prediction_vsa.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "31043315",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MaxPartialCharge</th>\n",
       "      <th>BCUT2D_MWHI</th>\n",
       "      <th>BCUT2D_MRLOW</th>\n",
       "      <th>BertzCT</th>\n",
       "      <th>Kappa1</th>\n",
       "      <th>PEOE_VSA3</th>\n",
       "      <th>PEOE_VSA6</th>\n",
       "      <th>PEOE_VSA7</th>\n",
       "      <th>PEOE_VSA8</th>\n",
       "      <th>PEOE_VSA9</th>\n",
       "      <th>...</th>\n",
       "      <th>SlogP_VSA2</th>\n",
       "      <th>SlogP_VSA3</th>\n",
       "      <th>EState_VSA4</th>\n",
       "      <th>VSA_EState10</th>\n",
       "      <th>VSA_EState2</th>\n",
       "      <th>VSA_EState7</th>\n",
       "      <th>VSA_EState9</th>\n",
       "      <th>FractionCSP3</th>\n",
       "      <th>MolLogP</th>\n",
       "      <th>logs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1144.000000</td>\n",
       "      <td>1144.000000</td>\n",
       "      <td>1144.000000</td>\n",
       "      <td>1144.000000</td>\n",
       "      <td>1144.000000</td>\n",
       "      <td>1144.000000</td>\n",
       "      <td>1144.000000</td>\n",
       "      <td>1144.000000</td>\n",
       "      <td>1144.000000</td>\n",
       "      <td>1144.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1144.000000</td>\n",
       "      <td>1144.000000</td>\n",
       "      <td>1144.000000</td>\n",
       "      <td>1144.000000</td>\n",
       "      <td>1144.000000</td>\n",
       "      <td>1144.000000</td>\n",
       "      <td>1144.000000</td>\n",
       "      <td>1144.000000</td>\n",
       "      <td>1144.000000</td>\n",
       "      <td>1144.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.170652</td>\n",
       "      <td>25.668258</td>\n",
       "      <td>0.485133</td>\n",
       "      <td>331.978414</td>\n",
       "      <td>10.013505</td>\n",
       "      <td>2.015724</td>\n",
       "      <td>25.227540</td>\n",
       "      <td>20.542790</td>\n",
       "      <td>7.954165</td>\n",
       "      <td>5.590735</td>\n",
       "      <td>...</td>\n",
       "      <td>14.177226</td>\n",
       "      <td>4.549725</td>\n",
       "      <td>7.837598</td>\n",
       "      <td>3.944850</td>\n",
       "      <td>8.467556</td>\n",
       "      <td>1.723926</td>\n",
       "      <td>0.235113</td>\n",
       "      <td>0.442626</td>\n",
       "      <td>2.449133</td>\n",
       "      <td>-3.057997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.141188</td>\n",
       "      <td>18.909027</td>\n",
       "      <td>0.697552</td>\n",
       "      <td>274.470826</td>\n",
       "      <td>4.934941</td>\n",
       "      <td>4.052089</td>\n",
       "      <td>21.591455</td>\n",
       "      <td>18.022845</td>\n",
       "      <td>8.715012</td>\n",
       "      <td>7.850892</td>\n",
       "      <td>...</td>\n",
       "      <td>16.417648</td>\n",
       "      <td>6.886043</td>\n",
       "      <td>10.118642</td>\n",
       "      <td>8.652606</td>\n",
       "      <td>10.841989</td>\n",
       "      <td>3.798256</td>\n",
       "      <td>1.368473</td>\n",
       "      <td>0.374868</td>\n",
       "      <td>1.866003</td>\n",
       "      <td>2.096502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.123544</td>\n",
       "      <td>12.011000</td>\n",
       "      <td>-0.803020</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-14.322623</td>\n",
       "      <td>-21.771026</td>\n",
       "      <td>-9.423007</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-7.571400</td>\n",
       "      <td>-11.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.046287</td>\n",
       "      <td>16.159724</td>\n",
       "      <td>-0.117291</td>\n",
       "      <td>80.464228</td>\n",
       "      <td>6.209275</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.600940</td>\n",
       "      <td>6.798008</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>4.427552</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.414900</td>\n",
       "      <td>-4.332250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.155198</td>\n",
       "      <td>16.531120</td>\n",
       "      <td>0.241606</td>\n",
       "      <td>278.794024</td>\n",
       "      <td>8.667304</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.268296</td>\n",
       "      <td>17.119040</td>\n",
       "      <td>6.076020</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>11.210494</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.573105</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.057488</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.350384</td>\n",
       "      <td>2.340300</td>\n",
       "      <td>-2.870500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.302194</td>\n",
       "      <td>35.495691</td>\n",
       "      <td>1.011138</td>\n",
       "      <td>516.291653</td>\n",
       "      <td>12.981490</td>\n",
       "      <td>4.390415</td>\n",
       "      <td>36.398202</td>\n",
       "      <td>28.837472</td>\n",
       "      <td>12.393687</td>\n",
       "      <td>9.871453</td>\n",
       "      <td>...</td>\n",
       "      <td>19.438325</td>\n",
       "      <td>6.560288</td>\n",
       "      <td>11.835812</td>\n",
       "      <td>5.281431</td>\n",
       "      <td>12.893261</td>\n",
       "      <td>2.647885</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>3.406475</td>\n",
       "      <td>-1.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.646588</td>\n",
       "      <td>126.922405</td>\n",
       "      <td>3.310650</td>\n",
       "      <td>1552.141814</td>\n",
       "      <td>40.506603</td>\n",
       "      <td>28.112393</td>\n",
       "      <td>167.947193</td>\n",
       "      <td>113.218325</td>\n",
       "      <td>61.843869</td>\n",
       "      <td>54.432782</td>\n",
       "      <td>...</td>\n",
       "      <td>168.110124</td>\n",
       "      <td>41.708701</td>\n",
       "      <td>64.208216</td>\n",
       "      <td>79.994907</td>\n",
       "      <td>67.989224</td>\n",
       "      <td>35.394277</td>\n",
       "      <td>11.465556</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.388600</td>\n",
       "      <td>1.580000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       MaxPartialCharge  BCUT2D_MWHI  BCUT2D_MRLOW      BertzCT       Kappa1  \\\n",
       "count       1144.000000  1144.000000   1144.000000  1144.000000  1144.000000   \n",
       "mean           0.170652    25.668258      0.485133   331.978414    10.013505   \n",
       "std            0.141188    18.909027      0.697552   274.470826     4.934941   \n",
       "min           -0.123544    12.011000     -0.803020     0.000000     0.000000   \n",
       "25%            0.046287    16.159724     -0.117291    80.464228     6.209275   \n",
       "50%            0.155198    16.531120      0.241606   278.794024     8.667304   \n",
       "75%            0.302194    35.495691      1.011138   516.291653    12.981490   \n",
       "max            0.646588   126.922405      3.310650  1552.141814    40.506603   \n",
       "\n",
       "         PEOE_VSA3    PEOE_VSA6    PEOE_VSA7    PEOE_VSA8    PEOE_VSA9  ...  \\\n",
       "count  1144.000000  1144.000000  1144.000000  1144.000000  1144.000000  ...   \n",
       "mean      2.015724    25.227540    20.542790     7.954165     5.590735  ...   \n",
       "std       4.052089    21.591455    18.022845     8.715012     7.850892  ...   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000  ...   \n",
       "25%       0.000000    11.600940     6.798008     0.000000     0.000000  ...   \n",
       "50%       0.000000    20.268296    17.119040     6.076020     0.000000  ...   \n",
       "75%       4.390415    36.398202    28.837472    12.393687     9.871453  ...   \n",
       "max      28.112393   167.947193   113.218325    61.843869    54.432782  ...   \n",
       "\n",
       "        SlogP_VSA2   SlogP_VSA3  EState_VSA4  VSA_EState10  VSA_EState2  \\\n",
       "count  1144.000000  1144.000000  1144.000000   1144.000000  1144.000000   \n",
       "mean     14.177226     4.549725     7.837598      3.944850     8.467556   \n",
       "std      16.417648     6.886043    10.118642      8.652606    10.841989   \n",
       "min       0.000000     0.000000     0.000000      0.000000   -14.322623   \n",
       "25%       4.427552     0.000000     0.000000      0.000000     0.000000   \n",
       "50%      11.210494     0.000000     5.573105      0.000000     3.057488   \n",
       "75%      19.438325     6.560288    11.835812      5.281431    12.893261   \n",
       "max     168.110124    41.708701    64.208216     79.994907    67.989224   \n",
       "\n",
       "       VSA_EState7  VSA_EState9  FractionCSP3      MolLogP         logs  \n",
       "count  1144.000000  1144.000000   1144.000000  1144.000000  1144.000000  \n",
       "mean      1.723926     0.235113      0.442626     2.449133    -3.057997  \n",
       "std       3.798256     1.368473      0.374868     1.866003     2.096502  \n",
       "min     -21.771026    -9.423007      0.000000    -7.571400   -11.600000  \n",
       "25%       0.000000     0.000000      0.100000     1.414900    -4.332250  \n",
       "50%       0.000000     0.000000      0.350384     2.340300    -2.870500  \n",
       "75%       2.647885     0.000000      0.800000     3.406475    -1.600000  \n",
       "max      35.394277    11.465556      1.000000    10.388600     1.580000  \n",
       "\n",
       "[8 rows x 22 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "full_df = pd.read_csv('vsa_descriptors.csv', index_col=0)\n",
    "full_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "75c81a61",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gasp/miniconda3/envs/sol_pred/lib/python3.7/site-packages/pandas/plotting/_matplotlib/tools.py:400: MatplotlibDeprecationWarning: \n",
      "The is_first_col function was deprecated in Matplotlib 3.4 and will be removed two minor releases later. Use ax.get_subplotspec().is_first_col() instead.\n",
      "  if ax.is_first_col():\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1800x1080 with 25 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist = full_df.hist(bins=15,figsize=(25, 15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7fdb0681",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data for Modeling: (1030, 22)\n",
      "Unseen Data For Predictions: (114, 22)\n"
     ]
    }
   ],
   "source": [
    "df = full_df.sample(frac=0.9, random_state=786)\n",
    "df_unseen = full_df.drop(df.index)\n",
    "\n",
    "df.reset_index(drop=True, inplace=True)\n",
    "df_unseen.reset_index(drop=True, inplace=True)\n",
    "\n",
    "print('Data for Modeling: ' + str(df.shape))\n",
    "print('Unseen Data For Predictions: ' + str(df_unseen.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7a1ab9f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['MaxPartialCharge',\n",
       " 'BCUT2D_MWHI',\n",
       " 'BCUT2D_MRLOW',\n",
       " 'BertzCT',\n",
       " 'Kappa1',\n",
       " 'PEOE_VSA3',\n",
       " 'PEOE_VSA6',\n",
       " 'PEOE_VSA7',\n",
       " 'PEOE_VSA8',\n",
       " 'PEOE_VSA9',\n",
       " 'SMR_VSA10',\n",
       " 'SMR_VSA7',\n",
       " 'SlogP_VSA2',\n",
       " 'SlogP_VSA3',\n",
       " 'EState_VSA4',\n",
       " 'VSA_EState10',\n",
       " 'VSA_EState2',\n",
       " 'VSA_EState7',\n",
       " 'VSA_EState9',\n",
       " 'FractionCSP3',\n",
       " 'MolLogP']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[i for i in df.columns if i not in ('logs')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "35a987e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "#T_40df7_row27_col1,#T_40df7_row29_col1{\n",
       "            background-color:  lightgreen;\n",
       "        }</style><table id=\"T_40df7_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Description</th>        <th class=\"col_heading level0 col1\" >Value</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_40df7_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_40df7_row0_col0\" class=\"data row0 col0\" >session_id</td>\n",
       "                        <td id=\"T_40df7_row0_col1\" class=\"data row0 col1\" >123</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_40df7_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_40df7_row1_col0\" class=\"data row1 col0\" >Target</td>\n",
       "                        <td id=\"T_40df7_row1_col1\" class=\"data row1 col1\" >logs</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_40df7_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_40df7_row2_col0\" class=\"data row2 col0\" >Original Data</td>\n",
       "                        <td id=\"T_40df7_row2_col1\" class=\"data row2 col1\" >(1030, 22)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_40df7_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_40df7_row3_col0\" class=\"data row3 col0\" >Missing Values</td>\n",
       "                        <td id=\"T_40df7_row3_col1\" class=\"data row3 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_40df7_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_40df7_row4_col0\" class=\"data row4 col0\" >Numeric Features</td>\n",
       "                        <td id=\"T_40df7_row4_col1\" class=\"data row4 col1\" >21</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_40df7_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_40df7_row5_col0\" class=\"data row5 col0\" >Categorical Features</td>\n",
       "                        <td id=\"T_40df7_row5_col1\" class=\"data row5 col1\" >0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_40df7_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_40df7_row6_col0\" class=\"data row6 col0\" >Ordinal Features</td>\n",
       "                        <td id=\"T_40df7_row6_col1\" class=\"data row6 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_40df7_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_40df7_row7_col0\" class=\"data row7 col0\" >High Cardinality Features</td>\n",
       "                        <td id=\"T_40df7_row7_col1\" class=\"data row7 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_40df7_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_40df7_row8_col0\" class=\"data row8 col0\" >High Cardinality Method</td>\n",
       "                        <td id=\"T_40df7_row8_col1\" class=\"data row8 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_40df7_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_40df7_row9_col0\" class=\"data row9 col0\" >Transformed Train Set</td>\n",
       "                        <td id=\"T_40df7_row9_col1\" class=\"data row9 col1\" >(824, 21)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_40df7_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "                        <td id=\"T_40df7_row10_col0\" class=\"data row10 col0\" >Transformed Test Set</td>\n",
       "                        <td id=\"T_40df7_row10_col1\" class=\"data row10 col1\" >(206, 21)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_40df7_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "                        <td id=\"T_40df7_row11_col0\" class=\"data row11 col0\" >Shuffle Train-Test</td>\n",
       "                        <td id=\"T_40df7_row11_col1\" class=\"data row11 col1\" >True</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_40df7_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "                        <td id=\"T_40df7_row12_col0\" class=\"data row12 col0\" >Stratify Train-Test</td>\n",
       "                        <td id=\"T_40df7_row12_col1\" class=\"data row12 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_40df7_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "                        <td id=\"T_40df7_row13_col0\" class=\"data row13 col0\" >Fold Generator</td>\n",
       "                        <td id=\"T_40df7_row13_col1\" class=\"data row13 col1\" >KFold</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_40df7_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "                        <td id=\"T_40df7_row14_col0\" class=\"data row14 col0\" >Fold Number</td>\n",
       "                        <td id=\"T_40df7_row14_col1\" class=\"data row14 col1\" >10</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_40df7_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "                        <td id=\"T_40df7_row15_col0\" class=\"data row15 col0\" >CPU Jobs</td>\n",
       "                        <td id=\"T_40df7_row15_col1\" class=\"data row15 col1\" >-1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_40df7_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "                        <td id=\"T_40df7_row16_col0\" class=\"data row16 col0\" >Use GPU</td>\n",
       "                        <td id=\"T_40df7_row16_col1\" class=\"data row16 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_40df7_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "                        <td id=\"T_40df7_row17_col0\" class=\"data row17 col0\" >Log Experiment</td>\n",
       "                        <td id=\"T_40df7_row17_col1\" class=\"data row17 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_40df7_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "                        <td id=\"T_40df7_row18_col0\" class=\"data row18 col0\" >Experiment Name</td>\n",
       "                        <td id=\"T_40df7_row18_col1\" class=\"data row18 col1\" >reg-default-name</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_40df7_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "                        <td id=\"T_40df7_row19_col0\" class=\"data row19 col0\" >USI</td>\n",
       "                        <td id=\"T_40df7_row19_col1\" class=\"data row19 col1\" >1438</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_40df7_level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "                        <td id=\"T_40df7_row20_col0\" class=\"data row20 col0\" >Imputation Type</td>\n",
       "                        <td id=\"T_40df7_row20_col1\" class=\"data row20 col1\" >simple</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_40df7_level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
       "                        <td id=\"T_40df7_row21_col0\" class=\"data row21 col0\" >Iterative Imputation Iteration</td>\n",
       "                        <td id=\"T_40df7_row21_col1\" class=\"data row21 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_40df7_level0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
       "                        <td id=\"T_40df7_row22_col0\" class=\"data row22 col0\" >Numeric Imputer</td>\n",
       "                        <td id=\"T_40df7_row22_col1\" class=\"data row22 col1\" >mean</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_40df7_level0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
       "                        <td id=\"T_40df7_row23_col0\" class=\"data row23 col0\" >Iterative Imputation Numeric Model</td>\n",
       "                        <td id=\"T_40df7_row23_col1\" class=\"data row23 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_40df7_level0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
       "                        <td id=\"T_40df7_row24_col0\" class=\"data row24 col0\" >Categorical Imputer</td>\n",
       "                        <td id=\"T_40df7_row24_col1\" class=\"data row24 col1\" >constant</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_40df7_level0_row25\" class=\"row_heading level0 row25\" >25</th>\n",
       "                        <td id=\"T_40df7_row25_col0\" class=\"data row25 col0\" >Iterative Imputation Categorical Model</td>\n",
       "                        <td id=\"T_40df7_row25_col1\" class=\"data row25 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_40df7_level0_row26\" class=\"row_heading level0 row26\" >26</th>\n",
       "                        <td id=\"T_40df7_row26_col0\" class=\"data row26 col0\" >Unknown Categoricals Handling</td>\n",
       "                        <td id=\"T_40df7_row26_col1\" class=\"data row26 col1\" >least_frequent</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_40df7_level0_row27\" class=\"row_heading level0 row27\" >27</th>\n",
       "                        <td id=\"T_40df7_row27_col0\" class=\"data row27 col0\" >Normalize</td>\n",
       "                        <td id=\"T_40df7_row27_col1\" class=\"data row27 col1\" >True</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_40df7_level0_row28\" class=\"row_heading level0 row28\" >28</th>\n",
       "                        <td id=\"T_40df7_row28_col0\" class=\"data row28 col0\" >Normalize Method</td>\n",
       "                        <td id=\"T_40df7_row28_col1\" class=\"data row28 col1\" >zscore</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_40df7_level0_row29\" class=\"row_heading level0 row29\" >29</th>\n",
       "                        <td id=\"T_40df7_row29_col0\" class=\"data row29 col0\" >Transformation</td>\n",
       "                        <td id=\"T_40df7_row29_col1\" class=\"data row29 col1\" >True</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_40df7_level0_row30\" class=\"row_heading level0 row30\" >30</th>\n",
       "                        <td id=\"T_40df7_row30_col0\" class=\"data row30 col0\" >Transformation Method</td>\n",
       "                        <td id=\"T_40df7_row30_col1\" class=\"data row30 col1\" >yeo-johnson</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_40df7_level0_row31\" class=\"row_heading level0 row31\" >31</th>\n",
       "                        <td id=\"T_40df7_row31_col0\" class=\"data row31 col0\" >PCA</td>\n",
       "                        <td id=\"T_40df7_row31_col1\" class=\"data row31 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_40df7_level0_row32\" class=\"row_heading level0 row32\" >32</th>\n",
       "                        <td id=\"T_40df7_row32_col0\" class=\"data row32 col0\" >PCA Method</td>\n",
       "                        <td id=\"T_40df7_row32_col1\" class=\"data row32 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_40df7_level0_row33\" class=\"row_heading level0 row33\" >33</th>\n",
       "                        <td id=\"T_40df7_row33_col0\" class=\"data row33 col0\" >PCA Components</td>\n",
       "                        <td id=\"T_40df7_row33_col1\" class=\"data row33 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_40df7_level0_row34\" class=\"row_heading level0 row34\" >34</th>\n",
       "                        <td id=\"T_40df7_row34_col0\" class=\"data row34 col0\" >Ignore Low Variance</td>\n",
       "                        <td id=\"T_40df7_row34_col1\" class=\"data row34 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_40df7_level0_row35\" class=\"row_heading level0 row35\" >35</th>\n",
       "                        <td id=\"T_40df7_row35_col0\" class=\"data row35 col0\" >Combine Rare Levels</td>\n",
       "                        <td id=\"T_40df7_row35_col1\" class=\"data row35 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_40df7_level0_row36\" class=\"row_heading level0 row36\" >36</th>\n",
       "                        <td id=\"T_40df7_row36_col0\" class=\"data row36 col0\" >Rare Level Threshold</td>\n",
       "                        <td id=\"T_40df7_row36_col1\" class=\"data row36 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_40df7_level0_row37\" class=\"row_heading level0 row37\" >37</th>\n",
       "                        <td id=\"T_40df7_row37_col0\" class=\"data row37 col0\" >Numeric Binning</td>\n",
