Demo 1 - Regression.ipynb

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   "metadata": {
    "slideshow": {
     "slide_type": "slide"
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   "source": [
    "# 👉 What is PyCaret?\n",
    "\n",
    "PyCaret is an open-source, low-code machine learning library in Python that automates machine learning workflows. It is an end-to-end machine learning and model management tool that speeds up the experiment cycle exponentially and makes you more productive.\n",
    "\n",
    "In comparison with the other open-source machine learning libraries, PyCaret is an alternate low-code library that can be used to replace hundreds of lines of code with few words only. This makes experiments exponentially fast and efficient. PyCaret is essentially a Python wrapper around several machine learning libraries and frameworks such as scikit-learn, XGBoost, LightGBM, CatBoost, spaCy, Optuna, Hyperopt, Ray, and many more.\n",
    "\n",
    "The design and simplicity of PyCaret is inspired by the emerging role of citizen data scientists, a term first used by Gartner. Citizen Data Scientists are power users who can perform both simple and moderately sophisticated analytical tasks that would previously have required more expertise. Seasoned data scientists are often difficult to find and expensive to hire but citizen data scientists can be an effective way to mitigate this gap and address data-related challenges in the business setting.\n",
    "\n",
    "Official Website: https://www.pycaret.org\n",
    "Documentation: https://pycaret.readthedocs.io/en/latest/"
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 👉 Install PyCaret\n",
    "Installing PyCaret is very easy and takes only a few minutes. We strongly recommend using a virtual environment to avoid potential conflicts with other libraries. PyCaret's default installation is a slim version of pycaret that only installs hard dependencies that are listed in [requirements.txt](https://github.com/pycaret/pycaret/blob/master/requirements.txt). To install the default version:\n",
    "\n",
    "- `pip install pycaret`\n",
    "\n",
    "When you install the full version of pycaret, all the optional dependencies as listed [here](https://github.com/pycaret/pycaret/blob/master/requirements-optional.txt) are also installed.To install version:\n",
    "\n",
    "- `pip install pycaret[full]`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 👉Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>bmi</th>\n",
       "      <th>children</th>\n",
       "      <th>smoker</th>\n",
       "      <th>region</th>\n",
       "      <th>charges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>female</td>\n",
       "      <td>27.900</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>southwest</td>\n",
       "      <td>16884.92400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>male</td>\n",
       "      <td>33.770</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>1725.55230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>male</td>\n",
       "      <td>33.000</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>4449.46200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>male</td>\n",
       "      <td>22.705</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>21984.47061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32</td>\n",
       "      <td>male</td>\n",
       "      <td>28.880</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>3866.85520</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age     sex     bmi  children smoker     region      charges\n",
       "0   19  female  27.900         0    yes  southwest  16884.92400\n",
       "1   18    male  33.770         1     no  southeast   1725.55230\n",
       "2   28    male  33.000         3     no  southeast   4449.46200\n",
       "3   33    male  22.705         0     no  northwest  21984.47061\n",
       "4   32    male  28.880         0     no  northwest   3866.85520"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pycaret.datasets import get_data\n",
    "data = get_data('insurance')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 👉 Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_53c6c_\" ><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_53c6c_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_53c6c_row0_col0\" class=\"data row0 col0\" >session_id</td>\n",
       "                        <td id=\"T_53c6c_row0_col1\" class=\"data row0 col1\" >123</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53c6c_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_53c6c_row1_col0\" class=\"data row1 col0\" >Target</td>\n",
       "                        <td id=\"T_53c6c_row1_col1\" class=\"data row1 col1\" >charges</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53c6c_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_53c6c_row2_col0\" class=\"data row2 col0\" >Original Data</td>\n",
       "                        <td id=\"T_53c6c_row2_col1\" class=\"data row2 col1\" >(1338, 7)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53c6c_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_53c6c_row3_col0\" class=\"data row3 col0\" >Missing Values</td>\n",
       "                        <td id=\"T_53c6c_row3_col1\" class=\"data row3 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53c6c_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_53c6c_row4_col0\" class=\"data row4 col0\" >Numeric Features</td>\n",
       "                        <td id=\"T_53c6c_row4_col1\" class=\"data row4 col1\" >2</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53c6c_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_53c6c_row5_col0\" class=\"data row5 col0\" >Categorical Features</td>\n",
       "                        <td id=\"T_53c6c_row5_col1\" class=\"data row5 col1\" >4</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53c6c_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_53c6c_row6_col0\" class=\"data row6 col0\" >Ordinal Features</td>\n",
       "                        <td id=\"T_53c6c_row6_col1\" class=\"data row6 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53c6c_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_53c6c_row7_col0\" class=\"data row7 col0\" >High Cardinality Features</td>\n",
       "                        <td id=\"T_53c6c_row7_col1\" class=\"data row7 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53c6c_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_53c6c_row8_col0\" class=\"data row8 col0\" >High Cardinality Method</td>\n",
       "                        <td id=\"T_53c6c_row8_col1\" class=\"data row8 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53c6c_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_53c6c_row9_col0\" class=\"data row9 col0\" >Transformed Train Set</td>\n",
       "                        <td id=\"T_53c6c_row9_col1\" class=\"data row9 col1\" >(936, 14)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53c6c_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "                        <td id=\"T_53c6c_row10_col0\" class=\"data row10 col0\" >Transformed Test Set</td>\n",
       "                        <td id=\"T_53c6c_row10_col1\" class=\"data row10 col1\" >(402, 14)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53c6c_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "                        <td id=\"T_53c6c_row11_col0\" class=\"data row11 col0\" >Shuffle Train-Test</td>\n",
       "                        <td id=\"T_53c6c_row11_col1\" class=\"data row11 col1\" >True</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53c6c_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "                        <td id=\"T_53c6c_row12_col0\" class=\"data row12 col0\" >Stratify Train-Test</td>\n",
       "                        <td id=\"T_53c6c_row12_col1\" class=\"data row12 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53c6c_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "                        <td id=\"T_53c6c_row13_col0\" class=\"data row13 col0\" >Fold Generator</td>\n",
       "                        <td id=\"T_53c6c_row13_col1\" class=\"data row13 col1\" >KFold</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53c6c_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "                        <td id=\"T_53c6c_row14_col0\" class=\"data row14 col0\" >Fold Number</td>\n",
       "                        <td id=\"T_53c6c_row14_col1\" class=\"data row14 col1\" >10</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53c6c_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "                        <td id=\"T_53c6c_row15_col0\" class=\"data row15 col0\" >CPU Jobs</td>\n",
       "                        <td id=\"T_53c6c_row15_col1\" class=\"data row15 col1\" >-1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53c6c_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "                        <td id=\"T_53c6c_row16_col0\" class=\"data row16 col0\" >Use GPU</td>\n",
       "                        <td id=\"T_53c6c_row16_col1\" class=\"data row16 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53c6c_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "                        <td id=\"T_53c6c_row17_col0\" class=\"data row17 col0\" >Log Experiment</td>\n",
       "                        <td id=\"T_53c6c_row17_col1\" class=\"data row17 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53c6c_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "                        <td id=\"T_53c6c_row18_col0\" class=\"data row18 col0\" >Experiment Name</td>\n",
       "                        <td id=\"T_53c6c_row18_col1\" class=\"data row18 col1\" >reg-default-name</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53c6c_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "                        <td id=\"T_53c6c_row19_col0\" class=\"data row19 col0\" >USI</td>\n",
       "                        <td id=\"T_53c6c_row19_col1\" class=\"data row19 col1\" >ecac</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53c6c_level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "                        <td id=\"T_53c6c_row20_col0\" class=\"data row20 col0\" >Imputation Type</td>\n",
       "                        <td id=\"T_53c6c_row20_col1\" class=\"data row20 col1\" >simple</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53c6c_level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
       "                        <td id=\"T_53c6c_row21_col0\" class=\"data row21 col0\" >Iterative Imputation Iteration</td>\n",
       "                        <td id=\"T_53c6c_row21_col1\" class=\"data row21 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53c6c_level0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
       "                        <td id=\"T_53c6c_row22_col0\" class=\"data row22 col0\" >Numeric Imputer</td>\n",
       "                        <td id=\"T_53c6c_row22_col1\" class=\"data row22 col1\" >mean</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53c6c_level0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
       "                        <td id=\"T_53c6c_row23_col0\" class=\"data row23 col0\" >Iterative Imputation Numeric Model</td>\n",
       "                        <td id=\"T_53c6c_row23_col1\" class=\"data row23 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53c6c_level0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
       "                        <td id=\"T_53c6c_row24_col0\" class=\"data row24 col0\" >Categorical Imputer</td>\n",
       "                        <td id=\"T_53c6c_row24_col1\" class=\"data row24 col1\" >constant</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53c6c_level0_row25\" class=\"row_heading level0 row25\" >25</th>\n",
       "                        <td id=\"T_53c6c_row25_col0\" class=\"data row25 col0\" >Iterative Imputation Categorical Model</td>\n",
       "                        <td id=\"T_53c6c_row25_col1\" class=\"data row25 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53c6c_level0_row26\" class=\"row_heading level0 row26\" >26</th>\n",
       "                        <td id=\"T_53c6c_row26_col0\" class=\"data row26 col0\" >Unknown Categoricals Handling</td>\n",
       "                        <td id=\"T_53c6c_row26_col1\" class=\"data row26 col1\" >least_frequent</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53c6c_level0_row27\" class=\"row_heading level0 row27\" >27</th>\n",
       "                        <td id=\"T_53c6c_row27_col0\" class=\"data row27 col0\" >Normalize</td>\n",
       "                        <td id=\"T_53c6c_row27_col1\" class=\"data row27 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53c6c_level0_row28\" class=\"row_heading level0 row28\" >28</th>\n",
       "                        <td id=\"T_53c6c_row28_col0\" class=\"data row28 col0\" >Normalize Method</td>\n",
       "                        <td id=\"T_53c6c_row28_col1\" class=\"data row28 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53c6c_level0_row29\" class=\"row_heading level0 row29\" >29</th>\n",
       "                        <td id=\"T_53c6c_row29_col0\" class=\"data row29 col0\" >Transformation</td>\n",
       "                        <td id=\"T_53c6c_row29_col1\" class=\"data row29 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53c6c_level0_row30\" class=\"row_heading level0 row30\" >30</th>\n",
       "                        <td id=\"T_53c6c_row30_col0\" class=\"data row30 col0\" >Transformation Method</td>\n",
       "                        <td id=\"T_53c6c_row30_col1\" class=\"data row30 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53c6c_level0_row31\" class=\"row_heading level0 row31\" >31</th>\n",
       "                        <td id=\"T_53c6c_row31_col0\" class=\"data row31 col0\" >PCA</td>\n",
       "                        <td id=\"T_53c6c_row31_col1\" class=\"data row31 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53c6c_level0_row32\" class=\"row_heading level0 row32\" >32</th>\n",
       "                        <td id=\"T_53c6c_row32_col0\" class=\"data row32 col0\" >PCA Method</td>\n",
       "                        <td id=\"T_53c6c_row32_col1\" class=\"data row32 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53c6c_level0_row33\" class=\"row_heading level0 row33\" >33</th>\n",
       "                        <td id=\"T_53c6c_row33_col0\" class=\"data row33 col0\" >PCA Components</td>\n",
       "                        <td id=\"T_53c6c_row33_col1\" class=\"data row33 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53c6c_level0_row34\" class=\"row_heading level0 row34\" >34</th>\n",
       "                        <td id=\"T_53c6c_row34_col0\" class=\"data row34 col0\" >Ignore Low Variance</td>\n",
       "                        <td id=\"T_53c6c_row34_col1\" class=\"data row34 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53c6c_level0_row35\" class=\"row_heading level0 row35\" >35</th>\n",
       "                        <td id=\"T_53c6c_row35_col0\" class=\"data row35 col0\" >Combine Rare Levels</td>\n",
       "                        <td id=\"T_53c6c_row35_col1\" class=\"data row35 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53c6c_level0_row36\" class=\"row_heading level0 row36\" >36</th>\n",
       "                        <td id=\"T_53c6c_row36_col0\" class=\"data row36 col0\" >Rare Level Threshold</td>\n",
       "                        <td id=\"T_53c6c_row36_col1\" class=\"data row36 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53c6c_level0_row37\" class=\"row_heading level0 row37\" >37</th>\n",
       "                        <td id=\"T_53c6c_row37_col0\" class=\"data row37 col0\" >Numeric Binning</td>\n",
       "                        <td id=\"T_53c6c_row37_col1\" class=\"data row37 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53c6c_level0_row38\" class=\"row_heading level0 row38\" >38</th>\n",
       "                        <td id=\"T_53c6c_row38_col0\" class=\"data row38 col0\" >Remove Outliers</td>\n",
       "                        <td id=\"T_53c6c_row38_col1\" class=\"data row38 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53c6c_level0_row39\" class=\"row_heading level0 row39\" >39</th>\n",
       "                        <td id=\"T_53c6c_row39_col0\" class=\"data row39 col0\" >Outliers Threshold</td>\n",
       "                        <td id=\"T_53c6c_row39_col1\" class=\"data row39 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53c6c_level0_row40\" class=\"row_heading level0 row40\" >40</th>\n",
       "                        <td id=\"T_53c6c_row40_col0\" class=\"data row40 col0\" >Remove Multicollinearity</td>\n",
       "                        <td id=\"T_53c6c_row40_col1\" class=\"data row40 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53c6c_level0_row41\" class=\"row_heading level0 row41\" >41</th>\n",
       "                        <td id=\"T_53c6c_row41_col0\" class=\"data row41 col0\" >Multicollinearity Threshold</td>\n",
       "                        <td id=\"T_53c6c_row41_col1\" class=\"data row41 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53c6c_level0_row42\" class=\"row_heading level0 row42\" >42</th>\n",
       "                        <td id=\"T_53c6c_row42_col0\" class=\"data row42 col0\" >Clustering</td>\n",
       "                        <td id=\"T_53c6c_row42_col1\" class=\"data row42 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53c6c_level0_row43\" class=\"row_heading level0 row43\" >43</th>\n",
       "                        <td id=\"T_53c6c_row43_col0\" class=\"data row43 col0\" >Clustering Iteration</td>\n",
       "                        <td id=\"T_53c6c_row43_col1\" class=\"data row43 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53c6c_level0_row44\" class=\"row_heading level0 row44\" >44</th>\n",
       "                        <td id=\"T_53c6c_row44_col0\" class=\"data row44 col0\" >Polynomial Features</td>\n",
       "                        <td id=\"T_53c6c_row44_col1\" class=\"data row44 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53c6c_level0_row45\" class=\"row_heading level0 row45\" >45</th>\n",
       "                        <td id=\"T_53c6c_row45_col0\" class=\"data row45 col0\" >Polynomial Degree</td>\n",
       "                        <td id=\"T_53c6c_row45_col1\" class=\"data row45 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53c6c_level0_row46\" class=\"row_heading level0 row46\" >46</th>\n",
       "                        <td id=\"T_53c6c_row46_col0\" class=\"data row46 col0\" >Trignometry Features</td>\n",
       "                        <td id=\"T_53c6c_row46_col1\" class=\"data row46 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53c6c_level0_row47\" class=\"row_heading level0 row47\" >47</th>\n",
       "                        <td id=\"T_53c6c_row47_col0\" class=\"data row47 col0\" >Polynomial Threshold</td>\n",
       "                        <td id=\"T_53c6c_row47_col1\" class=\"data row47 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53c6c_level0_row48\" class=\"row_heading level0 row48\" >48</th>\n",
       "                        <td id=\"T_53c6c_row48_col0\" class=\"data row48 col0\" >Group Features</td>\n",
       "                        <td id=\"T_53c6c_row48_col1\" class=\"data row48 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53c6c_level0_row49\" class=\"row_heading level0 row49\" >49</th>\n",
       "                        <td id=\"T_53c6c_row49_col0\" class=\"data row49 col0\" >Feature Selection</td>\n",
       "                        <td id=\"T_53c6c_row49_col1\" class=\"data row49 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53c6c_level0_row50\" class=\"row_heading level0 row50\" >50</th>\n",
       "                        <td id=\"T_53c6c_row50_col0\" class=\"data row50 col0\" >Feature Selection Method</td>\n",
       "                        <td id=\"T_53c6c_row50_col1\" class=\"data row50 col1\" >classic</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53c6c_level0_row51\" class=\"row_heading level0 row51\" >51</th>\n",
       "                        <td id=\"T_53c6c_row51_col0\" class=\"data row51 col0\" >Features Selection Threshold</td>\n",
