Boruta_dataset2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7a012275",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "aa169796",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(40964, 30)\n"
     ]
    }
   ],
   "source": [
    "dataset=pd.read_csv('Dataset 2.csv')\n",
    "## print shape of dataset with rows and columns\n",
    "print(dataset.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dcc2138a",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>COIL_ID</th>\n",
       "      <th>GRADE</th>\n",
       "      <th>COIL_GEN_TIME</th>\n",
       "      <th>FURN_EXIT_TEMP</th>\n",
       "      <th>FM_DOUT_TEMP</th>\n",
       "      <th>COILING_TEMP</th>\n",
       "      <th>SLAB_WID</th>\n",
       "      <th>SLAB_LEN</th>\n",
       "      <th>STRIP_EXIT_LEN</th>\n",
       "      <th>STRIP_EXIT_THIK</th>\n",
       "      <th>...</th>\n",
       "      <th>N</th>\n",
       "      <th>NI</th>\n",
       "      <th>P</th>\n",
       "      <th>S</th>\n",
       "      <th>SI</th>\n",
       "      <th>TI</th>\n",
       "      <th>V</th>\n",
       "      <th>UTS</th>\n",
       "      <th>YS</th>\n",
       "      <th>P_ELONGATION</th>\n",
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       "      <th>0</th>\n",
       "      <td>20081459</td>\n",
       "      <td>JVHGLD4AS0</td>\n",
       "      <td>8/10/2020 2:25</td>\n",
       "      <td>1081</td>\n",
       "      <td>869</td>\n",
       "      <td>680</td>\n",
       "      <td>1125</td>\n",
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       "      <td>69.81</td>\n",
       "      <td>34</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.018</td>\n",
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       "      <td>42</td>\n",
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       "      <th>1</th>\n",
       "      <td>20097626</td>\n",
       "      <td>JVHGLD4AS0</td>\n",
       "      <td>9/14/2020 2:30</td>\n",
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       "      <td>838</td>\n",
       "      <td>662</td>\n",
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       "      <td>10640.0</td>\n",
       "      <td>68.63</td>\n",
       "      <td>34</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0022</td>\n",
       "      <td>0.007</td>\n",
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       "      <th>2</th>\n",
       "      <td>20097722</td>\n",
       "      <td>JVHCM30A00</td>\n",
       "      <td>9/14/2020 6:47</td>\n",
       "      <td>1075</td>\n",
       "      <td>882</td>\n",
       "      <td>611</td>\n",
       "      <td>1040</td>\n",
       "      <td>10750.0</td>\n",
       "      <td>66.08</td>\n",
       "      <td>36</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0070</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.0030</td>\n",
       "      <td>0.164</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.001</td>\n",
       "      <td>730</td>\n",
       "      <td>575</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20098318</td>\n",
       "      <td>JVHST01C00</td>\n",
       "      <td>9/15/2020 11:55</td>\n",
       "      <td>1088</td>\n",
       "      <td>864</td>\n",
       "      <td>603</td>\n",
       "      <td>1260</td>\n",
       "      <td>9000.0</td>\n",
       "      <td>51.94</td>\n",
       "      <td>38</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0035</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.021</td>\n",
       "      <td>0.0131</td>\n",
       "      <td>0.099</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.001</td>\n",
       "      <td>495</td>\n",
       "      <td>397</td>\n",
       "      <td>30</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20098589</td>\n",
       "      <td>JVHWT02A00</td>\n",
       "      <td>9/16/2020 1:14</td>\n",
       "      <td>1061</td>\n",
       "      <td>848</td>\n",
       "      <td>650</td>\n",
       "      <td>1265</td>\n",
       "      <td>9811.0</td>\n",
       "      <td>61.23</td>\n",
       "      <td>35</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0032</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.0101</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>379</td>\n",
       "      <td>326</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    COIL_ID       GRADE    COIL_GEN_TIME  FURN_EXIT_TEMP  FM_DOUT_TEMP  \\\n",
       "0  20081459  JVHGLD4AS0   8/10/2020 2:25            1081           869   \n",
       "1  20097626  JVHGLD4AS0   9/14/2020 2:30            1070           838   \n",
       "2  20097722  JVHCM30A00   9/14/2020 6:47            1075           882   \n",
       "3  20098318  JVHST01C00  9/15/2020 11:55            1088           864   \n",
       "4  20098589  JVHWT02A00   9/16/2020 1:14            1061           848   \n",
       "\n",
       "   COILING_TEMP  SLAB_WID  SLAB_LEN  STRIP_EXIT_LEN  STRIP_EXIT_THIK  ...  \\\n",
       "0           680      1125   10750.0           69.81               34  ...   \n",
       "1           662      1245   10640.0           68.63               34  ...   \n",
       "2           611      1040   10750.0           66.08               36  ...   \n",
       "3           603      1260    9000.0           51.94               38  ...   \n",
       "4           650      1265    9811.0           61.23               35  ...   \n",
       "\n",
       "        N     NI      P       S     SI     TI      V  UTS   YS  P_ELONGATION  \n",
       "0  0.0025  0.007  0.018  0.0100  0.004  0.001  0.001  346  294            42  \n",
       "1  0.0022  0.007  0.014  0.0100  0.007  0.001  0.001  365  299            42  \n",
       "2  0.0070  0.008  0.015  0.0030  0.164  0.002  0.001  730  575            21  \n",
       "3  0.0035  0.008  0.021  0.0131  0.099  0.002  0.001  495  397            30  \n",
       "4  0.0032  0.007  0.015  0.0101  0.006  0.001  0.001  379  326            41  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## print the top5 records\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9fec6bb2",
   "metadata": {},
   "outputs": [
    {
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       "      <td>False</td>\n",
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      ],
      "text/plain": [
       "       COIL_ID  GRADE  COIL_GEN_TIME  FURN_EXIT_TEMP  FM_DOUT_TEMP  \\\n",
       "0        False  False          False           False         False   \n",
       "1        False  False          False           False         False   \n",
       "2        False  False          False           False         False   \n",
       "3        False  False          False           False         False   \n",
       "4        False  False          False           False         False   \n",
       "...        ...    ...            ...             ...           ...   \n",
       "40959    False  False          False           False         False   \n",
       "40960    False  False          False           False         False   \n",
       "40961    False  False          False           False         False   \n",
       "40962    False  False          False           False         False   \n",
       "40963    False  False          False           False         False   \n",
       "\n",
       "       COILING_TEMP  SLAB_WID  SLAB_LEN  STRIP_EXIT_LEN  STRIP_EXIT_THIK  ...  \\\n",
       "0             False     False     False           False            False  ...   \n",
       "1             False     False     False           False            False  ...   \n",
       "2             False     False     False           False            False  ...   \n",
       "3             False     False     False           False            False  ...   \n",
       "4             False     False     False           False            False  ...   \n",
       "...             ...       ...       ...             ...              ...  ...   \n",
       "40959         False     False     False           False            False  ...   \n",
       "40960         False     False     False           False            False  ...   \n",
       "40961         False     False     False           False            False  ...   \n",
       "40962         False     False     False           False            False  ...   \n",
       "40963         False     False     False           False            False  ...   \n",
       "\n",
       "           N     NI      P      S     SI     TI      V    UTS     YS  \\\n",
       "0      False  False  False  False  False  False  False  False  False   \n",
       "1      False  False  False  False  False  False  False  False  False   \n",
       "2      False  False  False  False  False  False  False  False  False   \n",
       "3      False  False  False  False  False  False  False  False  False   \n",
       "4      False  False  False  False  False  False  False  False  False   \n",
       "...      ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
       "40959  False  False  False  False  False  False  False  False  False   \n",
       "40960  False  False  False  False  False  False  False  False  False   \n",
       "40961  False  False  False  False  False  False  False  False  False   \n",
       "40962  False  False  False  False  False  False  False  False  False   \n",
       "40963  False  False  False  False  False  False  False  False  False   \n",
       "\n",
       "       P_ELONGATION  \n",
       "0             False  \n",
       "1             False  \n",
       "2             False  \n",
       "3             False  \n",
       "4             False  \n",
       "...             ...  \n",
       "40959         False  \n",
       "40960         False  \n",
       "40961         False  \n",
       "40962         False  \n",
       "40963         False  \n",
       "\n",
       "[40964 rows x 30 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.isna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ddc5eb65",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>COIL_ID</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GRADE</th>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>YS</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UTS</th>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>V</th>\n",
       "      <td>0.0</td>\n",
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       "      <th>TI</th>\n",
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       "      <th>P</th>\n",
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       "      <th>NI</th>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>N</th>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>MO</th>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>MN</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CU</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CR</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NB</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FM_EXIT_THIK</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STRIP_EXIT_THIK</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STRIP_EXIT_LEN</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SLAB_LEN</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SLAB_WID</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COILING_TEMP</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FM_DOUT_TEMP</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FURN_EXIT_TEMP</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COIL_GEN_TIME</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P_ELONGATION</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   0\n",
       "COIL_ID          0.0\n",
       "GRADE            0.0\n",
       "YS               0.0\n",
       "UTS              0.0\n",
       "V                0.0\n",
       "TI               0.0\n",
       "SI               0.0\n",
       "S                0.0\n",
       "P                0.0\n",
       "NI               0.0\n",
       "N                0.0\n",
       "MO               0.0\n",
       "MN               0.0\n",
       "CU               0.0\n",
       "CR               0.0\n",
       "NB               0.0\n",
       "CA               0.0\n",
       "C                0.0\n",
       "B                0.0\n",
       "AL               0.0\n",
       "FM_EXIT_THIK     0.0\n",
       "STRIP_EXIT_THIK  0.0\n",
       "STRIP_EXIT_LEN   0.0\n",
       "SLAB_LEN         0.0\n",
       "SLAB_WID         0.0\n",
       "COILING_TEMP     0.0\n",
       "FM_DOUT_TEMP     0.0\n",
       "FURN_EXIT_TEMP   0.0\n",
       "COIL_GEN_TIME    0.0\n",
       "P_ELONGATION     0.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame((dataset.isna().sum()/dataset.isna().count()).sort_values(ascending=False)).head(100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db79c524",
   "metadata": {},
   "source": [
    "# Numerical Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9ae693e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of numerical variables:  28\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>COIL_ID</th>\n",
       "      <th>FURN_EXIT_TEMP</th>\n",
       "      <th>FM_DOUT_TEMP</th>\n",
       "      <th>COILING_TEMP</th>\n",
       "      <th>SLAB_WID</th>\n",
       "      <th>SLAB_LEN</th>\n",
       "      <th>STRIP_EXIT_LEN</th>\n",
       "      <th>STRIP_EXIT_THIK</th>\n",
       "      <th>FM_EXIT_THIK</th>\n",
       "      <th>AL</th>\n",
       "      <th>...</th>\n",
       "      <th>N</th>\n",
       "      <th>NI</th>\n",
       "      <th>P</th>\n",
       "      <th>S</th>\n",
       "      <th>SI</th>\n",
       "      <th>TI</th>\n",
       "      <th>V</th>\n",
       "      <th>UTS</th>\n",
       "      <th>YS</th>\n",
       "      <th>P_ELONGATION</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20081459</td>\n",
       "      <td>1081</td>\n",
       "      <td>869</td>\n",
       "      <td>680</td>\n",
       "      <td>1125</td>\n",
       "      <td>10750.0</td>\n",
       "      <td>69.81</td>\n",
       "      <td>34</td>\n",
       "      <td>1.99</td>\n",
       "      <td>0.048</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0025</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.0100</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>346</td>\n",
       "      <td>294</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20097626</td>\n",
       "      <td>1070</td>\n",
       "      <td>838</td>\n",
       "      <td>662</td>\n",
       "      <td>1245</td>\n",
       "      <td>10640.0</td>\n",
       "      <td>68.63</td>\n",
       "      <td>34</td>\n",
       "      <td>2.05</td>\n",
       "      <td>0.051</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0022</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.0100</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>365</td>\n",
       "      <td>299</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20097722</td>\n",
       "      <td>1075</td>\n",
       "      <td>882</td>\n",
       "      <td>611</td>\n",
       "      <td>1040</td>\n",
       "      <td>10750.0</td>\n",
       "      <td>66.08</td>\n",
       "      <td>36</td>\n",
       "      <td>2.49</td>\n",
       "      <td>0.032</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0070</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.0030</td>\n",
       "      <td>0.164</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.001</td>\n",
       "      <td>730</td>\n",
       "      <td>575</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20098318</td>\n",
       "      <td>1088</td>\n",
       "      <td>864</td>\n",
       "      <td>603</td>\n",
       "      <td>1260</td>\n",
       "      <td>9000.0</td>\n",
       "      <td>51.94</td>\n",
       "      <td>38</td>\n",
       "      <td>4.99</td>\n",
       "      <td>0.053</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0035</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.021</td>\n",
       "      <td>0.0131</td>\n",
       "      <td>0.099</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.001</td>\n",
       "      <td>495</td>\n",
       "      <td>397</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20098589</td>\n",
       "      <td>1061</td>\n",
       "      <td>848</td>\n",
       "      <td>650</td>\n",
       "      <td>1265</td>\n",
       "      <td>9811.0</td>\n",
       "      <td>61.23</td>\n",
       "      <td>35</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.051</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0032</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.0101</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>379</td>\n",
       "      <td>326</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    COIL_ID  FURN_EXIT_TEMP  FM_DOUT_TEMP  COILING_TEMP  SLAB_WID  SLAB_LEN  \\\n",
       "0  20081459            1081           869           680      1125   10750.0   \n",
       "1  20097626            1070           838           662      1245   10640.0   \n",
       "2  20097722            1075           882           611      1040   10750.0   \n",
       "3  20098318            1088           864           603      1260    9000.0   \n",
       "4  20098589            1061           848           650      1265    9811.0   \n",
       "\n",
       "   STRIP_EXIT_LEN  STRIP_EXIT_THIK  FM_EXIT_THIK     AL  ...       N     NI  \\\n",
       "0           69.81               34          1.99  0.048  ...  0.0025  0.007   \n",
       "1           68.63               34          2.05  0.051  ...  0.0022  0.007   \n",
       "2           66.08               36          2.49  0.032  ...  0.0070  0.008   \n",
       "3           51.94               38          4.99  0.053  ...  0.0035  0.008   \n",
       "4           61.23               35          2.31  0.051  ...  0.0032  0.007   \n",
       "\n",
       "       P       S     SI     TI      V  UTS   YS  P_ELONGATION  \n",
       "0  0.018  0.0100  0.004  0.001  0.001  346  294            42  \n",
       "1  0.014  0.0100  0.007  0.001  0.001  365  299            42  \n",
       "2  0.015  0.0030  0.164  0.002  0.001  730  575            21  \n",
       "3  0.021  0.0131  0.099  0.002  0.001  495  397            30  \n",
       "4  0.015  0.0101  0.006  0.001  0.001  379  326            41  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# list of numerical variables\n",
    "numerical_features = [feature for feature in dataset.columns if dataset[feature].dtypes != 'O']\n",
    "\n",
    "print('Number of numerical variables: ', len(numerical_features))\n",
    "\n",
    "# visualise the numerical variables\n",
    "dataset[numerical_features].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb1ce12f",
   "metadata": {},
   "source": [
    "# Continuous Variable¶"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2a2033bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Discrete Variables Count: 0\n"
     ]
    }
   ],
   "source": [
    "## Numerical variables are usually of 2 type\n",
    "## 1. Continous variable and Discrete Variables\n",
    "\n",
    "discrete_feature=[feature for feature in numerical_features if len(dataset[feature].unique())<10 ]\n",
    "print(\"Discrete Variables Count: {}\".format(len(discrete_feature)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "121d85aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "discrete_feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "09638c07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Continuous feature Count 28\n"
     ]
    }
   ],
   "source": [
    "continuous_feature=[feature for feature in numerical_features if feature not in discrete_feature]\n",
    "print(\"Continuous feature Count {}\".format(len(continuous_feature)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d85c3f58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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fwX7xzL0t/772qvFlTszMLIsLiZmZZXEhMTOzLC4kZmaWxYXEzMyyuJCYmVkWFxIzM8viQmJmZllcSMzMLIsLiZmZZXEhMTOzLC4kZmaWpWOFRNI4SQ9I+tf0fKKkmyU9ku4nlPpeImmzpE2Sziq1z5K0Pi27VJI68VrMzMayTh6RXARsLD1fCqyJiBnAmvQcSccD84ETgLnA5ZLGpXWuAJYAM9Jtbnuim5nZgI4UEklTgHcAXyw1zwNWpscrgXNK7X0RsScitgCbgZMlTQLGR8TaiAjg6tI6ZmbWJip+B7d5p9J1wCeAVwN/FRFnS3omIo4o9Xk6IiZIugy4KyK+mtqvBG4EtgLLI+LM1H4a8NGIOLvG/pZQHLnQ3d09q6+vr66cu3fvpqurq/kX2kJVzVbVXOBszahqLmh/tvWPP1tXv+5DYcfzLQ4DzJx8eMPr5IzZnDlz1kVET61lbf9iK0lnAzsjYp2k3npWqdEWw7Tv3xixAlgB0NPTE7299ewW+vv7qbdvu1U1W1VzgbM1o6q5oP3Z6v2yqotn7uVT61v/q3Xrgt6G12nVmHXiGxJPBd4l6e3AIcB4SV8FdkiaFBHb07TVztR/GzC1tP4U4InUPqVGu5mZtVHbz5FExCURMSUiplGcRL8lIt4LrAYWpW6LgOvT49XAfEkHS5pOcVL9nojYDuySNDu9W2thaR0zM2uTKn1n+3JglaQLgEeB8wEiYoOkVcDDwF7gwojYl9Z5H3AVcCjFeZMb2x3azGys62ghiYh+oD89/gVwxhD9lgHLarTfB5zYuoRmZjYSf7LdzMyyuJCYmVkWFxIzM8viQmJmZllcSMzMLIsLiZmZZXEhMTOzLC4kZmaWxYXEzMyyuJCYmVkWFxIzM8viQmJmZllcSMzMLIsLiZmZZXEhMTOzLC4kZmaWpUrfkGhWCdOW3tBQ/63L39GiJGajg49IzMwsiwuJmZllcSExM7MsLiRmZpbFhcTMzLL4XVtmNiY1+u48G5qPSMzMLIsLiZmZZXEhMTOzLC4kZmaWxYXEzMyytL2QSJoq6VZJGyVtkHRRap8o6WZJj6T7CaV1LpG0WdImSWeV2mdJWp+WXSpJ7X49ZmZjXSeOSPYCF0fEccBs4EJJxwNLgTURMQNYk56Tls0HTgDmApdLGpe2dQWwBJiRbnPb+ULMzKwDhSQitkfE/enxLmAjMBmYB6xM3VYC56TH84C+iNgTEVuAzcDJkiYB4yNibUQEcHVpHTMzaxMVv4M7tHNpGnA7cCLwaEQcUVr2dERMkHQZcFdEfDW1XwncCGwFlkfEman9NOCjEXF2jf0soThyobu7e1ZfX19d+Xbv3k1XV1fTr6+VqpqtmVzrH3+2of4zJx/eUP8B9WZrV56yl9PPs11yszX6c65X96Gw4/mWbPolmvl3lzNmc+bMWRcRPbWWdeyT7ZK6gG8AH4yIXw5zeqPWghimff/GiBXACoCenp7o7e2tK2N/fz/19m23qmZrJtfiRr//Y0Fj2x9Qb7Z25Sl7Of082yU3W6M/53pdPHMvn1rf+l+tzfy7a9XPsyPv2pL0OxRF5GsR8c3UvCNNV5Hud6b2bcDU0upTgCdS+5Qa7WZm1kadeNeWgCuBjRHx6dKi1cCi9HgRcH2pfb6kgyVNpzipfk9EbAd2SZqdtrmwtI6ZmbVJJ6a2TgX+DFgv6cHU9jfAcmCVpAuAR4HzASJig6RVwMMU7/i6MCL2pfXeB1wFHEpx3uTGNr0GMzNL2l5IIuJ71D6/AXDGEOssA5bVaL+P4kS9mZl1iD/ZbmZmWVxIzMwsiwuJmZllcSExM7MsLiRmZpbFhcTMzLK4kJiZWRYXEjMzy+JCYmZmWVxIzMwsS8cuI2/WrGmNXuZ9+TtalMTMwEckZmaWyYXEzMyyuJCYmVkWnyMxs5bzea2XNx+RmJlZFhcSMzPL4qkts1HAU0NWZT4iMTOzLD4iMTMbhRo9SgW4au5hLUjiIxIzM8vkIxJ72Rv4y+3imXtZ3MRfcQdarb8kq5LNrBk+IjEzsyw+IjGzyqln/n/wUZzfqdY5LiRmmZo56WkHnn8OneOpLTMzy+Ijkga040Nh9e5j4LC+aofz6x9/1ieNzcYYFxKzl6EDNc0z1LvJqvYHjHWWC4mZNcznI6xs1BcSSXOBzwHjgC9GxPIOR/r//J/NzMaCUV1IJI0D/hH4D8A24F5JqyPi4c4max9fzM/MOm1UFxLgZGBzRPwEQFIfMA8YM4WkUa0+Srp4Zks3b2YVpIjodIamSToPmBsRf5Ge/xnwloh4/6B+S4Al6enrgU117uIo4OcHKO6BVtVsVc0FztaMquaC6marai7Iy/aaiDi61oLRfkSiGm37VcaIWAGsaHjj0n0R0dNMsFararaq5gJna0ZVc0F1s1U1F7Qu22j/QOI2YGrp+RTgiQ5lMTMbk0Z7IbkXmCFpuqRXAvOB1R3OZGY2pozqqa2I2Cvp/cB3KN7++6WI2HAAd9HwdFgbVTVbVXOBszWjqrmgutmqmgtalG1Un2w3M7POG+1TW2Zm1mEuJGZmlmXMFxJJF0l6SNIGSR9MbRMl3SzpkXQ/odT/EkmbJW2SdFabc/2tpMclPZhub29HLklfkrRT0kOltobHSNIsSevTsksl1Xr7dktySZom6fnS2H2hVbmGyXZ++nn+VlLPoP6dHLOauSoyZv9T0g8l/UDSv0g6orSsLWPWaLZ2jtsQuf4+ZXpQ0k2Sji0ta82YRcSYvQEnAg8Br6J448G/ATOAfwCWpj5LgU+mx8cD3wcOBqYDPwbGtTHX3wJ/VaN/S3MBpwNvAh4qtTU8RsA9wFspPv9zI/DHbcw1rdxv0HYOaK5hsh1H8YHYfqCnnp9fm8ZsqFxVGLP/CByUHn+yE//OmsjWtnEbItf40uO/BL7Q6jEb60ckxwF3RcSvImIvcBtwLsVlVlamPiuBc9LjeUBfROyJiC3AZorLtLQr11BamisibgeeqrHPusdI0iSKf+Bro/iXe3VpnXbkqqkVuYbKFhEbI6LWVRU6OmbD5KqpzWN2U/o/AHAXxWfFoI1j1kS2mtr48/xl6elhvPgh7ZaN2VgvJA8Bp0s6UtKrgLdTfMCxOyK2A6T7Y1L/ycBjpfW3pbZ25QJ4fzps/ZJenE5qV66yRsdocnrc6oxD5QKYLukBSbdJOq2Utx25htPpMRtOlcbsv1D8tTyQoUpjVs4GHR43ScskPQYsAP5Haf8tGbMxXUgiYiPFIenNwLcpDvv2DrNKXZdkaWGuK4DXAScB24FPtTNXnYbK0umM24HfjYg/AD4MXCNpfAVyMUyGTmerzJhJ+hjF/4GvDTQNkaEK2To+bhHxsYiYmjINXHuwZWM2pgsJQERcGRFviojTKQ4RHwF2pMO9gcPRnal72y7JUitXROyIiH0R8Vvgn3hx+qoTl4ppdIy28dJD/1ZlrJkrHc7/Ij1eRzE//PttzDWcTo9ZTVUZM0mLgLOBBWnqBSoyZrWyVWXckmuAP02PWzZmY76QSDom3f8u8CfAtRSXWVmUuiwCrk+PVwPzJR0saTrFCfB72pVr4Bdkci7FFFhbc5U0NEZpmmmXpNnpHSELS+u0PJeko1V8fw2SXpty/aSNuUbK3Mkxq6kKY6bii+s+CrwrIn5VWtTxMRsqW6fHTdKM0tN3AT9Mj1s3Zs2+W+DlcgO+S/H9Jd8HzkhtRwJrKI5O1gATS/0/RvEXxiYOwLtBGsz1FWA98IP0j2JSO3JRFNftwG8o/nq5oJkxAnooit+PgctIV1ZoRy6Kv8o2pPG8H3hnq3INk+3c9HgPsAP4TkXGrGauiozZZop5/QfT7QvtHrNGs7Vz3IbI9Y20jx8A3wImt3rMfIkUMzPLMuantszMLI8LiZmZZXEhMTOzLC4kZmaWxYXEzMyyuJCYmVkWFxKzOkn6mIrLrQ9covstkvo16JLwpf7nSgpJbyi1lS8x/n1Jd0p6/TD7fEDSSenxQZKek/Te0vJ1kt4kabGky1Jb+esGHpH0TUnHH7CBMBvEhcSsDpLeSnEpjDdFxL8HzuSlF8Cr5T3A94D5g9p/HBEnRcQbKa5Q/DfDbONO4JT0+I0UHyQ7JWU6DHgtxQffBvtM2scM4OvALZKOHiGvWVNcSMzqMwn4eUTsAYiIn0fEkNcjktQFnErxSePBhaRsPPD0MMvv4MVCcgrwBYqLdkJxrbX7I2LfcMEj4uvATcB/Hq6fWbNcSMzqcxMwVdKPJF0u6Q9H6H8O8O2I+BHwlKQ3lZa9Lk07/Zji6rCfHmY75SOSU4DbgT2SXp2e31Fn/vuBN4zYy6wJLiRmdYiI3cAsYAnwJPB1SYuHWeU9QF963JeeDxiY2nod8EFgxTD73Qq8UtK/oygEm4B7gbdQFJI763wJ2V83azaUgzodwGy0SFNI/UC/pPW8eJXhl5B0JPBHwImSAhgHhKSP1Oi+GvjyCLteC5wHbI+IkHQXxbTZyRTfzFePPwDuq7OvWUN8RGJWB0mvH3R57pOAnw7R/Tzg6oh4TURMi+ILhrYAb6vR920UV1wdzh3AhygKCul+IfCziHimjux/SvH94teO1NesGT4iMatPF/B5SUdQfBveZoppruuAGyT9JvVbCxwNLB+0/jcoTnZ/knSOhGK66QXgL0bY9x3AZ9K2iYjt6fsuhpvW+lB6m/BhFJcH/6OIeHLkl2nWOF9G3szMsnhqy8zMsnhqy6wCJJ1FMe1VtiUizu1EHrNGeGrLzMyyeGrLzMyyuJCYmVkWFxIzM8viQmJmZln+H1XZXTLfQcD0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  Lets analyse the continuous values by creating histograms to understand the distribution\n",