       "                        <td id=\"T_40df7_row37_col1\" class=\"data row37 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_40df7_level0_row38\" class=\"row_heading level0 row38\" >38</th>\n",
       "                        <td id=\"T_40df7_row38_col0\" class=\"data row38 col0\" >Remove Outliers</td>\n",
       "                        <td id=\"T_40df7_row38_col1\" class=\"data row38 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_40df7_level0_row39\" class=\"row_heading level0 row39\" >39</th>\n",
       "                        <td id=\"T_40df7_row39_col0\" class=\"data row39 col0\" >Outliers Threshold</td>\n",
       "                        <td id=\"T_40df7_row39_col1\" class=\"data row39 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_40df7_level0_row40\" class=\"row_heading level0 row40\" >40</th>\n",
       "                        <td id=\"T_40df7_row40_col0\" class=\"data row40 col0\" >Remove Multicollinearity</td>\n",
       "                        <td id=\"T_40df7_row40_col1\" class=\"data row40 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_40df7_level0_row41\" class=\"row_heading level0 row41\" >41</th>\n",
       "                        <td id=\"T_40df7_row41_col0\" class=\"data row41 col0\" >Multicollinearity Threshold</td>\n",
       "                        <td id=\"T_40df7_row41_col1\" class=\"data row41 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_40df7_level0_row42\" class=\"row_heading level0 row42\" >42</th>\n",
       "                        <td id=\"T_40df7_row42_col0\" class=\"data row42 col0\" >Clustering</td>\n",
       "                        <td id=\"T_40df7_row42_col1\" class=\"data row42 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_40df7_level0_row43\" class=\"row_heading level0 row43\" >43</th>\n",
       "                        <td id=\"T_40df7_row43_col0\" class=\"data row43 col0\" >Clustering Iteration</td>\n",
       "                        <td id=\"T_40df7_row43_col1\" class=\"data row43 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_40df7_level0_row44\" class=\"row_heading level0 row44\" >44</th>\n",
       "                        <td id=\"T_40df7_row44_col0\" class=\"data row44 col0\" >Polynomial Features</td>\n",
       "                        <td id=\"T_40df7_row44_col1\" class=\"data row44 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_40df7_level0_row45\" class=\"row_heading level0 row45\" >45</th>\n",
       "                        <td id=\"T_40df7_row45_col0\" class=\"data row45 col0\" >Polynomial Degree</td>\n",
       "                        <td id=\"T_40df7_row45_col1\" class=\"data row45 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_40df7_level0_row46\" class=\"row_heading level0 row46\" >46</th>\n",
       "                        <td id=\"T_40df7_row46_col0\" class=\"data row46 col0\" >Trignometry Features</td>\n",
       "                        <td id=\"T_40df7_row46_col1\" class=\"data row46 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_40df7_level0_row47\" class=\"row_heading level0 row47\" >47</th>\n",
       "                        <td id=\"T_40df7_row47_col0\" class=\"data row47 col0\" >Polynomial Threshold</td>\n",
       "                        <td id=\"T_40df7_row47_col1\" class=\"data row47 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_40df7_level0_row48\" class=\"row_heading level0 row48\" >48</th>\n",
       "                        <td id=\"T_40df7_row48_col0\" class=\"data row48 col0\" >Group Features</td>\n",
       "                        <td id=\"T_40df7_row48_col1\" class=\"data row48 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_40df7_level0_row49\" class=\"row_heading level0 row49\" >49</th>\n",
       "                        <td id=\"T_40df7_row49_col0\" class=\"data row49 col0\" >Feature Selection</td>\n",
       "                        <td id=\"T_40df7_row49_col1\" class=\"data row49 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_40df7_level0_row50\" class=\"row_heading level0 row50\" >50</th>\n",
       "                        <td id=\"T_40df7_row50_col0\" class=\"data row50 col0\" >Feature Selection Method</td>\n",
       "                        <td id=\"T_40df7_row50_col1\" class=\"data row50 col1\" >classic</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_40df7_level0_row51\" class=\"row_heading level0 row51\" >51</th>\n",
       "                        <td id=\"T_40df7_row51_col0\" class=\"data row51 col0\" >Features Selection Threshold</td>\n",
       "                        <td id=\"T_40df7_row51_col1\" class=\"data row51 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_40df7_level0_row52\" class=\"row_heading level0 row52\" >52</th>\n",
       "                        <td id=\"T_40df7_row52_col0\" class=\"data row52 col0\" >Feature Interaction</td>\n",
       "                        <td id=\"T_40df7_row52_col1\" class=\"data row52 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_40df7_level0_row53\" class=\"row_heading level0 row53\" >53</th>\n",
       "                        <td id=\"T_40df7_row53_col0\" class=\"data row53 col0\" >Feature Ratio</td>\n",
       "                        <td id=\"T_40df7_row53_col1\" class=\"data row53 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_40df7_level0_row54\" class=\"row_heading level0 row54\" >54</th>\n",
       "                        <td id=\"T_40df7_row54_col0\" class=\"data row54 col0\" >Interaction Threshold</td>\n",
       "                        <td id=\"T_40df7_row54_col1\" class=\"data row54 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_40df7_level0_row55\" class=\"row_heading level0 row55\" >55</th>\n",
       "                        <td id=\"T_40df7_row55_col0\" class=\"data row55 col0\" >Transform Target</td>\n",
       "                        <td id=\"T_40df7_row55_col1\" class=\"data row55 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_40df7_level0_row56\" class=\"row_heading level0 row56\" >56</th>\n",
       "                        <td id=\"T_40df7_row56_col0\" class=\"data row56 col0\" >Transform Target Method</td>\n",
       "                        <td id=\"T_40df7_row56_col1\" class=\"data row56 col1\" >box-cox</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x114c5f8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pycaret.regression import *\n",
    "reg = setup(\n",
    "    df, \n",
    "    target = 'logs', \n",
    "    session_id=123, \n",
    "    train_size=0.8, \n",
    "    numeric_features=[i for i in df.columns if i not in ('logs')],\n",
    "    transformation = True,\n",
    "    normalize = True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2b62dbbe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "    #T_5a560_ th {\n",
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       "            text-align:  left;\n",
       "            background-color:  yellow;\n",
       "        }#T_5a560_row0_col7,#T_5a560_row1_col7,#T_5a560_row2_col7,#T_5a560_row3_col7,#T_5a560_row4_col7,#T_5a560_row5_col7,#T_5a560_row6_col7,#T_5a560_row7_col7,#T_5a560_row8_col7,#T_5a560_row9_col7,#T_5a560_row10_col7,#T_5a560_row11_col7,#T_5a560_row12_col7,#T_5a560_row13_col7,#T_5a560_row14_col7,#T_5a560_row16_col7{\n",
       "            text-align:  left;\n",
       "            text-align:  left;\n",
       "            background-color:  lightgrey;\n",
       "        }#T_5a560_row15_col7{\n",
       "            text-align:  left;\n",
       "            text-align:  left;\n",
       "            background-color:  yellow;\n",
       "            background-color:  lightgrey;\n",
       "        }</style><table id=\"T_5a560_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Model</th>        <th class=\"col_heading level0 col1\" >MAE</th>        <th class=\"col_heading level0 col2\" >MSE</th>        <th class=\"col_heading level0 col3\" >RMSE</th>        <th class=\"col_heading level0 col4\" >R2</th>        <th class=\"col_heading level0 col5\" >RMSLE</th>        <th class=\"col_heading level0 col6\" >MAPE</th>        <th class=\"col_heading level0 col7\" >TT (Sec)</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_5a560_level0_row0\" class=\"row_heading level0 row0\" >et</th>\n",
       "                        <td id=\"T_5a560_row0_col0\" class=\"data row0 col0\" >Extra Trees Regressor</td>\n",
       "                        <td id=\"T_5a560_row0_col1\" class=\"data row0 col1\" >0.4584</td>\n",
       "                        <td id=\"T_5a560_row0_col2\" class=\"data row0 col2\" >0.4141</td>\n",
       "                        <td id=\"T_5a560_row0_col3\" class=\"data row0 col3\" >0.6401</td>\n",
       "                        <td id=\"T_5a560_row0_col4\" class=\"data row0 col4\" >0.9026</td>\n",
       "                        <td id=\"T_5a560_row0_col5\" class=\"data row0 col5\" >0.1800</td>\n",
       "                        <td id=\"T_5a560_row0_col6\" class=\"data row0 col6\" >0.5099</td>\n",
       "                        <td id=\"T_5a560_row0_col7\" class=\"data row0 col7\" >0.4210</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5a560_level0_row1\" class=\"row_heading level0 row1\" >lightgbm</th>\n",
       "                        <td id=\"T_5a560_row1_col0\" class=\"data row1 col0\" >Light Gradient Boosting Machine</td>\n",
       "                        <td id=\"T_5a560_row1_col1\" class=\"data row1 col1\" >0.4684</td>\n",
       "                        <td id=\"T_5a560_row1_col2\" class=\"data row1 col2\" >0.4144</td>\n",
       "                        <td id=\"T_5a560_row1_col3\" class=\"data row1 col3\" >0.6413</td>\n",
       "                        <td id=\"T_5a560_row1_col4\" class=\"data row1 col4\" >0.9018</td>\n",
       "                        <td id=\"T_5a560_row1_col5\" class=\"data row1 col5\" >0.1738</td>\n",
       "                        <td id=\"T_5a560_row1_col6\" class=\"data row1 col6\" >0.4542</td>\n",
       "                        <td id=\"T_5a560_row1_col7\" class=\"data row1 col7\" >0.1350</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5a560_level0_row2\" class=\"row_heading level0 row2\" >gbr</th>\n",
       "                        <td id=\"T_5a560_row2_col0\" class=\"data row2 col0\" >Gradient Boosting Regressor</td>\n",
       "                        <td id=\"T_5a560_row2_col1\" class=\"data row2 col1\" >0.4928</td>\n",
       "                        <td id=\"T_5a560_row2_col2\" class=\"data row2 col2\" >0.4500</td>\n",
       "                        <td id=\"T_5a560_row2_col3\" class=\"data row2 col3\" >0.6684</td>\n",
       "                        <td id=\"T_5a560_row2_col4\" class=\"data row2 col4\" >0.8943</td>\n",
       "                        <td id=\"T_5a560_row2_col5\" class=\"data row2 col5\" >0.1893</td>\n",
       "                        <td id=\"T_5a560_row2_col6\" class=\"data row2 col6\" >0.5031</td>\n",
       "                        <td id=\"T_5a560_row2_col7\" class=\"data row2 col7\" >0.1880</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5a560_level0_row3\" class=\"row_heading level0 row3\" >rf</th>\n",
       "                        <td id=\"T_5a560_row3_col0\" class=\"data row3 col0\" >Random Forest Regressor</td>\n",
       "                        <td id=\"T_5a560_row3_col1\" class=\"data row3 col1\" >0.4925</td>\n",
       "                        <td id=\"T_5a560_row3_col2\" class=\"data row3 col2\" >0.4743</td>\n",
       "                        <td id=\"T_5a560_row3_col3\" class=\"data row3 col3\" >0.6856</td>\n",
       "                        <td id=\"T_5a560_row3_col4\" class=\"data row3 col4\" >0.8876</td>\n",
       "                        <td id=\"T_5a560_row3_col5\" class=\"data row3 col5\" >0.1901</td>\n",
       "                        <td id=\"T_5a560_row3_col6\" class=\"data row3 col6\" >0.4828</td>\n",
       "                        <td id=\"T_5a560_row3_col7\" class=\"data row3 col7\" >0.6370</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5a560_level0_row4\" class=\"row_heading level0 row4\" >ada</th>\n",
       "                        <td id=\"T_5a560_row4_col0\" class=\"data row4 col0\" >AdaBoost Regressor</td>\n",
       "                        <td id=\"T_5a560_row4_col1\" class=\"data row4 col1\" >0.6446</td>\n",
       "                        <td id=\"T_5a560_row4_col2\" class=\"data row4 col2\" >0.6751</td>\n",
       "                        <td id=\"T_5a560_row4_col3\" class=\"data row4 col3\" >0.8189</td>\n",
       "                        <td id=\"T_5a560_row4_col4\" class=\"data row4 col4\" >0.8407</td>\n",
       "                        <td id=\"T_5a560_row4_col5\" class=\"data row4 col5\" >0.2362</td>\n",
       "                        <td id=\"T_5a560_row4_col6\" class=\"data row4 col6\" >0.7245</td>\n",
       "                        <td id=\"T_5a560_row4_col7\" class=\"data row4 col7\" >0.1720</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5a560_level0_row5\" class=\"row_heading level0 row5\" >br</th>\n",
       "                        <td id=\"T_5a560_row5_col0\" class=\"data row5 col0\" >Bayesian Ridge</td>\n",
       "                        <td id=\"T_5a560_row5_col1\" class=\"data row5 col1\" >0.6529</td>\n",
       "                        <td id=\"T_5a560_row5_col2\" class=\"data row5 col2\" >0.6773</td>\n",
       "                        <td id=\"T_5a560_row5_col3\" class=\"data row5 col3\" >0.8206</td>\n",
       "                        <td id=\"T_5a560_row5_col4\" class=\"data row5 col4\" >0.8404</td>\n",
       "                        <td id=\"T_5a560_row5_col5\" class=\"data row5 col5\" >0.2393</td>\n",
       "                        <td id=\"T_5a560_row5_col6\" class=\"data row5 col6\" >0.6394</td>\n",
       "                        <td id=\"T_5a560_row5_col7\" class=\"data row5 col7\" >0.0400</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5a560_level0_row6\" class=\"row_heading level0 row6\" >ridge</th>\n",
       "                        <td id=\"T_5a560_row6_col0\" class=\"data row6 col0\" >Ridge Regression</td>\n",
       "                        <td id=\"T_5a560_row6_col1\" class=\"data row6 col1\" >0.6530</td>\n",
       "                        <td id=\"T_5a560_row6_col2\" class=\"data row6 col2\" >0.6777</td>\n",
       "                        <td id=\"T_5a560_row6_col3\" class=\"data row6 col3\" >0.8208</td>\n",
       "                        <td id=\"T_5a560_row6_col4\" class=\"data row6 col4\" >0.8403</td>\n",
       "                        <td id=\"T_5a560_row6_col5\" class=\"data row6 col5\" >0.2391</td>\n",
       "                        <td id=\"T_5a560_row6_col6\" class=\"data row6 col6\" >0.6409</td>\n",
       "                        <td id=\"T_5a560_row6_col7\" class=\"data row6 col7\" >0.0400</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5a560_level0_row7\" class=\"row_heading level0 row7\" >lr</th>\n",
       "                        <td id=\"T_5a560_row7_col0\" class=\"data row7 col0\" >Linear Regression</td>\n",
       "                        <td id=\"T_5a560_row7_col1\" class=\"data row7 col1\" >0.6530</td>\n",
       "                        <td id=\"T_5a560_row7_col2\" class=\"data row7 col2\" >0.6780</td>\n",
       "                        <td id=\"T_5a560_row7_col3\" class=\"data row7 col3\" >0.8210</td>\n",
       "                        <td id=\"T_5a560_row7_col4\" class=\"data row7 col4\" >0.8402</td>\n",
       "                        <td id=\"T_5a560_row7_col5\" class=\"data row7 col5\" >0.2391</td>\n",
       "                        <td id=\"T_5a560_row7_col6\" class=\"data row7 col6\" >0.6415</td>\n",
       "                        <td id=\"T_5a560_row7_col7\" class=\"data row7 col7\" >0.6770</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5a560_level0_row8\" class=\"row_heading level0 row8\" >huber</th>\n",
       "                        <td id=\"T_5a560_row8_col0\" class=\"data row8 col0\" >Huber Regressor</td>\n",
       "                        <td id=\"T_5a560_row8_col1\" class=\"data row8 col1\" >0.6501</td>\n",
       "                        <td id=\"T_5a560_row8_col2\" class=\"data row8 col2\" >0.6895</td>\n",
       "                        <td id=\"T_5a560_row8_col3\" class=\"data row8 col3\" >0.8279</td>\n",
       "                        <td id=\"T_5a560_row8_col4\" class=\"data row8 col4\" >0.8374</td>\n",
       "                        <td id=\"T_5a560_row8_col5\" class=\"data row8 col5\" >0.2433</td>\n",
       "                        <td id=\"T_5a560_row8_col6\" class=\"data row8 col6\" >0.6467</td>\n",
       "                        <td id=\"T_5a560_row8_col7\" class=\"data row8 col7\" >0.0680</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5a560_level0_row9\" class=\"row_heading level0 row9\" >knn</th>\n",
       "                        <td id=\"T_5a560_row9_col0\" class=\"data row9 col0\" >K Neighbors Regressor</td>\n",
       "                        <td id=\"T_5a560_row9_col1\" class=\"data row9 col1\" >0.6391</td>\n",
       "                        <td id=\"T_5a560_row9_col2\" class=\"data row9 col2\" >0.7450</td>\n",
       "                        <td id=\"T_5a560_row9_col3\" class=\"data row9 col3\" >0.8605</td>\n",
       "                        <td id=\"T_5a560_row9_col4\" class=\"data row9 col4\" >0.8228</td>\n",
       "                        <td id=\"T_5a560_row9_col5\" class=\"data row9 col5\" >0.2330</td>\n",
       "                        <td id=\"T_5a560_row9_col6\" class=\"data row9 col6\" >0.7014</td>\n",
       "                        <td id=\"T_5a560_row9_col7\" class=\"data row9 col7\" >0.0850</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5a560_level0_row10\" class=\"row_heading level0 row10\" >lar</th>\n",
       "                        <td id=\"T_5a560_row10_col0\" class=\"data row10 col0\" >Least Angle Regression</td>\n",
       "                        <td id=\"T_5a560_row10_col1\" class=\"data row10 col1\" >0.6837</td>\n",
       "                        <td id=\"T_5a560_row10_col2\" class=\"data row10 col2\" >0.7537</td>\n",
       "                        <td id=\"T_5a560_row10_col3\" class=\"data row10 col3\" >0.8662</td>\n",
       "                        <td id=\"T_5a560_row10_col4\" class=\"data row10 col4\" >0.8212</td>\n",
       "                        <td id=\"T_5a560_row10_col5\" class=\"data row10 col5\" >0.2553</td>\n",
       "                        <td id=\"T_5a560_row10_col6\" class=\"data row10 col6\" >0.7810</td>\n",
       "                        <td id=\"T_5a560_row10_col7\" class=\"data row10 col7\" >0.0480</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5a560_level0_row11\" class=\"row_heading level0 row11\" >dt</th>\n",
       "                        <td id=\"T_5a560_row11_col0\" class=\"data row11 col0\" >Decision Tree Regressor</td>\n",
       "                        <td id=\"T_5a560_row11_col1\" class=\"data row11 col1\" >0.6680</td>\n",
       "                        <td id=\"T_5a560_row11_col2\" class=\"data row11 col2\" >0.8693</td>\n",
       "                        <td id=\"T_5a560_row11_col3\" class=\"data row11 col3\" >0.9241</td>\n",
       "                        <td id=\"T_5a560_row11_col4\" class=\"data row11 col4\" >0.7962</td>\n",
       "                        <td id=\"T_5a560_row11_col5\" class=\"data row11 col5\" >0.2451</td>\n",
       "                        <td id=\"T_5a560_row11_col6\" class=\"data row11 col6\" >0.6261</td>\n",
       "                        <td id=\"T_5a560_row11_col7\" class=\"data row11 col7\" >0.0360</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5a560_level0_row12\" class=\"row_heading level0 row12\" >omp</th>\n",
       "                        <td id=\"T_5a560_row12_col0\" class=\"data row12 col0\" >Orthogonal Matching Pursuit</td>\n",
       "                        <td id=\"T_5a560_row12_col1\" class=\"data row12 col1\" >0.7683</td>\n",
       "                        <td id=\"T_5a560_row12_col2\" class=\"data row12 col2\" >1.0162</td>\n",
       "                        <td id=\"T_5a560_row12_col3\" class=\"data row12 col3\" >1.0059</td>\n",
       "                        <td id=\"T_5a560_row12_col4\" class=\"data row12 col4\" >0.7622</td>\n",
       "                        <td id=\"T_5a560_row12_col5\" class=\"data row12 col5\" >0.2880</td>\n",
       "                        <td id=\"T_5a560_row12_col6\" class=\"data row12 col6\" >0.8954</td>\n",
       "                        <td id=\"T_5a560_row12_col7\" class=\"data row12 col7\" >0.0300</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5a560_level0_row13\" class=\"row_heading level0 row13\" >par</th>\n",
       "                        <td id=\"T_5a560_row13_col0\" class=\"data row13 col0\" >Passive Aggressive Regressor</td>\n",
       "                        <td id=\"T_5a560_row13_col1\" class=\"data row13 col1\" >0.8624</td>\n",
       "                        <td id=\"T_5a560_row13_col2\" class=\"data row13 col2\" >1.2186</td>\n",
       "                        <td id=\"T_5a560_row13_col3\" class=\"data row13 col3\" >1.0916</td>\n",
       "                        <td id=\"T_5a560_row13_col4\" class=\"data row13 col4\" >0.7065</td>\n",
       "                        <td id=\"T_5a560_row13_col5\" class=\"data row13 col5\" >0.3043</td>\n",
       "                        <td id=\"T_5a560_row13_col6\" class=\"data row13 col6\" >0.8668</td>\n",
       "                        <td id=\"T_5a560_row13_col7\" class=\"data row13 col7\" >0.0410</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5a560_level0_row14\" class=\"row_heading level0 row14\" >en</th>\n",
       "                        <td id=\"T_5a560_row14_col0\" class=\"data row14 col0\" >Elastic Net</td>\n",
       "                        <td id=\"T_5a560_row14_col1\" class=\"data row14 col1\" >1.0127</td>\n",
       "                        <td id=\"T_5a560_row14_col2\" class=\"data row14 col2\" >1.6470</td>\n",
       "                        <td id=\"T_5a560_row14_col3\" class=\"data row14 col3\" >1.2786</td>\n",
       "                        <td id=\"T_5a560_row14_col4\" class=\"data row14 col4\" >0.6183</td>\n",
       "                        <td id=\"T_5a560_row14_col5\" class=\"data row14 col5\" >0.3386</td>\n",
       "                        <td id=\"T_5a560_row14_col6\" class=\"data row14 col6\" >1.4951</td>\n",
       "                        <td id=\"T_5a560_row14_col7\" class=\"data row14 col7\" >0.0770</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5a560_level0_row15\" class=\"row_heading level0 row15\" >lasso</th>\n",
       "                        <td id=\"T_5a560_row15_col0\" class=\"data row15 col0\" >Lasso Regression</td>\n",
       "                        <td id=\"T_5a560_row15_col1\" class=\"data row15 col1\" >1.2454</td>\n",
       "                        <td id=\"T_5a560_row15_col2\" class=\"data row15 col2\" >2.4745</td>\n",
       "                        <td id=\"T_5a560_row15_col3\" class=\"data row15 col3\" >1.5673</td>\n",
       "                        <td id=\"T_5a560_row15_col4\" class=\"data row15 col4\" >0.4269</td>\n",
       "                        <td id=\"T_5a560_row15_col5\" class=\"data row15 col5\" >0.4145</td>\n",
       "                        <td id=\"T_5a560_row15_col6\" class=\"data row15 col6\" >1.9802</td>\n",
       "                        <td id=\"T_5a560_row15_col7\" class=\"data row15 col7\" >0.0290</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5a560_level0_row16\" class=\"row_heading level0 row16\" >llar</th>\n",