       "                        <td id=\"T_53c6c_row51_col1\" class=\"data row51 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53c6c_level0_row52\" class=\"row_heading level0 row52\" >52</th>\n",
       "                        <td id=\"T_53c6c_row52_col0\" class=\"data row52 col0\" >Feature Interaction</td>\n",
       "                        <td id=\"T_53c6c_row52_col1\" class=\"data row52 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53c6c_level0_row53\" class=\"row_heading level0 row53\" >53</th>\n",
       "                        <td id=\"T_53c6c_row53_col0\" class=\"data row53 col0\" >Feature Ratio</td>\n",
       "                        <td id=\"T_53c6c_row53_col1\" class=\"data row53 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53c6c_level0_row54\" class=\"row_heading level0 row54\" >54</th>\n",
       "                        <td id=\"T_53c6c_row54_col0\" class=\"data row54 col0\" >Interaction Threshold</td>\n",
       "                        <td id=\"T_53c6c_row54_col1\" class=\"data row54 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53c6c_level0_row55\" class=\"row_heading level0 row55\" >55</th>\n",
       "                        <td id=\"T_53c6c_row55_col0\" class=\"data row55 col0\" >Transform Target</td>\n",
       "                        <td id=\"T_53c6c_row55_col1\" class=\"data row55 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53c6c_level0_row56\" class=\"row_heading level0 row56\" >56</th>\n",
       "                        <td id=\"T_53c6c_row56_col0\" class=\"data row56 col0\" >Transform Target Method</td>\n",
       "                        <td id=\"T_53c6c_row56_col1\" class=\"data row56 col1\" >box-cox</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1bc25c423c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pycaret.regression import *\n",
    "s = setup(data, target = 'charges', session_id = 123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>bmi</th>\n",
       "      <th>sex_female</th>\n",
       "      <th>children_0</th>\n",
       "      <th>children_1</th>\n",
       "      <th>children_2</th>\n",
       "      <th>children_3</th>\n",
       "      <th>children_4</th>\n",
       "      <th>children_5</th>\n",
       "      <th>smoker_yes</th>\n",
       "      <th>region_northeast</th>\n",
       "      <th>region_northwest</th>\n",
       "      <th>region_southeast</th>\n",
       "      <th>region_southwest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>36.0</td>\n",
       "      <td>27.549999</td>\n",
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       "    <tr>\n",
       "      <th>904</th>\n",
       "      <td>60.0</td>\n",
       "      <td>35.099998</td>\n",
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       "    <tr>\n",
       "      <th>670</th>\n",
       "      <td>30.0</td>\n",
       "      <td>31.570000</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>617</th>\n",
       "      <td>49.0</td>\n",
       "      <td>25.600000</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>373</th>\n",
       "      <td>26.0</td>\n",
       "      <td>32.900002</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1238</th>\n",
       "      <td>37.0</td>\n",
       "      <td>22.705000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1147</th>\n",
       "      <td>20.0</td>\n",
       "      <td>31.920000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>19.0</td>\n",
       "      <td>28.400000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1041</th>\n",
       "      <td>18.0</td>\n",
       "      <td>23.084999</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1122</th>\n",
       "      <td>53.0</td>\n",
       "      <td>36.860001</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>936 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age        bmi  sex_female  children_0  children_1  children_2  \\\n",
       "300   36.0  27.549999         0.0         0.0         0.0         0.0   \n",
       "904   60.0  35.099998         1.0         1.0         0.0         0.0   \n",
       "670   30.0  31.570000         0.0         0.0         0.0         0.0   \n",
       "617   49.0  25.600000         0.0         0.0         0.0         1.0   \n",
       "373   26.0  32.900002         0.0         0.0         0.0         1.0   \n",
       "...    ...        ...         ...         ...         ...         ...   \n",
       "1238  37.0  22.705000         0.0         0.0         0.0         0.0   \n",
       "1147  20.0  31.920000         1.0         1.0         0.0         0.0   \n",
       "106   19.0  28.400000         1.0         0.0         1.0         0.0   \n",
       "1041  18.0  23.084999         0.0         1.0         0.0         0.0   \n",
       "1122  53.0  36.860001         1.0         0.0         0.0         0.0   \n",
       "\n",
       "      children_3  children_4  children_5  smoker_yes  region_northeast  \\\n",
       "300          1.0         0.0         0.0         0.0               1.0   \n",
       "904          0.0         0.0         0.0         0.0               0.0   \n",
       "670          1.0         0.0         0.0         0.0               0.0   \n",
       "617          0.0         0.0         0.0         1.0               0.0   \n",
       "373          0.0         0.0         0.0         1.0               0.0   \n",
       "...          ...         ...         ...         ...               ...   \n",
       "1238         1.0         0.0         0.0         0.0               1.0   \n",
       "1147         0.0         0.0         0.0         0.0               0.0   \n",
       "106          0.0         0.0         0.0         0.0               0.0   \n",
       "1041         0.0         0.0         0.0         0.0               1.0   \n",
       "1122         1.0         0.0         0.0         1.0               0.0   \n",
       "\n",
       "      region_northwest  region_southeast  region_southwest  \n",
       "300                0.0               0.0               0.0  \n",
       "904                0.0               0.0               1.0  \n",
       "670                0.0               1.0               0.0  \n",
       "617                0.0               0.0               1.0  \n",
       "373                0.0               0.0               1.0  \n",
       "...                ...               ...               ...  \n",
       "1238               0.0               0.0               0.0  \n",
       "1147               1.0               0.0               0.0  \n",
       "106                0.0               0.0               1.0  \n",
       "1041               0.0               0.0               0.0  \n",
       "1122               1.0               0.0               0.0  \n",
       "\n",
       "[936 rows x 14 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check transformed X_train\n",
    "get_config('X_train')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'bmi', 'sex_female', 'children_0', 'children_1', 'children_2',\n",
       "       'children_3', 'children_4', 'children_5', 'smoker_yes',\n",
       "       'region_northeast', 'region_northwest', 'region_southeast',\n",
       "       'region_southwest'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# list columns of transformed X_train \n",
    "get_config('X_train').columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 👉Model Training & Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "        }</style><table id=\"T_0c5c7_\" ><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_0c5c7_level0_row0\" class=\"row_heading level0 row0\" >gbr</th>\n",
       "                        <td id=\"T_0c5c7_row0_col0\" class=\"data row0 col0\" >Gradient Boosting Regressor</td>\n",
       "                        <td id=\"T_0c5c7_row0_col1\" class=\"data row0 col1\" >2702.7680</td>\n",
       "                        <td id=\"T_0c5c7_row0_col2\" class=\"data row0 col2\" >23242056.4409</td>\n",
       "                        <td id=\"T_0c5c7_row0_col3\" class=\"data row0 col3\" >4801.5704</td>\n",
       "                        <td id=\"T_0c5c7_row0_col4\" class=\"data row0 col4\" >0.8348</td>\n",
       "                        <td id=\"T_0c5c7_row0_col5\" class=\"data row0 col5\" >0.4397</td>\n",
       "                        <td id=\"T_0c5c7_row0_col6\" class=\"data row0 col6\" >0.3113</td>\n",
       "                        <td id=\"T_0c5c7_row0_col7\" class=\"data row0 col7\" >0.0280</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0c5c7_level0_row1\" class=\"row_heading level0 row1\" >catboost</th>\n",
       "                        <td id=\"T_0c5c7_row1_col0\" class=\"data row1 col0\" >CatBoost Regressor</td>\n",
       "                        <td id=\"T_0c5c7_row1_col1\" class=\"data row1 col1\" >2844.4446</td>\n",
       "                        <td id=\"T_0c5c7_row1_col2\" class=\"data row1 col2\" >24943135.5224</td>\n",
       "                        <td id=\"T_0c5c7_row1_col3\" class=\"data row1 col3\" >4977.1926</td>\n",
       "                        <td id=\"T_0c5c7_row1_col4\" class=\"data row1 col4\" >0.8228</td>\n",
       "                        <td id=\"T_0c5c7_row1_col5\" class=\"data row1 col5\" >0.4707</td>\n",
       "                        <td id=\"T_0c5c7_row1_col6\" class=\"data row1 col6\" >0.3364</td>\n",
       "                        <td id=\"T_0c5c7_row1_col7\" class=\"data row1 col7\" >1.0920</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0c5c7_level0_row2\" class=\"row_heading level0 row2\" >rf</th>\n",
       "                        <td id=\"T_0c5c7_row2_col0\" class=\"data row2 col0\" >Random Forest Regressor</td>\n",
       "                        <td id=\"T_0c5c7_row2_col1\" class=\"data row2 col1\" >2736.7455</td>\n",
       "                        <td id=\"T_0c5c7_row2_col2\" class=\"data row2 col2\" >24862762.2305</td>\n",
       "                        <td id=\"T_0c5c7_row2_col3\" class=\"data row2 col3\" >4970.6959</td>\n",
       "                        <td id=\"T_0c5c7_row2_col4\" class=\"data row2 col4\" >0.8213</td>\n",
       "                        <td id=\"T_0c5c7_row2_col5\" class=\"data row2 col5\" >0.4674</td>\n",
       "                        <td id=\"T_0c5c7_row2_col6\" class=\"data row2 col6\" >0.3294</td>\n",
       "                        <td id=\"T_0c5c7_row2_col7\" class=\"data row2 col7\" >0.1830</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0c5c7_level0_row3\" class=\"row_heading level0 row3\" >lightgbm</th>\n",
       "                        <td id=\"T_0c5c7_row3_col0\" class=\"data row3 col0\" >Light Gradient Boosting Machine</td>\n",
       "                        <td id=\"T_0c5c7_row3_col1\" class=\"data row3 col1\" >2959.5584</td>\n",
       "                        <td id=\"T_0c5c7_row3_col2\" class=\"data row3 col2\" >25236477.0456</td>\n",
       "                        <td id=\"T_0c5c7_row3_col3\" class=\"data row3 col3\" >5013.0892</td>\n",
       "                        <td id=\"T_0c5c7_row3_col4\" class=\"data row3 col4\" >0.8171</td>\n",
       "                        <td id=\"T_0c5c7_row3_col5\" class=\"data row3 col5\" >0.5427</td>\n",
       "                        <td id=\"T_0c5c7_row3_col6\" class=\"data row3 col6\" >0.3685</td>\n",
       "                        <td id=\"T_0c5c7_row3_col7\" class=\"data row3 col7\" >0.0240</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0c5c7_level0_row4\" class=\"row_heading level0 row4\" >ada</th>\n",
       "                        <td id=\"T_0c5c7_row4_col0\" class=\"data row4 col0\" >AdaBoost Regressor</td>\n",
       "                        <td id=\"T_0c5c7_row4_col1\" class=\"data row4 col1\" >4162.2323</td>\n",
       "                        <td id=\"T_0c5c7_row4_col2\" class=\"data row4 col2\" >28328260.0955</td>\n",
       "                        <td id=\"T_0c5c7_row4_col3\" class=\"data row4 col3\" >5316.6146</td>\n",
       "                        <td id=\"T_0c5c7_row4_col4\" class=\"data row4 col4\" >0.7985</td>\n",
       "                        <td id=\"T_0c5c7_row4_col5\" class=\"data row4 col5\" >0.6349</td>\n",
       "                        <td id=\"T_0c5c7_row4_col6\" class=\"data row4 col6\" >0.7263</td>\n",
       "                        <td id=\"T_0c5c7_row4_col7\" class=\"data row4 col7\" >0.0110</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0c5c7_level0_row5\" class=\"row_heading level0 row5\" >et</th>\n",
       "                        <td id=\"T_0c5c7_row5_col0\" class=\"data row5 col0\" >Extra Trees Regressor</td>\n",
       "                        <td id=\"T_0c5c7_row5_col1\" class=\"data row5 col1\" >2814.2964</td>\n",
       "                        <td id=\"T_0c5c7_row5_col2\" class=\"data row5 col2\" >28815493.0260</td>\n",
       "                        <td id=\"T_0c5c7_row5_col3\" class=\"data row5 col3\" >5339.0879</td>\n",
       "                        <td id=\"T_0c5c7_row5_col4\" class=\"data row5 col4\" >0.7964</td>\n",
       "                        <td id=\"T_0c5c7_row5_col5\" class=\"data row5 col5\" >0.4889</td>\n",
       "                        <td id=\"T_0c5c7_row5_col6\" class=\"data row5 col6\" >0.3350</td>\n",
       "                        <td id=\"T_0c5c7_row5_col7\" class=\"data row5 col7\" >0.1930</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0c5c7_level0_row6\" class=\"row_heading level0 row6\" >xgboost</th>\n",
       "                        <td id=\"T_0c5c7_row6_col0\" class=\"data row6 col0\" >Extreme Gradient Boosting</td>\n",
       "                        <td id=\"T_0c5c7_row6_col1\" class=\"data row6 col1\" >3302.3215</td>\n",
       "                        <td id=\"T_0c5c7_row6_col2\" class=\"data row6 col2\" >31739266.6000</td>\n",
       "                        <td id=\"T_0c5c7_row6_col3\" class=\"data row6 col3\" >5615.5941</td>\n",
       "                        <td id=\"T_0c5c7_row6_col4\" class=\"data row6 col4\" >0.7701</td>\n",
       "                        <td id=\"T_0c5c7_row6_col5\" class=\"data row6 col5\" >0.5661</td>\n",
       "                        <td id=\"T_0c5c7_row6_col6\" class=\"data row6 col6\" >0.4218</td>\n",
       "                        <td id=\"T_0c5c7_row6_col7\" class=\"data row6 col7\" >0.2820</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0c5c7_level0_row7\" class=\"row_heading level0 row7\" >llar</th>\n",
       "                        <td id=\"T_0c5c7_row7_col0\" class=\"data row7 col0\" >Lasso Least Angle Regression</td>\n",
       "                        <td id=\"T_0c5c7_row7_col1\" class=\"data row7 col1\" >4315.7895</td>\n",
       "                        <td id=\"T_0c5c7_row7_col2\" class=\"data row7 col2\" >38355976.5182</td>\n",
       "                        <td id=\"T_0c5c7_row7_col3\" class=\"data row7 col3\" >6173.8740</td>\n",
       "                        <td id=\"T_0c5c7_row7_col4\" class=\"data row7 col4\" >0.7311</td>\n",
       "                        <td id=\"T_0c5c7_row7_col5\" class=\"data row7 col5\" >0.6105</td>\n",
       "                        <td id=\"T_0c5c7_row7_col6\" class=\"data row7 col6\" >0.4415</td>\n",
       "                        <td id=\"T_0c5c7_row7_col7\" class=\"data row7 col7\" >0.0060</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0c5c7_level0_row8\" class=\"row_heading level0 row8\" >ridge</th>\n",
       "                        <td id=\"T_0c5c7_row8_col0\" class=\"data row8 col0\" >Ridge Regression</td>\n",
       "                        <td id=\"T_0c5c7_row8_col1\" class=\"data row8 col1\" >4336.2309</td>\n",
       "                        <td id=\"T_0c5c7_row8_col2\" class=\"data row8 col2\" >38381496.8000</td>\n",
       "                        <td id=\"T_0c5c7_row8_col3\" class=\"data row8 col3\" >6175.9541</td>\n",
       "                        <td id=\"T_0c5c7_row8_col4\" class=\"data row8 col4\" >0.7309</td>\n",
       "                        <td id=\"T_0c5c7_row8_col5\" class=\"data row8 col5\" >0.6193</td>\n",
       "                        <td id=\"T_0c5c7_row8_col6\" class=\"data row8 col6\" >0.4454</td>\n",
       "                        <td id=\"T_0c5c7_row8_col7\" class=\"data row8 col7\" >0.0090</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0c5c7_level0_row9\" class=\"row_heading level0 row9\" >br</th>\n",
       "                        <td id=\"T_0c5c7_row9_col0\" class=\"data row9 col0\" >Bayesian Ridge</td>\n",
       "                        <td id=\"T_0c5c7_row9_col1\" class=\"data row9 col1\" >4333.6881</td>\n",
       "                        <td id=\"T_0c5c7_row9_col2\" class=\"data row9 col2\" >38381669.3629</td>\n",
       "                        <td id=\"T_0c5c7_row9_col3\" class=\"data row9 col3\" >6175.9476</td>\n",
       "                        <td id=\"T_0c5c7_row9_col4\" class=\"data row9 col4\" >0.7308</td>\n",
       "                        <td id=\"T_0c5c7_row9_col5\" class=\"data row9 col5\" >0.6151</td>\n",
       "                        <td id=\"T_0c5c7_row9_col6\" class=\"data row9 col6\" >0.4450</td>\n",
       "                        <td id=\"T_0c5c7_row9_col7\" class=\"data row9 col7\" >0.0100</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0c5c7_level0_row10\" class=\"row_heading level0 row10\" >lr</th>\n",
       "                        <td id=\"T_0c5c7_row10_col0\" class=\"data row10 col0\" >Linear Regression</td>\n",
       "                        <td id=\"T_0c5c7_row10_col1\" class=\"data row10 col1\" >4323.6136</td>\n",
       "                        <td id=\"T_0c5c7_row10_col2\" class=\"data row10 col2\" >38380061.2000</td>\n",
       "                        <td id=\"T_0c5c7_row10_col3\" class=\"data row10 col3\" >6175.7164</td>\n",
       "                        <td id=\"T_0c5c7_row10_col4\" class=\"data row10 col4\" >0.7308</td>\n",
       "                        <td id=\"T_0c5c7_row10_col5\" class=\"data row10 col5\" >0.6175</td>\n",
       "                        <td id=\"T_0c5c7_row10_col6\" class=\"data row10 col6\" >0.4432</td>\n",
       "                        <td id=\"T_0c5c7_row10_col7\" class=\"data row10 col7\" >0.6420</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0c5c7_level0_row11\" class=\"row_heading level0 row11\" >lasso</th>\n",
       "                        <td id=\"T_0c5c7_row11_col0\" class=\"data row11 col0\" >Lasso Regression</td>\n",
       "                        <td id=\"T_0c5c7_row11_col1\" class=\"data row11 col1\" >4323.0688</td>\n",
       "                        <td id=\"T_0c5c7_row11_col2\" class=\"data row11 col2\" >38375137.8000</td>\n",
       "                        <td id=\"T_0c5c7_row11_col3\" class=\"data row11 col3\" >6175.3801</td>\n",
       "                        <td id=\"T_0c5c7_row11_col4\" class=\"data row11 col4\" >0.7308</td>\n",
       "                        <td id=\"T_0c5c7_row11_col5\" class=\"data row11 col5\" >0.6140</td>\n",
       "                        <td id=\"T_0c5c7_row11_col6\" class=\"data row11 col6\" >0.4431</td>\n",
       "                        <td id=\"T_0c5c7_row11_col7\" class=\"data row11 col7\" >0.0070</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0c5c7_level0_row12\" class=\"row_heading level0 row12\" >lar</th>\n",
       "                        <td id=\"T_0c5c7_row12_col0\" class=\"data row12 col0\" >Least Angle Regression</td>\n",
       "                        <td id=\"T_0c5c7_row12_col1\" class=\"data row12 col1\" >4454.3826</td>\n",
       "                        <td id=\"T_0c5c7_row12_col2\" class=\"data row12 col2\" >39745068.4096</td>\n",
       "                        <td id=\"T_0c5c7_row12_col3\" class=\"data row12 col3\" >6271.9943</td>\n",
       "                        <td id=\"T_0c5c7_row12_col4\" class=\"data row12 col4\" >0.7228</td>\n",
       "                        <td id=\"T_0c5c7_row12_col5\" class=\"data row12 col5\" >0.6486</td>\n",
       "                        <td id=\"T_0c5c7_row12_col6\" class=\"data row12 col6\" >0.4703</td>\n",
       "                        <td id=\"T_0c5c7_row12_col7\" class=\"data row12 col7\" >0.0100</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0c5c7_level0_row13\" class=\"row_heading level0 row13\" >dt</th>\n",