    "\n",
    "for feature in continuous_feature:\n",
    "    data=dataset.copy()\n",
    "    data[feature].hist(bins=25)\n",
    "    plt.xlabel(feature)\n",
    "    plt.ylabel(\"Count\")\n",
    "    plt.title(feature)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "47329f78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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qFoTjb4yzYO2jgb6gJERV3w36HtwaWi5WOr0a22EYHUFWSWFeoqJxwvo5BI1r1CQ1XavjyQa4RccmdMq/4XVEr3lkLwOffzxRJFPavhrvumD2VJOmggjXXtxvDm8ftsMwOoKopLBGTQre6KSwKCl/Pwc3Ssod7w07rVeusP4U040InN5d7Xx2LV1R+mZsUqciqkojo9yyaQ+3btozVV3X/3l6o6L8OxRvufWL5p/NDw6+OqWcJlTZsrvEsnNnA+l7k7QrFiVldARRkTsCmU8EUeawoeFSVdRSsSBsvC48Qsh/vQU/18OO545NTYCXnD+L518ZpTQyioiTNJcX3vammXx85dsiw2mzoK+nyPqrLwSoUqR+c5TLrN4ir49Npi4dH0YzwqrDyH0/jCwwhWHUwooN22LzDqImgkb3gyh2CWec3s3IiTFEgs04s3qLDH/uvYHXzuPOISkFEZ69+4pE30mjcT/3ehVVVLht2G8lyfeWZZ8QC6s1jIQkMc+EmajCwlOh2mw0NFyqyi8Impy8ppWwNduxE2OBk0/S8uF5ZUKVdUP7mq4soPJzr4ewgIKo30qS763RZtJGY05voyOotUAfJC+K504WUcloaQly1E/HRNtoHthxcLpFqML/u+gpFujrKQaODQsoiPqtJI1ay3PuhykMo2OopUAfJC+Kl8XKP2jyKUQlQhg1864LZk8tKNyKuuuvvjBRXS6XqN9K0qi1POd+mEnK6EiCTFRhE0FYPL//xm7WynBCNXWJi2JBGJ/QyAikvBHmiM6K518ZDfVLJPVfRf1WkphF8577YTsMoyPxm6jCejRA8uqvUSvDYkHo6ykiONE6xUL8LiFsI+HKGkeXMPXeNl63hA9e0lrdKutVFjMKQqEr+W4sTOF7d6ZPrL0s0r+wZtVCir7XLHbJlJLx/+Y+dMn8RL/BvGA7DKNjGRxIlpiVpPorhDvWZ/UWueOqC6uqxcaFlbr9I4J2QW5V2Ch/xgeWz5/K+7h1055qI32bc3JCHaVZDiuWsgINSyxsmCnI/zl7Hif9zeUVC6s1jAZSS/jtbQ/vDSxV0e+Jigq63rqhfZHO476eIm+MT7ZsRFXSKrlpKHYJM7q7OH6y8jNpVDhrWKhwHqreWlitYeSMtCtId2yYPyXqemEd71waGa3lleu07q5Mru1/nfcteQub/vlQQ687Nqm8qXcGv/+b4Yq4HrLqGpgXTGEYxjST1OTlp1mTkL+0xq0xBQjrwZt1v/GxZyqSHRvF4ZHRmkxDSXaPSQMkWhVTGIaRA/wT2NBwiRUbttU0OYGzQj+92FVTklqc+STOd1IPCrz86uvseuFoZgqxlsk7afJmmui7VsSipAwjZyStrBtWHbevp8jd1yzmjquqcwjicCc3V2EFVYVds2phoiivWplQ5YEdB+kp1jc9zZxRqIpYqnXyTpq8mSb6zkvU550nbIdhGDkjaWXdpKYsf5HCJ54N71fhhuv6V9O3bNrDnd9+ijuucor3xcW8dgmcdXqxLl/H6Phk6nwTL6+PTUZWq01DGt9EWnNXmtIz040pDMPIGUkmJ789/Z4blgZOLkGT14K1j4a+9uBAPys2bAucpI+dGOP2rfs4rbsr1rdQ6BLet+QtsT3Mo1B1FFhYFFkc3lLl3iCCWsjSN5FkgZB186+kmEnKMHJG2CTUJcLQcKnuZlBh9ZHc56N8B6NjE4l2DWMTyvb9R6bMM7VQEGFwoJ8vXb8ktWnNT5D5KA1JkzdrIW6B0IzmX0kxhWEYOSPMNzGhyu1b93Hnt59KZE8PY/3VFwZmI7u9IhrZItbNkn5+w5Wp8wZXL58HVPsFZvUWq+RPKk+t1OqbSELY5+0+n9R/0gzMJGVMC1E9A/Kw9Z5O3PcbZIoZHZsINfEknRCDOgOOTerUBBRX8yioqVAQ/okwrue5lxUXzOauwVPlT4KiyMIaEXWF9OyuVxFmlaUdF1mVp9wOUxhG0wlz8u164WiFzTvPzr+sGRzoT53vkHZCPHFyHKjsmX371n3cfc1ip1Krr68HOBOZ6/iOKm3iN9cMDZc4/sZ4YtmeePYoA59/vKqkikvU5B3UqCjPoa1xwQt5yu3ItDSIiPQB9wNvx4mr+B1V/X7AuF8CdgA3qOrmqGtaaZDWJ6x8QiFkZRiWF9BOu5GwRklBn1NQyY80pS3iOr95P+91Q/sqel+vXj6vYuXvyh3WwzxJzawoai3Z0W6/jSAFmPZzaYXSIF8Gvquq14nIDKDXP0BECsAfAI9lLIuRE8K20mGRMEHjWykUMY6w93Ltxf1VUUY9xcKUryHJhFhLx76Sx9m6ZXepItpoy+4Sy86dPfVaaVf6XlzFFNVvvdYOdK1e5M9LrZUAsiAzhSEiZwHvBj4MoKongZMBQ28GtgC/lJUsRr4I22KH7TCCtt5JcxXyRtIJfHRsgod2HmL18nlTtnn/RBFX1NBvUnIVUZzvwW3QFOdsTZL/EfVa7kIgzrfRLnWY6iEvCjDLHcb5wBHg6yKyBNgNfEJVj7sDRKQf+E3gMkxhdAxhTr6wFXWQ7TlPjkAvUaaQsJ1E2KTqrujTmB7iTEBux76ovIYJVYaGS6GTuF/usN1d3HfRJcK6oX2xvo16bPXtZJrKA1kqjG7gIuBmVd0pIl8G1gL/zTPmXuAzqjohEW0nReRG4EaA+fNbqwmMUU3cFttrM7/24uCVVZ4cgS5xZrKwFXvUBD46NsFtD+/llk17YjOW40xALnEd+/p6ilNyh5Fkdxe3c3BLgEThLVWSduJvJ7NlXsjM6S0iPw/sUNUF5ce/AqxV1Ss9Yw5wqr3IOcAJ4EZVHQq7rjm925c0zr1GOQLdazViFRrlzJ/U6PaotbRc3Xjdkgo5w17fj6twgnYi9RQtFODABuf2dr6fJxkdm0x9Hffzcr8LqC7/7r6eet6P/zvLc2+K6aARTu/MEvdU9WXgkIi49oTLgR/5xpynqgvKSmUz8J+jlIXR3qRJUGpUIlXaLNqoInFRzvwoZeHKXojYZfsZm1Du/PZTFc8lNccdLteG6p3RHdgidKTGiCagIhO9FmUBzud1zw1LeWLtZex64Si3bNoTqEzdz7Q0MsqazXurvrO8mi1bmayjpG4GHixHSD0HfEREbgJQ1fsyfm2jxUh7gzfCEZjGeR5n4kiTmOZl5aI5oY2Uojh2Yoyh4VJsvL4f70Qb5COptXy5Ams272XmjO66u/yteWQvn968l5MJu+2NTSiffHgP4HwX64b2hSrpdulNMR1Yi1YjN1nXaU0IcfIlkT8qpHNWb5GRE2OxeRGuSWTNI3uoZVHd11Nkzx3vBSrzHpLi9gyHdArHxf/5JvWF5JGeYoGL5p8dWpG3Ua1YW5FGmKRMYXQ4Yb6AsIilLG+2OL+EVwH09RZ57fXxiqqp/rH+axULwswZ3bw6Gq8E/BQLEtlbup4y3ADPb7iyronafe8At27aE1d9PJCCCJecP4vnXxmtSMTrEsig8d20cG9IVd9OINc+DKM1iMoBaHbBsyi/xNBwiU8+vGfK13DsxFhViW1/joBf/rEJZWR0rMJXsXLRnESVUKOUhfva9bBuaB+3Pby35ut4zWhnh1SjjWNClSeePTqlQN1oqg8sn08Ntf5ySacqi0ZhtaTanDizTCOyrhtJkF9iaLjELQnrKpVGRhkaLiWSc3RsYqoEd9LrZ0VceGkSSiOjkb0uamF0bKIhsuWBNEEFRjCmMNqYJHHo9WRdBykjaGwJA/c9pOH2rfs4uydZt7fSyGjqIn9Ga+KWS/djyX3JSa0wRGQWMKKt5vzoQJKUdiiNjE7Fs7skyboOUkZrNu8FZcpUFJUolfQmjSsvEcTo2ASnF7sS+xXsh5x/VlwwO7K1bBK27z9SEVUGltyXlkgfhoh8TkQWlf9/mohsB54F/k1E/kMzBDRqJ8ws494U7s5COZU96foN7hpcHJnnEOYjiPIruCTJfXDzHWoJ7wQYOTHG3dcsTt20pxVwTSudYmKZ1VvkBwdfrfs6Qb+zPDUnagXidhg3AF8o//8/lv+dA/wC8BfA/8hILqMBRJmb/DeJmzHrDa8My3OIqjMUxOGyX8HdUQQ1uPE6bRsR1jm3r4fBgf5p901kwYRq3VFVrUJPsYBq/UEFLv4cG0vuS0dclNRJj+lpFfDXqjqhqk9j/o/cE9aHuB6Hdi0+hbPLtYncHUXc6yc1Q73tTTOZ1VsdEZTnZjmNYt3QvtAM6Hbi7msW82oCX1QavL/zuPaoRiVxCuMNEXm7iMwBVgKPe45V9bYw8kVYmGp/HTdJ1GReLEhVr+WeYgGRZCtE9/WTru7+7pPvYfhz7+XeG5aGms6CFEoS+vt6Qj+nPNAukUtRFESmMugbifd6YYuqdl9w1ErcLuETODWe5gD3qOoBABG5AhjOWDajAYSZlWptYRllitp43RLnX58zO0kUkvf1k5S48CqCqBIhd1x1IWs2763Ko+jrKXLh3DP5p2ePVjm9vbK0u8knz1xy/iwgvsd4GCJwench8neep+ZErUCcwuhX1UX+J1X1O8B3shHJyJp6bpKwcFt3Nei9vktYNrW/Kql7XtwEUSzIVCmMOMLeKzjKwP9O3DIbQbWV4vpIGI3l+Vec30zQd7jg53rY8dyxyO/jg8vns+zc2bG/87w0J2oF4hTGOmBrMwQxmkutN0nYDRp144Y1TAorM+KfIM7uKSJCRV2nNLIHvdcVG7YFKqTeGd0VY8POrTV6y0hOlGny/cvm8+DvXlrRU9zF33vclEHjMMe1kYqoHUYYtexoGrHqi8r1qCc6phYTSU+xwGndXYmSCQ0H19cQliux64WjTa931unEKYxFIvJkwPMCqKq+IwOZjBxTyw4Dmr/tr7UUeRIHq/s+bnt4b6jydDsGuh3yVi6aw9/ufamet9RRFAvC8TfGOW/to6Fh2EGOf7dD4a2b9sQuTCzDOz1xCuMAcFUzBDFag/6QiTZvEUVxfS7WrFpY5QwvFiRxdMzgQH+oM3+ynCfhMjRcYs0je6uSGo1gusqlB9zdWFq/kTverT6w/ltPVVQoDsr1sQzvZMQpjDdU9YWmSGK0BGH+iLyFISYyOfnnoZTzeZJdytBwiVsf3oP5ypNRLAjjE8pYgz4wt0IxVCqFNI2zXGxHEq8wfkFEPul5rMBPgf/phtganUWrhCHGTeYbH3umasU/NqmRE4Yfp2lS9c7hpVedqrFSXimbrohHYKrHSZafl6sU0vqwbEfiEKcwNgJn+p5bAHxWRNar6l9nIpWRa1ohDDFoJ+S1i4dNSq6S8UbfeH0RXpPGnd9+KtDM5D5lu4rkKPCz0fGmhC27C500PqxadiTtSKTCUNU7g54Xkdk4daRMYRi5xL8TclevSaKU1g3tY9O/HJryb3ht4mHROUb9NCvHxd0VpzGtWs0ph5rCalX1qEiHlMo06sLbo9ofHx9EI+3E3p3Qig3bOHYiWUhrVNkNtxuhJfBNH27CZ9Q34DVx+dv4en9TSX9rYTuSLpGqkuntTE0KQ0QuA441WBajzVg3tK9i8p1QnXocpDTS2omTKpe01XXjMGUxvUyoIjilP4K+Ctd31Dujmyvf8Ra27z9S9RtJuzAJy72ZUA2NxGpHJKoPkojso9pnNxs4DPy2qu7PULZAli1bprt27Wr2yxo1cMHt3wnNU3j27iuqng/LoPaXXYdq5QLBSVudUALcCCfpbyJJwt/QcCk09ybuNfOAiOxW1WX1XCOuWu37cPIw3L/3AQtV9Z3ToSyM1iJtkl8aO3GYE/KWTXtYsPZRVmzYNrWKNGXRuQQ1Q6q1adLgQH+i3WU7N2CKc3pbDobRFIaGS4EZvRAcuRLnbCyNjLZl8yQjPaURJ8w5rnhkEgd20gKU7eoMt1pSxrRw3tpHKyrHrtkcvNXvEgIjV5KUQDcML3ETfZKyMEn9V+3agClThSEifcD9wNtxfCG/o6rf9xz/IPCZ8sPXgP9LVfdmKZPRPMLKiABTvbxv2bSHso8ykEmFXS8crbJBnzg53nB5jc6lWBBWLprDig3bIh3hUb9pF394bjtliEc6veu+uMhfAN9T1ftFZAbQq6ojnuPvAp5W1WMi8uvAelVdHnVNc3q3Do1yOHud5ObENrKiC5j0PC52CWec3s2xE2NTpqhZAaG6xYIwc0Z3YJRUrQ72LGiE0zuzHYaInAW8G/gwgKqeBE56x6jqP3ke7gDempU8RuPw5la49AfcKK5zMWoHkQTv65gT28iKSd/jsUmdyt1xf4PuYzek15/9v/GxZ7h10x42PvYMa1YtbLsM8cx2GCKyFPga8CNgCbAb+ISqHg8Z/ylgkap+LOq6tsOYXvy5FUH09RQ5fnK8qi1qPfT39XB4ZNTqMhm5oVgQNl63JLD6LTg7ibDFjQAHPBWNm0EzwmrroRu4CPiqqg4Ax4G1QQNFZCXwUU75M/zHbxSRXSKy68iRI1nJ29IMDZdYsWEb53lCSrPgoZ2HYseMjI41VFmA4+8wZWHkibEJ5c5vPwWEh+qG0apO8Syd3i8CL6rqzvLjzQQoDBF5B45j/NdV9ZWgC6nq13B2KyxbtszmDR9pMqTTOOCCxlqWs2GcwjVRpQmjzWM7gKRktsNQ1ZeBQyLifjKX45inphCR+Tg9w/9PVf1fWcnS7iRNRHIVi7tadxVL0G4kbKxVEDOMapLuGAoiucwCT0qWJimAm4EHy21elwJfFJGbROSm8vHPAT8H/JmI7BERc07UQJIMabesQVLFEjbWNhiGcQqh3FFx1UJ6ioXY8ZOqLassIGOFoap7VHWZqr5DVQdV9Ziq3qeq95WPf0xVZ6nq0vJfXQ6ZTiVsdeM+7+4WkpTkcNuJmunJMOJRmDL/3n3NYmb1FiPH++/VZvkeG0XWOwyjCQStbrx20rhQVO+PeP23gpsCGYYRzOjYBLc9vJdbN+3hZ6PRCaUnTo5PKYU0JuK8YKVBckjazNC42v5Rmal+B1ySBkOGYVTi7sjjdubHTozV1Vd8ujGFkTNq7R0c1TY1qmBaIxxwb3vTTP71J4HpNYZh+Ki1r3geMJNUzqi19HIUUaseN+nItaPWEgVlysIw0uFaAoLIc46GKYyckcWqoz/kB9jf11NlRzVft2HUj4gTQVUIWYG5ZuMo32MeMYWRM7JYdYSF/J04Oc76bz2VuDZTocuSMAwjCWefXuTAhiv50vVLQpXC4EA/117cP6VUCiJce3G4aTkPmMLIGVmsOgYH+rn7msX09VSG/B07MZbKyT1h0VOGkYhXy/eVe+/19/UgOLt61284NFxiy+5ShcN8y+6SRUkZyYmLeKrnuhsfe8aioAyjCZxePLUWDwtIsSgpoyFERTzVQ56jLwyjnXhj3F8svRqLkjJyTZ6jLwyjnUhivbUoKSO3DA2XOP6GtTU1jGYQFh3lpRWjpMwk1QGEtTWd1VtENV12t3sbdEUkA/YWuzgxFr8lN4w8UiwI4xNaV/+V1cvnTf0/rHJDVv7KLDGF0QGE1ZLqndHNmlULWfPI3soexV0SWU/qwIYrY3sVJ+nMZxjTgdtetSDC6uXzWHbu7KpJe9cLR2v6/brXvGtwMRBfuSErf2VWmMLoAGKda/7dszhtVoN2Hq59NW51dNfg4qq+34Yx3RS7hI3vX1I1Sfsfp62s0N/XwxNrL6t4zm0T4L8H8h4JFYUpjBYkbXHCuX09gQUI5/b1sPGxZ6raqY5NKCLVPYn99tW41ZEpCyNvjE1qosm63g56Q8MlbotoE5DnSKgozOndYtRSEjnIuVbsEk6cHA+tZDtyYiw04SgpSRx/htFo4goSJJmswyKVvFnZEH5ffPZv9kUmuuY5EioK22G0GLUk+/jNR2f3FDl+cnyqH3EQc/t6YncQcTud1cvnmR/DaDpxIa1JJus1qxZG+ujiOH4yvNxO3iOhojCF0WLUmuzjnfxXbNgWGRmV5AedpAy76/gzpWHkiZWL5sSOyTKCqZV7epvCyCFRK/cof0RSopRLf8IbI+lO567BxTz65EuRuxnDaCbb9x9JNK6eCCaBwLBcIbqvTd4xH0bOiPNRNCLZJ0y5uP2Ib920J7a/cJjSKY2MVvQoXje0z5SFkSua4XD+4CXzUz3fKtgOI2fErdwbsVUOss8WC8Jrr5/ya3hNTEGvF7bTEU61hC2NjJo5ysgdXSIMDZcyXem75lg3tNyfn9GqiLZY6OOyZct0165d0y1GZpy39tHQreyBDVeGnueasUojo1MtWb3mJb+Za+WiOWzff2Tq8fE3xlNlfPcWuxib1IqQ3LBtuGHkjTQO7HZBRHar6rK6rmEKI1+s2LAtcOUelBjkElb6A5wb49qL+9myuxQZ8RGmqKLoEqa69EX1DTeMPNLXU2Tmad0tU5ajXhqhMMyHkTNq8VGElf4Ax5z10M5DsX3Ca4kLn9RTLV1NWRitxsjoWIWv8JZNe1h65+O5bmA03ZjCyBlRHbrCiHPiJck2TRJqaBjtzsjoWGwibCdjJqk2IMyM5RJmLiqIMKnK3L4ejh5/g1GrMGsYQLQJuFXJvUlKRPpEZLOI7BeRp0XkUt9xEZE/EZEfi8iTInJRlvK0CkPDpYrQ1LjVTpAZy0uXBC8KJlSntuOmLAzjFKWRUdtlBJB1WO2Xge+q6nUiMgPo9R3/deBt5b/lwFfL/3YsSTKo/XhDbUsjo3RJZXkE0wWGkZ64+64TyWyHISJnAe8G/hxAVU+q6ohv2G8A31SHHUCfiLwlK5lqJe2Kvx6i8jCiGBzo54m1l/H8hit5y9mtWdjMMPJEkvuu08hyh3E+cAT4uogsAXYDn1DV454x/cAhz+MXy8+95L2QiNwI3Agwf35zMyVrWfHHXS8s6W5ouBTqi0ianRp1DcMw0nG4bJpqpa54WZKlD6MbuAj4qqoOAMeBtb4xQYWIqwzuqvo1VV2mqsvmzGluNE+tK/4gosp+uMfCSBL2GncNwzAc3vammYGTTxBrHtmbqp1AO5OlwngReFFVd5Yfb8ZRIP4x8zyP3woczlCm1NRaHTaIMOVz28N7ufPbT4XmUiStFRWVj2EYxin+9SfHEyWqKlS1K+5kU1VmJilVfVlEDonIQlV9Brgc+JFv2LeA3xORv8Zxdr+qqi/5r1UvSbaUYWMaUR3WJUzJTKhGFuiLysPwyt1aAdKG0bq0ase8esk6Supm4MFyhNRzwEdE5CYAVb0P+A5wBfBj4ATwkUYLkMQHETUmrJFKLQ1QwpRPFP3lRkZBRJUEMQwjO87uKU63CNNCpgpDVfcA/kSR+zzHFfh4ljIk6dsQNcZN3knr9ArasaxcNCdV9dZCl3B4ZJQFax8NrHZpJijDmB46tftw25c3j+vbEGXKcc9N20glbMdyejHeZdTf18PhkVF6ZxQq2jxOqE4pG1dpdOq22DCmm5EO7fHS9rWkwnwNbt+GKLt/rY3aw3YscY2EZs4o8MTayziw4UpeD8m2e2DHwakIjVZtJG8YrY7bU6PTaHuFEVQ2I0nfhnoatde68i8WTn0dUdVf3bC+uJIghmFkw4RqR4bXtr3CGBzo59qL+ymUjY4FkUhlkbRCbBRhK/++nmLkBP+qp4FRIcJI6vXBuJVtXdkNw2gOnRhe2/YKY2i4xJbdpakV+4Rq6MTa39fDgQ1X8sTay+rK5AzrabH+6gu5+5rFocrAq2hWL58XOMbFu4s5cXIcsG53htFsOs2P2PZO7yB/glJtlqrHBOUnSd/tuFBd17EdFlU1t6/HwmoNY5rpND9i2yuMsBWAcioiKYv6MFGRVUkUCjhKY9m5s0OVi4XVGkZzCOph38hFZqvQ9gojLFluuhukRCmUdUP7eGjnoQrHt9sEqd+jXG7dtKdJ0hpGZ+K936wIYQcojEZmatdD0vIk/3Xrk5wICKmdUJ2S2z2vlsxxwzCS4b/f0uZjtSMd0aJ1ulcGQb6GnmKhIhIrqT/C21Z15aI5bNldMrOUYWTEdFsiGkkjWrS2/Q4D6l8Z1Ktwai1PEoRrpiqNjLLpnw9xwzvn8eCOgxYhZRgZ0GlRUHF0hMKoh0Y0UIoqT3Le2kdrNi2NTSoP7jRlYRhZ0WlRUHG0fR5GvTSigVLUj85tylIrLWZRNIyWoROjoOIwhRFDIxooWQkPw2gtBLj2YnNy+zGFEUPY7iDNVtVbwiNt+Q4Bnt9wZcqzDMOIokuiJz8FtuwudVytqDhMYcQQVuYj7VZ1cKB/qhJtfwplYzZUw2g8H1g+n0IhevnWibWi4jCFEYN/d1BvYUIIVkLFglDsqvwBmw3VMBpPQYTt+49UZG2HYVFSlViUVAIanbATVhok6Dl3bH9IJFVBhBndwmhI/wzDMCpZvdwJRU+C7fArMYUxTYQpoTDFtGbVQtY8spexyVOromKXsPH9S5zjvmOGYVTzoUvmc9fgYrbvPxIbnWg7/GrMJNVK+E2u5ceDA/1sfP+SKbNZX0+RYox91jA6jf6+nqkq0IFm4S5hVm+xYabndsR2GC3CxseeqbK5jk3oVLa4d8eyYsM2Rkaj28EaRifh3y0krRhtVGIKo0VIkw9ijjrDOEVYToUVE0yPmaRahDT5IGFjwzr9+Z/tKRYo2i/DaBMsp6Jx2LTQIqTJBwkbu3r5vMDnP3jJ/Kqw4XELujLaCMupaAxmkmoR0thco8YuO3d2omtsfOyZRDWu3Fa3BRFO65bAXh6GkRXFglR1wQur+mym2vrJtB+GiDwP/G9gAhj312IXkbOBB4D5OMrrj1T161HXrKUfhpGeoP4cXeIoB1VHQaxePm8q6iTsHD/FLrHwX6Nh3HvD0qoFUNhip516W9RCq/TDWKmqPw059nHgR6p6lYjMAZ4RkQdV9WQT5DIiqCWKxD12S0Tr2I3vXzJ1Q7ttZ91/jc6jWBBmzugOjOpzJ/ildz4eeLyvpxjquM5Dl812ZLpNUgqcKSICnAEcBcanVyTDpZYoksGBftZ/66lUN/i6oX08EJN5W+wSEBKVczCmF5FkZfdn9Ra546oLgegJfv3VFwYmra6/+sLA61rIbHZkrTAUeFxEFPjvqvo13/GvAN8CDgNnAjeoapURXERuBG4EmD9/frYSG3WT9gbfvv9I4PPedrT+0il5UBv9Zblu3bQnF/JMJ0Eth6N2mvfesJTBgf6pbpajYxNTO81+3wRf627XFETjydqHMVdVD4vIm4C/A25W1X/0HL8OWAF8ErigPGaJqv4s7Jrmw2gN0rS1PW/to4ETrgAHQkq7X3D7d6bVjOWdIFds2FZXE6zpxp2gb3t4b+rPVCD0+12w9tHQ857fcGWiXvdG42iEDyPTsFpVPVz+9yfA3wDv9A35CLBVHX4MHAAWZSmT0Ry85dyfWHtZ5ARQS8+R1cvn1S1jPXjDNFcumjOtstSDa/oZHOjnS9cvSdXoqyDCPTcsDf1+V1wwO/A89/lGdLM0mktmCkNEZorIme7/gfcCP/QNOwhcXh7zZmAh8FxWMhn5JG3PkaHhUpUZqyDCigtmhyYnZoEbphlmUmsExYKETrz10tdTrFjNB5Xy/9Al85nVWww8f0KV27fuC02Ie/B3L62SfcUFs3nwdy8FGtPN0mguWfow3gz8jePPphv4K1X9rojcBKCq9wFfAL4hIvtwdrefiYioMtqUNDbqODNGktBecByur70+XleIr7sDavQE5+a2uKaiXS8c5Ylnj4aPF7jn+qWxuTOuKo3L4fE/f9fgYoaGS4EmK3dHELaDdJVDEHNDSvZbSfH8kpnCUNXngCUBz9/n+f9hnJ2H0eEkdVJGmTG81whzuLp+kRUbtnHsRHiBxmIXjGt4tI93BxQ28bm5Kn+796XQqLEL557JjueOTYUX+3NbAG57eG+onODI6H3vA59/PPC9icAfX3/K2bxiw7ZUTuRbQz7TWhXmmlULLfy1xZjusFrDSEUSM8bgQH/oajvJrqALQAT17T78K393gg2b+Nxdj7tCrzXMM60jeiREEU6qE76664WjbNldmpK3NDLK7Vv3AeH9WKDxOwILf209TGEYLUXSSStu9Rq1KzirpztwhT43JFM4ycRXT5hnlziTfRi9vkqRYe8NnN3YQzsPpTYtQTY7Agt/bS2s+KDRUiR1kMf1Yg+7zpeuXxK6Qo/alaSJCkvLad3ht2mXwBeveUfFc0HvzUvYjiXOtJRFf3ujtbAdhtFSpC3CGDaZRV0nzpzVbF6PKOjo+iS8uI/D8irCSrEkeX+2I+hsTGEYLUejJq2w6yQxvdTjk0hLmImpv68nViEGvY9rL+6v8GG4z5uz2YjDTFKG4SPO9OKG7pbKJUpcp3FWDXrS5qnEvY+7BhebacmoiUxLg2SBlQYxppuwUiBZls9u5o7GaE9apby5YbQV05GhbL4DIw+YScowUlJL7SvDaAdMYRhGSmr1KRhGq2MmKcNIiWUoG52KKQzDqAHzKRidiJmkDMMwjESYwjAMwzASYQrDMAzDSIQpDMMwDCMRpjAMwzCMRLRcaRAROQK8ME0vfw7QSi1kW0lekzUbTNbsaCV5zwFmquqcei7ScgpjOhGRXfXWYmkmrSSvyZoNJmt2tJK8jZLVTFKGYRhGIkxhGIZhGIkwhZGOr023AClpJXlN1mwwWbOjleRtiKzmwzAMwzASYTsMwzAMIxGmMAzDMIxEmMIoIyJ9IrJZRPaLyNMicqnv+CwR+RsReVJE/llE3u459msi8oyI/FhE1uZVVhGZJyLby+c8JSKfyKusnuMFERkWkb/NWtZ65Y07N2ey3lr+DfxQRB4SkdMzlHOhiOzx/P1MRG7xjRER+ZPyPfSkiFzkOda0+6seWZt9f9X7uZaPp7u/VNX+HD/OXwAfK/9/BtDnO74RuKP8/0XA35f/XwCeBc4vn7cX+Hc5lfUtwEXl/58J/K+8yuo5/kngr4C/zfPvIMm5eZEV6AcOAD3lxw8DH27S51sAXgbO9T1/BfD/AgJcAuz0jG/q/VWHrE2/v2qV1XM81f1lOwxARM4C3g38OYCqnlTVEd+wfwf8ffn4fmCBiLwZeCfwY1V9TlVPAn8N/EYeZVXVl1T1B+Xn/zfwNM7kkTtZy+e/FbgSuD8rGRslb8JzcyFr+Vg30CMi3UAvcDgrWX1cDjyrqv5qDb8BfFMddgB9IvIWmnx/1SNrs++vemSF2u4vUxgO5wNHgK+Xt2f3i8hM35i9wDUAIvJO4FzgrTg/iEOecS+S7Y+kHlmnEJEFwACwM8ey3gt8GpjMUEYv9cib5NxcyKqqJeCPgIPAS8Crqvp4hrJ6+S3goYDnw+6jZt9fXtLKOkWT7i8vtch6LynvL1MYDt3ARcBXVXUAOA74baUbgFkisge4GRgGxnG2en6yjFWuR1YAROQMYAtwi6r+LI+yisj7gJ+o6u4M5WuYvAnPzYWsIjILZ+V5HjAXmCkiH8pQVgBEZAZwNfBI0OGA5zTi+UypUVb33GbdX+7rpZa11vvLWrQ6vAi8qKruamAzvpuv/MV/BBxHEo4N+ADOdn6eZ+hbyXZ7X4+siEgR58f8oKpuzVDOemX9LeBqEbkCOB04S0QeUNUsJ7Z6fweR5+ZI1lXAAVU9Uj62FXgX8ECG8gL8OvADVf23gGMvEnwfzQh5PmtqkbXZ95dLLbJeRw33l+0wAFV9GTgkIgvLT10O/Mg7RpyIlBnlhx8D/rF8Q/4L8DYROa98/LeAb+VR1vKk8efA06r6x1nJ2AhZVfV2VX2rqi7A+Uy3Zaws6pU39ty8yIpjirpERHrLv4nLceztWbOaYLMJOPfMb5ejei7BMZO9RJPvr3pkbfb95SG1rDXfX2k88e38BywFdgFPAkPALOAm4Kby8UuBfwX2A1uBWZ5zr8CJiHgW+GxeZQV+GWfr/CSwp/x3RR5l9V3jPTQvSqqe30HVuTmW9c7y8z8E/hI4LWNZe4FXgLM9z3llFeBPy/fQPmCZZ1yz76+aZJ2m+6vmz9UzPvH9ZaVBDMMwjESYScowDMNIhCkMwzAMIxGmMAzDMIxEmMIwDMMwEmEKwzAMw0iEKQzDMAwjEaYwjNwjIhNSWcZ5gYh8WES+4hv3DyKyrPz/50Vknzglnf8/ETnXM05F5Euex58SkfURr79eREo+GfpE5BoR+XvPuF8uH+t25RORz3rO8b6P/xLwOqFjI2R4T/n9fNRznYHyc58qP/6GiBwon/MDybjsutG+mMIwWoFRVV3q+Xs+4XkrVfUdwD8A6zzPvwFcIyLnpJDhHp8MI+qUfnhdRD4gTtXXPwP+s6pO1e1S1d93z/G9jz/xv0CCsVUylJ/fB9zgudRv4RQe9LKmfN21wH9P8b4NYwpTGEYn8H0qq4mOA18Dbm3AtW8G7sLJnP4XVf2nBlwzLQeB08Upsy7Ar+H0QAjiH4H/o2mSGW2FFR80WoEecSquglM07zdTnv9rOKUzvPwp8KSI/GHCa9wqpyq6HlPVlQCq+pyIbAJ+D7ggpVxpCZShzGbg/TgVaX+As4sK4iqcHYlhpMYUhtEKjJbNKV7Catp4n98uTsOgn1BpkkKdYozfBP4LMJpAhntU9Y/8T4pIF/AfgNdw+k38NMG1aiVQhjIPA5twOus9hFN91stGEVmH00PjoxhGDZhJymhVXsEptudlNpUT9kqcSfwp4PMB17gXZ/Ksp9HRx3EK+H0U+NOySajpqFO9dgz4Vcpd9nysKfs9flVVf9hc6Yx2wRSG0ar8C7BCRH4eoBwddRqV3cVQ1VHgFpwSz7N9x47irMxrWnGXX/uTwKdV9btACaeM+HTxOeAzqjoxjTIYbYyZpIyWRFX/TUQ+AXynbBZ6DVitqlXtJtXpVfAQzm7gC77DX8LxP8Th9R8ADAJfBP5Qy42IcBTT90RkS6o3k5wgGaaYJoe70UFYeXPDMAwjEWaSMgzDMBJhJinDKCMin8UJTfXyiKr+fiu/lmE0CjNJGYZhGIkwk5RhGIaRCFMYhmEYRiJMYRiGYRiJMIVhGIZhJOL/B0wpdxGDMwbgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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wz7u+gm5bzdb67GHf/Sv7SxVhsFffu3mi4ik0p2prt2CKwDB8RE0Q7Zo8BmPcP279/04jJ9GBsXG/e//3s2zxKRTyuYoxvRqJZIrAMHxETRCtnjzcGv6jY8VYeQLu6rjG3NlRXHj2CSycPS3w3MLZ01i2+BQnvDQG3t3DknmDXP+R0xkcKCA4yrZbHcW1MB+BYfioVda51rlm4XdaK45TWAn3Gbir4w6LAQnFW6IiLGpoaHg0djadf/cQVrW117CoIcPIIEPDo3zhrk2Bk31YYbZuw1V6AyE9FFzi1lhqVWhoVnMTLGrIMDoIdycQ5iTdNVas2LV0U4SQF/fTh/VQcL+DuJ/f6yNIKyckqhNbFpRBGOYjMIyMUauZu2veWDJvkMeXn1PTmQrxnM2tZHCgwNuOr2xhno85G/mdvnE+v4s3esidtKMii5KStFBgVjBFYBgZIyoUNcgxXSu8EpyQ0iz4j93mM4vmTOeXv9pXca6UoMSp9zuK8/m9uBNzGpN2p+YmmCIwjIwRFg6ZEwm0ccddEWfBG+jKf+f6nQ3dx/sd1bPbSSsnpFNzE0wRGEbGCAtR/frH5wbamS88+4TI+02ZnGPhirVNlbEehMN28qSreC/+XdGyxaeQzyXb76SVE9KpuQnmLDaMjBEnfNWLG165av2OqrDRXJ9w4OChxA7lnAhHTBL2J7HX1MA7wdYqmRFGaEP7hLdaNGc680+cFlijaNGc6RMVU5M6kJP+7uKSdiSShY8aRgtoVUih/332vXEwsjVkEG7RtVqF7gD6xGlmUwt/6OY1Q5u5Y92OGlcFy1Zv+Kj/Po8vP6fq+1o0Zzr3bBxtSlHBZv3Om1Ul1cJHDaONNDukMGqC8SdInRQymbuGlKA5fCI7OeS8i7dK50nLHwwdGzR5zz9xGv+ybkfiFphB3109Nn33Gv/3tXDF2qYUFRwaHmXZ3Zsmur2NjhVZdvemCrnjEuXUbtZiwnwEhpEyzYxOCQt5vGZoMwtXrOWk5Q+ycMXaiRDIRuomRSkBv9077F6usvBPWCsf2VZ3H2T/d1ePTT/smmY5kK+9f0tgy89r79+S6D7NlCkKUwSGkTLNfJDDlMqqdTsC4+HTqpvkN0skvVejk5j3+qD3jiJKrmY5kMPMcUnNdM2UKQpTBEbLcAuo+Vet3fq+Ls18kMMmUP/q3Ws6CCusFnYOiIwymtqfr1rhJy3g1ugk5r0+6L2n9ucDrwsLwXXJYtRPK2QyH4HREtqVep+FlP+gukD1PshhDe2DCLODe/GfC3JM+jnvd9+SQOJgatVKiuoIl88J+944yEnLH6zwkdT6HHEcrM2K+pnanw9sGxqmoKJIKxLJS6qKQEQGgFuB03AWLZ9W1ScCxr0TWAcsVdU1acpktIe0HV5hDtQk71sryqPeKJBmPsiL5kyPHW1Tz6q7VnkLgMe27q46FqZwNzy/h8e27q763EvmDbLh+T0T3cdEoDCpj2LpUGS114H+PK+9fjgSKkyxN/KdN6Mi6ZfPP7WqbWg+J3z5/FPrul/aVVLT3hHcBDysqheIyGSg3z9ARHLA3wGPpCyL0UbSdHhFrfrjvm+tnUOjO4tmPchBkzBUR/jUu+OI8/sIGhOmcL1Ka3SsyLI1myZe37NxdCKXQBUU4YalZwSandxjC1esrVpphyn2dpaYbsUqvpmkpghE5BjgPcAlAKp6ADgQMPQK4B7gnWnJYrSfMJNGMxxeUav+uO9ba+fQihC+OET5CAYHCjUnnWuGNk+swnMiXHj2CRMJaRDP9NQnUmWWiavQS+PKlatHAhPK4nyfnVTLp5N6HaTpLD4Z2A3cJiLDInKriFSUGxSRQeBPgJtTlMPIAEFdpPJ90hSHV9TkENfRVmuCSXtHE9eZHaY4Bdh/4GDk+7hJXO4EPK7KHet2cM3Q5okxcbp9jatWRSclVehRJbaj6NRaPlknTUUwCTgT+KaqzgP2Act9Y24ErlLVSKOkiFwmIhtEZMPu3cFbY6MD8M8vdZbD9E+cxxaCHXAzBgqxo1lqTTBpTUBDw6MsW7OpIvRz2ZpNocogLFRScZq1u/e4cvUIZ1z3aMV9wgq93bFux8R3ueH5PaGTdFBxO3cVn6RdZBQKkcowi1E93UBqJSZE5LeAdao6q/z694HlqnqeZ8x2Dk8HxwH7gctUdSjsvlZiojMJKwPgzU6NQ1A0SD4noFQk8CRNwY8qeZATYcHJU/npjr1VUSgfPWsw0Bkal3lfeTQ0umT4S+9PLKsfr4yNNLAZKOTZWywFJpkJcMPSM6qco40Q9fvLagewrNOWEhOq+pKI7BSRU1R1G3Au8KRvzEkeIb8NfDdKCRidSxLTStSDHmSrL40rU/vz9E+eVHENELt4WJgTFhwzxuPP7GHh7Gk89+tiaF2aWpEyQZ8xSAkAoceHhke5Z2P8PAg32azR6XmsWGIwwt+y8pFtTVMCEO0vqGV7r+UH8WJKxSHtqKErgFXliKFngUtF5HIAVTW/QA8R12lbKzonTKGM7S9VrKDjRAF5J4A4q+V1z77CM9d/aOJ1WF0a78TrVw6jY0VE4jWXP/VLD7PvwPiEY3VwoMD+AwcT9ylu1vQc9h3NelOhoql8sxgdK3LS8gcZ6M+jCnuL1T2L/ZP+ydP7KxreuH4QoEoZZCHHJCukqghUdQTwb0UCFYCqXpKmLM2iE1YQ7ap0GfU+cZOqakXnNCMKCKiaAOIwrsrQ8GjNYmdBWb5e5RDXGrvvwPjE+yaRs9WkoQRcXN+Hi3ey3vD8ngoT2bhqVdczlzvX76xSBFmJBMsCllmcgE5YQbRKxqTvExZXDZXmm7DJbnSsyMIVa5n1puAxs95UiHWfXWPFWElTYXg/Y5Is384q9p5t3Mn6pb2vx74myAHeSaGoaWOKIAGdsIJoVQZv0AQYN2P3hqVnAHDdA1uqVntRpY9Hx4qhE++Pn9lTYY4JY0Y51r5evJ+xVpkEIxluHkQcpRl3nEtQxFOauS2dhhWdS0AnrCDSknFoeJR5X3mUK1ePRE60YRm7/vDIz981EugQVeqLKo0zKeRzTt5Cow+6t4bPR8/KxgKgG1g0ZzrbV5wXqwfxjIFC7F7NENzO00JRD2M7ggR0wgoiqYxx7PxxCpGFvU9YlE8UbpZs023i5bdNUq8n7DYLV6ydiBoymsMd63Zwx7odTJlcu6T0ojnTJ67x87bjp/Ds7v01o4Y6rQxEmiRWBCIyFRjTTutx2QSaWUUyLZLIGNfOH9emniRjN4qBkASxMGp10nIpHVK+cNcmjik0vv4ZHSs2JSzTqMZ1kkfx2NbdE/kn/lDR+SdOqzBf3rFuB49t3R04yQeFonrNn96IrW5WEpFPhIh8CbhLVbeKyBHAw8Bc4KCIfFJV/70VQmaFTlhBJJExrj8hzmSeE6mIynGvT+JQdXk1QZ/dpE3Qx1VDY/STYkqgfbh/k19bcnrFat+/uPFGXMXJ8ah1PWQnMKSZ1FoaLQW+Wv7/n5X/nQ68HfgnoKcUAXRGIakkCTdB+Cf+WpN5rk8YPxT8wNTjUB2P0w0dp3F6EiVgdA+uCdJv2tz3RnieRViOB1QuoKKuz1JgSDOp5Sw+4DEBLQa+o6rjqvoU5l/oSPyFx4Lw2/lrtQL0T9z+nYG/1s/FC2Y2pS5NTH1hdBmuCTIoEKHWTjKsk5tLrd1vlgJDmkmtyfwNETkN+H/AIuCvPOeqegsY2Ses8JhLkJ3fXQFduXok9vu4cf/+nIGVj2xj1bodDPTnGdsfXLumVSQ1Kxntx2uCrCfLOgjv5F5r95ulwJBmUmtH8FlgDbAVuEFVtwOIyIeA4ZRlM1IgauKL6jO7ZN5grLA+F4HKkNG7N1VU2Xxlf4lJOQksTZ3PSdW9GiHselMCnYfXZt8sX493co/a/WYtMKSZ1FIEg6o6R1XfpKqurwBVfUhVL0xZNiMFwmKvcyI8vvycSPtn0EMSNnH7p9jSIa0KGy2NK0cdOanCbLTyY3NZecHcimMXLZgZaZqKopDPcdGCmYmUmNE+GjcYOpVb4/6+/ZO715QJh5+XqEVSN1DLNHQNcG8rBDFaw4VnnxAYex2UcOMnqkxE0gJuLv5icf73cnFDAr2VP1f/ZGdF6Wk/AhUP76zlD8aWy2g9ccOAo8j3OX2Bva0tw/4ew0JCOyEgpNmYw7fHcEPt4pbp9RP2kHiPRT18fuLaXIPe1x8v7megP19xjfkEsk1TfjO+LUVYXk03r+7roZYimCMiPws4LoCq6u+mIJORMv7Y62YT9PDl+wSkMqu4UZurqxxOWv5g4CQy5rMhJ1EChXwffSKxkpuM7FAa14oQz07I/ckCtRTBduD8VghidAduXHexNF6VlQm1q4/W85AO9OcDHYf9k3MV954aMm6gkGfKEdVNbVY+so19ByqzS/fse4Ni6VDSr8Xw4X6naezSRseKFeXCe9HUk5Sa4aOq+nxLJDE6nqCszEI+x6I506uqj7rNYeKWso6qiRQ2j+w7MM6+A8WJe7uObb/TWoSK+4V9jmWLT+G6B7aYImgC/gY/UabEenwH3ZwFnAaRPYtF5DXgS55DCrwM/MgNJW011rM4u4Q90P4H2bXRhtn3vX2Mh4ZHq8pVg1NJdOUFcyNNQ0H05/solg5VjXdldLuANSs00ajG3485TlHDencOfodwJzSWSotGehavBI72HZsFfFFErlXV7zRBPqNLCFvVBWVzBk3u3vt4q3sGTRClceW6B7YkbhCzP2Q1H6eXgdEcXvf9Pt2J+At3bQqc7N3JvJ7eD95dJlR3prOdg0OkIlDV64KOi8g0nDpDpgiMCZKs2mqtuONU93xlf4nZVz9kkUAdRpBpzZ2IwyrnBjl998UsTugtI5H1xlLtoq7wUVXdI5KgK4TREzR7Qo5zN1MCncnsqx+qCFuOCjIIc/q6dbPiEFUjqFvrByWhLkUgIucArzRZFqPDCWsm04xEIaO7GFedmMTnnzgt1DkftVL/7qYXY7+fm6+S9cZS7aJWP4LNVD/D04BdwKfSEsrIHnGcbGF23CPzfRyZzzG2v5RoS290P3es28GDP3uxLpNN3L8hb75K1htLtYtaO4I/8r1W4Nequi8leYwm04woibhhnu7//Y5gxyYsXLRgJo9t3c1YsWS7BGOCMH9RIyabwYFC4N/8huf3VGTVf/QsyzGA2s5iyyHoYJLE6UcRt5OZy2+KB6uOFUvjFfZcUwJGLQb6nZal/sXMojnTeWzr7tDrpvbnJ8KPvQwNj3LPxtEJv9K4KvdsHGX+idN6XhlYraEuJskEHrVzCFuZ+Y+7isccuEYzGNtfqioUODpWjHQQ58pF54JIuqDpJVJVBCIyANwKnIazCPy0qj7hOX8RcFX55WvAf1XVTWnK1At4m28HETaB+3cObn/XsGnd72SL2+TeMOJQz3Iiqq5+3AWNSy8ln6W9I7gJeFhVLxCRyVR3NdsO/IGqviIiHwRuAc5OWaauZmh4lGV3b4oszxxnAvebcvwEOdksDM9oN6VDyhfuctaS/mziuAsa95peSj5LTRGIyDHAe4BLAFT1AHDAO0ZVf+x5uQ54a1rydALNWIFce/+WSCUAsO+NgxVFuZJm07rx3Rue3zORDZoToZDvC83cNYxWMa4amk3sJyxqqJYZKemzmvXdRZo7gpOB3cBtIjIX2Ah8NiLi6M+B76UoT6apdwXi/wOLE1I3VixV3DtJRrAAjy8/pyqZZ1yV/SXzDRjZICqb2CWsMQ1Em5GSPqudsLuo1aqyESYBZwLfVNV5wD5gedBAEVmEowiuCjl/mYhsEJENu3eHRwvEYWh4lIUr1nLS8gdZuGItQ8OjDd2vWUStQMJw/8C8vYHj4r13Eueuu41eFTOj0zDaxa6xYuiE7i5owibisCSzGQOFxM9qPc92q0lTEbwAvKCq68uv1+AohgpE5HdxHMp/rKq/DrqRqt6iqvNVdf706dPrFiho4rz63s2ZUAZJHVnQuHPWvffUcpheLfI5mdhG29rfyDrHFvKRE3oUQf25XTNS0me1nme71aSmCFT1JWCniLgGuHOBJ71jRGQmTk/kP1XVX6Qli0uWNXPSP9ih4dGGK2UO9OcZGh6t6uQVRmlcuXL1iPX+NTqCsWKJfW8cJJ+rLIsWJ5vY28ReqGxeH/ZM9okELirrVUatJO2ooSuAVeWIoWeBS0XkcgBVvRmn18GbgP9VrmF3MKxedjPIsmYO660a9Afr7mwa5fXSOMvu3mSre6NrGSuWyPcJU/vzEyVO4jpqwzqbhZVS8TqpvdclebbbRaqKQFVHAP/EfrPn/GeAz6Qpg5ewuvVZ0MxJeqs2K17fOm0ZvUDpkNI/eVJFM5xGiOqfEJSg1gl9kyM7lGWRejqUeROswrplZemXUoskHblc+7913DIMp2eGt/x1I4Q9hwJsX3Few/dvNlEdytJ0FmcCr4MYHCXgWgy9dr9OIskOpn/yJL58/qmBji/D6DXc8tfXDIWbVuNGFnaC7T8uXa8Igswobm/aqPCxLBMU0RDGrrEiS+YN8tGznHwBYKLqYn++63/9hhHInet3Vh0bGh5l3lce5crVI7EiC8MiixbNmZ7JEPUour7oXCMO4qxmAyZp2zdjoBBYdbFWG0jD6Gb8tn1/0peXsMJ0Qc+hv892FpPHguh6RVCvgzjr2YD+iIagP2R3dRLk1DIlYPQyOV+n3VoBGGELR/9zuHDF2o6scNr1toGoxJAoW2CWcw6CCIp7/uhZgxU7gXoY7EB7p2HU4sKzT6h4XctCENfun+UQ9Si6XhGEJYYAkVnGjf5C21HKYsm8QR5ffg43LD0DcNoANhJm6qbh+1dPhtGp5ES4eMHMqqihqIk+Scx/pzqQu940BMGJIbW2cI3kHLTTrBRl60yKArOvfsgazRgdjxsc4hLU9cxr23cZKOS59sOnxn5uOyF5LIiu3xGEUWvFH2VSqkU7zUrNbg5jSsBoNwOFPBcvmEm+r76dqf+5Dao5ds/GUT561mCF5eDGpWcw8uX3J1q8RZWmyDI9sSMIotaKv5FswHbaCbNuizSMuORE+PrH57Jk3iALV6yt2WfDS1jzeghfqD22dXdgr+OkhJWmyDI9qwjibOHq/YW2s5RF2HsbRifhz/hPssDxm4H8dKpDN0161jSU5hauEbNSGu9tGJ1E0LMYtojyG4viPGed6tBNk55VBHA4ymb7ivOammXcTjvhknmDnDnz2NTfxzAaISfCwtnTJp6RgUKeqf35qondJWxxddGCmYmfs3Yu1LJKTxSd6yX8LSQNI6u45h+o7iuczwlTJk9ib/Fw6WhoXgXPrFYNSJOoonOmCDqIoD9eqHw4XtxbJIFPzTDaipuwWMuv1YlVgrOGKYIuICg/IN8nHALGbeY3OhR/WfgoajmBjWiiFEHPRg1lkajtalDIW5JwOsPIIjMGCry09/VY+Sq9HNWTNj3tLM4Kccrf2kNgdBuugzZu0qJCx5R17jRMEbQZ1+QT1EHMm43cy6FtRvfh9sRYMm8wUWHDqP4ARv2YImghQYXo4pa/tfwAo5sYV+XOn+xkaHg08G/bbTgfRFC5lnYUeewmTBG0iKD6Jt4WmmF4S174cxMuXjAzfcENIyXGDylfvG9z4N/20t87gf7J4S5Mr6k07NkyZRAfcxa3iLD6JjmRUBtprZIXUX1XDaMT2HdgnKHh0Yq/7TgVdL2m0qgijxZuGg9TBC0izNk7rkohn6ur/G1Q31XD6DSuXD3C5+8a4ZNnz2T+idMCO+p58S+QrHZQ45giaBFhxeAGy2Gi9WQ5Woloo1s4pE4jpVpZ8YMBz0c7izx2C6YIWkRUtdNOLFtrGK0mLKGsU5vBZAlTBC2ikf4GQZgjzOglBMcJvHDFWhbNmc5jW3dXPEfXf+T0nqsd1ExSLTEhIgPArcBpOPkgn1bVJzznBbgJ+BCwH7hEVX8adc9eKzHhzTY+tpBHBF7ZX0qUmm8Y3YzVIYpHVImJtMNHbwIeVtU5wFzgKd/5DwJvK/9cBnwzZXk6Cn9Y3FixNJF4ZkrAMByKpXGuXD1i+QMNkJoiEJFjgPcA3wJQ1QOqOuYb9sfA7eqwDhgQkbekJVOn0ez+w4bRzVj+QP2k6SM4GdgN3CYic4GNwGdVdZ9nzCDgjYF8oXzsRe+NROQynB0DM2d2bxLVNUObuXP9TsZVEQELCjKMZBRL41x7/xbzFyQkNR+BiMwH1gELVXW9iNwE/EZV/9Yz5kHgelX9Ufn194G/VtWNYfftVh+BNZQxjPSIk5fj+uNGx4oTiZ5B4aqdSrt8BC8AL6jq+vLrNcCZAWNO8Lx+K7ArRZkyiyWHGUZ6jBVLkWYjrz8ODufo9Iq5KTVFoKovATtFxA3mPRd40jfsfuBT4rAA2KuqL9KDWHKYYaRLULE6lyh/XNR13ULaeQRXAKtEZDLwLHCpiFwOoKo3Aw/hhI4+jRM+emnK8mQGfxOaPsFaTBpGyoyOFSdqG/mPR9Ht5SpSVQSqOgL4bVI3e84r8JdpypBF/EW1RseKVgbWMFrE1fc6xRq9yiCq+CN0f7kKm3/aQNA29BBQyPeREwGg/I9hGE0myNSTpMhdN2IlJtpA2Dbz9dIhtq84b+L1ScsftMQxw0gBt1yFa5qd2p8P7BKYE+mJrGXbEbSBsG2m/3i3b0cNo514G9m89vpB8rnKbXghn+PrH5/b9UoATBG0haDWfEHbT2tPaRitoXRImTJ5UkWXtF7YCbiYaagNxK1E6h1XK6rBMIzGGCuWaiaddSumCDKEP6TU26tg9tUPBTq0BOirEfFgGEY8giKKegEzDbWBoGbby9ZsYtndm0IbcF949gmB97powUyeuf5D3Lj0DPJ9FmpkGI3QC8ljQaTajyANuqHW0MIVaxOZetx6Jxue3zNRlC4nMqEc3GOGYTSHbqox5BJVa8gUQRuoJyw0qPmGFaozjPTI54Qpkyext1jqiiqm7WxMYwRQT1ho0JbVCtUZRnqUxpWxYinQVNttmCJoA/WGhfoT0cwcZBito5s7oVnUUBuoNyzUv5OoVR/FMIzm4+4OIH50UVhEYFawHUGbWDJvkMeXn8PFC4I7ruX6qrMc/QlnYZFEXuwXbBjNJ0l0UVCUYNbMTDZPtJmvLTmdixfMnCg2lxPh4gUz+frH5tbMcvRf66dP4JMLZjJQyKf9MQyjq5jan68Zjh23NHVQkcmshala1FCXEBaSOjhQ4PHl54RuTYeGR7n2/i2MFasLbhlGL+J/ZsLMt+64WoRFCQpUFJlMm6ioIfMRdAlhqxP3uJuh7Mc9XuuP3jB6Aa8J1vtsePuH+MfVYsZAIfC5ylJRSTMNdQlxK5qG4fosLDfZ6GUOjo/zOV9k0JJ5g1z/kdPrLkgXt8hkO7EdQZewbPEpVasWgH1vHAxszRdG2OrFMHqB0iHnX39kUNiOOg5xi0y2E/MRdBFDw6Nc98CWqgYbQVnJUfcIUiiG0U0U8n0U3Vk/grh+gE7AMot7hCXzBumfXL3JSxKhELQNftvxU5osqWG0lyNjJnR2e9N6FzMNdRm1nMZx8G+DZ1/9UMNyGUZa1JNYOba/FOu6LDl008R2BF1Go07jICx72cgqfcAxheTr2RkDhZoJmVlz6KaJKYIuI40IhbCENcNoN7mcBDadj8J9HvwJmX3i+A6sVaXR8aQRoXDh2SdYuWsjc0ztzydWAlP783z5/MPtKL+25HS+tuT0NMTrKEwRdCGNhLoF4T4o/gY4+b7D4XYucaMxBBL3ZDAML2MRSmCwHAbt+gG6sdFMM0k1fFREngNeBcaBg/7QJRE5FrgDmImjlP5BVW+LuqeFj3YGcaotWmOdziefE1AoHWqtWh8s+7yiyqoYlbS7xMQiVX055NxfAk+q6vkiMh3YJiKrVPVAC+QyUqTWrmRoeNSUQIbxrqR37S0Stl5cecFcAK5cPRLrvkl3gkGKxuvzaqT0g3GYdpuGFDhaRAQ4CtgDHGyvSEbauElrRvZ42/FT+LfPv7fiWNjO7eIFMxP31lCClcFAIc+1Hz514l7enWTQMe8iI8sZu51C2qah7cArOL/3/62qt/jOHw3cD8wBjgaWquqDAfe5DLgMYObMmWc9//zzqclspE9YpVSj/TwXUg3zmqHNEz6inAgXnn0C80+cNjEJH1vIs+/AQUrj8eaTwYGCTd4tpp2moYWquktEjgf+TUS2quoPPOcXAyPAOcDs8pgfqupvvDcpK5BbwPERpCyzkTK9kq3ZaQxG5Jr4o2v8pUjGiiXyfcLU/jxj+51m7/veOBhY3txs+Nkj1TwCVd1V/vdXwH3A7/mGXArcqw5PA9txdgdGF9NotuaUybmJZjv15jh0ambEEZPSeWST2taDmq2UDin9kyexfcV5PL78HK798KmZr7ppOKSmCERkStn0g4hMAd4P/Nw3bAdwbnnMm4FTgGfTksnIBkFJb37cWkdT+/MMFPITST43Lj2D//4npzPliEkI8FvHHsnU/mQd2Ar5HBctmBm5As4iFy+YGfm9HXNEvPo5fnIiiZOn4pQyabR8s9E60jQNvRm4z/EDMwn4F1V9WEQuB1DVm4GvAt8Wkc04i7SrIiKMjC7BnQg+f9cIQVGHU/vzoaYDv0lidKxIvk/I5ySWfdo/6c1aXuWSiiRJ1MsRk/o4OK5NLdGxN6KTXOmQ4+z95a/2TRxbOHsaJ00/qioHxCVJZVovcZutNDunxUiH1BSBqj4LzA04frPn/7twdgpGj+FODsvWbKqYwPM54cvnnxp6XZhJwjUVRbXcDJr0BkMmtJwIxxQmVWWuhkW9BHHg4KGJVoRDw6OxQyzDuGPdjshs2mJpnF/+al9g8pRr34+T3xGHoP4XZvbpXKzWkNE2lswbZOUFcytMBysvmBs5MYWZJPYWS4x8+f3cuPSMULNS0Mo3rDbT1z8+NzRzVTnsmxgcKISapryr4yXzBptiinrt9YNObH0EblMVt8OWF7cTnWvHb6TZipl9ugdrTGN0FGGhp41EooStkmuFubo7DAhObPJPjM1q+jNQyPPq6wdrmpwsOsfwYo1pjK4hjeqqYavkWk5tt+FP3NWxOy6KOFFQe4slvv7xuTUd7hama8Sl3ZnFhpGIVvZ/jZM16062cZ2iS+YNht7Pu4KPqsM0Y6AQS7ZeaapiNI4pAqPjiDvpNsMx6r5XmJmonsk2jqP1a0tOZ/6J0yLHubIFmZzMcWskwUxDRlfiTo6jY0WUaAdqHJppkkpqSmrWOMMIw5zFRlfSSqeyYXQC7S5DbRgtJ07ma1IsOcroVsw0ZHQlYbZ7c6AaRjWmCIyuJI0wU8PoVsw0ZHQlrQwzNYxOxxSB0bWYTd8w4mGmIcMwjB7HFIFhGEaPY4rAMAyjxzFFYBiG0eOYIjAMw+hxOq7EhIi8CmxrtxwxOQ7ohNabJmfz6RRZTc7mk1VZT1TV6UEnOjF8dFtYvYysISIbOkFWk7P5dIqsJmfz6SRZXcw0ZBiG0eOYIjAMw+hxOlER3NJuARLQKbKanM2nU2Q1OZtPJ8kKdKCz2DAMw2gunbgjMAzDMJqIKQLDMIweJ1OKQEQGRGSNiGwVkadE5F0BY94rIiMiskVE/tNz/HPlYz8XkTtF5Mh2ySkiy8oyjpTlGReRaeVzHxCRbSLytIgsT0vGRmUVkRNE5LHyNVtE5LNZlNNzPiciwyLy3azKGefvO0OyZul5OlZEHhCRTWWZLvWca9nzVK+crX6W6kJVM/MD/BPwmfL/JwMDvvMDwJPAzPLr48v/DgLbgUL59V3AJe2S0zf2fGBt+f854Bng5PJ1m4Dfaed3GiHrW4Azy/8/GvhFmrLWK6fn2OeBfwG+m8XvM+m1bf7dZ+p5Av4G+Lvy/6cDe8rjWvo8NSBnS5+len4ysyMQkWOA9wDfAlDVA6o65hv2SeBeVd1RHvMrz7lJQEFEJgH9wK42yunlQuDO8v9/D3haVZ9V1QPAd4A/TkPORmVV1RdV9afl/78KPIUzQWRKzvL1bwXOA25NQz7P+9QtZx3Xtk3WMll6nhQ4WkQEOApngj1IC5+nRuRs5bNUL5lRBDhafTdwW3mLf6uITPGNeTswVUT+Q0Q2isinAFR1FPgHYAfwIrBXVR9to5wAiEg/8AHgnvKhQWCnZ8gLpPsH0Yis3nOzgHnA+ozKeSPw18ChlORrhpyxr223rBl8nr4BvANHGW0GPquqh2jt89SInBO04FmqiywpgknAmcA3VXUesA/w2/wmAWfhrP4WA38rIm8Xkak4K4GTgBnAFBG5uI1yupwPPK6qe8qvJWBMmvG7jcgKgIgchTNBXKmqv8manCLyR8CvVHVjSrI1Rc6E17ZV1gw+T4uBkbIsZwDfKK/QW/k8NSIn0LJnqS6ypAheAF5QVVdTrsH54v1jHlbVfar6MvADYC7wPmC7qu5W1RJwL/DuNsrp8gkqt9svACd4Xr+VlLbcnverV1ZEJI/zh7tKVe9NTcrG5FwIfFhEnsMxDZwjIndkUM4k1zaDRmTN2vN0KY5JWFX1aRz/xRxa+zw1Imcrn6W6yIwiUNWXgJ0ickr50Lk4jmEv/wr8vohMKm9nz8axt+0AFohIf9k+d275eLvkRESOBf6gLLPL/wXeJiInichknAfw/jTkbFTW8vf4LeApVf3HtGRsVE5VvVpV36qqs3C+z7WqmsrqtUE5Y12bBVnJ3vO0o3wcEXkzcArwLC18nhqRs5XPUt20w0Md9oOzndoA/AwYAqYClwOXe8Ysw/kF/Bxni+Uevw7YWj7+z8ARbZbzEuA7Add+CCdq4Bngixn5TqtkBf4Lzjb7Zzjb3RHgQ1mT03eP95J+1FAjv/uqazMsa2aeJxxTy6M4dvefAxd7rm3Z81SvnK1+lur5sRIThmEYPU5mTEOGYRhGezBFYBiG0eOYIjAMw+hxTBEYhmH0OKYIDMMwehxTBIZhGD2OKQKj4xCnXPKI52eWOOXJVUT+3DNuXvnYX0Xc69sisr1cOvgXInK7iAx6zh9bPvZM+ef2chKWWxL9uwH3u0BE7ivL9rSI7PXIWpWhGzW2XFdrm+fYmvI115Y/22977vO58rH55dfPicjm8md7VER+q5Hv3eheTBEYnUhRVc/w/DxXPr4ZWOoZ9wmc0sS1WKaqc3EyQYeBx8qZquBkhD6rqrNVdTZO2YCaVU5V9U9U9QzgM8APPbL+uI6xF3mOXeC5dHP5M7pcQHW266LyZ9uAUybZMKowRWB0EzuAI0XkzeW0/g8A34t7sTrcALwEfLC82j4L+Kpn2FeA+SIyu4ly18sQ5bLLInIysBenQmYQPwB+O+Sc0eOYIjA6kYLHVHKf79wa4GM4RdJ+CrxRx/1/ilMs7HeAEVUdd0+U/z8CnFqP4HWyyvN5V3qO/wan/s1pOP0EVkfc449wdhCGUcWkdgtgGHVQLJtSgrgLZ0Kcg1NRs56qmeL5N6gGi3s8rD5Ls+u2XKSqG0LOfQfHPLQYp+DZpb7zj4nIOE6dm2uaLJfRJdiOwOgq1KkSWQL+EPh+nbeZh1NtcwswT0QmnpPy/+eWz/8ap/CYl2nAy3W+bz08APwpsEODa9wvKvsWPqUpdkQzOhvbERjdyJdw+lmPO66CeJT9Clfg9Jh9WFUPiMgwzkr6K+Vh1wA/VdWnReQIYIaIvENVnxKRE3GUxEgTP0skqloUkatwKnAaRl2YIjC6jqDInBqsFJG/xenNuw5nFX2gfO7Pgf8hIk/jmISeKB9DVd8Qp3PXbSJyJM5O5DOqurcZn8PDKhEplv//sqq+z3tSVb/T5PczegwrQ20YhtHjmI/AMAyjxzHTkNETiMj/xOlv7OUmVb2txXLch9MU3stVqvpIK+UwDC9mGjIMw+hxzDRkGIbR45giMAzD6HFMERiGYfQ4pggMwzB6nP8PXaH4MNL07zIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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GGtYI+iRurStw+z4ueXiz6791Go4SAEt89GBMcfv/N9lDCRwIRGSyeK3+Imoybimiwz73MZzcnsb9i+e19IKjevlZR2CZ+bFgN7xLzpsKwP37uGnPYdfPtoajctkMBKWgct/ieZFuuekUnExnLol6dH1F5HsAnlTVXSJyEoAXUCoedxzAl1X1H4y1zEVnZ6f29vaG/bHUwmYue76uf79v1RUNakk81Xt9nCycNQVrbnIsRhz489rTbZg4IeVZNwgoBey7rpzTFAHbRNaQiGxTVXsWJ4DqcwSLAdxT/vtflP87FcC5AP43gNADAVHcLFy1MRFlJhpp057DWPLwZtdgEMSx4giOFUeqnnfkWHF0/iHu3yv7JvamVRsaGtITXYZFAH6qqsOq+ho40UwEIDllJhpt057DoX+mAvjOuldC/9y4qxYIPhSRT4nIVACXAHix4hgTrKkluBUxy2bSvgucmU7vS4pGbk7vxk/vIWmqBYJvoFQDaBeA+1T1TQAQkcsB9BluG1Eo3Cbnll81B5uWXYr7F89DOlU9P8JKQ6zcyWvhqo1N31MIa29fK+snDF6ZRElUbXgnV95dbAxVXQ9gvZkmEYXLGot1m5yzH3dLr0iJoKcvj2892Q8r4Sg/WMC3nuwf8z7N5vr5Zxi5Qdu3ifRT4K9RohiWirNqWUO/VlV76ehIMWuIouaV1dKebnMcemhPt+HVe75gslkNV7k4q9GcsoZqyU76SErwwXBt7Wv1bC+7erKGiBIhSLpezmU1ajaTdk1hbLZx6UYP06RTUnX/3yAF/qxicEtbvPx3WKoFgvPKJaTtBICq6vkG2kQUKqvIl1XfxcoCApyHc7oXzUb3U9vH7dd7dMh9e8Vm0+hhmuKw4u7ndmLp2n6IANb9vjK3v9oQlFMl0FoDgX1YKumqBYI3AVwZRkOIouJV5MspEHR15HD3czvHlV4o1jhEEUcmhoOs61X51keOFdH9dKmUx4quufjZtoMoOPSeUiINW6vhtZgtqaoFgg9V9a1QWkIUkVqKfNW7SUvcBRmmqVdxWPGdda947lU8rOrYSxPAdfLejmVA3FULBOeKyLcqvlYA7wL4v1YqKVGzc6tA6VXkq57N3JvB2VPb8fo7R0P7PD9zKIXiMG57stR7sG7oEye04cPj1f/txJQwCHioto5gNYCTK/6cgtKOYz8XkT833DaiUNRS5Mvt33hl3H/23pfraGV4evryoQaBIKyegbU2w08QAIChYQ1lsVqz8uwRqOrdTq+LyBSU6gz91ESjiMLktY7ALZvI7d94TV5a5Zjj/GTa05cPXH47bF7zN15q3dIyCWpKH1XVwyxFTa3EqchXT19+THZQfrCA7qe2jznf/m+cJpEr1XIDC9Mdz+zwXX47SrVs0hLWnEczqikQiMilAI40uC1EsbL82Z3jUkSLI4rlz+50vZlXu9eY3GUqKKfeTr37EofFmr/JpNscs4ychLkFZrPxDAQisgPjJ+WnADgE4CumGkUUB26LwwYLRdchnmo18aPaB9fOa+1EM7Dmbz4IsFDvpAnckNFNtR7Bn9q+VgC/U9V4ziQRhcRtwZlXOqPpXaaCcFs70Sys6x4keytI0EgazxCpqm/Z/uxnEKBmVEtF0MntaddjTmWne/rynjntK6+eG4v5gZ6+fMukvgYJrHHpjcUR+0rU8qxhkHy5cqjfjWTuunKOZ/lp+3i/134EuWwmNkEg7llBfljfO7/XNE69sTgyWnRORLIAHgHwKZR6zF9T1c0Vx5cA+Hb5y98D+M+q2vw/pRQrfktIVFbbTIng+vlnYPW1F7iueLU/YXo9ZVsbrDt9Rpgpjas37G6KrKBqvGpBOYlLbyyuTPcIHgDwQnlPgwsAvGY7/iaAPy4Xr7sHwEOG20MJ5KeEhFVt07rhD6visS370fvWYfz1ly7wteDMawOXx7bsx7l3rHf8jDAXOsUpa6keQXeEYxDwZqxHICKnAPgMgK8CgKoOARiqPEdV/7Hiyy0APmGqPZRcfkpIuFXbrFyEVJlqecl5U0eraQKlEtTV8tSHXIrShbnQqZVKY/gNaqw0Wp3JHsHZAAYAPCoifSLyiIhM8jj/PwL4ucH2UIsIOvHrp4SEV7EzoPREuWnZpXhz1RXoXjQba//pwJiFY9XSRr2EudCplcbJrUB+w4IZruew0qg/JucIJgC4EMCtqrpVRB4AsAzAd+0nisglKAWCP3J6IxG5GcDNADBjhvs3nVpf0L0DrNd73zo8Zmz+motOrAr2CiQCYNbt68eM6b+0a6ChJafD2hMYKF2LVtjMpTKQW72pKOdemp3nVpV1vbHIxwFsUdWZ5a//HYBlqnqF7bzzATwD4Auq+i/V3pdbVSbbwlUbHYc2ctkMNi271PHf2IMHULqRrLy6dKO47antkU6gnjNtEo4NjfjaHa0RatkSMm5YUjq4SLaqVNW3ReSAiMxW1d0ALgPwqq1hMwCsA/Af/AQBolr2DvDKGjo2dDzyLJo33jk6uv7ATw+HeG0azXTW0K0A1pS3u5wH4AcicouI3FI+/j0AHwPwYxHpFxE+6pMnt0VBXouF3IJEfrDgWSAuLPYwFDQjJohmKYVN4TIaCFS1X1U7VfV8Ve1S1SOq+qCqPlg+/p9UdbKqziv/cey2EFlq2TvALUiEOTYfVH6w4HsFdBBx3WcgiDh/35oVVxZTU+nqyGHl1XORy2YgKM0NVFss5BQ8BPEvS+x3BXTSXD//jKib0HKMriwmMsFpH4Bq5wMn1gGk2gTHm2R1ba2bsLQqAZgNZAADASVCZfBotqyZVlkN3AjNEb6bD4eGiGIu61EFNWlyrCBqBAMBUczFfCojNKwgag4DASWOR2XpWHqvjvIVrYQVRM3hHAE1Lac9d91uFJXnnppJ11UbKGzcUKVUT4hBwBz2CKgpOW02s3RtP+bd/eK4dEv7uYOFItJNspO54EShuFp2WYszAZDy8X24YcEMZgoZxh4BNSWnshFA6SZfuQn76g27HWsTFZskfVQBLF3bj2+u7Udbm4yWw2j2UhT7VpVKjlk9NbfS2CkRBoEQMBBQaIIM5VTjlVJZKA6j+6l+TEilmmpDdi8KjKuJ1AprDKy0XmtjIDsuHgsHAwGFopby0V6y7WnPOkHFEaA40hpBwEszrjHIZsanw7KUdLQYCCgUfvcN9qsRKZUfSQk+aOC+AlFotonkNgDLr5rjeGxF11ze+CPCQEChqKV8tBNreKkRWT/NHgSaMa8+1Wy5uwnBrCEKRS3lo+0qs3+oOfPqi8NqrMQ21Y49AnLkNrFb64TvJedNdZwMvOS8qb7b5JYplEQnTWhruiBgacZ5jVbHQEDjOE3sLl3bjzue2YHC0DBGyuflBwvofmo7et86jJd2DeDQYAHZ9jRUS6thKwPFS7sGHD+r8vU7e3Z4ThbyBnLCh8dHsOThzU25MXuzzWskAQNBAlR7iq88nm0vrbp1mow9OjT+abw4omOe9CszeazMoN63DrsO51g3d3v64LCeeF8rGEzPZjgsVGHTnsPo6cs3Vc+gGec1koBzBC3OaQVu5WYnPX15dD+9ffT4kWPOQaBWheIw1jgMCVmsp8Mnth5wPF75utMGM0nX/fT2QCuMF86aYrA13rKZdFPOayQBA0GL80rbBIC7n9uJouHsGa93t+YI3HYLq3y9qyOHay7KcavCCsVhxW1P+g8Ga266GCdNCPfXPpfN4P7F89B/1+cYBGKKQ0NNpJaJ2mppm1Fv3v7Ylv14adcABM4Bo02AOd97wXFYikqGVQMtzvvhNeej+6ntxststAG4d/E83vybAAOBYZ+99+UxG4afM20SfvGtPwn8PrWuzHUbV4/ThJ3XuP+IOs9N0FhBFudZ5yx/dqfRKqwMAs2DgcAgexAAgNffOYp/+531GB7RQOmXQVbm2ksup1MyZvincsIu22QlmcldkIl0p32f5939ouPPgltvzUsum2EQaCIMBAbZg4DluM8Kkk6BxM4+9GPvOVgllye3pzF47ERKJwAsXLWRQaCF1Dt3svyqOWN+doDSQ8M1F+VG04P9BITK0tnUHBgIPFTLa2+EQnEYS9f2Y/WG3aO9gyUPb8amPYd9/Xv7EI9Tz6E4omifOAF93/scgBOZQqYniSlcbhPuflkPI17zUDOXPe/5HgJgCTeRaTqJCAS13ND95LU3krU4629eer1qL8CSbpNxT15+avq4ZQrVMgRArcVpyKjSDQtmOK4QB0rDQfWUFqfoiDbZztidnZ3a29vr+3y3Ouf2XY/sGTm/ea8Ap6SKlAj2rLzc12f7GdpppHOmTcKxoRHHseKUCEZUuSirxVkbvpgURk+ZGk9Etqlqp9Mxoz0CEckCeATAp1B62Pyaqm6uOC4AHgBwOYBjAL6qqr9uZBse3+r89PL41v2jP7xOGTlugnS/p518UqiBwOuzrHaHFQTa0204VhypfiI1TFjrK1guuvWYXlnyAIAXVPU8ABcAeM12/AsAzin/uRnATxrdALdU6crXgxQz8/urFmSc36Qo1l5Nbk/j6os+Ef4HJxx386JaGesRiMgpAD4D4KsAoKpDAIZsp30RwP/R0vjUFhHJisi/UdXfmGqXkyDFzKz4Yb/RL5w1BWtuujj04aBqohj5O3Ks6DqOTGZwg3eqh8mhobMBDAB4VEQuALANwDdUtfIumQNQWWTmYPm1MYFARG5GqceAGTNmNKyBC1dtxKHBAtpEAg35OD3tb9pzuGpGBZEJuWyGQYDqYnJoaAKACwH8RFU7ABwFsMx2jtPAxbg7sqo+pKqdqto5dar/+vXVWIXWgqbdRTnkwyo7ZMecfaqXyUBwEMBBVd1a/vpplAKD/ZzKgc1PADhksE1N777F87Bv1RUMCASgtDKc6ZpUL2OBQFXfBnBARKzHlcsAvGo77VkAX5GSBQDeC3t+oJkITiz6iVOtIIqO20bwREGYXlB2K4A1IjIRwF4AN4rILQCgqg8CWI9S6ugbKKWP3mi4PU1NcWJew6mGECUPewPUCEYDgar2A7AvYHiw4rgC+LrJNrQaax2AvYYQwFXBRFSbli8xsXDWlFjk85tgryEEjF0hzcBARH60/A5la266ONLt+Uyzr4Ho6shh07JL8WYIpQaIqDW0fCAAgOs6ZyCXzUBQyrnOZtJ1v+c50ybh/sXzRt93Qls0eTycNCaierX80JBTHaF0SpBuk5q26rNWEFusyTqTi8ly2QwuOW8qfrYtP65WvFsO+ZKHNzu+Tq2jEQ80REACAoFjff5hxeT2NNonThjNwDk6dNxXBk5Y8w0CjBve6Txziq89i3v68i07L0InMHWUGqXlA4Fbtc0jx4qOk6xhlmj+SErwgUvwcXq1Wq144EQPiFofU0epUVo+ELSJcwVS+5C+/SYbdKjnnGmTAhWbmyDAhzUMTVUTpJIqNa+wSk5TMrR8IPBThtrOa3zdKQOppy+PY0PBau8frxIDJqZkdPFYtU3umTKaPCw5TY2UiKyhIKrtI7Bpz2HMun097uwpDb9YQzGNHlJSnCiKZ21y39OXH3de5eczCLSmNpzowaZEWHKaGq7lewRenJ64/UyyVu5f/NKuASNDMfaJ60JxGKs37B7XK+BQUGvLpNuw8urzOR9ARiU6EFhP8dYTd1BPbD2AkRB3fnHaQCfIpjrUfKZMOolBgIzj0FCZ9cQdxHB5M/hGmzQx5fi602dxQVlrY6CnMLR8jyCXzfgevz80WMBJE9rw4XF/E78pEXQvmj1mwVq9blgwA51nThn3npl0CjM/lsGs29djWBUpEVw//4yGfz7FixXoKxMCqiUPhCmu7aJgWr5H0L1oNjLpsU/Ybol307MZ/PCa88ellrq5fv4Z6OrIYeXV9U/cZdIp3L94HlZ0zR19z8qyGBfOOBWb9hwe3U3NmqfofevwmHOpdVgrx+0JAV7JA2GKa7souJYPBE431SULZowLDgDQPrENXR053PuleWPOr1a0rqsjh1wdQzS5bAYrr5475kmqqyOH7kWzMT2bwaHBgusk9hNbD6CrI4eZH8swaygmnH62/BLB6M+d9TPhlBBQy1Bmo8W1XRRcyw8NAc4rcrfu/d24BWCvv3MUn733ZRwbGhnT1b3tye2O7/vE1gOjaXzdi2Zj6dp+x/MEpS0mg3Sh7TWS3AyrVk15pfBkM2ksv2qO689CVTq+tIjbPEHU8wdxbRcFl4hA4MRtFXDl61ZX121z+8rXuzpyWP7sTgwWiuPOm57N+CoPAVRfx2CXEmEQCJlVBPDxrftHFyba0zxrDQRtIujpy4/5WZnuMs8VdaJAXNtFwbX80FC9vJ7I7cv8l181x3E+Ij9YwMJVG6uOndbyZM8VpuGyxu1XdM3F3pVXYN+q0p/X7vnCmJv3DQtm1PT+w6rjxtmd5rm8Ks+GJa7touAYCOpgvwlXzkcApSBg9Rn8TKQF7QlwhWm4UiLj5nLcdJ45ZdwvVxuAye3VS0fbx9md5rn8tsOkuLaLgkvs0FCQNFE7K3XTugnf2bMDT2w9MCat86VdA+O6zW6rg/3KpFOOv2itvB1nXAiAv/7SBb6/d6s37Ib9p2sEgGrp+1ht7sdp57k43mDj2i4KJrE9giBpopUEwJ6Vl48JAo9t2T8urdNt7UKtE2leT1trbroYp588sab3JX8Uwco+u32f3ysUxzxFu1UR5Tg7hSmxPQLrlzroHgT2X9Anth4I9Llev+ATU4Ihh/0JJqYEm5Zd6vm+W+/47LieidskN7lLtwFFh45i0PRgr4nUyqdop+wwjrNT2BLbIwBObPTul9MvaJCbbbVf8L+69oJxi8Kk/LofK7rmYs/Ky7Fv1RXYs/Jy3+2iEwv6Vl83ryEToH4nUjnOTnGQ2B5BJbcx9nOmTRq3psD+C+r3yTvnY+1AZS+lEUv23Z5uaSynSeB6vwdBvpccZ6eoiTbZ8EFnZ6f29vY2/H3tqZv2TerdWHMEXnLZTKCeR6ME3WUtidwm4IlajYhsU9VOp2NGewQisg/A+wCGARy3N0JETgXwGIAZ5bb8T1V91GSb3Pi56TuxJo2tsXm7dEo43htTfnppREkQxtDQJar6rsuxrwN4VVWvFJGpAHaLyBpVHQqhXQ2zomsuVnTNRU9fHt1PbUexch/MCDtcbvs1J10bgHsXzxszYcsKmpRkUU8WK4CTRUQAfBTAYQDHo21S7VZv2D02CAAojmhkRbi+PL+21a3NRlCq8eMnHTiTbhsXBFhBk5LOdI9AAbwoIgrgf6nqQ7bjPwLwLIBDAE4GsFhVx01visjNAG4GgBkz4ntzi1sRrhVdc/HmwO+bYrHZDQtmjFuU13nmlNEn9TaXSfnK+Zdanuy9KmiyV0BJYToQLFTVQyIyDcAvRGSXqv6y4vgiAP0ALgUwq3zOr1T1XyvfpBxAHgJKk8WG21yzuBXh6unLY/Pe+AeBXDYzOrxmFyTfvpbsm7gFb6IoGB0aUtVD5f++A+AZAJ+2nXIjgHVa8gaANwGcZ7JNJsWtCNfdz+30PUeQbhOkIxgoTLf5m0w3lW/vFqS5speSxFiPQEQmAWhT1ffLf/8cgL+0nbYfwGUAfiUipwOYDWCvqTaZ1uh1APU6cmx8SexKufKmN6dm0jg6dByR7HYZoMyHiXx7p60+ubKXksbk0NDpAJ4pzQNjAoDHVfUFEbkFAFT1QQD3APg7EdmB0i3h2x4ZRk2hWRYHVY6tL1y10XEfhTAUh9V1PN6pmF+jq63GLXgTRcFYIFDVvQDG1UYoBwDr74dQ6imQAdlM2vUGX/nEG/V4uNPn2xfqWcX8ABgJBrzxU5JFnT5KBi2/ag7SDjmVNyyYMW4HrCg5fb5bMb8nth5AT18eC1dtxFnLnve14Q8ReWMgaGFdHTmsvu6CMROs9y+eN+6J2mmSux5upZWduI3He20Pyrx/osZi0bkW52fYwzrutOdyuk2w+NNnVK2nZLFq9zzVu7/mQn6AdzE/5v0TNRYDAQE4ETDcFmU57bgGlLZebJ84Ydz5XR25mgv5AaVtQP0GHyD6eQ6iZsZAQGO49SDc0izvunKO65N4rYX8gPHF/Ly2AAVOzDOwbhBRcCxDTb7F4SbrtsJ45dWlwOF2jMGAki6yMtTUWuKQZumV979w1UbOHxDVgIGAmo5bQGLdIKLaMH2UWgbrBhHVhoGAWkbciv4RNQsODVHLYN0gotowEFBLicOENlGz4dAQEVHCMRAQESUcAwERUcIxEBARJRwDARFRwjVdrSERGQDwVtTtAHAagGbbVpNtDk8ztpttDk8U7T5TVac6HWi6QBAXItLrVsAprtjm8DRju9nm8MSt3RwaIiJKOAYCIqKEYyCo3UNRN6AGbHN4mrHdbHN4YtVuzhEQESUcewRERAnHQEBElHAMBAGIyH8XERWR01yOf15EdovIGyKyLOz2ObTnHhF5RUT6ReRFEZnuct4+EdlRPi/SDaEDtDlu13q1iOwqt/0ZEcm6nBena+23zbG51iJynYjsFJEREXFNv4zTdS63x2+7o7nWqso/Pv4AOAPABpQWs53mcDwFYA+AswFMBLAdwB9E3OZTKv7+3wA86HLePqf/p7i2OabX+nMAJpT//kMAP2yCa121zXG71gA+CWA2gJcBdHqcF5vr7LfdUV5r9gj8uw/A/wDgNrv+aQBvqOpeVR0C8FMAXwyrcU5U9V8rvpwE97bHhs82x/Fav6iqx8tfbgHwiSjb44fPNsfqWqvqa6q6O6rPr5XPdkd2rRkIfBCRqwDkVXW7x2k5AAcqvj5Yfi1SIvJ9ETkAYAmA77mcpgBeFJFtInJzeK1z5qPNsbzWFb4G4Ocux2J1rSu4tTnu19pNXK+zl8iuNXcoKxORfwDwcYdDdwD4DkrdaM+3cHjN+BO4V7tV9e9V9Q4Ad4jI7QD+K4C7HM5dqKqHRGQagF+IyC5V/WWM2xzLa10+5w4AxwGscXmbWF3r8jlebQ79Wvtpsw+hXmegIe2O5OcaYCAYpar/3ul1EZkL4CwA20UEKHWffy0in1bVtytOPYjSPILlEwAOGWruKLd2O3gcwPNwCASqeqj833dE5BmUuqjGfmka0OZYXmsR+QsAfwrgMi0P+jq8R6yutY82h36tA/x8eL1HqNe5/Fn1tjuSn2uAQ0NVqeoOVZ2mqjNVdSZK36wLbUEAAP4JwDkicpaITATw5wCeDbm5Y4jIORVfXgVgl8M5k0TkZOvvKPV8/jmcFo7np82I57X+PIBvA7hKVY+5nBO3a121zYjhta4mbtc5gOiuddSz6c32BxXZCACmA1hfcexyAP+C0sz/HTFo689Q+gV4BcBzAHL2dqOUobC9/Gdn1O320+aYXus3UBrf7S//ebAJrnXVNsftWgP4M5Qexj4E8FsAG+J+nf22O8przRITREQJx6EhIqKEYyAgIko4BgIiooRjICAiSjgGAiKihGMgIGoAEfm4iPxURPaIyKsisl5Ezo26XUR+MBAQ1UlKS86fAfCyqs5S1T9AqSzJ6dG2jMgflpggqt8lAIqq+qD1gqr2R9ccomDYIyCq36cAbIu6EUS1YiAgIko4BgKi+u0EcFHUjSCqFQMBUf02AjhJRG6yXhCRPxSRP46wTUS+segcUQOIyHQA96PUM/gApSq1S1X19QibReQLAwERUcJxaIiIKOEYCIiIEo6BgIgo4RgIiIgSjoGAiCjhGAiIiBKOgYCIKOH+P2pNtln5CSfqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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lemCPgCgEVm/eOxYEDLlRxerNe31/bbvN7I37jc1W7KZbetmTqdSWanx78cVV/9vigeFGDQJAFYFARKaKeLwrAlHMDQ7lXN3vJavN7FPJxNhc93KbrXid06/UlmrU0qPyuu5/WJUNBCLy9yJyYeHnM0TkKQD7AbwqIn8ZRAOJyF/GZvbp5pRllctym62s3rzXMqd/26N7quohVGpLkOK0f3OlMYLrAHyz8PMXCn9PB/A+AD8G8G8+tYsoVqZOSuL4ydKr/6mT/F9VCuRPwNWcbO16LEbwMHoIxmv42RavmYNAso5jBDO7tvg6NlMpNTSsp4sRLQTwiKqOqOpz4EAzkWdWLboIycT4jGsyIVi1yP9VpX4byo3g1vV9FUs4APnxiNkrtmJm1xbMXrHV0b8JSr3HCJyWwqhGpUDwtoi8X0SmA7gcwJNFj5XsLUBE1eloS6N7yVykC4OiCRHkRhTd2wbqMp/fD5VOYpUGpWsxeWKi8kFF0qbB6TD9Dh7a5W5XMycqBYIvI18DaB+A76rqSwAgIlcD6PW8NUQ1qtdcfC90tKXHBkvNqZUo/T/KsRt4LvdYuX/jRKY3i+FT7i7nzYPTtz/u/+wtp8ptiVmtSumdtKpeaL5TVbcC2Op9c4iql+nN4quP9o19UbKDQ/jqo30AgqnX4oVyC6rq8X+oVITNrXLlGcoNSteie9tAydRcwH6j+eZUsuS9thq/aSSVAsFKAJuCaAhRrf520zMlV0ujmr8/DIHASQ0dPxZUVcNI03jNrmCbnz0eu/dOkZ+aWhx4U8lEINU+0w43ubHidJ9jN7igjBpCpjdru3q02lWltTCnqFZm+h3Nt/djQZVbXgSBBbOnWd4/olqSssv0ZnFbYRW1nVp6JnbvnTE1tbje/ztsTrJe7wmwo+uKilVMrcY1mgCsqWGBnJ1KgeBCEXnG4k9/obQ0Ud0Zi5oqHRMUq0VW63YetEz53Lq+b9zsmJnvsj5pTZrYVDL24dd4SK05eQBYd9NluH5+i+XJzhwEv/5YP0YqJL537D9WdVsqLVJ7u2j84PjJnGWA9rqXsGDtdtuU1+SJCXzvukuw9xsfw/euu2Tcmop/uu4SX3q3ZbeqFJG9AK62e1xVX/a8RRVwq0oyW7B2e8VudnMqib5VHw1Ne6y4qYmTTAigGJf7TiUTniy+mtm1paZ/D+TXPwyezGFGcwonh09Z5tjTzSns6LrC8evVsl2jXVrO7ndltK3YrK4tNW9T6VQyIeheMtfTk365rSorjRG8XY+TPZEbTvLnQZRqAPInnGpzv25SWLmR0lNSLYPKxomy2rabGSf+cs8X5LiH3SI1N2Myf3L2ZDz/2gnP22YlN6K4/fG9gY1tVQoE7xORrxbdVgCvA/g/xlRSonqb4XDgbdl9T2PdTZd5+trFV5rNk5L4wx9Pefr8blVzcjVSWUHX2ncz7nHB2ZN9a4PVZ8fctpWZ/sCCgCHImUqVxgi6AUwp+nMm8juO/UJEPutz24gcscoBW6m0DaFbmd4sOjfuGRsLOH4yZzlNMUjVDCrXY8MVI0fvdFzj+ddOWC4scztOYj7+8gunOypyt86jGVRNIS3XWbZHoKq3W90vItOQrzP0iB+NInLD6D47SW3UMuhodvvjey1TNPWSbJKqqnR6lQ5yYyg3gtsf34tBF1e9xTOZjL0RimUHh3Dr+j7c/vherFp00djnojjtVbx2IDs4hJ/tzuJTH0jjqX1Hy07r9eq3PKqlU1btBBkzqqoXpKrHWIqawsT44gaZ4gjbIqMRzeeVv7K+D2elkhApbeOC2dNK0mMJkUD34TVU8/6t23mw4knZmPljKP5MmP/tUG4ET+07WjIw7Kc1i+fgtkf3VHzPg/yNVLWOQESuAHDc47YQ1aRee8qGxajmT4KK/OC41YnWKj1WjyBQLactNXocTj4TQS/W62hLY9TBe26ud+SnSvsR9FusITgMYC2AvwmmiUTOOPlC2y10qkZU+8Q79h8bl0sP8oQTpOMnc47SXk7GVa6f3+JFk2AUmK30mrVuxuNWpdTQx023FcAbqhrs8DmRA5VmD1mlRWoRoQvpErdt2IOvrO+DIp+LTjRJxUVdjcjpCfeOjjmelNyY8o78CuXOha0laUxj/CJtM07hp0qDxVxDQIFzUpPHitWXy6tFVlZqqRdTb8UnfTXdjpMz35Fw/Nnw4vf9ZmE9S/EEB7efcz9wcxkKFfOcdjc7XHn95aoUkKwCD0XLq28NO15f4sXvuzglFJad2ACfA4GINAO4H8D7kb/w+KKqPl30+DIAXyvc/AOA/66q5atPUUOrtQyzV1+ucgGp5+VjltMXKZqcTik2T1M2Zls1p5I4MXzK0VTiIPP+bvjdI7gLwBOqukREJqJ0V7OXAPyFqh4XkasA3Atgns9tohALSxlmu4C0YtMzGKr3noVUN3YXGube46mREbz61vDY402AbwXjvOBbIBCRMwF8CMANAKCqwwCGi49R1f9bdHMngPf61R6KBqdL/v1mF3gYBMiKESCMgPDaW8N1GfStlp/7EZwP4CiAB0SkV0TuF5FyBUP+CsAvfGwPRUClksFO1VqieZLLPW6JrMqPR2WbUT8DwQQAlwK4W1XbAJwA0GV1oIhcjnwg+JrN48tFpEdEeo4ePepXeykEOtrSWLN4zrga7MasH6cndy++kCeGOQBM7pQb3wo7P8cIDgM4rKq7Crc3wiIQiMjFyA8oX6Wqb1g9karei/z4Adrb2zlC1+Cs8rBuZhOFbd9fioewjG9Vw7cegaq+AuCQiBh9+isBPFt8jIi0IL8n8n9T1f/0qy0UfW6utqL8haToCsM2o9Xye8/iWwCsK2xreQmAb4vIzSJyc+HxvwfwLgA/FJE+EeHWY2TJzcm9li/kykw/Zq/Y6q5xRPBufKsefJ0+qqp9yO9fUOyeosf/GsBf+9kGCi83K4jtZhOdlUpiwdrt457DboVxpS+kF5u2U3yFbbWwG2X3LA4j7lncGKx2xSpXDiLTm8VtG/aMK4UgACYkZNxCHgGwbH4L2s+b5voL6cVevRRNteyHHBW17FlM5Au3A7o9Lx8rqYejKN27V5HfwOShXQfxuXktuKNjjtdNpwYzMRHRMrIe8nuMgMiS2wHdh3cdcvX8o5oPCFbbGxIV+86SufVuQt0xEFBduB3Qrbauj9sAQvEThRy+3xgIqC7czrBIVLkLDAvDEVXGQEB1UW4FsZX550+t6nWqDSBEccLBYvKFk6mhbkpGH3ijusVgI6pYsHZ7ZKbxUbAmNPFCAWAgIB/YlYPoefkYntp3dCw4XH7h9HG3zSfr4mBSS4LH6eY2xlaBFB+nRnXctGGvtzONCqaGyHN2U0Mf3HlwXCE48+3iwnDmwnF2kk35E3glTop/MQjQjv3HsOy+pysf2GDYI4igavf0DUq1NX2GciO4dX0fbl3f5/jfuNkegLWGyAmnO5Y1EgaCiKllT9+g2JWDqLezUkkA4Q+kREFjaihiolDz3GpqaBiIRHvzECK/sEcQMVEosWxVfOv4ibdxss7bPB4/mbNMO4UtkFJ9LZg9rd5NCBwDQcQEvadvtWkU89TQsFf2DGMqi/x35hkJ/P7t0z3suM4aYiCImM6Frfjqo30orr/WJPCl5rmX4xGbdh/2vH1eEgG4CDm6pk5K4vjJnKt/873rLuHYUAEDQcT0vHwMpiKcGNX8/Rt6Do6b8WC+ujFf3Zvn8Ztvn3j7lGdbPtY7LVQJg0B0pZIJrFp0Ebq3Dbjq2TEInMZAUGduUy/rdlmnV6zSLjv2H8O8b/0SExIJZAeHxi2YMubxo8xtO07GI8z/LyIr1S7iSzenLL8z5j0u7MRxHKAcBoI6qpR6sQoSbq9cX31reOxnry56J1SYa5bpzaJz456xvQKYfyc7373uElfrRgw7uq4ouc9uh7BKPWViIKirSlNB7YJEvZmzPOaAdfzE2yUbxhCZJUTQ0ZZ2HQiaC+tBrNjVrzrwxtDY5/PT7S1um9rwGAjqqNxUULsgERbGPsFnpZI4MXyKV//k2tJ55wLIX6E7Xc2bbBKsvvYix68RhQWYYcAFZQ5lerNYsHY7ZnVtwYK122tegJTpzaLJpkTyjEL+M8yMBVmDQzle/cdMQgQC5yW+Lzh7cklOfsHsaWPbiK676TJccPZkR8/V/em5rk7gUViAGQYMBA5kerPo3LBn3GrUzg17qg4GxlWK3aYpl184nQOsFEqpZAJ3fmYuXlp7De78TPktHg+svQYH1l6DL11+AX5z8M1xj/3m4Jvjvj9nTznDl/ZGYQFmGDAQOLB6817kTHM2c6OK1Zv3VvV8VlcpxZ7adxSdC1sdVdUk8tvkiQnLzYMqXZkbvehb1/dVvCp3mhpyWw7E7ZaoccUxAgcGh6wXqtjdX0mlq5Ejg0NVDaIR1SIhMq6XmhDB0nnnjqVw3Ko0lbOaq3K361g6F7aWtKPclqhxxUAQAPOsmuYKqyBnNKeQ6c1yoxQKTLJJXOffK6k0uaHaq/JqFo2x2mx5DAQeWZnpx8O7Do27okoXVuv+bHd23KyFZJOgSVCyQhg4fbXSvW2AQYCqcmDtNQCAWV1bHH2GmlNJrL72oqpOjukqS46br8ovOHsynn/thOvnccLNlqhx5WsgEJFmAPcDeD/yF7dfVNWnix4XAHcBuBrASQA3qOpv/GxTNcrVMcn0ZtHz8jHLlb3ZwSGs23mw5MtoHm8ofp1Viy5iWog8YVegMN2cslyQVQ271Ms7kk1le73FYw2Z3iwOvHHSk/ZQdfweLL4LwBOqeiGAuQCeMz1+FYALCn+WA7jb5/ZUZdUi+3nL3dsG8PCuQ7aPu72qN74cDmfmEdmy2hfC6/x4R1saaxbPQbo5NTagfGnLWRh0UQCue9sApyDXmW89AhE5E8CHANwAAKo6DGDYdNgnAPxEVRXAThFpFpH3qOrv/GpXNcpdode6sXqx4ydzaPvGk66rKBJZCSo/Xpx6cVpuvHhRl9vUUhMvkjznZ2rofABHATwgInMB7AbwZVUtTgSmARRfTh8u3DcuEIjIcuR7DGhpcb88fNl9T9dca8QuFzqjOYVX3vyj7ZoAoLSwVrlBYAYBqsXkieN7AEHnx9c53HOiePaPebZSJawU6z0/U0MTAFwK4G5VbQNwAkCX6Rir2F7ya1bVe1W1XVXbp0+f7qoR5iAA5OcsL7vvaZt/Ya1cN9tYKm8llUxg2fyWcV3nZfNZ64S8l2gSfOuT1U319Iqbc7QxfdRNEHD7GuSMnz2CwwAOq+quwu2NKA0EhwEUn0XfC+CIl42wW6jidAGLoVw323jMataQXVf853t+V/U6BCKDXTnmKDCmj7rdFMhpaQtyzrdAoKqviMghEWlV1QEAVwJ41nTYZgD/Q0QeATAPwJthGx8o5rSbbV6IY5Wa+vjc94R660aKBq9m/wSteNDabaqnXA+cquP3OoJbAKwTkYkAXgRwo4jcDACqeg+ArchPHX0B+emjN/rcHl+YB8hGVMduv3T0D5apKbc9EiKzqG2uUmvvpdaVzmTP10Cgqn0A2k1331P0uAL4kp9tsCtx6+WX6CGbXcMe2nXQctEYUa2iuLmKXe+l0gp6L9c9kLWGX1m87qbLPJk1ZDXoDOSvUuxO9gwC5Id0cypyQaCccl8T1gUKRsMHAgCOvjTl9g62CwKA+xkPRLUI+4nRrnRKubn/dlOzEyLjViCTf1iGGvkgcJtpv4HbivYbYD6fwsBcBjqMzrDZ0NrufsB+avadn/G2CB7Zi0WPoJKvP9aPEdNlzMio4uuP9fODSHV3/fyWyAyQ/tG8oXWF+wFWCA0DBgIAJ4aty+Xa3U8UlCgFAQA4K5W0XB9zVpkN5wFWCK03poYciNo0PWoMiSZB+3nR+uzZrfXiGrBwYyCAdZ2L4vvX3XSZbTDgKkfyy8ioRm6Tdbuqo26qkVLwmBoCsGx+i+Uq3+KaQOVmHs3s2uJLu4iitsm63R4I3CM43NgjAHBHxxxcP79l7Oo+IRK53Cw1pqidQIPYA4G8xx5BwR0dc3jip1CJ4gmUM4CiiYGAKECpZAKf+kB63D7WxYxyC+Uq14YdZwBFDwMBkUesTt52K9bbz5uG2x7dU7Iy3QgCrK1DQRKNWImE9vZ27enpqXczxuFgMQHAgbXXuDp+VtcW2zo7Ud5ngMJJRHarqrkIKAAOFhM5lkzYTxWuZhKx3UCwAOPKnazY1D9W7oTIDwwEDq3M9GP2iq2Y2bUFs1dsxcpMf72bRAFKN6fQvWQurrfZZrSa7UetZthYlWQ29vcl