Industrial Copper Modeling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0162d65d",
   "metadata": {},
   "source": [
    "## Importing Libraries and Reading file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7e6e85ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from warnings import filterwarnings\n",
    "filterwarnings('ignore')\n",
    "import pymongo\n",
    "import plotly.express as px"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d8af6d02",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scikit-learn version: 1.3.2\n"
     ]
    }
   ],
   "source": [
    "import sklearn\n",
    "print(\"scikit-learn version:\", sklearn.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4935f863",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"CSV/Copper modeling.xlsx.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0e9d5cec",
   "metadata": {},
   "outputs": [
    {
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       "\n",
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       "        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>id</th>\n",
       "      <th>item_date</th>\n",
       "      <th>quantity tons</th>\n",
       "      <th>customer</th>\n",
       "      <th>country</th>\n",
       "      <th>status</th>\n",
       "      <th>item type</th>\n",
       "      <th>application</th>\n",
       "      <th>thickness</th>\n",
       "      <th>width</th>\n",
       "      <th>material_ref</th>\n",
       "      <th>product_ref</th>\n",
       "      <th>delivery date</th>\n",
       "      <th>selling_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>EC06F063-9DF0-440C-8764-0B0C05A4F6AE</td>\n",
       "      <td>20210401.0</td>\n",
       "      <td>54.151139</td>\n",
       "      <td>30156308.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>Won</td>\n",
       "      <td>W</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>DEQ1 S460MC</td>\n",
       "      <td>1670798778</td>\n",
       "      <td>20210701.0</td>\n",
       "      <td>854.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4E5F4B3D-DDDF-499D-AFDE-A3227EC49425</td>\n",
       "      <td>20210401.0</td>\n",
       "      <td>768.024839</td>\n",
       "      <td>30202938.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>Won</td>\n",
       "      <td>W</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1210.0</td>\n",
       "      <td>104991</td>\n",
       "      <td>1668701718</td>\n",
       "      <td>20210401.0</td>\n",
       "      <td>1047.00</td>\n",
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       "      <th>2</th>\n",
       "      <td>E140FF1B-2407-4C02-A0DD-780A093B1158</td>\n",
       "      <td>20210401.0</td>\n",
       "      <td>386.127949</td>\n",
       "      <td>30153963.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Won</td>\n",
       "      <td>WI</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.38</td>\n",
       "      <td>952.0</td>\n",
       "      <td>S0380700</td>\n",
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       "      <td>20210101.0</td>\n",
       "      <td>644.33</td>\n",
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       "      <th>3</th>\n",
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       "      <td>20210401.0</td>\n",
       "      <td>202.411065</td>\n",
       "      <td>30349574.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>Won</td>\n",
       "      <td>S</td>\n",
       "      <td>59.0</td>\n",
       "      <td>2.30</td>\n",
       "      <td>1317.0</td>\n",
       "      <td>DX51D+ZM310MAO 2.3X1317</td>\n",
       "      <td>1668701718</td>\n",
       "      <td>20210101.0</td>\n",
       "      <td>768.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4E1C4E78-152B-430A-8094-ADD889C9D0AD</td>\n",
       "      <td>20210401.0</td>\n",
       "      <td>785.526262</td>\n",
       "      <td>30211560.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>Won</td>\n",
       "      <td>W</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>2_S275JR+AR-CL1</td>\n",
       "      <td>640665</td>\n",
       "      <td>20210301.0</td>\n",
       "      <td>577.00</td>\n",
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       "    <tr>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>181668</th>\n",
       "      <td>DE633116-D1DF-4846-982E-55EFC3658A76</td>\n",
       "      <td>20200702.0</td>\n",
       "      <td>102.4824216</td>\n",
       "      <td>30200854.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>Won</td>\n",
       "      <td>W</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.96</td>\n",
       "      <td>1220.0</td>\n",
       "      <td>1000777</td>\n",
       "      <td>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>591.00</td>\n",
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       "    <tr>\n",
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       "      <td>A48374B1-E6DB-45F2-889A-1F9C27C099EB</td>\n",
       "      <td>20200702.0</td>\n",
       "      <td>208.0864686</td>\n",
       "      <td>30200854.0</td>\n",
       "      <td>25.0</td>\n",
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       "      <td>W</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>1000227</td>\n",
       "      <td>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>589.00</td>\n",
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       "      <td>4.235593568</td>\n",
       "      <td>30200854.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>Won</td>\n",
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       "      <td>0.71</td>\n",
       "      <td>1250.0</td>\n",
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       "      <td>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>619.00</td>\n",
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       "      <td>-2000</td>\n",
       "      <td>30200854.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>Won</td>\n",
       "      <td>W</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1250.0</td>\n",
       "      <td>1001149</td>\n",
       "      <td>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>601.00</td>\n",
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       "      <th>181672</th>\n",
       "      <td>AD0CA853-AE3C-4B2F-9FBB-8B0B965F84BC</td>\n",
       "      <td>20200702.0</td>\n",
       "      <td>406.6865384</td>\n",
       "      <td>30200854.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>Won</td>\n",
       "      <td>W</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.71</td>\n",
       "      <td>1240.0</td>\n",
       "      <td>1005439</td>\n",
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       "<p>181673 rows × 14 columns</p>\n",
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      "text/plain": [
       "                                          id   item_date quantity tons  \\\n",
       "0       EC06F063-9DF0-440C-8764-0B0C05A4F6AE  20210401.0     54.151139   \n",
       "1       4E5F4B3D-DDDF-499D-AFDE-A3227EC49425  20210401.0    768.024839   \n",
       "2       E140FF1B-2407-4C02-A0DD-780A093B1158  20210401.0    386.127949   \n",
       "3       F8D507A0-9C62-4EFE-831E-33E1DA53BB50  20210401.0    202.411065   \n",
       "4       4E1C4E78-152B-430A-8094-ADD889C9D0AD  20210401.0    785.526262   \n",
       "...                                      ...         ...           ...   \n",
       "181668  DE633116-D1DF-4846-982E-55EFC3658A76  20200702.0   102.4824216   \n",
       "181669  A48374B1-E6DB-45F2-889A-1F9C27C099EB  20200702.0   208.0864686   \n",
       "181670  91643238-5C7B-4237-9A5F-63AE3D35F320  20200702.0   4.235593568   \n",
       "181671  7AFFD323-01D9-4E15-B80D-7D1B03498FC8  20200702.0         -2000   \n",
       "181672  AD0CA853-AE3C-4B2F-9FBB-8B0B965F84BC  20200702.0   406.6865384   \n",
       "\n",
       "          customer  country status item type  application  thickness   width  \\\n",
       "0       30156308.0     28.0    Won         W         10.0       2.00  1500.0   \n",
       "1       30202938.0     25.0    Won         W         41.0       0.80  1210.0   \n",
       "2       30153963.0     30.0    Won        WI         28.0       0.38   952.0   \n",
       "3       30349574.0     32.0    Won         S         59.0       2.30  1317.0   \n",
       "4       30211560.0     28.0    Won         W         10.0       4.00  2000.0   \n",
       "...            ...      ...    ...       ...          ...        ...     ...   \n",
       "181668  30200854.0     25.0    Won         W         41.0       0.96  1220.0   \n",
       "181669  30200854.0     25.0    Won         W         41.0       0.95  1500.0   \n",
       "181670  30200854.0     25.0    Won         W         41.0       0.71  1250.0   \n",
       "181671  30200854.0     25.0    Won         W         41.0       0.85  1250.0   \n",
       "181672  30200854.0     25.0    Won         W         41.0       0.71  1240.0   \n",
       "\n",
       "                   material_ref  product_ref  delivery date  selling_price  \n",
       "0                   DEQ1 S460MC   1670798778     20210701.0         854.00  \n",
       "1                        104991   1668701718     20210401.0        1047.00  \n",
       "2                      S0380700       628377     20210101.0         644.33  \n",
       "3       DX51D+ZM310MAO 2.3X1317   1668701718     20210101.0         768.00  \n",
       "4               2_S275JR+AR-CL1       640665     20210301.0         577.00  \n",
       "...                         ...          ...            ...            ...  \n",
       "181668                  1000777    164141591     20200701.0         591.00  \n",
       "181669                  1000227    164141591     20200701.0         589.00  \n",
       "181670                  1004216    164141591     20200701.0         619.00  \n",
       "181671                  1001149    164141591     20200701.0         601.00  \n",
       "181672                  1005439    164141591     20200701.0         607.00  \n",
       "\n",
       "[181673 rows x 14 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "879ee62f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(181673, 14)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "499a36b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 181673 entries, 0 to 181672\n",
      "Data columns (total 14 columns):\n",
      " #   Column         Non-Null Count   Dtype  \n",
      "---  ------         --------------   -----  \n",
      " 0   id             181671 non-null  object \n",
      " 1   item_date      181672 non-null  float64\n",
      " 2   quantity tons  181673 non-null  object \n",
      " 3   customer       181672 non-null  float64\n",
      " 4   country        181645 non-null  float64\n",
      " 5   status         181671 non-null  object \n",
      " 6   item type      181673 non-null  object \n",
      " 7   application    181649 non-null  float64\n",
      " 8   thickness      181672 non-null  float64\n",
      " 9   width          181673 non-null  float64\n",
      " 10  material_ref   103754 non-null  object \n",
      " 11  product_ref    181673 non-null  int64  \n",
      " 12  delivery date  181672 non-null  float64\n",
      " 13  selling_price  181672 non-null  float64\n",
      "dtypes: float64(8), int64(1), object(5)\n",
      "memory usage: 19.4+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "475b6a86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                   2\n",
       "item_date            1\n",
       "quantity tons        0\n",
       "customer             1\n",
       "country             28\n",
       "status               2\n",
       "item type            0\n",
       "application         24\n",
       "thickness            1\n",
       "width                0\n",
       "material_ref     77919\n",
       "product_ref          0\n",
       "delivery date        1\n",
       "selling_price        1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1330b420",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop([\"material_ref\",\"id\",\"item_date\",\"delivery date\"],axis = True ,inplace =True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "828eb792",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "quantity tons     0\n",
       "customer          1\n",
       "country          28\n",
       "status            2\n",
       "item type         0\n",
       "application      24\n",
       "thickness         1\n",
       "width             0\n",
       "product_ref       0\n",
       "selling_price     1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f8c0287",
   "metadata": {},
   "source": [
    "## Handling Unformatted Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d3231232",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>quantity tons</th>\n",
       "      <th>customer</th>\n",
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       "      <th>0</th>\n",
       "      <td>54.151139</td>\n",
       "      <td>30156308.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>Won</td>\n",