       "                        <td id=\"T_5a560_row16_col0\" class=\"data row16 col0\" >Lasso Least Angle Regression</td>\n",
       "                        <td id=\"T_5a560_row16_col1\" class=\"data row16 col1\" >1.6697</td>\n",
       "                        <td id=\"T_5a560_row16_col2\" class=\"data row16 col2\" >4.4232</td>\n",
       "                        <td id=\"T_5a560_row16_col3\" class=\"data row16 col3\" >2.0959</td>\n",
       "                        <td id=\"T_5a560_row16_col4\" class=\"data row16 col4\" >-0.0237</td>\n",
       "                        <td id=\"T_5a560_row16_col5\" class=\"data row16 col5\" >0.5304</td>\n",
       "                        <td id=\"T_5a560_row16_col6\" class=\"data row16 col6\" >2.7010</td>\n",
       "                        <td id=\"T_5a560_row16_col7\" class=\"data row16 col7\" >0.0480</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x114ccaf10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "best_top3_models = compare_models(n_select=3)\n",
    "# print(best_top3_models)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "908c1b13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "#T_aa821_row10_col0,#T_aa821_row10_col1,#T_aa821_row10_col2,#T_aa821_row10_col3,#T_aa821_row10_col4,#T_aa821_row10_col5{\n",
       "            background:  yellow;\n",
       "        }</style><table id=\"T_aa821_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >MAE</th>        <th class=\"col_heading level0 col1\" >MSE</th>        <th class=\"col_heading level0 col2\" >RMSE</th>        <th class=\"col_heading level0 col3\" >R2</th>        <th class=\"col_heading level0 col4\" >RMSLE</th>        <th class=\"col_heading level0 col5\" >MAPE</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_aa821_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_aa821_row0_col0\" class=\"data row0 col0\" >0.4948</td>\n",
       "                        <td id=\"T_aa821_row0_col1\" class=\"data row0 col1\" >0.4398</td>\n",
       "                        <td id=\"T_aa821_row0_col2\" class=\"data row0 col2\" >0.6632</td>\n",
       "                        <td id=\"T_aa821_row0_col3\" class=\"data row0 col3\" >0.8608</td>\n",
       "                        <td id=\"T_aa821_row0_col4\" class=\"data row0 col4\" >0.1644</td>\n",
       "                        <td id=\"T_aa821_row0_col5\" class=\"data row0 col5\" >0.4369</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_aa821_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_aa821_row1_col0\" class=\"data row1 col0\" >0.5540</td>\n",
       "                        <td id=\"T_aa821_row1_col1\" class=\"data row1 col1\" >0.5291</td>\n",
       "                        <td id=\"T_aa821_row1_col2\" class=\"data row1 col2\" >0.7274</td>\n",
       "                        <td id=\"T_aa821_row1_col3\" class=\"data row1 col3\" >0.8718</td>\n",
       "                        <td id=\"T_aa821_row1_col4\" class=\"data row1 col4\" >0.1856</td>\n",
       "                        <td id=\"T_aa821_row1_col5\" class=\"data row1 col5\" >0.4028</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_aa821_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_aa821_row2_col0\" class=\"data row2 col0\" >0.5068</td>\n",
       "                        <td id=\"T_aa821_row2_col1\" class=\"data row2 col1\" >0.4409</td>\n",
       "                        <td id=\"T_aa821_row2_col2\" class=\"data row2 col2\" >0.6640</td>\n",
       "                        <td id=\"T_aa821_row2_col3\" class=\"data row2 col3\" >0.8945</td>\n",
       "                        <td id=\"T_aa821_row2_col4\" class=\"data row2 col4\" >0.1654</td>\n",
       "                        <td id=\"T_aa821_row2_col5\" class=\"data row2 col5\" >0.2668</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_aa821_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_aa821_row3_col0\" class=\"data row3 col0\" >0.4780</td>\n",
       "                        <td id=\"T_aa821_row3_col1\" class=\"data row3 col1\" >0.4524</td>\n",
       "                        <td id=\"T_aa821_row3_col2\" class=\"data row3 col2\" >0.6726</td>\n",
       "                        <td id=\"T_aa821_row3_col3\" class=\"data row3 col3\" >0.8759</td>\n",
       "                        <td id=\"T_aa821_row3_col4\" class=\"data row3 col4\" >0.1757</td>\n",
       "                        <td id=\"T_aa821_row3_col5\" class=\"data row3 col5\" >0.2564</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_aa821_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_aa821_row4_col0\" class=\"data row4 col0\" >0.4130</td>\n",
       "                        <td id=\"T_aa821_row4_col1\" class=\"data row4 col1\" >0.2880</td>\n",
       "                        <td id=\"T_aa821_row4_col2\" class=\"data row4 col2\" >0.5366</td>\n",
       "                        <td id=\"T_aa821_row4_col3\" class=\"data row4 col3\" >0.9415</td>\n",
       "                        <td id=\"T_aa821_row4_col4\" class=\"data row4 col4\" >0.1433</td>\n",
       "                        <td id=\"T_aa821_row4_col5\" class=\"data row4 col5\" >0.1789</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_aa821_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_aa821_row5_col0\" class=\"data row5 col0\" >0.4458</td>\n",
       "                        <td id=\"T_aa821_row5_col1\" class=\"data row5 col1\" >0.3793</td>\n",
       "                        <td id=\"T_aa821_row5_col2\" class=\"data row5 col2\" >0.6159</td>\n",
       "                        <td id=\"T_aa821_row5_col3\" class=\"data row5 col3\" >0.9099</td>\n",
       "                        <td id=\"T_aa821_row5_col4\" class=\"data row5 col4\" >0.1703</td>\n",
       "                        <td id=\"T_aa821_row5_col5\" class=\"data row5 col5\" >0.2413</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_aa821_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_aa821_row6_col0\" class=\"data row6 col0\" >0.4977</td>\n",
       "                        <td id=\"T_aa821_row6_col1\" class=\"data row6 col1\" >0.5022</td>\n",
       "                        <td id=\"T_aa821_row6_col2\" class=\"data row6 col2\" >0.7087</td>\n",
       "                        <td id=\"T_aa821_row6_col3\" class=\"data row6 col3\" >0.9134</td>\n",
       "                        <td id=\"T_aa821_row6_col4\" class=\"data row6 col4\" >0.1828</td>\n",
       "                        <td id=\"T_aa821_row6_col5\" class=\"data row6 col5\" >0.6635</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_aa821_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_aa821_row7_col0\" class=\"data row7 col0\" >0.4700</td>\n",
       "                        <td id=\"T_aa821_row7_col1\" class=\"data row7 col1\" >0.3852</td>\n",
       "                        <td id=\"T_aa821_row7_col2\" class=\"data row7 col2\" >0.6207</td>\n",
       "                        <td id=\"T_aa821_row7_col3\" class=\"data row7 col3\" >0.9106</td>\n",
       "                        <td id=\"T_aa821_row7_col4\" class=\"data row7 col4\" >0.1781</td>\n",
       "                        <td id=\"T_aa821_row7_col5\" class=\"data row7 col5\" >0.4181</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_aa821_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_aa821_row8_col0\" class=\"data row8 col0\" >0.4056</td>\n",
       "                        <td id=\"T_aa821_row8_col1\" class=\"data row8 col1\" >0.3774</td>\n",
       "                        <td id=\"T_aa821_row8_col2\" class=\"data row8 col2\" >0.6143</td>\n",
       "                        <td id=\"T_aa821_row8_col3\" class=\"data row8 col3\" >0.9023</td>\n",
       "                        <td id=\"T_aa821_row8_col4\" class=\"data row8 col4\" >0.2053</td>\n",
       "                        <td id=\"T_aa821_row8_col5\" class=\"data row8 col5\" >0.4902</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_aa821_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_aa821_row9_col0\" class=\"data row9 col0\" >0.5036</td>\n",
       "                        <td id=\"T_aa821_row9_col1\" class=\"data row9 col1\" >0.4903</td>\n",
       "                        <td id=\"T_aa821_row9_col2\" class=\"data row9 col2\" >0.7002</td>\n",
       "                        <td id=\"T_aa821_row9_col3\" class=\"data row9 col3\" >0.9017</td>\n",
       "                        <td id=\"T_aa821_row9_col4\" class=\"data row9 col4\" >0.2250</td>\n",
       "                        <td id=\"T_aa821_row9_col5\" class=\"data row9 col5\" >1.4668</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_aa821_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "                        <td id=\"T_aa821_row10_col0\" class=\"data row10 col0\" >0.4769</td>\n",
       "                        <td id=\"T_aa821_row10_col1\" class=\"data row10 col1\" >0.4285</td>\n",
       "                        <td id=\"T_aa821_row10_col2\" class=\"data row10 col2\" >0.6524</td>\n",
       "                        <td id=\"T_aa821_row10_col3\" class=\"data row10 col3\" >0.8983</td>\n",
       "                        <td id=\"T_aa821_row10_col4\" class=\"data row10 col4\" >0.1796</td>\n",
       "                        <td id=\"T_aa821_row10_col5\" class=\"data row10 col5\" >0.4822</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_aa821_level0_row11\" class=\"row_heading level0 row11\" >SD</th>\n",
       "                        <td id=\"T_aa821_row11_col0\" class=\"data row11 col0\" >0.0430</td>\n",
       "                        <td id=\"T_aa821_row11_col1\" class=\"data row11 col1\" >0.0685</td>\n",
       "                        <td id=\"T_aa821_row11_col2\" class=\"data row11 col2\" >0.0538</td>\n",
       "                        <td id=\"T_aa821_row11_col3\" class=\"data row11 col3\" >0.0224</td>\n",
       "                        <td id=\"T_aa821_row11_col4\" class=\"data row11 col4\" >0.0215</td>\n",
       "                        <td id=\"T_aa821_row11_col5\" class=\"data row11 col5\" >0.3553</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x114d55390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# lgb = create_model('lightgbm')\n",
    "# tuned_lgb = tune_model(lgb, n_iter=50)\n",
    "\n",
    "# best = create_model(best_model)\n",
    "best = create_model('lightgbm', n_iter=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bb459e02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "#T_9871b_row10_col0,#T_9871b_row10_col1,#T_9871b_row10_col2,#T_9871b_row10_col3,#T_9871b_row10_col4,#T_9871b_row10_col5{\n",
       "            background:  yellow;\n",
       "        }</style><table id=\"T_9871b_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >MAE</th>        <th class=\"col_heading level0 col1\" >MSE</th>        <th class=\"col_heading level0 col2\" >RMSE</th>        <th class=\"col_heading level0 col3\" >R2</th>        <th class=\"col_heading level0 col4\" >RMSLE</th>        <th class=\"col_heading level0 col5\" >MAPE</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_9871b_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_9871b_row0_col0\" class=\"data row0 col0\" >0.5280</td>\n",
       "                        <td id=\"T_9871b_row0_col1\" class=\"data row0 col1\" >0.5027</td>\n",
       "                        <td id=\"T_9871b_row0_col2\" class=\"data row0 col2\" >0.7090</td>\n",
       "                        <td id=\"T_9871b_row0_col3\" class=\"data row0 col3\" >0.8409</td>\n",
       "                        <td id=\"T_9871b_row0_col4\" class=\"data row0 col4\" >0.1723</td>\n",
       "                        <td id=\"T_9871b_row0_col5\" class=\"data row0 col5\" >0.5935</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_9871b_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_9871b_row1_col0\" class=\"data row1 col0\" >0.5033</td>\n",
       "                        <td id=\"T_9871b_row1_col1\" class=\"data row1 col1\" >0.4793</td>\n",
       "                        <td id=\"T_9871b_row1_col2\" class=\"data row1 col2\" >0.6923</td>\n",
       "                        <td id=\"T_9871b_row1_col3\" class=\"data row1 col3\" >0.8839</td>\n",
       "                        <td id=\"T_9871b_row1_col4\" class=\"data row1 col4\" >0.1962</td>\n",
       "                        <td id=\"T_9871b_row1_col5\" class=\"data row1 col5\" >0.4001</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_9871b_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_9871b_row2_col0\" class=\"data row2 col0\" >0.5499</td>\n",
       "                        <td id=\"T_9871b_row2_col1\" class=\"data row2 col1\" >0.4708</td>\n",
       "                        <td id=\"T_9871b_row2_col2\" class=\"data row2 col2\" >0.6861</td>\n",
       "                        <td id=\"T_9871b_row2_col3\" class=\"data row2 col3\" >0.8874</td>\n",
       "                        <td id=\"T_9871b_row2_col4\" class=\"data row2 col4\" >0.1723</td>\n",
       "                        <td id=\"T_9871b_row2_col5\" class=\"data row2 col5\" >0.3510</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_9871b_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_9871b_row3_col0\" class=\"data row3 col0\" >0.5132</td>\n",
       "                        <td id=\"T_9871b_row3_col1\" class=\"data row3 col1\" >0.5362</td>\n",
       "                        <td id=\"T_9871b_row3_col2\" class=\"data row3 col2\" >0.7323</td>\n",
       "                        <td id=\"T_9871b_row3_col3\" class=\"data row3 col3\" >0.8529</td>\n",
       "                        <td id=\"T_9871b_row3_col4\" class=\"data row3 col4\" >0.1985</td>\n",
       "                        <td id=\"T_9871b_row3_col5\" class=\"data row3 col5\" >0.2842</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_9871b_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_9871b_row4_col0\" class=\"data row4 col0\" >0.4885</td>\n",
       "                        <td id=\"T_9871b_row4_col1\" class=\"data row4 col1\" >0.3934</td>\n",
       "                        <td id=\"T_9871b_row4_col2\" class=\"data row4 col2\" >0.6272</td>\n",
       "                        <td id=\"T_9871b_row4_col3\" class=\"data row4 col3\" >0.9201</td>\n",
       "                        <td id=\"T_9871b_row4_col4\" class=\"data row4 col4\" >0.1844</td>\n",
       "                        <td id=\"T_9871b_row4_col5\" class=\"data row4 col5\" >0.2390</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_9871b_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_9871b_row5_col0\" class=\"data row5 col0\" >0.5068</td>\n",
       "                        <td id=\"T_9871b_row5_col1\" class=\"data row5 col1\" >0.4690</td>\n",
       "                        <td id=\"T_9871b_row5_col2\" class=\"data row5 col2\" >0.6848</td>\n",
       "                        <td id=\"T_9871b_row5_col3\" class=\"data row5 col3\" >0.8887</td>\n",
       "                        <td id=\"T_9871b_row5_col4\" class=\"data row5 col4\" >0.1832</td>\n",
       "                        <td id=\"T_9871b_row5_col5\" class=\"data row5 col5\" >0.2931</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_9871b_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_9871b_row6_col0\" class=\"data row6 col0\" >0.6142</td>\n",
       "                        <td id=\"T_9871b_row6_col1\" class=\"data row6 col1\" >0.6103</td>\n",
       "                        <td id=\"T_9871b_row6_col2\" class=\"data row6 col2\" >0.7812</td>\n",
       "                        <td id=\"T_9871b_row6_col3\" class=\"data row6 col3\" >0.8948</td>\n",
       "                        <td id=\"T_9871b_row6_col4\" class=\"data row6 col4\" >0.2201</td>\n",
       "                        <td id=\"T_9871b_row6_col5\" class=\"data row6 col5\" >1.0236</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_9871b_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_9871b_row7_col0\" class=\"data row7 col0\" >0.5094</td>\n",
       "                        <td id=\"T_9871b_row7_col1\" class=\"data row7 col1\" >0.4289</td>\n",
       "                        <td id=\"T_9871b_row7_col2\" class=\"data row7 col2\" >0.6549</td>\n",
       "                        <td id=\"T_9871b_row7_col3\" class=\"data row7 col3\" >0.9005</td>\n",
       "                        <td id=\"T_9871b_row7_col4\" class=\"data row7 col4\" >0.2000</td>\n",
       "                        <td id=\"T_9871b_row7_col5\" class=\"data row7 col5\" >0.4561</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_9871b_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_9871b_row8_col0\" class=\"data row8 col0\" >0.4565</td>\n",
       "                        <td id=\"T_9871b_row8_col1\" class=\"data row8 col1\" >0.4684</td>\n",
       "                        <td id=\"T_9871b_row8_col2\" class=\"data row8 col2\" >0.6844</td>\n",
       "                        <td id=\"T_9871b_row8_col3\" class=\"data row8 col3\" >0.8788</td>\n",
       "                        <td id=\"T_9871b_row8_col4\" class=\"data row8 col4\" >0.2132</td>\n",
       "                        <td id=\"T_9871b_row8_col5\" class=\"data row8 col5\" >0.5255</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_9871b_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_9871b_row9_col0\" class=\"data row9 col0\" >0.6193</td>\n",
       "                        <td id=\"T_9871b_row9_col1\" class=\"data row9 col1\" >0.6524</td>\n",
       "                        <td id=\"T_9871b_row9_col2\" class=\"data row9 col2\" >0.8077</td>\n",
       "                        <td id=\"T_9871b_row9_col3\" class=\"data row9 col3\" >0.8692</td>\n",
       "                        <td id=\"T_9871b_row9_col4\" class=\"data row9 col4\" >0.2778</td>\n",
       "                        <td id=\"T_9871b_row9_col5\" class=\"data row9 col5\" >2.4825</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_9871b_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "                        <td id=\"T_9871b_row10_col0\" class=\"data row10 col0\" >0.5289</td>\n",
       "                        <td id=\"T_9871b_row10_col1\" class=\"data row10 col1\" >0.5011</td>\n",
       "                        <td id=\"T_9871b_row10_col2\" class=\"data row10 col2\" >0.7060</td>\n",
       "                        <td id=\"T_9871b_row10_col3\" class=\"data row10 col3\" >0.8817</td>\n",
       "                        <td id=\"T_9871b_row10_col4\" class=\"data row10 col4\" >0.2018</td>\n",
       "                        <td id=\"T_9871b_row10_col5\" class=\"data row10 col5\" >0.6649</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_9871b_level0_row11\" class=\"row_heading level0 row11\" >SD</th>\n",
       "                        <td id=\"T_9871b_row11_col0\" class=\"data row11 col0\" >0.0495</td>\n",
       "                        <td id=\"T_9871b_row11_col1\" class=\"data row11 col1\" >0.0751</td>\n",
       "                        <td id=\"T_9871b_row11_col2\" class=\"data row11 col2\" >0.0520</td>\n",
       "                        <td id=\"T_9871b_row11_col3\" class=\"data row11 col3\" >0.0218</td>\n",
       "                        <td id=\"T_9871b_row11_col4\" class=\"data row11 col4\" >0.0295</td>\n",
       "                        <td id=\"T_9871b_row11_col5\" class=\"data row11 col5\" >0.6429</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1278f5cd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tuned_best = tune_model(best)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "83ae374e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_12044_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Model</th>        <th class=\"col_heading level0 col1\" >MAE</th>        <th class=\"col_heading level0 col2\" >MSE</th>        <th class=\"col_heading level0 col3\" >RMSE</th>        <th class=\"col_heading level0 col4\" >R2</th>        <th class=\"col_heading level0 col5\" >RMSLE</th>        <th class=\"col_heading level0 col6\" >MAPE</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_12044_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_12044_row0_col0\" class=\"data row0 col0\" >Light Gradient Boosting Machine</td>\n",
       "                        <td id=\"T_12044_row0_col1\" class=\"data row0 col1\" >0.4929</td>\n",
       "                        <td id=\"T_12044_row0_col2\" class=\"data row0 col2\" >0.4463</td>\n",
       "                        <td id=\"T_12044_row0_col3\" class=\"data row0 col3\" >0.6681</td>\n",
       "                        <td id=\"T_12044_row0_col4\" class=\"data row0 col4\" >0.9014</td>\n",
       "                        <td id=\"T_12044_row0_col5\" class=\"data row0 col5\" >0.1746</td>\n",
       "                        <td id=\"T_12044_row0_col6\" class=\"data row0 col6\" >0.3320</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x114cfa890>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MaxPartialCharge</th>\n",
       "      <th>BCUT2D_MWHI</th>\n",
       "      <th>BCUT2D_MRLOW</th>\n",
       "      <th>BertzCT</th>\n",
       "      <th>Kappa1</th>\n",
       "      <th>PEOE_VSA3</th>\n",
       "      <th>PEOE_VSA6</th>\n",
       "      <th>PEOE_VSA7</th>\n",
       "      <th>PEOE_VSA8</th>\n",
       "      <th>PEOE_VSA9</th>\n",
       "      <th>...</th>\n",
       "      <th>SlogP_VSA3</th>\n",
       "      <th>EState_VSA4</th>\n",
       "      <th>VSA_EState10</th>\n",
       "      <th>VSA_EState2</th>\n",
       "      <th>VSA_EState7</th>\n",
       "      <th>VSA_EState9</th>\n",
       "      <th>FractionCSP3</th>\n",
       "      <th>MolLogP</th>\n",
       "      <th>logs</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.886795</td>\n",
       "      <td>-0.581488</td>\n",
       "      <td>-0.370221</td>\n",
       "      <td>-1.280562</td>\n",
       "      <td>-0.004362</td>\n",
       "      <td>-0.602252</td>\n",
       "      <td>0.287770</td>\n",
       "      <td>-0.305969</td>\n",
       "      <td>0.861641</td>\n",
       "      <td>-0.895095</td>\n",
       "      <td>...</td>\n",
       "      <td>0.626442</td>\n",
       "      <td>0.952940</td>\n",
       "      <td>-0.677352</td>\n",
       "      <td>-0.908988</td>\n",
       "      <td>0.908496</td>\n",
       "      <td>-0.144482</td>\n",
       "      <td>1.394043</td>\n",
       "      <td>0.096012</td>\n",
       "      <td>-1.850</td>\n",
       "      <td>-2.374565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.190633</td>\n",
       "      <td>-1.180687</td>\n",
       "      <td>0.902737</td>\n",
       "      <td>-1.276477</td>\n",
       "      <td>-1.376126</td>\n",
       "      <td>-0.602252</td>\n",
       "      <td>-0.978237</td>\n",
       "      <td>-0.925067</td>\n",
       "      <td>0.076147</td>\n",
       "      <td>-0.895095</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.835778</td>\n",
       "      <td>0.924917</td>\n",
       "      <td>-0.677352</td>\n",
       "      <td>-0.908988</td>\n",
       "      <td>1.351391</td>\n",
       "      <td>-0.144482</td>\n",
       "      <td>0.571753</td>\n",
       "      <td>-0.553420</td>\n",
       "      <td>-1.640</td>\n",
       "      <td>-1.823694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.356549</td>\n",
       "      <td>-0.609089</td>\n",
       "      <td>-0.774131</td>\n",
       "      <td>0.962357</td>\n",
       "      <td>0.896536</td>\n",
       "      <td>-0.602252</td>\n",
       "      <td>-0.531234</td>\n",
       "      <td>2.199393</td>\n",
       "      <td>-1.132877</td>\n",
       "      <td>1.185412</td>\n",
       "      <td>...</td>\n",
       "      <td>0.888340</td>\n",
       "      <td>1.782857</td>\n",
       "      <td>-0.677352</td>\n",