       "                        <td id=\"T_0c5c7_row13_col0\" class=\"data row13 col0\" >Decision Tree Regressor</td>\n",
       "                        <td id=\"T_0c5c7_row13_col1\" class=\"data row13 col1\" >3148.3402</td>\n",
       "                        <td id=\"T_0c5c7_row13_col2\" class=\"data row13 col2\" >43766011.6491</td>\n",
       "                        <td id=\"T_0c5c7_row13_col3\" class=\"data row13 col3\" >6584.7198</td>\n",
       "                        <td id=\"T_0c5c7_row13_col4\" class=\"data row13 col4\" >0.6855</td>\n",
       "                        <td id=\"T_0c5c7_row13_col5\" class=\"data row13 col5\" >0.5331</td>\n",
       "                        <td id=\"T_0c5c7_row13_col6\" class=\"data row13 col6\" >0.3455</td>\n",
       "                        <td id=\"T_0c5c7_row13_col7\" class=\"data row13 col7\" >0.0070</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0c5c7_level0_row14\" class=\"row_heading level0 row14\" >huber</th>\n",
       "                        <td id=\"T_0c5c7_row14_col0\" class=\"data row14 col0\" >Huber Regressor</td>\n",
       "                        <td id=\"T_0c5c7_row14_col1\" class=\"data row14 col1\" >3455.2997</td>\n",
       "                        <td id=\"T_0c5c7_row14_col2\" class=\"data row14 col2\" >48908984.4059</td>\n",
       "                        <td id=\"T_0c5c7_row14_col3\" class=\"data row14 col3\" >6971.2642</td>\n",
       "                        <td id=\"T_0c5c7_row14_col4\" class=\"data row14 col4\" >0.6545</td>\n",
       "                        <td id=\"T_0c5c7_row14_col5\" class=\"data row14 col5\" >0.4790</td>\n",
       "                        <td id=\"T_0c5c7_row14_col6\" class=\"data row14 col6\" >0.2174</td>\n",
       "                        <td id=\"T_0c5c7_row14_col7\" class=\"data row14 col7\" >0.0140</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0c5c7_level0_row15\" class=\"row_heading level0 row15\" >omp</th>\n",
       "                        <td id=\"T_0c5c7_row15_col0\" class=\"data row15 col0\" >Orthogonal Matching Pursuit</td>\n",
       "                        <td id=\"T_0c5c7_row15_col1\" class=\"data row15 col1\" >5754.7768</td>\n",
       "                        <td id=\"T_0c5c7_row15_col2\" class=\"data row15 col2\" >57503216.4290</td>\n",
       "                        <td id=\"T_0c5c7_row15_col3\" class=\"data row15 col3\" >7566.7093</td>\n",
       "                        <td id=\"T_0c5c7_row15_col4\" class=\"data row15 col4\" >0.5997</td>\n",
       "                        <td id=\"T_0c5c7_row15_col5\" class=\"data row15 col5\" >0.7418</td>\n",
       "                        <td id=\"T_0c5c7_row15_col6\" class=\"data row15 col6\" >0.8990</td>\n",
       "                        <td id=\"T_0c5c7_row15_col7\" class=\"data row15 col7\" >0.0060</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0c5c7_level0_row16\" class=\"row_heading level0 row16\" >par</th>\n",
       "                        <td id=\"T_0c5c7_row16_col0\" class=\"data row16 col0\" >Passive Aggressive Regressor</td>\n",
       "                        <td id=\"T_0c5c7_row16_col1\" class=\"data row16 col1\" >4164.7843</td>\n",
       "                        <td id=\"T_0c5c7_row16_col2\" class=\"data row16 col2\" >61324373.4835</td>\n",
       "                        <td id=\"T_0c5c7_row16_col3\" class=\"data row16 col3\" >7747.8332</td>\n",
       "                        <td id=\"T_0c5c7_row16_col4\" class=\"data row16 col4\" >0.5840</td>\n",
       "                        <td id=\"T_0c5c7_row16_col5\" class=\"data row16 col5\" >0.4724</td>\n",
       "                        <td id=\"T_0c5c7_row16_col6\" class=\"data row16 col6\" >0.2586</td>\n",
       "                        <td id=\"T_0c5c7_row16_col7\" class=\"data row16 col7\" >0.0100</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0c5c7_level0_row17\" class=\"row_heading level0 row17\" >en</th>\n",
       "                        <td id=\"T_0c5c7_row17_col0\" class=\"data row17 col0\" >Elastic Net</td>\n",
       "                        <td id=\"T_0c5c7_row17_col1\" class=\"data row17 col1\" >7369.0573</td>\n",
       "                        <td id=\"T_0c5c7_row17_col2\" class=\"data row17 col2\" >90443346.8000</td>\n",
       "                        <td id=\"T_0c5c7_row17_col3\" class=\"data row17 col3\" >9468.6782</td>\n",
       "                        <td id=\"T_0c5c7_row17_col4\" class=\"data row17 col4\" >0.3791</td>\n",
       "                        <td id=\"T_0c5c7_row17_col5\" class=\"data row17 col5\" >0.7377</td>\n",
       "                        <td id=\"T_0c5c7_row17_col6\" class=\"data row17 col6\" >0.9256</td>\n",
       "                        <td id=\"T_0c5c7_row17_col7\" class=\"data row17 col7\" >0.0060</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0c5c7_level0_row18\" class=\"row_heading level0 row18\" >knn</th>\n",
       "                        <td id=\"T_0c5c7_row18_col0\" class=\"data row18 col0\" >K Neighbors Regressor</td>\n",
       "                        <td id=\"T_0c5c7_row18_col1\" class=\"data row18 col1\" >7805.8425</td>\n",
       "                        <td id=\"T_0c5c7_row18_col2\" class=\"data row18 col2\" >126951808.0000</td>\n",
       "                        <td id=\"T_0c5c7_row18_col3\" class=\"data row18 col3\" >11221.6535</td>\n",
       "                        <td id=\"T_0c5c7_row18_col4\" class=\"data row18 col4\" >0.1218</td>\n",
       "                        <td id=\"T_0c5c7_row18_col5\" class=\"data row18 col5\" >0.8398</td>\n",
       "                        <td id=\"T_0c5c7_row18_col6\" class=\"data row18 col6\" >0.9147</td>\n",
       "                        <td id=\"T_0c5c7_row18_col7\" class=\"data row18 col7\" >0.0370</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1bc25f15b08>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# train all models using default hyperparameters\n",
    "best = compare_models()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GradientBoostingRegressor(alpha=0.9, ccp_alpha=0.0, criterion='friedman_mse',\n",
      "                          init=None, learning_rate=0.1, loss='ls', max_depth=3,\n",
      "                          max_features=None, max_leaf_nodes=None,\n",
      "                          min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "                          min_samples_leaf=1, min_samples_split=2,\n",
      "                          min_weight_fraction_leaf=0.0, n_estimators=100,\n",
      "                          n_iter_no_change=None, presort='deprecated',\n",
      "                          random_state=123, subsample=1.0, tol=0.0001,\n",
      "                          validation_fraction=0.1, verbose=0, warm_start=False)\n"
     ]
    }
   ],
   "source": [
    "print(best)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sklearn.ensemble._gb.GradientBoostingRegressor"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(best)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "#T_b660c_row10_col0,#T_b660c_row10_col1,#T_b660c_row10_col2,#T_b660c_row10_col3,#T_b660c_row10_col4,#T_b660c_row10_col5{\n",
       "            background:  yellow;\n",
       "        }</style><table id=\"T_b660c_\" ><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_b660c_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_b660c_row0_col0\" class=\"data row0 col0\" >3001.2294</td>\n",
       "                        <td id=\"T_b660c_row0_col1\" class=\"data row0 col1\" >37001480.2590</td>\n",
       "                        <td id=\"T_b660c_row0_col2\" class=\"data row0 col2\" >6082.8842</td>\n",
       "                        <td id=\"T_b660c_row0_col3\" class=\"data row0 col3\" >0.7790</td>\n",
       "                        <td id=\"T_b660c_row0_col4\" class=\"data row0 col4\" >0.4984</td>\n",
       "                        <td id=\"T_b660c_row0_col5\" class=\"data row0 col5\" >0.3140</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b660c_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_b660c_row1_col0\" class=\"data row1 col0\" >3389.8885</td>\n",
       "                        <td id=\"T_b660c_row1_col1\" class=\"data row1 col1\" >49305179.5732</td>\n",
       "                        <td id=\"T_b660c_row1_col2\" class=\"data row1 col2\" >7021.7647</td>\n",
       "                        <td id=\"T_b660c_row1_col3\" class=\"data row1 col3\" >0.7133</td>\n",
       "                        <td id=\"T_b660c_row1_col4\" class=\"data row1 col4\" >0.5574</td>\n",
       "                        <td id=\"T_b660c_row1_col5\" class=\"data row1 col5\" >0.3361</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b660c_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_b660c_row2_col0\" class=\"data row2 col0\" >2926.0191</td>\n",
       "                        <td id=\"T_b660c_row2_col1\" class=\"data row2 col1\" >42025684.6666</td>\n",
       "                        <td id=\"T_b660c_row2_col2\" class=\"data row2 col2\" >6482.7220</td>\n",
       "                        <td id=\"T_b660c_row2_col3\" class=\"data row2 col3\" >0.4679</td>\n",
       "                        <td id=\"T_b660c_row2_col4\" class=\"data row2 col4\" >0.6215</td>\n",
       "                        <td id=\"T_b660c_row2_col5\" class=\"data row2 col5\" >0.4025</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b660c_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_b660c_row3_col0\" class=\"data row3 col0\" >2744.7144</td>\n",
       "                        <td id=\"T_b660c_row3_col1\" class=\"data row3 col1\" >34078761.4507</td>\n",
       "                        <td id=\"T_b660c_row3_col2\" class=\"data row3 col2\" >5837.7017</td>\n",
       "                        <td id=\"T_b660c_row3_col3\" class=\"data row3 col3\" >0.7154</td>\n",
       "                        <td id=\"T_b660c_row3_col4\" class=\"data row3 col4\" >0.5412</td>\n",
       "                        <td id=\"T_b660c_row3_col5\" class=\"data row3 col5\" >0.3740</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b660c_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_b660c_row4_col0\" class=\"data row4 col0\" >3924.4816</td>\n",
       "                        <td id=\"T_b660c_row4_col1\" class=\"data row4 col1\" >59489464.3207</td>\n",
       "                        <td id=\"T_b660c_row4_col2\" class=\"data row4 col2\" >7712.9414</td>\n",
       "                        <td id=\"T_b660c_row4_col3\" class=\"data row4 col3\" >0.5575</td>\n",
       "                        <td id=\"T_b660c_row4_col4\" class=\"data row4 col4\" >0.6455</td>\n",
       "                        <td id=\"T_b660c_row4_col5\" class=\"data row4 col5\" >0.4796</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b660c_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_b660c_row5_col0\" class=\"data row5 col0\" >3322.5435</td>\n",
       "                        <td id=\"T_b660c_row5_col1\" class=\"data row5 col1\" >42747575.4453</td>\n",
       "                        <td id=\"T_b660c_row5_col2\" class=\"data row5 col2\" >6538.1630</td>\n",
       "                        <td id=\"T_b660c_row5_col3\" class=\"data row5 col3\" >0.7250</td>\n",
       "                        <td id=\"T_b660c_row5_col4\" class=\"data row5 col4\" >0.4869</td>\n",
       "                        <td id=\"T_b660c_row5_col5\" class=\"data row5 col5\" >0.2928</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b660c_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_b660c_row6_col0\" class=\"data row6 col0\" >3158.7047</td>\n",
       "                        <td id=\"T_b660c_row6_col1\" class=\"data row6 col1\" >49369669.1652</td>\n",
       "                        <td id=\"T_b660c_row6_col2\" class=\"data row6 col2\" >7026.3553</td>\n",
       "                        <td id=\"T_b660c_row6_col3\" class=\"data row6 col3\" >0.6641</td>\n",
       "                        <td id=\"T_b660c_row6_col4\" class=\"data row6 col4\" >0.4511</td>\n",
       "                        <td id=\"T_b660c_row6_col5\" class=\"data row6 col5\" >0.3089</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b660c_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_b660c_row7_col0\" class=\"data row7 col0\" >2405.2970</td>\n",
       "                        <td id=\"T_b660c_row7_col1\" class=\"data row7 col1\" >31318616.6440</td>\n",
       "                        <td id=\"T_b660c_row7_col2\" class=\"data row7 col2\" >5596.3038</td>\n",
       "                        <td id=\"T_b660c_row7_col3\" class=\"data row7 col3\" >0.8278</td>\n",
       "                        <td id=\"T_b660c_row7_col4\" class=\"data row7 col4\" >0.4497</td>\n",
       "                        <td id=\"T_b660c_row7_col5\" class=\"data row7 col5\" >0.1434</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b660c_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_b660c_row8_col0\" class=\"data row8 col0\" >3021.5461</td>\n",
       "                        <td id=\"T_b660c_row8_col1\" class=\"data row8 col1\" >39091793.3775</td>\n",
       "                        <td id=\"T_b660c_row8_col2\" class=\"data row8 col2\" >6252.3430</td>\n",
       "                        <td id=\"T_b660c_row8_col3\" class=\"data row8 col3\" >0.7475</td>\n",
       "                        <td id=\"T_b660c_row8_col4\" class=\"data row8 col4\" >0.5117</td>\n",
       "                        <td id=\"T_b660c_row8_col5\" class=\"data row8 col5\" >0.4381</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b660c_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_b660c_row9_col0\" class=\"data row9 col0\" >3588.9772</td>\n",
       "                        <td id=\"T_b660c_row9_col1\" class=\"data row9 col1\" >53231891.5889</td>\n",
       "                        <td id=\"T_b660c_row9_col2\" class=\"data row9 col2\" >7296.0189</td>\n",
       "                        <td id=\"T_b660c_row9_col3\" class=\"data row9 col3\" >0.6571</td>\n",
       "                        <td id=\"T_b660c_row9_col4\" class=\"data row9 col4\" >0.5679</td>\n",
       "                        <td id=\"T_b660c_row9_col5\" class=\"data row9 col5\" >0.3653</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b660c_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "                        <td id=\"T_b660c_row10_col0\" class=\"data row10 col0\" >3148.3402</td>\n",
       "                        <td id=\"T_b660c_row10_col1\" class=\"data row10 col1\" >43766011.6491</td>\n",
       "                        <td id=\"T_b660c_row10_col2\" class=\"data row10 col2\" >6584.7198</td>\n",
       "                        <td id=\"T_b660c_row10_col3\" class=\"data row10 col3\" >0.6855</td>\n",
       "                        <td id=\"T_b660c_row10_col4\" class=\"data row10 col4\" >0.5331</td>\n",
       "                        <td id=\"T_b660c_row10_col5\" class=\"data row10 col5\" >0.3455</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b660c_level0_row11\" class=\"row_heading level0 row11\" >SD</th>\n",
       "                        <td id=\"T_b660c_row11_col0\" class=\"data row11 col0\" >410.7953</td>\n",
       "                        <td id=\"T_b660c_row11_col1\" class=\"data row11 col1\" >8481549.4829</td>\n",
       "                        <td id=\"T_b660c_row11_col2\" class=\"data row11 col2\" >638.3390</td>\n",
       "                        <td id=\"T_b660c_row11_col3\" class=\"data row11 col3\" >0.1005</td>\n",
       "                        <td id=\"T_b660c_row11_col4\" class=\"data row11 col4\" >0.0631</td>\n",
       "                        <td id=\"T_b660c_row11_col5\" class=\"data row11 col5\" >0.0878</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1bc25f51fc8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# train individual model\n",
    "dt = create_model('dt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTreeRegressor(ccp_alpha=0.0, criterion='mse', max_depth=None,\n",
      "                      max_features=None, max_leaf_nodes=None,\n",
      "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "                      min_samples_leaf=1, min_samples_split=2,\n",
      "                      min_weight_fraction_leaf=0.0, presort='deprecated',\n",
      "                      random_state=123, splitter='best')\n"
     ]
    }
   ],
   "source": [
    "print(dt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tune Hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "#T_21644_row10_col0,#T_21644_row10_col1,#T_21644_row10_col2,#T_21644_row10_col3,#T_21644_row10_col4,#T_21644_row10_col5{\n",
       "            background:  yellow;\n",
       "        }</style><table id=\"T_21644_\" ><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_21644_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_21644_row0_col0\" class=\"data row0 col0\" >1710.0867</td>\n",
       "                        <td id=\"T_21644_row0_col1\" class=\"data row0 col1\" >18253568.8962</td>\n",
       "                        <td id=\"T_21644_row0_col2\" class=\"data row0 col2\" >4272.4196</td>\n",
       "                        <td id=\"T_21644_row0_col3\" class=\"data row0 col3\" >0.8910</td>\n",
       "                        <td id=\"T_21644_row0_col4\" class=\"data row0 col4\" >0.3435</td>\n",
       "                        <td id=\"T_21644_row0_col5\" class=\"data row0 col5\" >0.1349</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21644_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_21644_row1_col0\" class=\"data row1 col0\" >2342.9618</td>\n",
       "                        <td id=\"T_21644_row1_col1\" class=\"data row1 col1\" >33002910.7856</td>\n",
       "                        <td id=\"T_21644_row1_col2\" class=\"data row1 col2\" >5744.8160</td>\n",
       "                        <td id=\"T_21644_row1_col3\" class=\"data row1 col3\" >0.8081</td>\n",
       "                        <td id=\"T_21644_row1_col4\" class=\"data row1 col4\" >0.4462</td>\n",
       "                        <td id=\"T_21644_row1_col5\" class=\"data row1 col5\" >0.1421</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21644_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_21644_row2_col0\" class=\"data row2 col0\" >1992.6884</td>\n",
       "                        <td id=\"T_21644_row2_col1\" class=\"data row2 col1\" >23279759.5944</td>\n",
       "                        <td id=\"T_21644_row2_col2\" class=\"data row2 col2\" >4824.9103</td>\n",
       "                        <td id=\"T_21644_row2_col3\" class=\"data row2 col3\" >0.7053</td>\n",
       "                        <td id=\"T_21644_row2_col4\" class=\"data row2 col4\" >0.4672</td>\n",
       "                        <td id=\"T_21644_row2_col5\" class=\"data row2 col5\" >0.1580</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21644_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_21644_row3_col0\" class=\"data row3 col0\" >2250.2711</td>\n",
       "                        <td id=\"T_21644_row3_col1\" class=\"data row3 col1\" >25594847.8750</td>\n",
       "                        <td id=\"T_21644_row3_col2\" class=\"data row3 col2\" >5059.1351</td>\n",
       "                        <td id=\"T_21644_row3_col3\" class=\"data row3 col3\" >0.7863</td>\n",
       "                        <td id=\"T_21644_row3_col4\" class=\"data row3 col4\" >0.4246</td>\n",
       "                        <td id=\"T_21644_row3_col5\" class=\"data row3 col5\" >0.2126</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21644_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_21644_row4_col0\" class=\"data row4 col0\" >2157.4516</td>\n",
       "                        <td id=\"T_21644_row4_col1\" class=\"data row4 col1\" >24978154.4390</td>\n",
       "                        <td id=\"T_21644_row4_col2\" class=\"data row4 col2\" >4997.8150</td>\n",
       "                        <td id=\"T_21644_row4_col3\" class=\"data row4 col3\" >0.8142</td>\n",
       "                        <td id=\"T_21644_row4_col4\" class=\"data row4 col4\" >0.4363</td>\n",
       "                        <td id=\"T_21644_row4_col5\" class=\"data row4 col5\" >0.1531</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21644_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_21644_row5_col0\" class=\"data row5 col0\" >1991.3288</td>\n",