8gvHCBwot/EMZxrFg5GzN29LWstmKVYzbKzm4APRW09A0cJA4IDdlpIP7jzIQBADCZGSQV+vKmOaZ9gsWLudC7IocEwNEVUw//ypWLGpP5C8PRdkUT0wEBCVcc6UiTjwxlDJnH+/8vYdbWmsWTwH6eYUBNHYg4Cij6khB+z2VDVmipwxoQlvn7Kvt07RZGxpOstmerBfeXsuyKKgMRA4UKkoHYNA4yleE8BCatTomBpyoFJROrtS1CxRHW4TbdYFmO9n3p4aHXsEDpUrSme3gT03tg+37yyZi6+s7xuX9pPC/cVYSI0aHQOBBxIiPOlHTELE1QmeeXtqZAwEHmAQCK9zpkzEq28Nl9y/dN65AHiCJwJ8DgQicgDAWwBGAJwyFzwSkbMAPAigpdCWf1TVB/xskx/SZVaE2uFMI+8JgO9ed0nJiX1lpt+TlcBEjSqIHsHlqvq6zWNfAvCsqi4SkekABkRknaqWXsKFWOfCVqzY1D9urrndlFPD8KnRiseQOxOarAd/uekQUXn1njWkAKaIiAB4J4BjAE7VoyG1FJWzWgS0bH5LyUyTYjMKxzSCVDKBBbOnOZolZbxHzalkSTXPZELQnEqOvYfXz28Z954W37Z6rVwEN3snCgO/ewQK4EkRUQD/oqr3mh7/AYDNAI4AmALgOlUtyZeIyHIAywGgpcX9ydNucxCDF0XlrHLN7edNw+rNezE4lBt3vzH1sKMtbVvHKOzK1cu3q5dTvOHKgrXbS96X3Ihi8hkT0LfqoxVfP+hFXkSNzO9AsEBVj4jI2QB+KSL7VPVXRY8vBNAH4AoAswvH/FpVf1/8JIUAci+Q35jGTQMyvdlxaRujTgwwvpKklYd3HaoppWAEh3KBaOqkJI6fzFV4pmAIgJeKFlLNXrHVciA8IVJ2By2rVJl53r3dCdvpiZyLvIi842tqSFWPFP5+DcBjAD5oOuRGAJs07wUALwG40Ms2dG8bqFgnxu91AB1taezougIvrb0GO7quGAsCmd4s/vDH0kxYuQ1Q/GQ+iRoza8zs7jc4qZdjd8J2eiLnIi8i7/jWIxCRyQCaVPWtws8fBfAN02EHAVwJ4Ncicg6AVgAvetkOJ1eedusA/F4Z3L1tALnR0tedPDH/azGnTryUSibKXrEDp9Ni1cy4qTQt00mvodLzA1zkReQFP1ND5wB4LD8OjAkAHlLVJ0TkZgBQ1XsAfBPAj0SkH/nMxNfKzDCqipMUwtJ551rm6itd+dbKLki9OZTDpIn2A821EgBrFs9xdBL1a8aNFydyrgEg8oZvgUBVXwQw1+L+e4p+PoJ8T8E3Tq48a7nyrUW5IOV2XYIbinCcRMPQBiKKwcpip1ee9ZhrXi5I3bq+z7fXTYdkQLXSbC4iCkbDBwIgvFee5YKU1bRTL4RlQNXJbC4iCoZoxOrktLe3a09PT72b4btMbxadG/aMG0xONonl4LITAoTqqtvJWgMi8o6I7DaX+THEokcQRXa9he5tA5YnUAHwX2dPw479x0oe+55F/Z16q3UdARF5h4EgxOxSWlZ1jZYVNsqJSt7dbkC8SQSzuraEuu1EjYaBIGIqDX6HdTzEzGqgHDi9iI9jBkTBYSCIoKic7MsxB7Qmi0V9xgrwqP9ficKOgYDqpjigsYgcUf3Uuww1EYDaaw8RUfUYCCgUWESOqH6YGqJQYBE5ovphIKDQaIRBcKIoYmqIiCjmGAiIiGKOgYCIKOYYCIiIYo6BgIgo5iJXhlpEjgJ4GcC7AXi6rWWE8b04je/FaXwvTuN7AZynqtOtHohcIDCISI9dbe244XtxGt+L0/henMb3ojymhoiIYo6BgIgo5qIcCO6tdwNChO/FaXwvTuN7cRrfizIiO0ZARETeiHKPgIiIPMBAQEQUc5EJBCLSLSL7ROQZEXlMRJptjvuYiAyIyAsi0hVwMwMhIp8Wkb0iMioitlPiROSAiPSLSJ+I9ATZxqC4eC/i8LmYJiK/FJHnC39PtTmuYT8XlX7Pkvf9wuPPiMil9Whn2EQmEAD4JYD3q+rFAP4TwArzASKSAPDPAK4C8GcAlorInwXaymD8FsBiAL9ycOzlqnpJA8+hrvhexOhz0QXg31X1AgD/Xrhtp+E+Fw5/z1cBuKDwZzmAuwNtZEhFJhCo6pOqeqpwcyeA91oc9kEAL6jqi6o6DOARAJ8Iqo1BUdXnVHWg3u0IA4fvRSw+F8j/n35c+PnHADrq15S6cPJ7/gSAn2jeTgDNIvKeoBsaNpEJBCZfBPALi/vTAA4V3T5cuC+uFMCTIrJbRJbXuzF1FJfPxTmq+jsAKPx9ts1xjfq5cPJ7jstnwZVQ7VAmIv8G4L9YPPR1Vf3XwjFfB3AKwDqrp7C4L5LzY528Fw4sUNUjInI2gF+KyD5VdZJOChUP3otYfC5cPE1DfC4sOPk9N8xnwUuhCgSq+pflHheRLwD4OIAr1XoBxGEA5xbdfi+AI961MDiV3guHz3Gk8PdrIvIY8l3nyH3hPXgvYvG5EJFXReQ9qvq7QrrjNZvnaIjPhQUnv+eG+Sx4KTKpIRH5GICvAbhWVU/aHPb/AFwgIrNEZCKAzwLYHFQbw0REJovIFONnAB9FfmA1juLyudgM4AuFn78AoKS31OCfCye/580APl+YPTQfwJtGOi3WVDUSfwC8gHxur6/w557C/TMAbC067mrkZxXtRz51UPe2+/BefBL5K5u3AbwKYJv5vQBwPoA9hT974/xexOhz8S7kZws9X/h7Wtw+F1a/ZwA3A7i58LMgP7NoP4B+AO31bnMY/rDEBBFRzEUmNURERP5gICAiijkGAiKimGMgICKKOQYCIqKYYyAgcklEVER+WnR7gogcFZGfF27fUKiGenHRMb8VkZl1aC5RRQwERO6dAPB+EUkVbn8EQNZ0zGG4K/tAVDcMBETV+QWAawo/LwXwsOnxnwO4SERaA20VURUYCIiq8wiAz4rIOwBcDGCX6fFRAN8B8LdBN4zILQYCoiqo6jMAZiLfG9hqc9hDAOaLyKyg2kVUjVBVHyWKmM0A/hHAh5Gv8zOOqp4SkTuRL5ZIFFoMBETV+1/IV6/sF5EP2xzzIwD/E8CUgNpE5BpTQ0RVUtXDqnpXhWOGAXwf9ruFEdUdq48SEcUcewRERDHHQEBEFHMMBEREMcdAQEQUcwwEREQxx0BARBRzDARERDH3/wFLPJJeP5RK8gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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VO1X1UlXtUtUTqnq/VRBQ1eu5h4CC4PSoOm4eMy4Q9Tg2nHPsadRsdhfqWtsoTJpofSmxe9zgtEv31k0DZYvHdiWjAJAbGcXDvYerStu9dzqPpM3QciN53L55N76x7SXHFhZRwp3FRFUoDQZOC5aVnNeaClRPI6sKoHraKPzyN+/U9Lih0p21cRe+OjPoWDJaq1EFRhxaDuVVcdLmBWw6R+Qhq3zslxbMaPggEbcY42jkQjDzgym89ua7tj2Nmq10zaPePPiKB8uaBViqd10kN5LHxt7DrgWBRrHpHJFH7PKx65bOxYGht2taNPbSxt7DODuVHCs5rVXvqyewfP50y+BWzX4FL5Se4FWraqu6nM56mJRssb37NgQlCES16RwDAQWCXfXK2if24O13TzdtHB86cyJ+/dYp2+cVwDun6h9PXtW2p1FQ9xk4qRQEFs6aCgD4x53Ws7qNvYcxIRHcmn0R4LyzU6waMhORKQCG1asNCBRLdukWq1JBL/323TwWzprqeIEzb0qrVaY/iwNDb49bkD4w9HZD7xlEC2dNxcYbr0CmP2tby69o/N+zUWmHyqUV82eEMkDXynFDmYh8C8BmVd0rImcAeBrAPACnAXxRVf+lOcN8HzeURZPdxqIoSrZYL1QaF84wqKZWP91auJNGnbt7m6F041pQ9nZ4pZENZcsA3FX885eL/58G4GIA/wCg6YGAws9qUbh7cZvlDlc3DhMJGrt0eFDWQSqpdnf1WJAIaBAAgCtnTxv7s1Xn2bioVD56qiQFtBjAP6lqXlVfAdcXqA6Z/iy6t4xv/Na9ZTcAYN3SuUi3piB4/xAQCp5GymeD5ke7/73ssag3DbRSKRC8JyKXiMg0AFcCeKbkubKzBYgqWfP4HoyYEsYjo4o1j+9B36HjeO3Nd6EAXnvzXfQdCscdci0qde0MgyjV0Zurv4zqpridRlcpEPwZgK0A9gK4V1UPAICIXA2g3+OxUQTZlV0O50YsfwGdlM4egijdmsKGZe1ls5yJNlUydo8HTRTr6A1B2vVdz9kZ9aqU3kmr6mzzg6r6FICnvBkSUXVKu1MGbbHZqDe3q9H/2qaBcalzAfDXn5/XtPE1ontxG27bNADnyv9wmGI69zgou77NnVOzwznctnkAgDd9jirNCFa7/hmJPFDNYSlus9sJnBBxbOPc1ZHGvaaZwr0BbFFtp+/Q8UgEgWRCcOc1c8Y+dtoh3dLkydo3tr1UVnI7qoXHvcAFX2qqKTZthGtlTgcZF1G7vvhuSyYEyz46vazTZbW9/BvZzes3P9IkbhMAPZ+fN/Y9qLRD+owJzW3LZrfT+uTIKBauf871jW2VAsFsEbEKQQJAVfVS10ZCsXDnNXPQvXX3uE1EyYQ4bioyl5HabfPv6kij79Dxsr40tRxKU6o1lYSI9aa2fF7RecFUdF4wta4+PZ+854VxDdkuOncynr3tE3WMsvn8aI7nttZJyXHfp0qlu+9WaIHRTF60w64UCA4AuMaVz0QE+yZndpuT0iXPV7rYZvqzeHRXtiwIrFhQ2B06c+WTVY+z9HSq9rXPlC1yj6JQATVw56dq/mU0BwGg0KXzk/e8EIpgkBAJfTAYrnFWGrQF8loPD6qkUiB4T1UPufKZiIqsykTtNpQ5LbiaWdW3K4Dn9w41NF6nSqd61NuyOSjsmuaFSS0X9qA2mnOzjLdSILhYRG4r+VgBvA7gX41SUqJaOHWhXLd0ruWdvzl/W9rDpvT1dlVD2eEcPrLyybpTRDSeuWle2LRI+YE4F5072TYQN3t9oFpuzlIqBYIeAGeaHpsJ4A4RWaOq/+TaSCgWnOq0Oy+YWva41SLejv3H8cl7XsDRE++Oa1vtdKGv5XJlrhBxWuA2jlO0apsBNNbnP8g6L5iK5/cO4Vhxh3iYjGphVlr6vTh5yn4NYDg30vQjKisVVbg9S3EMBKq61upxEZmKQp8hBgKqiVOdtlXdtF3XSqu7NzdPryq15NIP26ZCjGMLzWcp3L5lN/Ilb1S6wGd393nRuZPdGL7nqu01FGQbew+P6ytUaQ+K2zn5Su68Zo7jmdjVVKbVoq7yUVU9LuLDcUoUei1SfqE1WNVN+8XYoOY0XqB40bcoWc1b/CXjYrJj5aJQVw1FoddQPT9aQWqt4XZAqisQiMgiACdcHQnFwhkTWpALUCmeHeMOsZpgVEue3HjfsFz0rQTpgtgIo4rMODynkmZWDhkzzWZxDAQiMojy4DkVwDEAf+zVoCi6glSP7ZcVD74YmnMHrDgtzIdRNe2/m1055BRsqw1ctag0I/gj08cK4A1VDUedGwVOI+f9RkVYzh2wY1XqG2VpHxb67YLtGRNaPLmJcKyLUtVDpv8OMwjEk1udEO1WlkLSeJNQyE9/7vI04vIt86MlulXvrFQygb/6nDfNHNhriCoyV4lUs8XdqpyyqyNtu6PT52NryYbdHo7n9w6Frmy0XqV7XUorjex+xt1gtwPfq1mJ45nFDb+5SCuAhwBcgkJa6Suq+mLJ8ysAfL344dsAvqqqu53ek2cWN59di+fS815LWZUXppIJfO7yNDbuPBzY82ubJSxnE9s1Yls4ayp+tv94bAKBISGC/euuBmD/M75u6Vz0HToeyLOPGzmzuFH3AXhaVT8vIhNRfqrZAQB/oKonROTTAB4AMN/jMVGN7Bau7B5f+8SesvxxbiRf1gwuji46d3IoggBgv5YR9jWOepVWh1mV0OZG8li17aVxVXF2s4mg8WzvtIicBeDjAP4eAFT1lKoOl75GVX+mqkYZai+A870aD9XPrmzO6vFMf9Z2R2TcgwDgvIM1CErXgmg84/yJ1ZlB26opu9LooLfu9nJGcCGAIQA/EJF5AHYB+DOHxeb/CuDHHo6H6mRVJSIArpw9rey1qzw6OCMqzLOo1ZnBsTSCAJg0MYGTp/K+tKSIwo5hLy2fP72sV1a1gt6TyctAMAHAZQBuUdWdInIfgJUAvml+oYhciUIg+H2rNxKRmwDcBAAzZszwbMBkzarPvwLY9G9HsKXvKN47XbgLYlO3yoxZVOGiOz6NoADeOVV5Qd6rRcoo7Bj2yuSJCdzdNRezVtV3Qm/Q+zB42VbvKICjqrqz+PFWFALDOCJyKQoLyp9R1Tes3khVH1DVTlXtnDat/C6U6ldtWahVlcjIqI4FAYBBoBrZ4RxWZwbRvXV3xR3WRkuKUsZde7bY7M0IGG4cbB6VHcNuSyUT+PZnC/n9eu/sJ8Q1EKjqawCOiIixHe8qAC+XvkZEZgDYBuC/qOovvBoLWTMOyC69qNy2ecDyosKLhHse7j3seCJbKfO/u90iZSMtCVZnBjFr1VMM5BZaZHyDN7tzqisJ+oZ6rxtt3wJgY/G4y3YAfykiN4vIzcXnvwXggwD+TkQGRIR1oU1UywHZQTuhKS7M/+61VnBVYuS8g57D9oL53GuzZEJwzxfax6XdFlw4xeth+cLTQKCqA8WUzqWq2qWqJ1T1flW9v/j8f1PVKaraXvzPssaVvOF0QLaZ1U5H8pZVf5taKriqEfRqFi859Q5Kt6bGHW5v6H01mqWzwTx6hwKnqyONdUuDWwcdFUbiISGCz11efkSnXeuBehuixXEmYPhfz//S8vGLzp2MHSsXWS7AR3UHPANBjNmlO+0ej8rpWkFmXGfyqnh0V7ZsvcYIyOnWFASFO9dGDimpN+cdBWE/O9pN7DUUYyvmz7CsiV4x37pEN9OfRbIl+AtfUWF3KlZXR/lMoV5ROIg+DIIebxkIYsx8CLlTXxSjbJFBoLm8rtYK+0H0YWF3cxUUDAQxd3fX3Kp6oHCzkT+aUa1l/AzMZFsJAM5nR591RgK/fa+234MvLZgR6D5DAAMBVSlKJ1IFlXlndrNOxTJ2KpPz2dErHnyx5iAABLvZnIGBgCgAjDbdz+8dakr/eQP7C1XfFryerqthWYxnICAKgEYqfxoR95Sf123Bl8+f7tl7u4nlo0Q+S4j4Vpob99YhR0+860qfJjthSAsBDAREvvPzrjHurUNq6dO0cNbUmt/fyyDjJgYCIpfUmw9+ZOcRzKzQ/dUrbB1S/ayonhRSWBbhuUZAVTljQsu4ltNUrt46fOPvOZ1BYMWNcwnMh6S3TkpCFXgzNxKbbqS1zIomJVtse3RZCUvqjYGAqpKocLM7eWJi7FAVqp/dbmKzTH8Wt2/Zjfzo+0Hk9i27AdTeCsRup3Ic9hXUWqJ7RjJRUyAIS+qNqSGqitMP/4Zl7djzF39Ysa0vVaeau8g7HhscCwKG/KjijscGXRvHlElJ194riKZMStZcrTVscx63nWbsA3EDZwTUMOMXqXtxG27dNODvYCLg7NT4C7BVCshu9lXvrMzqc9x5zRx0b91d9SE6YZPPj9Y8ezqvNVXT5sqwNGpkICDXdHWkGQhcMJwbwaxVT2H5/OnovGDquA1fpesIjSi98J+dSuKdU6fHLvjZ4Rxu3TQAESDK7Yfq2SXcvbit6g14YZpRMRDEnBsLjpn+bGjufMIir4qHew/j0V1Hy842bnQDmHk38XDOOt0R5SBQr9LFdaeZQTIhuPOaOc0aVsO4RhBjbh2E7tbh6VSu0gH3Vip9L+K+m7hUPT+3XR1p7Fi5CAfXL8GGZe1ja2NG+bDd6WZBxhlBjDkdhF7LD3E9f4e8c/vm3fjapgHbGV5YShqbodGfWzfPhvATA4EP3EjHuMHNg9Czwzn87jd/3OiQyEIqmRgXsFPJBE7n87ZnQ1Tal1DrgmeUGf8OQfmd9AsDQZNl+rPjKjGywzl0b62v/rtRdheEemuf60ljkDNBoSHdmsf3jOXy3z2drzp/nxvJ49ZNA1j7xJ6xjWKtk5JItghGRrkIAJTvl6h1Y18lRpDJDueQEEFeFemABRuuETTZ2if2lJXjjeQVa5/Y0/SxdC9uQ9K0UyyZkNDUPsfBfyz2tynd1V3PIu6JkyMYLu4WPnFyhEGgglp6EDnJ9GfRvWX32A2XebYWlLU1BoImO2GzIcXuca+dNgUl88fkr4Nv5Li46xM31lLWPL7HNui6FWzcwEAQY3c8NljWT0YB3LppwJcGaFQuO5xjPt8nbhwpY1eaawjKwj3XCJrMrmnVpGTzY7LTLtTSPGnfodpPZiKqRWsqGbhGd81Y8QpKLyIGgiabOMG6adXECcFrBWxMXV97812/h0IhZyyS2pl8xgS8dzofmYKDwh6dlyq+LijrcZ7ehopIq4hsFZG9IvKKiFxhel5E5H+KyK9E5CURuczL8QSB3VSx0hTSL9nhXN3tlYkM+9ddjYPrl9imW44N5/BuhILAbZsGKga1KZOSsakaug/A06o6G8A8AK+Ynv80gIuK/90E4Hsej8d3doeXBPWQ64RIYMdG4WOXCvkPyZbApEka1bN9X8W0UiqZCFQLCs9SQyJyFoCPA7geAFT1FIBTppd9BsD/UVUF0FucQXxYVf/dq3H5ze7uupG77lo3w6zODOKRnUeqem/OBshNV86ehod7D5c9nhsZxcwPpnD8nVOhr5CqtAActD0EgLczggsBDAH4gYj0i8hDIjLZ9Jo0gNIr0tHiY+OIyE0i0icifUNDQ96NuAlaU9YdCe0er8TYoFbaL6h7627bip/VmUE83HuYF3hqmtIZ5fN77X9/e189gXVL5yLdmoIA8KF+whWVZjYzP5gKVBAAvA0EEwBcBuB7qtoB4B0AK02vsco5lF2hVPUBVe1U1c5p06a5P9Imssuy1Jt9qXWDWrUzASK3lN50ON0t51XHGrodWL8Ev/zLJUiFMBp0L25zvLDu2H8cKx58sWnjqYaX/8pHARxV1Z3Fj7eiEBjMr5le8vH5AI55OCbfub2hrNb340yAmq305Dqnu2WrtagwVhF1daRxz7J2xyC2Y3+wSrI9CwSq+hqAIyJi1EddBeBl08seB/DHxeqhBQDejPL6AAC02Nz52z3uNi78kle+tGAGUsnxZdDmM4Gd7paXz59u80z4dHWksW7ppX4Po2pe7yO4BcBGEZkI4FUAN4jIzQCgqvcDeArA1QB+BeAkgBu8GESQOgvatXhpVuuXBRdOCdzdCEXD3V1z0XnBVMffNePPq7a9NHa33yLAF+fPwN1dc30Zt1eC0j6iGp4GAlUdANBpevj+kucVwJ94OQajpteYYGaHc7iteJxi0BZsmuHgG8HY0k7BUWmzVzWMGW01/fmj0sO/kkqtQeZ/+1nsvOOTTRqNs/CtxNRo1baXymp6R4uPx1FQepuQ/9KtKRxcvwT7113d8Ht9cf4MF0YULZWSsL9+6xRWZxo/f9oNkQ8EdotNYVyEckNUNu1QY8y5+0YsnDXVs7ROOoQ/r5n+LDr+4pmq+iYFpYov8oEgaNzeR1Cr7sVtSDRrZZoCI5kQtKaSEBQuruuWznUtPbPxxisqv6hO3YvbyhaggyzTn8XtW3ZXXQUYlCq+yDedaxHrhVi/roVrrp2D7i27x/UoT7YI1lzbnO3mfYeOI89DSWJDgKoKJOy64lbi9R27MebSBeggt+W+47HBUP5+RX5GYJe79Cun2dWRRs9188Z2T6ZbU+i5bp4ni2dWu4uDMhUl76VbUziwfgl2rFxU8efrLx1KHVsE2LCs3bfT7Eo3me1Yucjzz9cIp9buQRb5GYGRu3xk5xHkVZEQwfL5030tVWtW1YTVuatBmYqSt1LJBK6cPQ0L1z9XVdl0V0caW/oOW5YWj900WZ1iRJEQ+RkBUAgGRhvc/euujly9sh2ro/C4oSz60q0pfO7yNB7dlR3Xg6rSGbkbb7wCX1owY+xnJCGCLy0o1Pf3bN9XduTiyKj6Uiu/YVl70z9ntcL62xX5GUHcmctFl8+fbtn9kaJjx8pFWLj+ubIunsaNgdNs9O6uuZY3SnZlx36UI2/pC+7P74oFM2r6/QpKKyUGAh80c6ezuVzU+CXf2HuYM/sI+tKCQhrH7Qu33SKtH+XIQd4Zb5eK7rxgKm4tbmQt1XNde3MHaIOBoMkKR9gNjt2tlZ4N7EUwuHJ2ebdW465v5sonXf985B8jjQO4f+HuXtw27ucWcHcvQpTYzaoABKbVjVlAJibx0bN9n+2U3QtPvhTpHn6xkUq2YMOydlx0rvlIjwLzpi6r+vtGLtyFJmpzx1W7ubkXIQ7M1U9B+rfjjKDJ7GqgvaqNrre9NQVH6Z1+V0caKx58cVx6ZOGsqWWbuqzq7xu9Aw1Kj6CFs6YGOj0URgwETWbX4MvLap5MfzYQv8BUu3RrqizNUO1O3qBcuN1eE9t44xVMa7qMgaDJvDizuJLurbsBxLPbaphFIQfv1ZpYGGcFxlnhQdnPVIqBoMlEAKtrvpfl/cbRlXYbhih4EiKRyME7rYk1Oiswp8iCzDgr3JBXHfs4CMGAi8VNZnfj7/WG3xMnR0LzS0PAd77gTduRZvNy/8HGG6/AwfVLQrGJy661S1BavjAQEAVQFIIAYF+u6ub+gzC0VvcjJVwLBgIi8ozbZax2nyPoFzK7YpCgtHzhGgFRwEyZ1JyzKZrBizLWUmFZJ7Br7bJ8/nQfRlOOgYAoQJIJwZ3XNOdsimbxqow1LEEACGYX5FIMBE0msO7e6/UEMd2awswPpkLzixNXPZ+PxiJxMwTxZ9lpz4RT6wm/BT21Fjl2S0NeLhkZOdmNN14RyjNgoyQhYnssabo1xSBQJad22n4x9kzU0vo7KBgImszuQuzFBdqqJ0y1Z8B+6MyJro8n7lLJBL7zhXn4o3kftnzeqkEgWfPjHIRKmt1HzE1MDTWZ210cEwLkLaYTCQH2r1tS9njp4p1Tf6PX32aPIjelS9IEdheG5/cONXlU4eXHOQiVBOnMhloxEDSZ21UUVkHA6XFjDF0dacd+LUGpbw67g+vLg3GYLxhBEcRD7IN0ZkOtPA0EInIQwFsA8gBOq2qn6fmzATwMYEZxLH+jqj/wckxBEJRmYE7smuPFXSrZgtzIaFWvtSsACPMFIyi6F7dZHvTipzCf2dCMNYIrVbXdHASK/gTAy6o6D8AnAHxHRJicDgAGgXKpZALrll5adq6vHbt/wWZssoq6ro60baBtkffX3CpV47W4WK4X5jMb/E4NKYAzRUQAfADAcQCn/R1SuJwxoQXvnS6/Qz1jQuUYf9G5k/HL37zjxbAipzTH39WRHlcGuHD9c5Z3+HYFAF5vsooLu/OBvzj//fMbjHJOu+/PyVOnXT2zIwyzfSuiHt75icgBACdQuOD/b1V9wPT8mQAeBzAbwJkAlqlqWeJaRG4CcBMAzJgx4/JDhw55NuawyfRncdvmAYyWfBtbBLjnC+1V/UDO//az+PVbpzwcYWMmJgSnTAsehbtprTpFY0iIYMGFU3DwjRyOFUv8qlF6MIwVc6tlY4xhuRsMs0ZbO39k5ZO2PwepZAteuevT7gw0AERkl01mxvMZwUJVPSYi5wJ4VkT2qupPSp5fDGAAwCIAs4qv+amq/rb0TYoB5AEA6OzsZM6iRCN3l5n+LI4H/ASz/CiwYVl72ddXbX64BcA9y6yDYqXDTaq9sPAO3z/VbNJy2uTltOi8bumlro83qDwNBKp6rPj/34jIYwB+D0BpILgBwHotTEt+VZxBzAbwb16OK2rqnY6ufWIPRpzKiwIgr2r59d2+ebflOkaLAB8+O1XVBdnptLj9666uaZxhTQlEXaWDcawWeAWFtFOcvp+eBQIRmQygRVXfKv75UwD+wvSywwCuAvBTEfkQgDYAr3o1pqiq9yjAanOjC2dNxZ5jb2E4F5zZg91i9qgCO1Yuquo9gt4IjBpX6WAczuYKvJwRfAjAY4V1YEwA8I+q+rSI3AwAqno/gLsA/FBEBlEIxF9X1dc9HFPkeHUUoME4GD3Tn0X3lt0YGbW+ANv1UDK/BlW8zmzFgy+WndObtpnS17JDO+iNwKhx1ezZ4GzOw0Cgqq8CmGfx+P0lfz6GwkyB6uTVUYDmjVDmO6fWSUmoAsO5kaqCAADcu6y9rtpvq+ZibtVsB7kRGDWOezaq43f5KDXIbqHLi12XVndOdqWTVtzsuRLGKX29KTyqX5g3eTUTA0HIOS14VtKaSlrm/e26Y1qppS3CseGc7eesZceuIUxTeq9TeGQtjDcMfmD30ZBr5CzUNdfOQdK0tTLZIlhzbfUHo9QyxT6vNWX7OdctvRQXnTvZ8u8tnDW16s8RVGHuTBl2XR1p7Fi5CAfWL8GOlYsYBCwwEIRcI22tuzrS6Llu3rgt8T3X1XYwilW7hGRCyi72xnTc6XM+e9snyi76xmJ12LHRHAUZU0Mh12gOtNH0it3U2+ox47VOn9Pri75feXouWlKQMRCEXBByoHYX9qBNwf3M03PRkoKMgSACwrRo6ievSm2rEYSATWSHgYBiw+88PQM2BRUXiyk27PLxzNNT3DEQUGzwQBgia0wNUWwwT09kjYGAYoV5eqJyTA0REcUcAwERUcwxEBARxRwDARFRzDEQEBHFnGgV7YqDRESGAByq8uXnAIjK0Zf8WoKJX0sw8Wspd4GqTrN6InSBoBYi0qeqnX6Pww38WoKJX0sw8WupDVNDREQxx0BARBRzUQ8ED/g9ABfxawkmfi3BxK+lBpFeIyAiosqiPiMgIqIKGAiIiGIu0oFARDaJyEDxv4MiMuD3mBohIreIyD4R2SMif+33eOolImtEJFvyvbna7zE1SkT+XERURM7xeyz1EpG7ROSl4vfkGRE5z+8x1UtEekRkb/HreUxEWv0eU71E5Lri7/yoiHhSRhrpQKCqy1S1XVXbATwKYJvPQ6qbiFwJ4DMALlXVOQD+xuchNepe43ujqk/5PZhGiMh0AJ8EcNjvsTSoR1UvLf6+/AjAt3weTyOeBXCJql4K4BcAVvk8nkb8HMBSAD/x6hNEOhAYREQAfAHAI36PpQFfBbBeVd8DAFX9jc/joffdC+B/AAh15YWq/rbkw8kI8dejqs+o6unih70AzvdzPI1Q1VdUdZ+XnyMWgQDAxwD8WlV/6fdAGnAxgI+JyE4R+X8i8lG/B9SgPy1O278vIlP8Hky9RORaAFlV3e33WNwgIt8WkSMAViDcM4JSXwHwY78HEWShP6FMRP4FwO9YPHWHqv5z8c/LEYLZgNPXgsL3agqABQA+CmCziFyoAa3/rfC1fA/AXSjccd4F4Dso/LIGUoWv5RsAPtXcEdWv0u+Lqt4B4A4RWQXgTwHc2dQB1qCa330RuQPAaQAbmzm2WlV5HfPu8wf0OuIaEZkAIAvgclU96vd46iUiT6OQGnqh+PF+AAtUdcjXgTVIRGYC+JGqXuL3WGolInMB/F8AJ4sPnQ/gGIDfU9XXfBuYC0TkAgBPhvH7YhCRLwO4GcBVqnqy0uuDTkReAPDnqtrn9nvHITX0nwDsDXMQKMoAWAQAInIxgIkIaXdFEflwyYefRWExLHRUdVBVz1XVmao6E8BRAJeFNQiIyEUlH14LYK9fY2mUiPwhgK8DuDYKQcBroU8NVeE/IwRpoSp8H8D3ReTnAE4B+HJQ00JV+GsRaUchNXQQwH/3dTRkWC8ibQBGUWj1frPP42nEdwGcAeDZQq0IelU1lF+PiHwWwN8CmAbgSREZUNXFrn6O8F5LiIjIDXFIDRERkQMGAiKimGMgICKKOQYCIqKYYyAgIoo5BgKiBonIHcXukEbnzvki8oJXnSKJ3BaHfQREnhGRKwD8EQobyd4rtqGe6POwiGrCQEDUmA8DeL2kK+zrAFDcxEQUCkwNETXmGQDTReQXIvJ3IvIHfg+IqFYMBEQNUNW3AVwO4CYAQwA2icj1vg6KqEZMDRE1SFXzAF4A8IKIDAL4sr8jIqoNZwREDRCRNlPXznYUGrYRhQZnBESN+QCAvy0ejn4awK9QSBNt9XNQRLVg91EiophjaoiIKOYYCIiIYo6BgIgo5hgIiIhijoGAiCjmGAiIiGKOgYCIKOb+P7y7K5mttcCOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## We will be using logarithmic transformation\n",
    "\n",
    "\n",
    "for feature in continuous_feature:\n",
    "    data=dataset.copy()\n",
    "    if 0 in data[feature].unique():\n",
    "        pass\n",
    "    else:\n",
    "        data[feature]=np.log(data[feature])\n",
    "        data['UTS']=np.log(data['UTS'])\n",
    "        plt.scatter(data[feature],data['UTS'])\n",
    "        plt.xlabel(feature)\n",
    "        plt.ylabel('UTS')\n",
    "        plt.title(feature)\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5ba47cc",
   "metadata": {},
   "source": [
    "# Categorical Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cd3b8c39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['GRADE', 'COIL_GEN_TIME']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_features=[feature for feature in dataset.columns if data[feature].dtypes=='O']\n",
    "categorical_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "38d9a3df",
   "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>GRADE</th>\n",
       "      <th>COIL_GEN_TIME</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>JVHGLD4AS0</td>\n",
       "      <td>8/10/2020 2:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>JVHGLD4AS0</td>\n",
       "      <td>9/14/2020 2:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>JVHCM30A00</td>\n",
       "      <td>9/14/2020 6:47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>JVHST01C00</td>\n",
       "      <td>9/15/2020 11:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>JVHWT02A00</td>\n",
       "      <td>9/16/2020 1:14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        GRADE    COIL_GEN_TIME\n",
       "0  JVHGLD4AS0   8/10/2020 2:25\n",
       "1  JVHGLD4AS0   9/14/2020 2:30\n",
       "2  JVHCM30A00   9/14/2020 6:47\n",
       "3  JVHST01C00  9/15/2020 11:55\n",
       "4  JVHWT02A00   9/16/2020 1:14"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[categorical_features].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "63dadc89",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The feature is GRADE and number of categories are 110\n",
      "The feature is COIL_GEN_TIME and number of categories are 40959\n"
     ]
    }
   ],
   "source": [
    "for feature in categorical_features:\n",
    "    print('The feature is {} and number of categories are {}'.format(feature,len(dataset[feature].unique())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d57a0653",
   "metadata": {},
   "outputs": [],
   "source": [
    "for feature in categorical_features:\n",
    "    temp=dataset.groupby(feature)['UTS'].count()/len(dataset)\n",
    "    temp_df=temp[temp>0.01].index\n",
    "    dataset[feature]=np.where(dataset[feature].isin(temp_df),dataset[feature],'Rare_var')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a043315a",
   "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>COIL_ID</th>\n",
       "      <th>GRADE</th>\n",
       "      <th>COIL_GEN_TIME</th>\n",
       "      <th>FURN_EXIT_TEMP</th>\n",
       "      <th>FM_DOUT_TEMP</th>\n",
       "      <th>COILING_TEMP</th>\n",
       "      <th>SLAB_WID</th>\n",
       "      <th>SLAB_LEN</th>\n",
       "      <th>STRIP_EXIT_LEN</th>\n",
       "      <th>STRIP_EXIT_THIK</th>\n",
       "      <th>...</th>\n",
       "      <th>N</th>\n",
       "      <th>NI</th>\n",
       "      <th>P</th>\n",
       "      <th>S</th>\n",
       "      <th>SI</th>\n",
       "      <th>TI</th>\n",
       "      <th>V</th>\n",
       "      <th>UTS</th>\n",
       "      <th>YS</th>\n",
       "      <th>P_ELONGATION</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20081459</td>\n",
       "      <td>JVHGLD4AS0</td>\n",
       "      <td>Rare_var</td>\n",
       "      <td>1081</td>\n",
       "      <td>869</td>\n",
       "      <td>680</td>\n",
       "      <td>1125</td>\n",
       "      <td>10750.0</td>\n",
       "      <td>69.81</td>\n",
       "      <td>34</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0025</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.0100</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>346</td>\n",
       "      <td>294</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20097626</td>\n",
       "      <td>JVHGLD4AS0</td>\n",
       "      <td>Rare_var</td>\n",
       "      <td>1070</td>\n",
       "      <td>838</td>\n",
       "      <td>662</td>\n",
       "      <td>1245</td>\n",
       "      <td>10640.0</td>\n",
       "      <td>68.63</td>\n",
       "      <td>34</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0022</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.0100</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>365</td>\n",
       "      <td>299</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20097722</td>\n",
       "      <td>Rare_var</td>\n",
       "      <td>Rare_var</td>\n",
       "      <td>1075</td>\n",
       "      <td>882</td>\n",
       "      <td>611</td>\n",
       "      <td>1040</td>\n",
       "      <td>10750.0</td>\n",
       "      <td>66.08</td>\n",
       "      <td>36</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0070</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.0030</td>\n",
       "      <td>0.164</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.001</td>\n",
       "      <td>730</td>\n",
       "      <td>575</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20098318</td>\n",
       "      <td>JVHST01C00</td>\n",
       "      <td>Rare_var</td>\n",
       "      <td>1088</td>\n",
       "      <td>864</td>\n",
       "      <td>603</td>\n",
       "      <td>1260</td>\n",
       "      <td>9000.0</td>\n",
       "      <td>51.94</td>\n",
       "      <td>38</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0035</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.021</td>\n",
       "      <td>0.0131</td>\n",
       "      <td>0.099</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.001</td>\n",
       "      <td>495</td>\n",
       "      <td>397</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20098589</td>\n",
       "      <td>JVHWT02A00</td>\n",
       "      <td>Rare_var</td>\n",
       "      <td>1061</td>\n",
       "      <td>848</td>\n",
       "      <td>650</td>\n",
       "      <td>1265</td>\n",
       "      <td>9811.0</td>\n",
       "      <td>61.23</td>\n",
       "      <td>35</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0032</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.0101</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>379</td>\n",
       "      <td>326</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>20099556</td>\n",
       "      <td>JVHGLC4AS0</td>\n",
       "      <td>Rare_var</td>\n",
       "      <td>1017</td>\n",
       "      <td>839</td>\n",
       "      <td>641</td>\n",
       "      <td>1203</td>\n",
       "      <td>8750.0</td>\n",
       "      <td>57.28</td>\n",
       "      <td>34</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0023</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.0064</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>366</td>\n",
       "      <td>327</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>20099558</td>\n",
       "      <td>JVHGLD4AS0</td>\n",
       "      <td>Rare_var</td>\n",
       "      <td>1025</td>\n",
       "      <td>858</td>\n",
       "      <td>678</td>\n",
       "      <td>940</td>\n",
       "      <td>10750.0</td>\n",
       "      <td>65.42</td>\n",
       "      <td>36</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0031</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.0100</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>355</td>\n",
       "      <td>308</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>20099565</td>\n",
       "      <td>JVHGLD4AS0</td>\n",
       "      <td>Rare_var</td>\n",
       "      <td>1061</td>\n",
       "      <td>872</td>\n",
       "      <td>689</td>\n",
       "      <td>940</td>\n",
       "      <td>10750.0</td>\n",
       "      <td>67.29</td>\n",
       "      <td>35</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0031</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.0100</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>352</td>\n",
       "      <td>303</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>20099570</td>\n",
       "      <td>JVHGLD4AS0</td>\n",
       "      <td>Rare_var</td>\n",
       "      <td>1054</td>\n",
       "      <td>871</td>\n",
       "      <td>686</td>\n",
       "      <td>940</td>\n",
       "      <td>10750.0</td>\n",
       "      <td>67.29</td>\n",
       "      <td>35</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0036</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.0100</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>350</td>\n",
       "      <td>302</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>20099573</td>\n",
       "      <td>JVHWT01A00</td>\n",
       "      <td>Rare_var</td>\n",
       "      <td>1058</td>\n",
       "      <td>859</td>\n",
       "      <td>613</td>\n",
       "      <td>920</td>\n",
       "      <td>10750.0</td>\n",