       "      <td>-0.908988</td>\n",
       "      <td>0.646933</td>\n",
       "      <td>-0.144482</td>\n",
       "      <td>0.723230</td>\n",
       "      <td>0.083469</td>\n",
       "      <td>-4.955</td>\n",
       "      <td>-4.277392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.990820</td>\n",
       "      <td>1.229622</td>\n",
       "      <td>-1.012473</td>\n",
       "      <td>1.509644</td>\n",
       "      <td>1.569817</td>\n",
       "      <td>-0.602252</td>\n",
       "      <td>1.658530</td>\n",
       "      <td>1.435196</td>\n",
       "      <td>-1.132877</td>\n",
       "      <td>0.546389</td>\n",
       "      <td>...</td>\n",
       "      <td>1.580013</td>\n",
       "      <td>0.800004</td>\n",
       "      <td>1.651564</td>\n",
       "      <td>0.732067</td>\n",
       "      <td>0.102868</td>\n",
       "      <td>-0.144482</td>\n",
       "      <td>-0.278685</td>\n",
       "      <td>1.934099</td>\n",
       "      <td>-6.291</td>\n",
       "      <td>-6.446121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.399093</td>\n",
       "      <td>-1.196715</td>\n",
       "      <td>1.297037</td>\n",
       "      <td>-1.333785</td>\n",
       "      <td>-1.405602</td>\n",
       "      <td>-0.602252</td>\n",
       "      <td>-0.590060</td>\n",
       "      <td>-0.925067</td>\n",
       "      <td>-1.132877</td>\n",
       "      <td>-0.895095</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.835778</td>\n",
       "      <td>0.240059</td>\n",
       "      <td>-0.677352</td>\n",
       "      <td>-0.908988</td>\n",
       "      <td>0.823280</td>\n",
       "      <td>-0.144482</td>\n",
       "      <td>-0.555632</td>\n",
       "      <td>-0.371238</td>\n",
       "      <td>-2.090</td>\n",
       "      <td>-1.772832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>0.018880</td>\n",
       "      <td>1.229667</td>\n",
       "      <td>-0.148013</td>\n",
       "      <td>0.819907</td>\n",
       "      <td>0.792771</td>\n",
       "      <td>-0.602252</td>\n",
       "      <td>1.066501</td>\n",
       "      <td>0.799352</td>\n",
       "      <td>-1.132877</td>\n",
       "      <td>1.661174</td>\n",
       "      <td>...</td>\n",
       "      <td>0.626442</td>\n",
       "      <td>0.240059</td>\n",
       "      <td>1.927981</td>\n",
       "      <td>-1.360738</td>\n",
       "      <td>0.080824</td>\n",
       "      <td>-0.144482</td>\n",
       "      <td>1.073959</td>\n",
       "      <td>1.091366</td>\n",
       "      <td>-6.290</td>\n",
       "      <td>-5.947665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>-1.601615</td>\n",
       "      <td>-1.087209</td>\n",
       "      <td>0.160285</td>\n",
       "      <td>-1.283849</td>\n",
       "      <td>-0.552944</td>\n",
       "      <td>-0.602252</td>\n",
       "      <td>0.620677</td>\n",
       "      <td>-0.395410</td>\n",
       "      <td>-1.132877</td>\n",
       "      <td>-0.895095</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.835778</td>\n",
       "      <td>0.844631</td>\n",
       "      <td>-0.677352</td>\n",
       "      <td>-0.908988</td>\n",
       "      <td>-0.009549</td>\n",
       "      <td>-0.144482</td>\n",
       "      <td>1.394043</td>\n",
       "      <td>0.142066</td>\n",
       "      <td>-4.280</td>\n",
       "      <td>-3.973298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>-0.713711</td>\n",
       "      <td>1.228280</td>\n",
       "      <td>1.440386</td>\n",
       "      <td>1.025052</td>\n",
       "      <td>1.042898</td>\n",
       "      <td>-0.602252</td>\n",
       "      <td>1.990749</td>\n",
       "      <td>-0.368711</td>\n",
       "      <td>0.660969</td>\n",
       "      <td>1.874304</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.835778</td>\n",
       "      <td>-1.069928</td>\n",
       "      <td>1.920619</td>\n",
       "      <td>-0.908988</td>\n",
       "      <td>-0.408972</td>\n",
       "      <td>-0.144482</td>\n",
       "      <td>-1.265530</td>\n",
       "      <td>2.525058</td>\n",
       "      <td>-8.600</td>\n",
       "      <td>-8.110401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>0.787289</td>\n",
       "      <td>-0.550699</td>\n",
       "      <td>-1.684012</td>\n",
       "      <td>0.377827</td>\n",
       "      <td>0.033386</td>\n",
       "      <td>-0.602252</td>\n",
       "      <td>-1.546475</td>\n",
       "      <td>-0.293008</td>\n",
       "      <td>0.711765</td>\n",
       "      <td>1.403111</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.835778</td>\n",
       "      <td>0.186061</td>\n",
       "      <td>-0.677352</td>\n",
       "      <td>1.169553</td>\n",
       "      <td>-0.408972</td>\n",
       "      <td>-0.144482</td>\n",
       "      <td>-0.749748</td>\n",
       "      <td>-0.336513</td>\n",
       "      <td>-2.820</td>\n",
       "      <td>-2.713965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>1.039645</td>\n",
       "      <td>-0.619813</td>\n",
       "      <td>0.203278</td>\n",
       "      <td>0.446923</td>\n",
       "      <td>-0.905789</td>\n",
       "      <td>1.573171</td>\n",
       "      <td>-1.546475</td>\n",
       "      <td>-1.655528</td>\n",
       "      <td>-1.132877</td>\n",
       "      <td>0.606906</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.835778</td>\n",
       "      <td>-1.069928</td>\n",
       "      <td>-0.677352</td>\n",
       "      <td>0.827508</td>\n",
       "      <td>0.025739</td>\n",
       "      <td>-0.144482</td>\n",
       "      <td>-1.265530</td>\n",
       "      <td>-1.556138</td>\n",
       "      <td>-3.401</td>\n",
       "      <td>-1.705870</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>206 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     MaxPartialCharge  BCUT2D_MWHI  BCUT2D_MRLOW   BertzCT    Kappa1  \\\n",
       "0           -0.886795    -0.581488     -0.370221 -1.280562 -0.004362   \n",
       "1           -1.190633    -1.180687      0.902737 -1.276477 -1.376126   \n",
       "2           -0.356549    -0.609089     -0.774131  0.962357  0.896536   \n",
       "3            0.990820     1.229622     -1.012473  1.509644  1.569817   \n",
       "4           -1.399093    -1.196715      1.297037 -1.333785 -1.405602   \n",
       "..                ...          ...           ...       ...       ...   \n",
       "201          0.018880     1.229667     -0.148013  0.819907  0.792771   \n",
       "202         -1.601615    -1.087209      0.160285 -1.283849 -0.552944   \n",
       "203         -0.713711     1.228280      1.440386  1.025052  1.042898   \n",
       "204          0.787289    -0.550699     -1.684012  0.377827  0.033386   \n",
       "205          1.039645    -0.619813      0.203278  0.446923 -0.905789   \n",
       "\n",
       "     PEOE_VSA3  PEOE_VSA6  PEOE_VSA7  PEOE_VSA8  PEOE_VSA9  ...  SlogP_VSA3  \\\n",
       "0    -0.602252   0.287770  -0.305969   0.861641  -0.895095  ...    0.626442   \n",
       "1    -0.602252  -0.978237  -0.925067   0.076147  -0.895095  ...   -0.835778   \n",
       "2    -0.602252  -0.531234   2.199393  -1.132877   1.185412  ...    0.888340   \n",
       "3    -0.602252   1.658530   1.435196  -1.132877   0.546389  ...    1.580013   \n",
       "4    -0.602252  -0.590060  -0.925067  -1.132877  -0.895095  ...   -0.835778   \n",
       "..         ...        ...        ...        ...        ...  ...         ...   \n",
       "201  -0.602252   1.066501   0.799352  -1.132877   1.661174  ...    0.626442   \n",
       "202  -0.602252   0.620677  -0.395410  -1.132877  -0.895095  ...   -0.835778   \n",
       "203  -0.602252   1.990749  -0.368711   0.660969   1.874304  ...   -0.835778   \n",
       "204  -0.602252  -1.546475  -0.293008   0.711765   1.403111  ...   -0.835778   \n",
       "205   1.573171  -1.546475  -1.655528  -1.132877   0.606906  ...   -0.835778   \n",
       "\n",
       "     EState_VSA4  VSA_EState10  VSA_EState2  VSA_EState7  VSA_EState9  \\\n",
       "0       0.952940     -0.677352    -0.908988     0.908496    -0.144482   \n",
       "1       0.924917     -0.677352    -0.908988     1.351391    -0.144482   \n",
       "2       1.782857     -0.677352    -0.908988     0.646933    -0.144482   \n",
       "3       0.800004      1.651564     0.732067     0.102868    -0.144482   \n",
       "4       0.240059     -0.677352    -0.908988     0.823280    -0.144482   \n",
       "..           ...           ...          ...          ...          ...   \n",
       "201     0.240059      1.927981    -1.360738     0.080824    -0.144482   \n",
       "202     0.844631     -0.677352    -0.908988    -0.009549    -0.144482   \n",
       "203    -1.069928      1.920619    -0.908988    -0.408972    -0.144482   \n",
       "204     0.186061     -0.677352     1.169553    -0.408972    -0.144482   \n",
       "205    -1.069928     -0.677352     0.827508     0.025739    -0.144482   \n",
       "\n",
       "     FractionCSP3   MolLogP   logs     Label  \n",
       "0        1.394043  0.096012 -1.850 -2.374565  \n",
       "1        0.571753 -0.553420 -1.640 -1.823694  \n",
       "2        0.723230  0.083469 -4.955 -4.277392  \n",
       "3       -0.278685  1.934099 -6.291 -6.446121  \n",
       "4       -0.555632 -0.371238 -2.090 -1.772832  \n",
       "..            ...       ...    ...       ...  \n",
       "201      1.073959  1.091366 -6.290 -5.947665  \n",
       "202      1.394043  0.142066 -4.280 -3.973298  \n",
       "203     -1.265530  2.525058 -8.600 -8.110401  \n",
       "204     -0.749748 -0.336513 -2.820 -2.713965  \n",
       "205     -1.265530 -1.556138 -3.401 -1.705870  \n",
       "\n",
       "[206 rows x 23 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# predicting labels for the test set\n",
    "predict_model(tuned_best)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f6e920ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model(tuned_best)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d6e2618c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model(tuned_best, plot='error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4396cad5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model(tuned_best, plot='feature')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9a556b06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_9dea4_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Model</th>        <th class=\"col_heading level0 col1\" >MAE</th>        <th class=\"col_heading level0 col2\" >MSE</th>        <th class=\"col_heading level0 col3\" >RMSE</th>        <th class=\"col_heading level0 col4\" >R2</th>        <th class=\"col_heading level0 col5\" >RMSLE</th>        <th class=\"col_heading level0 col6\" >MAPE</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_9dea4_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_9dea4_row0_col0\" class=\"data row0 col0\" >Light Gradient Boosting Machine</td>\n",
       "                        <td id=\"T_9dea4_row0_col1\" class=\"data row0 col1\" >0.1924</td>\n",
       "                        <td id=\"T_9dea4_row0_col2\" class=\"data row0 col2\" >0.0666</td>\n",
       "                        <td id=\"T_9dea4_row0_col3\" class=\"data row0 col3\" >0.2581</td>\n",
       "                        <td id=\"T_9dea4_row0_col4\" class=\"data row0 col4\" >0.9853</td>\n",
       "                        <td id=\"T_9dea4_row0_col5\" class=\"data row0 col5\" >0.0849</td>\n",
       "                        <td id=\"T_9dea4_row0_col6\" class=\"data row0 col6\" >0.1592</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
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       "<pandas.io.formats.style.Styler at 0x137216b50>"
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MaxPartialCharge</th>\n",
       "      <th>BCUT2D_MWHI</th>\n",
       "      <th>BCUT2D_MRLOW</th>\n",
       "      <th>BertzCT</th>\n",
       "      <th>Kappa1</th>\n",
       "      <th>PEOE_VSA3</th>\n",
       "      <th>PEOE_VSA6</th>\n",
       "      <th>PEOE_VSA7</th>\n",
       "      <th>PEOE_VSA8</th>\n",
       "      <th>PEOE_VSA9</th>\n",
       "      <th>...</th>\n",
       "      <th>SlogP_VSA3</th>\n",
       "      <th>EState_VSA4</th>\n",
       "      <th>VSA_EState10</th>\n",
       "      <th>VSA_EState2</th>\n",
       "      <th>VSA_EState7</th>\n",
       "      <th>VSA_EState9</th>\n",
       "      <th>FractionCSP3</th>\n",
       "      <th>MolLogP</th>\n",
       "      <th>logs</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.886795</td>\n",
       "      <td>-0.581488</td>\n",
       "      <td>-0.370221</td>\n",
       "      <td>-1.280562</td>\n",
       "      <td>-0.004362</td>\n",
       "      <td>-0.602252</td>\n",
       "      <td>0.287770</td>\n",
       "      <td>-0.305969</td>\n",
       "      <td>0.861641</td>\n",
       "      <td>-0.895095</td>\n",
       "      <td>...</td>\n",
       "      <td>0.626442</td>\n",
       "      <td>0.952940</td>\n",
       "      <td>-0.677352</td>\n",
       "      <td>-0.908988</td>\n",
       "      <td>0.908496</td>\n",
       "      <td>-0.144482</td>\n",
       "      <td>1.394043</td>\n",
       "      <td>0.096012</td>\n",
       "      <td>-1.850</td>\n",
       "      <td>-2.034845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.190633</td>\n",
       "      <td>-1.180687</td>\n",
       "      <td>0.902737</td>\n",
       "      <td>-1.276477</td>\n",
       "      <td>-1.376126</td>\n",
       "      <td>-0.602252</td>\n",
       "      <td>-0.978237</td>\n",
       "      <td>-0.925067</td>\n",
       "      <td>0.076147</td>\n",
       "      <td>-0.895095</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.835778</td>\n",
       "      <td>0.924917</td>\n",
       "      <td>-0.677352</td>\n",
       "      <td>-0.908988</td>\n",
       "      <td>1.351391</td>\n",
       "      <td>-0.144482</td>\n",
       "      <td>0.571753</td>\n",
       "      <td>-0.553420</td>\n",
       "      <td>-1.640</td>\n",
       "      <td>-1.601070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.356549</td>\n",
       "      <td>-0.609089</td>\n",
       "      <td>-0.774131</td>\n",
       "      <td>0.962357</td>\n",
       "      <td>0.896536</td>\n",
       "      <td>-0.602252</td>\n",
       "      <td>-0.531234</td>\n",
       "      <td>2.199393</td>\n",
       "      <td>-1.132877</td>\n",
       "      <td>1.185412</td>\n",
       "      <td>...</td>\n",
       "      <td>0.888340</td>\n",
       "      <td>1.782857</td>\n",
       "      <td>-0.677352</td>\n",
       "      <td>-0.908988</td>\n",
       "      <td>0.646933</td>\n",
       "      <td>-0.144482</td>\n",
       "      <td>0.723230</td>\n",
       "      <td>0.083469</td>\n",
       "      <td>-4.955</td>\n",
       "      <td>-4.646206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.990820</td>\n",
       "      <td>1.229622</td>\n",
       "      <td>-1.012473</td>\n",
       "      <td>1.509644</td>\n",
       "      <td>1.569817</td>\n",
       "      <td>-0.602252</td>\n",
       "      <td>1.658530</td>\n",
       "      <td>1.435196</td>\n",
       "      <td>-1.132877</td>\n",
       "      <td>0.546389</td>\n",
       "      <td>...</td>\n",
       "      <td>1.580013</td>\n",
       "      <td>0.800004</td>\n",
       "      <td>1.651564</td>\n",
       "      <td>0.732067</td>\n",
       "      <td>0.102868</td>\n",
       "      <td>-0.144482</td>\n",
       "      <td>-0.278685</td>\n",
       "      <td>1.934099</td>\n",
       "      <td>-6.291</td>\n",
       "      <td>-6.239954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.399093</td>\n",
       "      <td>-1.196715</td>\n",
       "      <td>1.297037</td>\n",
       "      <td>-1.333785</td>\n",
       "      <td>-1.405602</td>\n",
       "      <td>-0.602252</td>\n",
       "      <td>-0.590060</td>\n",
       "      <td>-0.925067</td>\n",
       "      <td>-1.132877</td>\n",
       "      <td>-0.895095</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.835778</td>\n",
       "      <td>0.240059</td>\n",
       "      <td>-0.677352</td>\n",
       "      <td>-0.908988</td>\n",
       "      <td>0.823280</td>\n",
       "      <td>-0.144482</td>\n",
       "      <td>-0.555632</td>\n",
       "      <td>-0.371238</td>\n",
       "      <td>-2.090</td>\n",
       "      <td>-2.092519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>0.018880</td>\n",
       "      <td>1.229667</td>\n",
       "      <td>-0.148013</td>\n",
       "      <td>0.819907</td>\n",
       "      <td>0.792771</td>\n",
       "      <td>-0.602252</td>\n",
       "      <td>1.066501</td>\n",
       "      <td>0.799352</td>\n",
       "      <td>-1.132877</td>\n",
       "      <td>1.661174</td>\n",
       "      <td>...</td>\n",
       "      <td>0.626442</td>\n",
       "      <td>0.240059</td>\n",
       "      <td>1.927981</td>\n",
       "      <td>-1.360738</td>\n",
       "      <td>0.080824</td>\n",
       "      <td>-0.144482</td>\n",
       "      <td>1.073959</td>\n",
       "      <td>1.091366</td>\n",
       "      <td>-6.290</td>\n",
       "      <td>-6.256551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>-1.601615</td>\n",
       "      <td>-1.087209</td>\n",
       "      <td>0.160285</td>\n",
       "      <td>-1.283849</td>\n",
       "      <td>-0.552944</td>\n",
       "      <td>-0.602252</td>\n",
       "      <td>0.620677</td>\n",
       "      <td>-0.395410</td>\n",
       "      <td>-1.132877</td>\n",
       "      <td>-0.895095</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.835778</td>\n",
       "      <td>0.844631</td>\n",
       "      <td>-0.677352</td>\n",
       "      <td>-0.908988</td>\n",
       "      <td>-0.009549</td>\n",
       "      <td>-0.144482</td>\n",
       "      <td>1.394043</td>\n",
       "      <td>0.142066</td>\n",
       "      <td>-4.280</td>\n",
       "      <td>-4.111535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>-0.713711</td>\n",
       "      <td>1.228280</td>\n",
       "      <td>1.440386</td>\n",
       "      <td>1.025052</td>\n",
       "      <td>1.042898</td>\n",
       "      <td>-0.602252</td>\n",
       "      <td>1.990749</td>\n",
       "      <td>-0.368711</td>\n",
       "      <td>0.660969</td>\n",
       "      <td>1.874304</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.835778</td>\n",
       "      <td>-1.069928</td>\n",
       "      <td>1.920619</td>\n",
       "      <td>-0.908988</td>\n",
       "      <td>-0.408972</td>\n",
       "      <td>-0.144482</td>\n",
       "      <td>-1.265530</td>\n",
       "      <td>2.525058</td>\n",
       "      <td>-8.600</td>\n",
       "      <td>-8.367751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>0.787289</td>\n",
       "      <td>-0.550699</td>\n",
       "      <td>-1.684012</td>\n",
       "      <td>0.377827</td>\n",
       "      <td>0.033386</td>\n",
       "      <td>-0.602252</td>\n",
       "      <td>-1.546475</td>\n",
       "      <td>-0.293008</td>\n",
       "      <td>0.711765</td>\n",
       "      <td>1.403111</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.835778</td>\n",
       "      <td>0.186061</td>\n",
       "      <td>-0.677352</td>\n",
       "      <td>1.169553</td>\n",
       "      <td>-0.408972</td>\n",
       "      <td>-0.144482</td>\n",
       "      <td>-0.749748</td>\n",
       "      <td>-0.336513</td>\n",
       "      <td>-2.820</td>\n",
       "      <td>-2.591698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>1.039645</td>\n",
       "      <td>-0.619813</td>\n",
       "      <td>0.203278</td>\n",
       "      <td>0.446923</td>\n",
       "      <td>-0.905789</td>\n",
       "      <td>1.573171</td>\n",
       "      <td>-1.546475</td>\n",
       "      <td>-1.655528</td>\n",
       "      <td>-1.132877</td>\n",
       "      <td>0.606906</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.835778</td>\n",
       "      <td>-1.069928</td>\n",
       "      <td>-0.677352</td>\n",
       "      <td>0.827508</td>\n",
       "      <td>0.025739</td>\n",
       "      <td>-0.144482</td>\n",
       "      <td>-1.265530</td>\n",
       "      <td>-1.556138</td>\n",
       "      <td>-3.401</td>\n",
       "      <td>-2.716679</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>206 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     MaxPartialCharge  BCUT2D_MWHI  BCUT2D_MRLOW   BertzCT    Kappa1  \\\n",
       "0           -0.886795    -0.581488     -0.370221 -1.280562 -0.004362   \n",
       "1           -1.190633    -1.180687      0.902737 -1.276477 -1.376126   \n",
       "2           -0.356549    -0.609089     -0.774131  0.962357  0.896536   \n",
       "3            0.990820     1.229622     -1.012473  1.509644  1.569817   \n",
       "4           -1.399093    -1.196715      1.297037 -1.333785 -1.405602   \n",
       "..                ...          ...           ...       ...       ...   \n",
       "201          0.018880     1.229667     -0.148013  0.819907  0.792771   \n",
       "202         -1.601615    -1.087209      0.160285 -1.283849 -0.552944   \n",
       "203         -0.713711     1.228280      1.440386  1.025052  1.042898   \n",
       "204          0.787289    -0.550699     -1.684012  0.377827  0.033386   \n",
       "205          1.039645    -0.619813      0.203278  0.446923 -0.905789   \n",
       "\n",
       "     PEOE_VSA3  PEOE_VSA6  PEOE_VSA7  PEOE_VSA8  PEOE_VSA9  ...  SlogP_VSA3  \\\n",
       "0    -0.602252   0.287770  -0.305969   0.861641  -0.895095  ...    0.626442   \n",
       "1    -0.602252  -0.978237  -0.925067   0.076147  -0.895095  ...   -0.835778   \n",
       "2    -0.602252  -0.531234   2.199393  -1.132877   1.185412  ...    0.888340   \n",
       "3    -0.602252   1.658530   1.435196  -1.132877   0.546389  ...    1.580013   \n",
       "4    -0.602252  -0.590060  -0.925067  -1.132877  -0.895095  ...   -0.835778   \n",
       "..         ...        ...        ...        ...        ...  ...         ...   \n",
       "201  -0.602252   1.066501   0.799352  -1.132877   1.661174  ...    0.626442   \n",
       "202  -0.602252   0.620677  -0.395410  -1.132877  -0.895095  ...   -0.835778   \n",
       "203  -0.602252   1.990749  -0.368711   0.660969   1.874304  ...   -0.835778   \n",
       "204  -0.602252  -1.546475  -0.293008   0.711765   1.403111  ...   -0.835778   \n",
       "205   1.573171  -1.546475  -1.655528  -1.132877   0.606906  ...   -0.835778   \n",
       "\n",
       "     EState_VSA4  VSA_EState10  VSA_EState2  VSA_EState7  VSA_EState9  \\\n",
       "0       0.952940     -0.677352    -0.908988     0.908496    -0.144482   \n",
       "1       0.924917     -0.677352    -0.908988     1.351391    -0.144482   \n",
       "2       1.782857     -0.677352    -0.908988     0.646933    -0.144482   \n",
       "3       0.800004      1.651564     0.732067     0.102868    -0.144482   \n",
       "4       0.240059     -0.677352    -0.908988     0.823280    -0.144482   \n",
       "..           ...           ...          ...          ...          ...   \n",
       "201     0.240059      1.927981    -1.360738     0.080824    -0.144482   \n",
       "202     0.844631     -0.677352    -0.908988    -0.009549    -0.144482   \n",
       "203    -1.069928      1.920619    -0.908988    -0.408972    -0.144482   \n",
       "204     0.186061     -0.677352     1.169553    -0.408972    -0.144482   \n",
       "205    -1.069928     -0.677352     0.827508     0.025739    -0.144482   \n",
       "\n",
       "     FractionCSP3   MolLogP   logs     Label  \n",