       "                        <td id=\"T_21644_row5_col1\" class=\"data row5 col1\" >18794342.2788</td>\n",
       "                        <td id=\"T_21644_row5_col2\" class=\"data row5 col2\" >4335.2442</td>\n",
       "                        <td id=\"T_21644_row5_col3\" class=\"data row5 col3\" >0.8791</td>\n",
       "                        <td id=\"T_21644_row5_col4\" class=\"data row5 col4\" >0.3399</td>\n",
       "                        <td id=\"T_21644_row5_col5\" class=\"data row5 col5\" >0.1565</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21644_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_21644_row6_col0\" class=\"data row6 col0\" >1688.3935</td>\n",
       "                        <td id=\"T_21644_row6_col1\" class=\"data row6 col1\" >20093049.8225</td>\n",
       "                        <td id=\"T_21644_row6_col2\" class=\"data row6 col2\" >4482.5272</td>\n",
       "                        <td id=\"T_21644_row6_col3\" class=\"data row6 col3\" >0.8633</td>\n",
       "                        <td id=\"T_21644_row6_col4\" class=\"data row6 col4\" >0.3137</td>\n",
       "                        <td id=\"T_21644_row6_col5\" class=\"data row6 col5\" >0.1210</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21644_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_21644_row7_col0\" class=\"data row7 col0\" >2060.8145</td>\n",
       "                        <td id=\"T_21644_row7_col1\" class=\"data row7 col1\" >26178263.6299</td>\n",
       "                        <td id=\"T_21644_row7_col2\" class=\"data row7 col2\" >5116.4698</td>\n",
       "                        <td id=\"T_21644_row7_col3\" class=\"data row7 col3\" >0.8561</td>\n",
       "                        <td id=\"T_21644_row7_col4\" class=\"data row7 col4\" >0.4613</td>\n",
       "                        <td id=\"T_21644_row7_col5\" class=\"data row7 col5\" >0.1332</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21644_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_21644_row8_col0\" class=\"data row8 col0\" >2088.2260</td>\n",
       "                        <td id=\"T_21644_row8_col1\" class=\"data row8 col1\" >23545921.7229</td>\n",
       "                        <td id=\"T_21644_row8_col2\" class=\"data row8 col2\" >4852.4140</td>\n",
       "                        <td id=\"T_21644_row8_col3\" class=\"data row8 col3\" >0.8479</td>\n",
       "                        <td id=\"T_21644_row8_col4\" class=\"data row8 col4\" >0.3741</td>\n",
       "                        <td id=\"T_21644_row8_col5\" class=\"data row8 col5\" >0.1592</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21644_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_21644_row9_col0\" class=\"data row9 col0\" >2233.1985</td>\n",
       "                        <td id=\"T_21644_row9_col1\" class=\"data row9 col1\" >27217915.9631</td>\n",
       "                        <td id=\"T_21644_row9_col2\" class=\"data row9 col2\" >5217.0793</td>\n",
       "                        <td id=\"T_21644_row9_col3\" class=\"data row9 col3\" >0.8247</td>\n",
       "                        <td id=\"T_21644_row9_col4\" class=\"data row9 col4\" >0.4302</td>\n",
       "                        <td id=\"T_21644_row9_col5\" class=\"data row9 col5\" >0.1662</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21644_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "                        <td id=\"T_21644_row10_col0\" class=\"data row10 col0\" >2051.5421</td>\n",
       "                        <td id=\"T_21644_row10_col1\" class=\"data row10 col1\" >24093873.5007</td>\n",
       "                        <td id=\"T_21644_row10_col2\" class=\"data row10 col2\" >4890.2830</td>\n",
       "                        <td id=\"T_21644_row10_col3\" class=\"data row10 col3\" >0.8276</td>\n",
       "                        <td id=\"T_21644_row10_col4\" class=\"data row10 col4\" >0.4037</td>\n",
       "                        <td id=\"T_21644_row10_col5\" class=\"data row10 col5\" >0.1537</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21644_level0_row11\" class=\"row_heading level0 row11\" >SD</th>\n",
       "                        <td id=\"T_21644_row11_col0\" class=\"data row11 col0\" >206.3066</td>\n",
       "                        <td id=\"T_21644_row11_col1\" class=\"data row11 col1\" >4191347.7243</td>\n",
       "                        <td id=\"T_21644_row11_col2\" class=\"data row11 col2\" >423.0902</td>\n",
       "                        <td id=\"T_21644_row11_col3\" class=\"data row11 col3\" >0.0514</td>\n",
       "                        <td id=\"T_21644_row11_col4\" class=\"data row11 col4\" >0.0529</td>\n",
       "                        <td id=\"T_21644_row11_col5\" class=\"data row11 col5\" >0.0238</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1bc25f3ce88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 889 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# tune hyperparameters of model\n",
    "tuned_dt = tune_model(dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "#T_3c5ac_row10_col0,#T_3c5ac_row10_col1,#T_3c5ac_row10_col2,#T_3c5ac_row10_col3,#T_3c5ac_row10_col4,#T_3c5ac_row10_col5{\n",
       "            background:  yellow;\n",
       "        }</style><table id=\"T_3c5ac_\" ><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_3c5ac_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_3c5ac_row0_col0\" class=\"data row0 col0\" >2579.8299</td>\n",
       "                        <td id=\"T_3c5ac_row0_col1\" class=\"data row0 col1\" >20553234.9577</td>\n",
       "                        <td id=\"T_3c5ac_row0_col2\" class=\"data row0 col2\" >4533.5676</td>\n",
       "                        <td id=\"T_3c5ac_row0_col3\" class=\"data row0 col3\" >0.8772</td>\n",
       "                        <td id=\"T_3c5ac_row0_col4\" class=\"data row0 col4\" >0.4036</td>\n",
       "                        <td id=\"T_3c5ac_row0_col5\" class=\"data row0 col5\" >0.2978</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3c5ac_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_3c5ac_row1_col0\" class=\"data row1 col0\" >3185.6429</td>\n",
       "                        <td id=\"T_3c5ac_row1_col1\" class=\"data row1 col1\" >31391105.8784</td>\n",
       "                        <td id=\"T_3c5ac_row1_col2\" class=\"data row1 col2\" >5602.7766</td>\n",
       "                        <td id=\"T_3c5ac_row1_col3\" class=\"data row1 col3\" >0.8175</td>\n",
       "                        <td id=\"T_3c5ac_row1_col4\" class=\"data row1 col4\" >0.4625</td>\n",
       "                        <td id=\"T_3c5ac_row1_col5\" class=\"data row1 col5\" >0.3315</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3c5ac_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_3c5ac_row2_col0\" class=\"data row2 col0\" >2685.6595</td>\n",
       "                        <td id=\"T_3c5ac_row2_col1\" class=\"data row2 col1\" >23097034.7695</td>\n",
       "                        <td id=\"T_3c5ac_row2_col2\" class=\"data row2 col2\" >4805.9374</td>\n",
       "                        <td id=\"T_3c5ac_row2_col3\" class=\"data row2 col3\" >0.7076</td>\n",
       "                        <td id=\"T_3c5ac_row2_col4\" class=\"data row2 col4\" >0.4799</td>\n",
       "                        <td id=\"T_3c5ac_row2_col5\" class=\"data row2 col5\" >0.3230</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3c5ac_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_3c5ac_row3_col0\" class=\"data row3 col0\" >2953.7995</td>\n",
       "                        <td id=\"T_3c5ac_row3_col1\" class=\"data row3 col1\" >25833993.3635</td>\n",
       "                        <td id=\"T_3c5ac_row3_col2\" class=\"data row3 col2\" >5082.7152</td>\n",
       "                        <td id=\"T_3c5ac_row3_col3\" class=\"data row3 col3\" >0.7843</td>\n",
       "                        <td id=\"T_3c5ac_row3_col4\" class=\"data row3 col4\" >0.4615</td>\n",
       "                        <td id=\"T_3c5ac_row3_col5\" class=\"data row3 col5\" >0.3829</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3c5ac_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_3c5ac_row4_col0\" class=\"data row4 col0\" >2837.2551</td>\n",
       "                        <td id=\"T_3c5ac_row4_col1\" class=\"data row4 col1\" >24784197.5159</td>\n",
       "                        <td id=\"T_3c5ac_row4_col2\" class=\"data row4 col2\" >4978.3730</td>\n",
       "                        <td id=\"T_3c5ac_row4_col3\" class=\"data row4 col3\" >0.8157</td>\n",
       "                        <td id=\"T_3c5ac_row4_col4\" class=\"data row4 col4\" >0.4479</td>\n",
       "                        <td id=\"T_3c5ac_row4_col5\" class=\"data row4 col5\" >0.3076</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3c5ac_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_3c5ac_row5_col0\" class=\"data row5 col0\" >2886.9283</td>\n",
       "                        <td id=\"T_3c5ac_row5_col1\" class=\"data row5 col1\" >21983688.3215</td>\n",
       "                        <td id=\"T_3c5ac_row5_col2\" class=\"data row5 col2\" >4688.6766</td>\n",
       "                        <td id=\"T_3c5ac_row5_col3\" class=\"data row5 col3\" >0.8586</td>\n",
       "                        <td id=\"T_3c5ac_row5_col4\" class=\"data row5 col4\" >0.3628</td>\n",
       "                        <td id=\"T_3c5ac_row5_col5\" class=\"data row5 col5\" >0.2822</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3c5ac_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_3c5ac_row6_col0\" class=\"data row6 col0\" >2857.3329</td>\n",
       "                        <td id=\"T_3c5ac_row6_col1\" class=\"data row6 col1\" >21418065.8828</td>\n",
       "                        <td id=\"T_3c5ac_row6_col2\" class=\"data row6 col2\" >4627.9656</td>\n",
       "                        <td id=\"T_3c5ac_row6_col3\" class=\"data row6 col3\" >0.8543</td>\n",
       "                        <td id=\"T_3c5ac_row6_col4\" class=\"data row6 col4\" >0.3993</td>\n",
       "                        <td id=\"T_3c5ac_row6_col5\" class=\"data row6 col5\" >0.3484</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3c5ac_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_3c5ac_row7_col0\" class=\"data row7 col0\" >2606.6486</td>\n",
       "                        <td id=\"T_3c5ac_row7_col1\" class=\"data row7 col1\" >24784213.2073</td>\n",
       "                        <td id=\"T_3c5ac_row7_col2\" class=\"data row7 col2\" >4978.3746</td>\n",
       "                        <td id=\"T_3c5ac_row7_col3\" class=\"data row7 col3\" >0.8637</td>\n",
       "                        <td id=\"T_3c5ac_row7_col4\" class=\"data row7 col4\" >0.4704</td>\n",
       "                        <td id=\"T_3c5ac_row7_col5\" class=\"data row7 col5\" >0.2649</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3c5ac_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_3c5ac_row8_col0\" class=\"data row8 col0\" >2888.3808</td>\n",
       "                        <td id=\"T_3c5ac_row8_col1\" class=\"data row8 col1\" >23394063.2467</td>\n",
       "                        <td id=\"T_3c5ac_row8_col2\" class=\"data row8 col2\" >4836.7410</td>\n",
       "                        <td id=\"T_3c5ac_row8_col3\" class=\"data row8 col3\" >0.8489</td>\n",
       "                        <td id=\"T_3c5ac_row8_col4\" class=\"data row8 col4\" >0.4585</td>\n",
       "                        <td id=\"T_3c5ac_row8_col5\" class=\"data row8 col5\" >0.3843</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3c5ac_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_3c5ac_row9_col0\" class=\"data row9 col0\" >2708.3053</td>\n",
       "                        <td id=\"T_3c5ac_row9_col1\" class=\"data row9 col1\" >24775400.8000</td>\n",
       "                        <td id=\"T_3c5ac_row9_col2\" class=\"data row9 col2\" >4977.4894</td>\n",
       "                        <td id=\"T_3c5ac_row9_col3\" class=\"data row9 col3\" >0.8404</td>\n",
       "                        <td id=\"T_3c5ac_row9_col4\" class=\"data row9 col4\" >0.4566</td>\n",
       "                        <td id=\"T_3c5ac_row9_col5\" class=\"data row9 col5\" >0.3203</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3c5ac_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "                        <td id=\"T_3c5ac_row10_col0\" class=\"data row10 col0\" >2818.9783</td>\n",
       "                        <td id=\"T_3c5ac_row10_col1\" class=\"data row10 col1\" >24201499.7943</td>\n",
       "                        <td id=\"T_3c5ac_row10_col2\" class=\"data row10 col2\" >4911.2617</td>\n",
       "                        <td id=\"T_3c5ac_row10_col3\" class=\"data row10 col3\" >0.8268</td>\n",
       "                        <td id=\"T_3c5ac_row10_col4\" class=\"data row10 col4\" >0.4403</td>\n",
       "                        <td id=\"T_3c5ac_row10_col5\" class=\"data row10 col5\" >0.3243</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3c5ac_level0_row11\" class=\"row_heading level0 row11\" >SD</th>\n",
       "                        <td id=\"T_3c5ac_row11_col0\" class=\"data row11 col0\" >172.0921</td>\n",
       "                        <td id=\"T_3c5ac_row11_col1\" class=\"data row11 col1\" >2884644.6917</td>\n",
       "                        <td id=\"T_3c5ac_row11_col2\" class=\"data row11 col2\" >284.6197</td>\n",
       "                        <td id=\"T_3c5ac_row11_col3\" class=\"data row11 col3\" >0.0475</td>\n",
       "                        <td id=\"T_3c5ac_row11_col4\" class=\"data row11 col4\" >0.0362</td>\n",
       "                        <td id=\"T_3c5ac_row11_col5\" class=\"data row11 col5\" >0.0374</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1bc27c79fc8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 1.05 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# tune hyperparameters of model\n",
    "tuned_dt = tune_model(dt, search_library = 'optuna')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "#T_b0b94_row10_col0,#T_b0b94_row10_col1,#T_b0b94_row10_col2,#T_b0b94_row10_col3,#T_b0b94_row10_col4,#T_b0b94_row10_col5{\n",
       "            background:  yellow;\n",
       "        }</style><table id=\"T_b0b94_\" ><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_b0b94_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_b0b94_row0_col0\" class=\"data row0 col0\" >1737.9461</td>\n",
       "                        <td id=\"T_b0b94_row0_col1\" class=\"data row0 col1\" >19084226.9033</td>\n",
       "                        <td id=\"T_b0b94_row0_col2\" class=\"data row0 col2\" >4368.5497</td>\n",
       "                        <td id=\"T_b0b94_row0_col3\" class=\"data row0 col3\" >0.8860</td>\n",
       "                        <td id=\"T_b0b94_row0_col4\" class=\"data row0 col4\" >0.3535</td>\n",
       "                        <td id=\"T_b0b94_row0_col5\" class=\"data row0 col5\" >0.1298</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b0b94_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_b0b94_row1_col0\" class=\"data row1 col0\" >2301.3648</td>\n",
       "                        <td id=\"T_b0b94_row1_col1\" class=\"data row1 col1\" >32666312.6667</td>\n",
       "                        <td id=\"T_b0b94_row1_col2\" class=\"data row1 col2\" >5715.4451</td>\n",
       "                        <td id=\"T_b0b94_row1_col3\" class=\"data row1 col3\" >0.8101</td>\n",
       "                        <td id=\"T_b0b94_row1_col4\" class=\"data row1 col4\" >0.4302</td>\n",
       "                        <td id=\"T_b0b94_row1_col5\" class=\"data row1 col5\" >0.1221</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b0b94_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_b0b94_row2_col0\" class=\"data row2 col0\" >1926.0696</td>\n",
       "                        <td id=\"T_b0b94_row2_col1\" class=\"data row2 col1\" >22308633.4826</td>\n",
       "                        <td id=\"T_b0b94_row2_col2\" class=\"data row2 col2\" >4723.2016</td>\n",
       "                        <td id=\"T_b0b94_row2_col3\" class=\"data row2 col3\" >0.7175</td>\n",
       "                        <td id=\"T_b0b94_row2_col4\" class=\"data row2 col4\" >0.4736</td>\n",
       "                        <td id=\"T_b0b94_row2_col5\" class=\"data row2 col5\" >0.1489</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b0b94_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_b0b94_row3_col0\" class=\"data row3 col0\" >2026.0820</td>\n",
       "                        <td id=\"T_b0b94_row3_col1\" class=\"data row3 col1\" >22473178.7795</td>\n",
       "                        <td id=\"T_b0b94_row3_col2\" class=\"data row3 col2\" >4740.5884</td>\n",
       "                        <td id=\"T_b0b94_row3_col3\" class=\"data row3 col3\" >0.8124</td>\n",
       "                        <td id=\"T_b0b94_row3_col4\" class=\"data row3 col4\" >0.4318</td>\n",
       "                        <td id=\"T_b0b94_row3_col5\" class=\"data row3 col5\" >0.2012</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b0b94_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_b0b94_row4_col0\" class=\"data row4 col0\" >2038.5257</td>\n",
       "                        <td id=\"T_b0b94_row4_col1\" class=\"data row4 col1\" >25854437.0548</td>\n",
       "                        <td id=\"T_b0b94_row4_col2\" class=\"data row4 col2\" >5084.7259</td>\n",
       "                        <td id=\"T_b0b94_row4_col3\" class=\"data row4 col3\" >0.8077</td>\n",
       "                        <td id=\"T_b0b94_row4_col4\" class=\"data row4 col4\" >0.4618</td>\n",
       "                        <td id=\"T_b0b94_row4_col5\" class=\"data row4 col5\" >0.1242</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b0b94_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_b0b94_row5_col0\" class=\"data row5 col0\" >2034.3579</td>\n",
       "                        <td id=\"T_b0b94_row5_col1\" class=\"data row5 col1\" >20359122.7557</td>\n",
       "                        <td id=\"T_b0b94_row5_col2\" class=\"data row5 col2\" >4512.1085</td>\n",
       "                        <td id=\"T_b0b94_row5_col3\" class=\"data row5 col3\" >0.8690</td>\n",
       "                        <td id=\"T_b0b94_row5_col4\" class=\"data row5 col4\" >0.3448</td>\n",
       "                        <td id=\"T_b0b94_row5_col5\" class=\"data row5 col5\" >0.1493</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b0b94_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_b0b94_row6_col0\" class=\"data row6 col0\" >1638.3514</td>\n",
       "                        <td id=\"T_b0b94_row6_col1\" class=\"data row6 col1\" >17652018.4487</td>\n",
       "                        <td id=\"T_b0b94_row6_col2\" class=\"data row6 col2\" >4201.4305</td>\n",
       "                        <td id=\"T_b0b94_row6_col3\" class=\"data row6 col3\" >0.8799</td>\n",
       "                        <td id=\"T_b0b94_row6_col4\" class=\"data row6 col4\" >0.2901</td>\n",
       "                        <td id=\"T_b0b94_row6_col5\" class=\"data row6 col5\" >0.1070</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b0b94_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_b0b94_row7_col0\" class=\"data row7 col0\" >2370.2470</td>\n",
       "                        <td id=\"T_b0b94_row7_col1\" class=\"data row7 col1\" >28957688.5789</td>\n",
       "                        <td id=\"T_b0b94_row7_col2\" class=\"data row7 col2\" >5381.2349</td>\n",
       "                        <td id=\"T_b0b94_row7_col3\" class=\"data row7 col3\" >0.8408</td>\n",
       "                        <td id=\"T_b0b94_row7_col4\" class=\"data row7 col4\" >0.4534</td>\n",
       "                        <td id=\"T_b0b94_row7_col5\" class=\"data row7 col5\" >0.1456</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b0b94_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_b0b94_row8_col0\" class=\"data row8 col0\" >2061.5307</td>\n",
       "                        <td id=\"T_b0b94_row8_col1\" class=\"data row8 col1\" >23682198.8277</td>\n",
       "                        <td id=\"T_b0b94_row8_col2\" class=\"data row8 col2\" >4866.4359</td>\n",