       "      <td>69.63</td>\n",
       "      <td>34</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0025</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.0090</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>384</td>\n",
       "      <td>336</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     COIL_ID       GRADE COIL_GEN_TIME  FURN_EXIT_TEMP  FM_DOUT_TEMP  \\\n",
       "0   20081459  JVHGLD4AS0      Rare_var            1081           869   \n",
       "1   20097626  JVHGLD4AS0      Rare_var            1070           838   \n",
       "2   20097722    Rare_var      Rare_var            1075           882   \n",
       "3   20098318  JVHST01C00      Rare_var            1088           864   \n",
       "4   20098589  JVHWT02A00      Rare_var            1061           848   \n",
       "..       ...         ...           ...             ...           ...   \n",
       "95  20099556  JVHGLC4AS0      Rare_var            1017           839   \n",
       "96  20099558  JVHGLD4AS0      Rare_var            1025           858   \n",
       "97  20099565  JVHGLD4AS0      Rare_var            1061           872   \n",
       "98  20099570  JVHGLD4AS0      Rare_var            1054           871   \n",
       "99  20099573  JVHWT01A00      Rare_var            1058           859   \n",
       "\n",
       "    COILING_TEMP  SLAB_WID  SLAB_LEN  STRIP_EXIT_LEN  STRIP_EXIT_THIK  ...  \\\n",
       "0            680      1125   10750.0           69.81               34  ...   \n",
       "1            662      1245   10640.0           68.63               34  ...   \n",
       "2            611      1040   10750.0           66.08               36  ...   \n",
       "3            603      1260    9000.0           51.94               38  ...   \n",
       "4            650      1265    9811.0           61.23               35  ...   \n",
       "..           ...       ...       ...             ...              ...  ...   \n",
       "95           641      1203    8750.0           57.28               34  ...   \n",
       "96           678       940   10750.0           65.42               36  ...   \n",
       "97           689       940   10750.0           67.29               35  ...   \n",
       "98           686       940   10750.0           67.29               35  ...   \n",
       "99           613       920   10750.0           69.63               34  ...   \n",
       "\n",
       "         N     NI      P       S     SI     TI      V  UTS   YS  P_ELONGATION  \n",
       "0   0.0025  0.007  0.018  0.0100  0.004  0.001  0.001  346  294            42  \n",
       "1   0.0022  0.007  0.014  0.0100  0.007  0.001  0.001  365  299            42  \n",
       "2   0.0070  0.008  0.015  0.0030  0.164  0.002  0.001  730  575            21  \n",
       "3   0.0035  0.008  0.021  0.0131  0.099  0.002  0.001  495  397            30  \n",
       "4   0.0032  0.007  0.015  0.0101  0.006  0.001  0.001  379  326            41  \n",
       "..     ...    ...    ...     ...    ...    ...    ...  ...  ...           ...  \n",
       "95  0.0023  0.008  0.010  0.0064  0.008  0.001  0.001  366  327            41  \n",
       "96  0.0031  0.008  0.016  0.0100  0.006  0.001  0.001  355  308            43  \n",
       "97  0.0031  0.008  0.016  0.0100  0.006  0.001  0.001  352  303            44  \n",
       "98  0.0036  0.008  0.015  0.0100  0.009  0.001  0.001  350  302            44  \n",
       "99  0.0025  0.007  0.020  0.0090  0.012  0.001  0.001  384  336            36  \n",
       "\n",
       "[100 rows x 30 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f6035005",
   "metadata": {},
   "outputs": [],
   "source": [
    "for feature in categorical_features:\n",
    "    labels_ordered=dataset.groupby([feature])['UTS'].mean().sort_values().index\n",
    "    labels_ordered={k:i for i,k in enumerate(labels_ordered,0)}\n",
    "    dataset[feature]=dataset[feature].map(labels_ordered)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "150f8585",
   "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>COIL_ID</th>\n",
       "      <th>GRADE</th>\n",
       "      <th>COIL_GEN_TIME</th>\n",
       "      <th>FURN_EXIT_TEMP</th>\n",
       "      <th>FM_DOUT_TEMP</th>\n",
       "      <th>COILING_TEMP</th>\n",
       "      <th>SLAB_WID</th>\n",
       "      <th>SLAB_LEN</th>\n",
       "      <th>STRIP_EXIT_LEN</th>\n",
       "      <th>STRIP_EXIT_THIK</th>\n",
       "      <th>...</th>\n",
       "      <th>N</th>\n",
       "      <th>NI</th>\n",
       "      <th>P</th>\n",
       "      <th>S</th>\n",
       "      <th>SI</th>\n",
       "      <th>TI</th>\n",
       "      <th>V</th>\n",
       "      <th>UTS</th>\n",
       "      <th>YS</th>\n",
       "      <th>P_ELONGATION</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20081459</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1081</td>\n",
       "      <td>869</td>\n",
       "      <td>680</td>\n",
       "      <td>1125</td>\n",
       "      <td>10750.0</td>\n",
       "      <td>69.81</td>\n",
       "      <td>34</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0025</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.0100</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>346</td>\n",
       "      <td>294</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20097626</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1070</td>\n",
       "      <td>838</td>\n",
       "      <td>662</td>\n",
       "      <td>1245</td>\n",
       "      <td>10640.0</td>\n",
       "      <td>68.63</td>\n",
       "      <td>34</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0022</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.0100</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>365</td>\n",
       "      <td>299</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20097722</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>1075</td>\n",
       "      <td>882</td>\n",
       "      <td>611</td>\n",
       "      <td>1040</td>\n",
       "      <td>10750.0</td>\n",
       "      <td>66.08</td>\n",
       "      <td>36</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0070</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.0030</td>\n",
       "      <td>0.164</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.001</td>\n",
       "      <td>730</td>\n",
       "      <td>575</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20098318</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>1088</td>\n",
       "      <td>864</td>\n",
       "      <td>603</td>\n",
       "      <td>1260</td>\n",
       "      <td>9000.0</td>\n",
       "      <td>51.94</td>\n",
       "      <td>38</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0035</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.021</td>\n",
       "      <td>0.0131</td>\n",
       "      <td>0.099</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.001</td>\n",
       "      <td>495</td>\n",
       "      <td>397</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20098589</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1061</td>\n",
       "      <td>848</td>\n",
       "      <td>650</td>\n",
       "      <td>1265</td>\n",
       "      <td>9811.0</td>\n",
       "      <td>61.23</td>\n",
       "      <td>35</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0032</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.0101</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>379</td>\n",
       "      <td>326</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>20099556</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1017</td>\n",
       "      <td>839</td>\n",
       "      <td>641</td>\n",
       "      <td>1203</td>\n",
       "      <td>8750.0</td>\n",
       "      <td>57.28</td>\n",
       "      <td>34</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0023</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.0064</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>366</td>\n",
       "      <td>327</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>20099558</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1025</td>\n",
       "      <td>858</td>\n",
       "      <td>678</td>\n",
       "      <td>940</td>\n",
       "      <td>10750.0</td>\n",
       "      <td>65.42</td>\n",
       "      <td>36</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0031</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.0100</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>355</td>\n",
       "      <td>308</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>20099565</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1061</td>\n",
       "      <td>872</td>\n",
       "      <td>689</td>\n",
       "      <td>940</td>\n",
       "      <td>10750.0</td>\n",
       "      <td>67.29</td>\n",
       "      <td>35</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0031</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.0100</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>352</td>\n",
       "      <td>303</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>20099570</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1054</td>\n",
       "      <td>871</td>\n",
       "      <td>686</td>\n",
       "      <td>940</td>\n",
       "      <td>10750.0</td>\n",
       "      <td>67.29</td>\n",
       "      <td>35</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0036</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.0100</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>350</td>\n",
       "      <td>302</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>20099573</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1058</td>\n",
       "      <td>859</td>\n",
       "      <td>613</td>\n",
       "      <td>920</td>\n",
       "      <td>10750.0</td>\n",
       "      <td>69.63</td>\n",
       "      <td>34</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0025</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.0090</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>384</td>\n",
       "      <td>336</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     COIL_ID  GRADE  COIL_GEN_TIME  FURN_EXIT_TEMP  FM_DOUT_TEMP  \\\n",
       "0   20081459      0              0            1081           869   \n",
       "1   20097626      0              0            1070           838   \n",
       "2   20097722     12              0            1075           882   \n",
       "3   20098318     14              0            1088           864   \n",
       "4   20098589      7              0            1061           848   \n",
       "..       ...    ...            ...             ...           ...   \n",
       "95  20099556      1              0            1017           839   \n",
       "96  20099558      0              0            1025           858   \n",
       "97  20099565      0              0            1061           872   \n",
       "98  20099570      0              0            1054           871   \n",
       "99  20099573      5              0            1058           859   \n",
       "\n",
       "    COILING_TEMP  SLAB_WID  SLAB_LEN  STRIP_EXIT_LEN  STRIP_EXIT_THIK  ...  \\\n",
       "0            680      1125   10750.0           69.81               34  ...   \n",
       "1            662      1245   10640.0           68.63               34  ...   \n",
       "2            611      1040   10750.0           66.08               36  ...   \n",
       "3            603      1260    9000.0           51.94               38  ...   \n",
       "4            650      1265    9811.0           61.23               35  ...   \n",
       "..           ...       ...       ...             ...              ...  ...   \n",
       "95           641      1203    8750.0           57.28               34  ...   \n",
       "96           678       940   10750.0           65.42               36  ...   \n",
       "97           689       940   10750.0           67.29               35  ...   \n",
       "98           686       940   10750.0           67.29               35  ...   \n",
       "99           613       920   10750.0           69.63               34  ...   \n",
       "\n",
       "         N     NI      P       S     SI     TI      V  UTS   YS  P_ELONGATION  \n",
       "0   0.0025  0.007  0.018  0.0100  0.004  0.001  0.001  346  294            42  \n",
       "1   0.0022  0.007  0.014  0.0100  0.007  0.001  0.001  365  299            42  \n",
       "2   0.0070  0.008  0.015  0.0030  0.164  0.002  0.001  730  575            21  \n",
       "3   0.0035  0.008  0.021  0.0131  0.099  0.002  0.001  495  397            30  \n",
       "4   0.0032  0.007  0.015  0.0101  0.006  0.001  0.001  379  326            41  \n",
       "..     ...    ...    ...     ...    ...    ...    ...  ...  ...           ...  \n",
       "95  0.0023  0.008  0.010  0.0064  0.008  0.001  0.001  366  327            41  \n",
       "96  0.0031  0.008  0.016  0.0100  0.006  0.001  0.001  355  308            43  \n",
       "97  0.0031  0.008  0.016  0.0100  0.006  0.001  0.001  352  303            44  \n",
       "98  0.0036  0.008  0.015  0.0100  0.009  0.001  0.001  350  302            44  \n",
       "99  0.0025  0.007  0.020  0.0090  0.012  0.001  0.001  384  336            36  \n",
       "\n",
       "[100 rows x 30 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2b0d426a",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaling_feature=[feature for feature in dataset.columns if feature not in ['UTS','YS','P_ELONGATION']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "adcec980",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(scaling_feature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6add27e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['COIL_ID',\n",
       " 'GRADE',\n",
       " 'COIL_GEN_TIME',\n",
       " 'FURN_EXIT_TEMP',\n",
       " 'FM_DOUT_TEMP',\n",
       " 'COILING_TEMP',\n",
       " 'SLAB_WID',\n",
       " 'SLAB_LEN',\n",
       " 'STRIP_EXIT_LEN',\n",
       " 'STRIP_EXIT_THIK',\n",
       " 'FM_EXIT_THIK',\n",
       " 'AL',\n",
       " 'B',\n",
       " 'C',\n",
       " 'CA',\n",
       " 'NB',\n",
       " 'CR',\n",
       " 'CU',\n",
       " 'MN',\n",
       " 'MO',\n",
       " 'N',\n",
       " 'NI',\n",
       " 'P',\n",
       " 'S',\n",
       " 'SI',\n",
       " 'TI',\n",
       " 'V']"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaling_feature"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "984bf8a9",
   "metadata": {},
   "source": [
    "# Feature Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "baef1b12",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_scale=[feature for feature in dataset.columns if feature not in ['UTS','YS','P_ELONGATION']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c50b4764",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MinMaxScaler()"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler=MinMaxScaler()\n",
    "scaler.fit(dataset[feature_scale])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c391591b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.        , 0.        , 0.        , ..., 0.00769428, 0.02777778,\n",
       "        0.02      ],\n",
       "       [0.01564043, 0.        , 0.        , ..., 0.01538856, 0.02777778,\n",
       "        0.02      ],\n",
       "       [0.01573331, 0.75      , 0.        , ..., 0.41805591, 0.05555556,\n",
       "        0.02      ],\n",
       "       ...,\n",
       "       [0.99994873, 0.4375    , 0.        , ..., 0.04103616, 0.02777778,\n",
       "        0.02      ],\n",
       "       [0.9999971 , 0.625     , 0.        , ..., 0.04360092, 0.02777778,\n",
       "        0.02      ],\n",
       "       [1.        , 0.875     , 0.        , ..., 0.35393691, 0.08333333,\n",
       "        0.02      ]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler.transform(dataset[feature_scale])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b87a2196",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.concat([dataset[['UTS','YS','P_ELONGATION']].reset_index(drop=True),\n",
    "                    pd.DataFrame(scaler.transform(dataset[feature_scale]), columns=feature_scale)],\n",
    "                    axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a73d1afa",
   "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>UTS</th>\n",
       "      <th>YS</th>\n",
       "      <th>P_ELONGATION</th>\n",
       "      <th>COIL_ID</th>\n",
       "      <th>GRADE</th>\n",
       "      <th>COIL_GEN_TIME</th>\n",
       "      <th>FURN_EXIT_TEMP</th>\n",
       "      <th>FM_DOUT_TEMP</th>\n",
       "      <th>COILING_TEMP</th>\n",
       "      <th>SLAB_WID</th>\n",
       "      <th>...</th>\n",
       "      <th>CU</th>\n",
       "      <th>MN</th>\n",
       "      <th>MO</th>\n",
       "      <th>N</th>\n",
       "      <th>NI</th>\n",
       "      <th>P</th>\n",
       "      <th>S</th>\n",
       "      <th>SI</th>\n",
       "      <th>TI</th>\n",
       "      <th>V</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>346</td>\n",
       "      <td>294</td>\n",
       "      <td>42</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.644295</td>\n",
       "      <td>0.540323</td>\n",
       "      <td>0.758974</td>\n",
       "      <td>0.556962</td>\n",
       "      <td>...</td>\n",
       "      <td>0.008863</td>\n",
       "      <td>0.038462</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.3125</td>\n",
       "      <td>0.012136</td>\n",
       "      <td>0.177632</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.007694</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>365</td>\n",
       "      <td>299</td>\n",
       "      <td>42</td>\n",
       "      <td>0.015640</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.570470</td>\n",
       "      <td>0.290323</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.860759</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005908</td>\n",
       "      <td>0.061538</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.012136</td>\n",
       "      <td>0.133772</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.015389</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>730</td>\n",
       "      <td>575</td>\n",
       "      <td>21</td>\n",
       "      <td>0.015733</td>\n",
       "      <td>0.7500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.604027</td>\n",
       "      <td>0.645161</td>\n",
       "      <td>0.405128</td>\n",
       "      <td>0.341772</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005908</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.8750</td>\n",
       "      <td>0.016181</td>\n",
       "      <td>0.144737</td>\n",
       "      <td>0.150</td>\n",
       "      <td>0.418056</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>495</td>\n",
       "      <td>397</td>\n",
       "      <td>30</td>\n",
       "      <td>0.016310</td>\n",
       "      <td>0.8750</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.691275</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.364103</td>\n",
       "      <td>0.898734</td>\n",
       "      <td>...</td>\n",
       "      <td>0.017725</td>\n",
       "      <td>0.461538</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.4375</td>\n",
       "      <td>0.016181</td>\n",
       "      <td>0.210526</td>\n",
       "      <td>0.655</td>\n",
       "      <td>0.251346</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>379</td>\n",
       "      <td>326</td>\n",
       "      <td>41</td>\n",
       "      <td>0.016572</td>\n",
       "      <td>0.4375</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.510067</td>\n",
       "      <td>0.370968</td>\n",
       "      <td>0.605128</td>\n",
       "      <td>0.911392</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002954</td>\n",
       "      <td>0.130769</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.012136</td>\n",
       "      <td>0.144737</td>\n",
       "      <td>0.505</td>\n",
       "      <td>0.012824</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   UTS   YS  P_ELONGATION   COIL_ID   GRADE  COIL_GEN_TIME  FURN_EXIT_TEMP  \\\n",
       "0  346  294            42  0.000000  0.0000            0.0        0.644295   \n",
       "1  365  299            42  0.015640  0.0000            0.0        0.570470   \n",
       "2  730  575            21  0.015733  0.7500            0.0        0.604027   \n",
       "3  495  397            30  0.016310  0.8750            0.0        0.691275   \n",
       "4  379  326            41  0.016572  0.4375            0.0        0.510067   \n",
       "\n",
       "   FM_DOUT_TEMP  COILING_TEMP  SLAB_WID  ...        CU        MN    MO  \\\n",
       "0      0.540323      0.758974  0.556962  ...  0.008863  0.038462  0.05   \n",
       "1      0.290323      0.666667  0.860759  ...  0.005908  0.061538  0.05   \n",
       "2      0.645161      0.405128  0.341772  ...  0.005908  0.923077  0.05   \n",
       "3      0.500000      0.364103  0.898734  ...  0.017725  0.461538  0.05   \n",
       "4      0.370968      0.605128  0.911392  ...  0.002954  0.130769  0.05   \n",
       "\n",
       "        N        NI         P      S        SI        TI     V  \n",
       "0  0.3125  0.012136  0.177632  0.500  0.007694  0.027778  0.02  \n",
       "1  0.2750  0.012136  0.133772  0.500  0.015389  0.027778  0.02  \n",
       "2  0.8750  0.016181  0.144737  0.150  0.418056  0.055556  0.02  \n",
       "3  0.4375  0.016181  0.210526  0.655  0.251346  0.055556  0.02  \n",
       "4  0.4000  0.012136  0.144737  0.505  0.012824  0.027778  0.02  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f16dc998",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7cdc16b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "## for feature slection\n",
    "\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.feature_selection import SelectFromModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "fd97a03d",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Capture the dependent feature\n",
    "y_train=data[['UTS','YS','P_ELONGATION']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "78756acd",
   "metadata": {},
   "outputs": [],
   "source": [
    "## drop dependent feature from dataset\n",
    "X_train=data.drop(['UTS','YS','P_ELONGATION'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "3628ce83",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>COIL_ID</th>\n",
       "      <th>GRADE</th>\n",
       "      <th>COIL_GEN_TIME</th>\n",
       "      <th>FURN_EXIT_TEMP</th>\n",
       "      <th>FM_DOUT_TEMP</th>\n",
       "      <th>COILING_TEMP</th>\n",
       "      <th>SLAB_WID</th>\n",
       "      <th>SLAB_LEN</th>\n",
       "      <th>STRIP_EXIT_LEN</th>\n",
       "      <th>STRIP_EXIT_THIK</th>\n",
       "      <th>...</th>\n",
       "      <th>CU</th>\n",
       "      <th>MN</th>\n",
       "      <th>MO</th>\n",
       "      <th>N</th>\n",
       "      <th>NI</th>\n",
       "      <th>P</th>\n",
       "      <th>S</th>\n",
       "      <th>SI</th>\n",
       "      <th>TI</th>\n",
       "      <th>V</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.644295</td>\n",
       "      <td>0.540323</td>\n",
       "      <td>0.758974</td>\n",
       "      <td>0.556962</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.973836</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.008863</td>\n",