       "0        1.394043  0.096012 -1.850 -2.034845  \n",
       "1        0.571753 -0.553420 -1.640 -1.601070  \n",
       "2        0.723230  0.083469 -4.955 -4.646206  \n",
       "3       -0.278685  1.934099 -6.291 -6.239954  \n",
       "4       -0.555632 -0.371238 -2.090 -2.092519  \n",
       "..            ...       ...    ...       ...  \n",
       "201      1.073959  1.091366 -6.290 -6.256551  \n",
       "202      1.394043  0.142066 -4.280 -4.111535  \n",
       "203     -1.265530  2.525058 -8.600 -8.367751  \n",
       "204     -0.749748 -0.336513 -2.820 -2.591698  \n",
       "205     -1.265530 -1.556138 -3.401 -2.716679  \n",
       "\n",
       "[206 rows x 23 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train on full dataset and create the final model\n",
    "final_model = finalize_model(tuned_best)\n",
    "predict_model(final_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a242556e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MaxPartialCharge</th>\n",
       "      <th>BCUT2D_MWHI</th>\n",
       "      <th>BCUT2D_MRLOW</th>\n",
       "      <th>BertzCT</th>\n",
       "      <th>Kappa1</th>\n",
       "      <th>PEOE_VSA3</th>\n",
       "      <th>PEOE_VSA6</th>\n",
       "      <th>PEOE_VSA7</th>\n",
       "      <th>PEOE_VSA8</th>\n",
       "      <th>PEOE_VSA9</th>\n",
       "      <th>...</th>\n",
       "      <th>SlogP_VSA3</th>\n",
       "      <th>EState_VSA4</th>\n",
       "      <th>VSA_EState10</th>\n",
       "      <th>VSA_EState2</th>\n",
       "      <th>VSA_EState7</th>\n",
       "      <th>VSA_EState9</th>\n",
       "      <th>FractionCSP3</th>\n",
       "      <th>MolLogP</th>\n",
       "      <th>logs</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.104589</td>\n",
       "      <td>35.539166</td>\n",
       "      <td>1.387534</td>\n",
       "      <td>8.754888</td>\n",
       "      <td>4.580000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.923737</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.080247</td>\n",
       "      <td>-0.222222</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.81000</td>\n",
       "      <td>-1.290</td>\n",
       "      <td>-1.140979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.154255</td>\n",
       "      <td>16.675371</td>\n",
       "      <td>-0.122813</td>\n",
       "      <td>39.793194</td>\n",
       "      <td>7.920000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.771212</td>\n",
       "      <td>13.213764</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>9.473726</td>\n",
       "      <td>13.213764</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.037037</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.40540</td>\n",
       "      <td>-0.430</td>\n",
       "      <td>-0.429935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.032898</td>\n",
       "      <td>79.922000</td>\n",
       "      <td>1.506933</td>\n",
       "      <td>205.919883</td>\n",
       "      <td>9.229767</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>75.852510</td>\n",
       "      <td>17.890878</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.890878</td>\n",
       "      <td>13.529815</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.73660</td>\n",
       "      <td>-6.980</td>\n",
       "      <td>-4.697492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.060661</td>\n",
       "      <td>35.499014</td>\n",
       "      <td>1.704597</td>\n",
       "      <td>219.543000</td>\n",
       "      <td>7.200011</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>34.802820</td>\n",
       "      <td>18.199101</td>\n",
       "      <td>5.022633</td>\n",
       "      <td>10.045267</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>16.761481</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.64680</td>\n",
       "      <td>-3.590</td>\n",
       "      <td>-4.054611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.039755</td>\n",
       "      <td>13.931090</td>\n",
       "      <td>1.322148</td>\n",
       "      <td>160.800515</td>\n",
       "      <td>6.341655</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>34.889456</td>\n",
       "      <td>20.771212</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>2.61186</td>\n",
       "      <td>-3.400</td>\n",
       "      <td>-3.216557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>0.241599</td>\n",
       "      <td>32.092047</td>\n",
       "      <td>-0.147378</td>\n",
       "      <td>307.946252</td>\n",
       "      <td>13.096534</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.192033</td>\n",
       "      <td>30.977423</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>9.589074</td>\n",
       "      <td>12.841643</td>\n",
       "      <td>4.797601</td>\n",
       "      <td>24.049915</td>\n",
       "      <td>2.295448</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>1.34990</td>\n",
       "      <td>-3.360</td>\n",
       "      <td>-3.111525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>0.118789</td>\n",
       "      <td>16.255418</td>\n",
       "      <td>0.464180</td>\n",
       "      <td>251.049732</td>\n",
       "      <td>8.119800</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>25.980209</td>\n",
       "      <td>30.034913</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.126903</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>2.82402</td>\n",
       "      <td>-2.220</td>\n",
       "      <td>-2.389812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>0.281950</td>\n",
       "      <td>35.539345</td>\n",
       "      <td>0.189699</td>\n",
       "      <td>267.237707</td>\n",
       "      <td>16.430000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>46.564705</td>\n",
       "      <td>27.694949</td>\n",
       "      <td>17.836535</td>\n",
       "      <td>5.032286</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.761885</td>\n",
       "      <td>17.836171</td>\n",
       "      <td>13.663351</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>4.84390</td>\n",
       "      <td>-4.880</td>\n",
       "      <td>-4.952471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>0.362333</td>\n",
       "      <td>35.582521</td>\n",
       "      <td>0.176285</td>\n",
       "      <td>183.232219</td>\n",
       "      <td>12.980000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>34.802820</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.219595</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>13.612543</td>\n",
       "      <td>14.219595</td>\n",
       "      <td>15.822569</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.559576</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.16090</td>\n",
       "      <td>-0.220</td>\n",
       "      <td>-2.633336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>0.411839</td>\n",
       "      <td>16.549804</td>\n",
       "      <td>0.202664</td>\n",
       "      <td>300.450826</td>\n",
       "      <td>9.577032</td>\n",
       "      <td>4.794537</td>\n",
       "      <td>6.066367</td>\n",
       "      <td>37.107112</td>\n",
       "      <td>7.047672</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.126903</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.870474</td>\n",
       "      <td>-0.439815</td>\n",
       "      <td>1.532726</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>2.02164</td>\n",
       "      <td>-2.581</td>\n",
       "      <td>-1.762005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>114 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     MaxPartialCharge  BCUT2D_MWHI  BCUT2D_MRLOW     BertzCT     Kappa1  \\\n",
       "0            0.104589    35.539166      1.387534    8.754888   4.580000   \n",
       "1            0.154255    16.675371     -0.122813   39.793194   7.920000   \n",
       "2            0.032898    79.922000      1.506933  205.919883   9.229767   \n",
       "3            0.060661    35.499014      1.704597  219.543000   7.200011   \n",
       "4           -0.039755    13.931090      1.322148  160.800515   6.341655   \n",
       "..                ...          ...           ...         ...        ...   \n",
       "109          0.241599    32.092047     -0.147378  307.946252  13.096534   \n",
       "110          0.118789    16.255418      0.464180  251.049732   8.119800   \n",
       "111          0.281950    35.539345      0.189699  267.237707  16.430000   \n",
       "112          0.362333    35.582521      0.176285  183.232219  12.980000   \n",
       "113          0.411839    16.549804      0.202664  300.450826   9.577032   \n",
       "\n",
       "     PEOE_VSA3  PEOE_VSA6  PEOE_VSA7  PEOE_VSA8  PEOE_VSA9  ...  SlogP_VSA3  \\\n",
       "0     0.000000   0.000000   6.923737   0.000000   0.000000  ...    0.000000   \n",
       "1     0.000000   0.000000  20.771212  13.213764   0.000000  ...    9.473726   \n",
       "2     0.000000   0.000000  75.852510  17.890878   0.000000  ...    0.000000   \n",
       "3     0.000000  34.802820  18.199101   5.022633  10.045267  ...    0.000000   \n",
       "4     0.000000  34.889456  20.771212   0.000000   0.000000  ...    0.000000   \n",
       "..         ...        ...        ...        ...        ...  ...         ...   \n",
       "109   0.000000  27.192033  30.977423   0.000000   0.000000  ...    9.589074   \n",
       "110   0.000000  25.980209  30.034913   0.000000   0.000000  ...    0.000000   \n",
       "111   0.000000  46.564705  27.694949  17.836535   5.032286  ...    0.000000   \n",
       "112   0.000000  34.802820   0.000000  14.219595   0.000000  ...   13.612543   \n",
       "113   4.794537   6.066367  37.107112   7.047672   0.000000  ...    0.000000   \n",
       "\n",
       "     EState_VSA4  VSA_EState10  VSA_EState2  VSA_EState7  VSA_EState9  \\\n",
       "0       0.000000     10.080247    -0.222222     0.000000     0.000000   \n",
       "1      13.213764      0.000000     0.000000    -0.037037     0.000000   \n",
       "2      17.890878     13.529815     0.000000     0.000000     0.000000   \n",
       "3       0.000000     16.761481     0.000000     0.000000     0.000000   \n",
       "4       0.000000      0.000000     0.000000     0.000000     0.000000   \n",
       "..           ...           ...          ...          ...          ...   \n",
       "109    12.841643      4.797601    24.049915     2.295448     0.000000   \n",
       "110    11.126903      0.000000     0.000000     0.000000     0.000000   \n",
       "111    11.761885     17.836171    13.663351     0.000000     0.000000   \n",
       "112    14.219595     15.822569     0.000000     0.000000    -1.559576   \n",
       "113    11.126903      0.000000    10.870474    -0.439815     1.532726   \n",
       "\n",
       "     FractionCSP3  MolLogP   logs     Label  \n",
       "0        1.000000  1.81000 -1.290 -1.140979  \n",
       "1        1.000000  1.40540 -0.430 -0.429935  \n",
       "2        0.000000  4.73660 -6.980 -4.697492  \n",
       "3        0.000000  3.64680 -3.590 -4.054611  \n",
       "4        0.333333  2.61186 -3.400 -3.216557  \n",
       "..            ...      ...    ...       ...  \n",
       "109      0.727273  1.34990 -3.360 -3.111525  \n",
       "110      0.400000  2.82402 -2.220 -2.389812  \n",
       "111      0.700000  4.84390 -4.880 -4.952471  \n",
       "112      1.000000  2.16090 -0.220 -2.633336  \n",
       "113      0.300000  2.02164 -2.581 -1.762005  \n",
       "\n",
       "[114 rows x 23 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unseen_predictions = predict_model(final_model, data=df_unseen)\n",
    "unseen_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "467a4ef3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.8481, 0.5969, 0.6185)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pycaret.utils import check_metric\n",
    "check_metric(unseen_predictions.logs, unseen_predictions.Label, 'R2'), check_metric(unseen_predictions.logs, unseen_predictions.Label, 'MAE'), check_metric(unseen_predictions.logs, unseen_predictions.Label, 'RMSE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "83ea0d0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "#T_ec5a7_row10_col0,#T_ec5a7_row10_col1,#T_ec5a7_row10_col2,#T_ec5a7_row10_col3,#T_ec5a7_row10_col4,#T_ec5a7_row10_col5{\n",
       "            background:  yellow;\n",
       "        }</style><table id=\"T_ec5a7_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >MAE</th>        <th class=\"col_heading level0 col1\" >MSE</th>        <th class=\"col_heading level0 col2\" >RMSE</th>        <th class=\"col_heading level0 col3\" >R2</th>        <th class=\"col_heading level0 col4\" >RMSLE</th>        <th class=\"col_heading level0 col5\" >MAPE</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_ec5a7_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_ec5a7_row0_col0\" class=\"data row0 col0\" >0.4866</td>\n",
       "                        <td id=\"T_ec5a7_row0_col1\" class=\"data row0 col1\" >0.4250</td>\n",
       "                        <td id=\"T_ec5a7_row0_col2\" class=\"data row0 col2\" >0.6520</td>\n",
       "                        <td id=\"T_ec5a7_row0_col3\" class=\"data row0 col3\" >0.8655</td>\n",
       "                        <td id=\"T_ec5a7_row0_col4\" class=\"data row0 col4\" >0.1610</td>\n",
       "                        <td id=\"T_ec5a7_row0_col5\" class=\"data row0 col5\" >0.5820</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ec5a7_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_ec5a7_row1_col0\" class=\"data row1 col0\" >0.5186</td>\n",
       "                        <td id=\"T_ec5a7_row1_col1\" class=\"data row1 col1\" >0.5263</td>\n",
       "                        <td id=\"T_ec5a7_row1_col2\" class=\"data row1 col2\" >0.7255</td>\n",
       "                        <td id=\"T_ec5a7_row1_col3\" class=\"data row1 col3\" >0.8725</td>\n",
       "                        <td id=\"T_ec5a7_row1_col4\" class=\"data row1 col4\" >0.1975</td>\n",
       "                        <td id=\"T_ec5a7_row1_col5\" class=\"data row1 col5\" >0.3994</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ec5a7_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_ec5a7_row2_col0\" class=\"data row2 col0\" >0.5105</td>\n",
       "                        <td id=\"T_ec5a7_row2_col1\" class=\"data row2 col1\" >0.4521</td>\n",
       "                        <td id=\"T_ec5a7_row2_col2\" class=\"data row2 col2\" >0.6724</td>\n",
       "                        <td id=\"T_ec5a7_row2_col3\" class=\"data row2 col3\" >0.8918</td>\n",
       "                        <td id=\"T_ec5a7_row2_col4\" class=\"data row2 col4\" >0.1668</td>\n",
       "                        <td id=\"T_ec5a7_row2_col5\" class=\"data row2 col5\" >0.4283</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ec5a7_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_ec5a7_row3_col0\" class=\"data row3 col0\" >0.4328</td>\n",
       "                        <td id=\"T_ec5a7_row3_col1\" class=\"data row3 col1\" >0.3867</td>\n",
       "                        <td id=\"T_ec5a7_row3_col2\" class=\"data row3 col2\" >0.6219</td>\n",
       "                        <td id=\"T_ec5a7_row3_col3\" class=\"data row3 col3\" >0.8939</td>\n",
       "                        <td id=\"T_ec5a7_row3_col4\" class=\"data row3 col4\" >0.1683</td>\n",
       "                        <td id=\"T_ec5a7_row3_col5\" class=\"data row3 col5\" >0.2236</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ec5a7_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_ec5a7_row4_col0\" class=\"data row4 col0\" >0.4521</td>\n",
       "                        <td id=\"T_ec5a7_row4_col1\" class=\"data row4 col1\" >0.3444</td>\n",
       "                        <td id=\"T_ec5a7_row4_col2\" class=\"data row4 col2\" >0.5869</td>\n",
       "                        <td id=\"T_ec5a7_row4_col3\" class=\"data row4 col3\" >0.9300</td>\n",
       "                        <td id=\"T_ec5a7_row4_col4\" class=\"data row4 col4\" >0.1602</td>\n",
       "                        <td id=\"T_ec5a7_row4_col5\" class=\"data row4 col5\" >0.2145</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ec5a7_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_ec5a7_row5_col0\" class=\"data row5 col0\" >0.5121</td>\n",
       "                        <td id=\"T_ec5a7_row5_col1\" class=\"data row5 col1\" >0.4607</td>\n",
       "                        <td id=\"T_ec5a7_row5_col2\" class=\"data row5 col2\" >0.6787</td>\n",
       "                        <td id=\"T_ec5a7_row5_col3\" class=\"data row5 col3\" >0.8906</td>\n",
       "                        <td id=\"T_ec5a7_row5_col4\" class=\"data row5 col4\" >0.1729</td>\n",
       "                        <td id=\"T_ec5a7_row5_col5\" class=\"data row5 col5\" >0.3299</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ec5a7_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_ec5a7_row6_col0\" class=\"data row6 col0\" >0.5757</td>\n",
       "                        <td id=\"T_ec5a7_row6_col1\" class=\"data row6 col1\" >0.6082</td>\n",
       "                        <td id=\"T_ec5a7_row6_col2\" class=\"data row6 col2\" >0.7799</td>\n",
       "                        <td id=\"T_ec5a7_row6_col3\" class=\"data row6 col3\" >0.8952</td>\n",
       "                        <td id=\"T_ec5a7_row6_col4\" class=\"data row6 col4\" >0.2086</td>\n",
       "                        <td id=\"T_ec5a7_row6_col5\" class=\"data row6 col5\" >0.7711</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ec5a7_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_ec5a7_row7_col0\" class=\"data row7 col0\" >0.4619</td>\n",
       "                        <td id=\"T_ec5a7_row7_col1\" class=\"data row7 col1\" >0.3628</td>\n",
       "                        <td id=\"T_ec5a7_row7_col2\" class=\"data row7 col2\" >0.6023</td>\n",
       "                        <td id=\"T_ec5a7_row7_col3\" class=\"data row7 col3\" >0.9158</td>\n",
       "                        <td id=\"T_ec5a7_row7_col4\" class=\"data row7 col4\" >0.1714</td>\n",
       "                        <td id=\"T_ec5a7_row7_col5\" class=\"data row7 col5\" >0.4520</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ec5a7_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_ec5a7_row8_col0\" class=\"data row8 col0\" >0.4075</td>\n",
       "                        <td id=\"T_ec5a7_row8_col1\" class=\"data row8 col1\" >0.4150</td>\n",
       "                        <td id=\"T_ec5a7_row8_col2\" class=\"data row8 col2\" >0.6442</td>\n",
       "                        <td id=\"T_ec5a7_row8_col3\" class=\"data row8 col3\" >0.8926</td>\n",
       "                        <td id=\"T_ec5a7_row8_col4\" class=\"data row8 col4\" >0.2128</td>\n",
       "                        <td id=\"T_ec5a7_row8_col5\" class=\"data row8 col5\" >0.4892</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ec5a7_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_ec5a7_row9_col0\" class=\"data row9 col0\" >0.5049</td>\n",
       "                        <td id=\"T_ec5a7_row9_col1\" class=\"data row9 col1\" >0.4426</td>\n",
       "                        <td id=\"T_ec5a7_row9_col2\" class=\"data row9 col2\" >0.6653</td>\n",
       "                        <td id=\"T_ec5a7_row9_col3\" class=\"data row9 col3\" >0.9113</td>\n",
       "                        <td id=\"T_ec5a7_row9_col4\" class=\"data row9 col4\" >0.2326</td>\n",
       "                        <td id=\"T_ec5a7_row9_col5\" class=\"data row9 col5\" >1.8519</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ec5a7_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "                        <td id=\"T_ec5a7_row10_col0\" class=\"data row10 col0\" >0.4863</td>\n",
       "                        <td id=\"T_ec5a7_row10_col1\" class=\"data row10 col1\" >0.4424</td>\n",
       "                        <td id=\"T_ec5a7_row10_col2\" class=\"data row10 col2\" >0.6629</td>\n",
       "                        <td id=\"T_ec5a7_row10_col3\" class=\"data row10 col3\" >0.8959</td>\n",
       "                        <td id=\"T_ec5a7_row10_col4\" class=\"data row10 col4\" >0.1852</td>\n",
       "                        <td id=\"T_ec5a7_row10_col5\" class=\"data row10 col5\" >0.5742</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ec5a7_level0_row11\" class=\"row_heading level0 row11\" >SD</th>\n",
       "                        <td id=\"T_ec5a7_row11_col0\" class=\"data row11 col0\" >0.0463</td>\n",
       "                        <td id=\"T_ec5a7_row11_col1\" class=\"data row11 col1\" >0.0742</td>\n",
       "                        <td id=\"T_ec5a7_row11_col2\" class=\"data row11 col2\" >0.0544</td>\n",
       "                        <td id=\"T_ec5a7_row11_col3\" class=\"data row11 col3\" >0.0182</td>\n",
       "                        <td id=\"T_ec5a7_row11_col4\" class=\"data row11 col4\" >0.0243</td>\n",
       "                        <td id=\"T_ec5a7_row11_col5\" class=\"data row11 col5\" >0.4536</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x137227ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Improving the model using ensemble techniques\n",
    "bagged_dt = ensemble_model(tuned_best)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0b86fca2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "#T_31094_row10_col0,#T_31094_row10_col1,#T_31094_row10_col2,#T_31094_row10_col3,#T_31094_row10_col4,#T_31094_row10_col5{\n",
       "            background:  yellow;\n",
       "        }</style><table id=\"T_31094_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >MAE</th>        <th class=\"col_heading level0 col1\" >MSE</th>        <th class=\"col_heading level0 col2\" >RMSE</th>        <th class=\"col_heading level0 col3\" >R2</th>        <th class=\"col_heading level0 col4\" >RMSLE</th>        <th class=\"col_heading level0 col5\" >MAPE</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_31094_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_31094_row0_col0\" class=\"data row0 col0\" >0.5035</td>\n",
       "                        <td id=\"T_31094_row0_col1\" class=\"data row0 col1\" >0.4425</td>\n",
       "                        <td id=\"T_31094_row0_col2\" class=\"data row0 col2\" >0.6652</td>\n",
       "                        <td id=\"T_31094_row0_col3\" class=\"data row0 col3\" >0.8600</td>\n",
       "                        <td id=\"T_31094_row0_col4\" class=\"data row0 col4\" >0.1656</td>\n",
       "                        <td id=\"T_31094_row0_col5\" class=\"data row0 col5\" >0.3814</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_31094_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_31094_row1_col0\" class=\"data row1 col0\" >0.5398</td>\n",
       "                        <td id=\"T_31094_row1_col1\" class=\"data row1 col1\" >0.5182</td>\n",
       "                        <td id=\"T_31094_row1_col2\" class=\"data row1 col2\" >0.7198</td>\n",
       "                        <td id=\"T_31094_row1_col3\" class=\"data row1 col3\" >0.8745</td>\n",
       "                        <td id=\"T_31094_row1_col4\" class=\"data row1 col4\" >0.1957</td>\n",
       "                        <td id=\"T_31094_row1_col5\" class=\"data row1 col5\" >0.4163</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_31094_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_31094_row2_col0\" class=\"data row2 col0\" >0.5282</td>\n",
       "                        <td id=\"T_31094_row2_col1\" class=\"data row2 col1\" >0.4613</td>\n",
       "                        <td id=\"T_31094_row2_col2\" class=\"data row2 col2\" >0.6792</td>\n",