       "                        <td id=\"T_b0b94_row8_col3\" class=\"data row8 col3\" >0.8470</td>\n",
       "                        <td id=\"T_b0b94_row8_col4\" class=\"data row8 col4\" >0.3571</td>\n",
       "                        <td id=\"T_b0b94_row8_col5\" class=\"data row8 col5\" >0.1312</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b0b94_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_b0b94_row9_col0\" class=\"data row9 col0\" >2422.1612</td>\n",
       "                        <td id=\"T_b0b94_row9_col1\" class=\"data row9 col1\" >30646993.2491</td>\n",
       "                        <td id=\"T_b0b94_row9_col2\" class=\"data row9 col2\" >5535.9727</td>\n",
       "                        <td id=\"T_b0b94_row9_col3\" class=\"data row9 col3\" >0.8026</td>\n",
       "                        <td id=\"T_b0b94_row9_col4\" class=\"data row9 col4\" >0.4793</td>\n",
       "                        <td id=\"T_b0b94_row9_col5\" class=\"data row9 col5\" >0.2119</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b0b94_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "                        <td id=\"T_b0b94_row10_col0\" class=\"data row10 col0\" >2055.6636</td>\n",
       "                        <td id=\"T_b0b94_row10_col1\" class=\"data row10 col1\" >24368481.0747</td>\n",
       "                        <td id=\"T_b0b94_row10_col2\" class=\"data row10 col2\" >4912.9693</td>\n",
       "                        <td id=\"T_b0b94_row10_col3\" class=\"data row10 col3\" >0.8273</td>\n",
       "                        <td id=\"T_b0b94_row10_col4\" class=\"data row10 col4\" >0.4076</td>\n",
       "                        <td id=\"T_b0b94_row10_col5\" class=\"data row10 col5\" >0.1471</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b0b94_level0_row11\" class=\"row_heading level0 row11\" >SD</th>\n",
       "                        <td id=\"T_b0b94_row11_col0\" class=\"data row11 col0\" >242.3217</td>\n",
       "                        <td id=\"T_b0b94_row11_col1\" class=\"data row11 col1\" >4784954.6251</td>\n",
       "                        <td id=\"T_b0b94_row11_col2\" class=\"data row11 col2\" >480.8467</td>\n",
       "                        <td id=\"T_b0b94_row11_col3\" class=\"data row11 col3\" >0.0470</td>\n",
       "                        <td id=\"T_b0b94_row11_col4\" class=\"data row11 col4\" >0.0623</td>\n",
       "                        <td id=\"T_b0b94_row11_col5\" class=\"data row11 col5\" >0.0323</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1bc258fd208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 2.06 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# tune hyperparameters of model\n",
    "tuned_dt = tune_model(dt, search_library = 'scikit-optimize')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ensemble Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "#T_740cb_row10_col0,#T_740cb_row10_col1,#T_740cb_row10_col2,#T_740cb_row10_col3,#T_740cb_row10_col4,#T_740cb_row10_col5{\n",
       "            background:  yellow;\n",
       "        }</style><table id=\"T_740cb_\" ><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_740cb_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_740cb_row0_col0\" class=\"data row0 col0\" >1714.3042</td>\n",
       "                        <td id=\"T_740cb_row0_col1\" class=\"data row0 col1\" >18502255.0953</td>\n",
       "                        <td id=\"T_740cb_row0_col2\" class=\"data row0 col2\" >4301.4248</td>\n",
       "                        <td id=\"T_740cb_row0_col3\" class=\"data row0 col3\" >0.8895</td>\n",
       "                        <td id=\"T_740cb_row0_col4\" class=\"data row0 col4\" >0.3512</td>\n",
       "                        <td id=\"T_740cb_row0_col5\" class=\"data row0 col5\" >0.1272</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_740cb_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_740cb_row1_col0\" class=\"data row1 col0\" >2422.6145</td>\n",
       "                        <td id=\"T_740cb_row1_col1\" class=\"data row1 col1\" >30056553.2436</td>\n",
       "                        <td id=\"T_740cb_row1_col2\" class=\"data row1 col2\" >5482.3857</td>\n",
       "                        <td id=\"T_740cb_row1_col3\" class=\"data row1 col3\" >0.8252</td>\n",
       "                        <td id=\"T_740cb_row1_col4\" class=\"data row1 col4\" >0.3943</td>\n",
       "                        <td id=\"T_740cb_row1_col5\" class=\"data row1 col5\" >0.1723</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_740cb_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_740cb_row2_col0\" class=\"data row2 col0\" >1791.3911</td>\n",
       "                        <td id=\"T_740cb_row2_col1\" class=\"data row2 col1\" >18943828.4002</td>\n",
       "                        <td id=\"T_740cb_row2_col2\" class=\"data row2 col2\" >4352.4508</td>\n",
       "                        <td id=\"T_740cb_row2_col3\" class=\"data row2 col3\" >0.7601</td>\n",
       "                        <td id=\"T_740cb_row2_col4\" class=\"data row2 col4\" >0.4316</td>\n",
       "                        <td id=\"T_740cb_row2_col5\" class=\"data row2 col5\" >0.1539</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_740cb_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_740cb_row3_col0\" class=\"data row3 col0\" >1969.4230</td>\n",
       "                        <td id=\"T_740cb_row3_col1\" class=\"data row3 col1\" >19619134.5779</td>\n",
       "                        <td id=\"T_740cb_row3_col2\" class=\"data row3 col2\" >4429.3492</td>\n",
       "                        <td id=\"T_740cb_row3_col3\" class=\"data row3 col3\" >0.8362</td>\n",
       "                        <td id=\"T_740cb_row3_col4\" class=\"data row3 col4\" >0.3761</td>\n",
       "                        <td id=\"T_740cb_row3_col5\" class=\"data row3 col5\" >0.1680</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_740cb_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_740cb_row4_col0\" class=\"data row4 col0\" >2349.1782</td>\n",
       "                        <td id=\"T_740cb_row4_col1\" class=\"data row4 col1\" >27075816.8911</td>\n",
       "                        <td id=\"T_740cb_row4_col2\" class=\"data row4 col2\" >5203.4428</td>\n",
       "                        <td id=\"T_740cb_row4_col3\" class=\"data row4 col3\" >0.7986</td>\n",
       "                        <td id=\"T_740cb_row4_col4\" class=\"data row4 col4\" >0.4591</td>\n",
       "                        <td id=\"T_740cb_row4_col5\" class=\"data row4 col5\" >0.1597</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_740cb_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_740cb_row5_col0\" class=\"data row5 col0\" >2182.2224</td>\n",
       "                        <td id=\"T_740cb_row5_col1\" class=\"data row5 col1\" >19450947.0951</td>\n",
       "                        <td id=\"T_740cb_row5_col2\" class=\"data row5 col2\" >4410.3228</td>\n",
       "                        <td id=\"T_740cb_row5_col3\" class=\"data row5 col3\" >0.8749</td>\n",
       "                        <td id=\"T_740cb_row5_col4\" class=\"data row5 col4\" >0.3434</td>\n",
       "                        <td id=\"T_740cb_row5_col5\" class=\"data row5 col5\" >0.1559</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_740cb_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_740cb_row6_col0\" class=\"data row6 col0\" >1664.9644</td>\n",
       "                        <td id=\"T_740cb_row6_col1\" class=\"data row6 col1\" >18708131.5532</td>\n",
       "                        <td id=\"T_740cb_row6_col2\" class=\"data row6 col2\" >4325.2898</td>\n",
       "                        <td id=\"T_740cb_row6_col3\" class=\"data row6 col3\" >0.8727</td>\n",
       "                        <td id=\"T_740cb_row6_col4\" class=\"data row6 col4\" >0.2950</td>\n",
       "                        <td id=\"T_740cb_row6_col5\" class=\"data row6 col5\" >0.1120</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_740cb_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_740cb_row7_col0\" class=\"data row7 col0\" >2222.6272</td>\n",
       "                        <td id=\"T_740cb_row7_col1\" class=\"data row7 col1\" >25396879.4405</td>\n",
       "                        <td id=\"T_740cb_row7_col2\" class=\"data row7 col2\" >5039.5317</td>\n",
       "                        <td id=\"T_740cb_row7_col3\" class=\"data row7 col3\" >0.8604</td>\n",
       "                        <td id=\"T_740cb_row7_col4\" class=\"data row7 col4\" >0.4220</td>\n",
       "                        <td id=\"T_740cb_row7_col5\" class=\"data row7 col5\" >0.1422</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_740cb_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_740cb_row8_col0\" class=\"data row8 col0\" >1948.6908</td>\n",
       "                        <td id=\"T_740cb_row8_col1\" class=\"data row8 col1\" >20971053.5198</td>\n",
       "                        <td id=\"T_740cb_row8_col2\" class=\"data row8 col2\" >4579.4163</td>\n",
       "                        <td id=\"T_740cb_row8_col3\" class=\"data row8 col3\" >0.8645</td>\n",
       "                        <td id=\"T_740cb_row8_col4\" class=\"data row8 col4\" >0.3513</td>\n",
       "                        <td id=\"T_740cb_row8_col5\" class=\"data row8 col5\" >0.1752</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_740cb_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_740cb_row9_col0\" class=\"data row9 col0\" >2219.5333</td>\n",
       "                        <td id=\"T_740cb_row9_col1\" class=\"data row9 col1\" >25927191.7862</td>\n",
       "                        <td id=\"T_740cb_row9_col2\" class=\"data row9 col2\" >5091.8751</td>\n",
       "                        <td id=\"T_740cb_row9_col3\" class=\"data row9 col3\" >0.8330</td>\n",
       "                        <td id=\"T_740cb_row9_col4\" class=\"data row9 col4\" >0.4135</td>\n",
       "                        <td id=\"T_740cb_row9_col5\" class=\"data row9 col5\" >0.1654</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_740cb_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "                        <td id=\"T_740cb_row10_col0\" class=\"data row10 col0\" >2048.4949</td>\n",
       "                        <td id=\"T_740cb_row10_col1\" class=\"data row10 col1\" >22465179.1603</td>\n",
       "                        <td id=\"T_740cb_row10_col2\" class=\"data row10 col2\" >4721.5489</td>\n",
       "                        <td id=\"T_740cb_row10_col3\" class=\"data row10 col3\" >0.8415</td>\n",
       "                        <td id=\"T_740cb_row10_col4\" class=\"data row10 col4\" >0.3838</td>\n",
       "                        <td id=\"T_740cb_row10_col5\" class=\"data row10 col5\" >0.1532</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_740cb_level0_row11\" class=\"row_heading level0 row11\" >SD</th>\n",
       "                        <td id=\"T_740cb_row11_col0\" class=\"data row11 col0\" >254.7410</td>\n",
       "                        <td id=\"T_740cb_row11_col1\" class=\"data row11 col1\" >4013793.7153</td>\n",
       "                        <td id=\"T_740cb_row11_col2\" class=\"data row11 col2\" >414.9159</td>\n",
       "                        <td id=\"T_740cb_row11_col3\" class=\"data row11 col3\" >0.0375</td>\n",
       "                        <td id=\"T_740cb_row11_col4\" class=\"data row11 col4\" >0.0470</td>\n",
       "                        <td id=\"T_740cb_row11_col5\" class=\"data row11 col5\" >0.0194</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1bc24fa1f08>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bagged_tunned_dt = ensemble_model(tuned_dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BaggingRegressor(base_estimator=DecisionTreeRegressor(ccp_alpha=0.0,\n",
      "                                                      criterion='mae',\n",
      "                                                      max_depth=10,\n",
      "                                                      max_features=0.7926939286102865,\n",
      "                                                      max_leaf_nodes=None,\n",
      "                                                      min_impurity_decrease=3.626887508557946e-07,\n",
      "                                                      min_impurity_split=None,\n",
      "                                                      min_samples_leaf=5,\n",
      "                                                      min_samples_split=3,\n",
      "                                                      min_weight_fraction_leaf=0.0,\n",
      "                                                      presort='deprecated',\n",
      "                                                      random_state=123,\n",
      "                                                      splitter='best'),\n",
      "                 bootstrap=True, bootstrap_features=False, max_features=1.0,\n",
      "                 max_samples=1.0, n_estimators=10, n_jobs=None, oob_score=False,\n",
      "                 random_state=123, verbose=0, warm_start=False)\n"
     ]
    }
   ],
   "source": [
    "print(bagged_tunned_dt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Voting Ensemble"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "#T_2172d_row10_col0,#T_2172d_row10_col1,#T_2172d_row10_col2,#T_2172d_row10_col3,#T_2172d_row10_col4,#T_2172d_row10_col5{\n",
       "            background:  yellow;\n",
       "        }</style><table id=\"T_2172d_\" ><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_2172d_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_2172d_row0_col0\" class=\"data row0 col0\" >4317.6330</td>\n",
       "                        <td id=\"T_2172d_row0_col1\" class=\"data row0 col1\" >40225874.7120</td>\n",
       "                        <td id=\"T_2172d_row0_col2\" class=\"data row0 col2\" >6342.3871</td>\n",
       "                        <td id=\"T_2172d_row0_col3\" class=\"data row0 col3\" >0.7597</td>\n",
       "                        <td id=\"T_2172d_row0_col4\" class=\"data row0 col4\" >0.5377</td>\n",
       "                        <td id=\"T_2172d_row0_col5\" class=\"data row0 col5\" >0.5095</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2172d_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_2172d_row1_col0\" class=\"data row1 col0\" >4603.8770</td>\n",
       "                        <td id=\"T_2172d_row1_col1\" class=\"data row1 col1\" >47671358.8401</td>\n",
       "                        <td id=\"T_2172d_row1_col2\" class=\"data row1 col2\" >6904.4449</td>\n",
       "                        <td id=\"T_2172d_row1_col3\" class=\"data row1 col3\" >0.7228</td>\n",
       "                        <td id=\"T_2172d_row1_col4\" class=\"data row1 col4\" >0.5528</td>\n",
       "                        <td id=\"T_2172d_row1_col5\" class=\"data row1 col5\" >0.4544</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2172d_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_2172d_row2_col0\" class=\"data row2 col0\" >3526.5657</td>\n",
       "                        <td id=\"T_2172d_row2_col1\" class=\"data row2 col1\" >30025112.7222</td>\n",
       "                        <td id=\"T_2172d_row2_col2\" class=\"data row2 col2\" >5479.5176</td>\n",
       "                        <td id=\"T_2172d_row2_col3\" class=\"data row2 col3\" >0.6198</td>\n",
       "                        <td id=\"T_2172d_row2_col4\" class=\"data row2 col4\" >0.6098</td>\n",
       "                        <td id=\"T_2172d_row2_col5\" class=\"data row2 col5\" >0.5355</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2172d_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_2172d_row3_col0\" class=\"data row3 col0\" >4059.9577</td>\n",
       "                        <td id=\"T_2172d_row3_col1\" class=\"data row3 col1\" >33028436.9777</td>\n",
       "                        <td id=\"T_2172d_row3_col2\" class=\"data row3 col2\" >5747.0372</td>\n",
       "                        <td id=\"T_2172d_row3_col3\" class=\"data row3 col3\" >0.7242</td>\n",
       "                        <td id=\"T_2172d_row3_col4\" class=\"data row3 col4\" >0.6159</td>\n",
       "                        <td id=\"T_2172d_row3_col5\" class=\"data row3 col5\" >0.6499</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2172d_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_2172d_row4_col0\" class=\"data row4 col0\" >4719.1236</td>\n",
       "                        <td id=\"T_2172d_row4_col1\" class=\"data row4 col1\" >47134827.6069</td>\n",
       "                        <td id=\"T_2172d_row4_col2\" class=\"data row4 col2\" >6865.4809</td>\n",
       "                        <td id=\"T_2172d_row4_col3\" class=\"data row4 col3\" >0.6494</td>\n",
       "                        <td id=\"T_2172d_row4_col4\" class=\"data row4 col4\" >0.5783</td>\n",
       "                        <td id=\"T_2172d_row4_col5\" class=\"data row4 col5\" >0.5456</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2172d_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_2172d_row5_col0\" class=\"data row5 col0\" >4036.9877</td>\n",
       "                        <td id=\"T_2172d_row5_col1\" class=\"data row5 col1\" >39416780.6753</td>\n",
       "                        <td id=\"T_2172d_row5_col2\" class=\"data row5 col2\" >6278.2785</td>\n",
       "                        <td id=\"T_2172d_row5_col3\" class=\"data row5 col3\" >0.7464</td>\n",
       "                        <td id=\"T_2172d_row5_col4\" class=\"data row5 col4\" >0.4745</td>\n",
       "                        <td id=\"T_2172d_row5_col5\" class=\"data row5 col5\" >0.3684</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2172d_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_2172d_row6_col0\" class=\"data row6 col0\" >4098.2194</td>\n",
       "                        <td id=\"T_2172d_row6_col1\" class=\"data row6 col1\" >43644758.2312</td>\n",
       "                        <td id=\"T_2172d_row6_col2\" class=\"data row6 col2\" >6606.4180</td>\n",
       "                        <td id=\"T_2172d_row6_col3\" class=\"data row6 col3\" >0.7030</td>\n",
       "                        <td id=\"T_2172d_row6_col4\" class=\"data row6 col4\" >0.5116</td>\n",
       "                        <td id=\"T_2172d_row6_col5\" class=\"data row6 col5\" >0.4656</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2172d_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_2172d_row7_col0\" class=\"data row7 col0\" >3938.6892</td>\n",
       "                        <td id=\"T_2172d_row7_col1\" class=\"data row7 col1\" >37270337.0487</td>\n",
       "                        <td id=\"T_2172d_row7_col2\" class=\"data row7 col2\" >6104.9437</td>\n",
       "                        <td id=\"T_2172d_row7_col3\" class=\"data row7 col3\" >0.7951</td>\n",
       "                        <td id=\"T_2172d_row7_col4\" class=\"data row7 col4\" >0.4289</td>\n",
       "                        <td id=\"T_2172d_row7_col5\" class=\"data row7 col5\" >0.3185</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2172d_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_2172d_row8_col0\" class=\"data row8 col0\" >4493.1798</td>\n",
       "                        <td id=\"T_2172d_row8_col1\" class=\"data row8 col1\" >43777521.8925</td>\n",
       "                        <td id=\"T_2172d_row8_col2\" class=\"data row8 col2\" >6616.4584</td>\n",
       "                        <td id=\"T_2172d_row8_col3\" class=\"data row8 col3\" >0.7172</td>\n",
       "                        <td id=\"T_2172d_row8_col4\" class=\"data row8 col4\" >0.5800</td>\n",
       "                        <td id=\"T_2172d_row8_col5\" class=\"data row8 col5\" >0.6167</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2172d_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_2172d_row9_col0\" class=\"data row9 col0\" >4452.8107</td>\n",
       "                        <td id=\"T_2172d_row9_col1\" class=\"data row9 col1\" >44338906.9474</td>\n",
       "                        <td id=\"T_2172d_row9_col2\" class=\"data row9 col2\" >6658.7466</td>\n",
       "                        <td id=\"T_2172d_row9_col3\" class=\"data row9 col3\" >0.7144</td>\n",