       "      <td>0.038462</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.3125</td>\n",
       "      <td>0.012136</td>\n",
       "      <td>0.177632</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.007694</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.015640</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.570470</td>\n",
       "      <td>0.290323</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.860759</td>\n",
       "      <td>0.969444</td>\n",
       "      <td>0.941338</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005908</td>\n",
       "      <td>0.061538</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.012136</td>\n",
       "      <td>0.133772</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.015389</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.015733</td>\n",
       "      <td>0.7500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.604027</td>\n",
       "      <td>0.645161</td>\n",
       "      <td>0.405128</td>\n",
       "      <td>0.341772</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.871110</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005908</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.8750</td>\n",
       "      <td>0.016181</td>\n",
       "      <td>0.144737</td>\n",
       "      <td>0.150</td>\n",
       "      <td>0.418056</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.016310</td>\n",
       "      <td>0.8750</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.691275</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.364103</td>\n",
       "      <td>0.898734</td>\n",
       "      <td>0.513889</td>\n",
       "      <td>0.481685</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>...</td>\n",
       "      <td>0.017725</td>\n",
       "      <td>0.461538</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.4375</td>\n",
       "      <td>0.016181</td>\n",
       "      <td>0.210526</td>\n",
       "      <td>0.655</td>\n",
       "      <td>0.251346</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.016572</td>\n",
       "      <td>0.4375</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.510067</td>\n",
       "      <td>0.370968</td>\n",
       "      <td>0.605128</td>\n",
       "      <td>0.911392</td>\n",
       "      <td>0.739167</td>\n",
       "      <td>0.737538</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002954</td>\n",
       "      <td>0.130769</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.012136</td>\n",
       "      <td>0.144737</td>\n",
       "      <td>0.505</td>\n",
       "      <td>0.012824</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40959</th>\n",
       "      <td>0.999947</td>\n",
       "      <td>0.4375</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.604027</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>0.528205</td>\n",
       "      <td>0.924051</td>\n",
       "      <td>0.986111</td>\n",
       "      <td>0.966951</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005908</td>\n",
       "      <td>0.138462</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.3375</td>\n",
       "      <td>0.012136</td>\n",
       "      <td>0.111842</td>\n",
       "      <td>0.430</td>\n",
       "      <td>0.015389</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40960</th>\n",
       "      <td>0.999948</td>\n",
       "      <td>0.4375</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.543624</td>\n",
       "      <td>0.612903</td>\n",
       "      <td>0.579487</td>\n",
       "      <td>0.924051</td>\n",
       "      <td>0.986111</td>\n",
       "      <td>0.966951</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005908</td>\n",
       "      <td>0.138462</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.4250</td>\n",
       "      <td>0.016181</td>\n",
       "      <td>0.100877</td>\n",
       "      <td>0.315</td>\n",
       "      <td>0.035907</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40961</th>\n",
       "      <td>0.999949</td>\n",
       "      <td>0.4375</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.644295</td>\n",
       "      <td>0.572581</td>\n",
       "      <td>0.528205</td>\n",
       "      <td>0.924051</td>\n",
       "      <td>0.986111</td>\n",
       "      <td>0.966951</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005908</td>\n",
       "      <td>0.146154</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.012136</td>\n",
       "      <td>0.100877</td>\n",
       "      <td>0.225</td>\n",
       "      <td>0.041036</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40962</th>\n",
       "      <td>0.999997</td>\n",
       "      <td>0.6250</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.604027</td>\n",
       "      <td>0.741935</td>\n",
       "      <td>0.615385</td>\n",
       "      <td>0.924051</td>\n",
       "      <td>0.986111</td>\n",
       "      <td>0.765354</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005908</td>\n",
       "      <td>0.246154</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.4250</td>\n",
       "      <td>0.012136</td>\n",
       "      <td>0.144737</td>\n",
       "      <td>0.320</td>\n",
       "      <td>0.043601</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40963</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.8750</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.570470</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>0.487179</td>\n",
       "      <td>0.911392</td>\n",
       "      <td>0.986111</td>\n",
       "      <td>0.560176</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005908</td>\n",
       "      <td>0.453846</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.4125</td>\n",
       "      <td>0.016181</td>\n",
       "      <td>0.254386</td>\n",
       "      <td>0.205</td>\n",
       "      <td>0.353937</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>40964 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        COIL_ID   GRADE  COIL_GEN_TIME  FURN_EXIT_TEMP  FM_DOUT_TEMP  \\\n",
       "0      0.000000  0.0000            0.0        0.644295      0.540323   \n",
       "1      0.015640  0.0000            0.0        0.570470      0.290323   \n",
       "2      0.015733  0.7500            0.0        0.604027      0.645161   \n",
       "3      0.016310  0.8750            0.0        0.691275      0.500000   \n",
       "4      0.016572  0.4375            0.0        0.510067      0.370968   \n",
       "...         ...     ...            ...             ...           ...   \n",
       "40959  0.999947  0.4375            0.0        0.604027      0.548387   \n",
       "40960  0.999948  0.4375            0.0        0.543624      0.612903   \n",
       "40961  0.999949  0.4375            0.0        0.644295      0.572581   \n",
       "40962  0.999997  0.6250            0.0        0.604027      0.741935   \n",
       "40963  1.000000  0.8750            0.0        0.570470      0.548387   \n",
       "\n",
       "       COILING_TEMP  SLAB_WID  SLAB_LEN  STRIP_EXIT_LEN  STRIP_EXIT_THIK  ...  \\\n",
       "0          0.758974  0.556962  1.000000        0.973836         0.000000  ...   \n",
       "1          0.666667  0.860759  0.969444        0.941338         0.000000  ...   \n",
       "2          0.405128  0.341772  1.000000        0.871110         0.111111  ...   \n",
       "3          0.364103  0.898734  0.513889        0.481685         0.222222  ...   \n",
       "4          0.605128  0.911392  0.739167        0.737538         0.055556  ...   \n",
       "...             ...       ...       ...             ...              ...  ...   \n",
       "40959      0.528205  0.924051  0.986111        0.966951         0.000000  ...   \n",
       "40960      0.579487  0.924051  0.986111        0.966951         0.000000  ...   \n",
       "40961      0.528205  0.924051  0.986111        0.966951         0.000000  ...   \n",
       "40962      0.615385  0.924051  0.986111        0.765354         0.222222  ...   \n",
       "40963      0.487179  0.911392  0.986111        0.560176         0.500000  ...   \n",
       "\n",
       "             CU        MN    MO       N        NI         P      S        SI  \\\n",
       "0      0.008863  0.038462  0.05  0.3125  0.012136  0.177632  0.500  0.007694   \n",
       "1      0.005908  0.061538  0.05  0.2750  0.012136  0.133772  0.500  0.015389   \n",
       "2      0.005908  0.923077  0.05  0.8750  0.016181  0.144737  0.150  0.418056   \n",
       "3      0.017725  0.461538  0.05  0.4375  0.016181  0.210526  0.655  0.251346   \n",
       "4      0.002954  0.130769  0.05  0.4000  0.012136  0.144737  0.505  0.012824   \n",
       "...         ...       ...   ...     ...       ...       ...    ...       ...   \n",
       "40959  0.005908  0.138462  0.05  0.3375  0.012136  0.111842  0.430  0.015389   \n",
       "40960  0.005908  0.138462  0.05  0.4250  0.016181  0.100877  0.315  0.035907   \n",
       "40961  0.005908  0.146154  0.05  0.4000  0.012136  0.100877  0.225  0.041036   \n",
       "40962  0.005908  0.246154  0.05  0.4250  0.012136  0.144737  0.320  0.043601   \n",
       "40963  0.005908  0.453846  0.05  0.4125  0.016181  0.254386  0.205  0.353937   \n",
       "\n",
       "             TI     V  \n",
       "0      0.027778  0.02  \n",
       "1      0.027778  0.02  \n",
       "2      0.055556  0.02  \n",
       "3      0.055556  0.02  \n",
       "4      0.027778  0.02  \n",
       "...         ...   ...  \n",
       "40959  0.027778  0.02  \n",
       "40960  0.222222  0.02  \n",
       "40961  0.027778  0.02  \n",
       "40962  0.027778  0.02  \n",
       "40963  0.083333  0.02  \n",
       "\n",
       "[40964 rows x 27 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ecd48c14",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Apply Feature Selection\n",
    "# first, I specify the Lasso Regression model, and I\n",
    "# select a suitable alpha (equivalent of penalty).\n",
    "# The bigger the alpha the less features that will be selected.\n",
    "\n",
    "# Then I use the selectFromModel object from sklearn, which\n",
    "# will select the features which coefficients are non-zero"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3c047ee2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SelectFromModel(estimator=Lasso(alpha=0.1, random_state=0))"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_sel_model = SelectFromModel(Lasso(alpha=0.1, random_state=0)) # remember to set the seed, the random state in this function\n",
    "feature_sel_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "754fe193",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True, False, False,  True,  True,  True, False,  True,\n",
       "       False,  True, False, False,  True,  True,  True, False, False,\n",
       "        True, False,  True, False, False, False,  True,  True, False])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_sel_model.get_support()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "4df592fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total features: 27\n",
      "selected features: 14\n"
     ]
    }
   ],
   "source": [
    "# let's print the number of total and selected features\n",
    "\n",
    "# this is how we can make a list of the selected features\n",
    "selected_feat = X_train.columns[(feature_sel_model.get_support())]\n",
    "\n",
    "# let's print some stats\n",
    "print('total features: {}'.format((X_train.shape[1])))\n",
    "print('selected features: {}'.format(len(selected_feat)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "632f50c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['COIL_ID', 'GRADE', 'FM_DOUT_TEMP', 'COILING_TEMP', 'SLAB_WID',\n",
       "       'STRIP_EXIT_LEN', 'FM_EXIT_THIK', 'C', 'CA', 'NB', 'MN', 'N', 'SI',\n",
       "       'TI'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_feat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ed74195e",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train=X_train[selected_feat]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "fd3a5395",
   "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>COIL_ID</th>\n",
       "      <th>GRADE</th>\n",
       "      <th>FM_DOUT_TEMP</th>\n",
       "      <th>COILING_TEMP</th>\n",
       "      <th>SLAB_WID</th>\n",
       "      <th>STRIP_EXIT_LEN</th>\n",
       "      <th>FM_EXIT_THIK</th>\n",
       "      <th>C</th>\n",
       "      <th>CA</th>\n",
       "      <th>NB</th>\n",
       "      <th>MN</th>\n",
       "      <th>N</th>\n",
       "      <th>SI</th>\n",
       "      <th>TI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.540323</td>\n",
       "      <td>0.758974</td>\n",
       "      <td>0.556962</td>\n",
       "      <td>0.973836</td>\n",
       "      <td>0.040468</td>\n",
       "      <td>0.044611</td>\n",
       "      <td>0.075</td>\n",
       "      <td>0.019685</td>\n",
       "      <td>0.038462</td>\n",
       "      <td>0.3125</td>\n",
       "      <td>0.007694</td>\n",
       "      <td>0.027778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.015640</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.290323</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.860759</td>\n",
       "      <td>0.941338</td>\n",
       "      <td>0.045863</td>\n",
       "      <td>0.056667</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.019685</td>\n",
       "      <td>0.061538</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.015389</td>\n",
       "      <td>0.027778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.015733</td>\n",
       "      <td>0.7500</td>\n",
       "      <td>0.645161</td>\n",
       "      <td>0.405128</td>\n",
       "      <td>0.341772</td>\n",
       "      <td>0.871110</td>\n",
       "      <td>0.085432</td>\n",
       "      <td>0.641186</td>\n",
       "      <td>0.700</td>\n",
       "      <td>0.019685</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>0.8750</td>\n",
       "      <td>0.418056</td>\n",
       "      <td>0.055556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.016310</td>\n",
       "      <td>0.8750</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.364103</td>\n",
       "      <td>0.898734</td>\n",
       "      <td>0.481685</td>\n",
       "      <td>0.310252</td>\n",
       "      <td>0.338558</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.019685</td>\n",
       "      <td>0.461538</td>\n",
       "      <td>0.4375</td>\n",
       "      <td>0.251346</td>\n",
       "      <td>0.055556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.016572</td>\n",
       "      <td>0.4375</td>\n",
       "      <td>0.370968</td>\n",
       "      <td>0.605128</td>\n",
       "      <td>0.911392</td>\n",
       "      <td>0.737538</td>\n",
       "      <td>0.069245</td>\n",
       "      <td>0.110924</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.019685</td>\n",
       "      <td>0.130769</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.012824</td>\n",
       "      <td>0.027778</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40959</th>\n",
       "      <td>0.999947</td>\n",
       "      <td>0.4375</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>0.528205</td>\n",
       "      <td>0.924051</td>\n",
       "      <td>0.966951</td>\n",
       "      <td>0.040468</td>\n",
       "      <td>0.103689</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.019685</td>\n",
       "      <td>0.138462</td>\n",
       "      <td>0.3375</td>\n",
       "      <td>0.015389</td>\n",
       "      <td>0.027778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40960</th>\n",
       "      <td>0.999948</td>\n",
       "      <td>0.4375</td>\n",
       "      <td>0.612903</td>\n",
       "      <td>0.579487</td>\n",
       "      <td>0.924051</td>\n",
       "      <td>0.966951</td>\n",
       "      <td>0.040468</td>\n",
       "      <td>0.125392</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.019685</td>\n",
       "      <td>0.138462</td>\n",
       "      <td>0.4250</td>\n",
       "      <td>0.035907</td>\n",
       "      <td>0.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40961</th>\n",
       "      <td>0.999949</td>\n",
       "      <td>0.4375</td>\n",
       "      <td>0.572581</td>\n",
       "      <td>0.528205</td>\n",
       "      <td>0.924051</td>\n",
       "      <td>0.966951</td>\n",
       "      <td>0.040468</td>\n",
       "      <td>0.120569</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.019685</td>\n",
       "      <td>0.146154</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.041036</td>\n",
       "      <td>0.027778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40962</th>\n",
       "      <td>0.999997</td>\n",
       "      <td>0.6250</td>\n",
       "      <td>0.741935</td>\n",
       "      <td>0.615385</td>\n",
       "      <td>0.924051</td>\n",
       "      <td>0.765354</td>\n",
       "      <td>0.381295</td>\n",
       "      <td>0.212202</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.019685</td>\n",
       "      <td>0.246154</td>\n",
       "      <td>0.4250</td>\n",
       "      <td>0.043601</td>\n",
       "      <td>0.027778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40963</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.8750</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>0.487179</td>\n",
       "      <td>0.911392</td>\n",
       "      <td>0.560176</td>\n",
       "      <td>0.561151</td>\n",
       "      <td>0.361707</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.019685</td>\n",
       "      <td>0.453846</td>\n",
       "      <td>0.4125</td>\n",
       "      <td>0.353937</td>\n",
       "      <td>0.083333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>40964 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        COIL_ID   GRADE  FM_DOUT_TEMP  COILING_TEMP  SLAB_WID  STRIP_EXIT_LEN  \\\n",
       "0      0.000000  0.0000      0.540323      0.758974  0.556962        0.973836   \n",
       "1      0.015640  0.0000      0.290323      0.666667  0.860759        0.941338   \n",
       "2      0.015733  0.7500      0.645161      0.405128  0.341772        0.871110   \n",
       "3      0.016310  0.8750      0.500000      0.364103  0.898734        0.481685   \n",
       "4      0.016572  0.4375      0.370968      0.605128  0.911392        0.737538   \n",
       "...         ...     ...           ...           ...       ...             ...   \n",
       "40959  0.999947  0.4375      0.548387      0.528205  0.924051        0.966951   \n",
       "40960  0.999948  0.4375      0.612903      0.579487  0.924051        0.966951   \n",
       "40961  0.999949  0.4375      0.572581      0.528205  0.924051        0.966951   \n",
       "40962  0.999997  0.6250      0.741935      0.615385  0.924051        0.765354   \n",
       "40963  1.000000  0.8750      0.548387      0.487179  0.911392        0.560176   \n",
       "\n",
       "       FM_EXIT_THIK         C     CA        NB        MN       N        SI  \\\n",
       "0          0.040468  0.044611  0.075  0.019685  0.038462  0.3125  0.007694   \n",
       "1          0.045863  0.056667  0.025  0.019685  0.061538  0.2750  0.015389   \n",
       "2          0.085432  0.641186  0.700  0.019685  0.923077  0.8750  0.418056   \n",
       "3          0.310252  0.338558  0.050  0.019685  0.461538  0.4375  0.251346   \n",
       "4          0.069245  0.110924  0.025  0.019685  0.130769  0.4000  0.012824   \n",
       "...             ...       ...    ...       ...       ...     ...       ...   \n",
       "40959      0.040468  0.103689  0.025  0.019685  0.138462  0.3375  0.015389   \n",
       "40960      0.040468  0.125392  0.025  0.019685  0.138462  0.4250  0.035907   \n",
       "40961      0.040468  0.120569  0.025  0.019685  0.146154  0.4000  0.041036   \n",
       "40962      0.381295  0.212202  0.025  0.019685  0.246154  0.4250  0.043601   \n",
       "40963      0.561151  0.361707  0.025  0.019685  0.453846  0.4125  0.353937   \n",
       "\n",
       "             TI  \n",
       "0      0.027778  \n",
       "1      0.027778  \n",
       "2      0.055556  \n",
       "3      0.055556  \n",
       "4      0.027778  \n",
       "...         ...  \n",
       "40959  0.027778  \n",
       "40960  0.222222  \n",
       "40961  0.027778  \n",
       "40962  0.027778  \n",
       "40963  0.083333  \n",
       "\n",
       "[40964 rows x 14 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "6b967110",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "42e9939f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: Boruta in c:\\users\\user\\anaconda3\\lib\\site-packages (0.3)\n",
      "Requirement already satisfied: numpy>=1.10.4 in c:\\users\\user\\anaconda3\\lib\\site-packages (from Boruta) (1.20.1)\n",
      "Requirement already satisfied: scikit-learn>=0.17.1 in c:\\users\\user\\anaconda3\\lib\\site-packages (from Boruta) (0.24.1)\n",
      "Requirement already satisfied: scipy>=0.17.0 in c:\\users\\user\\anaconda3\\lib\\site-packages (from Boruta) (1.6.2)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\user\\anaconda3\\lib\\site-packages (from scikit-learn>=0.17.1->Boruta) (2.1.0)\n",
      "Requirement already satisfied: joblib>=0.11 in c:\\users\\user\\anaconda3\\lib\\site-packages (from scikit-learn>=0.17.1->Boruta) (1.0.1)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install Boruta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "b6049698",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from boruta import BorutaPy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "52a3fa61",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UTS</th>\n",
       "      <th>YS</th>\n",
       "      <th>P_ELONGATION</th>\n",
       "      <th>COIL_ID</th>\n",
       "      <th>GRADE</th>\n",
       "      <th>COIL_GEN_TIME</th>\n",
       "      <th>FURN_EXIT_TEMP</th>\n",
       "      <th>FM_DOUT_TEMP</th>\n",
       "      <th>COILING_TEMP</th>\n",
       "      <th>SLAB_WID</th>\n",
       "      <th>...</th>\n",
       "      <th>CU</th>\n",
       "      <th>MN</th>\n",
       "      <th>MO</th>\n",
       "      <th>N</th>\n",
       "      <th>NI</th>\n",
       "      <th>P</th>\n",
       "      <th>S</th>\n",
       "      <th>SI</th>\n",
       "      <th>TI</th>\n",
       "      <th>V</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>346</td>\n",
       "      <td>294</td>\n",
       "      <td>42</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.644295</td>\n",
       "      <td>0.540323</td>\n",
       "      <td>0.758974</td>\n",
       "      <td>0.556962</td>\n",
       "      <td>...</td>\n",
       "      <td>0.008863</td>\n",
       "      <td>0.038462</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.3125</td>\n",
       "      <td>0.012136</td>\n",
       "      <td>0.177632</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.007694</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>365</td>\n",
       "      <td>299</td>\n",
       "      <td>42</td>\n",
       "      <td>0.015640</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.570470</td>\n",
       "      <td>0.290323</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.860759</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005908</td>\n",