       "                        <td id=\"T_31094_row2_col3\" class=\"data row2 col3\" >0.8896</td>\n",
       "                        <td id=\"T_31094_row2_col4\" class=\"data row2 col4\" >0.1592</td>\n",
       "                        <td id=\"T_31094_row2_col5\" class=\"data row2 col5\" >0.2763</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_31094_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_31094_row3_col0\" class=\"data row3 col0\" >0.4402</td>\n",
       "                        <td id=\"T_31094_row3_col1\" class=\"data row3 col1\" >0.3959</td>\n",
       "                        <td id=\"T_31094_row3_col2\" class=\"data row3 col2\" >0.6292</td>\n",
       "                        <td id=\"T_31094_row3_col3\" class=\"data row3 col3\" >0.8914</td>\n",
       "                        <td id=\"T_31094_row3_col4\" class=\"data row3 col4\" >0.1593</td>\n",
       "                        <td id=\"T_31094_row3_col5\" class=\"data row3 col5\" >0.2277</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_31094_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_31094_row4_col0\" class=\"data row4 col0\" >0.4840</td>\n",
       "                        <td id=\"T_31094_row4_col1\" class=\"data row4 col1\" >0.3648</td>\n",
       "                        <td id=\"T_31094_row4_col2\" class=\"data row4 col2\" >0.6040</td>\n",
       "                        <td id=\"T_31094_row4_col3\" class=\"data row4 col3\" >0.9259</td>\n",
       "                        <td id=\"T_31094_row4_col4\" class=\"data row4 col4\" >0.1610</td>\n",
       "                        <td id=\"T_31094_row4_col5\" class=\"data row4 col5\" >0.2203</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_31094_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_31094_row5_col0\" class=\"data row5 col0\" >0.4513</td>\n",
       "                        <td id=\"T_31094_row5_col1\" class=\"data row5 col1\" >0.3621</td>\n",
       "                        <td id=\"T_31094_row5_col2\" class=\"data row5 col2\" >0.6018</td>\n",
       "                        <td id=\"T_31094_row5_col3\" class=\"data row5 col3\" >0.9140</td>\n",
       "                        <td id=\"T_31094_row5_col4\" class=\"data row5 col4\" >0.1609</td>\n",
       "                        <td id=\"T_31094_row5_col5\" class=\"data row5 col5\" >0.3137</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_31094_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_31094_row6_col0\" class=\"data row6 col0\" >0.5294</td>\n",
       "                        <td id=\"T_31094_row6_col1\" class=\"data row6 col1\" >0.4881</td>\n",
       "                        <td id=\"T_31094_row6_col2\" class=\"data row6 col2\" >0.6986</td>\n",
       "                        <td id=\"T_31094_row6_col3\" class=\"data row6 col3\" >0.9159</td>\n",
       "                        <td id=\"T_31094_row6_col4\" class=\"data row6 col4\" >0.1938</td>\n",
       "                        <td id=\"T_31094_row6_col5\" class=\"data row6 col5\" >0.4520</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_31094_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_31094_row7_col0\" class=\"data row7 col0\" >0.4675</td>\n",
       "                        <td id=\"T_31094_row7_col1\" class=\"data row7 col1\" >0.4021</td>\n",
       "                        <td id=\"T_31094_row7_col2\" class=\"data row7 col2\" >0.6341</td>\n",
       "                        <td id=\"T_31094_row7_col3\" class=\"data row7 col3\" >0.9067</td>\n",
       "                        <td id=\"T_31094_row7_col4\" class=\"data row7 col4\" >0.1768</td>\n",
       "                        <td id=\"T_31094_row7_col5\" class=\"data row7 col5\" >0.3863</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_31094_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_31094_row8_col0\" class=\"data row8 col0\" >0.4259</td>\n",
       "                        <td id=\"T_31094_row8_col1\" class=\"data row8 col1\" >0.4731</td>\n",
       "                        <td id=\"T_31094_row8_col2\" class=\"data row8 col2\" >0.6879</td>\n",
       "                        <td id=\"T_31094_row8_col3\" class=\"data row8 col3\" >0.8776</td>\n",
       "                        <td id=\"T_31094_row8_col4\" class=\"data row8 col4\" >0.2255</td>\n",
       "                        <td id=\"T_31094_row8_col5\" class=\"data row8 col5\" >0.5619</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_31094_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_31094_row9_col0\" class=\"data row9 col0\" >0.5517</td>\n",
       "                        <td id=\"T_31094_row9_col1\" class=\"data row9 col1\" >0.5893</td>\n",
       "                        <td id=\"T_31094_row9_col2\" class=\"data row9 col2\" >0.7677</td>\n",
       "                        <td id=\"T_31094_row9_col3\" class=\"data row9 col3\" >0.8819</td>\n",
       "                        <td id=\"T_31094_row9_col4\" class=\"data row9 col4\" >0.2430</td>\n",
       "                        <td id=\"T_31094_row9_col5\" class=\"data row9 col5\" >1.4671</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_31094_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "                        <td id=\"T_31094_row10_col0\" class=\"data row10 col0\" >0.4921</td>\n",
       "                        <td id=\"T_31094_row10_col1\" class=\"data row10 col1\" >0.4497</td>\n",
       "                        <td id=\"T_31094_row10_col2\" class=\"data row10 col2\" >0.6687</td>\n",
       "                        <td id=\"T_31094_row10_col3\" class=\"data row10 col3\" >0.8937</td>\n",
       "                        <td id=\"T_31094_row10_col4\" class=\"data row10 col4\" >0.1841</td>\n",
       "                        <td id=\"T_31094_row10_col5\" class=\"data row10 col5\" >0.4703</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_31094_level0_row11\" class=\"row_heading level0 row11\" >SD</th>\n",
       "                        <td id=\"T_31094_row11_col0\" class=\"data row11 col0\" >0.0425</td>\n",
       "                        <td id=\"T_31094_row11_col1\" class=\"data row11 col1\" >0.0681</td>\n",
       "                        <td id=\"T_31094_row11_col2\" class=\"data row11 col2\" >0.0502</td>\n",
       "                        <td id=\"T_31094_row11_col3\" class=\"data row11 col3\" >0.0201</td>\n",
       "                        <td id=\"T_31094_row11_col4\" class=\"data row11 col4\" >0.0285</td>\n",
       "                        <td id=\"T_31094_row11_col5\" class=\"data row11 col5\" >0.3469</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x137216b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "boosted_dt = ensemble_model(tuned_best, method = 'Boosting')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "54fce145",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "#T_0eab9_row10_col0,#T_0eab9_row10_col1,#T_0eab9_row10_col2,#T_0eab9_row10_col3,#T_0eab9_row10_col4,#T_0eab9_row10_col5{\n",
       "            background:  yellow;\n",
       "        }</style><table id=\"T_0eab9_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >MAE</th>        <th class=\"col_heading level0 col1\" >MSE</th>        <th class=\"col_heading level0 col2\" >RMSE</th>        <th class=\"col_heading level0 col3\" >R2</th>        <th class=\"col_heading level0 col4\" >RMSLE</th>        <th class=\"col_heading level0 col5\" >MAPE</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_0eab9_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_0eab9_row0_col0\" class=\"data row0 col0\" >0.4626</td>\n",
       "                        <td id=\"T_0eab9_row0_col1\" class=\"data row0 col1\" >0.3883</td>\n",
       "                        <td id=\"T_0eab9_row0_col2\" class=\"data row0 col2\" >0.6231</td>\n",
       "                        <td id=\"T_0eab9_row0_col3\" class=\"data row0 col3\" >0.8771</td>\n",
       "                        <td id=\"T_0eab9_row0_col4\" class=\"data row0 col4\" >0.1526</td>\n",
       "                        <td id=\"T_0eab9_row0_col5\" class=\"data row0 col5\" >0.3944</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0eab9_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_0eab9_row1_col0\" class=\"data row1 col0\" >0.5082</td>\n",
       "                        <td id=\"T_0eab9_row1_col1\" class=\"data row1 col1\" >0.4905</td>\n",
       "                        <td id=\"T_0eab9_row1_col2\" class=\"data row1 col2\" >0.7003</td>\n",
       "                        <td id=\"T_0eab9_row1_col3\" class=\"data row1 col3\" >0.8812</td>\n",
       "                        <td id=\"T_0eab9_row1_col4\" class=\"data row1 col4\" >0.1906</td>\n",
       "                        <td id=\"T_0eab9_row1_col5\" class=\"data row1 col5\" >0.3889</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0eab9_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_0eab9_row2_col0\" class=\"data row2 col0\" >0.4916</td>\n",
       "                        <td id=\"T_0eab9_row2_col1\" class=\"data row2 col1\" >0.4120</td>\n",
       "                        <td id=\"T_0eab9_row2_col2\" class=\"data row2 col2\" >0.6419</td>\n",
       "                        <td id=\"T_0eab9_row2_col3\" class=\"data row2 col3\" >0.9014</td>\n",
       "                        <td id=\"T_0eab9_row2_col4\" class=\"data row2 col4\" >0.1563</td>\n",
       "                        <td id=\"T_0eab9_row2_col5\" class=\"data row2 col5\" >0.2940</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0eab9_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_0eab9_row3_col0\" class=\"data row3 col0\" >0.4220</td>\n",
       "                        <td id=\"T_0eab9_row3_col1\" class=\"data row3 col1\" >0.3688</td>\n",
       "                        <td id=\"T_0eab9_row3_col2\" class=\"data row3 col2\" >0.6073</td>\n",
       "                        <td id=\"T_0eab9_row3_col3\" class=\"data row3 col3\" >0.8989</td>\n",
       "                        <td id=\"T_0eab9_row3_col4\" class=\"data row3 col4\" >0.1667</td>\n",
       "                        <td id=\"T_0eab9_row3_col5\" class=\"data row3 col5\" >0.2193</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0eab9_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_0eab9_row4_col0\" class=\"data row4 col0\" >0.4303</td>\n",
       "                        <td id=\"T_0eab9_row4_col1\" class=\"data row4 col1\" >0.3180</td>\n",
       "                        <td id=\"T_0eab9_row4_col2\" class=\"data row4 col2\" >0.5639</td>\n",
       "                        <td id=\"T_0eab9_row4_col3\" class=\"data row4 col3\" >0.9354</td>\n",
       "                        <td id=\"T_0eab9_row4_col4\" class=\"data row4 col4\" >0.1553</td>\n",
       "                        <td id=\"T_0eab9_row4_col5\" class=\"data row4 col5\" >0.2035</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0eab9_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_0eab9_row5_col0\" class=\"data row5 col0\" >0.4741</td>\n",
       "                        <td id=\"T_0eab9_row5_col1\" class=\"data row5 col1\" >0.3978</td>\n",
       "                        <td id=\"T_0eab9_row5_col2\" class=\"data row5 col2\" >0.6307</td>\n",
       "                        <td id=\"T_0eab9_row5_col3\" class=\"data row5 col3\" >0.9055</td>\n",
       "                        <td id=\"T_0eab9_row5_col4\" class=\"data row5 col4\" >0.1636</td>\n",
       "                        <td id=\"T_0eab9_row5_col5\" class=\"data row5 col5\" >0.3282</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0eab9_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_0eab9_row6_col0\" class=\"data row6 col0\" >0.5474</td>\n",
       "                        <td id=\"T_0eab9_row6_col1\" class=\"data row6 col1\" >0.5312</td>\n",
       "                        <td id=\"T_0eab9_row6_col2\" class=\"data row6 col2\" >0.7289</td>\n",
       "                        <td id=\"T_0eab9_row6_col3\" class=\"data row6 col3\" >0.9084</td>\n",
       "                        <td id=\"T_0eab9_row6_col4\" class=\"data row6 col4\" >0.1968</td>\n",
       "                        <td id=\"T_0eab9_row6_col5\" class=\"data row6 col5\" >0.6315</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0eab9_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_0eab9_row7_col0\" class=\"data row7 col0\" >0.4553</td>\n",
       "                        <td id=\"T_0eab9_row7_col1\" class=\"data row7 col1\" >0.3508</td>\n",
       "                        <td id=\"T_0eab9_row7_col2\" class=\"data row7 col2\" >0.5923</td>\n",
       "                        <td id=\"T_0eab9_row7_col3\" class=\"data row7 col3\" >0.9186</td>\n",
       "                        <td id=\"T_0eab9_row7_col4\" class=\"data row7 col4\" >0.1686</td>\n",
       "                        <td id=\"T_0eab9_row7_col5\" class=\"data row7 col5\" >0.4245</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0eab9_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_0eab9_row8_col0\" class=\"data row8 col0\" >0.4123</td>\n",
       "                        <td id=\"T_0eab9_row8_col1\" class=\"data row8 col1\" >0.4234</td>\n",
       "                        <td id=\"T_0eab9_row8_col2\" class=\"data row8 col2\" >0.6507</td>\n",
       "                        <td id=\"T_0eab9_row8_col3\" class=\"data row8 col3\" >0.8904</td>\n",
       "                        <td id=\"T_0eab9_row8_col4\" class=\"data row8 col4\" >0.2143</td>\n",
       "                        <td id=\"T_0eab9_row8_col5\" class=\"data row8 col5\" >0.5230</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0eab9_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_0eab9_row9_col0\" class=\"data row9 col0\" >0.5078</td>\n",
       "                        <td id=\"T_0eab9_row9_col1\" class=\"data row9 col1\" >0.4666</td>\n",
       "                        <td id=\"T_0eab9_row9_col2\" class=\"data row9 col2\" >0.6831</td>\n",
       "                        <td id=\"T_0eab9_row9_col3\" class=\"data row9 col3\" >0.9065</td>\n",
       "                        <td id=\"T_0eab9_row9_col4\" class=\"data row9 col4\" >0.2334</td>\n",
       "                        <td id=\"T_0eab9_row9_col5\" class=\"data row9 col5\" >1.5802</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0eab9_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "                        <td id=\"T_0eab9_row10_col0\" class=\"data row10 col0\" >0.4712</td>\n",
       "                        <td id=\"T_0eab9_row10_col1\" class=\"data row10 col1\" >0.4147</td>\n",
       "                        <td id=\"T_0eab9_row10_col2\" class=\"data row10 col2\" >0.6422</td>\n",
       "                        <td id=\"T_0eab9_row10_col3\" class=\"data row10 col3\" >0.9023</td>\n",
       "                        <td id=\"T_0eab9_row10_col4\" class=\"data row10 col4\" >0.1798</td>\n",
       "                        <td id=\"T_0eab9_row10_col5\" class=\"data row10 col5\" >0.4988</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0eab9_level0_row11\" class=\"row_heading level0 row11\" >SD</th>\n",
       "                        <td id=\"T_0eab9_row11_col0\" class=\"data row11 col0\" >0.0410</td>\n",
       "                        <td id=\"T_0eab9_row11_col1\" class=\"data row11 col1\" >0.0621</td>\n",
       "                        <td id=\"T_0eab9_row11_col2\" class=\"data row11 col2\" >0.0479</td>\n",
       "                        <td id=\"T_0eab9_row11_col3\" class=\"data row11 col3\" >0.0163</td>\n",
       "                        <td id=\"T_0eab9_row11_col4\" class=\"data row11 col4\" >0.0263</td>\n",
       "                        <td id=\"T_0eab9_row11_col5\" class=\"data row11 col5\" >0.3811</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x137227ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bagged_dt2 = ensemble_model(tuned_best, n_estimators=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7c03e819",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "#T_210ce_row10_col0,#T_210ce_row10_col1,#T_210ce_row10_col2,#T_210ce_row10_col3,#T_210ce_row10_col4,#T_210ce_row10_col5{\n",
       "            background:  yellow;\n",
       "        }</style><table id=\"T_210ce_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >MAE</th>        <th class=\"col_heading level0 col1\" >MSE</th>        <th class=\"col_heading level0 col2\" >RMSE</th>        <th class=\"col_heading level0 col3\" >R2</th>        <th class=\"col_heading level0 col4\" >RMSLE</th>        <th class=\"col_heading level0 col5\" >MAPE</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_210ce_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_210ce_row0_col0\" class=\"data row0 col0\" >0.5020</td>\n",
       "                        <td id=\"T_210ce_row0_col1\" class=\"data row0 col1\" >0.4197</td>\n",
       "                        <td id=\"T_210ce_row0_col2\" class=\"data row0 col2\" >0.6478</td>\n",
       "                        <td id=\"T_210ce_row0_col3\" class=\"data row0 col3\" >0.8672</td>\n",
       "                        <td id=\"T_210ce_row0_col4\" class=\"data row0 col4\" >0.1654</td>\n",
       "                        <td id=\"T_210ce_row0_col5\" class=\"data row0 col5\" >0.3640</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_210ce_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_210ce_row1_col0\" class=\"data row1 col0\" >0.5336</td>\n",
       "                        <td id=\"T_210ce_row1_col1\" class=\"data row1 col1\" >0.4741</td>\n",
       "                        <td id=\"T_210ce_row1_col2\" class=\"data row1 col2\" >0.6886</td>\n",
       "                        <td id=\"T_210ce_row1_col3\" class=\"data row1 col3\" >0.8852</td>\n",
       "                        <td id=\"T_210ce_row1_col4\" class=\"data row1 col4\" >0.1949</td>\n",
       "                        <td id=\"T_210ce_row1_col5\" class=\"data row1 col5\" >0.4280</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_210ce_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_210ce_row2_col0\" class=\"data row2 col0\" >0.4801</td>\n",
       "                        <td id=\"T_210ce_row2_col1\" class=\"data row2 col1\" >0.3948</td>\n",
       "                        <td id=\"T_210ce_row2_col2\" class=\"data row2 col2\" >0.6284</td>\n",
       "                        <td id=\"T_210ce_row2_col3\" class=\"data row2 col3\" >0.9055</td>\n",
       "                        <td id=\"T_210ce_row2_col4\" class=\"data row2 col4\" >0.1491</td>\n",
       "                        <td id=\"T_210ce_row2_col5\" class=\"data row2 col5\" >0.2674</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_210ce_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_210ce_row3_col0\" class=\"data row3 col0\" >0.4549</td>\n",
       "                        <td id=\"T_210ce_row3_col1\" class=\"data row3 col1\" >0.4261</td>\n",
       "                        <td id=\"T_210ce_row3_col2\" class=\"data row3 col2\" >0.6528</td>\n",
       "                        <td id=\"T_210ce_row3_col3\" class=\"data row3 col3\" >0.8831</td>\n",
       "                        <td id=\"T_210ce_row3_col4\" class=\"data row3 col4\" >0.1751</td>\n",
       "                        <td id=\"T_210ce_row3_col5\" class=\"data row3 col5\" >0.2396</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_210ce_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_210ce_row4_col0\" class=\"data row4 col0\" >0.4401</td>\n",
       "                        <td id=\"T_210ce_row4_col1\" class=\"data row4 col1\" >0.3196</td>\n",
       "                        <td id=\"T_210ce_row4_col2\" class=\"data row4 col2\" >0.5653</td>\n",
       "                        <td id=\"T_210ce_row4_col3\" class=\"data row4 col3\" >0.9351</td>\n",
       "                        <td id=\"T_210ce_row4_col4\" class=\"data row4 col4\" >0.1514</td>\n",
       "                        <td id=\"T_210ce_row4_col5\" class=\"data row4 col5\" >0.2024</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_210ce_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_210ce_row5_col0\" class=\"data row5 col0\" >0.4844</td>\n",
       "                        <td id=\"T_210ce_row5_col1\" class=\"data row5 col1\" >0.3948</td>\n",
       "                        <td id=\"T_210ce_row5_col2\" class=\"data row5 col2\" >0.6283</td>\n",
       "                        <td id=\"T_210ce_row5_col3\" class=\"data row5 col3\" >0.9063</td>\n",
       "                        <td id=\"T_210ce_row5_col4\" class=\"data row5 col4\" >0.1629</td>\n",
       "                        <td id=\"T_210ce_row5_col5\" class=\"data row5 col5\" >0.3350</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_210ce_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_210ce_row6_col0\" class=\"data row6 col0\" >0.5385</td>\n",
       "                        <td id=\"T_210ce_row6_col1\" class=\"data row6 col1\" >0.4997</td>\n",
       "                        <td id=\"T_210ce_row6_col2\" class=\"data row6 col2\" >0.7069</td>\n",
       "                        <td id=\"T_210ce_row6_col3\" class=\"data row6 col3\" >0.9139</td>\n",
       "                        <td id=\"T_210ce_row6_col4\" class=\"data row6 col4\" >0.1960</td>\n",
       "                        <td id=\"T_210ce_row6_col5\" class=\"data row6 col5\" >0.5082</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_210ce_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_210ce_row7_col0\" class=\"data row7 col0\" >0.4808</td>\n",
       "                        <td id=\"T_210ce_row7_col1\" class=\"data row7 col1\" >0.3813</td>\n",
       "                        <td id=\"T_210ce_row7_col2\" class=\"data row7 col2\" >0.6175</td>\n",
       "                        <td id=\"T_210ce_row7_col3\" class=\"data row7 col3\" >0.9115</td>\n",
       "                        <td id=\"T_210ce_row7_col4\" class=\"data row7 col4\" >0.1699</td>\n",
       "                        <td id=\"T_210ce_row7_col5\" class=\"data row7 col5\" >0.4076</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_210ce_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_210ce_row8_col0\" class=\"data row8 col0\" >0.4286</td>\n",
       "                        <td id=\"T_210ce_row8_col1\" class=\"data row8 col1\" >0.4367</td>\n",
       "                        <td id=\"T_210ce_row8_col2\" class=\"data row8 col2\" >0.6608</td>\n",
       "                        <td id=\"T_210ce_row8_col3\" class=\"data row8 col3\" >0.8870</td>\n",
       "                        <td id=\"T_210ce_row8_col4\" class=\"data row8 col4\" >0.2255</td>\n",
       "                        <td id=\"T_210ce_row8_col5\" class=\"data row8 col5\" >0.5641</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_210ce_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_210ce_row9_col0\" class=\"data row9 col0\" >0.5422</td>\n",
       "                        <td id=\"T_210ce_row9_col1\" class=\"data row9 col1\" >0.5384</td>\n",
       "                        <td id=\"T_210ce_row9_col2\" class=\"data row9 col2\" >0.7338</td>\n",
       "                        <td id=\"T_210ce_row9_col3\" class=\"data row9 col3\" >0.8921</td>\n",
       "                        <td id=\"T_210ce_row9_col4\" class=\"data row9 col4\" >0.2422</td>\n",
       "                        <td id=\"T_210ce_row9_col5\" class=\"data row9 col5\" >1.4254</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_210ce_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "                        <td id=\"T_210ce_row10_col0\" class=\"data row10 col0\" >0.4885</td>\n",
       "                        <td id=\"T_210ce_row10_col1\" class=\"data row10 col1\" >0.4285</td>\n",