       "      <td>W</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>1670798778</td>\n",
       "      <td>854.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>768.024839</td>\n",
       "      <td>30202938.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>Won</td>\n",
       "      <td>W</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1210.0</td>\n",
       "      <td>1668701718</td>\n",
       "      <td>1047.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>386.127949</td>\n",
       "      <td>30153963.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Won</td>\n",
       "      <td>WI</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.38</td>\n",
       "      <td>952.0</td>\n",
       "      <td>628377</td>\n",
       "      <td>644.33</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>202.411065</td>\n",
       "      <td>30349574.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>Won</td>\n",
       "      <td>S</td>\n",
       "      <td>59.0</td>\n",
       "      <td>2.30</td>\n",
       "      <td>1317.0</td>\n",
       "      <td>1668701718</td>\n",
       "      <td>768.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>785.526262</td>\n",
       "      <td>30211560.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>Won</td>\n",
       "      <td>W</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>640665</td>\n",
       "      <td>577.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  quantity tons    customer  country status item type  application  thickness  \\\n",
       "0     54.151139  30156308.0     28.0    Won         W         10.0       2.00   \n",
       "1    768.024839  30202938.0     25.0    Won         W         41.0       0.80   \n",
       "2    386.127949  30153963.0     30.0    Won        WI         28.0       0.38   \n",
       "3    202.411065  30349574.0     32.0    Won         S         59.0       2.30   \n",
       "4    785.526262  30211560.0     28.0    Won         W         10.0       4.00   \n",
       "\n",
       "    width  product_ref  selling_price  \n",
       "0  1500.0   1670798778         854.00  \n",
       "1  1210.0   1668701718        1047.00  \n",
       "2   952.0       628377         644.33  \n",
       "3  1317.0   1668701718         768.00  \n",
       "4  2000.0       640665         577.00  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1bf3760d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "quantity tons     object\n",
       "customer         float64\n",
       "country          float64\n",
       "status            object\n",
       "item type         object\n",
       "application      float64\n",
       "thickness        float64\n",
       "width            float64\n",
       "product_ref        int64\n",
       "selling_price    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7dbd5c98",
   "metadata": {},
   "outputs": [],
   "source": [
    "# errors='coerce' ---> If Any column cannot be converted to a numeric type, Pandas will replace that value with NaN\n",
    "df['quantity tons'] = pd.to_numeric(df['quantity tons'], errors='coerce')\n",
    "df['customer'] = pd.to_numeric(df['customer'], errors='coerce')\n",
    "df['country'] = pd.to_numeric(df['country'], errors='coerce')\n",
    "df['application'] = pd.to_numeric(df['application'], errors='coerce')\n",
    "df['thickness'] = pd.to_numeric(df['thickness'], errors='coerce')\n",
    "df['width'] = pd.to_numeric(df['width'], errors='coerce')\n",
    "df['product_ref'] = pd.to_numeric(df['product_ref'], errors='coerce')\n",
    "df['selling_price'] = pd.to_numeric(df['selling_price'], errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f2591701",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "quantity tons     1\n",
       "customer          1\n",
       "country          28\n",
       "status            2\n",
       "item type         0\n",
       "application      24\n",
       "thickness         1\n",
       "width             0\n",
       "product_ref       0\n",
       "selling_price     1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f928b832",
   "metadata": {},
   "source": [
    "## Statistical Information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "742eca81",
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>quantity tons</th>\n",
       "      <td>181672.0</td>\n",
       "      <td>5.874926e+03</td>\n",
       "      <td>2.349081e+06</td>\n",
       "      <td>-2000.00</td>\n",
       "      <td>1.097030e+01</td>\n",
       "      <td>3.036464e+01</td>\n",
       "      <td>6.716061e+01</td>\n",
       "      <td>1.000000e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer</th>\n",
       "      <td>181672.0</td>\n",
       "      <td>3.051221e+07</td>\n",
       "      <td>2.433382e+07</td>\n",
       "      <td>12458.00</td>\n",
       "      <td>3.019688e+07</td>\n",
       "      <td>3.020524e+07</td>\n",
       "      <td>3.028042e+07</td>\n",
       "      <td>2.147484e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <td>181645.0</td>\n",
       "      <td>4.489302e+01</td>\n",
       "      <td>2.440421e+01</td>\n",
       "      <td>25.00</td>\n",
       "      <td>2.600000e+01</td>\n",
       "      <td>3.000000e+01</td>\n",
       "      <td>7.800000e+01</td>\n",
       "      <td>1.130000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>application</th>\n",
       "      <td>181649.0</td>\n",
       "      <td>2.561581e+01</td>\n",
       "      <td>1.775417e+01</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.000000e+01</td>\n",
       "      <td>1.500000e+01</td>\n",
       "      <td>4.100000e+01</td>\n",
       "      <td>9.900000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thickness</th>\n",
       "      <td>181672.0</td>\n",
       "      <td>2.564827e+00</td>\n",
       "      <td>6.572321e+00</td>\n",
       "      <td>0.18</td>\n",
       "      <td>7.000000e-01</td>\n",
       "      <td>1.500000e+00</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>2.500000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>width</th>\n",
       "      <td>181673.0</td>\n",
       "      <td>1.295287e+03</td>\n",
       "      <td>2.616318e+02</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.180000e+03</td>\n",
       "      <td>1.250000e+03</td>\n",
       "      <td>1.500000e+03</td>\n",
       "      <td>2.990000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>product_ref</th>\n",
       "      <td>181673.0</td>\n",
       "      <td>4.739679e+08</td>\n",
       "      <td>7.175101e+08</td>\n",
       "      <td>611728.00</td>\n",
       "      <td>6.119930e+05</td>\n",
       "      <td>6.406650e+05</td>\n",
       "      <td>1.332077e+09</td>\n",
       "      <td>1.722208e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>selling_price</th>\n",
       "      <td>181672.0</td>\n",
       "      <td>1.918036e+03</td>\n",
       "      <td>3.317956e+05</td>\n",
       "      <td>-1160.00</td>\n",
       "      <td>6.690000e+02</td>\n",
       "      <td>8.120000e+02</td>\n",
       "      <td>9.530000e+02</td>\n",
       "      <td>1.000010e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  count          mean           std        min           25%  \\\n",
       "quantity tons  181672.0  5.874926e+03  2.349081e+06   -2000.00  1.097030e+01   \n",
       "customer       181672.0  3.051221e+07  2.433382e+07   12458.00  3.019688e+07   \n",
       "country        181645.0  4.489302e+01  2.440421e+01      25.00  2.600000e+01   \n",
       "application    181649.0  2.561581e+01  1.775417e+01       2.00  1.000000e+01   \n",
       "thickness      181672.0  2.564827e+00  6.572321e+00       0.18  7.000000e-01   \n",
       "width          181673.0  1.295287e+03  2.616318e+02       1.00  1.180000e+03   \n",
       "product_ref    181673.0  4.739679e+08  7.175101e+08  611728.00  6.119930e+05   \n",
       "selling_price  181672.0  1.918036e+03  3.317956e+05   -1160.00  6.690000e+02   \n",
       "\n",
       "                        50%           75%           max  \n",
       "quantity tons  3.036464e+01  6.716061e+01  1.000000e+09  \n",
       "customer       3.020524e+07  3.028042e+07  2.147484e+09  \n",
       "country        3.000000e+01  7.800000e+01  1.130000e+02  \n",
       "application    1.500000e+01  4.100000e+01  9.900000e+01  \n",
       "thickness      1.500000e+00  3.000000e+00  2.500000e+03  \n",
       "width          1.250000e+03  1.500000e+03  2.990000e+03  \n",
       "product_ref    6.406650e+05  1.332077e+09  1.722208e+09  \n",
       "selling_price  8.120000e+02  9.530000e+02  1.000010e+08  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "cd33c29a",
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>quantity tons</th>\n",
       "      <td>181668.0</td>\n",
       "      <td>5.875066e+03</td>\n",
       "      <td>2.349107e+06</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>1.097122e+01</td>\n",
       "      <td>3.036501e+01</td>\n",
       "      <td>6.716134e+01</td>\n",
       "      <td>1.000000e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer</th>\n",
       "      <td>181672.0</td>\n",
       "      <td>3.051221e+07</td>\n",
       "      <td>2.433382e+07</td>\n",
       "      <td>12458.00000</td>\n",
       "      <td>3.019688e+07</td>\n",
       "      <td>3.020524e+07</td>\n",
       "      <td>3.028042e+07</td>\n",
       "      <td>2.147484e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <td>181645.0</td>\n",
       "      <td>4.489302e+01</td>\n",
       "      <td>2.440421e+01</td>\n",
       "      <td>25.00000</td>\n",
       "      <td>2.600000e+01</td>\n",
       "      <td>3.000000e+01</td>\n",
       "      <td>7.800000e+01</td>\n",
       "      <td>1.130000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>application</th>\n",
       "      <td>181649.0</td>\n",
       "      <td>2.561581e+01</td>\n",
       "      <td>1.775417e+01</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>1.000000e+01</td>\n",
       "      <td>1.500000e+01</td>\n",
       "      <td>4.100000e+01</td>\n",
       "      <td>9.900000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thickness</th>\n",
       "      <td>181672.0</td>\n",
       "      <td>2.564827e+00</td>\n",
       "      <td>6.572321e+00</td>\n",
       "      <td>0.18000</td>\n",
       "      <td>7.000000e-01</td>\n",
       "      <td>1.500000e+00</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>2.500000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>width</th>\n",
       "      <td>181673.0</td>\n",
       "      <td>1.295287e+03</td>\n",
       "      <td>2.616318e+02</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.180000e+03</td>\n",
       "      <td>1.250000e+03</td>\n",
       "      <td>1.500000e+03</td>\n",
       "      <td>2.990000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>product_ref</th>\n",
       "      <td>181673.0</td>\n",
       "      <td>4.739679e+08</td>\n",
       "      <td>7.175101e+08</td>\n",
       "      <td>611728.00000</td>\n",
       "      <td>6.119930e+05</td>\n",
       "      <td>6.406650e+05</td>\n",
       "      <td>1.332077e+09</td>\n",
       "      <td>1.722208e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>selling_price</th>\n",
       "      <td>181665.0</td>\n",
       "      <td>1.918124e+03</td>\n",
       "      <td>3.318020e+05</td>\n",
       "      <td>0.10000</td>\n",
       "      <td>6.690000e+02</td>\n",
       "      <td>8.120000e+02</td>\n",
       "      <td>9.530000e+02</td>\n",
       "      <td>1.000010e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  count          mean           std           min  \\\n",
       "quantity tons  181668.0  5.875066e+03  2.349107e+06       0.00001   \n",
       "customer       181672.0  3.051221e+07  2.433382e+07   12458.00000   \n",
       "country        181645.0  4.489302e+01  2.440421e+01      25.00000   \n",
       "application    181649.0  2.561581e+01  1.775417e+01       2.00000   \n",
       "thickness      181672.0  2.564827e+00  6.572321e+00       0.18000   \n",
       "width          181673.0  1.295287e+03  2.616318e+02       1.00000   \n",
       "product_ref    181673.0  4.739679e+08  7.175101e+08  611728.00000   \n",
       "selling_price  181665.0  1.918124e+03  3.318020e+05       0.10000   \n",
       "\n",
       "                        25%           50%           75%           max  \n",
       "quantity tons  1.097122e+01  3.036501e+01  6.716134e+01  1.000000e+09  \n",
       "customer       3.019688e+07  3.020524e+07  3.028042e+07  2.147484e+09  \n",
       "country        2.600000e+01  3.000000e+01  7.800000e+01  1.130000e+02  \n",
       "application    1.000000e+01  1.500000e+01  4.100000e+01  9.900000e+01  \n",
       "thickness      7.000000e-01  1.500000e+00  3.000000e+00  2.500000e+03  \n",
       "width          1.180000e+03  1.250000e+03  1.500000e+03  2.990000e+03  \n",
       "product_ref    6.119930e+05  6.406650e+05  1.332077e+09  1.722208e+09  \n",
       "selling_price  6.690000e+02  8.120000e+02  9.530000e+02  1.000010e+08  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# quantity and selling price values sholud not be below 0. so we converting 0 to null value.\n",