       "                        <td id=\"T_2172d_row9_col4\" class=\"data row9 col4\" >0.5613</td>\n",
       "                        <td id=\"T_2172d_row9_col5\" class=\"data row9 col5\" >0.4606</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2172d_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "                        <td id=\"T_2172d_row10_col0\" class=\"data row10 col0\" >4224.7044</td>\n",
       "                        <td id=\"T_2172d_row10_col1\" class=\"data row10 col1\" >40653391.5654</td>\n",
       "                        <td id=\"T_2172d_row10_col2\" class=\"data row10 col2\" >6360.3713</td>\n",
       "                        <td id=\"T_2172d_row10_col3\" class=\"data row10 col3\" >0.7152</td>\n",
       "                        <td id=\"T_2172d_row10_col4\" class=\"data row10 col4\" >0.5451</td>\n",
       "                        <td id=\"T_2172d_row10_col5\" class=\"data row10 col5\" >0.4925</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2172d_level0_row11\" class=\"row_heading level0 row11\" >SD</th>\n",
       "                        <td id=\"T_2172d_row11_col0\" class=\"data row11 col0\" >341.8405</td>\n",
       "                        <td id=\"T_2172d_row11_col1\" class=\"data row11 col1\" >5548059.5251</td>\n",
       "                        <td id=\"T_2172d_row11_col2\" class=\"data row11 col2\" >446.1712</td>\n",
       "                        <td id=\"T_2172d_row11_col3\" class=\"data row11 col3\" >0.0480</td>\n",
       "                        <td id=\"T_2172d_row11_col4\" class=\"data row11 col4\" >0.0561</td>\n",
       "                        <td id=\"T_2172d_row11_col5\" class=\"data row11 col5\" >0.0970</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1bc25f2b908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt = create_model('dt', verbose=False)\n",
    "lasso = create_model('lasso', verbose=False)\n",
    "knn = create_model('knn', verbose=False)\n",
    "blender = blend_models([dt,lasso,knn])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VotingRegressor(estimators=[('dt',\n",
      "                             DecisionTreeRegressor(ccp_alpha=0.0,\n",
      "                                                   criterion='mse',\n",
      "                                                   max_depth=None,\n",
      "                                                   max_features=None,\n",
      "                                                   max_leaf_nodes=None,\n",
      "                                                   min_impurity_decrease=0.0,\n",
      "                                                   min_impurity_split=None,\n",
      "                                                   min_samples_leaf=1,\n",
      "                                                   min_samples_split=2,\n",
      "                                                   min_weight_fraction_leaf=0.0,\n",
      "                                                   presort='deprecated',\n",
      "                                                   random_state=123,\n",
      "                                                   splitter='best')),\n",
      "                            ('lasso',\n",
      "                             Lasso(alpha=1.0, copy_X=True, fit_intercept=True,\n",
      "                                   max_iter=1000, normalize=False,\n",
      "                                   positive=False, precompute=False,\n",
      "                                   random_state=123, selection='cyclic',\n",
      "                                   tol=0.0001, warm_start=False)),\n",
      "                            ('knn',\n",
      "                             KNeighborsRegressor(algorithm='auto', leaf_size=30,\n",
      "                                                 metric='minkowski',\n",
      "                                                 metric_params=None, n_jobs=-1,\n",
      "                                                 n_neighbors=5, p=2,\n",
      "                                                 weights='uniform'))],\n",
      "                n_jobs=-1, verbose=False, weights=None)\n"
     ]
    }
   ],
   "source": [
    "print(blender)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sklearn.ensemble._voting.VotingRegressor"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(blender)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stacking Ensemble"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "#T_80e92_row10_col0,#T_80e92_row10_col1,#T_80e92_row10_col2,#T_80e92_row10_col3,#T_80e92_row10_col4,#T_80e92_row10_col5{\n",
       "            background:  yellow;\n",
       "        }</style><table id=\"T_80e92_\" ><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_80e92_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_80e92_row0_col0\" class=\"data row0 col0\" >3330.1372</td>\n",
       "                        <td id=\"T_80e92_row0_col1\" class=\"data row0 col1\" >26118092.6166</td>\n",
       "                        <td id=\"T_80e92_row0_col2\" class=\"data row0 col2\" >5110.5863</td>\n",
       "                        <td id=\"T_80e92_row0_col3\" class=\"data row0 col3\" >0.8440</td>\n",
       "                        <td id=\"T_80e92_row0_col4\" class=\"data row0 col4\" >0.4528</td>\n",
       "                        <td id=\"T_80e92_row0_col5\" class=\"data row0 col5\" >0.3698</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_80e92_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_80e92_row1_col0\" class=\"data row1 col0\" >3823.1055</td>\n",
       "                        <td id=\"T_80e92_row1_col1\" class=\"data row1 col1\" >36671240.2394</td>\n",
       "                        <td id=\"T_80e92_row1_col2\" class=\"data row1 col2\" >6055.6783</td>\n",
       "                        <td id=\"T_80e92_row1_col3\" class=\"data row1 col3\" >0.7868</td>\n",
       "                        <td id=\"T_80e92_row1_col4\" class=\"data row1 col4\" >0.5307</td>\n",
       "                        <td id=\"T_80e92_row1_col5\" class=\"data row1 col5\" >0.3557</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_80e92_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_80e92_row2_col0\" class=\"data row2 col0\" >3330.5109</td>\n",
       "                        <td id=\"T_80e92_row2_col1\" class=\"data row2 col1\" >29504475.5264</td>\n",
       "                        <td id=\"T_80e92_row2_col2\" class=\"data row2 col2\" >5431.8022</td>\n",
       "                        <td id=\"T_80e92_row2_col3\" class=\"data row2 col3\" >0.6264</td>\n",
       "                        <td id=\"T_80e92_row2_col4\" class=\"data row2 col4\" >0.5701</td>\n",
       "                        <td id=\"T_80e92_row2_col5\" class=\"data row2 col5\" >0.3860</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_80e92_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_80e92_row3_col0\" class=\"data row3 col0\" >3029.4249</td>\n",
       "                        <td id=\"T_80e92_row3_col1\" class=\"data row3 col1\" >21804106.7675</td>\n",
       "                        <td id=\"T_80e92_row3_col2\" class=\"data row3 col2\" >4669.4868</td>\n",
       "                        <td id=\"T_80e92_row3_col3\" class=\"data row3 col3\" >0.8179</td>\n",
       "                        <td id=\"T_80e92_row3_col4\" class=\"data row3 col4\" >0.4804</td>\n",
       "                        <td id=\"T_80e92_row3_col5\" class=\"data row3 col5\" >0.4295</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_80e92_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_80e92_row4_col0\" class=\"data row4 col0\" >3931.7436</td>\n",
       "                        <td id=\"T_80e92_row4_col1\" class=\"data row4 col1\" >36775712.8160</td>\n",
       "                        <td id=\"T_80e92_row4_col2\" class=\"data row4 col2\" >6064.2982</td>\n",
       "                        <td id=\"T_80e92_row4_col3\" class=\"data row4 col3\" >0.7265</td>\n",
       "                        <td id=\"T_80e92_row4_col4\" class=\"data row4 col4\" >0.5132</td>\n",
       "                        <td id=\"T_80e92_row4_col5\" class=\"data row4 col5\" >0.3751</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_80e92_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_80e92_row5_col0\" class=\"data row5 col0\" >3461.5432</td>\n",
       "                        <td id=\"T_80e92_row5_col1\" class=\"data row5 col1\" >29482846.8211</td>\n",
       "                        <td id=\"T_80e92_row5_col2\" class=\"data row5 col2\" >5429.8109</td>\n",
       "                        <td id=\"T_80e92_row5_col3\" class=\"data row5 col3\" >0.8103</td>\n",
       "                        <td id=\"T_80e92_row5_col4\" class=\"data row5 col4\" >0.4937</td>\n",
       "                        <td id=\"T_80e92_row5_col5\" class=\"data row5 col5\" >0.3541</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_80e92_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_80e92_row6_col0\" class=\"data row6 col0\" >3479.7849</td>\n",
       "                        <td id=\"T_80e92_row6_col1\" class=\"data row6 col1\" >31955532.4915</td>\n",
       "                        <td id=\"T_80e92_row6_col2\" class=\"data row6 col2\" >5652.9225</td>\n",
       "                        <td id=\"T_80e92_row6_col3\" class=\"data row6 col3\" >0.7826</td>\n",
       "                        <td id=\"T_80e92_row6_col4\" class=\"data row6 col4\" >0.4684</td>\n",
       "                        <td id=\"T_80e92_row6_col5\" class=\"data row6 col5\" >0.3600</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_80e92_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_80e92_row7_col0\" class=\"data row7 col0\" >3610.5786</td>\n",
       "                        <td id=\"T_80e92_row7_col1\" class=\"data row7 col1\" >30708816.3945</td>\n",
       "                        <td id=\"T_80e92_row7_col2\" class=\"data row7 col2\" >5541.5536</td>\n",
       "                        <td id=\"T_80e92_row7_col3\" class=\"data row7 col3\" >0.8311</td>\n",
       "                        <td id=\"T_80e92_row7_col4\" class=\"data row7 col4\" >0.5030</td>\n",
       "                        <td id=\"T_80e92_row7_col5\" class=\"data row7 col5\" >0.2904</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_80e92_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_80e92_row8_col0\" class=\"data row8 col0\" >3761.7961</td>\n",
       "                        <td id=\"T_80e92_row8_col1\" class=\"data row8 col1\" >27523649.5059</td>\n",
       "                        <td id=\"T_80e92_row8_col2\" class=\"data row8 col2\" >5246.2986</td>\n",
       "                        <td id=\"T_80e92_row8_col3\" class=\"data row8 col3\" >0.8222</td>\n",
       "                        <td id=\"T_80e92_row8_col4\" class=\"data row8 col4\" >0.5596</td>\n",
       "                        <td id=\"T_80e92_row8_col5\" class=\"data row8 col5\" >0.4627</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_80e92_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_80e92_row9_col0\" class=\"data row9 col0\" >3913.4878</td>\n",
       "                        <td id=\"T_80e92_row9_col1\" class=\"data row9 col1\" >35744866.0161</td>\n",
       "                        <td id=\"T_80e92_row9_col2\" class=\"data row9 col2\" >5978.7010</td>\n",
       "                        <td id=\"T_80e92_row9_col3\" class=\"data row9 col3\" >0.7698</td>\n",
       "                        <td id=\"T_80e92_row9_col4\" class=\"data row9 col4\" >0.6368</td>\n",
       "                        <td id=\"T_80e92_row9_col5\" class=\"data row9 col5\" >0.4228</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_80e92_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "                        <td id=\"T_80e92_row10_col0\" class=\"data row10 col0\" >3567.2113</td>\n",
       "                        <td id=\"T_80e92_row10_col1\" class=\"data row10 col1\" >30628933.9195</td>\n",
       "                        <td id=\"T_80e92_row10_col2\" class=\"data row10 col2\" >5518.1139</td>\n",
       "                        <td id=\"T_80e92_row10_col3\" class=\"data row10 col3\" >0.7818</td>\n",
       "                        <td id=\"T_80e92_row10_col4\" class=\"data row10 col4\" >0.5209</td>\n",
       "                        <td id=\"T_80e92_row10_col5\" class=\"data row10 col5\" >0.3806</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_80e92_level0_row11\" class=\"row_heading level0 row11\" >SD</th>\n",
       "                        <td id=\"T_80e92_row11_col0\" class=\"data row11 col0\" >278.9752</td>\n",
       "                        <td id=\"T_80e92_row11_col1\" class=\"data row11 col1\" >4611695.3476</td>\n",
       "                        <td id=\"T_80e92_row11_col2\" class=\"data row11 col2\" >423.5013</td>\n",
       "                        <td id=\"T_80e92_row11_col3\" class=\"data row11 col3\" >0.0612</td>\n",
       "                        <td id=\"T_80e92_row11_col4\" class=\"data row11 col4\" >0.0525</td>\n",
       "                        <td id=\"T_80e92_row11_col5\" class=\"data row11 col5\" >0.0458</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1bc27d06ec8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stacker = stack_models([dt,lasso,knn])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "StackingRegressor(cv=KFold(n_splits=10, random_state=RandomState(MT19937) at 0x1BC27FFD268,\n",
      "   shuffle=False),\n",
      "                  estimators=[('dt',\n",
      "                               DecisionTreeRegressor(ccp_alpha=0.0,\n",
      "                                                     criterion='mse',\n",
      "                                                     max_depth=None,\n",
      "                                                     max_features=None,\n",
      "                                                     max_leaf_nodes=None,\n",
      "                                                     min_impurity_decrease=0.0,\n",
      "                                                     min_impurity_split=None,\n",
      "                                                     min_samples_leaf=1,\n",
      "                                                     min_samples_split=2,\n",
      "                                                     min_weight_fraction_leaf=0.0,\n",
      "                                                     presor...\n",
      "                                     positive=False, precompute=False,\n",
      "                                     random_state=123, selection='cyclic',\n",
      "                                     tol=0.0001, warm_start=False)),\n",
      "                              ('knn',\n",
      "                               KNeighborsRegressor(algorithm='auto',\n",
      "                                                   leaf_size=30,\n",
      "                                                   metric='minkowski',\n",
      "                                                   metric_params=None,\n",
      "                                                   n_jobs=-1, n_neighbors=5,\n",
      "                                                   p=2, weights='uniform'))],\n",
      "                  final_estimator=LinearRegression(copy_X=True,\n",
      "                                                   fit_intercept=True,\n",
      "                                                   n_jobs=-1, normalize=False),\n",
      "                  n_jobs=-1, passthrough=True, verbose=0)\n"
     ]
    }
   ],
   "source": [
    "print(stacker)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyze Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b3e79dd44bd34d7c95e1522e60644df7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(ToggleButtons(description='Plot Type:', icons=('',), options=(('Hyperparameters', 'param…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluate_model(best)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x511.2 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "interpret_model(dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "object-assign\n",
       "(c) Sindre Sorhus\n",
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n.childContextTypes?u(\"107\",this.getName()||\"ReactCompositeComponent\"):void 0;for(var i in e)i in n.childContextTypes?void 0:u(\"108\",this.getName()||\"ReactCompositeComponent\",i);return c({},t,e)}return t},_checkContextTypes:function(t,e,n){},receiveComponent:function(t,e,n){var r=this._currentElement,i=this._context;this._pendingElement=null,this.updateComponent(e,r,t,i,n)},performUpdateIfNecessary:function(t){null!=this._pendingElement?v.receiveComponent(this,this._pendingElement,t,this._context):null!==this._pendingStateQueue||this._pendingForceUpdate?this.updateComponent(t,this._currentElement,this._currentElement,this._context,this._context):this._updateBatchNumber=null},updateComponent:function(t,e,n,r,i){var o=this._instance;null==o?u(\"136\",this.getName()||\"ReactCompositeComponent\"):void 0;var a,c=!1;this._context===i?a=o.context:(a=this._processContext(i),c=!0);var s=e.props,l=n.props;e!==n&&(c=!0),c&&o.componentWillReceiveProps&&o.componentWillReceiveProps(l,a);var f=this._processPendingState(l,a),p=!0;this._pendingForceUpdate||(o.shouldComponentUpdate?p=o.shouldComponentUpdate(l,f,a):this._compositeType===_.PureClass&&(p=!m(s,l)||!m(o.state,f))),this._updateBatchNumber=null,p?(this._pendingForceUpdate=!1,this._performComponentUpdate(n,l,f,a,t,i)):(this._currentElement=n,this._context=i,o.props=l,o.state=f,o.context=a)},_processPendingState:function(t,e){var n=this._instance,r=this._pendingStateQueue,i=this._pendingReplaceState;if(this._pendingReplaceState=!1,this._pendingStateQueue=null,!r)return n.state;if(i&&1===r.length)return r[0];for(var o=c({},i?r[0]:n.state),a=i?1:0;a<r.length;a++){var u=r[a];c(o,\"function\"==typeof u?u.call(n,o,t,e):u)}return o},_performComponentUpdate:function(t,e,n,r,i,o){var a,u,c,s=this._instance,l=Boolean(s.componentDidUpdate);l&&(a=s.props,u=s.state,c=s.context),s.componentWillUpdate&&s.componentWillUpdate(e,n,r),this._currentElement=t,this._context=o,s.props=e,s.state=n,s.context=r,this._updateRenderedComponent(i,o),l&&i.getReactMountReady().enqueue(s.componentDidUpdate.bind(s,a,u,c),s)},_updateRenderedComponent:function(t,e){var n=this._renderedComponent,r=n._currentElement,i=this._renderValidatedComponent(),o=0;if(y(r,i))v.receiveComponent(n,i,t,this._processChildContext(e));else{var a=v.getHostNode(n);v.unmountComponent(n,!1);var u=d.getType(i);this._renderedNodeType=u;var c=this._instantiateReactComponent(i,u!==d.EMPTY);this._renderedComponent=c;var s=v.mountComponent(c,t,this._hostParent,this._hostContainerInfo,this._processChildContext(e),o);this._replaceNodeWithMarkup(a,s,n)}},_replaceNodeWithMarkup:function(t,e,n){l.replaceNodeWithMarkup(t,e,n)},_renderValidatedComponentWithoutOwnerOrContext:function(){var t,e=this._instance;return t=e.render()},_renderValidatedComponent:function(){var t;if(this._compositeType!==_.StatelessFunctional){f.current=this;try{t=this._renderValidatedComponentWithoutOwnerOrContext()}finally{f.current=null}}else t=this._renderValidatedComponentWithoutOwnerOrContext();return null===t||t===!1||s.isValidElement(t)?void 0:u(\"109\",this.getName()||\"ReactCompositeComponent\"),t},attachRef:function(t,e){var n=this.getPublicInstance();null==n?u(\"110\"):void 0;var r=e.getPublicInstance(),i=n.refs===g?n.refs={}:n.refs;i[t]=r},detachRef:function(t){var e=this.getPublicInstance().refs;delete e[t]},getName:function(){var t=this._currentElement.type,e=this._instance&&this._instance.constructor;return t.displayName||e&&e.displayName||t.name||e&&e.name||null},getPublicInstance:function(){var t=this._instance;return this._compositeType===_.StatelessFunctional?null:t},_instantiateReactComponent:null};t.exports=x},function(t,e,n){\"use strict\";var r=n(4),i=n(358),o=n(163),a=n(24),u=n(11),c=n(371),s=n(387),l=n(167),f=n(395);n(1);i.inject();var p={findDOMNode:s,render:o.render,unmountComponentAtNode:o.unmountComponentAtNode,version:c,unstable_batchedUpdates:u.batchedUpdates,unstable_renderSubtreeIntoContainer:f};\"undefined\"!