       "                        <td id=\"T_210ce_row10_col2\" class=\"data row10 col2\" >0.6530</td>\n",
       "                        <td id=\"T_210ce_row10_col3\" class=\"data row10 col3\" >0.8987</td>\n",
       "                        <td id=\"T_210ce_row10_col4\" class=\"data row10 col4\" >0.1832</td>\n",
       "                        <td id=\"T_210ce_row10_col5\" class=\"data row10 col5\" >0.4742</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_210ce_level0_row11\" class=\"row_heading level0 row11\" >SD</th>\n",
       "                        <td id=\"T_210ce_row11_col0\" class=\"data row11 col0\" >0.0385</td>\n",
       "                        <td id=\"T_210ce_row11_col1\" class=\"data row11 col1\" >0.0598</td>\n",
       "                        <td id=\"T_210ce_row11_col2\" class=\"data row11 col2\" >0.0458</td>\n",
       "                        <td id=\"T_210ce_row11_col3\" class=\"data row11 col3\" >0.0185</td>\n",
       "                        <td id=\"T_210ce_row11_col4\" class=\"data row11 col4\" >0.0295</td>\n",
       "                        <td id=\"T_210ce_row11_col5\" class=\"data row11 col5\" >0.3352</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1369572d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "boosted_dt2 = ensemble_model(tuned_best, method='Boosting', n_estimators=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8d6d76e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# train individual models to blend\n",
    "# can I tune them before blending?\n",
    "lightgbm = create_model('lightgbm', verbose=False)\n",
    "et = create_model('et', verbose=False)\n",
    "rf = create_model('rf', verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e6fa33be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "#T_8efe6_row10_col0,#T_8efe6_row10_col1,#T_8efe6_row10_col2,#T_8efe6_row10_col3,#T_8efe6_row10_col4,#T_8efe6_row10_col5{\n",
       "            background:  yellow;\n",
       "        }</style><table id=\"T_8efe6_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >MAE</th>        <th class=\"col_heading level0 col1\" >MSE</th>        <th class=\"col_heading level0 col2\" >RMSE</th>        <th class=\"col_heading level0 col3\" >R2</th>        <th class=\"col_heading level0 col4\" >RMSLE</th>        <th class=\"col_heading level0 col5\" >MAPE</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_8efe6_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_8efe6_row0_col0\" class=\"data row0 col0\" >0.4352</td>\n",
       "                        <td id=\"T_8efe6_row0_col1\" class=\"data row0 col1\" >0.3797</td>\n",
       "                        <td id=\"T_8efe6_row0_col2\" class=\"data row0 col2\" >0.6162</td>\n",
       "                        <td id=\"T_8efe6_row0_col3\" class=\"data row0 col3\" >0.8798</td>\n",
       "                        <td id=\"T_8efe6_row0_col4\" class=\"data row0 col4\" >0.1559</td>\n",
       "                        <td id=\"T_8efe6_row0_col5\" class=\"data row0 col5\" >0.3930</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_8efe6_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_8efe6_row1_col0\" class=\"data row1 col0\" >0.5272</td>\n",
       "                        <td id=\"T_8efe6_row1_col1\" class=\"data row1 col1\" >0.4910</td>\n",
       "                        <td id=\"T_8efe6_row1_col2\" class=\"data row1 col2\" >0.7007</td>\n",
       "                        <td id=\"T_8efe6_row1_col3\" class=\"data row1 col3\" >0.8811</td>\n",
       "                        <td id=\"T_8efe6_row1_col4\" class=\"data row1 col4\" >0.1922</td>\n",
       "                        <td id=\"T_8efe6_row1_col5\" class=\"data row1 col5\" >0.4512</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_8efe6_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_8efe6_row2_col0\" class=\"data row2 col0\" >0.4665</td>\n",
       "                        <td id=\"T_8efe6_row2_col1\" class=\"data row2 col1\" >0.3819</td>\n",
       "                        <td id=\"T_8efe6_row2_col2\" class=\"data row2 col2\" >0.6180</td>\n",
       "                        <td id=\"T_8efe6_row2_col3\" class=\"data row2 col3\" >0.9086</td>\n",
       "                        <td id=\"T_8efe6_row2_col4\" class=\"data row2 col4\" >0.1442</td>\n",
       "                        <td id=\"T_8efe6_row2_col5\" class=\"data row2 col5\" >0.2500</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_8efe6_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_8efe6_row3_col0\" class=\"data row3 col0\" >0.4476</td>\n",
       "                        <td id=\"T_8efe6_row3_col1\" class=\"data row3 col1\" >0.4111</td>\n",
       "                        <td id=\"T_8efe6_row3_col2\" class=\"data row3 col2\" >0.6412</td>\n",
       "                        <td id=\"T_8efe6_row3_col3\" class=\"data row3 col3\" >0.8872</td>\n",
       "                        <td id=\"T_8efe6_row3_col4\" class=\"data row3 col4\" >0.1632</td>\n",
       "                        <td id=\"T_8efe6_row3_col5\" class=\"data row3 col5\" >0.2368</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_8efe6_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_8efe6_row4_col0\" class=\"data row4 col0\" >0.4000</td>\n",
       "                        <td id=\"T_8efe6_row4_col1\" class=\"data row4 col1\" >0.2750</td>\n",
       "                        <td id=\"T_8efe6_row4_col2\" class=\"data row4 col2\" >0.5245</td>\n",
       "                        <td id=\"T_8efe6_row4_col3\" class=\"data row4 col3\" >0.9441</td>\n",
       "                        <td id=\"T_8efe6_row4_col4\" class=\"data row4 col4\" >0.1388</td>\n",
       "                        <td id=\"T_8efe6_row4_col5\" class=\"data row4 col5\" >0.1723</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_8efe6_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_8efe6_row5_col0\" class=\"data row5 col0\" >0.4412</td>\n",
       "                        <td id=\"T_8efe6_row5_col1\" class=\"data row5 col1\" >0.3814</td>\n",
       "                        <td id=\"T_8efe6_row5_col2\" class=\"data row5 col2\" >0.6176</td>\n",
       "                        <td id=\"T_8efe6_row5_col3\" class=\"data row5 col3\" >0.9095</td>\n",
       "                        <td id=\"T_8efe6_row5_col4\" class=\"data row5 col4\" >0.1639</td>\n",
       "                        <td id=\"T_8efe6_row5_col5\" class=\"data row5 col5\" >0.2509</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_8efe6_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_8efe6_row6_col0\" class=\"data row6 col0\" >0.4964</td>\n",
       "                        <td id=\"T_8efe6_row6_col1\" class=\"data row6 col1\" >0.4641</td>\n",
       "                        <td id=\"T_8efe6_row6_col2\" class=\"data row6 col2\" >0.6813</td>\n",
       "                        <td id=\"T_8efe6_row6_col3\" class=\"data row6 col3\" >0.9200</td>\n",
       "                        <td id=\"T_8efe6_row6_col4\" class=\"data row6 col4\" >0.1862</td>\n",
       "                        <td id=\"T_8efe6_row6_col5\" class=\"data row6 col5\" >0.5532</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_8efe6_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_8efe6_row7_col0\" class=\"data row7 col0\" >0.4480</td>\n",
       "                        <td id=\"T_8efe6_row7_col1\" class=\"data row7 col1\" >0.3459</td>\n",
       "                        <td id=\"T_8efe6_row7_col2\" class=\"data row7 col2\" >0.5881</td>\n",
       "                        <td id=\"T_8efe6_row7_col3\" class=\"data row7 col3\" >0.9198</td>\n",
       "                        <td id=\"T_8efe6_row7_col4\" class=\"data row7 col4\" >0.1700</td>\n",
       "                        <td id=\"T_8efe6_row7_col5\" class=\"data row7 col5\" >0.4265</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_8efe6_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_8efe6_row8_col0\" class=\"data row8 col0\" >0.3822</td>\n",
       "                        <td id=\"T_8efe6_row8_col1\" class=\"data row8 col1\" >0.3932</td>\n",
       "                        <td id=\"T_8efe6_row8_col2\" class=\"data row8 col2\" >0.6271</td>\n",
       "                        <td id=\"T_8efe6_row8_col3\" class=\"data row8 col3\" >0.8982</td>\n",
       "                        <td id=\"T_8efe6_row8_col4\" class=\"data row8 col4\" >0.2026</td>\n",
       "                        <td id=\"T_8efe6_row8_col5\" class=\"data row8 col5\" >0.4804</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_8efe6_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_8efe6_row9_col0\" class=\"data row9 col0\" >0.5398</td>\n",
       "                        <td id=\"T_8efe6_row9_col1\" class=\"data row9 col1\" >0.5515</td>\n",
       "                        <td id=\"T_8efe6_row9_col2\" class=\"data row9 col2\" >0.7426</td>\n",
       "                        <td id=\"T_8efe6_row9_col3\" class=\"data row9 col3\" >0.8895</td>\n",
       "                        <td id=\"T_8efe6_row9_col4\" class=\"data row9 col4\" >0.2357</td>\n",
       "                        <td id=\"T_8efe6_row9_col5\" class=\"data row9 col5\" >1.4784</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_8efe6_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "                        <td id=\"T_8efe6_row10_col0\" class=\"data row10 col0\" >0.4584</td>\n",
       "                        <td id=\"T_8efe6_row10_col1\" class=\"data row10 col1\" >0.4075</td>\n",
       "                        <td id=\"T_8efe6_row10_col2\" class=\"data row10 col2\" >0.6357</td>\n",
       "                        <td id=\"T_8efe6_row10_col3\" class=\"data row10 col3\" >0.9038</td>\n",
       "                        <td id=\"T_8efe6_row10_col4\" class=\"data row10 col4\" >0.1753</td>\n",
       "                        <td id=\"T_8efe6_row10_col5\" class=\"data row10 col5\" >0.4693</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_8efe6_level0_row11\" class=\"row_heading level0 row11\" >SD</th>\n",
       "                        <td id=\"T_8efe6_row11_col0\" class=\"data row11 col0\" >0.0481</td>\n",
       "                        <td id=\"T_8efe6_row11_col1\" class=\"data row11 col1\" >0.0739</td>\n",
       "                        <td id=\"T_8efe6_row11_col2\" class=\"data row11 col2\" >0.0579</td>\n",
       "                        <td id=\"T_8efe6_row11_col3\" class=\"data row11 col3\" >0.0195</td>\n",
       "                        <td id=\"T_8efe6_row11_col4\" class=\"data row11 col4\" >0.0279</td>\n",
       "                        <td id=\"T_8efe6_row11_col5\" class=\"data row11 col5\" >0.3566</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1369bf290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "blender = blend_models(estimator_list = [lightgbm, et, rf])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ab6b8a07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_580a3_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Model</th>        <th class=\"col_heading level0 col1\" >MAE</th>        <th class=\"col_heading level0 col2\" >MSE</th>        <th class=\"col_heading level0 col3\" >RMSE</th>        <th class=\"col_heading level0 col4\" >R2</th>        <th class=\"col_heading level0 col5\" >RMSLE</th>        <th class=\"col_heading level0 col6\" >MAPE</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_580a3_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_580a3_row0_col0\" class=\"data row0 col0\" >Voting Regressor</td>\n",
       "                        <td id=\"T_580a3_row0_col1\" class=\"data row0 col1\" >0.0978</td>\n",
       "                        <td id=\"T_580a3_row0_col2\" class=\"data row0 col2\" >0.0199</td>\n",
       "                        <td id=\"T_580a3_row0_col3\" class=\"data row0 col3\" >0.1410</td>\n",
       "                        <td id=\"T_580a3_row0_col4\" class=\"data row0 col4\" >0.9956</td>\n",
       "                        <td id=\"T_580a3_row0_col5\" class=\"data row0 col5\" >0.0472</td>\n",
       "                        <td id=\"T_580a3_row0_col6\" class=\"data row0 col6\" >0.0735</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1372301d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MaxPartialCharge</th>\n",
       "      <th>BCUT2D_MWHI</th>\n",
       "      <th>BCUT2D_MRLOW</th>\n",
       "      <th>BertzCT</th>\n",
       "      <th>Kappa1</th>\n",
       "      <th>PEOE_VSA3</th>\n",
       "      <th>PEOE_VSA6</th>\n",
       "      <th>PEOE_VSA7</th>\n",
       "      <th>PEOE_VSA8</th>\n",
       "      <th>PEOE_VSA9</th>\n",
       "      <th>...</th>\n",
       "      <th>SlogP_VSA3</th>\n",
       "      <th>EState_VSA4</th>\n",
       "      <th>VSA_EState10</th>\n",
       "      <th>VSA_EState2</th>\n",
       "      <th>VSA_EState7</th>\n",
       "      <th>VSA_EState9</th>\n",
       "      <th>FractionCSP3</th>\n",
       "      <th>MolLogP</th>\n",
       "      <th>logs</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.886795</td>\n",
       "      <td>-0.581488</td>\n",
       "      <td>-0.370221</td>\n",
       "      <td>-1.280562</td>\n",
       "      <td>-0.004362</td>\n",
       "      <td>-0.602252</td>\n",
       "      <td>0.287770</td>\n",
       "      <td>-0.305969</td>\n",
       "      <td>0.861641</td>\n",
       "      <td>-0.895095</td>\n",
       "      <td>...</td>\n",
       "      <td>0.626442</td>\n",
       "      <td>0.952940</td>\n",
       "      <td>-0.677352</td>\n",
       "      <td>-0.908988</td>\n",
       "      <td>0.908496</td>\n",
       "      <td>-0.144482</td>\n",
       "      <td>1.394043</td>\n",
       "      <td>0.096012</td>\n",
       "      <td>-1.850</td>\n",
       "      <td>-2.028879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.190633</td>\n",
       "      <td>-1.180687</td>\n",
       "      <td>0.902737</td>\n",
       "      <td>-1.276477</td>\n",
       "      <td>-1.376126</td>\n",
       "      <td>-0.602252</td>\n",
       "      <td>-0.978237</td>\n",
       "      <td>-0.925067</td>\n",
       "      <td>0.076147</td>\n",
       "      <td>-0.895095</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.835778</td>\n",
       "      <td>0.924917</td>\n",
       "      <td>-0.677352</td>\n",
       "      <td>-0.908988</td>\n",
       "      <td>1.351391</td>\n",
       "      <td>-0.144482</td>\n",
       "      <td>0.571753</td>\n",
       "      <td>-0.553420</td>\n",
       "      <td>-1.640</td>\n",
       "      <td>-1.645419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.356549</td>\n",
       "      <td>-0.609089</td>\n",
       "      <td>-0.774131</td>\n",
       "      <td>0.962357</td>\n",
       "      <td>0.896536</td>\n",
       "      <td>-0.602252</td>\n",
       "      <td>-0.531234</td>\n",
       "      <td>2.199393</td>\n",
       "      <td>-1.132877</td>\n",
       "      <td>1.185412</td>\n",
       "      <td>...</td>\n",
       "      <td>0.888340</td>\n",
       "      <td>1.782857</td>\n",
       "      <td>-0.677352</td>\n",
       "      <td>-0.908988</td>\n",
       "      <td>0.646933</td>\n",
       "      <td>-0.144482</td>\n",
       "      <td>0.723230</td>\n",
       "      <td>0.083469</td>\n",
       "      <td>-4.955</td>\n",
       "      <td>-4.619308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.990820</td>\n",
       "      <td>1.229622</td>\n",
       "      <td>-1.012473</td>\n",
       "      <td>1.509644</td>\n",
       "      <td>1.569817</td>\n",
       "      <td>-0.602252</td>\n",
       "      <td>1.658530</td>\n",
       "      <td>1.435196</td>\n",
       "      <td>-1.132877</td>\n",
       "      <td>0.546389</td>\n",
       "      <td>...</td>\n",
       "      <td>1.580013</td>\n",
       "      <td>0.800004</td>\n",
       "      <td>1.651564</td>\n",
       "      <td>0.732067</td>\n",
       "      <td>0.102868</td>\n",
       "      <td>-0.144482</td>\n",
       "      <td>-0.278685</td>\n",
       "      <td>1.934099</td>\n",
       "      <td>-6.291</td>\n",
       "      <td>-6.399493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.399093</td>\n",
       "      <td>-1.196715</td>\n",
       "      <td>1.297037</td>\n",
       "      <td>-1.333785</td>\n",
       "      <td>-1.405602</td>\n",
       "      <td>-0.602252</td>\n",
       "      <td>-0.590060</td>\n",
       "      <td>-0.925067</td>\n",
       "      <td>-1.132877</td>\n",
       "      <td>-0.895095</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.835778</td>\n",
       "      <td>0.240059</td>\n",
       "      <td>-0.677352</td>\n",
       "      <td>-0.908988</td>\n",
       "      <td>0.823280</td>\n",
       "      <td>-0.144482</td>\n",
       "      <td>-0.555632</td>\n",
       "      <td>-0.371238</td>\n",
       "      <td>-2.090</td>\n",
       "      <td>-2.137436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>0.018880</td>\n",
       "      <td>1.229667</td>\n",
       "      <td>-0.148013</td>\n",
       "      <td>0.819907</td>\n",
       "      <td>0.792771</td>\n",
       "      <td>-0.602252</td>\n",
       "      <td>1.066501</td>\n",
       "      <td>0.799352</td>\n",
       "      <td>-1.132877</td>\n",
       "      <td>1.661174</td>\n",
       "      <td>...</td>\n",
       "      <td>0.626442</td>\n",
       "      <td>0.240059</td>\n",
       "      <td>1.927981</td>\n",
       "      <td>-1.360738</td>\n",
       "      <td>0.080824</td>\n",
       "      <td>-0.144482</td>\n",
       "      <td>1.073959</td>\n",
       "      <td>1.091366</td>\n",
       "      <td>-6.290</td>\n",
       "      <td>-6.185720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>-1.601615</td>\n",
       "      <td>-1.087209</td>\n",
       "      <td>0.160285</td>\n",
       "      <td>-1.283849</td>\n",
       "      <td>-0.552944</td>\n",
       "      <td>-0.602252</td>\n",
       "      <td>0.620677</td>\n",
       "      <td>-0.395410</td>\n",
       "      <td>-1.132877</td>\n",
       "      <td>-0.895095</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.835778</td>\n",
       "      <td>0.844631</td>\n",
       "      <td>-0.677352</td>\n",
       "      <td>-0.908988</td>\n",
       "      <td>-0.009549</td>\n",
       "      <td>-0.144482</td>\n",
       "      <td>1.394043</td>\n",
       "      <td>0.142066</td>\n",
       "      <td>-4.280</td>\n",
       "      <td>-4.186963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>-0.713711</td>\n",
       "      <td>1.228280</td>\n",
       "      <td>1.440386</td>\n",
       "      <td>1.025052</td>\n",
       "      <td>1.042898</td>\n",
       "      <td>-0.602252</td>\n",
       "      <td>1.990749</td>\n",
       "      <td>-0.368711</td>\n",
       "      <td>0.660969</td>\n",
       "      <td>1.874304</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.835778</td>\n",
       "      <td>-1.069928</td>\n",
       "      <td>1.920619</td>\n",
       "      <td>-0.908988</td>\n",
       "      <td>-0.408972</td>\n",
       "      <td>-0.144482</td>\n",
       "      <td>-1.265530</td>\n",
       "      <td>2.525058</td>\n",
       "      <td>-8.600</td>\n",
       "      <td>-8.527445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>0.787289</td>\n",
       "      <td>-0.550699</td>\n",
       "      <td>-1.684012</td>\n",
       "      <td>0.377827</td>\n",
       "      <td>0.033386</td>\n",
       "      <td>-0.602252</td>\n",
       "      <td>-1.546475</td>\n",
       "      <td>-0.293008</td>\n",
       "      <td>0.711765</td>\n",
       "      <td>1.403111</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.835778</td>\n",
       "      <td>0.186061</td>\n",
       "      <td>-0.677352</td>\n",
       "      <td>1.169553</td>\n",
       "      <td>-0.408972</td>\n",
       "      <td>-0.144482</td>\n",
       "      <td>-0.749748</td>\n",
       "      <td>-0.336513</td>\n",
       "      <td>-2.820</td>\n",
       "      <td>-2.747061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>1.039645</td>\n",
       "      <td>-0.619813</td>\n",
       "      <td>0.203278</td>\n",
       "      <td>0.446923</td>\n",
       "      <td>-0.905789</td>\n",
       "      <td>1.573171</td>\n",
       "      <td>-1.546475</td>\n",
       "      <td>-1.655528</td>\n",
       "      <td>-1.132877</td>\n",
       "      <td>0.606906</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.835778</td>\n",
       "      <td>-1.069928</td>\n",
       "      <td>-0.677352</td>\n",
       "      <td>0.827508</td>\n",
       "      <td>0.025739</td>\n",
       "      <td>-0.144482</td>\n",
       "      <td>-1.265530</td>\n",
       "      <td>-1.556138</td>\n",
       "      <td>-3.401</td>\n",
       "      <td>-3.164846</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>206 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     MaxPartialCharge  BCUT2D_MWHI  BCUT2D_MRLOW   BertzCT    Kappa1  \\\n",
       "0           -0.886795    -0.581488     -0.370221 -1.280562 -0.004362   \n",
       "1           -1.190633    -1.180687      0.902737 -1.276477 -1.376126   \n",
       "2           -0.356549    -0.609089     -0.774131  0.962357  0.896536   \n",
       "3            0.990820     1.229622     -1.012473  1.509644  1.569817   \n",
       "4           -1.399093    -1.196715      1.297037 -1.333785 -1.405602   \n",
       "..                ...          ...           ...       ...       ...   \n",
       "201          0.018880     1.229667     -0.148013  0.819907  0.792771   \n",
       "202         -1.601615    -1.087209      0.160285 -1.283849 -0.552944   \n",
       "203         -0.713711     1.228280      1.440386  1.025052  1.042898   \n",
       "204          0.787289    -0.550699     -1.684012  0.377827  0.033386   \n",
       "205          1.039645    -0.619813      0.203278  0.446923 -0.905789   \n",
       "\n",
       "     PEOE_VSA3  PEOE_VSA6  PEOE_VSA7  PEOE_VSA8  PEOE_VSA9  ...  SlogP_VSA3  \\\n",
       "0    -0.602252   0.287770  -0.305969   0.861641  -0.895095  ...    0.626442   \n",
       "1    -0.602252  -0.978237  -0.925067   0.076147  -0.895095  ...   -0.835778   \n",
       "2    -0.602252  -0.531234   2.199393  -1.132877   1.185412  ...    0.888340   \n",
       "3    -0.602252   1.658530   1.435196  -1.132877   0.546389  ...    1.580013   \n",
       "4    -0.602252  -0.590060  -0.925067  -1.132877  -0.895095  ...   -0.835778   \n",
       "..         ...        ...        ...        ...        ...  ...         ...   \n",
       "201  -0.602252   1.066501   0.799352  -1.132877   1.661174  ...    0.626442   \n",
       "202  -0.602252   0.620677  -0.395410  -1.132877  -0.895095  ...   -0.835778   \n",
       "203  -0.602252   1.990749  -0.368711   0.660969   1.874304  ...   -0.835778   \n",
       "204  -0.602252  -1.546475  -0.293008   0.711765   1.403111  ...   -0.835778   \n",
       "205   1.573171  -1.546475  -1.655528  -1.132877   0.606906  ...   -0.835778   \n",
       "\n",
       "     EState_VSA4  VSA_EState10  VSA_EState2  VSA_EState7  VSA_EState9  \\\n",
       "0       0.952940     -0.677352    -0.908988     0.908496    -0.144482   \n",
       "1       0.924917     -0.677352    -0.908988     1.351391    -0.144482   \n",
       "2       1.782857     -0.677352    -0.908988     0.646933    -0.144482   \n",
       "3       0.800004      1.651564     0.732067     0.102868    -0.144482   \n",
       "4       0.240059     -0.677352    -0.908988     0.823280    -0.144482   \n",
       "..           ...           ...          ...          ...          ...   \n",
       "201     0.240059      1.927981    -1.360738     0.080824    -0.144482   \n",