    "df['quantity tons'] = df['quantity tons'].apply(lambda x: np.nan if x<=0 else x)\n",
    "df['selling_price'] = df['selling_price'].apply(lambda x: np.nan if x<=0 else x)\n",
    "df.describe().T    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6da1025a",
   "metadata": {},
   "source": [
    "## Imputing Missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b26f3686",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "quantity tons     5\n",
       "customer          1\n",
       "country          28\n",
       "status            2\n",
       "item type         0\n",
       "application      24\n",
       "thickness         1\n",
       "width             0\n",
       "product_ref       0\n",
       "selling_price     8\n",
       "dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2d12ac2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "quantity tons    0.000028\n",
       "customer         0.000006\n",
       "country          0.000154\n",
       "status           0.000011\n",
       "item type        0.000000\n",
       "application      0.000132\n",
       "thickness        0.000006\n",
       "width            0.000000\n",
       "product_ref      0.000000\n",
       "selling_price    0.000044\n",
       "dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()/df.isnull().count().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7aa1f341",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=df,orient='h')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b7edd303",
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop the null rows \n",
    "df=df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "bb71dec1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "quantity tons    0\n",
       "customer         0\n",
       "country          0\n",
       "status           0\n",
       "item type        0\n",
       "application      0\n",
       "thickness        0\n",
       "width            0\n",
       "product_ref      0\n",
       "selling_price    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6a51f42a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(181628, 10)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9dfa02ce",
   "metadata": {},
   "source": [
    "## Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8722d1bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# sns.pairplot(df,diag_kind='kde')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9cc90223",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['quantity tons', 'customer', 'country', 'status', 'item type',\n",
       "       'application', 'thickness', 'width', 'product_ref', 'selling_price'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7b81bae1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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UUIa2gTLU8FyYIqAFlKFtIPcxDHEM/mAeQtcvM8ZfX7MF9n7NEPLvt+HYJhAtJo+Dz5RxSFixzhiTtPp7uA/phcD50+EQHIjA+dPhPqgHkr7YWLs7V8MOn1NjcLgdOgSK4eUmwnODZSjX6HHmiulB2vGDZRjRw874/sg5NYJ9xRjYSQoPFwEDO0kR1EKMw+dMGeBDZ9Xo3laCriESeLgKGNPbDq6OAo5fVKMhYL1VH+vs4bDeqo919nBYb9XHOqsdgkhUZ6+GqFo/Ivzvf/8bRUVFePzxx+Hk5ITXX38d+fn5ZjHLly/HlClT0LdvXzRr1gyfffaZ2YyCo0ePxty5czFr1iyoVCqMGDEC7733HhYtWlStgs+fPx+TJk1C27ZtUVpaisTExAoxPXv2xIwZM/DMM88gOzsbCxcuNH5O69at0bNnT2RnZ6Nbt25Vflbz5s2xePFivP3225gyZQomTpyIDRs24OOPP4ZOp8Pzzz+PwsJChIeHY+/evXB1dX2gfRCLxcjOzsbEiRORkZEBd3d3PPHEExWeq6przp3bo8eBTcb3bT99BwCQ8v0vOD9tAWTeTWF/u6MFAKVJN3B61Itou3wB/F6eAFVqJi7N/RDp2/cZY3L/ikb0hHkIXjwHwYtfRUl8CqLHz0XeqfO1t2O14OAZNaQSAU/2k8FeJuB6hg5f7yyD6q7vb1cnEfR6082BpHQdNu0tw/DuMgzvZofsfD027i1DcoYp5uw1DRzkwLAudlA6CEjL1mHNrlLkFjaMIQ2st+pjnT0c1lv1sc4eDuut+lhnZIsE/YM+TFWJ/v37IywsDKtWrbJSkWqHXq9HmzZt8NJLL1V4jsnW7JZWf1KRxm6EOg5zVxfVdTFsyspZjqyzh8B6qz7W2cNhvVUf6+zhsN6qb+Usx7ouQqXODOpdZ5/d6cCx+wfZmGplrhqKzMxMbNq0CTdv3sSUKVPqujhERERERNQANMrOlaenJ9zd3bFmzZoHHsJHRERERERUlb/duTp06JAVilG7/uZISCIiIiKiBkHE37myqoY5TQcREREREVEta5TDAomIiIiICBBEzFxZEzNXREREREREVsDMFRERERFRI9VQf8y3rrA2iYiIiIiIrICdKyIiIiIiIivgsEAiIiIiokaKE1pYFzNXREREREREVsDMFRERERFRI8UfEbYuZq6IiIiIiIisgJ0rIiIiIiIiK+CwQCIiIiKiRooTWlgXM1dERERERERWwMwVEREREVEjJYiYa7Em1iYREREREZEVMHNFRERERNRI8Zkr62LmioiIiIiIyArYuSIiIiIiIrICDgskIiIiImqkOCzQupi5IiIiIiIisgJmroiIiIiIGilmrqyLmSsiIiIiIiIrYOeKiIiIiIjICjgskIiIiIiokRJEzLVYE2uTiIiIiIjICpi5IiIiIiJqpERiTmhhTcxcERERERERWQEzV0REREREjRSnYrcuZq6IiIiIiIisgJ0rIiIiIiIiKxD0er2+rgtBRERERES1L3Hq43X22QHrfq2zz64pfOaqAZi7uqiui2BzVs5yxG5pcF0Xw6aMUMfhk226ui6GzXlznIjnaDWtnOXIY+0hvDlOhMX/p67rYtiUhf+QYsay3Louhs35+i1Xfq9V08pZjnVdBKol7FwRERERETVSnNDCuvjMFRERERERkRWwc0VERERERGQFHBZIRERERNRIcVigdTFzRUREREREZAXMXBERERERNVKCiLkWa2JtEhERERERWQE7V0RERERERFbAYYFERERERI0UJ7SwLmauiIiIiIiIrICZKyIiIiKiRooTWlgXa5OIiIiIiMgKmLkiIiIiImqsBD5zZU3MXBEREREREVkBO1dERERERERWwGGBRERERESNFKdity5mroiIiIiIiKyAmSsiIiIiokaKU7FbF2uTiIiIiIjICti5IiIiIiKieu/LL79EQEAA5HI5OnfujKNHj1YZv3nzZoSGhkKhUMDb2xtTpkxBdnZ2jZaRnSsiIiIiokZKEAl19qqOrVu3Ys6cOXj33XcRHR2NPn36YPjw4UhOTrYYf+zYMUycOBHTpk3DpUuX8N///henT5/GCy+8YI1qqxQ7V0REREREVK+tWLEC06ZNwwsvvICQkBCsWrUKPj4++OqrryzGnzhxAv7+/nj11VcREBCA3r1746WXXkJkZGSNlpOdKyIiIiKiRkoQierspVKpUFBQYPZSqVQVylheXo6oqCgMHTrUbPnQoUNx/Phxi/vVs2dP3LhxA3v27IFer0dGRgZ+/vlnjBgxokbq8Q52roiIiIiIqNYtXboUzs7OZq+lS5dWiMvKyoJWq4Wnp6fZck9PT6Snp1vcds+ePbF582Y888wzsLOzg5eXF1xcXPDFF1/UyL7cwc4VEREREVEjVZfPXC1YsAD5+flmrwULFlReVsH8OS29Xl9h2R0xMTF49dVX8f777yMqKgp//PEHEhMTMWPGDKvW3734O1dERERERFTrZDIZZDLZfePc3d0hFosrZKkyMzMrZLPuWLp0KXr16oU33ngDAPDII4/AwcEBffr0wQcffABvb++/vwMWMHNFRERERET1lp2dHTp37oyIiAiz5REREejZs6fFdUpKSiC65weSxWIxAEPGq6Ywc0VERERE1EhVd0r0ujJv3jw8//zzCA8PR48ePbBmzRokJycbh/ktWLAAN2/exPfffw8AGDVqFKZPn46vvvoKw4YNQ1paGubMmYOuXbuiWbNmNVZOdq6IiIiIiKhee+aZZ5CdnY0lS5YgLS0N7du3x549e+Dn5wcASEtLM/vNq8mTJ6OwsBCrV6/G66+/DhcXFwwcOBDLli2r0XKyc0VERERE1FiJbOcpoZkzZ2LmzJkW/7Zhw4YKy2bPno3Zs2fXcKnM2U5tEhERERER1WONMnN16NAhDBgwALm5uXBxcanr4tiUYV3t0KOdBPYyAckZOmw7rEJ6jq7KdR5pKcbwbjK4OwvIytdjzwkVLiRozWJ6tZdgQCc7KBUC0nN02HFUhYS0qrdbn7n1Dkfg69Pg3Kk95M08EDluJjJ+PVD1On26oO2nb8OxbWuoUjMRv/xbJK/ZYhbjNXYogha9BkVLX5TEJyPu/ZXI2Lm/Jnel1sWc+AEXjq5DaeEtuHi0QvcRC+AVEF5pfFrCKZzcswx5mdegcPJAh77TENLtWbOYi39uxOWTW1CUlwa5gyv82w9F+NB5kEjvP0ORrajuOdSymQije8vg5SZCQbEeB8+U4/gljVnMg5y7tszax5pOq8a5Q2twNXonSgoy4OwegC6Pvo4WQX1qY3dqTXiQCD3biuBkD2TmAXsjtUi+VfnD4X4eAoZ2FsPDBSgsAf6M0SHqqunYDA0UMKZnxebIBz+oobXdy4BFI3vJ0TtUBoVcQFKaBj9GlCAtq+qd7BgkxeN97OHuIkJWng47j5Ti7FW18e99w+zQt6MMTZwND+qnZWmx+3gpLiVoKtukzeD3GtkiZq7qIb1eD42m/n0pDuwkRf8wKbYdVmHlT6UoKNZhxmg5ZNLK1/HzEmHiMDki49T4948liIxTY9IwOXw9TYdeWCsJxvSRISKyHJ9uLUFCqhYvjrKHi6NtPGBpidhBgYLzcbj02pIHirf3b4Euv61BzrEoHOsyBteWfY12K9+F11jTL5G7dA9Dxx9W4ubmnTjaeTRubt6JTj+ugkvXR2pqN2pdwvk9OLn7Y4T1fwljZv0CL//O2LvxJRTlpVqML8y5gX0bZ8DLvzPGzPoFof1fxIldHyHx4j5jzLWzvyFy7wp0HPgKxs3djd5PfIDE878jct+K2tqtGlfdc8jNScD0UfZISNXi060liIgqx9i+MjzSUmyMeZBz15bVxLEWGfEZLp/+CT1GvYsn5uxCm67PYP//zUZWakxt7VaNa+cn4NHOIhy9qMU3uzVIztRhwkAxlArL8S4OwPiBYiRn6vDNbg2OXtJieLgIIT7mx2ZZuR6f/qw2ezW0jtXQbjIM6iLHlv0l+Pj7AuQX6/Ha006Q2VW+TkAzMV4Y7YATl8rxwfoCnLhUjumjHeDvbTpXcwv12HG4FEs3FmDpxgLEXVfj5Scc4e1u2+cqv9dqjyAIdfZqiOr10aTT6bBs2TK0atUKMpkMvr6++PDDD3Ho0CEIgoC8vDxj7NmzZyEIApKSkgAA169fx6hRo+Dq6goHBwe0a9cOe/bsQVJSEgYMGAAAcHV1hSAImDx5MgBApVLh1VdfhYeHB+RyOXr37o3Tp08bP+PO5+7duxcdO3aEvb09Bg4ciMzMTPz+++8ICQmBUqnEc889h5KSEuN6er0en3zyCQIDA2Fvb4/Q0FD8/PPPFrcbHh4OmUyGo0eP1lzFPqR+oVJERJbjQoIW6Tk6/LBfBTuJgE5BlSdA+4VKcSVFiwNRamTm6XEgSo0rN7ToF2rqkfUPk+JkjAYnYzTIzNVjx7Fy5BXp0atDFb22eu7W3iO4snAV0ndE3D8YgN+Lz6IsOQ0xr3+EossJSFn3M1I2/ILAeVONMQGzJyFr/3HEf7IGxXEJiP9kDbIOnoD/7Ek1tRu17uKxjQjq/ASCuzwFF4+W6D7yHTg4eyH25BaL8bGntsDBxRvdR74DF4+WCO7yFII6P4ELR9cZYzKTz8LDtxNaho2Ek2tztGjdC4GhI5B141Jt7VaNq+451LO9FHmFhrjMXD1OxmhwKlaDAR1NrbwHOXdtWU0ca/HRvyK034vwCe4HpZsPQro/h+ate+PisQ21tFc1r3uICNHxOkRf0yOrANgbpUN+CdAlyHJzIjxIhPxiQ1xWARB9TY/oeB16tK0YX1xm/mpoBoXL8ftfpTh7RY3ULB027i6GnRToGlJ572pQuByxSRrsPVGGjBwd9p4ow+XrGgwKlxtjLsSrcTFBg8xcHTJzddh5tAyqcj0Cmtn24CR+r5GtqtedqwULFmDZsmV47733EBMTgx9++KHSHwq71yuvvAKVSoUjR47gwoULWLZsGRwdHeHj44Nt27YBAOLi4pCWlobPPvsMAPDmm29i27Zt2LhxI86cOYNWrVph2LBhyMnJMdv2okWLsHr1ahw/fhwpKSl4+umnsWrVKvzwww/YvXs3IiIi8MUXXxjj//nPf2L9+vX46quvcOnSJcydOxf/+Mc/cPjwYbPtvvnmm1i6dCliY2PxyCP1KxvRRClA6SBCXLIpda7VAdduahFw1x20e/l7ic3WAYC4ZC38vQzriEVACw8R4lLMM3VxKRpjTGPg0j0Mt/b/abbs1r6jcO7cHoLEcIF07R6GrP3HzGKyIo7CtUfHWitnTdJqypGVegnNW/cyW968VS9kXo+2uE5m8lk0b3VPfOteyLp5CTqtYdiMp18nZKdewq2U8wCAgpwUpMQdgU+bfjWwF7XvYc4hfy9xhfjLyRr4NBUZn2u+37lry2rqWNNqyiG+Z6ipRCpDRlKUFUtfd0QioJmbgPg08yGACWk6tGhq+Q50C3ehwjCu+DQ9mjURcPfsz3YS4LUxEswdK8Fz/cXwcrV68euUu7MIzo4ixCaazjuNFriaokFg88o7QYHNJYhNVJsti0lUI7C55fNQEIDwECnspAISb9a/ETAPit9rtUsQiers1RDV29sahYWF+Oyzz7B69WpMmmS4M9+yZUv07t0bhw4duu/6ycnJGDduHDp06AAACAwMNP7Nzc0NAODh4WF85qq4uBhfffUVNmzYgOHDhwMA1q5di4iICHz33XfGX3cGgA8++AC9ehkustOmTcOCBQsQHx9v/Iwnn3wS//vf//DWW2+huLgYK1aswMGDB9GjRw9jWY4dO4ZvvvkG/fqZGnhLlizBkCFDKt0nlUoFlUpltuxBftXaGpwUhqtgYan5RbWoVA9Xp8rTuk4KocI6haV6KB0M6zjYCxCLBBSW3BNToodS0TDTxZbIPN2hysgyW1aemQ2RVAo7d1eo0m9B5uUOVUa2WYwqIxsyr6a1WdQaU1aSB71OC3tHd7Pl9k5NUHo1y+I6pYVZsA9qYh7v6A69ToOy4lwolB5oGToCZcW52LXmH9Dr9dDrNGjT7VmE9pteY/tSmx7mHHJyEFCYXDFeLBbgKBdQUKK/77lry2rqWLuTpfLyD4fSzRep8X/heuxB6HUN43kOhQwQiQQUlZovLyoFWjazfFw42gsoSr33ugGIRQIUcsO/swqAHX9pkZmrh0wqoFsbEaYOk+Dr3RrkFNbU3tQu5e2hbAUl5h3NgmI93Jwrb2AqHQQUFN+7jg5KB/N1mrmL8ObzSkglgKpcj2+2FyEt23bHVfJ7jWxZve1cxcbGQqVSYdCgQQ+1/quvvoqXX34Z+/btw+DBgzFu3Lgqs0Hx8fFQq9XGThMASKVSdO3aFbGxsWaxd2/H09MTCoXCrPPm6emJU6dOAQBiYmJQVlZWodNUXl6Ojh3NMw7h4ZU/SA0AS5cuxeLFi82WLVy4EHCfX+V6D6NTkARP9zd13Nbuun01tfDM8v1+5NrS3+9dZmkTNffb2fXUvZVyZyzy3cstxdTgr4zXiXuvcXq9hYV3h1dY4fYfDMvTEk7h3KFv0PPx99DUJxQF2ddxYtdSRDt9iY4DLU/naouqew7d+zfBwvIHOXdtmpWPte4j38Gx7e9j28oRgCBA6eaDoE5jceXMdqsVuT4SBFTrC/ver7abWXrcNPZp9Ui+pcVLj0nQNViEPyJts4PQta0dxg8zPYj2n5+LAFTyNX+/a+g97y09p5KRo8OH6wtgLxfQKUiKSSMcsOKHQpvuYAH8XqsttvIjwrai3nau7O3tK/2b6HYaUX/X2aBWm6fNX3jhBQwbNgy7d+/Gvn37sHTpUixfvrzSue7vbOveLy29Xl9hmVRqGpsrCILZ+zvLdDrDF9qd/+7evRvNmzc3i7s36+Tg4GCxbHcsWLAA8+bNq7CNt9eqK1nj4V1K1ODTDNPdVonYUAdOCsPdnzsc7QUUlVb+rWTpLpOTveluVHGpHlqdhRhFxTtWDZkqI6tCBsquqRt0ajXKs/MMMelZkHmZ32mXebhVyHjZKrnCBYJIjNJC8/0pLcqBvWMTi+vYO7mjpEJ8NgSRBHKFCwAgKuJztOr4OIK7PAUAcPMKgqa8FMd2LERY/xk2PyzhYc6hwuKK8Y4KAVqtHsVlhnXud+7aspo61uwd3TDk+dXQqFVQleRBofTA6b3L4eTa3MIWbU+JCtDp9HC85/LsIAeKyiwfF0WlluO1Oj1KVRZXAQCkZuvhVsWoiPru3LVyJKaahqjdHt0NZwcRCopN11YnRcXM1N0KivVwvidLZWkdrQ64lWdYlpyuhZ+3BAPC5fhhbwlsEb/XyJbV21ZF69atYW9vjwMHKk5f3bSpoRGalpZmXHb27NkKcT4+PpgxYwZ++eUXvP7661i7di0AwM7O8HCjVmv6gmvVqhXs7Oxw7JjpmRa1Wo3IyEiEhIQ89H60bdsWMpkMycnJaNWqldnLx8enWtuSyWRQKpVmr5oaFqhSA1n5euMrPUeHgmIdgn1M45LFIqBVczES0yof8pKUrkWQj/lY5mBfMZLSDetodcCNTB2CfMz7+UE+EmNMY5B34izcB/U0W9Z0SG/kR12E/vbMkbknzsJ9kPkzH+6DeyP3L8vPiNgascQO7s3a4ea142bLU68dh4ef5efKPHzDkHpP/M2rf8K9eTuIxIabHhp1qelW+W2CSAzo9dA3gPzow5xDhvPSPD7YR4KUWzrcvh9033PXltXUsXaHRCqDg7Mn9DoNki5GwC/k4UZg1Dc6HZCao0egl/n5FOglwo1KpmK/kaVHoJd5U6Olt4DUbD10VZx+nm6oMPzQlqjKDZ2dO6+0LB3yi3QI8Tedd2IR0NpHgoQqno1KuKlBiL/58RUSIEXCzarPQwGA1IYfI+L3Gtmyetu5ksvleOutt/Dmm2/i+++/R3x8PE6cOIHvvvvO2DFZtGgRrly5gt27d2P58uVm68+ZMwd79+5FYmIizpw5g4MHDxo7SX5+fhAEAbt27cKtW7dQVFQEBwcHvPzyy3jjjTfwxx9/ICYmBtOnT0dJSQmmTZv20Pvh5OSE+fPnY+7cudi4cSPi4+MRHR2N//znP9i4cePfqqPadvicGoPD7dAhUAwvNxGeGyxDuUaPM1dMF4bxg2UY0cM0M8+Rc2oE+4oxsJMUHi4CBnaSIqiFGIfPmbJth86q0b2tBF1DJPBwFTCmtx1cHQUcv2j9jFxtETsooAxtA2VoGwCAIqAFlKFtIPfxBgAEfzAPoeuXGeOvr9kCe79mCPn323BsE4gWk8fBZ8o4JKwwzUSWtPp7uA/phcD50+EQHIjA+dPhPqgHkr6wreOoKu17T8KVyG24ErkNeZnxOLF7KYry09Cm6zMAgNN7V+Dwf98yxod0fRZFeak4sftj5GXGG9aN+gUd+phmWfRtMwCXT25B/LndKMy5gZtX/0RUxOfwDRkAkciGWx93ud85NKKHHcYPNt2IOX5RDVcnAaN728HDVUDXEAm6tZXgf9HlxpgHOXdtWU0ca5kp55B0cR8KclKQnhiJP9a/COh16ND34a8h9c2JWB06tRIhrKUAdyUwrLMIzg5A5O3frRoUJsKYnqbzKvKKDs6OwNDOIrgrgbCWAjq2FOGvGFPmpV8HEVp6C3BxBDxdgce7i+HlKhi32VAciCzDoz3kCGstRTN3ESaNcEC5GjgVazrvJo9QYExf00yAB6PKEBIgwdBuMni6iTC0mwwhfhIciDRNpzi6rxytWkjQRClCM3cRRveRI8hXglMx5bBl/F6rRSJR3b0aoHo7LBAA3nvvPUgkErz//vtITU2Ft7c3ZsyYAalUih9//BEvv/wyQkND0aVLF3zwwQd46qmnjOtqtVq88soruHHjBpRKJR599FGsXLkSANC8eXMsXrwYb7/9NqZMmYKJEydiw4YN+Pjjj6HT6fD888+jsLAQ4eHh2Lt3L1xd/960Rf/617/g4eGBpUuXIiEhAS4uLujUqRPeeeedv7Xd2nbwjBpSiYAn+8lgLxNwPUOHr3eWQXXXd5Krkwh6vemCmJSuw6a9ZRjeXYbh3eyQna/Hxr1lSM4wxZy9poGDHBjWxQ5KBwFp2Tqs2VWK3ELbzSo4d26PHgc2Gd+3/dTw/zrl+19wftoCyLybwv52RwsASpNu4PSoF9F2+QL4vTwBqtRMXJr7IdK3m35DJ/evaERPmIfgxXMQvPhVlMSnIHr8XOSdOl97O1bDAh95DGUleYg++CVKCm/B1bM1hk762jisqrTwForyTBlrJ7cWGDrpa5zc8zFiT/wAhdID3Ue+g4D2pt8HCxswAxAEREV8jpKCDMgd3ODbpj86D51T27tXY+53DikVAlydTBexnEI91v5WijG9ZejdQYr8Yj22H1HhfLzp7u2DnLu2rCaONa1ahaiIz1GYmwKJnQI+wX3R7+llkNkra33/asql63rYy3To10EMx9s/Irz5f1rkFxv+7mgvwPmuEe55xcAPB7UYFi5GlyARCkuB3yN1iE0xfb/L7YCR3QzbU6mBtBw9NuzTIjXbdq8Bluw7afj5kueGKqCQC0hM1eDznwqhuqsP5KYUmT3/k3BTi+9+LcbjfezxeB973MrTYe2vxUi6a8SI0kGEKSMVUDqIUKrS4+YtLb74bxFik2x3tkCA32tkuwS9vrE/xmf75q4uqusi2JyVsxyxWxpc18WwKSPUcfhkGy9A1fXmOBHP0WpaOcuRx9pDeHOcCIv/r5Hfga+mhf+QYsay3Louhs35+i1Xfq9V08pZjnVdhErlfPBSnX222z+/qbPPrikNMx9HRERERERUy9i5IiIiIiIisoJ6/cwVERERERHVHEFgrsWaWJtERERERERWwMwVEREREVFjJbLdH+yuj5i5IiIiIiIisgJmroiIiIiIGimhgf6Yb11hbRIREREREVkBO1dERERERERWwGGBRERERESNlMAJLayKmSsiIiIiIiIrYOaKiIiIiKix4o8IWxVrk4iIiIiIyArYuSIiIiIiIrICDgskIiIiImqkOKGFdTFzRUREREREZAXMXBERERERNVYi5lqsibVJRERERERkBcxcERERERE1UoLAZ66siZkrIiIiIiIiK2DnioiIiIiIyAo4LJCIiIiIqLHihBZWxdokIiIiIiKyAmauiIiIiIgaKf6IsHUxc0VERERERGQF7FwRERERERFZAYcFEhERERE1VgJzLdbE2iQiIiIiIrICZq6IiIiIiBorTmhhVcxcERERERERWQEzV0REREREjZTAZ66sirVJRERERERkBexcERERERERWYGg1+v1dV0IIiIiIiKqfcVr/1lnn+0w/YM6++yawmeuGoC5q4vqugg2Z+UsR3yyTVfXxbApb44TYbc0uK6LYXNGqOMwa0V+XRfDpqye54yXPs6p62LYnG/edsOSzZq6LoZNeX+CBI9NvVDXxbA5e9Z1wOtfFtd1MWzK8pkOdV0EqiXsXBERERERNVKCiE8JWRNrk4iIiIiIyArYuSIiIiIiIrICDgskIiIiImqsBKGuS9CgMHNFRERERERkBcxcERERERE1VpzQwqpYm0RERERERFbAzhUREREREZEVcFggEREREVFjxQktrIqZKyIiIiIiIitg5oqIiIiIqJESOKGFVbE2iYiIiIiIrICZKyIiIiKixkpgrsWaWJtERERERERWwM4VERERERGRFXBYIBERERFRYyXiVOzWxMwVERERERGRFTBzRURERETUSAmc0MKqWJtERERERERWwM4VERERERGRFXBYIBERERFRY8UJLayKmSsiIiIiIiIrYOaKiIiIiKix4oQWVsXaJCIiIiIisgJmroiIiIiIGiuBz1xZEzNXREREREREVsDOFRERERERkRVwWCARERERUWMlYq7FmlibREREREREVsDMFRERERFRY8Wp2K2KtUlERERERGQFzFxZUVJSEgICAhAdHY2wsLC6Lk6NGNbVDj3aSWAvE5CcocO2wyqk5+iqXOeRlmIM7yaDu7OArHw99pxQ4UKC1iymV3sJBnSyg1IhID1Hhx1HVUhIq3q7tiDmxA+4cHQdSgtvwcWjFbqPWACvgPBK49MSTuHknmXIy7wGhZMHOvSdhpBuz5rFXPxzIy6f3IKivDTIHVzh334owofOg0Qqq+ndqXFuvcMR+Po0OHdqD3kzD0SOm4mMXw9UvU6fLmj76dtwbNsaqtRMxC//FslrtpjFeI0diqBFr0HR0hcl8cmIe38lMnbur8ldqROP9ZChVwc72MsFXE/TYuvBUqRnV30ehbWWYERPOdydRcjK1+G3P8tw/prG+PehXWQIbS2Bp5sYao0eCala7Dxahsxc2z8/7xjZ2x59QmVQyAUkpmnw474SpGVpq1ynY7AUj/dRoKmLCLfydNh5pARnr6gtxj7aXY6x/RU4cLoMPx0oqYldqFXhrQX0aCuCkz2QmQfsi9Ii+Vbl8X4ewJBOYni4AIUlwPFYHaKu6s1iZFJgYJgIbXwE2NsBuUVAxBkdrqXqLW/URk0Y7YFH+7nBUSFGXEIJvvy/VCSnqiqN920mw/NjPNHK3x6e7nb45sdU7IzIrrDNCaM9zZbl5Kvxj7mXa2QfalPPdhL07yg1tg12/lmOxCraBoHNRHi8px283EQoKNbjf2fV+OuSxiymQ6AYj3a1M7ZJfj9ZjouJVZ/vRNXBzFUdKC8vr+siPJSBnaToHybFtsMqrPypFAXFOswYLYdMWvk6fl4iTBwmR2ScGv/+sQSRcWpMGiaHr6fp0AtrJcGYPjJERJbj060lSEjV4sVR9nBxtO3fXUg4vwcnd3+MsP4vYcysX+Dl3xl7N76EorxUi/GFOTewb+MMePl3xphZvyC0/4s4sesjJF7cZ4y5dvY3RO5dgY4DX8G4ubvR+4kPkHj+d0TuW1Fbu1WjxA4KFJyPw6XXljxQvL1/C3T5bQ1yjkXhWJcxuLbsa7Rb+S68xg41xrh0D0PHH1bi5uadONp5NG5u3olOP66CS9dHamo36sTgLnYY0EmGnw6W4t+bi1BQrMPscQ5Vnp8B3mJMGaHA6Rg1Pt5UhNMxakwboYCfl9gY08pHjCNny/Hpj0VY/XMxxCJg1jgH2DWQW3PDuskxuIscWyKKsXRjAQqKdJjzjBNkdpWvE9hMgumjHXHyogr/WpePkxdVeHG0I/y9xRVi/bzE6BMmQ0qmxsKWbE9bPwHDOotw7KIOa/ZokXxLj/EDxFAqLMe7OADPDRAj+ZYea/ZoceySDo92NnSi7hCJgH8MEsPFAfj5qBb/+U2LXSe1KCxpWB2rJ4e7Y+xQd3z1f6mY869ryM3X4MP5AbCXV94Uk9mJkHarHOt/TkdOnuXOOwAk3SjDhDmxxtfM96/WxC7UqrBWYozubYcDUWqs+G8pEtN0mD5SXmnbwM1JwAsj5EhM02HFf0tx4IwaY3rboUOg6bz08xTh+aEyRF3RYPnWUkRd0WDiUBl8PRp5c1gk1N2rAWpQR5NOp8OyZcvQqlUryGQy+Pr64sMPPwQAXLhwAQMHDoS9vT2aNGmCF198EUVFRcZ1+/fvjzlz5phtb8yYMZg8ebLxvb+/Pz766CNMnToVTk5O8PX1xZo1a4x/DwgIAAB07NgRgiCgf//+AIDJkydjzJgxWLp0KZo1a4agoCAsWbIEHTp0qLAPnTt3xvvvv2+lGrGufqFSRESW40KCFuk5OvywXwU7iYBOQZW3svqFSnElRYsDUWpk5ulxIEqNKze06BdqavH1D5PiZIwGJ2M0yMzVY8excuQV6dGrQxWtQhtw8dhGBHV+AsFdnoKLR0t0H/kOHJy9EHtyi8X42FNb4ODije4j34GLR0sEd3kKQZ2fwIWj64wxmcln4eHbCS3DRsLJtTlatO6FwNARyLpxqbZ2q0bd2nsEVxauQvqOiAeK93vxWZQlpyHm9Y9QdDkBKet+RsqGXxA4b6oxJmD2JGTtP474T9agOC4B8Z+sQdbBE/CfPammdqNODOgow95TZTh3TYO0bB027S2FVCIgvE3lvYT+nexw+boG+06rkJGrw77TKsSlaDCgk2mdL38pwckYNdKzdbiZpcP/7S2Fm1IEH8+KHQlbNKiLHL8fL0X0FTVSs7TYsLsYdlKga9vKM8GDusgQm6jGHyfKkJGjwx8nynD5uhqDusjN4mRSYNrjjtj0ezFKyhpGR6FHGxGi4/WIjtcjqwDYF6VDfgkQHmS5OdG5tQj5xYa4rAIY1k3Qo2eIKb5jS0O2authHVJuAfnFQMotICOvlnaqlowZ4o4tuzJx/EwBrt9UYfl3NyCzE6F/N5dK17maVIp1/03HkVP5UGsqP4a0Oj1yCzTGV0Gh7Wdi+oZKcSpWg5OxhrbBzj8NbYOe7S23OXq0kyCvyBCXmavHyVgNTl3WoH+Y1GybV1K0OHjG0CY5eEaNqze16Btq2+0Nql8aVOdqwYIFWLZsGd577z3ExMTghx9+gKenJ0pKSvDoo4/C1dUVp0+fxn//+1/s378fs2bNqvZnLF++HOHh4YiOjsbMmTPx8ssv4/JlQ+r91KlTAID9+/cjLS0Nv/zyi3G9AwcOIDY2FhEREdi1axemTp2KmJgYnD592hhz/vx5REdHm3Xo6osmSgFKBxHikk1f2FodcO2mFgEW7tbe4e8lNlsHAOKStfC/fWdcLAJaeIgQl2J+VzcuRWOMsUVaTTmyUi+heeteZsubt+qFzOvRFtfJTD6L5q3uiW/dC1k3L0GnNdyx9PTrhOzUS7iVch4AUJCTgpS4I/Bp068G9qL+c+kehlv7/zRbdmvfUTh3bg9BYrgAu3YPQ9b+Y2YxWRFH4dqjY62Vs6Y1cRbg7CjC5STTeaTRAtduaBDYrPLzKMBbgsvXzc+92CQNAptVfsNELjPcaWwInQV3ZxGcHUWISTJlBDRa4EqKBi2bV14Hgc0kZusAwKVEdYV1nhvqgAvx6gp1bKtEIsDbDYhPM/9/n5Cmh4+75TvQLZoKSLgnPj5VD+8mppvWQc0F3MjSY3gXEeY9IcaMEWL0bidAaEA3tb2aSuHmIsWZS6abuhqNHhfiihHSqpK0XzU095Rh04o2WLcsGG+95AOvprbdWRCLgBZNRYhLuaf9kKKFfyU3dvy8xBXjk7XwaSoyzjTu5ynCFQsxfl4NqjlcfYKo7l4NUAMZ2AEUFhbis88+w+rVqzFpkuGOdMuWLdG7d2+sXbsWpaWl+P777+Hg4AAAWL16NUaNGoVly5bB09Ozqk2beeyxxzBz5kwAwFtvvYWVK1fi0KFDaNOmDZo2bQoAaNKkCby8vMzWc3BwwLfffgs7O9Md4WHDhmH9+vXo0qULAGD9+vXo168fAgMDLX62SqWCSmU+Nlsmq53nbJwUhqtcYan5RbKoVA9Xp8qvgE4KocI6haV6KB0M6zjYCxCLhArDPwpL9FAqbPfKWlaSB71OC3tHd7Pl9k5NUHo1y+I6pYVZsA9qYh7v6A69ToOy4lwolB5oGToCZcW52LXmH9Dr9dDrNGjT7VmE9pteY/tSn8k83aHKMK/P8sxsiKRS2Lm7QpV+CzIvd6gyzJ9RUGVkQ+bVtDaLWqOUCsMFytJ55Kas/DxSOlg+95yqOPfG9ZPj2g1DdszWKR0N9VZQbL4vhcU6uCkr75QqHQ3Pc9ytoFgPpYOpoRAeYgdfTzE+2lhgxRLXLYUMEIkEFN/TsS4u08PB3vIx4yiHxXixSIBCBhSVAa6OAgIcgQuJevx4SAs3JwHDu4ggEnQ4ctH2O/EA4Ko0dHbyCsw72nkFGng0+XsdobiEEiz/NgU301VwcZbg2ZEe+PSdlnj5n1dRWGybGSwHuaFtUHRvm6NEDycfy8eaUiEgrqRiG0UsFuAgF4zfbRbbJDbc3qD6p8F0rmJjY6FSqTBo0CCLfwsNDTV2rACgV69e0Ol0iIuLq1bn6pFHTM9pCIIALy8vZGZm3ne9Dh06mHWsAGD69OmYOnUqVqxYAbFYjM2bN2P58uWVbmPp0qVYvHix2bKFCxcC7vMfuPwPqlOQBE/3N3Xc1u4qNfzDwnVOf59rn6W/37vM0iYaxCX13u9rvd7CwrvDK6xw+w+G5WkJp3Du0Dfo+fh7aOoTioLs6zixayminb5Ex4EzrVZsm3LvwXTndvfdyy3F3O/ArcfC20jx3GB74/uvdhQDsHDOCPc/j6pTDU8PlKOZuxgrtxbdP7ge6trWDhMeNV0HVv+3EEBldXC/Lzbzt3efua5OIjwzWIHPthZCY5tt2+qrorru/ZPxFL3rfXEZsOuUDno9kJajh5O9Dj3ainDkom1WYP/uLpg9sZnx/cJV1wFY+CqysKy6Ii/cdT7eVCH2WhK+WxaMwb1csX2f5Zt5tqJC3dynD1RpVeorD7LG/wOb15DSxPVAg+lc2dvbV/o3vV4PoZID585ykUgE/T1nl1pd8eFRqdT8DpMgCNDp7n8H9+6O3R2jRo2CTCbD9u3bIZPJoFKpMG7cuEq3sWDBAsybN89smUwmw9trK3/I9WFdStTg0wzTRU0iNtSTk0JAwV13hhztK95ZupulDJSTvelueXGpHlqdhRhFxTvqtkSucIEgEqO00PzCVlqUA3vHJhbXsXdyR0mF+GwIIgnkChcAQFTE52jV8XEEd3kKAODmFQRNeSmO7ViIsP4zIDSyX1lXZWRVyEDZNXWDTq1GeXaeISY9CzIv8wyizMOtQsbLllyIVyMp/e7z0/BfpUIwy6g42QsoLK78PDJkWx7s3HtqgBwdWkqxamsR8ops89w8d60cietMmQOJxLDvzo4iFNx1h9/JoWJm6m4FRToo73mo3slBMGbAfL3EUDqI8M5kpfHvYpGA1j4S9O8swyv/zrXJxlyJCtDp9HCQC7i7heogF1BcZnmdojLA8Z54hUyAVqdH6e2BGEWlhmeG7q6TrALD8SsSAQ9wia13Tp4tQFyCaWZI6e1jzdVZgtx80zHorJRUyGb9XapyPa7fKEMzzypmZannissMbYN7s+iO9pW3DQosZN0d7QVotXoUqwzrWMrMO9pXzGYR/R0NpiXWunVr2Nvb48CBitM2t23bFmfPnkVxcbFx2Z9//gmRSISgoCAAQNOmTZGWlmb8u1arxcWLF6tVhjuZKa32we60SSQSTJo0CevXr8f69evx7LPPQqGofOy1TCaDUqk0e9XUsECVGsjK1xtf6Tk6FBTrEOxjGiojFgGtmouRmFb5/ialaxHkYz68JthXbGwYanXAjUwdgnzM+/lBPhKzxqOtEUvs4N6sHW5eO262PPXacXj4WX7Wx8M3DKn3xN+8+ifcm7eDSGzo1GvUpRXuMAkiMaDXQ98wcn3VknfiLNwH9TRb1nRIb+RHXYReY2iw5J44C/dB5s+yuQ/ujdy/LD/7ZgtUaiArT2d8pWfrkF+kQxs/03kkFgGtWkiQkFr5eZSYpjFbBwDa+EmQkGre2HtqoByhraX4/L/FyC6w3eNMVQ7cytMZX2lZWuQX6RDib7ppJhYZvn/ib1be4E1I1ZitAwBt/aXGdS5fV2Pxt/n4YF2B8ZWUpsGpS+X4YF2BTXasAEMnJy0HCPQ2/w4K9BaQkmV5p27c0leIb+ktIC0b0N1eJeWWHm73DC93czI0hG2xYwUApWU6pGWWG1/JqSrk5KnRqa2jMUYiFtAh2AGx16w7Pb9EIsDHW46cPNt91k+rA27c0lVoPwS1ECMpw/J32vV0LYJa3NPe8BEj5ZbOeBxdz7CwTR8xrqfb6IFG9VKD6VzJ5XK89dZbePPNN/H9998jPj4eJ06cwHfffYcJEyZALpdj0qRJuHjxIv73v/9h9uzZeP75541DAgcOHIjdu3dj9+7duHz5MmbOnIm8vLxqlcHDwwP29vb4448/kJGRgfz8/Puu88ILL+DgwYP4/fffMXXq1PvG16XD59QYHG6Y1tTLTYTnBstQrtHjzBXTF/j4wTKM6GG6W3bknBrBvmIM7CSFh4uAgZ2kCGohxuFzpmzbobNqdG8rQdcQCTxcBYzpbQdXRwHHL1o/I1eb2veehCuR23AlchvyMuNxYvdSFOWnoU3XZwAAp/euwOH/vmWMD+n6LIryUnFi98fIy4w3rBv1Czr0MR0Xvm0G4PLJLYg/txuFOTdw8+qfiIr4HL4hAyAS2e4EIHeIHRRQhraBMrQNAEAR0ALK0DaQ+3gDAII/mIfQ9cuM8dfXbIG9XzOE/PttOLYJRIvJ4+AzZRwSVphmWExa/T3ch/RC4PzpcAgOROD86XAf1ANJX2ys3Z2rYf+LVmFoVzkeaSWBdxMRnn/UHmqNHpGXTT/98Pyj9ni8t+mGzKEz5WjjJ8HgLnbwdBVhcBc7tPGV4H9nTOs8PVCOLm3ssGFPCcrKDXd9nRQCpA1k3MOB02UY3kOOsCApmrmLMXmEA8rVwKkY0/Otk0c6YEw/0+iIA5EqtA2QYlg3OTzdRBjWTY4QfykOnDakb1TlQGqW1uylUutRXKpH6n1+P6u+++uyDp1aCggLFOCuBIZ2EsFZAURdNTROB4aJMLqHqWkRdVUHZwdDnLsSCAsU0LGlgOOxpsZs5FUd7GXAo+EiuDkBrZsJ6N1OhNNXGlaDd0dEFp4e6YEenZTway7DvGktoCrX4dDJPGPM6y+0wORxpkcVJGIBgT5yBPrIIZEIaOIiRaCPHN4epuvstKe90D7IAZ7uUgQH2uPdmb5Q2Itw4Hhube6e1R05p0a3EAm6tjG0DR7vZQdXJwF/XTS0OR7rLsVzg0z18NclDVydBDze0w4ergK6tjG0Kw6dNbUljp5XI8hHjAEdDW2SAR0NbZIj52y7vfG3iUR192qAGsjl0eC9996DRCLB+++/j9TUVHh7e2PGjBlQKBTYu3cvXnvtNXTp0gUKhQLjxo3DihWm3waaOnUqzp07h4kTJ0IikWDu3LkYMGBAtT5fIpHg888/x5IlS/D++++jT58+OHToUJXrtG7dGj179kR2dja6dev2MLtdaw6eUUMqEfBkPxnsZQKuZ+jw9c4yqO76TnJ1EkGvN10Qk9J12LS3DMO7yzC8mx2y8/XYuLcMyRmmmLPXNHCQA8O62EHpICAtW4c1u0qRW2ijt3dvC3zkMZSV5CH64JcoKbwFV8/WGDrpazi5NgcAlBbeQlGeKVvq5NYCQyd9jZN7PkbsiR+gUHqg+8h3ENDe9JtNYQNmAIKAqIjPUVKQAbmDG3zb9EfnoXNqe/dqhHPn9uhxYJPxfdtP3wEApHz/C85PWwCZd1PY3+5oAUBp0g2cHvUi2i5fAL+XJ0CVmolLcz9E+nbTb4Pl/hWN6AnzELx4DoIXv4qS+BREj5+LvFPna2/HasH+0+Wwkwh4ZqA9FHIBSelarN5WbHZ+ujmJzLImiWlarN9dgpG95BjZU46sPB3W7S7B9buyxn3DDJ2xOU+b7rgDwKY/DFO027q9J8sglQoYP9TB8CPCqRp8trUQqrt+jtBNaV5vCTc1+HZnEUb3VeDxvva4lavD2p1FSKoii99QxFzXQ2GnQ98OIjje/hHhHw5pkX97YIijHHC+a6hpXjHw4/+0GNpZjPAgMQpLgT+idLicYqrQghJg80FDzIwRYhSUAKfidPgzxravAff6+fcsyOxEeOUfzeDoYPgR4X8uT0Rpmel62NRNapatc3ORYPXi1sb3Tw5viieHN8X5y0V4+5NEAIC7qxRvzfCB0lGM/EIt4uJLMPfDeGRm2/b5efaaFgpZOYaES6F0sENatg7f7ipD7u1hyUqFABdHU+M8p1CPb3eXYXQvO/TqYI/8YsNPu1xIMJ2XSek6/N8+FYZ3s8OjXaXILtBjU4QKyZkNqyNPdUvQ3/ugEdUqvV6PNm3a4KWXXqrwPNWDmrvaNh8ur0srZznik238Mq2ON8eJsFsaXNfFsDkj1HGYteL+WWwyWT3PGS99nFPXxbA537zthiWbbXcoWF14f4IEj029UNfFsDl71nXA618W3z+QjJbPrPjsfX1RtvvrOvts+YgZdfbZNaVBZa5sTWZmJjZt2oSbN29iypQpdV0cIiIiIiL6G9i5qkOenp5wd3fHmjVr4OrqWtfFISIiIiKiv4GdqzrEEZlEREREVKeEhjmxRF1hbRIRERERUb335ZdfIiAgAHK5HJ07d8bRo0erjFepVHj33Xfh5+cHmUyGli1bYt26dVWu83cxc0VERERE1FjZyJToW7duxZw5c/Dll1+iV69e+OabbzB8+HDExMTA19fX4jpPP/00MjIy8N1336FVq1bIzMyERlOzE/+wc0VERERERLVOpVJBpVKZLZPJZJDJZBViV6xYgWnTpuGFF14AAKxatQp79+7FV199haVLl1aI/+OPP3D48GEkJCTAzc0NAODv72/9nbiHbXRViYiIiIjI+gShzl5Lly6Fs7Oz2ctSR6m8vBxRUVEYOnSo2fKhQ4fi+PHjFnfr119/RXh4OD755BM0b94cQUFBmD9/PkpLS2ukGu9g5oqIiIiIiGrdggULKvzOq6WsVVZWFrRaLTw9Pc2We3p6Ij093eK2ExIScOzYMcjlcmzfvh1ZWVmYOXMmcnJyavS5K3auiIiIiIio1lU2BLAygiCYvdfr9RWW3aHT6SAIAjZv3gxnZ2cAhqGFTz75JP7zn//A3t7+4QteBQ4LJCIiIiJqrARR3b0ekLu7O8RicYUsVWZmZoVs1h3e3t5o3ry5sWMFACEhIdDr9bhx48bD1dUDYOeKiIiIiIjqLTs7O3Tu3BkRERFmyyMiItCzZ0+L6/Tq1QupqakoKioyLrty5QpEIhFatGhRY2Vl54qIiIiIqLGqwwktqmPevHn49ttvsW7dOsTGxmLu3LlITk7GjBkzABie35o4caIxfvz48WjSpAmmTJmCmJgYHDlyBG+88QamTp1aY0MCAT5zRURERERE9dwzzzyD7OxsLFmyBGlpaWjfvj327NkDPz8/AEBaWhqSk5ON8Y6OjoiIiMDs2bMRHh6OJk2a4Omnn8YHH3xQo+Vk54qIiIiIiOq9mTNnYubMmRb/tmHDhgrL2rRpU2EoYU1j54qIiIiIqLES8Skha2JtEhERERERWQEzV0REREREjZS+mhNLUNWYuSIiIiIiIrICZq6IiIiIiBqravyYL90fa5OIiIiIiMgK2LkiIiIiIiKyAg4LJCIiIiJqrDgs0KpYm0RERERERFbAzBURERERUSPFqditi5krIiIiIiIiK2DnioiIiIiIyAo4LJCIiIiIqLHihBZWxdokIiIiIiKyAmauiIiIiIgaK05oYVXMXBEREREREVkBM1dERERERI2ViLkWa2JtEhERERERWQE7V0RERERERFYg6PV6fV0XgoiIiIiIal/x8V/q7LMdej5RZ59dU/jMVQMwd3VRXRfB5qyc5ch6q6aVsxwxa0V+XRfD5qye54zd0uC6LoZNGaGOw8jpMXVdDJuza21b9B51uK6LYVOO/dYPo16Kreti2JzfvgnhNbSaVs5yrOsiUC1h54qIiIiIqLHijwhbFWuTiIiIiIjICti5IiIiIiIisgIOCyQiIiIiaqT0HBZoVaxNIiIiIiIiK2DmioiIiIiosRKEui5Bg8LMFRERERERkRWwc0VERERERGQFHBZIRERERNRIcUIL62JtEhERERERWQEzV0REREREjRUntLAqZq6IiIiIiIisgJkrIiIiIqLGis9cWRVrk4iIiIiIyArYuSIiIiIiIrICDgskIiIiImqk9JzQwqqYuSIiIiIiIrICZq6IiIiIiBorTmhhVaxNIiIiIiIiK2DnioiIiIiIyAo4LJCIiIiIqJHSgxNaWBMzV0RERERERFbAzBURERERUSOl54QWVsXaJCIiIiIisgJmroiIiIiIGitmrqyKtUlERERERGQF7FwRERERERFZAYcFEhERERE1UnqBU7FbEzNXREREREREVsDMFRERERFRI8Wp2K2rwdXmhg0b4OLiYny/aNEihIWF1fjn+vv7Y9WqVTX+OUREREREVD81+MzV/PnzMXv2bKttb8OGDZgzZw7y8vLMlp8+fRoODg5W+5z6alhXO/RoJ4G9TEByhg7bDquQnqOrcp1HWooxvJsM7s4CsvL12HNChQsJWrOYXu0lGNDJDkqFgPQcHXYcVSEhrert2oLq7lfLZiKM7i2Dl5sIBcV6HDxTjuOXNGYxD1KfDcFjPWTo1cEO9nIB19O02HqwFOnZVR8TYa0lGNFTDndnEbLydfjtzzKcv2aqv6FdZAhtLYGnmxhqjR4JqVrsPFqGzFzbPdbceocj8PVpcO7UHvJmHogcNxMZvx6oep0+XdD207fh2LY1VKmZiF/+LZLXbDGL8Ro7FEGLXoOipS9K4pMR9/5KZOzcX5O7UmfGj2qKYX1d4KgQ40piKb76IR3JqapK432byTDh8aZo5SeHp7sd1mxJx68HciqNf2p4E0x6whM792dj7daMmtiFWjf1OT88PswbTo4SxFwpxIqvryIxuaTS+FFDvfDoQC8E+ikAAHHXivDN94mIvVpoFjf2sWZ47okWaOIqQ1JyMT5bG4/zMfk1ui+16bmR7hjWx3Ssff1jOpLTyiuN9/W2w4THm6Klr+FYW/tTOn49kGsWM7yvC4b3c4VnEykAIDlNhS27shB1qbhG96U28BpKtqjBZa7u5ejoiCZNmtT45zRt2hQKhaLGP6cuDewkRf8wKbYdVmHlT6UoKNZhxmg5ZNLK1/HzEmHiMDki49T4948liIxTY9IwOXw9TYdeWCsJxvSRISKyHJ9uLUFCqhYvjrKHi6NtP2BZ3f1ycxIwfZQ9ElK1+HRrCSKiyjG2rwyPtBQbYx6kPhuCwV3sMKCTDD8dLMW/NxehoFiH2eMcqjzWArzFmDJCgdMxany8qQinY9SYNkIBPy9T/bXyEePI2XJ8+mMRVv9cDLEImDXOAXY2fJtJ7KBAwfk4XHptyQPF2/u3QJff1iDnWBSOdRmDa8u+RruV78Jr7FBjjEv3MHT8YSVubt6Jo51H4+bmnej04yq4dH2kpnajzox7tAnGDHHD1z+kY96HicjN1+Bfc31hL6v8nJLZCUjPKsfGXzKRk6eucvut/eUY1tcViSll1i56nZkwzgfPjGmBFd9cwwvzziA7txwrlzwCe3txpet07OCC/UcyMfudc3jpjWhk3CrDiiWPwN3NzhgzsHdTvPpCS3z/UzKmvhaFc5fy8emiDvBsKquN3apx44Y1wZjBbvhmSwbmLU1CboEGS+bc71gTIT1LjY3bbyEnX2MxJitPg43bMzH3oyTM/SgJ5y+X4N2ZPvD1trMYbyt4Da1FglB3rwaoVo+mP/74A71794aLiwuaNGmCkSNHIj4+HgCQlJQEQRCwZcsW9OzZE3K5HO3atcOhQ4eM6x86dAiCIGD37t0IDQ2FXC5Ht27dcOHChUo/09KwwHXr1qFdu3aQyWTw9vbGrFmzjH9bsWIFOnToAAcHB/j4+GDmzJkoKioyfv6UKVOQn58PQRAgCAIWLVoEoOKwwOTkZIwePRqOjo5QKpV4+umnkZFhumN5p1ybNm2Cv78/nJ2d8eyzz6Kw0PwuXn3SL1SKiMhyXEjQIj1Hhx/2q2AnEdApqPKWab9QKa6kaHEgSo3MPD0ORKlx5YYW/UJNreT+YVKcjNHgZIwGmbl67DhWjrwiPXp1qKIlbQOqu18920uRV2iIy8zV42SMBqdiNRjQ0XSBfJD6bAgGdJRh76kynLumQVq2Dpv2lkIqERDepvLGQv9Odrh8XYN9p1XIyNVh32kV4lI0GNDJtM6Xv5TgZIwa6dk63MzS4f/2lsJNKYKPZ+WNwvru1t4juLJwFdJ3RDxQvN+Lz6IsOQ0xr3+EossJSFn3M1I2/ILAeVONMQGzJyFr/3HEf7IGxXEJiP9kDbIOnoD/7Ek1tRt1ZvQgN2zdk4W/ogtxPVWFFetTIbMToV83ZaXrXE0qw/qfM3HkdAHUGn2lcXKZgPkvNMcX36ehqKTh3Bl/6vHm+P6nZBz5KwuJySX4cOVlyGRiDO3nUek6S5ZfxvY9qbiWWIzkG6VYtvoKRCIgPNTVGPPsmBbYFZGOXfvScf1GCT7/Nh6ZWWUYM7xZbexWjXt8kBt++j0bf0UXIjlVhZUb0gzHWtcqjrXrZVi/LRNHIwugVlvO2Jw+X4Soi8VIzSxHamY5Nu28hTKVDsGB9jW1K7WC11CyVbXauSouLsa8efNw+vRpHDhwACKRCGPHjoVOZ/rCeOONN/D6668jOjoaPXv2xOOPP47s7Gyz7bzxxhv49NNPcfr0aXh4eODxxx+HWl313cM7vvrqK7zyyit48cUXceHCBfz6669o1aqV8e8ikQiff/45Ll68iI0bN+LgwYN48803AQA9e/bEqlWroFQqkZaWhrS0NMyfP7/CZ+j1eowZMwY5OTk4fPgwIiIiEB8fj2eeecYsLj4+Hjt27MCuXbuwa9cuHD58GB9//PED12dtaqIUoHQQIS7Z1EDQ6oBrN7UI8K68YervJTZbBwDikrXwv51NEIuAFh4ixKWY35GLS9EYY2zRw+yXv5e4QvzlZA18moogEt