=typeof __REACT_DEVTOOLS_GLOBAL_HOOK__&&\"function\"==typeof __REACT_DEVTOOLS_GLOBAL_HOOK__.inject&&__REACT_DEVTOOLS_GLOBAL_HOOK__.inject({ComponentTree:{getClosestInstanceFromNode:r.getClosestInstanceFromNode,getNodeFromInstance:function(t){return t._renderedComponent&&(t=l(t)),t?r.getNodeFromInstance(t):null}},Mount:o,Reconciler:a});t.exports=p},function(t,e,n){\"use strict\";function r(t){if(t){var e=t._currentElement._owner||null;if(e){var n=e.getName();if(n)return\" This DOM node was rendered by `\"+n+\"`.\"}}return\"\"}function i(t,e){e&&(G[t._tag]&&(null!=e.children||null!=e.dangerouslySetInnerHTML?v(\"137\",t._tag,t._currentElement._owner?\" Check the render method of \"+t._currentElement._owner.getName()+\".\":\"\"):void 0),null!=e.dangerouslySetInnerHTML&&(null!=e.children?v(\"60\"):void 0,\"object\"==typeof e.dangerouslySetInnerHTML&&V in e.dangerouslySetInnerHTML?void 0:v(\"61\")),null!=e.style&&\"object\"!=typeof e.style?v(\"62\",r(t)):void 0)}function o(t,e,n,r){if(!(r instanceof I)){var i=t._hostContainerInfo,o=i._node&&i._node.nodeType===H,u=o?i._node:i._ownerDocument;F(e,u),r.getReactMountReady().enqueue(a,{inst:t,registrationName:e,listener:n})}}function a(){var t=this;C.putListener(t.inst,t.registrationName,t.listener)}function u(){var t=this;S.postMountWrapper(t)}function c(){var t=this;A.postMountWrapper(t)}function s(){var t=this;P.postMountWrapper(t)}function l(){var t=this;t._rootNodeID?void 0:v(\"63\");var e=U(t);switch(e?void 0:v(\"64\"),t._tag){case\"iframe\":case\"object\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"video\":case\"audio\":t._wrapperState.listeners=[];for(var n in q)q.hasOwnProperty(n)&&t._wrapperState.listeners.push(k.trapBubbledEvent(n,q[n],e));break;case\"source\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topError\",\"error\",e)];break;case\"img\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topError\",\"error\",e),k.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"form\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topReset\",\"reset\",e),k.trapBubbledEvent(\"topSubmit\",\"submit\",e)];break;case\"input\":case\"select\":case\"textarea\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topInvalid\",\"invalid\",e)]}}function f(){N.postUpdateWrapper(this)}function p(t){Z.call(X,t)||($.test(t)?void 0:v(\"65\",t),X[t]=!0)}function h(t,e){return t.indexOf(\"-\")>=0||null!=e.is}function d(t){var e=t.type;p(e),this._currentElement=t,this._tag=e.toLowerCase(),this._namespaceURI=null,this._renderedChildren=null,this._previousStyle=null,this._previousStyleCopy=null,this._hostNode=null,this._hostParent=null,this._rootNodeID=0,this._domID=0,this._hostContainerInfo=null,this._wrapperState=null,this._topLevelWrapper=null,this._flags=0}var v=n(2),g=n(3),m=n(332),y=n(334),_=n(20),b=n(82),x=n(21),w=n(156),C=n(22),M=n(83),k=n(51),E=n(157),T=n(4),S=n(351),P=n(352),N=n(158),A=n(355),O=(n(9),n(364)),I=n(369),D=(n(8),n(54)),R=(n(0),n(94),n(80),n(96),n(1),E),L=C.deleteListener,U=T.getNodeFromInstance,F=k.listenTo,j=M.registrationNameModules,B={string:!0,number:!0},W=\"style\",V=\"__html\",z={children:null,dangerouslySetInnerHTML:null,suppressContentEditableWarning:null},H=11,q={topAbort:\"abort\",topCanPlay:\"canplay\",topCanPlayThrough:\"canplaythrough\",topDurationChange:\"durationchange\",topEmptied:\"emptied\",topEncrypted:\"encrypted\",topEnded:\"ended\",topError:\"error\",topLoadedData:\"loadeddata\",topLoadedMetadata:\"loadedmetadata\",topLoadStart:\"loadstart\",topPause:\"pause\",topPlay:\"play\",topPlaying:\"playing\",topProgress:\"progress\",topRateChange:\"ratechange\",topSeeked:\"seeked\",topSeeking:\"seeking\",topStalled:\"stalled\",topSuspend:\"suspend\",topTimeUpdate:\"timeupdate\",topVolumeChange:\"volumechange\",topWaiting:\"waiting\"},Y={area:!0,base:!0,br:!0,col:!0,embed:!0,hr:!0,img:!0,input:!0,keygen:!0,link:!0,meta:!0,param:!0,source:!0,track:!0,wbr:!0},K={listing:!0,pre:!0,textarea:!0},G=g({menuitem:!0},Y),$=/^[a-zA-Z][a-zA-Z:_\\.\\-\\d]*$/,X={},Z={}.hasOwnProperty,Q=1;d.displayName=\"ReactDOMComponent\",d.Mixin={mountComponent:function(t,e,n,r){this._rootNodeID=Q++,this._domID=n._idCounter++,this._hostParent=e,this._hostContainerInfo=n;var o=this._currentElement.props;switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":this._wrapperState={listeners:null},t.getReactMountReady().enqueue(l,this);break;case\"input\":S.mountWrapper(this,o,e),o=S.getHostProps(this,o),t.getReactMountReady().enqueue(l,this);break;case\"option\":P.mountWrapper(this,o,e),o=P.getHostProps(this,o);break;case\"select\":N.mountWrapper(this,o,e),o=N.getHostProps(this,o),t.getReactMountReady().enqueue(l,this);break;case\"textarea\":A.mountWrapper(this,o,e),o=A.getHostProps(this,o),t.getReactMountReady().enqueue(l,this)}i(this,o);var a,f;null!=e?(a=e._namespaceURI,f=e._tag):n._tag&&(a=n._namespaceURI,f=n._tag),(null==a||a===b.svg&&\"foreignobject\"===f)&&(a=b.html),a===b.html&&(\"svg\"===this._tag?a=b.svg:\"math\"===this._tag&&(a=b.mathml)),this._namespaceURI=a;var p;if(t.useCreateElement){var h,d=n._ownerDocument;if(a===b.html)if(\"script\"===this._tag){var v=d.createElement(\"div\"),g=this._currentElement.type;v.innerHTML=\"<\"+g+\"></\"+g+\">\",h=v.removeChild(v.firstChild)}else h=o.is?d.createElement(this._currentElement.type,o.is):d.createElement(this._currentElement.type);else h=d.createElementNS(a,this._currentElement.type);T.precacheNode(this,h),this._flags|=R.hasCachedChildNodes,this._hostParent||w.setAttributeForRoot(h),this._updateDOMProperties(null,o,t);var y=_(h);this._createInitialChildren(t,o,r,y),p=y}else{var x=this._createOpenTagMarkupAndPutListeners(t,o),C=this._createContentMarkup(t,o,r);p=!C&&Y[this._tag]?x+\"/>\":x+\">\"+C+\"</\"+this._currentElement.type+\">\"}switch(this._tag){case\"input\":t.getReactMountReady().enqueue(u,this),o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"textarea\":t.getReactMountReady().enqueue(c,this),o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"select\":o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"button\":o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"option\":t.getReactMountReady().enqueue(s,this)}return p},_createOpenTagMarkupAndPutListeners:function(t,e){var n=\"<\"+this._currentElement.type;for(var r in e)if(e.hasOwnProperty(r)){var i=e[r];if(null!=i)if(j.hasOwnProperty(r))i&&o(this,r,i,t);else{r===W&&(i&&(i=this._previousStyleCopy=g({},e.style)),i=y.createMarkupForStyles(i,this));var a=null;null!=this._tag&&h(this._tag,e)?z.hasOwnProperty(r)||(a=w.createMarkupForCustomAttribute(r,i)):a=w.createMarkupForProperty(r,i),a&&(n+=\" \"+a)}}return t.renderToStaticMarkup?n:(this._hostParent||(n+=\" \"+w.createMarkupForRoot()),n+=\" \"+w.createMarkupForID(this._domID))},_createContentMarkup:function(t,e,n){var r=\"\",i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&(r=i.__html);else{var o=B[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)r=D(o);else if(null!=a){var u=this.mountChildren(a,t,n);r=u.join(\"\")}}return K[this._tag]&&\"\\n\"===r.charAt(0)?\"\\n\"+r:r},_createInitialChildren:function(t,e,n,r){var i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&_.queueHTML(r,i.__html);else{var o=B[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)\"\"!==o&&_.queueText(r,o);else if(null!=a)for(var u=this.mountChildren(a,t,n),c=0;c<u.length;c++)_.queueChild(r,u[c])}},receiveComponent:function(t,e,n){var r=this._currentElement;this._currentElement=t,this.updateComponent(e,r,t,n)},updateComponent:function(t,e,n,r){var o=e.props,a=this._currentElement.props;switch(this._tag){case\"input\":o=S.getHostProps(this,o),a=S.getHostProps(this,a);break;case\"option\":o=P.getHostProps(this,o),a=P.getHostProps(this,a);break;case\"select\":o=N.getHostProps(this,o),a=N.getHostProps(this,a);break;case\"textarea\":o=A.getHostProps(this,o),a=A.getHostProps(this,a)}switch(i(this,a),this._updateDOMProperties(o,a,t),this._updateDOMChildren(o,a,t,r),this._tag){case\"input\":S.updateWrapper(this);break;case\"textarea\":A.updateWrapper(this);break;case\"select\":t.getReactMountReady().enqueue(f,this)}},_updateDOMProperties:function(t,e,n){var r,i,a;for(r in t)if(!e.hasOwnProperty(r)&&t.hasOwnProperty(r)&&null!=t[r])if(r===W){var u=this._previousStyleCopy;for(i in u)u.hasOwnProperty(i)&&(a=a||{},a[i]=\"\");this._previousStyleCopy=null}else j.hasOwnProperty(r)?t[r]&&L(this,r):h(this._tag,t)?z.hasOwnProperty(r)||w.deleteValueForAttribute(U(this),r):(x.properties[r]||x.isCustomAttribute(r))&&w.deleteValueForProperty(U(this),r);for(r in e){var c=e[r],s=r===W?this._previousStyleCopy:null!=t?t[r]:void 0;if(e.hasOwnProperty(r)&&c!==s&&(null!=c||null!=s))if(r===W)if(c?c=this._previousStyleCopy=g({},c):this._previousStyleCopy=null,s){for(i in s)!s.hasOwnProperty(i)||c&&c.hasOwnProperty(i)||(a=a||{},a[i]=\"\");for(i in c)c.hasOwnProperty(i)&&s[i]!==c[i]&&(a=a||{},a[i]=c[i])}else a=c;else if(j.hasOwnProperty(r))c?o(this,r,c,n):s&&L(this,r);else if(h(this._tag,e))z.hasOwnProperty(r)||w.setValueForAttribute(U(this),r,c);else if(x.properties[r]||x.isCustomAttribute(r)){var l=U(this);null!=c?w.setValueForProperty(l,r,c):w.deleteValueForProperty(l,r)}}a&&y.setValueForStyles(U(this),a,this)},_updateDOMChildren:function(t,e,n,r){var i=B[typeof t.children]?t.children:null,o=B[typeof e.children]?e.children:null,a=t.dangerouslySetInnerHTML&&t.dangerouslySetInnerHTML.__html,u=e.dangerouslySetInnerHTML&&e.dangerouslySetInnerHTML.__html,c=null!=i?null:t.children,s=null!=o?null:e.children,l=null!=i||null!=a,f=null!=o||null!=u;null!=c&&null==s?this.updateChildren(null,n,r):l&&!f&&this.updateTextContent(\"\"),null!=o?i!==o&&this.updateTextContent(\"\"+o):null!=u?a!==u&&this.updateMarkup(\"\"+u):null!=s&&this.updateChildren(s,n,r)},getHostNode:function(){return U(this)},unmountComponent:function(t){switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":var e=this._wrapperState.listeners;if(e)for(var n=0;n<e.length;n++)e[n].remove();break;case\"html\":case\"head\":case\"body\":v(\"66\",this._tag)}this.unmountChildren(t),T.uncacheNode(this),C.deleteAllListeners(this),this._rootNodeID=0,this._domID=0,this._wrapperState=null},getPublicInstance:function(){return U(this)}},g(d.prototype,d.Mixin,O.Mixin),t.exports=d},function(t,e,n){\"use strict\";function r(t,e){var n={_topLevelWrapper:t,_idCounter:1,_ownerDocument:e?e.nodeType===i?e:e.ownerDocument:null,_node:e,_tag:e?e.nodeName.toLowerCase():null,_namespaceURI:e?e.namespaceURI:null};return n}var i=(n(96),9);t.exports=r},function(t,e,n){\"use strict\";var r=n(3),i=n(20),o=n(4),a=function(t){this._currentElement=null,this._hostNode=null,this._hostParent=null,this._hostContainerInfo=null,this._domID=0};r(a.prototype,{mountComponent:function(t,e,n,r){var a=n._idCounter++;this._domID=a,this._hostParent=e,this._hostContainerInfo=n;var u=\" react-empty: \"+this._domID+\" \";if(t.useCreateElement){var c=n._ownerDocument,s=c.createComment(u);return o.precacheNode(this,s),i(s)}return t.renderToStaticMarkup?\"\":\"<!--\"+u+\"-->\"},receiveComponent:function(){},getHostNode:function(){return o.getNodeFromInstance(this)},unmountComponent:function(){o.uncacheNode(this)}}),t.exports=a},function(t,e,n){\"use strict\";var r={useCreateElement:!0,useFiber:!1};t.exports=r},function(t,e,n){\"use strict\";var r=n(81),i=n(4),o={dangerouslyProcessChildrenUpdates:function(t,e){var n=i.getNodeFromInstance(t);r.processUpdates(n,e)}};t.exports=o},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&f.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=c.executeOnChange(e,t);l.asap(r,this);var i=e.name;if(\"radio\"===e.type&&null!=i){for(var a=s.getNodeFromInstance(this),u=a;u.parentNode;)u=u.parentNode;for(var f=u.querySelectorAll(\"input[name=\"+JSON.stringify(\"\"+i)+'][type=\"radio\"]'),p=0;p<f.length;p++){var h=f[p];if(h!==a&&h.form===a.form){var d=s.getInstanceFromNode(h);d?void 0:o(\"90\"),l.asap(r,d)}}}return n}var o=n(2),a=n(3),u=n(156),c=n(85),s=n(4),l=n(11),f=(n(0),n(1),{getHostProps:function(t,e){var n=c.getValue(e),r=c.getChecked(e),i=a({type:void 0,step:void 0,min:void 0,max:void 0},e,{defaultChecked:void 0,defaultValue:void 0,value:null!=n?n:t._wrapperState.initialValue,checked:null!=r?r:t._wrapperState.initialChecked,onChange:t._wrapperState.onChange});return i},mountWrapper:function(t,e){var n=e.defaultValue;t._wrapperState={initialChecked:null!=e.checked?e.checked:e.defaultChecked,initialValue:null!=e.value?e.value:n,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=e.checked;null!=n&&u.setValueForProperty(s.getNodeFromInstance(t),\"checked\",n||!1);var r=s.getNodeFromInstance(t),i=c.getValue(e);if(null!=i){var o=\"\"+i;o!==r.value&&(r.value=o)}else null==e.value&&null!=e.defaultValue&&r.defaultValue!==\"\"+e.defaultValue&&(r.defaultValue=\"\"+e.defaultValue),null==e.checked&&null!=e.defaultChecked&&(r.defaultChecked=!!e.defaultChecked)},postMountWrapper:function(t){var e=t._currentElement.props,n=s.getNodeFromInstance(t);switch(e.type){case\"submit\":case\"reset\":break;case\"color\":case\"date\":case\"datetime\":case\"datetime-local\":case\"month\":case\"time\":case\"week\":n.value=\"\",n.value=n.defaultValue;break;default:n.value=n.value}var r=n.name;\"\"!==r&&(n.name=\"\"),n.defaultChecked=!n.defaultChecked,n.defaultChecked=!n.defaultChecked,\"\"!==r&&(n.name=r)}});t.exports=f},function(t,e,n){\"use strict\";function r(t){var e=\"\";return o.Children.forEach(t,function(t){null!=t&&(\"string\"==typeof t||\"number\"==typeof t?e+=t:c||(c=!0))}),e}var i=n(3),o=n(26),a=n(4),u=n(158),c=(n(1),!1),s={mountWrapper:function(t,e,n){var i=null;if(null!=n){var o=n;\"optgroup\"===o._tag&&(o=o._hostParent),null!=o&&\"select\"===o._tag&&(i=u.getSelectValueContext(o))}var a=null;if(null!=i){var c;if(c=null!=e.value?e.value+\"\":r(e.children),a=!1,Array.isArray(i)){for(var s=0;s<i.length;s++)if(\"\"+i[s]===c){a=!0;break}}else a=\"\"+i===c}t._wrapperState={selected:a}},postMountWrapper:function(t){var e=t._currentElement.props;if(null!=e.value){var n=a.getNodeFromInstance(t);n.setAttribute(\"value\",e.value)}},getHostProps:function(t,e){var n=i({selected:void 0,children:void 0},e);null!=t._wrapperState.selected&&(n.selected=t._wrapperState.selected);var o=r(e.children);return o&&(n.children=o),n}};t.exports=s},function(t,e,n){\"use strict\";function r(t,e,n,r){return t===n&&e===r}function i(t){var e=document.selection,n=e.createRange(),r=n.text.length,i=n.duplicate();i.moveToElementText(t),i.setEndPoint(\"EndToStart\",n);var o=i.text.length,a=o+r;return{start:o,end:a}}function o(t){var e=window.getSelection&&window.getSelection();if(!e||0===e.rangeCount)return null;var n=e.anchorNode,i=e.anchorOffset,o=e.focusNode,a=e.focusOffset,u=e.getRangeAt(0);try{u.startContainer.nodeType,u.endContainer.nodeType}catch(t){return null}var c=r(e.anchorNode,e.anchorOffset,e.focusNode,e.focusOffset),s=c?0:u.toString().length,l=u.cloneRange();l.selectNodeContents(t),l.setEnd(u.startContainer,u.startOffset);var f=r(l.startContainer,l.startOffset,l.endContainer,l.endOffset),p=f?0:l.toString().length,h=p+s,d=document.createRange();d.setStart(n,i),d.setEnd(o,a);var v=d.collapsed;return{start:v?h:p,end:v?p:h}}function a(t,e){var n,r,i=document.selection.createRange().duplicate();void 0===e.end?(n=e.start,r=n):e.start>e.end?(n=e.end,r=e.start):(n=e.start,r=e.end),i.moveToElementText(t),i.moveStart(\"character\",n),i.setEndPoint(\"EndToStart\",i),i.moveEnd(\"character\",r-n),i.select()}function u(t,e){if(window.getSelection){var n=window.getSelection(),r=t[l()].length,i=Math.min(e.start,r),o=void 0===e.end?i:Math.min(e.end,r);if(!n.extend&&i>o){var a=o;o=i,i=a}var u=s(t,i),c=s(t,o);if(u&&c){var f=document.createRange();f.setStart(u.node,u.offset),n.removeAllRanges(),i>o?(n.addRange(f),n.extend(c.node,c.offset)):(f.setEnd(c.node,c.offset),n.addRange(f))}}}var c=n(6),s=n(392),l=n(168),f=c.canUseDOM&&\"selection\"in document&&!(\"getSelection\"in window),p={getOffsets:f?i:o,setOffsets:f?a:u};t.exports=p},function(t,e,n){\"use strict\";var r=n(2),i=n(3),o=n(81),a=n(20),u=n(4),c=n(54),s=(n(0),n(96),function(t){this._currentElement=t,this._stringText=\"\"+t,this._hostNode=null,this._hostParent=null,this._domID=0,this._mountIndex=0,this._closingComment=null,this._commentNodes=null});i(s.prototype,{mountComponent:function(t,e,n,r){var i=n._idCounter++,o=\" react-text: \"+i+\" \",s=\" /react-text \";if(this._domID=i,this._hostParent=e,t.useCreateElement){var l=n._ownerDocument,f=l.createComment(o),p=l.createComment(s),h=a(l.createDocumentFragment());return a.queueChild(h,a(f)),this._stringText&&a.queueChild(h,a(l.createTextNode(this._stringText))),a.queueChild(h,a(p)),u.precacheNode(this,f),this._closingComment=p,h}var d=c(this._stringText);return t.renderToStaticMarkup?d:\"<!--\"+o+\"-->\"+d+\"<!--\"+s+\"-->\"},receiveComponent:function(t,e){if(t!==this._currentElement){this._currentElement=t;var n=\"\"+t;if(n!==this._stringText){this._stringText=n;var r=this.getHostNode();o.replaceDelimitedText(r[0],r[1],n)}}},getHostNode:function(){var t=this._commentNodes;if(t)return t;if(!this._closingComment)for(var e=u.getNodeFromInstance(this),n=e.nextSibling;;){if(null==n?r(\"67\",this._domID):void 0,8===n.nodeType&&\" /react-text \"===n.nodeValue){this._closingComment=n;break}n=n.nextSibling}return t=[this._hostNode,this._closingComment],this._commentNodes=t,t},unmountComponent:function(){this._closingComment=null,this._commentNodes=null,u.uncacheNode(this)}}),t.exports=s},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&l.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=u.executeOnChange(e,t);return s.asap(r,this),n}var o=n(2),a=n(3),u=n(85),c=n(4),s=n(11),l=(n(0),n(1),{getHostProps:function(t,e){null!=e.dangerouslySetInnerHTML?o(\"91\"):void 0;var n=a({},e,{value:void 0,defaultValue:void 0,children:\"\"+t._wrapperState.initialValue,onChange:t._wrapperState.onChange});return n},mountWrapper:function(t,e){var n=u.getValue(e),r=n;if(null==n){var a=e.defaultValue,c=e.children;null!=c&&(null!=a?o(\"92\"):void 0,Array.isArray(c)&&(c.length<=1?void 0:o(\"93\"),c=c[0]),a=\"\"+c),null==a&&(a=\"\"),r=a}t._wrapperState={initialValue:\"\"+r,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=c.getNodeFromInstance(t),r=u.getValue(e);if(null!=r){var i=\"\"+r;i!==n.value&&(n.value=i),null==e.defaultValue&&(n.defaultValue=i)}null!=e.defaultValue&&(n.defaultValue=e.defaultValue)},postMountWrapper:function(t){var e=c.getNodeFromInstance(t),n=e.textContent;\n",