       "      <td>0.061538</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.012136</td>\n",
       "      <td>0.133772</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.015389</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>730</td>\n",
       "      <td>575</td>\n",
       "      <td>21</td>\n",
       "      <td>0.015733</td>\n",
       "      <td>0.7500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.604027</td>\n",
       "      <td>0.645161</td>\n",
       "      <td>0.405128</td>\n",
       "      <td>0.341772</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005908</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.8750</td>\n",
       "      <td>0.016181</td>\n",
       "      <td>0.144737</td>\n",
       "      <td>0.150</td>\n",
       "      <td>0.418056</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>495</td>\n",
       "      <td>397</td>\n",
       "      <td>30</td>\n",
       "      <td>0.016310</td>\n",
       "      <td>0.8750</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.691275</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.364103</td>\n",
       "      <td>0.898734</td>\n",
       "      <td>...</td>\n",
       "      <td>0.017725</td>\n",
       "      <td>0.461538</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.4375</td>\n",
       "      <td>0.016181</td>\n",
       "      <td>0.210526</td>\n",
       "      <td>0.655</td>\n",
       "      <td>0.251346</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>379</td>\n",
       "      <td>326</td>\n",
       "      <td>41</td>\n",
       "      <td>0.016572</td>\n",
       "      <td>0.4375</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.510067</td>\n",
       "      <td>0.370968</td>\n",
       "      <td>0.605128</td>\n",
       "      <td>0.911392</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002954</td>\n",
       "      <td>0.130769</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.012136</td>\n",
       "      <td>0.144737</td>\n",
       "      <td>0.505</td>\n",
       "      <td>0.012824</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.02</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>40959</th>\n",
       "      <td>398</td>\n",
       "      <td>352</td>\n",
       "      <td>42</td>\n",
       "      <td>0.999947</td>\n",
       "      <td>0.4375</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.604027</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>0.528205</td>\n",
       "      <td>0.924051</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005908</td>\n",
       "      <td>0.138462</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.3375</td>\n",
       "      <td>0.012136</td>\n",
       "      <td>0.111842</td>\n",
       "      <td>0.430</td>\n",
       "      <td>0.015389</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40960</th>\n",
       "      <td>405</td>\n",
       "      <td>347</td>\n",
       "      <td>35</td>\n",
       "      <td>0.999948</td>\n",
       "      <td>0.4375</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.543624</td>\n",
       "      <td>0.612903</td>\n",
       "      <td>0.579487</td>\n",
       "      <td>0.924051</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005908</td>\n",
       "      <td>0.138462</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.4250</td>\n",
       "      <td>0.016181</td>\n",
       "      <td>0.100877</td>\n",
       "      <td>0.315</td>\n",
       "      <td>0.035907</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40961</th>\n",
       "      <td>389</td>\n",
       "      <td>330</td>\n",
       "      <td>39</td>\n",
       "      <td>0.999949</td>\n",
       "      <td>0.4375</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.644295</td>\n",
       "      <td>0.572581</td>\n",
       "      <td>0.528205</td>\n",
       "      <td>0.924051</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005908</td>\n",
       "      <td>0.146154</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.012136</td>\n",
       "      <td>0.100877</td>\n",
       "      <td>0.225</td>\n",
       "      <td>0.041036</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40962</th>\n",
       "      <td>405</td>\n",
       "      <td>311</td>\n",
       "      <td>37</td>\n",
       "      <td>0.999997</td>\n",
       "      <td>0.6250</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.604027</td>\n",
       "      <td>0.741935</td>\n",
       "      <td>0.615385</td>\n",
       "      <td>0.924051</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005908</td>\n",
       "      <td>0.246154</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.4250</td>\n",
       "      <td>0.012136</td>\n",
       "      <td>0.144737</td>\n",
       "      <td>0.320</td>\n",
       "      <td>0.043601</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.02</td>\n",
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       "    <tr>\n",
       "      <th>40963</th>\n",
       "      <td>486</td>\n",
       "      <td>359</td>\n",
       "      <td>30</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.8750</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.570470</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>0.487179</td>\n",
       "      <td>0.911392</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005908</td>\n",
       "      <td>0.453846</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.4125</td>\n",
       "      <td>0.016181</td>\n",
       "      <td>0.254386</td>\n",
       "      <td>0.205</td>\n",
       "      <td>0.353937</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>40964 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       UTS   YS  P_ELONGATION   COIL_ID   GRADE  COIL_GEN_TIME  \\\n",
       "0      346  294            42  0.000000  0.0000            0.0   \n",
       "1      365  299            42  0.015640  0.0000            0.0   \n",
       "2      730  575            21  0.015733  0.7500            0.0   \n",
       "3      495  397            30  0.016310  0.8750            0.0   \n",
       "4      379  326            41  0.016572  0.4375            0.0   \n",
       "...    ...  ...           ...       ...     ...            ...   \n",
       "40959  398  352            42  0.999947  0.4375            0.0   \n",
       "40960  405  347            35  0.999948  0.4375            0.0   \n",
       "40961  389  330            39  0.999949  0.4375            0.0   \n",
       "40962  405  311            37  0.999997  0.6250            0.0   \n",
       "40963  486  359            30  1.000000  0.8750            0.0   \n",
       "\n",
       "       FURN_EXIT_TEMP  FM_DOUT_TEMP  COILING_TEMP  SLAB_WID  ...        CU  \\\n",
       "0            0.644295      0.540323      0.758974  0.556962  ...  0.008863   \n",
       "1            0.570470      0.290323      0.666667  0.860759  ...  0.005908   \n",
       "2            0.604027      0.645161      0.405128  0.341772  ...  0.005908   \n",
       "3            0.691275      0.500000      0.364103  0.898734  ...  0.017725   \n",
       "4            0.510067      0.370968      0.605128  0.911392  ...  0.002954   \n",
       "...               ...           ...           ...       ...  ...       ...   \n",
       "40959        0.604027      0.548387      0.528205  0.924051  ...  0.005908   \n",
       "40960        0.543624      0.612903      0.579487  0.924051  ...  0.005908   \n",
       "40961        0.644295      0.572581      0.528205  0.924051  ...  0.005908   \n",
       "40962        0.604027      0.741935      0.615385  0.924051  ...  0.005908   \n",
       "40963        0.570470      0.548387      0.487179  0.911392  ...  0.005908   \n",
       "\n",
       "             MN    MO       N        NI         P      S        SI        TI  \\\n",
       "0      0.038462  0.05  0.3125  0.012136  0.177632  0.500  0.007694  0.027778   \n",
       "1      0.061538  0.05  0.2750  0.012136  0.133772  0.500  0.015389  0.027778   \n",
       "2      0.923077  0.05  0.8750  0.016181  0.144737  0.150  0.418056  0.055556   \n",
       "3      0.461538  0.05  0.4375  0.016181  0.210526  0.655  0.251346  0.055556   \n",
       "4      0.130769  0.05  0.4000  0.012136  0.144737  0.505  0.012824  0.027778   \n",
       "...         ...   ...     ...       ...       ...    ...       ...       ...   \n",
       "40959  0.138462  0.05  0.3375  0.012136  0.111842  0.430  0.015389  0.027778   \n",
       "40960  0.138462  0.05  0.4250  0.016181  0.100877  0.315  0.035907  0.222222   \n",
       "40961  0.146154  0.05  0.4000  0.012136  0.100877  0.225  0.041036  0.027778   \n",
       "40962  0.246154  0.05  0.4250  0.012136  0.144737  0.320  0.043601  0.027778   \n",
       "40963  0.453846  0.05  0.4125  0.016181  0.254386  0.205  0.353937  0.083333   \n",
       "\n",
       "          V  \n",
       "0      0.02  \n",
       "1      0.02  \n",
       "2      0.02  \n",
       "3      0.02  \n",
       "4      0.02  \n",
       "...     ...  \n",
       "40959  0.02  \n",
       "40960  0.02  \n",
       "40961  0.02  \n",
       "40962  0.02  \n",
       "40963  0.02  \n",
       "\n",
       "[40964 rows x 30 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "ab0b5842",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GRADE</th>\n",
       "      <th>COIL_GEN_TIME</th>\n",
       "      <th>FURN_EXIT_TEMP</th>\n",
       "      <th>FM_DOUT_TEMP</th>\n",
       "      <th>COILING_TEMP</th>\n",
       "      <th>SLAB_WID</th>\n",
       "      <th>SLAB_LEN</th>\n",
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       "      <th>STRIP_EXIT_THIK</th>\n",
       "      <th>FM_EXIT_THIK</th>\n",
       "      <th>...</th>\n",
       "      <th>CU</th>\n",
       "      <th>MN</th>\n",
       "      <th>MO</th>\n",
       "      <th>N</th>\n",
       "      <th>NI</th>\n",
       "      <th>P</th>\n",
       "      <th>S</th>\n",
       "      <th>SI</th>\n",
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       "      <td>...</td>\n",
       "      <td>0.008863</td>\n",
       "      <td>0.038462</td>\n",
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       "      <td>0.177632</td>\n",
       "      <td>0.500</td>\n",
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       "      <td>0.027778</td>\n",
       "      <td>0.02</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.570470</td>\n",
       "      <td>0.290323</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.860759</td>\n",
       "      <td>0.969444</td>\n",
       "      <td>0.941338</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.045863</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005908</td>\n",
       "      <td>0.061538</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.012136</td>\n",
       "      <td>0.133772</td>\n",
       "      <td>0.500</td>\n",
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       "      <td>0.027778</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.7500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.604027</td>\n",
       "      <td>0.645161</td>\n",
       "      <td>0.405128</td>\n",
       "      <td>0.341772</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.871110</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.085432</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005908</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.8750</td>\n",
       "      <td>0.016181</td>\n",
       "      <td>0.144737</td>\n",
       "      <td>0.150</td>\n",
       "      <td>0.418056</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.8750</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.691275</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.364103</td>\n",
       "      <td>0.898734</td>\n",
       "      <td>0.513889</td>\n",
       "      <td>0.481685</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.310252</td>\n",
       "      <td>...</td>\n",
       "      <td>0.017725</td>\n",
       "      <td>0.461538</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.4375</td>\n",
       "      <td>0.016181</td>\n",
       "      <td>0.210526</td>\n",
       "      <td>0.655</td>\n",
       "      <td>0.251346</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.02</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.4375</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.510067</td>\n",
       "      <td>0.370968</td>\n",
       "      <td>0.605128</td>\n",
       "      <td>0.911392</td>\n",
       "      <td>0.739167</td>\n",
       "      <td>0.737538</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.069245</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002954</td>\n",
       "      <td>0.130769</td>\n",
       "      <td>0.05</td>\n",
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       "      <td>0.012824</td>\n",
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       "      <td>0.3375</td>\n",
       "      <td>0.012136</td>\n",
       "      <td>0.111842</td>\n",
       "      <td>0.430</td>\n",
       "      <td>0.015389</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.02</td>\n",
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       "    <tr>\n",
       "      <th>40960</th>\n",
       "      <td>0.4375</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.543624</td>\n",
       "      <td>0.612903</td>\n",
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       "      <td>0.924051</td>\n",
       "      <td>0.986111</td>\n",
       "      <td>0.966951</td>\n",
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       "      <td>0.040468</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.4250</td>\n",
       "      <td>0.016181</td>\n",
       "      <td>0.100877</td>\n",
       "      <td>0.315</td>\n",
       "      <td>0.035907</td>\n",
       "      <td>0.222222</td>\n",
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       "    <tr>\n",
       "      <th>40961</th>\n",
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       "      <td>0.040468</td>\n",
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       "      <td>0.05</td>\n",
       "      <td>0.4000</td>\n",
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       "      <td>0.100877</td>\n",
       "      <td>0.225</td>\n",
       "      <td>0.041036</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.02</td>\n",
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       "    <tr>\n",
       "      <th>40962</th>\n",
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       "      <td>0.741935</td>\n",
       "      <td>0.615385</td>\n",
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       "      <td>0.986111</td>\n",
       "      <td>0.765354</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.381295</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005908</td>\n",
       "      <td>0.246154</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.4250</td>\n",
       "      <td>0.012136</td>\n",
       "      <td>0.144737</td>\n",
       "      <td>0.320</td>\n",
       "      <td>0.043601</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.02</td>\n",
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       "    <tr>\n",
       "      <th>40963</th>\n",
       "      <td>0.8750</td>\n",
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       "      <td>0.487179</td>\n",
       "      <td>0.911392</td>\n",
       "      <td>0.986111</td>\n",
       "      <td>0.560176</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.561151</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005908</td>\n",
       "      <td>0.453846</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.4125</td>\n",
       "      <td>0.016181</td>\n",
       "      <td>0.254386</td>\n",
       "      <td>0.205</td>\n",
       "      <td>0.353937</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>40964 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        GRADE  COIL_GEN_TIME  FURN_EXIT_TEMP  FM_DOUT_TEMP  COILING_TEMP  \\\n",
       "0      0.0000            0.0        0.644295      0.540323      0.758974   \n",
       "1      0.0000            0.0        0.570470      0.290323      0.666667   \n",
       "2      0.7500            0.0        0.604027      0.645161      0.405128   \n",
       "3      0.8750            0.0        0.691275      0.500000      0.364103   \n",
       "4      0.4375            0.0        0.510067      0.370968      0.605128   \n",
       "...       ...            ...             ...           ...           ...   \n",
       "40959  0.4375            0.0        0.604027      0.548387      0.528205   \n",
       "40960  0.4375            0.0        0.543624      0.612903      0.579487   \n",
       "40961  0.4375            0.0        0.644295      0.572581      0.528205   \n",
       "40962  0.6250            0.0        0.604027      0.741935      0.615385   \n",
       "40963  0.8750            0.0        0.570470      0.548387      0.487179   \n",
       "\n",
       "       SLAB_WID  SLAB_LEN  STRIP_EXIT_LEN  STRIP_EXIT_THIK  FM_EXIT_THIK  ...  \\\n",
       "0      0.556962  1.000000        0.973836         0.000000      0.040468  ...   \n",
       "1      0.860759  0.969444        0.941338         0.000000      0.045863  ...   \n",
       "2      0.341772  1.000000        0.871110         0.111111      0.085432  ...   \n",
       "3      0.898734  0.513889        0.481685         0.222222      0.310252  ...   \n",
       "4      0.911392  0.739167        0.737538         0.055556      0.069245  ...   \n",
       "...         ...       ...             ...              ...           ...  ...   \n",
       "40959  0.924051  0.986111        0.966951         0.000000      0.040468  ...   \n",
       "40960  0.924051  0.986111        0.966951         0.000000      0.040468  ...   \n",
       "40961  0.924051  0.986111        0.966951         0.000000      0.040468  ...   \n",
       "40962  0.924051  0.986111        0.765354         0.222222      0.381295  ...   \n",
       "40963  0.911392  0.986111        0.560176         0.500000      0.561151  ...   \n",
       "\n",
       "             CU        MN    MO       N        NI         P      S        SI  \\\n",
       "0      0.008863  0.038462  0.05  0.3125  0.012136  0.177632  0.500  0.007694   \n",
       "1      0.005908  0.061538  0.05  0.2750  0.012136  0.133772  0.500  0.015389   \n",
       "2      0.005908  0.923077  0.05  0.8750  0.016181  0.144737  0.150  0.418056   \n",
       "3      0.017725  0.461538  0.05  0.4375  0.016181  0.210526  0.655  0.251346   \n",
       "4      0.002954  0.130769  0.05  0.4000  0.012136  0.144737  0.505  0.012824   \n",
       "...         ...       ...   ...     ...       ...       ...    ...       ...   \n",
       "40959  0.005908  0.138462  0.05  0.3375  0.012136  0.111842  0.430  0.015389   \n",
       "40960  0.005908  0.138462  0.05  0.4250  0.016181  0.100877  0.315  0.035907   \n",
       "40961  0.005908  0.146154  0.05  0.4000  0.012136  0.100877  0.225  0.041036   \n",
       "40962  0.005908  0.246154  0.05  0.4250  0.012136  0.144737  0.320  0.043601   \n",
       "40963  0.005908  0.453846  0.05  0.4125  0.016181  0.254386  0.205  0.353937   \n",
       "\n",
       "             TI     V  \n",
       "0      0.027778  0.02  \n",
       "1      0.027778  0.02  \n",
       "2      0.055556  0.02  \n",
       "3      0.055556  0.02  \n",
       "4      0.027778  0.02  \n",
       "...         ...   ...  \n",
       "40959  0.027778  0.02  \n",
       "40960  0.222222  0.02  \n",
       "40961  0.027778  0.02  \n",
       "40962  0.027778  0.02  \n",
       "40963  0.083333  0.02  \n",
       "\n",
       "[40964 rows x 26 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X=data.drop(['COIL_ID','UTS','YS','P_ELONGATION'],axis=1)\n",
    "\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "952db689",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[346],\n",
       "       [365],\n",
       "       [730],\n",
       "       ...,\n",
       "       [389],\n",
       "       [405],\n",
       "       [486]], dtype=int64)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=data[['UTS'\n",
    "       ]].values\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "f0fbd44e",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = y.ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "908f0b06",
   "metadata": {},
   "outputs": [],
   "source": [
    "model= RandomForestRegressor(n_estimators=100, max_depth=5, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "75c9bfc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "feat_selector = BorutaPy(\n",
    "    verbose=2,\n",
    "    estimator=model,\n",
    "    n_estimators='auto',\n",
    "    max_iter=10,  \n",
    "    random_state=0,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "5c980b1f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration: \t1 / 10\n",
      "Confirmed: \t0\n",
      "Tentative: \t26\n",
      "Rejected: \t0\n",
      "Iteration: \t2 / 10\n",
      "Confirmed: \t0\n",
      "Tentative: \t26\n",
      "Rejected: \t0\n",
      "Iteration: \t3 / 10\n",
      "Confirmed: \t0\n",
      "Tentative: \t26\n",
      "Rejected: \t0\n",
      "Iteration: \t4 / 10\n",
      "Confirmed: \t0\n",
      "Tentative: \t26\n",
      "Rejected: \t0\n",
      "Iteration: \t5 / 10\n",
      "Confirmed: \t0\n",
      "Tentative: \t26\n",
      "Rejected: \t0\n",
      "Iteration: \t6 / 10\n",
      "Confirmed: \t0\n",
      "Tentative: \t26\n",
      "Rejected: \t0\n",
      "Iteration: \t7 / 10\n",
      "Confirmed: \t0\n",
      "Tentative: \t26\n",
      "Rejected: \t0\n",
      "Iteration: \t8 / 10\n",
      "Confirmed: \t7\n",
      "Tentative: \t8\n",
      "Rejected: \t11\n",
      "Iteration: \t9 / 10\n",
      "Confirmed: \t7\n",
      "Tentative: \t8\n",
      "Rejected: \t11\n",
      "\n",
      "\n",
      "BorutaPy finished running.\n",
      "\n",
      "Iteration: \t10 / 10\n",
      "Confirmed: \t7\n",
      "Tentative: \t2\n",
      "Rejected: \t11\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "BorutaPy(estimator=RandomForestRegressor(max_depth=5, n_estimators=109,\n",
       "                                         random_state=RandomState(MT19937) at 0x7ED4A15440),\n",
       "         max_iter=10, n_estimators='auto',\n",
       "         random_state=RandomState(MT19937) at 0x7ED4A15440, verbose=2)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_selector.fit(np.array(X), np.array(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "a503c958",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "6d211aec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "------Support and Ranking for each feature------\n",
      "\n",
      "Passes the test:  GRADE  - Ranking:  1 ✔️\n",
      "Doesn't pass the test:  COIL_GEN_TIME  - Ranking:  18 X\n",
      "Doesn't pass the test:  FURN_EXIT_TEMP  - Ranking:  13 X\n",
      "Doesn't pass the test:  FM_DOUT_TEMP  - Ranking:  2 X\n",
      "Doesn't pass the test:  COILING_TEMP  - Ranking:  2 X\n",
      "Doesn't pass the test:  SLAB_WID  - Ranking:  13 X\n",
      "Doesn't pass the test:  SLAB_LEN  - Ranking:  15 X\n",
      "Doesn't pass the test:  STRIP_EXIT_LEN  - Ranking:  8 X\n",
      "Doesn't pass the test:  STRIP_EXIT_THIK  - Ranking:  3 X\n",
      "Passes the test:  FM_EXIT_THIK  - Ranking:  1 ✔️\n",