0VU0V9NgRNnAU4O4pwOclUFxotcO2GBoHNKt/PAG8JLl83r7/YJA0Cm1Xe+ZfLDHfNSsoqbyA3NC7dw3Br/59my27tOwrnzu0hSAx15do9DFn7j5nFZEUchWuPjrVWztrg6S6Fm4sU0XcNn9Jo9Lh4pQQhLf/+SISXx3vj9PkinIu1/eFZdzTzlMPdTYZT0aahaWqNHmcv5qF9m8o7CfeSycSQiAUUFBmu3RKJgKBWTjgdbT688nR0LtqHPPh26ytPdyncnCWIjikyLrtzrLVpab1OkEgA+oQrIbcTcDmh1GrbrW28htYuvSCqs1dDVKuDYcaNG2f2/rvvvoOHhwdiYmLg6OgIAJg1a5Yx7quvvsIff/yB7777ztjBAYCFCxdiyJAhAICNGzeiRYsW2L59O55++un7luGDDz7A66+/jtdee824rEuXLsZ/z5kzx/jvgIAA/Otf/8LLL7+ML7/8EnZ2dnB2doYgCPDy8qr0M/bv34/z588jMTERPj4+AIBNmzahXbt2OH36tPHzdDodNmzYACcnJwDA888/jwMHDuDDDz+0uF2VSgWVyvw5AJmsdoZLOCkMjdDCUvNGaFGpHq5Olad1nRRChXUKS/VQOhjWcbAXIBYJKCy5J6ZED6XCdtPFD7NfTg4CCpMrxovFAhzlAgpK9Petz4ZAqTB82VqqOzdl5fupdLBc305VHEfj+slx7YYhO9ZYyDzdocrIMltWnpkNkVQKO3dXqNJvQeblDlWG+U0tVUY2ZF5Na7OoNc7V2XAJzCswb5DlFWjg0eTv3cnu20WJlr5yzP0w8W9tp75xczVkAXLyzJ8Tys0rh6eH/IG38/KkANzKLkfkWUMnzVkphUQsVBhmmZOnRhMX2x7eBgCuyjvHmnnDPq9QAw+3v5818Wsmw7/f8oedVECpSocPv76BlCqe5arveA0lW1arnav4+Hi89957OHHiBLKysowZq+TkZLRt2xYA0KNHD1PhJBKEh4cjNjbWbDt3x7i5uSE4OLhCjCWZmZlITU3FoEGDKo353//+h48++ggxMTEoKCiARqNBWVkZiouLH3jCitjYWPj4+Bg7VgDQtm1buLi4IDY21ti58vf3N3asAMDb2xuZmZmVbnfp0qVYvHix2bKFCxcC7hWzZ39XpyAJnu5v6rit3XX7DpiFG/z6+9z0t/T3e5dZ2kRDyCVUd7/u/ZtgYfmD1KctCW8jxXODTXduv9phuMtfYZeE+x8T1amHpwfK0cxdjJVbi+4f3NDcW1F3xr3fvdxSjC0faAD6d1PilX+Yhpgt/iIZgOXz7u/sqrurBNOf9cL7K69XOWzQFgzp54E3Xgkyvn9zye1h+BUq7QFO0NvGP+GDwX09MPudcyhXm69k8bCrZpnrg35dlXhlgrfx/ZLVKQAs7B+ss383M1R47YMEOCjE6NnRCXMnN8OC5ddtuoMF8BpaW/Rg59KaarVzNWrUKPj4+GDt2rVo1qwZdDod2rdvj/Lyqk9+4QEeeHuQGHv7qlPv169fx2OPPYYZM2bgX//6F9zc3HDs2DFMmzbtgYcdAoZhgZbKc+9yqdT8bpUgCGZDJO+1YMECzJs3z2yZTCbD22sfvGwP6lKiBp9mmO6wScSGcjspDHd/7nC0F1BUWvm3kqW7TE72prtRxaV6aHUWYhQV71jZkofZr8LiivGOCgFarR7Ft4et3a8+bdGFeDWS0u8+1gz/VSoEFBSb9svJXkBhceX7WVBc8e5jZfX91AA5OrSUYtXWIuQV2W7dPQxVRlaFDJRdUzfo1GqUZ+cZYtKzIPNyN4uRebhVyHjZmpNnixCXEG98L5UasqSuSgly75oswFkpqZDNqo5WfvZwVUqw6p+BxmVisYB2rRUYOcANY1+Ohc5GDrtjp7IRcyXS+N7udp25udohO9d07XZ1llbIZlny3NgWeP4pX8x57xzik0zDJfML1NBo9Wjian5dfNDt1jenzhXhSmKC8b1UYvhucnUWI/euY8vZ6e8da3dotEDaLTUANa5dL0Nrf3s8PtAN/9mc/re3XRd4DSVbVmuDHbOzsxEbG4t//vOfGDRoEEJCQpCbm1sh7sSJE8Z/azQaREVFoU2bNpXG5Obm4sqVKxViLHFycoK/vz8OHLA8TXFkZCQ0Gg2WL1+O7t27IygoCKmpqWYxdnZ20GqrfjC5bdu2SE5ORkpKinFZTEwM8vPzERISct9yVkYmk0GpVJq9ampYoEoNZOXrja/0HB0KinUI9jGNSxaLgFbNxUhMq7w+ktK1CPIxH8sc7Cs2Nqa1OuBGpg5BPub9/CAfiVmD29Y8zH4Z6so8PthHgpRbOtzpc9+vPm2RSg1k5emMr/RsHfKLdGjjZ6oLsQho1UKChNTK9zMxTWO2DgC08ZMgIdW84fLUQDlCW0vx+X+LkV3Q+C6oeSfOwn1QT7NlTYf0Rn7UReg1hrrKPXEW7oN6mcW4D+6N3L+ia62cNaFUpUPaLbXxlZyqQk6eGh3bmkYlSMRA+yAFYuMrn1b8fs7FFuOVhfF4dUmC8XUlqRSHTubj1SUJNtOxAoDSUi1uppUZX4nJJcjKUaFLmGkiColEQFh7F1y8XFDltp4b2wKTnvHD/EXnEXfNPGOs0ehx5VohunR0NVseHuaKi7FVb7c+qnCspZUjJ1+DsJCKx9rleOs/GyUIpg6dLeI1lGxZrXWuXF1d0aRJE6xZswbXrl3DwYMHK2RhAOA///kPtm/fjsuXL+OVV15Bbm4upk6dahazZMkSHDhwABcvXsTkyZPh7u6OMWPGPFA5Fi1ahOXLl+Pzzz/H1atXcebMGXzxxRcAgJYtW0Kj0eCLL75AQkICNm3ahK+//tpsfX9/fxQVFeHAgQPIyspCSUnFC/DgwYPxyCOPYMKECThz5gxOnTqFiRMnol+/fggPD3/AGqt/Dp9TY3C4HToEiuHlJsJzg2Uo1+hx5oqp8Tp+sAwjepjGxx85p0awrxgDO0nh4SJgYCcpglqIcficKdt26Kwa3dtK0DVEAg9XAWN628HVUcDxi9bPyNWm++3XiB52GD/Y1Dk+flENVycBo3vbwcNVQNcQCbq1leB/0aa7tg9Snw3B/6JVGNpVjkdaSeDdRITnH7WHWqNH5GVTXTz/qD0e722qv0NnytHGT4LBXezg6SrC4C52aOMrwf/OmNZ5eqAcXdrYYcOeEpSVG8bfOykESG18KnZlaBsoQw03mBQBLaAMbQO5j2FIUvAH8xC6fpkx/vqaLbD3a4aQf78NxzaBaDF5HHymjEPCinXGmKTV38N9SC8Ezp8Oh+BABM6fDvdBPZD0xcba3blasPNADp56zB09OjrBr5kMc6Y0h6pch8MnTQ36eVObYdJY00x4EjEQ4CNDgI8MEomAJq4SBPjI4N3UkHUpVelwPVVl9lKpdCgs1uJ6Fb+fZSv+++tNPP+UL/p2b4IAXwXenRMMlUqLfYdNw9r/OTcYL00MML4f/4QPpj8fgKWfxyEtowxuLobJROzlpmbIlh03MHKIN0YM9oJfCwVmv9ASnk3l2PG7+U1OW/XrgRw8Ndwd3cOc4NtMhjmTmxmOtVOmY23uZG9MHGPKLEvEQEALGQJa3D7WXKQIaGE61gDg+TFN0baVPTyaSOHXTIbnRzdF+yAFDp2yvU7p3XgNrT2c0MK6aq1JIRKJsGXLFrz66qto3749goOD8fnnn6N///5mcR9//DGWLVuG6OhotGzZEjt37oS7u3uFmNdeew1Xr15FaGgofv31V9jZPdgDr5MmTUJZWRlWrlyJ+fPnw93dHU8++SQAICwsDCtWrMCyZcuwYMEC9O3bF0uXLsXEiRON6/fs2RMzZszAM888g+zsbCxcuNA4HfsdgiBgx44dmD17Nvr27QuRSIRHH33U2ImzVQfPqCGVCHiynwz2MgHXM3T4emcZVHd9J7k6iaDXm4Y2JqXrsGlvGYZ3l2F4Nztk5+uxcW8ZkjNMMWevaeAgB4Z1sYPSQUBatg5rdpUit9CGbu9acL/9UioEuDqZvlhyCvVY+1spxvSWoXcHKfKL9dh+RIXz8aY7ag9Snw3B/tPlsJMIeGagPRRyAUnpWqzeVmx2rLk5iczGySemabF+dwlG9pJjZE85svJ0WLe7BNfvuiPZN8xwIZ7ztKPZ5236wzBFuy1y7twePQ5sMr5v++k7AICU73/B+WkLIPNuCnsf07MfpUk3cHrUi2i7fAH8Xp4AVWomLs39EOnb9xljcv+KRvSEeQhePAfBi19FSXwKosfPRd6p87W3Y7Vk2x/ZkElFeHm8FxwdxIhLKMX7K5NRqjKdU03dpGbZJjcXKb54v6Xx/bhh7hg3zB0X4oqx4NPrtVn8OrF5WwpkdiLMe7k1nByliLlSgLnvn0dpqelc82wqN6uzsY81g51UhA8XtDPb1rofkrDuR0OdHTx2C85KKSY/64cmbnZIvF6MNxZfQMYt2++QAsC2vdmwkwqGY00hwpXEUrz/WUqFY01/z7H2+Xum4aVPDG2CJ4Y2wYW4YryzwvDMoIuTBPOmNIObswTFpTok3VRh0ecpOGvjs1TyGkq2StDr68djfElJSQgICEB0dHSF36W649ChQxgwYAByc3Ph4uJSq+Wrz+auboQP5P9NK2c5st6qaeUsR8xakV/XxbA5q+c5Y7c0uK6LYVNGqOMwcnpMXRfD5uxa2xa9Rx2u62LYlGO/9cOol+4/IRaZ++2bEF5Dq2nlLMf7B9WRWzGn6uyzm7btWmefXVMaZj6OiIiIiIiolrFzRUREREREZAX15jFuf39/3G+EYv/+/e8bQ0RERERED0bPXItVsTaJiIiIiIisoN5kroiIiIiIqHbpBdv9TbT6iJkrIiIiIiIiK2DmioiIiIiokWqoP+ZbV1ibREREREREVsDOFRERERERkRVwWCARERERUSOlBye0sCZmroiIiIiIiKyAmSsiIiIiokaKE1pYF2uTiIiIiIjICti5IiIiIiIisgIOCyQiIiIiaqT0Aie0sCZmroiIiIiIiKyAmSsiIiIiokaKU7FbFzNXREREREREVsDMFRERERFRI8Wp2K2LtUlERERERGQF7FwRERERERFZAYcFEhERERE1UpzQwrqYuSIiIiIiIrICZq6IiIiIiBopTmhhXaxNIiIiIiIiK2DnioiIiIiIyAo4LJCIiIiIqJHihBbWxcwVERERERGRFbBzRURERETUSOkFUZ29quvLL79EQEAA5HI5OnfujKNHjz7Qen/++SckEgnCwsKq/ZnVxc4VERERERHVa1u3bsWcOXPw7rvvIjo6Gn369MHw4cORnJxc5Xr5+fmYOHEiBg0aVCvlZOeKiIiIiKiR0kOos1d1rFixAtOmTcMLL7yAkJAQrFq1Cj4+Pvjqq6+qXO+ll17C+PHj0aNHj79TTQ+MnSsiIiIiIqp1KpUKBQUFZi+VSlUhrry8HFFRURg6dKjZ8qFDh+L48eOVbn/9+vWIj4/HwoULrV72yrBzRUREREREtW7p0qVwdnY2ey1durRCXFZWFrRaLTw9Pc2We3p6Ij093eK2r169irfffhubN2+GRFJ7E6QLer1eX2ufRkRERERE9UZ8QkKdfXaL5s0rZKpkMhlkMpnZstTUVDRv3hzHjx83G9734YcfYtOmTbh8+bJZvFarRffu3TFt2jTMmDEDALBo0SLs2LEDZ8+erZmduY2/c9UAzF1dVNdFsDkrZznik226ui6GTXlznAgvfZxT18WwOd+87YaR02Pquhg2ZdfattgtDa7rYticEeo4XLqWVtfFsCntWnlj6U/aui6GzVnwtBhzvmDbozpWzXas6yLUS5Y6Upa4u7tDLBZXyFJlZmZWyGYBQGFhISIjIxEdHY1Zs2YBAHQ6HfR6PSQSCfbt24eBAwdaZyfuwc4VEREREVEjpdfX/x8RtrOzQ+fOnREREYGxY8cal0dERGD06NEV4pVKJS5cuGC27Msvv8TBgwfx888/IyAgoMbKys4VERERERHVa/PmzcPzzz+P8PBw9OjRA2vWrEFycrJx2N+CBQtw8+ZNfP/99xCJRGjfvr3Z+h4eHpDL5RWWWxs7V0REREREVK8988wzyM7OxpIlS5CWlob27dtjz5498PPzAwCkpaXd9zevagM7V0REREREjZTehiYPnzlzJmbOnGnxbxs2bKhy3UWLFmHRokXWL9Q9bKc2iYiIiIiI6jFmroiIiIiIGik96v+EFraEmSsiIiIiIiIrYOeKiIiIiIjICjgskIiIiIiokeKwQOti5oqIiIiIiMgKmLkiIiIiImqkmLmyLmauiIiIiIiIrICZKyIiIiKiRoqZK+ti5oqIiIiIiMgK2LkiIiIiIiKyAg4LJCIiIiJqpPR6Dgu0JmauiIiIiIiIrICZKyIiIiKiRooTWlgXM1dERERERERWwM4VERERERGRFXBYIBERERFRI8VhgdbFzBUREREREZEVMHNFRERERNRIMXNlXcxcERERERERWQEzV0REREREjRR/RNi6mLkiIiIiIiKyAnauiIiIiIiIrIDDAomIiIiIGikdJ7SwKmauiIiIiIiIrICZKyIiIiKiRopTsVuXzWSuDh06BEEQkJeXV2nMokWLEBYW9sDbFAQBO3bs+NtlIyIiIiIiqreZq/79+yMsLAyrVq164HXmz5+P2bNn11yhCMO62qFHOwnsZQKSM3TYdliF9Bxdles80lKM4d1kcHcWkJWvx54TKlxI0JrF9GovwYBOdlAqBKTn6LDjqAoJaVVv1xbEnPgBF46uQ2nhLbh4tEL3EQvgFRBeaXxawimc3LMMeZnXoHDyQIe+0xDS7Vnj33VaNc4dWoOr0TtRUpABZ/cAdHn0dbQI6lMbu1OrRva2R59QGRRyAYlpGvy4rwRpWdoq1+kYLMXjfRRo6iLCrTwddh4pwdkraouxj3aXY2x/BQ6cLsNPB0pqYhdq3fhRTTGsrwscFWJcSSzFVz+kIzlVVWm8bzMZJjzeFK385PB0t8OaLen49UBOpfFPDW+CSU94Yuf+bKzdmlETu1Br3HqHI/D1aXDu1B7yZh6IHDcTGb8eqHqdPl3Q9tO34di2NVSpmYhf/i2S12wxi/EaOxRBi16DoqUvSuKTEff+SmTs3F+Tu1Lrft+1Azt/2YLcnGz4+AZg6ouz0Lb9IxZjYy+dx/fr1+DmjWSUq8rQ1MMTQx99HKPGPmUWV1xUiM3ff4cTx4+guKgQHp7emPzCTHTu0r02dqnGdWopoFuwAEd74FY+sP+sDjeyKo/3aQoMChWhqTNQWAqcjNMjOl5vFtOltYCOLQUoFUBpOXD5hh6Hzuuhtf1Lp5lH77Q75AKS03X4+QHbHY91N7U7dv9lod3RQYKBHe2gdDC0O7YfVSEhtYFVHtUJm8lcPQhHR0c0adKkrovRYA3sJEX/MCm2HVZh5U+lKCjWYcZoOWTSytfx8xJh4jA5IuPU+PePJYiMU2PSMDl8PU2HXlgrCcb0kSEishyfbi1BQqoWL46yh4ujbaepE87vwcndHyOs/0sYM+sXePl3xt6NL6EoL9VifGHODezbOANe/p0xZtYvCO3/Ik7s+giJF/cZYyIjPsPl0z+hx6h38cScXWjT9Rns/7/ZyEqNqa3dqhXDuskxuIscWyKKsXRjAQqKdJjzjBNkdpWvE9hMgumjHXHyogr/WpePkxdVeHG0I/y9xRVi/bzE6BMmQ0qmpgb3onaNe7QJxgxxw9c/pGPeh4nIzdfgX3N9YS+r/GteZicgPascG3/JRE6e5U7oHa395RjW1xWJKWXWLnqdEDsoUHA+DpdeW/JA8fb+LdDltzXIORaFY13G4Nqyr9Fu5bvwGjvUGOPSPQwdf1iJm5t34mjn0bi5eSc6/bgKLl0tdzxs0bEjB7F+7WqMe+YfWP75twhp3wEfLHwTtzItd7Zlcns8NnIsPlj2GT7/eiOefOZ5/LDpO+z7/TdjjFqtxqJ/zkdmRjreeGcxvlizCTNfnQ+3Ju61tVs1KsRHwOAwAcdj9Vi3T4cbWXo800cEpcJyvLMD8HQfEW5kGeL/itVjSJiA4OammHa+Avo/IuBYjB5r/9Bhz2kdQnwMyxqSQZ2k6N9Rim1HVFixtRQFJTq8fJ92h7+XCJMelSPyshqf/FiCyMtqTH5UDr+72h0dW0sw9k67Y4uh3fFSA2h3PCy9XqizV0NULztXkydPxuHDh/HZZ59BEAQIgoCkpCQAQFRUFMLDw6FQKNCzZ0/ExcUZ17M0LHDdunVo164dZDIZvL29MWvWrEo/d8mSJfD09MTZs2cBAP7+/vjoo48wdepUODk5wdfXF2vWrDFb5+bNm3jmmWfg6uqKJk2aYPTo0cayAobhjF27doWDgwNcXFzQq1cvXL9+HQBw7tw5DBgwAE5OTlAqlejcuTMiIyMfvuJqWL9QKSIiy3EhQYv0HB1+2K+CnURAp6DKE6D9QqW4kqLFgSg1MvP0OBClxpUbWvQLNX0z9g+T4mSMBidjNMjM1WPHsXLkFenRq0MV35424OKxjQjq/ASCuzwFF4+W6D7yHTg4eyH25BaL8bGntsDBxRvdR74DF4+WCO7yFII6P4ELR9cZY+Kjf0VovxfhE9wPSjcfhHR/Ds1b98bFYxtqaa9qx6Aucvx+vBTRV9RIzdJiw+5i2EmBrm1lVawjQ2yiGn+cKENGjg5/nCjD5etqDOoiN4uTSYFpjzti0+/FKCnTV7I12zN6kBu27snCX9GFuJ6qwor1qZDZidCvm7LSda4mlWH9z5k4croAak3ldSGXCZj/QnN88X0aikqqzh7ailt7j+DKwlVI3xHxQPF+Lz6LsuQ0xLz+EYouJyBl3c9I2fALAudNNcYEzJ6ErP3HEf/JGhTHJSD+kzXIOngC/rMn1dRu1Lrftv8Xg4Y+hiHDRqKFrx+mvTgbTdw9sHfPTovxgS1bo0//QfD1C4CHpzf6DRyKsE5dEHvpvDHmYMQeFBUW4u33PkBI2w7w8PBCSLtHEBDYqrZ2q0Z1DRJwLlGPc4l6ZBcC+8/qUVAKdGxpuWHZsaWAghJDXHYhDOsm6dEt2NRka94EuJEFxCTrkV8CJGYY/u3t2rAaq33DpIg4XY7z8YZ2x+YIFeykAjpX1e4IM7Q79kepkZmrx/477Y6wiu2OEzEaZOTqsf2ood3R28bbHVQ/1MvO1WeffYYePXpg+vTpSEtLQ1paGnx8fAAA7777LpYvX47IyEhIJBJMnTq10u189dVXeOWVV/Diiy/iwoUL+PXXX9GqVcUva71ej9deew3fffcdjh07ZtZBW758OcLDwxEdHY2ZM2fi5ZdfxuXLlwEAJSUlGDBgABwdHXHkyBEcO3YMjo6OePTRR1FeXg6NRoMxY8agX79+OH/+PP766y+8+OKLEATDl9+ECRPQokULnD59GlFRUXj77bchldbPE7uJUoDSQYS4ZFPDSqsDrt3UIsBCZuAOfy+x2ToAEJeshb+XYR2xCGjhIUJcinkGIS5FY4yxRVpNObJSL6F5615my5u36oXM69EW18lMPovmre6Jb90LWTcvQadVG7crlpp3MCRSGTKSoqxY+rrl7iyCs6MIMUmmTIpGC1xJ0aBl88ovqIHNJGbrAMClRHWFdZ4b6oAL8Wpcvt5wslae7lK4uUgRfanYuEyj0ePilRKEtKzk9ng1vDzeG6fPF+FcbPH9gxsol+5huLX/T7Nlt/YdhXPn9hAkhmPMtXsYsvYfM4vJijgK1x4da62cNUmtViP+WhxCO3YxWx7WqQsux156oG0kxF9FXOxFtO0Qalx2+uRxBLdpi7VfrsKUCWPx2szJ+Hnr/0Grtf2OvEgEeLkCiRnmNy8S0/Vo0cRyR6h5EwGJ6RXjvdwA0e1VUrIM2/V2M7x3cQBaegu4ltZwbhg1UQpwdhDhsoV2h6URCXf4e4nN1gGAyxbaHZeTNffEaKrcbkOmh1Bnr4aoXj5z5ezsDDs7OygUCnh5eQGAsUPz4Ycfol+/fgCAt99+GyNGjEBZWRnkcnmF7XzwwQd4/fXX8dprrxmXdeliflHQaDSYOHEiIiMj8eeff6JFixZmf3/ssccwc+ZMAMBbb72FlStX4tChQ2jTpg22bNkCkUiEb7/91thhWr9+PVxcXHDo0CGEh4cjPz8fI0eORMuWLQEAISEhxm0nJyfjjTfeQJs2bQAArVu3rrJeVCoVVCrz5ydkssrv5FuTk8Kwf4Wl5l/cRaV6uDpVfnI4KYQK6xSW6qF0MKzjYC9ALBJQWHJPTIkeSoXtnnRlJXnQ67SwdzQf1mLv1ASlVy0PtC8tzIJ9kPmwVntHd+h1GpQV50Kh9DBmqbz8w6F080Vq/F+4HnsQep3tN0LuUDoa7vkUFJuPfS8s1sFNWfmFT+koQkGx+XFUUKyH0sF0Dyk8xA6+nmJ8tLHAiiWue67Ohq/yvALzxkJegQYeTf7eDZu+XZRo6SvH3A8T/9Z2bJ3M0x2qDPNztzwzGyKpFHburlCl34LMyx2qjGyzGFVGNmReTWuzqDWmsCAfOp0OLi6uZsudXVyRl1v5s3oA8MLEJ1GQnw+dTounx0/GkGEjjX/LSE/FhYx09O0/BP9c9DHSUm9gzVefQafV4unxtp31U9gBIpGA4ntG0xarAIeKzRYAgKMcSLjnUcniMkAsEmAvM/w7NkUPhQx4foAIEAx/O3NNhxOXG07nqrJ2R2GJHm73a3dYalPcr91RatvtDqo/6mXnqiqPPGIau+7t7Q0AyMzMhK+vr1lcZmYmUlNTMWjQoCq3N3fuXMhkMpw4cQLu7hXHd9/9eYIgwMvLC5mZmQAMQxSvXbsGJycns3XKysoQHx+PoUOHYvLkyRg2bBiGDBmCwYMH4+mnnzaWe968eXjhhRewadMmDB48GE899ZSxE2bJ0qVLsXjxYrNlCxcuBNznV7mPD6NTkARP9zd13NbuKjX8w8L3tv4+3+WW/n7vMkubaBCXiHu/p/V6CwvvDq+wwu0/GJZ3H/kOjm1/H9tWjgAEAUo3HwR1GosrZ7Zbrci1rWtbO0x41MH4fvV/CwFUdlzd72Azf3t3bbo6ifDMYAU+21oIjY33Rft3U+KVfzQzvl/8RTKAirUj4P7nZ1XcXSWY/qwX3l95vcphg43GvZV5+7w0W24p5u/8T6iH7txMNNLrce+ie334yRcoKyvFlcsx2LRhDby9m6NPf8P1WafTw9nFFTNmvw6xWIyWrYORk5ONHdu22HznqjL3bcLf55DxbQr0DBGw94weqTl6uDoCg8NE6NUW+DPGNo+3zkESPD3A1O5Y85vldocgPED7wMK14H6noVBxtUajoT77VFdsrnN197C5O1/wOl3F2V3s7e0faHtDhgzBjz/+iL1792LChAlVft6dz7zzeTqdDp07d8bmzZsrrNe0qeFO5fr16/Hqq6/ijz/+wNatW/HPf/4TERER6N69OxYtWoTx48dj9+7d+P3337Fw4UJs2bIFY8eOtVjWBQsWYN68eWbLZDIZ3l5b9YPoD+NSogafZphaoBKxoa6dFAIK7rrb42gvoKi08q8jSxkoJ3vTHaPiUj20OgsxFu482RK5wgWCSIzSQvM73aVFObB3tDzpir2TO0oqxGdDEEkgV7gYYhzdMOT51dCoVVCV5EGh9MDpvcvh5NrcwhZtw7lr5UhcZ8q4SCSGY8HZUYSCYtMx6ORQMTN1t4IiHZT3PIzs5CAYM2C+XmIoHUR4Z7LpGSSxSEBrHwn6d5bhlX/n2kwb+OTZIsQlxBvfS6WG7JyrUoLcfFNdOislFbJZ1dHKzx6uSglW/TPQuEwsFtCutQIjB7hh7Mux0NlInf1dqoysChkou6Zu0KnVKM/OM8SkZ0HmZX6TTubhViHjZauclM4QiUTIvSdLlZ+fB2cXtyrX9fQy3FT08w9EXl4Otv6wwdi5cnVrAolYDLHYlJlu4eOHvNwcqNXqejtc/kGUlBs6j/dmqRS3M1CWFJVVzGo5yAGtTo/S2xmtvu1FuHjd8BwXYJiBUCrWY3i4YLOdq4uJGlx/wHZHVe2DwhI9nBzMrwWOiortDqd72h332y7Rg6q3nSs7O7u/Nd7ayckJ/v7+OHDgAAYMGFBp3OOPP45Ro0Zh/PjxEIvFePbZZyuNvVenTp2wdetWeHh4QKms/KHxjh07omPHjliwYAF69OiBH374Ad27G6aXDQoKQlBQEObOnYvnnnsO69evr7RzJZPJKhkGaP3OlUoNqPLv/pLRo6BYh2AfMW5mGRqrYhHQqrkYvx2vfKrnpHQtgnzEOHzOVMZgXzGS0g3/b7U64EamDkE+ErNpUoN8JLiYaLvPxIgldnBv1g43rx2Hf7shxuWp147Dt+1Ai+t4+IYhJfaQ2bKbV/+Ee/N2EInNGxcSqQwSZ0/otGokXYxAYIdHrb4PtUVVDtwqN79Bkl+kQ4i/FCm3L7RikeGY+OVQaaXbSUjVIMRfigOnTcdjW38p4m8ajqPL19VY/G2+2TqTRjggPVuLvSfKbKZjBQClKh1Kb5nXWU6eGh3bOiDh9mx+EjHQPkiBDdsefsr0c7HFeGVhvNmy16Y0w400Fbb9kd1oOlYAkHfiLDxGmF9Lmg7pjfyoi9BrDMdY7omzcB/UC4mfbTTGuA/ujdy/LD9naWukUilatgrGuehIdO9p+vmHc9GR6Nq9VxVrmtPrAbW63Pi+Tdv2OHpoP3Q6HUQiw42C1JspcHVrYtMdKwDQ6YD0XCDAU8CVm6YTJsBTwJVUyyfQzWw9Wnub51ECPAWk58B4zknEFbMsd77DbDVZaqndkV+sQ7CvhXbHn1W3O4J9xDh81tTuaGOh3RF8T7sj2FeCiwm22+6g+qNeTmgBGGbqO3nyJJKSkpCVlWUxO3U/ixYtwvLly/H555/j6tWrOHPmDL744osKcWPHjsWmTZswZcoU/Pzzzw+8/QkTJsDd3R2jR4/G0aNHkZiYiMOHD+O1117DjRs3kJiYiAULFuCvv/7C9evXsW/fPly5cgUhISEoLS3FrFmzcOjQIVy/fh1//vknTp8+bfZMVn1z+Jwag8Pt0CFQDC83EZ4bLEO5Ro8zV0xfRuMHyzCih2m+7CPn1Aj2FWNgJyk8XAQM7CRFUAvzztahs2p0bytB1xAJPFwFjOltB1dHAccvWr/TWJva956EK5HbcCVyG/Iy43Fi91IU5aehTddnAACn967A4f++ZYwP6fosivJScWL3x8jLjDesG/ULOvQxTdqSmXIOSRf3oSAnBemJkfhj/YuAXocOfafV+v7VpAOnyzC8hxxhQVI0cxdj8ggHlKuBUzGmC+rkkQ4Y08+UoT4QqULbACmGdZPD002EYd3ktztbhs6GqhxIzdKavVRqPYpL9Ui9z+9n2YKdB3Lw1GPu6NHRCX7NZJgzpTlU5TocPml6vmze1GaYNNbD+F4iBgJ8ZAjwkUEiEdDEVYIAHxm8mxoatKUqHa6nqsxeKpUOhcVaXK/i97NsgdhBAWVoGyhDDc+8KgJaQBnaBnIfQ4Yl+IN5CF2/zBh/fc0W2Ps1Q8i/34Zjm0C0mDwOPlPGIWGFaTbPpNXfw31ILwTOnw6H4EAEzp8O90E9kPTFRjQUo8Y+hQP7duPAvj24kXwd69asRtatDAx97HEAwP9tWIPPln9kjP9913acPnkcqTdvIPXmDRyI+B2//rIV/QaYbjo9+thoFBYW4LtvvkDqzRREnvoL237ajOEjxtT27tWIU1f0CA0Q8EiAgCZOwKAww29T3fndqn4dBIzsasqiRMfroXQABoUa4h8JEBAaIOBknKkddC1Nj04tBYT4CHB2APw9gb7tBVxN1dtkx6oyR86qMeSudsf4wTKUq/WIuqvdMWGIDCPvanccPmtodwzqJIWHq4BBd9odZ+9pd7SToFuIBJ53tTv+tPF2x8PihBbWVW8zV/Pnz8ekSZPQtm1blJaWYv369dXexqRJk1BWVoaVK1di/vz5cHd3x5NPPmkx9sknn4ROp8Pzzz8PkUiEJ5544r7bVygUOHLkCN566y088cQTKCwsRPPmzTFo0CAolUqUlpbi8uXL2LhxI7Kzs41Twb/00kvQaDTIzs7GxIkTkZGRAXd3dzzxxBMVnqmqTw6eUUMqEfBkPxnsZQKuZ+jw9c4yqO76LnJ1EkGvN10AktJ12LS3DMO7yzC8mx2y8/XYuLcMyRmmmLPXNHCQA8O6GH7MLy1bhzW7SpFbaNtXiMBHHkNZSR6iD36JksJbcPVsjaGTvjYO4SstvIWivDRjvJNbCwyd9DVO7vkYsSd+gELpge4j30FAe9Pv6GjVKkRFfI7C3BRI7BTwCe6Lfk8vg8y+8sypLdp7sgxSqYDxQx0MPyKcqsFnWwuhMt3shptSZNaISLipwbc7izC6rwKP97XHrVwd1u4sQlKa7XecHsS2P7Ihk4rw8ngvODqIEZdQivdXJqNUZTrXmrpJzbJNbi5SfPG+6TnPccPcMW6YOy7EFWPBp9drs/i1zrlze/Q4sMn4vu2n7wAAUr7/BeenLYDMuynsb3e0AKA06QZOj3oRbZcvgN/LE6BKzcSluR8ifbvpd+hy/4pG9IR5CF48B8GLX0VJfAqix89F3inTtOO2rnffgSgsKMBPP25Ebk4OfP0C8O7iZfDwMEw+lZuTjaxbpmypTqfH/21cg8z0dIjFYnh6N8M/Jr+IocNHGWPcm3pg4b8+xbq1qzH3lalwa9IUIx4fh7FPPlfr+1cTYlP0sLcDerUV4CgXcCsf+OmoDgW3f7vcUY7bQ+MNJ2d+seHvg8NE6NRKQFEpEHFWj7ibpm3+GaMH9EC/9gIc7QWUqAwdrsMXbPu6ea8Dd9od/WVQ3G53fHVvu8OxYrvj+z/K8FgPGYZ3N7U7rt/V7oi+qoFCDgzramp3fPOb7bc7qH4Q9PqGdI+jcZq7uqiui2BzVs5yxCfb+Evs1fHmOBFe+rjqGcGoom/edsPI6Q3rR55r2q61bbFbGlzXxbA5I9RxuHQt7f6BZNSulTeW/tQ4bsBY04KnxZjzBdse1bFqtmNdF6FSpy7n3z+ohnRt41xnn11T6u2wQCIiIiIiIlvCzhUREREREZEV1NtnroiIiIiIqGbxIQnrYuaKiIiIiIjICpi5IiIiIiJqpPT6hjklel1h5oqIiIiIiMgKmLkiIiIiImqkGuqP+dYVZq6IiIiIiIisgJ0rIiIiIiIiK+CwQCIiIiKiRooTWlgXM1dERERERERWwMwVEREREVEjxQktrIuZKyIiIiIiIitg54qIiIiIiMgKOCyQiIiIiKiR0unrugQNCzNXREREREREVsDMFRERERFRI8UJLayLmSsiIiIiIiIrYOaKiIiIiKiR4o8IWxczV0RERERERFbAzhUREREREZEVcFggEREREVEjpedU7FbFzBUREREREZEVMHNFRERERNRI6TgVu1Uxc0VERERERGQF7FwRERERERFZAYcFEhERERE1UvydK+ti5oqIiIiIiMgKmLkiIiIiImqkOBW7dTFzRUREREREZAXMXBERERERNVJ6TsVuVcxcERERERERWQE7V0RERERERFYg6PV8jI2IiIiIqDH642x5nX32o2F2dfbZNYXPXDUAc1cX1XURbM7KWY5Y/H/qui6GTVn4DymWbNbUdTFszvsTJOg96nBdF8OmHPutHy5dS6vrYticdq28sVsaXNfFsCkj1HGYsSy3rothc75+y5Vtj2paOcuxrotAtYSdKyIiIiKiRoo/ImxdfOaKiIiIiIjICti5IiIiIiIisgIOCyQiIiIiaqQ4tZ11MXNFRERERERkBcxcERERERE1UjpwQgtrYuaKiIiIiIjICti5IiIiIiIisgIOCyQiIiIiaqQ4oYV1MXNFRERERERkBcxcERERERE1Uno9J7SwJmauiIiIiIiIrICZKyIiIiKiRkrHZ66sipkrIiIiIiIiK2DnioiIiIiIyAo4LJCIiIiIqJHiVOzWxcwVERERERGRFTBzRURERETUSOnBqditiZkrIiIiIiKq97788ksEBARALpejc+fOOHr0aKWxv/zyC4YMGYKmTZtCqVSiR48e2Lt3b42XkZ0rIiIiIiKq17Zu3Yo5c+bg3XffRXR0NPr06YPhw4cjOTnZYvyRI0cwZMgQ7NmzB1FRURgwYABGjRqF6OjoGi0nhwUSERERETVStvI7VytWrMC0adPwwgsvAABWrVqFvXv34quvvsLSpUsrxK9atcrs/UcffYSdO3fit99+Q8eOHWusnMxcERERERFRrVOpVCgoKDB7qVSqCnHl5eWIiorC0KFDzZYPHToUx48ff6DP0ul0KCwshJubm1XKXpn/b+/Ow6Iq3z6Af88MMOybgKAiiwuCGyruC+5Wpqj1c31dQi01y92yci3TMpcylzI1s7TMSsu0XMFdY3NFUEFBBQER2beZ8/4xMTgyoNjAGeD7ua65LufMMzP3eTycOc+5n4WNKyIiIiKiGkoUpXssW7YMNjY2Wg9dWaiUlBQolUrUrl1ba3vt2rWRmJj4TPu5cuVKZGVlYejQoXqpt9KwWyAREREREVW6efPmYebMmVrbFApFqeUFQXtmQ1EUS2zTZefOnVi0aBH27t0LJyen5wv2GbFxRURERERUQ0m5iLBCoSizMVXEwcEBcrm8RJYqKSmpRDbrST/99BPGjx+Pn3/+Gb179/5P8T4LdgskIiIiIiKDZWJigjZt2uDQoUNa2w8dOoROnTqV+r6dO3di3Lhx2LFjB/r371/RYQJg5oqIiIiIiAzczJkzMXr0aPj5+aFjx474+uuvERcXh0mTJgFQdzG8e/cuvvvuOwDqhtWYMWPw+eefo0OHDpqsl5mZGWxsbCosTjauiIiIiIhqKJX49DFLhmDYsGF48OABlixZgoSEBDRr1gz79++Hm5sbACAhIUFrzauvvvoKhYWFePPNN/Hmm29qto8dOxbffvtthcXJxhURERERERm8KVOmYMqUKTpfe7LBFBQUVPEB6cDGFRERERFRDSXlhBbVESe0KKdvv/0Wtra2ZZZZtGgRfH19yyxz69YtCIKAiIgIvcVGRERERETSYeaqnIYNG4aXXnqpXO8ZN24c0tLSsGfPnooJqhL1a2eCjk2NYKYQEHdfhV+C85CYqirzPS0ayPFiewUcbASkPBKx/2weLsUotcp0bmaEHq1NYG0uIDFVhT0n8hCTUPbnVgV+jWXo5CODlRmQlAb8HaJEXHLpt4jcnAT0bSOHky2QkQ2cuqpC6PXiemjpKWBQp5J/th/tKICy6lcXAMCvkYCOj9XZwVAl4pJLL+/mBPRpXVxnpyNVCL2uXccKY6CnrwxNXAWYmQAPM4FDYSrcuFe9btcFjnDDwH4usLI0wtXoDKzaeB2xcdmllh/Q1xkv9HSGp5s5ACDqRia++i4WkdcztMoNfqkORgyph1p2CtyKy8Lnm27i4tVHFbovleHAvj3Y++uPeJj6AK71PRD4+lT4NGuhs2zklYv4buvXuHsnDvl5uXB0qo2+LwzEgMH/0yqXlZmBH77bjLOnjyMrMwNOtV0wbsIUtGnboTJ2qULZd/GD56zxsGndDKZ1nBDyyhTc//1I2e/p2hY+n70LS59GyLuXhJsrv0Hc1z9qlXEe3BeNF02DeYP6yL4Zh6gFq3F/7+GK3BXJvNzZFF1aKmBuKuBWQiF2HspGQkrZJ+9WjY0xsKsZHGxlSElTYe/xHERcL9C83s3XBN1aKVDLRg4ASEhR4s/TObgSU1ih+1IZyntt0KCODAFdFHC2lyE9S8TRsHycvqJdD89yTUL0X7BxVU5mZmYwMzOTOgxJ9GxtjO6+xthxOBfJaSL6+BljUoApln2fjbwC3e9xc5ZhTD9THDiXj0s3C9G8gRHG9jPFF7/mIO6++gTp29AIg7oqsDs4D7EJSnRqaozXB5hh+Y5spGVW3Yvfpm4CXmgjw5//KBGfJKJNIxlG9ZRj3R+FSNdxvWtrAYzsKUfYdRV+O6WCq5OA/m3lyM4VERlfXA+5+SK+/F37x6K6NKx83AT0ayPD/n9UiE8W0bqRDCN7yLF+n7LUOhvRQ46wGyL2nFbB1VHAS21lyMpV4dq/dSaTAf/XS12Pu0+oP8faHMgv5Zitqka94ophg+ph6ZooxN/Nxthhbli9pAVGTP4HOTm6LxxaNbfF4eNJuBT5CPkFKowa4opVS1pg9Jv/ICU1HwDQs4sj3p7QACs3Xselq+kIeMEFny1qjtFv/oP7yXmVuYt6dfL4UWzd9CUmTpkOb+/m+Puv3/HRwrn4fMM2ODqVXDNFYWqGl14eDDcPT5iamiLyyiVs/HIVFKam6PviAABAQUEBFn0wGzY2dpjz3mLUcnDEg+QkmJqZV/buVQi5hTnSL0bhzrZf0ebnL59a3sy9Htr+8TXiN/+MiLFzYNepNZqtXYj85FQk/nYQAGDbwRetdqxG9MLPkbj3MJwDeqP1zjU4030k0s5frOhdqlR92yvQq60ptu3PQlKqEi92MsO0oVZY+M0j5OXrfo9HHTkmBFjg9xO5iIjOh29jE0wMsMCKHzJwK0H9d/0wQ8Se4BwkPVT/EHRsZoLJQyyx9Nv0pzbcDFl5rw3srQRMHGCGs1cK8P2hXHi4yPGqvwKZuSIu3lTX1bNck9RE7BaoX+wWCOCPP/6Ara0tVCr1H1ZERAQEQcCcOXM0Zd544w2MGDFCZ7fA5cuXo3bt2rCyssL48eORm5ureW3RokXYtm0b9u7dC0EQIAiC1gC7mJgY9OjRA+bm5mjZsiXOnDlTofv6X/i3NMahkHxcilEiMVWFHYfzYGIkoHXj0tvo/i2NER2vxJHQAiSliTgSWoDoO0r4tzTWlOnua4xzVwtx7mohkh6K2HMyH2mZIjo3Ny71c6uCDt4yhN9UIfyGiJR04O9QFR5lA20b6/6z82ssw6MsdbmUdCD8hojwmyp09ClZPitX+1FddGwiQ/hNEeE31XV28N868yulzto0UtfZwaI6uykiPEZEJ+/i8q0aqLNVPwWrEJ8MPMoC4pOB+2mVtFOV5H8D6+K7XXE4fiYFsXHZWLr6GhQKOfr6l74S/ZKV1/Db/nu4EZuFuDs5+OTLaMhkgF9LO02Z4YPqYd+hROw7mIjbd7LxxTc3kZSSi0Ev1qmM3aowf/z2M3r1fQl9+r2MevXdMP71t1DLwQl/79+rs7xng0bo2r0X6rt5wKm2C/x79oVv67aIvFLcADh6aD8yMzLw7vyP4O3THE5OzvBu2gIeng0ra7cqVPLfxxG9cA0S9xx6emEAbq8PR25cAq7O+hiZ12IQv2U34r/9FZ4zAzVlPN4ai5TDp3Hz06+RFRWDm59+jZSjZ+H+1tiK2g3J9PIzxYEzOYiILsC9FBW2/ZkFE2OgnbdJme+JvFWIv8/m4n6qCn+fzcW124Xo5WeqKXPpZgEuxxQi6aEKSQ9V2HsiF3n5IjzqVO375+W9NujUzBhpGepySQ9FnLtaiPORhejRqrh+n+WahOi/YuMKQLdu3ZCRkYHw8HAAQHBwMBwcHBAcHKwpExQUBH9//xLv3bVrFxYuXIilS5ciJCQELi4uWL9+veb12bNnY+jQoXjhhReQkJCAhIQErcXO3n//fcyePRsRERFo3LgxRowYgcJCw0vl17IWYG0hQ1Rc8R1wpQq4cVcJDxd5qe9zd5ZrvQcAouKUcHdWv0cuA+o5yRAVr73PUfGFmjJVkUwG1LEXcDNB+3ZQTIIK9Rx1T3laz0Eo0d3hZoKIOrUEyB57i4kRMG2QEWYMNsKI7nI426FakMkAF3voqDMRrg6l1JmjgJgnyt+8J8KlFjR11riugDspIl5sK8PMIXJM6i9Hl6YChKox8+wzqVPbFA72CpwPf6jZVlAoIuJyGpo1sX7mz1Eo5DCSC0jPVKf1jIwENG5ohX/CU7XK/RP+EM28n/1zDU1BQQFu3ohCy1Zttbb7tm6La5FXnukzYm5eR1TkZfg0b6nZ9s+50/Bq4oNN69fgtVGDMW3KOOz+6XsolTWzy5FtB18kHz6ltS354AnYtGkGwUh94W/XwRcph09qlUk5dAJ2HVtVWpyVwcFGBhtLGSJji3/rCpXA9fhCeNYtvRHkWdcIkbHaafarsQXwrKv791EQAD9vY5gYC4i9a3jXEs/qea4N3J3lJcpfiyuEq6MMMtljZcq4JqmpVKJ0j+qoat/W0BMbGxv4+voiKCgIbdq0QVBQEGbMmIHFixcjIyMDWVlZiI6ORvfu3XH27Fmt965ZswaBgYGYMGECAOCjjz7C4cOHNdkrS0tLmJmZIS8vD87OziW+e/bs2ZoVoxcvXoymTZvixo0baNKkSYmyeXl5yMvT7oajUCj0UgdPY2WuvhLNyNH+S8jMEWFnVfpVqpW5UOI9GTkirC3U77EwEyCXCcjIfqJMtghr86p79WuuAGQyAZk52tszc4AGdXTvl6WZgMx7T9YvIJcJMDdV/zslHdhzRomkhyIUxgLaN5EhsJ8RNv5ZiNQMnR9bZRTVWVaudh1k5YqwMCulzkyhs7xcJsBcAWTmAnaWAjwsgUuxInYGKWFvJeDFtjLIBBWOX64eZ3Z7O/Wd2dQ07b5FD9PyUdvJVNdbdJo81gPJD/IREqFupNlYG8NILiA1TfviLjWtALVsS7/bbugy0h9BpVLB1lb7zoSNrR3SHqaW8i61CWNeRfqjR1CplBg6chz69HtZ89r9xHu4dD8R3br3wQeLliPh3h18veFzqJRKDB1Z/TIxT6Oo7YC8+yla2/KTHkBmbAwTBzvkJSZD4eyAvPsPtMrk3X8AhbNjZYZa4awt1eew9GztG2jpWSLsbUq/z21tISA968n3qGBtof2eOg4yzB1tDWMjIC9fxFe/ZSLhQdXt5vY81wZWFgIy4kqWl8sFWJoKSM8Wn3pNQqQPzFz9q3v37ggKCoIoijhx4gQCAgLQrFkznDx5EseOHUPt2rV1NngiIyPRsWNHrW1PPi9LixbFg6ddXFwAAElJSTrLLlu2DDY2NlqPZcuWPfN3lUfrxkZY/rqF5iEvOlJ0XIs+ra+urtef3KbrI6rHZa82QUC5dqwou1JUX3dTRFyKFXE/DYhLFvHzCSUepAPtvKr5n3IZdfbkS5o6e+x5Vi6w77wKCanAldsiTl5WoU0pXQ2rgj7+Tji4q4vmYWT0xE4XEYRnPt5GDnFF725OeH/ZFeQXaL/pyb/XcnysQROeTF+K4lMzmks/XYsVn3+FN96ciX17d+NEUPGEDiqVCBtbO0x6axYaNPJCF/9eeHXY/+GvUroa1gi6Dp4nt+s8wKr2EdbOxwRrZthqHvJ/U+k6q+Npv6FPPC9x3AK4n6rC0q3p+GR7Bo6H52Fsfwu41Kq657gi5b02KFFXOrY/yzVJTSOKgmSP6oiZq391794dmzdvxoULFyCTyeDj4wN/f38EBwfj4cOHOrsE6oOxcXE/36ITZtHYryfNmzcPM2fO1NqmUCjw7ib9j8y/EluIz+4Xp86N5OrYrMzVd3+KWJoJyMwp/ayk6y6TlVnx3aisHBFKlY4y5iXvWFUl2XnqCy3LJ+Y+sTAFMnN171dmju7ySpWInDLmDbj3QIR9GdnDqqKozixMta82LEyFUseVZeYClk+UN1cIWnWWmaOuw8d/PFPS1cehTAaU8udm0E6ef4Cr0SGa5ybG6osoezsTPHhYnL2yszEukc3SZcTgehj9v/qYPv8Cbt7K0mx/lF6AQqWIWnba4xGe9XMNlZW1DWQyGR4+kaV69CgNNrb2Zb63trP6JpibuyfS0lLx045v0bV7LwCAnX0tGMnlkMuLuxjVc3VD2sNUFBQUaJ3va4K8+yklMlAmjvZQFRQg/0GaukxiChTODlplFE72JTJeVc2FG/mIvVfcRe3fXpCwsZAhPav4t9XKvGRm6nHpWSJsnshS6XqPUgUkp6m3xSUq4eZihB5+ptjxd+mzhRqy57k2yMgqWd7SXIBSKWp6ODztmoRIH6r+bQ09KRp3tWbNGvj7+0MQBPj7+yMoKKjU8VYA4O3tXaKr4JPPTUxM9NLnXqFQwNraWutRUd0C8wqAlEei5pGYqkJ6lgpersUXDXIZ0LCuHLEJpe/brUQlGrtq92X2qi/HrUT1e5Qq4E6SCo1dtdv5jV2NNGWqIpUKuJcqwtNZ+yTu6SzDnVKmYr+TIsLTWftPsoGLgHsPxDL7Jde2R4nuh1WRSgUkpAKeLk/UmYuA+JRS6ixZLFG+gYuAhAfFfbnjk0s2Pu2t1D+yVbFhBQA5OUrcTcjVPGLjspGSmoe2vsXd3IyMBPg2s8Xla+llftaIwfUwdpgbZi+6iKgbmVqvFRaKiL6RgbattLvP+fna4XJk2Z9ryIyNjdGgoRcuhIdobb8QHoIm3k2f+XNEESgoKG5kNvFphoSEu1o3yO7djYedfa0a17ACgLSzEXDo1Ulrm2OfLngUehniv2OLH56NgEOvzlplHHp3wcMz4ZUWZ0XIy1c3dooeCSkqPMpUwdu9+LdOLgMauRohpoyxUTF3C+Htrn3seHsYI+Zu2b+PAgDjKjyM6HmuDdTXG9rlvVyNEJ+s0pzrn3ZNQqQPbFz9q2jc1ffff4/u3bsDUDe4wsLCNOOtdJk2bRq2bNmCLVu2IDo6GgsXLsSVK9oDot3d3XHx4kVERUUhJSUFBQVVcw7o4AsF6O1nguaecjjbyzCitwL5hSLCoot/GEb2VqB/x+KxGMcvFMCrvhw9WxvDyVZAz9bGaFxPjuALxXUQFFGADj5GaOdtBCc7AYO6mMDOUsDpy1WznoqcjVShdUMZfBsIcLAG+rWRwcYCCPl33apevjIM6lR8kg+JVsHGEujbRgYHa8C3gYBWDWQ4c7X4Qs2/uQwNXATYWgK17YCBHeRwthM0n1nVnbmmQusGAnw91XXWt7UMNubQrPXV01eGgI7Fp63Q6yrYWKjLOVgDvp4CWjUQcDqyuD5CrqtgpgBe8JPB3gpoVEdAl6Yy/BNdPeqsyM+/38Xo/9VHtw614FHfHO9P90JenhIHg4u7GX8wwwtvjPHQPB85xBUTR3tg2RdRSLifC3tbY9jbGsPMtLiOf9xzBy/3cUH/3s5wq2eOtyY0QG1HU+w5cK9S90/fBgz+H44c/BNHDu7Hnbjb2PL1l0hJvo++Lw0EAHz/7df4fOXHmvIH9v2Gf86dxr27d3Dv7h0cOXQAv//6E/x79NGUeeGlAGRkpGPzV2tx7248Qs6fwS+7fsCL/QdV9u5VCLmFOaxbNoF1S3UXeXOPerBu2QSmrupsntdHM9Fy6yea8re//hFmbnXgveJdWDbxRL1xr8D1tVcQs2qLpsytL7+DQ5/O8Jw9ERZenvCcPREOvTri1tptlbtzleBISC5e6GgK30bGqOMgw9j+FsgvAM5HFjfQx/U3x6BuxeMkj4bmwtvDCH3bK1DbXoa+7RXwdjPCkZDidH5AN1M0rGeEWtYy1HGQIaCrKRrXN8L5q1U3uww8/dqgf0cTjOxdfIP59OUC2FkJCOhiAic7Ae28jdDexwjHwovr4VmuSWoiUZTuUR2xW+BjevTogbCwME1Dys7ODj4+Prh37x68vb11vmfYsGG4efMm3nnnHeTm5uKVV17B5MmT8ffff2vKTJw4EUFBQfDz80NmZiaOHTsGd3f3Stgj/ToaVgBjIwGv+itgphBw+74KG/fmaq1xZWclgygWX7TeSlRh+9+5eLGDAi+2N8GDRyK2/Z2rtZ5ExI1CWJgC/dqawNpCQMIDFb7el4OHGVX7r+7KbRFmChX8m8th+e+CuD8cU+LRv72uLM0E2FgUl0/LAnYcVaKfnxxtG8uQkQMcCFFprXFlagK83F79eXkFQEKqiG8PKnHvQdWuqyJXb4swN1GhW3OZps52BD1WZ6aAzWMDj9OygJ3HlOjbRg6/xnJk5AB/hRavcQUA6dnAD0fVZSb1lyM9GzgfpcKpq9Wjzor88Es8FCYyzJzcCFaWxrganY4ZCy5qrXFV29FUKws6+KU6MDGWYek87WzNlh23sGXnbQDA0ZPJsLE2xrjhbqhlb4LY21mYs/hSlV7jCgC6dOuJjPR07Nq5DQ9TU1HfzQPvL/4ETk7qiYcepj5ASvJ9TXmVSsT3275GUmIi5HI5arvUwf+Ne12zxhUAODg6YeGHn2HLpi8x481A2NdyRP+Br2DwqyMqff8qgk2bZuh4ZLvmuc9n7wEA4r/7FRfHz4PCxRFm/za0ACDn1h38M+B1+KycB7fJo5B3LwlXZizVrHEFAA/PhCN81Ex4LZ4Or8VvI/tmPMJHzqh2a1wBwMFz6uVLRvQ1h7mpgNh7hfhiV4bWGlf21jKtC86Yu0ps/j0LA7uaYWBXMySnqbDp9yzNGlcAYG0hw2svm8PaQoacPBF3k5VY+3MmIm9V3dkCgadfG1ibC7CzKr4RlJohYtMfORjURYEuzY3xKEvEb8fzNGtcAc92TUL0XwmiWF3bjTXHjC8zn16ItKyeaonF39fsO1XltfD/jLHkh6r9Yy2FBaOM0GVA8NMLksbJP/xx5UaC1GFUOU0buuBPYy+pw6hS+hdEYdInD6UOo8rZ+I4drz3KafVUS6lDKNW3QdJ997ju0n13RWG3QCIiIiIiIj1g44qIiIiIiEgPOOaKiIiIiKiG4gAh/WLmioiIiIiISA+YuSIiIiIiqqGYudIvZq6IiIiIiIj0gJkrIiIiIqIaSsXMlV4xc0VERERERKQHbFwRERERERHpAbsFEhERERHVUJzQQr+YuSIiIiIiItIDZq6IiIiIiGoolUrqCKoXZq6IiIiIiIj0gI0rIiIiIiIiPWC3QCIiIiKiGooTWugXM1dERERERER6wMwVEREREVENxcyVfjFzRUREREREpAfMXBERERER1VAqZq70ipkrIiIiIiIiPWDjioiIiIiISA/YLZCIiIiIqIYSJZ3RQpDwuysGM1dERERERER6wMwVEREREVENxanY9YuZKyIiIiIiIj1g44qIiIiIiEgP2C2QiIiIiKiGUqmkjqB6YeaKiIiIiIhID5i5IiIiIiKqoTihhX4xc0VERERERKQHzFwREREREdVQKmau9IqZKyIiIiIiIj1g44qIiIiIiEgPBFHkMDYiIiIioppo5R7pmgKzBgmSfXdF4ZiramDGl5lSh1DlrJ5qiUmfPJQ6jCpl4zt2eCnwktRhVDn7tzTHgDcipQ6jSvnjK28s26WUOowqZ95QOc9r5bTxHTv8aewldRhVTv+CKF57lNPqqZZSh0CVhI0rIiIiIqIaSpR0Rovql7nimCsiIiIiIiI9YOOKiIiIiIhID9gtkIiIiIiohuI6V/rFzBUREREREZEeMHNFRERERFRDcVEm/WLmioiIiIiISA/YuCIiIiIiItIDdgskIiIiIqqhVJzRQq+YuSIiIiIiItIDZq6IiIiIiGooTmihX8xcERERERER6QEzV0RERERENRQzV/rFzBUREREREZEesHFFRERERESkB+wWSERERERUQ6nYL1CvmLkiIiIiIiLSA2auiIiIiIhqKFEldQTVCzNXREREREREesDGFRERERERkR6wWyARERERUQ0lckILvWLmioiIiIiISA+YuSIiIiIiqqFUnNBCr5i5IiIiIiIi0gNmroiIiIiIaiiOudIvZq6IiIiIiIj0gI0rIiIiIiIiPWC3QCIiIiKiGkrFXoF6xcwVERERERGRHjBzRURERERUQ4lMXelVtcpcubu7Y82aNVKH8VyuXbuGDh06wNTUFL6+vlKHQ0RERERE5cTMVRnGjRuHtLQ07Nmzp8K/a+HChbCwsEBUVBQsLS0r/PueV792JujY1AhmCgFx91X4JTgPiallrz7XooEcL7ZXwMFGQMojEfvP5uFSjFKrTOdmRujR2gTW5gISU1XYcyIPMQnVY1W7lzuboktLBcxNBdxKKMTOQ9lISCl731o1NsbArmZwsJUhJU2FvcdzEHG9QPN6N18TdGulQC0bOQAgIUWJP0/n4EpMYYXuS2UaFeCEF/ztYWkuR1RMNtZ/fw9x9/JKLV+/jgKjB9VGQ3cz1HYwwVc772HvoQclPnNUQG2tbamPCvB/M65VyD5UthEvO6BfV1tYmssRHZuDjTsTEZeQX2r5+i4mGDXQEQ3qm6K2gwk27UrE70ceapV5sZstXvS3Q+1axgCAuIQ8/LgvBaFXsip0XypD6wYC2nsJsDQDkh8BhyNUuJNSenlXR6BXSxkcbYCMHOBclIjwm9p3fNs2EtCqgQBrcyAnH7h2R0TQRRHK6nE60+B57dnZd/GD56zxsGndDKZ1nBDyyhTc//1I2e/p2hY+n70LS59GyLuXhJsrv0Hc1z9qlXEe3BeNF02DeYP6yL4Zh6gFq3F/7+GK3JVKV95rgwZ1ZAjoooCzvQzpWSKOhuXj9BXt4+dZrkmI/guDy1zl55d+IVAVFRQUPL0QgJs3b