       "n===t._wrapperState.initialValue&&(e.value=n)}});t.exports=l},function(t,e,n){\"use strict\";function r(t,e){\"_hostNode\"in t?void 0:c(\"33\"),\"_hostNode\"in e?void 0:c(\"33\");for(var n=0,r=t;r;r=r._hostParent)n++;for(var i=0,o=e;o;o=o._hostParent)i++;for(;n-i>0;)t=t._hostParent,n--;for(;i-n>0;)e=e._hostParent,i--;for(var a=n;a--;){if(t===e)return t;t=t._hostParent,e=e._hostParent}return null}function i(t,e){\"_hostNode\"in t?void 0:c(\"35\"),\"_hostNode\"in e?void 0:c(\"35\");for(;e;){if(e===t)return!0;e=e._hostParent}return!1}function o(t){return\"_hostNode\"in t?void 0:c(\"36\"),t._hostParent}function a(t,e,n){for(var r=[];t;)r.push(t),t=t._hostParent;var i;for(i=r.length;i-- >0;)e(r[i],\"captured\",n);for(i=0;i<r.length;i++)e(r[i],\"bubbled\",n)}function u(t,e,n,i,o){for(var a=t&&e?r(t,e):null,u=[];t&&t!==a;)u.push(t),t=t._hostParent;for(var c=[];e&&e!==a;)c.push(e),e=e._hostParent;var s;for(s=0;s<u.length;s++)n(u[s],\"bubbled\",i);for(s=c.length;s-- 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r(){C||(C=!0,y.EventEmitter.injectReactEventListener(m),y.EventPluginHub.injectEventPluginOrder(u),y.EventPluginUtils.injectComponentTree(p),y.EventPluginUtils.injectTreeTraversal(d),y.EventPluginHub.injectEventPluginsByName({SimpleEventPlugin:w,EnterLeaveEventPlugin:c,ChangeEventPlugin:a,SelectEventPlugin:x,BeforeInputEventPlugin:o}),y.HostComponent.injectGenericComponentClass(f),y.HostComponent.injectTextComponentClass(v),y.DOMProperty.injectDOMPropertyConfig(i),y.DOMProperty.injectDOMPropertyConfig(s),y.DOMProperty.injectDOMPropertyConfig(b),y.EmptyComponent.injectEmptyComponentFactory(function(t){return new h(t)}),y.Updates.injectReconcileTransaction(_),y.Updates.injectBatchingStrategy(g),y.Component.injectEnvironment(l))}var i=n(331),o=n(333),a=n(335),u=n(337),c=n(338),s=n(341),l=n(343),f=n(346),p=n(4),h=n(348),d=n(356),v=n(354),g=n(357),m=n(361),y=n(362),_=n(367),b=n(372),x=n(373),w=n(374),C=!1;t.exports={inject:r}},function(t,e,n){\"use strict\";var r=\"function\"==typeof 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v={_enabled:!0,_handleTopLevel:null,WINDOW_HANDLE:s.canUseDOM?window:null,setHandleTopLevel:function(t){v._handleTopLevel=t},setEnabled:function(t){v._enabled=!!t},isEnabled:function(){return v._enabled},trapBubbledEvent:function(t,e,n){return n?c.listen(n,e,v.dispatchEvent.bind(null,t)):null},trapCapturedEvent:function(t,e,n){return n?c.capture(n,e,v.dispatchEvent.bind(null,t)):null},monitorScrollValue:function(t){var e=a.bind(null,t);c.listen(window,\"scroll\",e)},dispatchEvent:function(t,e){if(v._enabled){var n=i.getPooled(t,e);try{p.batchedUpdates(o,n)}finally{i.release(n)}}}};t.exports=v},function(t,e,n){\"use strict\";var r=n(21),i=n(22),o=n(50),a=n(86),u=n(159),c=n(51),s=n(161),l=n(11),f={Component:a.injection,DOMProperty:r.injection,EmptyComponent:u.injection,EventPluginHub:i.injection,EventPluginUtils:o.injection,EventEmitter:c.injection,HostComponent:s.injection,Updates:l.injection};t.exports=f},function(t,e,n){\"use strict\";var r=n(385),i=/\\/?>/,o=/^<\\!\\-\\-/,a={CHECKSUM_ATTR_NAME:\"data-react-checksum\",addChecksumToMarkup:function(t){var e=r(t);return o.test(t)?t:t.replace(i,\" \"+a.CHECKSUM_ATTR_NAME+'=\"'+e+'\"$&')},canReuseMarkup:function(t,e){var n=e.getAttribute(a.CHECKSUM_ATTR_NAME);n=n&&parseInt(n,10);var i=r(t);return i===n}};t.exports=a},function(t,e,n){\"use strict\";function r(t,e,n){return{type:\"INSERT_MARKUP\",content:t,fromIndex:null,fromNode:null,toIndex:n,afterNode:e}}function i(t,e,n){return{type:\"MOVE_EXISTING\",content:null,fromIndex:t._mountIndex,fromNode:p.getHostNode(t),toIndex:n,afterNode:e}}function o(t,e){return{type:\"REMOVE_NODE\",content:null,fromIndex:t._mountIndex,fromNode:e,toIndex:null,afterNode:null}}function a(t){return{type:\"SET_MARKUP\",content:t,fromIndex:null,fromNode:null,toIndex:null,afterNode:null}}function u(t){return{type:\"TEXT_CONTENT\",content:t,fromIndex:null,fromNode:null,toIndex:null,afterNode:null}}function c(t,e){return e&&(t=t||[],t.push(e)),t}function s(t,e){f.processChildrenUpdates(t,e)}var l=n(2),f=n(86),p=(n(40),n(9),n(15),n(24)),h=n(342),d=(n(8),n(388)),v=(n(0),{Mixin:{_reconcilerInstantiateChildren:function(t,e,n){return h.instantiateChildren(t,e,n)},_reconcilerUpdateChildren:function(t,e,n,r,i,o){var a,u=0;return a=d(e,u),h.updateChildren(t,a,n,r,i,this,this._hostContainerInfo,o,u),a},mountChildren:function(t,e,n){var r=this._reconcilerInstantiateChildren(t,e,n);this._renderedChildren=r;var i=[],o=0;for(var a in r)if(r.hasOwnProperty(a)){var u=r[a],c=0,s=p.mountComponent(u,e,this,this._hostContainerInfo,n,c);u._mountIndex=o++,i.push(s)}return i},updateTextContent:function(t){var e=this._renderedChildren;h.unmountChildren(e,!1);for(var n in e)e.hasOwnProperty(n)&&l(\"118\");var r=[u(t)];s(this,r)},updateMarkup:function(t){var e=this._renderedChildren;h.unmountChildren(e,!1);for(var n in e)e.hasOwnProperty(n)&&l(\"118\");var r=[a(t)];s(this,r)},updateChildren:function(t,e,n){this._updateChildren(t,e,n)},_updateChildren:function(t,e,n){var r=this._renderedChildren,i={},o=[],a=this._reconcilerUpdateChildren(r,t,o,i,e,n);if(a||r){var u,l=null,f=0,h=0,d=0,v=null;for(u in a)if(a.hasOwnProperty(u)){var g=r&&r[u],m=a[u];g===m?(l=c(l,this.moveChild(g,v,f,h)),h=Math.max(g._mountIndex,h),g._mountIndex=f):(g&&(h=Math.max(g._mountIndex,h)),l=c(l,this._mountChildAtIndex(m,o[d],v,f,e,n)),d++),f++,v=p.getHostNode(m)}for(u in i)i.hasOwnProperty(u)&&(l=c(l,this._unmountChild(r[u],i[u])));l&&s(this,l),this._renderedChildren=a}},unmountChildren:function(t){var e=this._renderedChildren;h.unmountChildren(e,t),this._renderedChildren=null},moveChild:function(t,e,n,r){if(t._mountIndex<r)return i(t,e,n)},createChild:function(t,e,n){return r(n,e,t._mountIndex)},removeChild:function(t,e){return o(t,e)},_mountChildAtIndex:function(t,e,n,r,i,o){return t._mountIndex=r,this.createChild(t,n,e)},_unmountChild:function(t,e){var n=this.removeChild(t,e);return t._mountIndex=null,n}}});t.exports=v},function(t,e,n){\"use strict\";function r(t){return!(!t||\"function\"!=typeof t.attachRef||\"function\"!=typeof t.detachRef)}var i=n(2),o=(n(0),{addComponentAsRefTo:function(t,e,n){r(n)?void 0:i(\"119\"),n.attachRef(e,t)},removeComponentAsRefFrom:function(t,e,n){r(n)?void 0:i(\"120\");var o=n.getPublicInstance();o&&o.refs[e]===t.getPublicInstance()&&n.detachRef(e)}});t.exports=o},function(t,e,n){\"use strict\";var r=\"SECRET_DO_NOT_PASS_THIS_OR_YOU_WILL_BE_FIRED\";t.exports=r},function(t,e,n){\"use strict\";function r(t){this.reinitializeTransaction(),this.renderToStaticMarkup=!1,this.reactMountReady=o.getPooled(null),this.useCreateElement=t}var i=n(3),o=n(155),a=n(17),u=n(51),c=n(162),s=(n(9),n(53)),l=n(88),f={initialize:c.getSelectionInformation,close:c.restoreSelection},p={initialize:function(){var t=u.isEnabled();return u.setEnabled(!1),t},close:function(t){u.setEnabled(t)}},h={initialize:function(){this.reactMountReady.reset()},close:function(){this.reactMountReady.notifyAll()}},d=[f,p,h],v={getTransactionWrappers:function(){return d},getReactMountReady:function(){return this.reactMountReady},getUpdateQueue:function(){return l},checkpoint:function(){return this.reactMountReady.checkpoint()},rollback:function(t){this.reactMountReady.rollback(t)},destructor:function(){o.release(this.reactMountReady),this.reactMountReady=null}};i(r.prototype,s,v),a.addPoolingTo(r),t.exports=r},function(t,e,n){\"use strict\";function r(t,e,n){\"function\"==typeof t?t(e.getPublicInstance()):o.addComponentAsRefTo(e,t,n)}function i(t,e,n){\"function\"==typeof t?t(null):o.removeComponentAsRefFrom(e,t,n)}var o=n(365),a={};a.attachRefs=function(t,e){if(null!==e&&\"object\"==typeof e){var n=e.ref;null!=n&&r(n,t,e._owner)}},a.shouldUpdateRefs=function(t,e){var n=null,r=null;null!==t&&\"object\"==typeof 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       "    SHAP.React.createElement(SHAP.AdditiveForceVisualizer, {\"outNames\": [\"f(x)\"], \"baseValue\": 13225.435243916309, \"outValue\": 5148.552734375, \"link\": \"identity\", \"featureNames\": [\"age\", \"bmi\", \"sex_female\", \"children_0\", \"children_1\", \"children_2\", \"children_3\", \"children_4\", \"children_5\", \"smoker_yes\", \"region_northeast\", \"region_northwest\", \"region_southeast\", \"region_southwest\"], \"features\": {\"0\": {\"effect\": -3739.178109286851, \"value\": 32.0}, \"1\": {\"effect\": 831.0202551035051, \"value\": 37.334999084472656}, \"2\": {\"effect\": -245.07367748139188, \"value\": 0.0}, \"3\": {\"effect\": 882.8649841997568, \"value\": 0.0}, \"4\": {\"effect\": -84.0744661881798, \"value\": 1.0}, \"5\": {\"effect\": -33.30702664613415, \"value\": 0.0}, \"6\": {\"effect\": -18.662350002961063, \"value\": 0.0}, \"7\": {\"effect\": -314.0905376650926, \"value\": 0.0}, \"8\": {\"effect\": -18.45188449596271, \"value\": 0.0}, \"9\": {\"effect\": -5831.206520644553, \"value\": 0.0}, \"10\": {\"effect\": 777.2117505892569, \"value\": 1.0}, \"11\": {\"effect\": -81.1812065995856, \"value\": 0.0}, \"12\": {\"effect\": -206.55677576825022, \"value\": 0.0}, \"13\": {\"effect\": 3.803055345134416, \"value\": 0.0}}, \"plot_cmap\": \"RdBu\", \"labelMargin\": 20}),\n",
       "    document.getElementById('iQVG916C2YTBSZDQL7JV9')\n",
       "  );\n",
       "</script>"
      ],
      "text/plain": [
       "<shap.plots._force.AdditiveForceVisualizer at 0x1bc31168848>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interpret_model(dt, plot = 'reason', observation=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_9639c_\" ><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_9639c_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_9639c_row0_col0\" class=\"data row0 col0\" >Gradient Boosting Regressor</td>\n",
       "                        <td id=\"T_9639c_row0_col1\" class=\"data row0 col1\" >2386.2018</td>\n",
       "                        <td id=\"T_9639c_row0_col2\" class=\"data row0 col2\" >17296249.1379</td>\n",
       "                        <td id=\"T_9639c_row0_col3\" class=\"data row0 col3\" >4158.8759</td>\n",
       "                        <td id=\"T_9639c_row0_col4\" class=\"data row0 col4\" >0.8789</td>\n",
       "                        <td id=\"T_9639c_row0_col5\" class=\"data row0 col5\" >0.3985</td>\n",
       "                        <td id=\"T_9639c_row0_col6\" class=\"data row0 col6\" >0.2922</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1bc31287888>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# predict on holdout / test set\n",
    "pred_holdout = predict_model(best);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    .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>age</th>\n",
       "      <th>bmi</th>\n",
       "      <th>sex_female</th>\n",
       "      <th>children_0</th>\n",
       "      <th>children_1</th>\n",
       "      <th>children_2</th>\n",
       "      <th>children_3</th>\n",
       "      <th>children_4</th>\n",
       "      <th>children_5</th>\n",
       "      <th>smoker_yes</th>\n",
       "      <th>region_northeast</th>\n",
       "      <th>region_northwest</th>\n",
       "      <th>region_southeast</th>\n",
       "      <th>region_southwest</th>\n",
       "      <th>charges</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>49.0</td>\n",
       "      <td>42.680000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9800.888672</td>\n",
       "      <td>10621.483595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>32.0</td>\n",
       "      <td>37.334999</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4667.607422</td>\n",
       "      <td>7290.151941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>27.0</td>\n",
       "      <td>31.400000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>34838.871094</td>\n",
       "      <td>36012.959871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
       "      <td>24.129999</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5125.215820</td>\n",
       "      <td>7553.788882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60.0</td>\n",
       "      <td>25.740000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12142.578125</td>\n",
       "      <td>14904.032497</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age        bmi  sex_female  children_0  children_1  children_2  \\\n",
       "0  49.0  42.680000         1.0         0.0         0.0         1.0   \n",
       "1  32.0  37.334999         0.0         0.0         1.0         0.0   \n",
       "2  27.0  31.400000         1.0         1.0         0.0         0.0   \n",
       "3  35.0  24.129999         0.0         0.0         1.0         0.0   \n",
       "4  60.0  25.740000         0.0         1.0         0.0         0.0   \n",
       "\n",
       "   children_3  children_4  children_5  smoker_yes  region_northeast  \\\n",
       "0         0.0         0.0         0.0         0.0               0.0   \n",
       "1         0.0         0.0         0.0         0.0               1.0   \n",
       "2         0.0         0.0         0.0         1.0               0.0   \n",
       "3         0.0         0.0         0.0         0.0               0.0   \n",
       "4         0.0         0.0         0.0         0.0               0.0   \n",
       "\n",
       "   region_northwest  region_southeast  region_southwest       charges  \\\n",
       "0               0.0               1.0               0.0   9800.888672   \n",
       "1               0.0               0.0               0.0   4667.607422   \n",
       "2               0.0               0.0               1.0  34838.871094   \n",
       "3               1.0               0.0               0.0   5125.215820   \n",
       "4               0.0               1.0               0.0  12142.578125   \n",
       "\n",
       "          Label  \n",
       "0  10621.483595  \n",
       "1   7290.151941  \n",
       "2  36012.959871  \n",
       "3   7553.788882  \n",
       "4  14904.032497  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_holdout.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>bmi</th>\n",
       "      <th>children</th>\n",
       "      <th>smoker</th>\n",
       "      <th>region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>female</td>\n",
       "      <td>27.900</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>southwest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>male</td>\n",
       "      <td>33.770</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>male</td>\n",
       "      <td>33.000</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>male</td>\n",
       "      <td>22.705</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32</td>\n",
       "      <td>male</td>\n",
       "      <td>28.880</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age     sex     bmi  children smoker     region\n",
       "0   19  female  27.900         0    yes  southwest\n",
       "1   18    male  33.770         1     no  southeast\n",
       "2   28    male  33.000         3     no  southeast\n",
       "3   33    male  22.705         0     no  northwest\n",
       "4   32    male  28.880         0     no  northwest"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# predict on new data\n",
    "data2 = data.copy()\n",
    "data2.drop('charges', axis=1, inplace=True)\n",
    "data2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# finalize model\n",
    "best_final = finalize_model(best)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>bmi</th>\n",
       "      <th>children</th>\n",
       "      <th>smoker</th>\n",
       "      <th>region</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>female</td>\n",
       "      <td>27.900</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>southwest</td>\n",
       "      <td>18894.260073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>male</td>\n",
       "      <td>33.770</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>3698.287534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>male</td>\n",
       "      <td>33.000</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>6029.271578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>male</td>\n",
       "      <td>22.705</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>8958.189116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32</td>\n",
       "      <td>male</td>\n",
       "      <td>28.880</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>3900.039002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age     sex     bmi  children smoker     region         Label\n",
       "0   19  female  27.900         0    yes  southwest  18894.260073\n",
       "1   18    male  33.770         1     no  southeast   3698.287534\n",
       "2   28    male  33.000         3     no  southeast   6029.271578\n",
       "3   33    male  22.705         0     no  northwest   8958.189116\n",
       "4   32    male  28.880         0     no  northwest   3900.039002"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# predict on data2\n",
    "predictions = predict_model(best_final, data=data2)\n",
    "predictions.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 👉 Save / Load / Deploy Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transformation Pipeline and Model Succesfully Saved\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(Pipeline(memory=None,\n",
       "          steps=[('dtypes',\n",
       "                  DataTypes_Auto_infer(categorical_features=[],\n",
       "                                       display_types=False, features_todrop=[],\n",
       "                                       id_columns=[], ml_usecase='regression',\n",
       "                                       numerical_features=[], target='charges',\n",
       "                                       time_features=[])),\n",
       "                 ('imputer',\n",
       "                  Simple_Imputer(categorical_strategy='not_available',\n",
       "                                 fill_value_categorical=None,\n",
       "                                 fill_value_numerical=None,\n",
       "                                 numeric_strateg...\n",
       "                                            learning_rate=0.1, loss='ls',\n",
       "                                            max_depth=3, max_features=None,\n",
       "                                            max_leaf_nodes=None,\n",
       "                                            min_impurity_decrease=0.0,\n",
       "                                            min_impurity_split=None,\n",
       "                                            min_samples_leaf=1,\n",
       "                                            min_samples_split=2,\n",
       "                                            min_weight_fraction_leaf=0.0,\n",
       "                                            n_estimators=100,\n",
       "                                            n_iter_no_change=None,\n",
       "                                            presort='deprecated',\n",
       "                                            random_state=123, subsample=1.0,\n",
       "                                            tol=0.0001, validation_fraction=0.1,\n",
       "                                            verbose=0, warm_start=False)]],\n",
       "          verbose=False), 'insurance-pipeline.pkl')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "save_model(best_final, 'insurance-pipeline')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transformation Pipeline and Model Successfully Loaded\n"
     ]
    }
   ],
   "source": [
    "loaded_pipeline = load_model('insurance-pipeline')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(memory=None,\n",
      "         steps=[('dtypes',\n",
      "                 DataTypes_Auto_infer(categorical_features=[],\n",
      "                                      display_types=False, features_todrop=[],\n",
      "                                      id_columns=[], ml_usecase='regression',\n",
      "                                      numerical_features=[], target='charges',\n",
      "                                      time_features=[])),\n",
      "                ('imputer',\n",
      "                 Simple_Imputer(categorical_strategy='not_available',\n",
      "                                fill_value_categorical=None,\n",
      "                                fill_value_numerical=None,\n",
      "                                numeric_strateg...\n",
      "                                           learning_rate=0.1, loss='ls',\n",
      "                                           max_depth=3, max_features=None,\n",
      "                                           max_leaf_nodes=None,\n",
      "                                           min_impurity_decrease=0.0,\n",
      "                                           min_impurity_split=None,\n",
      "                                           min_samples_leaf=1,\n",
      "                                           min_samples_split=2,\n",
      "                                           min_weight_fraction_leaf=0.0,\n",
      "                                           n_estimators=100,\n",
      "                                           n_iter_no_change=None,\n",
      "                                           presort='deprecated',\n",
      "                                           random_state=123, subsample=1.0,\n",
      "                                           tol=0.0001, validation_fraction=0.1,\n",
      "                                           verbose=0, warm_start=False)]],\n",
      "         verbose=False)\n"
     ]
    }
   ],
   "source": [
    "print(loaded_pipeline)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Succesfully Deployed on AWS S3\n"
     ]
    }
   ],
   "source": [
    "# deploy model on AWS S3\n",
    "deploy_model(best_final, 'insurance-pipeline-aws', platform = 'aws',\n",
    "             authentication = {'bucket' : 'pycaret-test'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## THE END"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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