      "Doesn't pass the test:  AL  - Ranking:  10 X\n",
      "Doesn't pass the test:  B  - Ranking:  17 X\n",
      "Passes the test:  C  - Ranking:  1 ✔️\n",
      "Doesn't pass the test:  CA  - Ranking:  6 X\n",
      "Passes the test:  NB  - Ranking:  1 ✔️\n",
      "Passes the test:  CR  - Ranking:  1 ✔️\n",
      "Doesn't pass the test:  CU  - Ranking:  5 X\n",
      "Passes the test:  MN  - Ranking:  1 ✔️\n",
      "Doesn't pass the test:  MO  - Ranking:  14 X\n",
      "Doesn't pass the test:  N  - Ranking:  7 X\n",
      "Doesn't pass the test:  NI  - Ranking:  5 X\n",
      "Doesn't pass the test:  P  - Ranking:  3 X\n",
      "Doesn't pass the test:  S  - Ranking:  9 X\n",
      "Passes the test:  SI  - Ranking:  1 ✔️\n",
      "Doesn't pass the test:  TI  - Ranking:  11 X\n",
      "Doesn't pass the test:  V  - Ranking:  16 X\n"
     ]
    }
   ],
   "source": [
    "# print support and ranking for each feature\n",
    "print(\"\\n------Support and Ranking for each feature------\\n\")\n",
    "for i in range(len(feat_selector.support_)):\n",
    "    if feat_selector.support_[i]:\n",
    "        print(\"Passes the test: \", X.columns[i],\n",
    "              \" - Ranking: \", feat_selector.ranking_[i], \"✔️\")\n",
    "    else:\n",
    "        print(\"Doesn't pass the test: \",\n",
    "              X.columns[i], \" - Ranking: \", feat_selector.ranking_[i], \"X\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "ba913abb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# features selected by Boruta\n",
    "X_filtered = feat_selector.transform(np.array(X))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "f7cba3d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "------Selected Features------\n",
      "\n",
      "[[0.         0.04046763 0.04461056 ... 0.02159309 0.03846154 0.00769428]\n",
      " [0.         0.04586331 0.05666747 ... 0.01679463 0.06153846 0.01538856]\n",
      " [0.75       0.08543165 0.6411864  ... 0.02639155 0.92307692 0.41805591]\n",
      " ...\n",
      " [0.4375     0.04046763 0.12056909 ... 0.01439539 0.14615385 0.04103616]\n",
      " [0.625      0.38129496 0.21220159 ... 0.02399232 0.24615385 0.04360092]\n",
      " [0.875      0.56115108 0.36170726 ... 0.02399232 0.45384615 0.35393691]]\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n------Selected Features------\\n\")\n",
    "(print(X_filtered))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "8421c9f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
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       "      <th>6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.040468</td>\n",
       "      <td>0.044611</td>\n",
       "      <td>0.019685</td>\n",
       "      <td>0.021593</td>\n",
       "      <td>0.038462</td>\n",
       "      <td>0.007694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.045863</td>\n",
       "      <td>0.056667</td>\n",
       "      <td>0.019685</td>\n",
       "      <td>0.016795</td>\n",
       "      <td>0.061538</td>\n",
       "      <td>0.015389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.7500</td>\n",
       "      <td>0.085432</td>\n",
       "      <td>0.641186</td>\n",
       "      <td>0.019685</td>\n",
       "      <td>0.026392</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>0.418056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.8750</td>\n",
       "      <td>0.310252</td>\n",
       "      <td>0.338558</td>\n",
       "      <td>0.019685</td>\n",
       "      <td>0.031190</td>\n",
       "      <td>0.461538</td>\n",
       "      <td>0.251346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.4375</td>\n",
       "      <td>0.069245</td>\n",
       "      <td>0.110924</td>\n",
       "      <td>0.019685</td>\n",
       "      <td>0.014395</td>\n",
       "      <td>0.130769</td>\n",
       "      <td>0.012824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.019685</td>\n",
       "      <td>0.009597</td>\n",
       "      <td>0.138462</td>\n",
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       "      <th>40960</th>\n",
       "      <td>0.4375</td>\n",
       "      <td>0.040468</td>\n",
       "      <td>0.125392</td>\n",
       "      <td>0.019685</td>\n",
       "      <td>0.019194</td>\n",
       "      <td>0.138462</td>\n",
       "      <td>0.035907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40961</th>\n",
       "      <td>0.4375</td>\n",
       "      <td>0.040468</td>\n",
       "      <td>0.120569</td>\n",
       "      <td>0.019685</td>\n",
       "      <td>0.014395</td>\n",
       "      <td>0.146154</td>\n",
       "      <td>0.041036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40962</th>\n",
       "      <td>0.6250</td>\n",
       "      <td>0.381295</td>\n",
       "      <td>0.212202</td>\n",
       "      <td>0.019685</td>\n",
       "      <td>0.023992</td>\n",
       "      <td>0.246154</td>\n",
       "      <td>0.043601</td>\n",
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       "    <tr>\n",
       "      <th>40963</th>\n",
       "      <td>0.8750</td>\n",
       "      <td>0.561151</td>\n",
       "      <td>0.361707</td>\n",
       "      <td>0.019685</td>\n",
       "      <td>0.023992</td>\n",
       "      <td>0.453846</td>\n",
       "      <td>0.353937</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>40964 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            0         1         2         3         4         5         6\n",
       "0      0.0000  0.040468  0.044611  0.019685  0.021593  0.038462  0.007694\n",
       "1      0.0000  0.045863  0.056667  0.019685  0.016795  0.061538  0.015389\n",
       "2      0.7500  0.085432  0.641186  0.019685  0.026392  0.923077  0.418056\n",
       "3      0.8750  0.310252  0.338558  0.019685  0.031190  0.461538  0.251346\n",
       "4      0.4375  0.069245  0.110924  0.019685  0.014395  0.130769  0.012824\n",
       "...       ...       ...       ...       ...       ...       ...       ...\n",
       "40959  0.4375  0.040468  0.103689  0.019685  0.009597  0.138462  0.015389\n",
       "40960  0.4375  0.040468  0.125392  0.019685  0.019194  0.138462  0.035907\n",
       "40961  0.4375  0.040468  0.120569  0.019685  0.014395  0.146154  0.041036\n",
       "40962  0.6250  0.381295  0.212202  0.019685  0.023992  0.246154  0.043601\n",
       "40963  0.8750  0.561151  0.361707  0.019685  0.023992  0.453846  0.353937\n",
       "\n",
       "[40964 rows x 7 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(X_filtered)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "d5ed87d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: BorutaShap in c:\\users\\user\\anaconda3\\lib\\site-packages (1.0.16)Note: you may need to restart the kernel to use updated packages.\n",
      "Requirement already satisfied: tqdm in c:\\users\\user\\anaconda3\\lib\\site-packages (from BorutaShap) (4.59.0)\n",
      "Requirement already satisfied: pandas in c:\\users\\user\\anaconda3\\lib\\site-packages (from BorutaShap) (1.2.4)\n",
      "Requirement already satisfied: statsmodels in c:\\users\\user\\anaconda3\\lib\\site-packages (from BorutaShap) (0.12.2)\n",
      "Requirement already satisfied: scipy in c:\\users\\user\\anaconda3\\lib\\site-packages (from BorutaShap) (1.6.2)\n",
      "Requirement already satisfied: numpy in c:\\users\\user\\anaconda3\\lib\\site-packages (from BorutaShap) (1.20.1)\n",
      "Requirement already satisfied: scikit-learn in c:\\users\\user\\anaconda3\\lib\\site-packages (from BorutaShap) (0.24.1)\n",
      "Requirement already satisfied: matplotlib in c:\\users\\user\\anaconda3\\lib\\site-packages (from BorutaShap) (3.3.4)\n",
      "Requirement already satisfied: shap>=0.34.0 in c:\\users\\user\\anaconda3\\lib\\site-packages (from BorutaShap) (0.40.0)\n",
      "Requirement already satisfied: seaborn in c:\\users\\user\\anaconda3\\lib\\site-packages (from BorutaShap) (0.11.1)\n",
      "Requirement already satisfied: cloudpickle in c:\\users\\user\\anaconda3\\lib\\site-packages (from shap>=0.34.0->BorutaShap) (1.6.0)\n",
      "Requirement already satisfied: numba in c:\\users\\user\\anaconda3\\lib\\site-packages (from shap>=0.34.0->BorutaShap) (0.53.1)\n",
      "Requirement already satisfied: packaging>20.9 in c:\\users\\user\\anaconda3\\lib\\site-packages (from shap>=0.34.0->BorutaShap) (21.3)\n",
      "Requirement already satisfied: slicer==0.0.7 in c:\\users\\user\\anaconda3\\lib\\site-packages (from shap>=0.34.0->BorutaShap) (0.0.7)\n",
      "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in c:\\users\\user\\anaconda3\\lib\\site-packages (from packaging>20.9->shap>=0.34.0->BorutaShap) (2.4.7)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in c:\\users\\user\\anaconda3\\lib\\site-packages (from matplotlib->BorutaShap) (2.8.1)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\user\\anaconda3\\lib\\site-packages (from matplotlib->BorutaShap) (1.3.1)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\user\\anaconda3\\lib\\site-packages (from matplotlib->BorutaShap) (0.10.0)\n",
      "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\user\\anaconda3\\lib\\site-packages (from matplotlib->BorutaShap) (8.2.0)\n",
      "Requirement already satisfied: six in c:\\users\\user\\anaconda3\\lib\\site-packages (from cycler>=0.10->matplotlib->BorutaShap) (1.15.0)\n",
      "Requirement already satisfied: llvmlite<0.37,>=0.36.0rc1 in c:\\users\\user\\anaconda3\\lib\\site-packages (from numba->shap>=0.34.0->BorutaShap) (0.36.0)\n",
      "\n",
      "Requirement already satisfied: setuptools in c:\\users\\user\\anaconda3\\lib\\site-packages (from numba->shap>=0.34.0->BorutaShap) (52.0.0.post20210125)\n",
      "Requirement already satisfied: pytz>=2017.3 in c:\\users\\user\\anaconda3\\lib\\site-packages (from pandas->BorutaShap) (2021.1)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\user\\anaconda3\\lib\\site-packages (from scikit-learn->BorutaShap) (2.1.0)\n",
      "Requirement already satisfied: joblib>=0.11 in c:\\users\\user\\anaconda3\\lib\\site-packages (from scikit-learn->BorutaShap) (1.0.1)\n",
      "Requirement already satisfied: patsy>=0.5 in c:\\users\\user\\anaconda3\\lib\\site-packages (from statsmodels->BorutaShap) (0.5.1)\n"
     ]
    }
   ],
   "source": [
    "pip install BorutaShap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "15a649ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "from BorutaShap import BorutaShap\n",
    "# Creates a BorutaShap selector for regression\n",
    "selector = BorutaShap(importance_measure = 'shap', classification = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "38834358",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>GRADE</th>\n",
       "      <th>COIL_GEN_TIME</th>\n",
       "      <th>FURN_EXIT_TEMP</th>\n",
       "      <th>FM_DOUT_TEMP</th>\n",
       "      <th>COILING_TEMP</th>\n",
       "      <th>SLAB_WID</th>\n",
       "      <th>SLAB_LEN</th>\n",
       "      <th>STRIP_EXIT_LEN</th>\n",
       "      <th>STRIP_EXIT_THIK</th>\n",
       "      <th>FM_EXIT_THIK</th>\n",
       "      <th>...</th>\n",
       "      <th>CU</th>\n",
       "      <th>MN</th>\n",
       "      <th>MO</th>\n",
       "      <th>N</th>\n",
       "      <th>NI</th>\n",
       "      <th>P</th>\n",
       "      <th>S</th>\n",
       "      <th>SI</th>\n",
       "      <th>TI</th>\n",
       "      <th>V</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.644295</td>\n",
       "      <td>0.540323</td>\n",
       "      <td>0.758974</td>\n",
       "      <td>0.556962</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.973836</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.040468</td>\n",
       "      <td>...</td>\n",
       "      <td>0.008863</td>\n",
       "      <td>0.038462</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.3125</td>\n",
       "      <td>0.012136</td>\n",
       "      <td>0.177632</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.007694</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.570470</td>\n",
       "      <td>0.290323</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.860759</td>\n",
       "      <td>0.969444</td>\n",
       "      <td>0.941338</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.045863</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005908</td>\n",
       "      <td>0.061538</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.012136</td>\n",
       "      <td>0.133772</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.015389</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.7500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.604027</td>\n",
       "      <td>0.645161</td>\n",
       "      <td>0.405128</td>\n",
       "      <td>0.341772</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.871110</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.085432</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005908</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.8750</td>\n",
       "      <td>0.016181</td>\n",
       "      <td>0.144737</td>\n",
       "      <td>0.150</td>\n",
       "      <td>0.418056</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.8750</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.691275</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.364103</td>\n",
       "      <td>0.898734</td>\n",
       "      <td>0.513889</td>\n",
       "      <td>0.481685</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.310252</td>\n",
       "      <td>...</td>\n",
       "      <td>0.017725</td>\n",
       "      <td>0.461538</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.4375</td>\n",
       "      <td>0.016181</td>\n",
       "      <td>0.210526</td>\n",
       "      <td>0.655</td>\n",
       "      <td>0.251346</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.4375</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.510067</td>\n",
       "      <td>0.370968</td>\n",
       "      <td>0.605128</td>\n",
       "      <td>0.911392</td>\n",
       "      <td>0.739167</td>\n",
       "      <td>0.737538</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.069245</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002954</td>\n",
       "      <td>0.130769</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.012136</td>\n",
       "      <td>0.144737</td>\n",
       "      <td>0.505</td>\n",
       "      <td>0.012824</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40959</th>\n",
       "      <td>0.4375</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.604027</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>0.528205</td>\n",
       "      <td>0.924051</td>\n",
       "      <td>0.986111</td>\n",
       "      <td>0.966951</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.040468</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005908</td>\n",
       "      <td>0.138462</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.3375</td>\n",
       "      <td>0.012136</td>\n",
       "      <td>0.111842</td>\n",
       "      <td>0.430</td>\n",
       "      <td>0.015389</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40960</th>\n",
       "      <td>0.4375</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.543624</td>\n",
       "      <td>0.612903</td>\n",
       "      <td>0.579487</td>\n",
       "      <td>0.924051</td>\n",
       "      <td>0.986111</td>\n",
       "      <td>0.966951</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.040468</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005908</td>\n",
       "      <td>0.138462</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.4250</td>\n",
       "      <td>0.016181</td>\n",
       "      <td>0.100877</td>\n",
       "      <td>0.315</td>\n",
       "      <td>0.035907</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40961</th>\n",
       "      <td>0.4375</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.644295</td>\n",
       "      <td>0.572581</td>\n",
       "      <td>0.528205</td>\n",
       "      <td>0.924051</td>\n",
       "      <td>0.986111</td>\n",
       "      <td>0.966951</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.040468</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005908</td>\n",
       "      <td>0.146154</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.012136</td>\n",
       "      <td>0.100877</td>\n",
       "      <td>0.225</td>\n",
       "      <td>0.041036</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40962</th>\n",
       "      <td>0.6250</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.604027</td>\n",
       "      <td>0.741935</td>\n",
       "      <td>0.615385</td>\n",
       "      <td>0.924051</td>\n",
       "      <td>0.986111</td>\n",
       "      <td>0.765354</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.381295</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005908</td>\n",
       "      <td>0.246154</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.4250</td>\n",
       "      <td>0.012136</td>\n",
       "      <td>0.144737</td>\n",
       "      <td>0.320</td>\n",
       "      <td>0.043601</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40963</th>\n",
       "      <td>0.8750</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.570470</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>0.487179</td>\n",
       "      <td>0.911392</td>\n",
       "      <td>0.986111</td>\n",
       "      <td>0.560176</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.561151</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005908</td>\n",
       "      <td>0.453846</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.4125</td>\n",
       "      <td>0.016181</td>\n",
       "      <td>0.254386</td>\n",
       "      <td>0.205</td>\n",
       "      <td>0.353937</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>40964 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        GRADE  COIL_GEN_TIME  FURN_EXIT_TEMP  FM_DOUT_TEMP  COILING_TEMP  \\\n",
       "0      0.0000            0.0        0.644295      0.540323      0.758974   \n",
       "1      0.0000            0.0        0.570470      0.290323      0.666667   \n",
       "2      0.7500            0.0        0.604027      0.645161      0.405128   \n",
       "3      0.8750            0.0        0.691275      0.500000      0.364103   \n",
       "4      0.4375            0.0        0.510067      0.370968      0.605128   \n",
       "...       ...            ...             ...           ...           ...   \n",
       "40959  0.4375            0.0        0.604027      0.548387      0.528205   \n",
       "40960  0.4375            0.0        0.543624      0.612903      0.579487   \n",
       "40961  0.4375            0.0        0.644295      0.572581      0.528205   \n",
       "40962  0.6250            0.0        0.604027      0.741935      0.615385   \n",
       "40963  0.8750            0.0        0.570470      0.548387      0.487179   \n",
       "\n",
       "       SLAB_WID  SLAB_LEN  STRIP_EXIT_LEN  STRIP_EXIT_THIK  FM_EXIT_THIK  ...  \\\n",
       "0      0.556962  1.000000        0.973836         0.000000      0.040468  ...   \n",
       "1      0.860759  0.969444        0.941338         0.000000      0.045863  ...   \n",
       "2      0.341772  1.000000        0.871110         0.111111      0.085432  ...   \n",
       "3      0.898734  0.513889        0.481685         0.222222      0.310252  ...   \n",
       "4      0.911392  0.739167        0.737538         0.055556      0.069245  ...   \n",
       "...         ...       ...             ...              ...           ...  ...   \n",
       "40959  0.924051  0.986111        0.966951         0.000000      0.040468  ...   \n",
       "40960  0.924051  0.986111        0.966951         0.000000      0.040468  ...   \n",
       "40961  0.924051  0.986111        0.966951         0.000000      0.040468  ...   \n",
       "40962  0.924051  0.986111        0.765354         0.222222      0.381295  ...   \n",
       "40963  0.911392  0.986111        0.560176         0.500000      0.561151  ...   \n",
       "\n",
       "             CU        MN    MO       N        NI         P      S        SI  \\\n",
       "0      0.008863  0.038462  0.05  0.3125  0.012136  0.177632  0.500  0.007694   \n",
       "1      0.005908  0.061538  0.05  0.2750  0.012136  0.133772  0.500  0.015389   \n",
       "2      0.005908  0.923077  0.05  0.8750  0.016181  0.144737  0.150  0.418056   \n",
       "3      0.017725  0.461538  0.05  0.4375  0.016181  0.210526  0.655  0.251346   \n",
       "4      0.002954  0.130769  0.05  0.4000  0.012136  0.144737  0.505  0.012824   \n",
       "...         ...       ...   ...     ...       ...       ...    ...       ...   \n",
       "40959  0.005908  0.138462  0.05  0.3375  0.012136  0.111842  0.430  0.015389   \n",
       "40960  0.005908  0.138462  0.05  0.4250  0.016181  0.100877  0.315  0.035907   \n",
       "40961  0.005908  0.146154  0.05  0.4000  0.012136  0.100877  0.225  0.041036   \n",
       "40962  0.005908  0.246154  0.05  0.4250  0.012136  0.144737  0.320  0.043601   \n",
       "40963  0.005908  0.453846  0.05  0.4125  0.016181  0.254386  0.205  0.353937   \n",
       "\n",
       "             TI     V  \n",
       "0      0.027778  0.02  \n",
       "1      0.027778  0.02  \n",
       "2      0.055556  0.02  \n",
       "3      0.055556  0.02  \n",
       "4      0.027778  0.02  \n",
       "...         ...   ...  \n",
       "40959  0.027778  0.02  \n",
       "40960  0.222222  0.02  \n",
       "40961  0.027778  0.02  \n",
       "40962  0.027778  0.02  \n",
       "40963  0.083333  0.02  \n",
       "\n",
       "[40964 rows x 26 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fits the selector\n",
    "\n",
    "X=data.drop(['COIL_ID','UTS','YS','P_ELONGATION'],axis=1)\n",
    "\n",
    "X\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "c2229ff4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[346],\n",
       "       [365],\n",
       "       [730],\n",
       "       ...,\n",
       "       [389],\n",
       "       [405],\n",
       "       [486]], dtype=int64)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y=data[['UTS'\n",
    "       ]].values\n",
    "Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2bd49158",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "02401e0d778d438a9334892bce2c8299",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "selector.fit(X , Y , n_trials = 100, sample = False, verbose = True)\n",
    "# n_trials -> number of iterations for Boruta algorithm\n",
    "# sample -> samples the data so it goes faster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa50e97f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Display features to be removed\n",
    "features_to_remove = selector.features_to_remove\n",
    "print(features_to_remove)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "abcdbed5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removes them\n",
    "X_train_boruta_shap = X_train.drop(columns = features_to_remove)\n",
    "X_test_boruta_shap = X_test.drop(columns = features_to_remove)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2847642c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "437788f4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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