       "202     0.844631     -0.677352    -0.908988    -0.009549    -0.144482   \n",
       "203    -1.069928      1.920619    -0.908988    -0.408972    -0.144482   \n",
       "204     0.186061     -0.677352     1.169553    -0.408972    -0.144482   \n",
       "205    -1.069928     -0.677352     0.827508     0.025739    -0.144482   \n",
       "\n",
       "     FractionCSP3   MolLogP   logs     Label  \n",
       "0        1.394043  0.096012 -1.850 -2.028879  \n",
       "1        0.571753 -0.553420 -1.640 -1.645419  \n",
       "2        0.723230  0.083469 -4.955 -4.619308  \n",
       "3       -0.278685  1.934099 -6.291 -6.399493  \n",
       "4       -0.555632 -0.371238 -2.090 -2.137436  \n",
       "..            ...       ...    ...       ...  \n",
       "201      1.073959  1.091366 -6.290 -6.185720  \n",
       "202      1.394043  0.142066 -4.280 -4.186963  \n",
       "203     -1.265530  2.525058 -8.600 -8.527445  \n",
       "204     -0.749748 -0.336513 -2.820 -2.747061  \n",
       "205     -1.265530 -1.556138 -3.401 -3.164846  \n",
       "\n",
       "[206 rows x 23 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_model = finalize_model(blender)\n",
    "predict_model(final_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "15a12da3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.8843, 0.4522, 0.4712)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unseen_predictions = predict_model(final_model, data=df_unseen)\n",
    "check_metric(unseen_predictions.logs, unseen_predictions.Label, 'R2'), check_metric(unseen_predictions.logs, unseen_predictions.Label, 'MAE'), check_metric(unseen_predictions.logs, unseen_predictions.Label, 'RMSE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "208fbe00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "            background:  yellow;\n",
       "        }</style><table id=\"T_eea24_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >MAE</th>        <th class=\"col_heading level0 col1\" >MSE</th>        <th class=\"col_heading level0 col2\" >RMSE</th>        <th class=\"col_heading level0 col3\" >R2</th>        <th class=\"col_heading level0 col4\" >RMSLE</th>        <th class=\"col_heading level0 col5\" >MAPE</th>    </tr></thead><tbody>\n",
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       "                        <th id=\"T_eea24_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_eea24_row0_col0\" class=\"data row0 col0\" >0.4398</td>\n",
       "                        <td id=\"T_eea24_row0_col1\" class=\"data row0 col1\" >0.3819</td>\n",
       "                        <td id=\"T_eea24_row0_col2\" class=\"data row0 col2\" >0.6180</td>\n",
       "                        <td id=\"T_eea24_row0_col3\" class=\"data row0 col3\" >0.8791</td>\n",
       "                        <td id=\"T_eea24_row0_col4\" class=\"data row0 col4\" >0.1540</td>\n",
       "                        <td id=\"T_eea24_row0_col5\" class=\"data row0 col5\" >0.2782</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_eea24_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_eea24_row1_col0\" class=\"data row1 col0\" >0.5286</td>\n",
       "                        <td id=\"T_eea24_row1_col1\" class=\"data row1 col1\" >0.4752</td>\n",
       "                        <td id=\"T_eea24_row1_col2\" class=\"data row1 col2\" >0.6894</td>\n",
       "                        <td id=\"T_eea24_row1_col3\" class=\"data row1 col3\" >0.8849</td>\n",
       "                        <td id=\"T_eea24_row1_col4\" class=\"data row1 col4\" >0.1989</td>\n",
       "                        <td id=\"T_eea24_row1_col5\" class=\"data row1 col5\" >0.4649</td>\n",
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       "            <tr>\n",
       "                        <th id=\"T_eea24_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
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       "                        <td id=\"T_eea24_row2_col1\" class=\"data row2 col1\" >0.3828</td>\n",
       "                        <td id=\"T_eea24_row2_col2\" class=\"data row2 col2\" >0.6187</td>\n",
       "                        <td id=\"T_eea24_row2_col3\" class=\"data row2 col3\" >0.9084</td>\n",
       "                        <td id=\"T_eea24_row2_col4\" class=\"data row2 col4\" >0.1513</td>\n",
       "                        <td id=\"T_eea24_row2_col5\" class=\"data row2 col5\" >0.3457</td>\n",
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       "            <tr>\n",
       "                        <th id=\"T_eea24_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_eea24_row3_col0\" class=\"data row3 col0\" >0.3909</td>\n",
       "                        <td id=\"T_eea24_row3_col1\" class=\"data row3 col1\" >0.3075</td>\n",
       "                        <td id=\"T_eea24_row3_col2\" class=\"data row3 col2\" >0.5546</td>\n",
       "                        <td id=\"T_eea24_row3_col3\" class=\"data row3 col3\" >0.9156</td>\n",
       "                        <td id=\"T_eea24_row3_col4\" class=\"data row3 col4\" >0.1373</td>\n",
       "                        <td id=\"T_eea24_row3_col5\" class=\"data row3 col5\" >0.2125</td>\n",
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       "            <tr>\n",
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       "                        <td id=\"T_eea24_row4_col0\" class=\"data row4 col0\" >0.4077</td>\n",
       "                        <td id=\"T_eea24_row4_col1\" class=\"data row4 col1\" >0.2812</td>\n",
       "                        <td id=\"T_eea24_row4_col2\" class=\"data row4 col2\" >0.5303</td>\n",
       "                        <td id=\"T_eea24_row4_col3\" class=\"data row4 col3\" >0.9429</td>\n",
       "                        <td id=\"T_eea24_row4_col4\" class=\"data row4 col4\" >0.1476</td>\n",
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       "                        <td id=\"T_eea24_row5_col0\" class=\"data row5 col0\" >0.4517</td>\n",
       "                        <td id=\"T_eea24_row5_col1\" class=\"data row5 col1\" >0.3592</td>\n",
       "                        <td id=\"T_eea24_row5_col2\" class=\"data row5 col2\" >0.5993</td>\n",
       "                        <td id=\"T_eea24_row5_col3\" class=\"data row5 col3\" >0.9147</td>\n",
       "                        <td id=\"T_eea24_row5_col4\" class=\"data row5 col4\" >0.1674</td>\n",
       "                        <td id=\"T_eea24_row5_col5\" class=\"data row5 col5\" >0.2809</td>\n",
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       "            <tr>\n",
       "                        <th id=\"T_eea24_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_eea24_row6_col0\" class=\"data row6 col0\" >0.5226</td>\n",
       "                        <td id=\"T_eea24_row6_col1\" class=\"data row6 col1\" >0.4702</td>\n",
       "                        <td id=\"T_eea24_row6_col2\" class=\"data row6 col2\" >0.6857</td>\n",
       "                        <td id=\"T_eea24_row6_col3\" class=\"data row6 col3\" >0.9190</td>\n",
       "                        <td id=\"T_eea24_row6_col4\" class=\"data row6 col4\" >0.1908</td>\n",
       "                        <td id=\"T_eea24_row6_col5\" class=\"data row6 col5\" >0.7690</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_eea24_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_eea24_row7_col0\" class=\"data row7 col0\" >0.4689</td>\n",
       "                        <td id=\"T_eea24_row7_col1\" class=\"data row7 col1\" >0.3528</td>\n",
       "                        <td id=\"T_eea24_row7_col2\" class=\"data row7 col2\" >0.5940</td>\n",
       "                        <td id=\"T_eea24_row7_col3\" class=\"data row7 col3\" >0.9182</td>\n",
       "                        <td id=\"T_eea24_row7_col4\" class=\"data row7 col4\" >0.1689</td>\n",
       "                        <td id=\"T_eea24_row7_col5\" class=\"data row7 col5\" >0.3961</td>\n",
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       "            <tr>\n",
       "                        <th id=\"T_eea24_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_eea24_row8_col0\" class=\"data row8 col0\" >0.4016</td>\n",
       "                        <td id=\"T_eea24_row8_col1\" class=\"data row8 col1\" >0.3942</td>\n",
       "                        <td id=\"T_eea24_row8_col2\" class=\"data row8 col2\" >0.6279</td>\n",
       "                        <td id=\"T_eea24_row8_col3\" class=\"data row8 col3\" >0.8980</td>\n",
       "                        <td id=\"T_eea24_row8_col4\" class=\"data row8 col4\" >0.2060</td>\n",
       "                        <td id=\"T_eea24_row8_col5\" class=\"data row8 col5\" >0.4939</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_eea24_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_eea24_row9_col0\" class=\"data row9 col0\" >0.5125</td>\n",
       "                        <td id=\"T_eea24_row9_col1\" class=\"data row9 col1\" >0.4693</td>\n",
       "                        <td id=\"T_eea24_row9_col2\" class=\"data row9 col2\" >0.6851</td>\n",
       "                        <td id=\"T_eea24_row9_col3\" class=\"data row9 col3\" >0.9059</td>\n",
       "                        <td id=\"T_eea24_row9_col4\" class=\"data row9 col4\" >0.2188</td>\n",
       "                        <td id=\"T_eea24_row9_col5\" class=\"data row9 col5\" >1.2600</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_eea24_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "                        <td id=\"T_eea24_row10_col0\" class=\"data row10 col0\" >0.4596</td>\n",
       "                        <td id=\"T_eea24_row10_col1\" class=\"data row10 col1\" >0.3874</td>\n",
       "                        <td id=\"T_eea24_row10_col2\" class=\"data row10 col2\" >0.6203</td>\n",
       "                        <td id=\"T_eea24_row10_col3\" class=\"data row10 col3\" >0.9087</td>\n",
       "                        <td id=\"T_eea24_row10_col4\" class=\"data row10 col4\" >0.1741</td>\n",
       "                        <td id=\"T_eea24_row10_col5\" class=\"data row10 col5\" >0.4698</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_eea24_level0_row11\" class=\"row_heading level0 row11\" >SD</th>\n",
       "                        <td id=\"T_eea24_row11_col0\" class=\"data row11 col0\" >0.0480</td>\n",
       "                        <td id=\"T_eea24_row11_col1\" class=\"data row11 col1\" >0.0640</td>\n",
       "                        <td id=\"T_eea24_row11_col2\" class=\"data row11 col2\" >0.0518</td>\n",
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       "                        <td id=\"T_eea24_row11_col4\" class=\"data row11 col4\" >0.0264</td>\n",
       "                        <td id=\"T_eea24_row11_col5\" class=\"data row11 col5\" >0.3080</td>\n",
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      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x137234990>"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stacker = stack_models(best_top3_models)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c927008f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style><table id=\"T_d8407_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Model</th>        <th class=\"col_heading level0 col1\" >MAE</th>        <th class=\"col_heading level0 col2\" >MSE</th>        <th class=\"col_heading level0 col3\" >RMSE</th>        <th class=\"col_heading level0 col4\" >R2</th>        <th class=\"col_heading level0 col5\" >RMSLE</th>        <th class=\"col_heading level0 col6\" >MAPE</th>    </tr></thead><tbody>\n",
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       "      <th></th>\n",
       "      <th>MaxPartialCharge</th>\n",
       "      <th>BCUT2D_MWHI</th>\n",
       "      <th>BCUT2D_MRLOW</th>\n",
       "      <th>BertzCT</th>\n",
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       "      <td>0.902737</td>\n",
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       "      <td>-0.925067</td>\n",
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       "      <td>-1.640</td>\n",
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       "    <tr>\n",
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       "      <td>-0.356549</td>\n",
       "      <td>-0.609089</td>\n",
       "      <td>-0.774131</td>\n",
       "      <td>0.962357</td>\n",
       "      <td>0.896536</td>\n",
       "      <td>-0.602252</td>\n",
       "      <td>-0.531234</td>\n",
       "      <td>2.199393</td>\n",
       "      <td>-1.132877</td>\n",
       "      <td>1.185412</td>\n",
       "      <td>...</td>\n",
       "      <td>0.888340</td>\n",
       "      <td>1.782857</td>\n",
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       "      <td>0.646933</td>\n",
       "      <td>-0.144482</td>\n",
       "      <td>0.723230</td>\n",
       "      <td>0.083469</td>\n",
       "      <td>-4.955</td>\n",
       "      <td>-4.567109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.990820</td>\n",
       "      <td>1.229622</td>\n",
       "      <td>-1.012473</td>\n",
       "      <td>1.509644</td>\n",
       "      <td>1.569817</td>\n",
       "      <td>-0.602252</td>\n",
       "      <td>1.658530</td>\n",
       "      <td>1.435196</td>\n",
       "      <td>-1.132877</td>\n",
       "      <td>0.546389</td>\n",
       "      <td>...</td>\n",
       "      <td>1.580013</td>\n",
       "      <td>0.800004</td>\n",
       "      <td>1.651564</td>\n",
       "      <td>0.732067</td>\n",
       "      <td>0.102868</td>\n",
       "      <td>-0.144482</td>\n",
       "      <td>-0.278685</td>\n",
       "      <td>1.934099</td>\n",
       "      <td>-6.291</td>\n",
       "      <td>-6.601302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.399093</td>\n",
       "      <td>-1.196715</td>\n",
       "      <td>1.297037</td>\n",
       "      <td>-1.333785</td>\n",
       "      <td>-1.405602</td>\n",
       "      <td>-0.602252</td>\n",
       "      <td>-0.590060</td>\n",
       "      <td>-0.925067</td>\n",
       "      <td>-1.132877</td>\n",
       "      <td>-0.895095</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.835778</td>\n",
       "      <td>0.240059</td>\n",
       "      <td>-0.677352</td>\n",
       "      <td>-0.908988</td>\n",
       "      <td>0.823280</td>\n",
       "      <td>-0.144482</td>\n",
       "      <td>-0.555632</td>\n",
       "      <td>-0.371238</td>\n",
       "      <td>-2.090</td>\n",
       "      <td>-2.121426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>0.018880</td>\n",
       "      <td>1.229667</td>\n",
       "      <td>-0.148013</td>\n",
       "      <td>0.819907</td>\n",
       "      <td>0.792771</td>\n",
       "      <td>-0.602252</td>\n",
       "      <td>1.066501</td>\n",
       "      <td>0.799352</td>\n",
       "      <td>-1.132877</td>\n",
       "      <td>1.661174</td>\n",
       "      <td>...</td>\n",
       "      <td>0.626442</td>\n",
       "      <td>0.240059</td>\n",
       "      <td>1.927981</td>\n",
       "      <td>-1.360738</td>\n",
       "      <td>0.080824</td>\n",
       "      <td>-0.144482</td>\n",
       "      <td>1.073959</td>\n",
       "      <td>1.091366</td>\n",
       "      <td>-6.290</td>\n",
       "      <td>-6.143054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>-1.601615</td>\n",
       "      <td>-1.087209</td>\n",
       "      <td>0.160285</td>\n",
       "      <td>-1.283849</td>\n",
       "      <td>-0.552944</td>\n",
       "      <td>-0.602252</td>\n",
       "      <td>0.620677</td>\n",
       "      <td>-0.395410</td>\n",
       "      <td>-1.132877</td>\n",
       "      <td>-0.895095</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.835778</td>\n",
       "      <td>0.844631</td>\n",
       "      <td>-0.677352</td>\n",
       "      <td>-0.908988</td>\n",
       "      <td>-0.009549</td>\n",
       "      <td>-0.144482</td>\n",
       "      <td>1.394043</td>\n",
       "      <td>0.142066</td>\n",
       "      <td>-4.280</td>\n",
       "      <td>-4.266251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>-0.713711</td>\n",
       "      <td>1.228280</td>\n",
       "      <td>1.440386</td>\n",
       "      <td>1.025052</td>\n",
       "      <td>1.042898</td>\n",
       "      <td>-0.602252</td>\n",
       "      <td>1.990749</td>\n",
       "      <td>-0.368711</td>\n",
       "      <td>0.660969</td>\n",
       "      <td>1.874304</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.835778</td>\n",
       "      <td>-1.069928</td>\n",
       "      <td>1.920619</td>\n",
       "      <td>-0.908988</td>\n",
       "      <td>-0.408972</td>\n",
       "      <td>-0.144482</td>\n",
       "      <td>-1.265530</td>\n",
       "      <td>2.525058</td>\n",
       "      <td>-8.600</td>\n",
       "      <td>-8.621841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>0.787289</td>\n",
       "      <td>-0.550699</td>\n",
       "      <td>-1.684012</td>\n",
       "      <td>0.377827</td>\n",
       "      <td>0.033386</td>\n",
       "      <td>-0.602252</td>\n",
       "      <td>-1.546475</td>\n",
       "      <td>-0.293008</td>\n",
       "      <td>0.711765</td>\n",
       "      <td>1.403111</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.835778</td>\n",
       "      <td>0.186061</td>\n",
       "      <td>-0.677352</td>\n",
       "      <td>1.169553</td>\n",
       "      <td>-0.408972</td>\n",
       "      <td>-0.144482</td>\n",
       "      <td>-0.749748</td>\n",
       "      <td>-0.336513</td>\n",
       "      <td>-2.820</td>\n",
       "      <td>-2.965900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>1.039645</td>\n",
       "      <td>-0.619813</td>\n",
       "      <td>0.203278</td>\n",
       "      <td>0.446923</td>\n",
       "      <td>-0.905789</td>\n",
       "      <td>1.573171</td>\n",
       "      <td>-1.546475</td>\n",
       "      <td>-1.655528</td>\n",
       "      <td>-1.132877</td>\n",
       "      <td>0.606906</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.835778</td>\n",
       "      <td>-1.069928</td>\n",
       "      <td>-0.677352</td>\n",
       "      <td>0.827508</td>\n",
       "      <td>0.025739</td>\n",
       "      <td>-0.144482</td>\n",
       "      <td>-1.265530</td>\n",
       "      <td>-1.556138</td>\n",
       "      <td>-3.401</td>\n",
       "      <td>-3.277988</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>206 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     MaxPartialCharge  BCUT2D_MWHI  BCUT2D_MRLOW   BertzCT    Kappa1  \\\n",
       "0           -0.886795    -0.581488     -0.370221 -1.280562 -0.004362   \n",
       "1           -1.190633    -1.180687      0.902737 -1.276477 -1.376126   \n",
       "2           -0.356549    -0.609089     -0.774131  0.962357  0.896536   \n",
       "3            0.990820     1.229622     -1.012473  1.509644  1.569817   \n",
       "4           -1.399093    -1.196715      1.297037 -1.333785 -1.405602   \n",
       "..                ...          ...           ...       ...       ...   \n",
       "201          0.018880     1.229667     -0.148013  0.819907  0.792771   \n",
       "202         -1.601615    -1.087209      0.160285 -1.283849 -0.552944   \n",
       "203         -0.713711     1.228280      1.440386  1.025052  1.042898   \n",
       "204          0.787289    -0.550699     -1.684012  0.377827  0.033386   \n",
       "205          1.039645    -0.619813      0.203278  0.446923 -0.905789   \n",
       "\n",
       "     PEOE_VSA3  PEOE_VSA6  PEOE_VSA7  PEOE_VSA8  PEOE_VSA9  ...  SlogP_VSA3  \\\n",
       "0    -0.602252   0.287770  -0.305969   0.861641  -0.895095  ...    0.626442   \n",
       "1    -0.602252  -0.978237  -0.925067   0.076147  -0.895095  ...   -0.835778   \n",
       "2    -0.602252  -0.531234   2.199393  -1.132877   1.185412  ...    0.888340   \n",
       "3    -0.602252   1.658530   1.435196  -1.132877   0.546389  ...    1.580013   \n",
       "4    -0.602252  -0.590060  -0.925067  -1.132877  -0.895095  ...   -0.835778   \n",
       "..         ...        ...        ...        ...        ...  ...         ...   \n",
       "201  -0.602252   1.066501   0.799352  -1.132877   1.661174  ...    0.626442   \n",
       "202  -0.602252   0.620677  -0.395410  -1.132877  -0.895095  ...   -0.835778   \n",
       "203  -0.602252   1.990749  -0.368711   0.660969   1.874304  ...   -0.835778   \n",
       "204  -0.602252  -1.546475  -0.293008   0.711765   1.403111  ...   -0.835778   \n",
       "205   1.573171  -1.546475  -1.655528  -1.132877   0.606906  ...   -0.835778   \n",
       "\n",
       "     EState_VSA4  VSA_EState10  VSA_EState2  VSA_EState7  VSA_EState9  \\\n",
       "0       0.952940     -0.677352    -0.908988     0.908496    -0.144482   \n",
       "1       0.924917     -0.677352    -0.908988     1.351391    -0.144482   \n",
       "2       1.782857     -0.677352    -0.908988     0.646933    -0.144482   \n",
       "3       0.800004      1.651564     0.732067     0.102868    -0.144482   \n",
       "4       0.240059     -0.677352    -0.908988     0.823280    -0.144482   \n",
       "..           ...           ...          ...          ...          ...   \n",
       "201     0.240059      1.927981    -1.360738     0.080824    -0.144482   \n",
       "202     0.844631     -0.677352    -0.908988    -0.009549    -0.144482   \n",
       "203    -1.069928      1.920619    -0.908988    -0.408972    -0.144482   \n",
       "204     0.186061     -0.677352     1.169553    -0.408972    -0.144482   \n",
       "205    -1.069928     -0.677352     0.827508     0.025739    -0.144482   \n",
       "\n",
       "     FractionCSP3   MolLogP   logs     Label  \n",
       "0        1.394043  0.096012 -1.850 -1.855294  \n",
       "1        0.571753 -0.553420 -1.640 -1.650233  \n",
       "2        0.723230  0.083469 -4.955 -4.567109  \n",
       "3       -0.278685  1.934099 -6.291 -6.601302  \n",
       "4       -0.555632 -0.371238 -2.090 -2.121426  \n",
       "..            ...       ...    ...       ...  \n",
       "201      1.073959  1.091366 -6.290 -6.143054  \n",
       "202      1.394043  0.142066 -4.280 -4.266251  \n",
       "203     -1.265530  2.525058 -8.600 -8.621841  \n",
       "204     -0.749748 -0.336513 -2.820 -2.965900  \n",
       "205     -1.265530 -1.556138 -3.401 -3.277988  \n",
       "\n",
       "[206 rows x 23 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_model = finalize_model(stacker)\n",
    "predict_model(final_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "cc907874",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.8874, 0.4558, 0.4587)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unseen_predictions = predict_model(final_model, data=df_unseen)\n",
    "check_metric(unseen_predictions.logs, unseen_predictions.Label, 'R2'), check_metric(unseen_predictions.logs, unseen_predictions.Label, 'MAE'), check_metric(unseen_predictions.logs, unseen_predictions.Label, 'RMSE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2d992a86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model(final_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b76cb217",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transformation Pipeline and Model Succesfully Saved\n",
      "Transformation Pipeline and Model Successfully Loaded\n"
     ]
    }
   ],
   "source": [
    "save_model(final_model,'sol_pred_model_vsa')\n",
    "saved_final_model = load_model('sol_pred_model_vsa')\n",
    "new_prediction = predict_model(saved_final_model, data=df_unseen)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}