6JLly5wc3NDrVq1Kjiq59OztTG6+xrjl+A8rN6Vg/QsFSYFmEJhXPp73JxlGNPPFCFRBVixMxshUQUY288U9WsXH3q+DY0wqKsCh0Ly8dlP2Yi5p8TrA8xgaylUwl5VrL7tFejV1hQ/Hs7G8u/S8ShLxLShVlCYlP4ejzpyTAiwwNkr+fhoazrOXsnHxAALuLvINWUeZojYE5yDZdvSsWxbOqJuF2DyEEu4OBjcn/RzefVFBwzu64AN39/D9A9v4OGjQiyd7QEz09L3T2EiQ0JyPrbuTkRqWul/d7fu5GLU9EjNY8qC6xWxC5XulX61MKi3Pb768T5mLruFh+mFWDK9PswUZddZYkoBtv2WjNRHui9gU9IKse23JMz4+BZmfHwLF69l4/0prqjvUsZBXAV4uwro7SvgdKSILQdVuJMiYlhXGazNdZe3sQCGdpXhToq6/JlIEX18BXjVLS7TtL6A7i0EnLwqYtNfKuz/RwVvV/W26oTntfKRW5gj/WIUrkxb8kzlzdzroe0fXyP1ZChOth2EG59sRNPV78N5cF9NGdsOvmi1YzXu/rAXJ9oE4O4Pe9F65xrYtmtRUbtR6cp7bWBvJWDiADPE3FPis5+ycSg0H4O7KdCiQfEx9izXJDWRKEr3qI4q/Gjq3r07pk6diqlTp8LW1ha1atXCBx98oFmwzN3dHR999BHGjRsHGxsbTJw4EQDwyy+/oGnTplAoFHB3d8fKlSu1PjcpKQkDBgyAmZkZPDw88MMPP2i9fuvWLQiCgIiICM22tLQ0CIKAoKAgzbYrV66gf//+sLa2hpWVFbp27YqbN29i0aJF2LZtG/bu3QtBEEq8T5ei79y1axe6d+8OU1NTfP/99wCArVu3wtvbG6ampmjSpAnWr1+veZ8gCAgNDcWSJUsgCAIWLVpUzlquHP4tjXEoJB+XYpRITFVhx+E8mBgJaN249ASof0tjRMcrcSS0AElpIo6EFiD6jhL+LYtbZN19jXHuaiHOXS1E0kMRe07mIy1TROfmZbTaqohefqY4cCYHEdEFuJeiwrY/s2BiDLTzLv0qpJefKSJvFeLvs7m4n6rC32dzce12IXr5mWrKXLpZgMsxhUh6qELSQxX2nshFXr4IjzrVIxk9qI8DftyXhNNh6bh9Nw8rN9+BwkSG7u1tS33P9Vs52PJzIo6ff4SCwtLP2EqViIfphZpHekb1uGM5sJc9dh14gDPhGYi7l4fV3yZAYSKDfzvrUt9z/XYutv6ShBMh6Sgo0H03+J+LmQi9nIV7Sfm4l5SP7XuTkZungpenWUXtSqVo11jAhVgRF2JFPMgADkeISM8BWjXQfeHWqoGA9Gx1uQcZUL/3loj2XsU/o3VrAXdSgKtxIh5lA7H31f92satejSue18on+e/jiF64Bol7Dj1TebfXhyM3LgFXZ32MzGsxiN+yG/Hf/grPmYGaMh5vjUXK4dO4+enXyIqKwc1Pv0bK0bNwf2tsRe1GpSvvtUGnZsZIy1CXS3oo4tzVQpyPLESPVsXH5bNckxD9V5XSVN+2bRuMjIxw7tw5fPHFF1i9ejW++eYbzesrVqxAs2bNEBoaivnz5yM0NBRDhw7F8OHDcenSJSxatAjz58/Ht99+q3nPuHHjcOvWLRw9ehS7d+/G+vXrkZSUVK647t69i27dusHU1BRHjx5FaGgoAgMDUVhYiNmzZ2Po0KF44YUXkJCQgISEBHTq1OmZPvedd97B22+/jcjISPTr1w+bNm3C+++/j6VLlyIyMhIff/wx5s+fj23btgEAEhIS0LRpU8yaNQsJCQmYPXt2ufajMtSyFmBtIUNUXPGFqFIF3LirhMdjdx6f5O4s13oPAETFKeHurH6PXAbUc5IhKl77rnlUfKGmTFXlYCODjaUMkbHF+1aoBK7HF8KzbukXC551jRAZq515uRpbAM+6uutDEAA/b2OYGAuIvVu1u88AgLOjMextjRF2JVOzrbBQxKWoLHg3LCWtUA51ayuwfVUTbPnEC++84Qpnx6r/o1rbwRj2NkYIv6pdZ5ejs9Gkgf4aQTIB6OpnDVMTAddicvT2uZVNJgOc7YDY+9qN8NhEEfVq6W4I1a0lIDaxZHlne3W9AEB8ivpzXezVz20tgAYuAm4kVJ/bszyvVTzbDr5IPnxKa1vywROwadMMgpG6ju06+CLl8EmtMimHTsCuY6tKi7MiPc+1gbuzvET5a3GFcHWUQSZ7rEwZ1yQ1lUolSvaojirldpCrqytWr14NQRDg5eWFS5cuYfXq1ZosVc+ePbUaFKNGjUKvXr0wf/58AEDjxo1x9epVrFixAuPGjUN0dDQOHDiAs2fPon379gCAzZs3w9vbu1xxrVu3DjY2Nvjxxx9hbGys+a4iZmZmyMvLg7Ozc7k+d/r06RgyZIjm+YcffoiVK1dqtnl4eODq1av46quvMHbsWDg7O8PIyAiWlpZlfldeXh7y8rTHnCgUinLF9ryszNVXDxk52n8ImTki7KxKvytrZS6UeE9GjghrC/V7LMwEyGUCMrKfKJMtwtq8at/ttf6360J6tnZGID1LhL1N6fc1rC0EpGc9+R4VrC2031PHQYa5o61hbATk5Yv46rdMJDyo+gM77KzVf4tp6do/kmnphXCq9d8aQlEx2Vj5TTzuJubB1sYIw192wmfvNcDkD64jI6vqZrDsrNWn8rR07X1IyyiEk/1/bzy61VFgxTvuMDEWkJOnwtKNdxBfxlguQ2duAshkArJytbdn5QEWprrfY2kKxDwx5C8rF5DLBJgp1P+OjBdhrgBG95ABgvq1sBsqnL1WfS4geF6reIraDsi7rz34Lz/pAWTGxjBxsENeYjIUzg7Iu689pjTv/gMonB0rM9QK8zzXBlYWAjLiSpaXywVYmgpIzxafek1CpA+V0rjq0KEDBKH4wO3YsSNWrlwJpVJ9IeDn56dVPjIyEgEBAVrbOnfujDVr1kCpVCIyMhJGRkZa72vSpAlsbW3LFVdERAS6du2qaVjpy+NxJScnIz4+HuPHj9c0JgGgsLAQNjY25frcZcuWYfHixVrbFi5cCDjoP9PVurERhnYvbrht2vfvXWod1whP6zOr6/Unt+n6iKp2OdLOxwQj+xVnVtbtVmcRntxXQcBTd+7Jlx//+ylyP1WFpVvTYWYqoHVjY4ztb4FVOzKq3IVI9w62eGtMHc3zhWtuA9BRbzq2lVfIpeLMDu7mIfLGLWz+xAu9O9vht4NlzGRgYPzbWePNUS6a50u+jAdQSp3p4fvu3s/DtI9iYGEuR6dWVpgxrg7mrbxdpRtYujz18uoplVnfEejkLeDvMBH3UkXYWQK9fWXo7AOculrVzmhqPK9JRGcFP7FdV5lqNoilvNcGJY4xHduf5ZqkphFregXomUF0ZLawsNB6LopiiZPu4//xRf/WdWIuIvs3B/z4+56cXMLMrGLGDDy+PyqV+gdh06ZNmixbEbm8fGnoefPmYebMmVrbFAoF3t30bJNmlMeV2EJ8dr/4LriRXF3XVubquz9FLM0EZOaU/kep6y6TlVnx3aisHBFKlY4y5iXvWBm6CzfyEXuvONvyb+8N2FjIkP5YVsTKvOQd3MelZ4mweeJurq73KFVAcpp6W1yiEm4uRujhZ4odf2f/112pVOci0hEVUxyzsZH6WLCzMcLDxyZZsLE2KpHN+q/y8kXcvpOLOrWr1uQM5y9kIjo2RvO8uM7kePhYHdlY6afOCpVAQnIBgALcuJ2LRu5mGNjTHut+SPzPny2F7Hx1N5gns1Tm/2agdMnMLZnVsjBVj+HL+Tej1a2ZDJdvq8dxAeoZCI3lIl70E6ps44rntcqXdz+lRAbKxNEeqoIC5D9IU5dJTIHC2UGrjMLJvkTGq6p6nmuDjKyS5S3NBSiVIrJy1e952jUJkT5Uypirs2fPlnjeqFGjUhsXPj4+OHlSuy/x6dOn0bhxY8jlcnh7e6OwsBAhISGa16OiopCWlqZ57uioPjElJCRotj0+uQUAtGjRAidOnCh1Rj8TExNNdu151a5dG3Xr1kVMTAwaNmyo9fDw8CjXZykUClhbW2s9KqpbYF4BkPJI1DwSU1VIz1LBy7X4/0wuAxrWlSM2ofQ6upWoRGNX7f9nr/py3EpUv0epAu4kqdDYVbud39jVSFOmqsjLV18UFD0SUlR4lKmCt3vxvsllQCNXI8SUMYYg5m4hvN21s6neHsaIuVt2fQgAjKtgt/GcXBUSkvI1j7h7eUhNK0Brn+IlCYzkApp7WSDyhn4vsIyMBLi6mCI1rWqN6cjJUyEhuUDziEvIR+qjQvh6F9/YMZIDzRqb49pN/Y+NEoTiBl1VpFIBiQ8Bj9ra++BRW8CdB7ovsu4+EHWWT0wFioYNGMlL3jkvur9Xxr1Ag8bzWuVLOxsBh17aY7wd+3TBo9DLEAvVdfzwbAQcenXWKuPQuwsengmvtDgr0vNcG6ivN7TLe7kaIT5ZhX/vcz/1moRIHyqlcRUfH4+ZM2ciKioKO3fuxNq1azFt2rRSy8+aNQtHjhzBhx9+iOjoaGzbtg1ffvmlZlyWl5cXXnjhBUycOBHnzp1DaGgoJkyYoJWJMjMzQ4cOHbB8+XJcvXoVx48fxwcffKD1PVOnTkV6ejqGDx+OkJAQXL9+Hdu3b0dUVBQA9UyGFy9eRFRUFFJSUp55WvUnLVq0CMuWLcPnn3+O6OhoXLp0CVu3bsWqVaue6/OkEnyhAL39TNDcUw5nexlG9FYgv1BEWHTxD+rI3gr071icBTh+oQBe9eXo2doYTrYCerY2RuN6cgRfKK7LoIgCdPAxQjtvIzjZCRjUxQR2lgJOX9Z/Rq6yHQnJxQsdTeHbyBh1HGQY298C+QXA+cji7lTj+ptjULfiW+JHQ3Ph7WGEvu0VqG0vQ9/2Cni7GeFISPEt9YBupmhYzwi1rGWo4yBDQFdTNK5vhPNXq0c3rT2HUjD0ZSd0bG0Nt7oKzBxfD3n5KgSdS9OUmTWhHsa9UrxmlZFcgKerKTxdTWFkJKCWrTE8XU3h4lR8PI4f6oxmjS1Q28EYXp5meH9KfZibyXDktPbaTlXR70dS8b8XHdDB1wr16ygwfVwd5OWrEHw+XVNmxjgXjBlUfEfcSA541FPAo55CU2ce9RRweWySj9GDHOHT0AxOtYzhVkeB0QGOaNbYHEGPfW5VdD5aREsPAS08BNSyAnr5qtemKlq3yr+5gJfbFbeIwm+KsLYAerVUl2/hIaClh4BzUcWZlxsJIlo3EODtKsDGAnCvDXRrJuD6PbFadTviea185BbmsG7ZBNYtmwAAzD3qwbplE5i6qrv2en00Ey23fqIpf/vrH2HmVgfeK96FZRNP1Bv3ClxfewUxq7Zoytz68js49OkMz9kTYeHlCc/ZE+HQqyNurd1WuTtXgZ52bdC/owlG9i6+wXz6cgHsrAQEdDGBk52Adt5GaO9jhGPhxcfPs1yT1ESiSrpHdVQp3QLHjBmDnJwctGvXDnK5HG+99RZef/31Usu3bt0au3btwoIFC/Dhhx/CxcUFS5Yswbhx4zRltm7digkTJsDf3x+1a9fGRx99pJkAo8iWLVsQGBgIPz8/eHl54dNPP0XfvsXrRNSqVQtHjx7FnDlz4O/vD7lcDl9fX3TurL4bNHHiRAQFBcHPzw+ZmZk4duwYunfvXu79nzBhAszNzbFixQrMnTsXFhYWaN68OaZPn17uz5LS0bACGBsJeNVfATOFgNv3Vdi4Nxd5j52T7KxkEB/7a7mVqML2v3PxYgcFXmxvggePRGz7Oxdx94vLRNwohIUp0K+tCawtBCQ8UOHrfTl4mFH1r0YOnlNPVz+irznMTQXE3ivEF7sykPfYtYK9tUzrwivmrhKbf8/CwK5mGNjVDMlpKmz6PQu3HssQWlvI8NrL5rC2kCEnT8TdZCXW/pyJyFtVKwNTmt0HUqAwkeHN/6sDSwv1IsIfrIxFTm7xceNob6y5GwkA9rZG+HJxI83zV190xKsvOuLitUy8+2ksAMDBzhjvTHKFtaUcjzKUiLqZjRlLbyLpQdX/Yf3l7wcwMRYweaQzLM1liI7NwYLP45GTp11njx9r9rbG+GK+p+b5kL61MKRvLVyKysJ7q+IAALZWRpj5Wh3Y2xghK0eFW3fzsOiLeEREVu1FhCPjRZiZAJ191IPdkx8Bu06okP5vctTSFP92H1JX2KMs9eu9fWVo3VBAZg5wKEJE1N3izzx1VQREwL+ZAEszAdl56gZX8KWqfy57HM9r5WPTphk6Htmuee7z2XsAgPjvfsXF8fOgcHGEmWvxGMqcW3fwz4DX4bNyHtwmj0LevSRcmbEUib8d1JR5eCYc4aNmwmvxdHgtfhvZN+MRPnIG0s5frLwdq2BPuzawNhdgZ1WcI0jNELHpjxwM6qJAl+bGeJQl4rfjebh4s/gYe5ZrEqL/ShAreBRb9+7d4evrizVr1lTk19RoM77MfHoh0rJ6qiUmfVL1sxWVaeM7dngp8JLUYVQ5+7c0x4A3IqUOo0r54ytvLNvFbjrlNW+onOe1ctr4jh3+NPaSOowqp39BFK89ymn1VMunF5LI7A3SjWn8bPJ/X2LF0NTsJamJiIiIiIj0hI2rcvj4449haWmp8/Hiiy9KHR4RERERUbW1fv16eHh4wNTUFG3atMGJEyfKLB8cHIw2bdrA1NQUnp6e2LhxY4XHWOFjroKCgir6KyrNpEmTMHToUJ2vVdS07kREREREFaWqrHP1008/Yfr06Vi/fj06d+6Mr776Ci+++CKuXr2K+vXrlygfGxuLl156CRMnTsT333+PU6dOYcqUKXB0dMQrr7xSYXEaxDpXVYW9vT3s7e2lDoOIiIiIqEZZtWoVxo8fjwkTJgAA1qxZg7///hsbNmzAsmXLSpTfuHEj6tevr5n3wdvbGyEhIfjss88qtHHFboFERERERDWUSiVK9sjLy0N6errWIy8vr0SM+fn5CA0N1Zr1GwD69u2L06dP69yvM2fOlCjfr18/hISEPPfySs+CjSsiIiIiIqp0y5Ytg42NjdZDVxYqJSUFSqUStWvX1tpeu3ZtJCYm6vzsxMREneULCwuRkpKiv514ArsFEhERERHVUFIOuZo3bx5mzpyptU2hUJRSGhAEQeu5KIoltj2tvK7t+sTGFRERERERVTqFQlFmY6qIg4MD5HJ5iSxVUlJSiexUEWdnZ53ljYyMUKtWrecP+inYLZCIiIiIiAyWiYkJ2rRpg0OHDmltP3ToEDp16qTzPR07dixR/uDBg/Dz84OxsXGFxcrGFRERERFRDSWqRMke5TFz5kx888032LJlCyIjIzFjxgzExcVh0qRJANRdDMeMGaMpP2nSJNy+fRszZ85EZGQktmzZgs2bN2P27Nl6rb8nsVsgEREREREZtGHDhuHBgwdYsmQJEhIS0KxZM+zfvx9ubm4AgISEBMTFxWnKe3h4YP/+/ZgxYwbWrVuHOnXq4IsvvqjQadgBNq6IiIiIiGosVRVZRBgApkyZgilTpuh87dtvvy2xzd/fH2FhYRUclTZ2CyQiIiIiItIDNq6IiIiIiIj0gN0CiYiIiIhqqPJOLEFlY+aKiIiIiIhID5i5IiIiIiKqoZi50i9mroiIiIiIiPSAmSsiIiIiohqKiSv9YuaKiIiIiIhID9i4IiIiIiIi0gN2CyQiIiIiqqE4oYV+MXNFRERERESkB8xcERERERHVUKLIzJU+MXNFRERERESkB2xcERERERER6QG7BRIRERER1VAqTmihV8xcERERERER6QEzV0RERERENRQntNAvZq6IiIiIiIj0gJkrIiIiIqIaiosI6xczV0RERERERHrAxhUREREREZEeCCJHsRERERER1UiBi5Mk++4tC50k++6KwjFX1cCMLzOlDqHKWT3VkvVWTqunWmLW+iypw6hyVk6x4LFWTqunWmL6WtZZea15i+e18uJvwfNZPdUSfxp7SR1GldK/IErqEKiSsHFFRERERFRDqdiJTa845oqIiIiIiEgP2LgiIiIiIiLSA3YLJCIiIiKqobjOlX4xc0VERERERKQHzFwREREREdVQXJVJv5i5IiIiIiIi0gM2roiIiIiIiPSA3QKJiIiIiGooFSe00CtmroiIiIiIiPSAmSsiIiIiohqKU7HrFzNXREREREREesDMFRERERFRDcWp2PWLmSsiIiIiIiI9YOOKiIiIiIhID9gtkIiIiIiohhJVKqlDqFaYuSIiIiIiItIDZq6IiIiIiGooLiKsX8xcERERERER6QEbV0RERERERHrAboFERERERDUU17nSL2auiIiIiIiI9ICZKyIiIiKiGkrkhBZ6xcwVERERERGRHjBzRURERERUQzFzpV/MXBEREREREekBG1dERERERER6wG6BREREREQ1lEpUSR1CtcLMFRERERERkR4wc0VEREREVENxQgv9kjxzdevWLQiCgIiICABAUFAQBEFAWloaAODbb7+Fra2tZPGVx5OxExERERFRzWHwmathw4bhpZdekjqMZ9KpUyckJCTAxsZG6lAqTL92JujY1AhmCgFx91X4JTgPiall99Vt0UCOF9sr4GAjIOWRiP1n83ApRqlVpnMzI/RobQJrcwGJqSrsOZGHmISq3we4vPvVoI4MAV0UcLaXIT1LxNGwfJy+UqhV5lnqsyrr1NQI3VsZa+ps76l8xJZRZ551ZBjYyURTZ8ciCnDmiTpr7inHC+1MNHV24Fw+LsdWnzoDeKz9Fy8UnddMBcQlqrD7Gc9rL3Uorps/z+g4rzU3Qs9WJrC2UP+f/HYiDzH3eF6ricca66x87Lv4wXPWeNi0bgbTOk4IeWUK7v9+pOz3dG0Ln8/ehaVPI+TdS8LNld8g7usftco4D+6LxoumwbxBfWTfjEPUgtW4v/dwRe4K1UCSZ66exszMDE5OTlKH8VQFBQUwMTGBs7MzBEGQOpwK0bO1Mbr7GuOX4Dys3pWD9CwVJgWYQmFc+nvcnGUY088UIVEFWLEzGyFRBRjbzxT1axcfer4NjTCoqwKHQvLx2U/ZiLmnxOsDzGBrWbXrsbz7ZW8lYOIAM8TcU+Kzn7JxKDQfg7sp0KKBXFPmWeqzKvNtKEdAFxMcCS3Aqp9zEJugwsSXTcusswn9TRGboMKqn3NwJKwAg7qYoLnnY3VWW4bRfRUIjS7Eyp9yEBpdiDF9FajvVD3qDOCx9l/0am2M7q2M8cvxPKz6KQfp2SpMfsp5zd1ZhrEvmCLkWgE+3ZmNkGsFGPeCKdweq5tWjYwwuOj/5Ef1/8kbPK/VyGONdVZ+cgtzpF+MwpVpS56pvJl7PbT942ukngzFybaDcOOTjWi6+n04D+6rKWPbwRetdqzG3R/24kSbANz9YS9a71wD23YtKmo3qgxRJUr2qI709le4e/duNG/eHGZmZqhVqxZ69+6NrKwsAMDWrVvh7e0NU1NTNGnSBOvXr3/mz32yW+CiRYvg6+uL7du3w93dHTY2Nhg+fDgyMjI0ZTIyMjBq1ChYWFjAxcUFq1evRvfu3TF9+vRn+k53d3d8+OGHGDlyJCwtLVGnTh2sXbtWq4wgCNi4cSMCAgJgYWGBjz76SGe3wFOnTsHf3x/m5uaws7NDv3798PDhQwCAKIr49NNP4enpCTMzM7Rs2RK7d+9+5rqpbP4tjXEoJB+XYpRITFVhx+E8mBgJaN249ASof0tjRMcrcSS0AElpIo6EFiD6jhL+LYuvXLr7GuPc1UKcu1qIpIci9pzMR1qmiM7Ny7i6qQLKu1+dmhkjLUNdLumhiHNXC3E+shA9WployjxLfVZl3Voa43xkIc5Fquts7yl1nXVqpvsY69jUCGmZ6nJJD0WciyzE+WuF6O5rrPWZ0fFKHA1T19nRsAJcv6tEt2pSZwCPtf+im68xDv2Tj4s31ee1Hw7lwcRYQJuyzmu+6ro5HFqApIciDhfVjW/J89rZq4W4/1DEbyfU/yddeF6rccca66z8kv8+juiFa5C459AzlXd7fThy4xJwddbHyLwWg/gtuxH/7a/wnBmoKePx1likHD6Nm59+jayoGNz89GukHD0L97fGVtRuUA2ll8ZVQkICRowYgcDAQERGRiIoKAhDhgyBKIrYtGkT3n//fSxduhSRkZH4+OOPMX/+fGzbtu25v+/mzZvYs2cP9u3bh3379iE4OBjLly/XvD5z5kycOnUKv//+Ow4dOoQTJ04gLCysXN+xYsUKtGjRAmFhYZg3bx5mzJiBQ4e0/8gXLlyIgIAAXLp0CYGBgSU+IyIiAr169ULTpk1x5swZnDx5EgMGDIBSqU7bf/DBB9i6dSs2bNiAK1euYMaMGfi///s/BAcHP0etVKxa1gKsLWSIiivucqBUATfuKuHhIi/1fe7Ocq33AEBUnBLuzur3yGVAPScZouK1uztExRdqylRFz7Nf7s7yEuWvxRXC1VEGmeyxMmXUZ1UmlwH1HGWIin9i/+KVcK+te//cnOUly8cpterMrbYM0TrKuDlXkzu8PNaeWy1rATYWMlzTcV5zf8p57doTdXNNx3ntWlzJOi7rcw0dj7XyY51VDtsOvkg+fEprW/LBE7Bp0wyCkfpGiV0HX6QcPqlVJuXQCdh1bFVpcRoqURQle1RHehlzlZCQgMLCQgwZMgRubm4AgObNmwMAPvzwQ6xcuRJDhgwBAHh4eODq1av46quvMHbs890tUKlU+Pbbb2FlZQUAGD16NI4cOYKlS5ciIyMD27Ztw44dO9CrVy8A6sxZnTp1yvUdnTt3xrvvvgsAaNy4MU6dOoXVq1ejT58+mjIjR47UalTFxsZqfcann34KPz8/rUxd06ZNAQBZWVlYtWoVjh49io4dOwIAPD09cfLkSXz11Vfw9/cvEVNeXh7y8vK0tikUinLt1/OyMld3X8jI0f5DyMwRYWdVejcXK3OhxHsyckRYW6jfY2EmQC4TkJH9RJlsEdbmVbf7zPPsl5WFgIy4kuXlcgGWpgLSs8Wn1mdVZmGqrrPMJ4+xbBFWrrr3z9pcQFR2yWNSLhdgYaqu/1LrrAofX4/jsfb8SjuvZWSLsH/aeU1XfT/tvFbFjzsea+XHOqscitoOyLuforUtP+kBZMbGMHGwQ15iMhTODsi7/0CrTN79B1A4O1ZmqFQD6KVx1bJlS/Tq1QvNmzdHv3790LdvX7z66qsoLCxEfHw8xo8fj4kTJ2rKFxYW/qdJH9zd3TUNKwBwcXFBUlISACAmJgYFBQVo166d5nUbGxt4eXmV6zuKGjyPP1+zZo3WNj8/vzI/IyIiAv/73/90vnb16lXk5uZqNdYAID8/H61a6b6LsmzZMixevFhr28KFCwGH2WXG8TxaNzbC0O7FDbdN+3LU/9Bxk+FpNx50vf7kNl0fUR3uZ5R3v558TdCx/VnqsyorsS9PuVYoddfF0gsJur6niuOx9nRtGhthaI/i89rXf+g+rwnCM5x/nuOYEkq+rUrisVZ+rLNK8OTOF41/f3y7rjI1utLUVKqqP9GOIdFL40oul+PQoUM4ffo0Dh48iLVr1+L999/HH3/8AQDYtGkT2rdvX+I9z8vYWLtPsSAImgOjKMX45KQS+kg9PvmZFhYWZZY3MzMr9bWieP/880/UrVtX67XSslHz5s3DzJkzS5R9d1NBmXE8jyuxhfjsfnGXAyO5et+tzNV3zYpYmpXMNDxO1905K7Piu3hZOSKUKh1ldNwZrkqeZ78yskqWtzQXoFSKyMpVv+dp9VmVZeWq68zqyTooY/+K7uA+WV6pFJGVV1xnOj+zjOO2KuGx9uwuxxbi9jOe18raz4xsEVYWJevvyfNaeY7lqoDHWvmxzipH3v2UEhkoE0d7qAoKkP8gTV0mMQUKZwetMgon+xIZL6L/Sm+DDgRBQOfOnbF48WKEh4fDxMQEp06dQt26dRETE4OGDRtqPTw8PPT11VoaNGgAY2NjnD9/XrMtPT0d169fL9fnnD17tsTzJk2alOszWrRogSNHdE8d6uPjA4VCgbi4uBJ14+rqqvM9CoUC1tbWWo+K6haYVwCkPBI1j8RUFdKzVPByLW4Uy2VAw7pyxCaUPvXrrUQlGrtqN6S96stxK1H9HqUKuJOkQmNX7XZ+Y1cjTZmq6Hn2S11X2uW9XI0Qn6xC0U2lp9VnVaZUAXeSVSX2r3E9OW7d171/txOVaFzvifpwlWvV2e37Oj7TVY7bidXjTh2PtWen67z2KEsFr/olz2u3nnJe83qibproOK95PVnH9Y3K/FxDx2Ot/FhnlSPtbAQcenXS2ubYpwsehV6GWKgev/bwbAQcenXWKuPQuwsengmvtDipZtBL4+rcuXP4+OOPERISgri4OPz6669ITk6Gt7c3Fi1ahGXLluHzzz9HdHQ0Ll26hK1bt2LVqlX6+OoSrKysMHbsWMyZMwfHjh3DlStXEBgYCJlMVq4p0k+dOoVPP/0U0dHRWLduHX7++WdMmzatXLHMmzcP//zzD6ZMmYKLFy/i2rVr2LBhA1JSUmBlZYXZs2djxowZ2LZtG27evInw8HCsW7fuP032UZGCLxSgt596mmtnexlG9FYgv1BEWHTxwNuRvRXo37F4RqPjFwrgVV+Onq2N4WQroGdrYzSuJ0fwheJsW1BEATr4GKGdtxGc7AQM6mICO0sBpy/rPyNXmZ62X/07mmBk7+LG8enLBbCzEhDQxQROdgLaeRuhvY8RjoXna8o8S31WZccvFKC9txHaNVHX2cDOJrCzEnDmsvoYe6mDMUb0Kj6+zlwphJ2VgIGd/q2zJur6Doooro8TFwvQ2FWOHq3UddajlbrOjleTOgN4rP0XxyMK0Oex89rI3grkF4gIfey8NqqPAi8/dl4LjlDXTa/WxnCyE9CrqG4injivNTVCe28j1H7s/+QUz2s17lhjnZWf3MIc1i2bwLql+qa2uUc9WLdsAlNXFwCA10cz0XLrJ5ryt7/+EWZudeC94l1YNvFEvXGvwPW1VxCzaoumzK0vv4NDn87wnD0RFl6e8Jw9EQ69OuLWWsO85qpMnIpdv/TSLdDa2hrHjx/HmjVrkJ6eDjc3N6xcuRIvvvgiAMDc3BwrVqzA3LlzYWFhgebNmz/ztOjPY9WqVZg0aRJefvllWFtbY+7cuYiPj4epqekzf8asWbMQGhqKxYsXw8rKCitXrkS/fv3KFUfjxo1x8OBBvPfee2jXrh3MzMzQvn17jBgxAoB6sg8nJycsW7YMMTExsLW1RevWrfHee++V63sqy9GwAhgbCXjVXwEzhYDb91XYuDcXeY+dy+2sZBDF4ozArUQVtv+dixc7KPBiexM8eCRi29+5iLtfXCbiRiEsTIF+bdWLbSY8UOHrfTl4mFG1/+ietl/W5gLsrIrvb6RmiNj0Rw4GdVGgS3NjPMoS8dvxPFy8WXwn8lnqsyqLuKGEuSIfffyMYW1hgoQHKnyzLxcPM4vrzNZSu86++TMXAZ1N0Lm5GR5lqacvfnwhzVuJKnx/MA8vtjfBC+2M8SBdxPZDeYhLqh51BvBY+y+OFJ3Xuitg/u95bcOT5zXLkue17/7KxUsdFXixQ3Hd3H6sbsKvF8LcVL3wetH/yVd/8LxWE4811ln52bRpho5Htmue+3ymvi6K/+5XXBw/DwoXR5j929ACgJxbd/DPgNfhs3Ie3CaPQt69JFyZsRSJvx3UlHl4Jhzho2bCa/F0eC1+G9k34xE+cgbSzl+svB2jGkEQq+s8iI/JyspC3bp1sXLlSowfP/6p5d3d3TF9+vQKbQDq04wvM6UOocpZPdWS9VZOq6daYtb6LKnDqHJWTrHgsVZOq6daYvpa1ll5rXmL57Xy4m/B81k91RJ/GpdvorCarn9BlNQhlKr/hMuSffef3zST7Lsril4yV4YmPDwc165dQ7t27fDo0SMsWaJe4TsgIEDiyIiIiIiIqLqqlo0rAPjss88QFRUFExMTtGnTBidOnICDgwNOnDih6a6oS2Ym72AREREREVH5VcvGVatWrRAaGqrzNT8/P0RERJT5/lu3buk/KCIiIiIiA1NdJ5aQSrVsXJXFzMwMDRs2lDoMIiIiIiKqZmpc44qIiIiIiNSYudIvvS0iTEREREREVJMxc0VEREREVEOpxOqxPpqhYOaKiIiIiIhID9i4IiIiIiIi0gN2CyQiIiIiqqE4oYV+MXNFRERERESkB8xcERERERHVUKKKE1roEzNXREREREREesDGFRERERERkR6wWyARERERUQ3FCS30i5krIiIiIiIiPWDmioiIiIiohhJFTmihT8xcERERERER6QEzV0RERERENZSKY670ipkrIiIiIiIiPWDjioiIiIiISA/YLZCIiIiIqIYSVZzQQp+YuSIiIiIiItIDZq6IiIiIiGooLiKsX8xcERERERER6QEbV0RERERERHrAboFERERERDWUKHJCC31i5oqIiIiIiKqNhw8fYvTo0bCxsYGNjQ1Gjx6NtLS0UssXFBTgnXfeQfPmzWFhYYE6depgzJgxuHfvXrm/m40rIiIiIqIaSlSJkj0qysiRIxEREYG//voLf/31FyIiIjB69OhSy2dnZyMsLAzz589HWFgYfv31V0RHR2PgwIHl/m52CyQiIiIiomohMjISf/31F86ePYv27dsDADZt2oSOHTsiKioKXl5eJd5jY2ODQ4cOaW1bu3Yt2rVrh7i4ONSvX/+Zv5+NKyIiIiKiGkrKRYTz8vKQl5entU2hUEChUDz3Z545cwY2NjaahhUAdOjQATY2Njh9+rTOxpUujx49giAIsLW1Ldf3s1sgERERERFVumXLlmnGRRU9li1b9p8+MzExEU5OTiW2Ozk5ITEx8Zk+Izc3F++++y5GjhwJa2vrcn0/G1dERERERFTp5s2bh0ePHmk95s2bp7PsokWLIAhCmY+QkBAAgCAIJd4viqLO7U8qKCjA8OHDoVKpsH79+nLvkyCKIpdlJr3Ly8vDsmXLMG/evP+U2q1JWGfPh/VWfqyz58N6Kz/W2fNhvZUf66z6S0lJQUpKSpll3N3dsWPHDsycObPE7IC2trZYvXo1XnvttVLfX1BQgKFDhyImJgZHjx5FrVq1yh0nG1dUIdLT02FjY4NHjx6VO51aU7HOng/rrfxYZ8+H9VZ+rLPnw3orP9YZFYmMjISPjw/OnTuHdu3aAQDOnTuHDh064Nq1a6WOuSpqWF2/fh3Hjh2Do6Pjc30/uwUSEREREVG14O3tjRdeeAETJ07E2bNncfbsWUycOBEvv/yyVsOqSZMm+O233wAAhYWFePXVVxESEoIffvgBSqUSiYmJSExMRH5+frm+n40rIiIiIiKqNn744Qc0b94cffv2Rd++fdGiRQts375dq0xUVBQePXoEALhz5w5+//133LlzB76+vnBxcdE8Tp8+Xa7v5lTsRERERERUbdjb2+P7778vs8zjI6Pc3d2hr5FSzFxRhVAoFFi4cCEHlZYD6+z5sN7Kj3X2fFhv5cc6ez6st/JjnZGh4IQWREREREREesDMFRERERERkR6wcUVERERERKQHbFwRERERERHpARtXREREREREesDGFZFElEolgoOD8fDhQ6lDISIiIiI9YOOK9CIsLAyXLl3SPN+7dy8GDRqE9957r9wrW9cUcrkc/fr1Q1pamtShVDmLFi3C7du3pQ6DiIiISAsbV6QXb7zxBqKjowEAMTExGD58OMzNzfHzzz9j7ty5EkdnuJo3b46YmBipw6hy/vjjDzRo0AC9evXCjh07kJubK3VIBi8rKwvz589Hp06d0LBhQ3h6emo9qCTeNHp+KpUK0dHROHnyJI4fP671oGKtW7fW9F5YsmQJsrOzJY6o6jlx4gT+7//+Dx07dsTdu3cBANu3b8fJkycljoxqKq5zRXphY2ODsLAwNGjQAJ988gmOHj2Kv//+G6dOncLw4cMRHx8vdYgG6eDBg3jnnXfw4Ycfok2bNrCwsNB63draWqLIDN/FixexdetW7NixA/n5+Rg+fDgCAwPRtm1bqUMzSCNGjEBwcDBGjx4NFxcXCIKg9fq0adMkisxwtW3bFu+++y5eeeUVxMTEoGnTphg8eDD++ecf9O/fH2vWrJE6RIN09uxZjBw5Erdv38aTlxiCIECpVEoUmeExMzPD9evXUa9ePcjlciQkJMDJyUnqsKqMX375BaNHj8aoUaOwfft2XL16FZ6enli/fj327duH/fv3Sx0i1UBsXJFeWFtbIzQ0FI0aNUKfPn3w8ssvY9q0aYiLi4OXlxdycnKkDtEgyWTFyePHL3ZFUeRFyDMqLCzEH3/8ga1bt+Kvv/6Cl5cXJkyYgHHjxsHGxkbq8AyGra0t/vzzT3Tu3FnqUKoM3jR6Pr6+vmjcuDEWL16ssyHPv8tiHTt2hKWlJbp06YLFixdj9uzZsLS01Fl2wYIFlRyd4WvVqhVmzJiBMWPGwMrKChcuXICnpyciIiLwwgsvIDExUeoQqQYykjoAqh78/Pzw0UcfoXfv3ggODsaGDRsAALGxsahdu7bE0RmuY8eOSR1CladSqZCfn4+8vDyIogh7e3ts2LAB8+fPx6ZNmzBs2DCpQzQIdnZ2sLe3lzqMKkUURahUKgDA4cOH8fLLLwMAXF1dkZKSImVoBu369evYvXs3GjZsKHUoBu/bb7/FwoULsW/fPgiCgAMHDsDIqOSlmSAIbFzpEBUVhW7dupXYbm1tzfHMJBk2rkgv1qxZg1GjRmHPnj14//33NT+qu3fvRqdOnSSOznD5+/tLHUKVFRoaiq1bt2Lnzp1QKBQYM2YM1q1bpzn2Vq5cibfffpuNq399+OGHWLBgAbZt2wZzc3Opw6kSeNPo+bRv3x43btxg4+oZeHl54ccffwSg7slw5MgRdgssBxcXF9y4cQPu7u5a20+ePMmxpCQZdgukCpWbmwu5XA5jY2OpQzFYJ06cwFdffYWYmBj8/PPPqFu3LrZv3w4PDw906dJF6vAMUosWLRAZGYm+ffti4sSJGDBgAORyuVaZ5ORk1K5dW5N5qOlatWqFmzdvQhRFuLu7l/ibDAsLkygyw3Xx4kWMGjUKcXFxmDlzJhYuXAgAeOutt/DgwQPs2LFD4ggNx8WLFzX/vnnzJj744APMmTMHzZs3L3GstWjRorLDo2rq008/xbZt27Blyxb06dMH+/fvx+3btzFjxgwsWLAAU6dOlTpEqoGYuSK9ys/PR1JSUokL2vr160sUkWF7fDBuWFgY8vLyAAAZGRn4+OOPORi3FP/73/8QGBiIunXrllrG0dGRDavHDBo0SOoQqpwWLVpozRZYZMWKFSUa8zWdr68vBEHQmsAiMDBQ8++i1ziWtGzbt2/Hxo0bERsbizNnzsDNzQ2rV6+Gp6cnAgICpA7P4MydOxePHj1Cjx49kJubi27dukGhUGD27NlsWJFkmLkivYiOjsb48eNx+vRpre38MS0bB+OWX0FBAby8vLBv3z74+PhIHQ5VY/Hx8RAEAfXq1QMAnD9/Hjt27ICPjw9ef/11iaMzLOVZd87Nza0CI6m6NmzYgAULFmD69OlYunQpLl++DE9PT3z77bfYtm0bx+iWITs7G1evXoVKpYKPj0+pk4IQVQZmrkgvXnvtNRgZGWHfvn06Z4ci3TgYt/yMjY2Rl5fHY+w5hYaGIjIyEoIgwMfHB61atZI6JIM1cuRIvP766xg9ejQSExPRp08fNG3aFN9//z0SExM5wcBjHm8wHT9+HJ06dSoxMUNhYSFOnz7NxlUp1q5di02bNmHQoEFYvny5Zrufnx9mz54tYWSG69GjR1AqlbC3t4efn59me2pqKoyMjLicCUmCjSvSi4iICISGhqJJkyZSh1KlcDDu83nrrbfwySef4JtvvtE5sxaVlJSUhOHDhyMoKAi2trYQRVHTnebHH3+Eo6Oj1CEanMuXL6Ndu3YAgF27dqFZs2Y4deoUDh48iEmTJrFxVYoePXroXK+p6HhjTwbdYmNjdd7sUCgUyMrKkiAiwzd8+HAMGDAAU6ZM0dq+a9cu/P777+xaT5KQPb0I0dP5+PhwauLn8MYbb2DatGk4d+4cBEHAvXv38MMPP2D27Nklfiyo2Llz5/Drr7+ifv366NevH4YMGaL1oJLeeustpKen48qVK0hNTcXDhw9x+fJlpKen4+2335Y6PINUUFAAhUIBQD0V+8CBAwEATZo0QUJCgpShGbSi7uBPevDgQYmF0qmYh4cHIiIiSmw/cOAAu0CX4ty5c+jRo0eJ7d27d8e5c+ckiIiImSvSk08++QRz587Fxx9/rHN2KKbmdeNg3Odja2uLV155ReowqpS//voLhw8fhre3t2abj48P1q1bh759+0oYmeFq2rQpNm7ciP79++PQoUP48MMPAQD37t1DrVq1JI7O8BTd2BAEAePGjdM0TAFAqVTi4sWLXJqjDHPmzMGbb76J3NxciKKI8+fPY+fOnVi2bBm++eYbqcMzSHl5eSgsLCyxvaCgADk5ORJERMQJLUhPZDJ1EvTJu5Wc0OLZcDAuVTQrKyucOHECvr6+WtvDw8Ph7++P9PR0aQIzYEFBQRg8eDDS09MxduxYbNmyBQDw3nvv4dq1a/j1118ljtCwvPbaawCAbdu2YejQoTAzM9O8ZmJiAnd3d0ycOBEODg5ShWjwNm3ahI8++gjx8fEAgLp162LRokUYP368xJEZpu7du6N58+ZYu3at1vY333wTFy9exIkTJySKjGoyNq5IL4KDg8t8nYvlkj717NkTv/76K2xtbbW2p6enY9CgQTh69Kg0gRmwgIAApKWlYefOnahTpw4A4O7duxg1ahTs7Ozw22+/SRyhYVIqlUhPT4ednZ1m261bt2Bubs7FXkuxePFizJ49m10Ay6GwsBA//PAD+vXrB2dnZ6SkpEClUvEYe4pTp06hd+/eaNu2LXr16gUAOHLkCP755x8cPHgQXbt2lThCqonYuCKSUG5uLtauXYtjx47pXB+MC7vqJpPJkJiYWOLCIykpCXXr1kVBQYFEkRmu+Ph4BAQE4PLly3B1dYUgCIiLi0Pz5s2xd+9ezXTjpK2wsBBBQUG4efMmRo4cCSsrK9y7dw/W1tbMMJNemZubIzIykrMpllNERARWrFiBiIgImJmZoUWLFpg3bx4aNWokdWhUQ3HMFelNWloaNm/erDXNc2BgIGxsbKQOzWAFBgbi0KFDePXVV9GuXTtOL/4UFy9e1Pz76tWrWuuAKZVK/PXXX2UuLFyTubq6IiwsDIcOHcK1a9cgiiJ8fHzQu3dvqUMzWLdv38YLL7yAuLg45OXloU+fPrCyssKnn36K3NxcbNy4UeoQDUarVq2e+fzFm0a6tW/fHuHh4WxclZOvry9++OEHqcMg0mDmivQiJCQE/fr1g5mZGdq1awdRFBESEoKcnBwcPHgQrVu3ljpEg2RjY4P9+/ejc+fOUodSJchkMs0FnK5Tl5mZGdauXYvAwMDKDo2qoUGDBsHKygqbN29GrVq1NIt8BwcHY8KECbh+/brUIRqMxYsXa/6dm5uL9evXw8fHBx07dgQAnD17FleuXMGUKVOwbNkyqcI0aD///DPeffddzJgxA23atCnRrbJFixYSRWZY0tPTNZNkPW2sKCfTIimwcUV60bVrVzRs2BCbNm3SrDtUWFiICRMmICYmBsePH5c4QsPk4+ODH3/8kT+az+j27dsQRRGenp44f/681tpMJiYmcHJyglwulzBCw/LFF1/g9ddfh6mpKb744osyy3I69pIcHBxw6tQpeHl5wcrKStO4unXrFnx8fJCdnS11iAZpwoQJcHFx0cyuWGThwoWIj4/XTAxC2oomhnqcIAicGOoJcrlcs47a4zfcHsc6IymxcUV6YWZmhvDw8BKLCF+9ehV+fn68CCnFgQMH8MUXX2Djxo3sCkJ65+HhgZCQENSqVQseHh6llhMEATExMZUYWdVgb2+PkydPwsfHR6txdfLkSbzyyiu4f/++1CEaJBsbG4SEhJQY83L9+nX4+fnh0aNHEkVm2G7fvl3m6/yNUAsODkbnzp1hZGTEybTIIHHMFemFtbU14uLiSjSu4uPjYWVlJVFUhs/Pzw+5ubnw9PSEubl5ifXBUlNTJYrM8EVHRyMoKEjnRCALFiyQKCrDEhsbq/Pf9Gz69OmDNWvW4OuvvwagboRmZmZi4cKFeOmllySOznCZmZnh5MmTJRpXJ0+ehKmpqURRGb5nbTz1798f33zzDVxcXCo4IsNU1GAqmmwmMDAQrq6uEkdFVIyNK9KLYcOGYfz48fjss8/QqVMnCIKAkydPYs6cORgxYoTU4RmsESNG4O7du/j4449Ru3ZtTmjxjDZt2oTJkyfDwcEBzs7OWvUmCAIbVzosWbIEs2fPhrm5udb2nJwcrFixgnWmw+rVq9GjRw/4+PggNzcXI0eOxPXr1+Hg4ICdO3dKHZ7Bmj59OiZPnozQ0FB06NABgHrM1ZYtW3ic6cHx48e5QC4AIyMjfPbZZxg7dqzUoRBpYbdA0ov8/HzMmTMHGzdu1KyWbmxsjMmTJ2P58uVQKBQSR2iYzM3NcebMGbRs2VLqUKoUNzc3TJkyBe+8847UoVQZj49TeNyDBw/g5OTEsQmlyMnJwc6dOxEWFgaVSoXWrVtj1KhRWgvkUkm7du3C559/jsjISACAt7c3pk2bhqFDh0ocWdX3eBfVmm7QoEEYNGgQxo0bJ3UoRBpsXJFeZWdn4+bNmxBFEQ0bNixxl5y0tW7dGuvXr9fc3aVnY21tjYiICF5clINMJsP9+/e1JgEBgKNHj2LYsGFITk6WKDIiKg82rop99dVXWLRoEUaNGqVzhsWBAwdKFBnVZGxckV4EBgbi888/LzG+KisrC2+99RZnhyrFwYMHsXjxYixduhTNmzcvMeaK08jqNn78eLRt2xaTJk2SOhSDZ2dnB0EQ8OjRI1hbW2t1oVQqlcjMzMSkSZOwbt06CaM0XBzbR4aGjatiumZYLMLZAkkqbFyRXpTW5SglJQXOzs6aroKkreiH4cmxVpxGtmzLli3DqlWr0L9/f52NUk4rXmzbtm0QRRGBgYFYs2aN1qLeJiYmcHd316xFRNqeNraPi+EWs7e3R3R0NBwcHDQN+tJwop7/ho0rIsPGCS3oP0lPT4coihBFERkZGVozQSmVSuzfv79Eg4uKHTt2TOoQqqSvv/4alpaWCA4OLjEVryAIbFw9pmiwt4eHBzp16lSiIUql++ijj7B06VKO7XsGq1ev1vRcWLNmjbTBEBFJiJkr+k9KW8CviCAIWLx4Md5///1KjIqIypKTk4OCggKtbeyCWhLH9j2fUaNGwd/fH927d0fjxo2lDqfKOH78ODp16gQjI+373oWFhTh9+jS6desGQJ25nzx5MmxtbSWI0vAcOXIEq1evRmRkJARBQJMmTTB9+nT07t1b6tCohmLjiv6T4OBgiKKInj174pdffoG9vb3mNRMTE7i5uaFOnToSRmj40tLSsHnzZs0Pg4+PDwIDA7W6bxH9V9nZ2Zg7dy527dqFBw8elHidXVBL4ti+5zNp0iQEBQUhOjoazs7O8Pf31zS2nlwLkYpxRs/y+/LLLzFjxgy8+uqrmu7NZ8+exe7du7Fq1SpMnTpV4gipJmLjivTi9u3bqF+/PtdpKqeQkBD069cPZmZmaNeuHURRREhICHJycnDw4EG0bt1a6hANUmBgYJmvcwKVkt58800cO3YMS5YswZgxY7Bu3TrcvXsXX331FZYvX45Ro0ZJHaLB4di+/yYxMRFBQUEICgpCcHAwoqOj4eTkhISEBKlDM0ilzegZHR0NPz8/pKenSxSZ4apbty7mzZtXohG1bt06LF26FPfu3ZMoMqrJ2LgiklDXrl3RsGFDbNq0SdMVpLCwEBMmTEBMTAyOHz8ucYSGafDgwVrPCwoKcPnyZaSlpaFnz5749ddfJYrMcNWvXx/fffcdunfvDmtra4SFhaFhw4bYvn07du7cif3790sdosHx8PAo9TVBEBATE1OJ0VQ9WVlZOHnypKaBFRYWBh8fH4SHh0sdmkEZMmQIAGDv3r144YUXtNaFVCqVuHjxIry8vPDXX39JFaLBsrKyQnh4OBo2bKi1/fr162jVqhUyMzMlioxqMk5oQSShkJAQrYYVoF51fu7cufDz85MwMsP222+/ldimUqkwZcoUjo8pRWpqqqaxYG1trZmxrUuXLpg8ebKUoRms2NhYqUOokt555x0EBwfjwoULaNasGbp164Z58+ahW7duHCekQ1EXcFEUYWVlpbVAtYmJCTp06ICJEydKFZ5BGzhwIH777TfMmTNHa/vevXsxYMAAiaKimo6NKyIJWVtbIy4ursQ4hPj4+BJrhlHZZDIZZsyYge7du2Pu3LlSh2NwPD09cevWLbi5ucHHxwe7du1Cu3bt8Mcff/CCtxQXL15EixYtdL62Z88eDBo0qHIDqiJWrFgBR0dHLFy4EAEBAfD29pY6JIO2detWAIC7uzvmzJkDc3NziSOqOry9vbF06VIEBQVpjbk6deoUZs2ahS+++EJTlt14qbKwWyCRhN5++2389ttv+Oyzz9CpUycIgoCTJ09izpw5eOWVVzilcTnt378fY8eORXJystShGJzVq1dDLpfj7bffxrFjx9C/f38olUoUFhZi1apVmDZtmtQhGhwXFxecOnWqRDb0l19+wZgxY5CVlSVRZIbtwoULCA4ORlBQEE6cOAG5XK6Z0KJ79+5sbJUiNjYWhYWFaNSokdb269evw9jYGO7u7tIEZsDK6rr7OHbjpcrExhXpxaJFi/Daa6/Bzc1N6lCqlPz8fMyZMwcbN27ULLRsbGyMyZMnY/ny5Vp976nYzJkztZ6LooiEhAT8+eefGDt2LL788kuJIqs64uLiEBISggYNGqBly5ZSh2OQlixZgq1bt+L06dNwcXEBAPz0008IDAzEt99+i//9738SR1g1XLhwAWvWrMH3338PlUrFWe9K4e/vj8DAQM3adEW+//57fPPNNwgKCpImMCIqFzauSC/atGmDCxcuwN/fH+PHj8eQIUO0FhSmsmVnZ+PmzZsQRRENGzZkt5Cn6NGjh9ZzmUwGR0dH9OzZE4GBgSXWiSF6XtOmTcPhw4dx4sQJ/PXXX5gwYQK2b9+OV155RerQDFp4eLhmIosTJ04gPT0dvr6+6NGjB1asWCF1eAbp8YlmHnfjxg34+fkhLS1NmsCqAa5ZR5WJjSvSm4sXL2Lr1q3YsWMH8vPzMXz4cAQGBqJt27ZSh2awAgMD8fnnn5cYX5WVlYW33nqLU4qT3rz99tto2LBhiXEHX375JW7cuMEuqGUYPXo0zp07h7t372LHjh0ICAiQOiSDZmdnh8zMTLRs2VLTFbBbt25cqPopbGxsEBQUhFatWmltDw0NRffu3ZGRkSFRZFWflZUVLly4wMYVVQo2rkjvCgsL8ccff2Dr1q3466+/4OXlhQkTJmDcuHFcGPcJpS0amZKSAmdnZ01XQdItOTkZUVFREAQBjRs3LrE+DBWrW7cufv/9d7Rp00Zre1hYGAYOHIg7d+5IFJlh+f3330tsKygowIwZM9C3b18MHDhQs/3xf1Oxffv2sTH1HF5++WWYm5tj586dkMvlANRTsQ8bNgxZWVk4cOCAxBFWXWxcUWVi3xnSO5VKhfz8fOTl5UEURdjb22PDhg2YP38+Nm3ahGHDhkkdouTS09MhiiJEUURGRoZWF0qlUon9+/eXaHBRsaLM3nfffQeVSgVA3VAdM2YM1q5dy26VOjx48EDnzQ1ra2ukpKRIEJFhKmsGwC1btmiyyYIgcOxQKV5++WWpQ6iSPv30U3Tr1g1eXl7o2rUrAGi6VB49elTi6IjoWcmkDoCqj9DQUEydOhUuLi6YMWMGWrVqhcjISAQHB+PatWtYuHAhp0L9l62tLezt7TUZFzs7O83DwcEBgYGBePPNN6UO02DNnDkTwcHB+OOPP5CWloa0tDTs3bsXwcHBmDVrltThGaSGDRvqXIT0wIEDvJv7GJVK9UwPNqxI33x8fHDx4kUMHToUSUlJyMjIwJgxY3Dt2jU0a9ZM6vCI6BmxWyDpRYsWLRAZGYm+ffti4sSJGDBggKZbQ5Hk5GTUrl1bk2moyYKDgyGKInr27IlffvkF9vb2mtdMTEzg5uaGOnXqSBihYXNwcMDu3bvRvXt3re3Hjh3D0KFDORW7Dlu2bMHUqVMxZ84c9OzZEwBw5MgRrFy5EmvWrOEipURUbXFCC6pM7BZIevG///0PgYGBqFu3bqllHB0d2bD6l7+/PwD1uib169eHIAgSR1S1ZGdno3bt2iW2Ozk5ITs7W4KIDF9gYCDy8vKwdOlSfPjhhwDUi5Zu2LABY8aMkTg6w8RJQKgyHT9+vMzXu3XrVkmRVD/MI1BlYuaK9GLJkiWYPXt2ibEuOTk5WLFiBRYsWCBRZIbtr7/+gqWlJbp06QIAWLduHTZt2gQfHx+sW7cOdnZ2EkdomHr16oVatWrhu+++04xXy8nJwdixY5GamorDhw9LHKFhS05OhpmZGSwtLaUOxaBxEhCqTDJZyZEaj994Y1fU53fy5Em0bduWa0dSpWDjivSitFnvHjx4ACcnJ/4olKJ58+b45JNP8NJLL+HSpUvw8/PDrFmzcPToUXh7e2Pr1q1Sh2iQLl26hBdffBG5ublo2bIlBEFAREQEFAoFDh48iKZNm0odIlUDpqamuHz5ss51h5o1a4bc3FyJIqPq6NGjR1rPCwoKEB4ejvnz52Pp0qXo1auXRJEZricXlC8iCAJMTU3RsGFDBAQEaHW9J6po7BZIeiGKos6ubRcuXOBJrQyxsbHw8fEBAPzyyy8YMGAAPv74Y4SFheGll16SODrD1bx5c1y/fh3ff/89rl27BlEUMXz4cIwaNQpmZmZSh2cwWrdujSNHjsDOzg6tWrUqs/tpWFhYJUZWNRRNAjJ16lSt7ZwEhCqCrtk8+/TpA4VCgRkzZiA0NFSCqAxbeHg4wsLCoFQq4eXlBVEUcf36dcjlcjRp0gTr16/HrFmzcPLkSc1vLVFFY+OK/hM7OzsIgqCZ9e7JLgyZmZmYNGmShBEaNhMTE80YocOHD2vGvtjb2yM9PV3K0AzasmXLULt27RKTMGzZsgXJycl45513JIrMsAQEBGi6wZQ1xTjpNnPmTEydOhXJyck6JwEhqgyOjo6IioqSOgyDVJSV2rp1q2ZdtfT0dIwfPx5dunTBxIkTMXLkSMyYMQN///23xNFSTcFugfSfbNu2DaIoIjAwEGvWrNG682ZiYgJ3d3d07NhRwggN28CBA5Gfn4/OnTvjww8/RGxsLOrWrYuDBw9i6tSpiI6OljpEg+Tu7o4dO3agU6dOWtvPnTuH4cOHIzY2VqLIqLrZsGEDli5dinv37gFQH3uLFi3iJCCkdxcvXtR6LooiEhISsHz5chQUFODUqVMSRWa46tati0OHDpXISl25cgV9+/bF3bt3ERYWhr59+3I9P6o0zFzRfzJ27FgAgIeHBzp16gRjY2OJI6pavvzyS0yZMgW7d+/Ghg0bNLMtHjhwAC+88ILE0RmuxMREuLi4lNju6OiIhIQECSKi6mry5MmYPHkyJwGhCufr6wtBEErMbNehQwfN4tWk7dGjR0hKSirRuEpOTtb0/rC1tUV+fr4U4VENxcYVPbf09HRNGr5Vq1bIyclBTk6OzrJF5Uhb/fr1sW/fvhLbV69eLUE0VYerqytOnToFDw8Pre2nTp3i+mCPKeq2+yxSU1MrOJqqzdHRUeoQqJp7MuMuk8ng6OiomRGVSgoICEBgYCBWrlyJtm3bQhAEnD9/HrNnz9Z0hT5//jwaN24sbaBUo7BxRc/Nzs5OM0Ogra2tzou4ookuOFugbnFxcWW+Xr9+/UqKpGqZMGECpk+fjoKCAq2xMHPnzsWsWbMkjs5wcFxQ+XESEJKKm5ub1CFUOV999RVmzJiB4cOHo7CwEABgZGSEsWPHam5SNmnSBN98842UYVINw8YVPbejR49qZgI8duyYxNFUTe7u7mVevLFRqtvcuXORmpqKKVOmaLp7mJqa4p133sG8efMkjs5wFHXbpWfHSUCoMn3xxRfPXPbJxawJsLS0xKZNm7B69WrExMRAFEU0aNBAq/uur6+vdAFSjcQJLUgv4uLi4OrqWqKhIIoi4uPjmYEpxYULF7SeF61rsmrVKixduhRDhgyRKLKqITMzE5GRkTAzM0OjRo24QORTKJVK/Pbbb4iMjIQgCPD29kZAQACMjHifjUgKT3ZtTk5ORnZ2NmxtbQEAaWlpMDc3h5OTE2JiYiSIkIjKi40r0gsuIqxff/75J1asWIGgoCCpQ6Fq4vLlywgICEBiYiK8vLwAANHR0XB0dMTvv/+O5s2bSxyh4crPz0dSUhJUKpXWdt40In3asWMH1q9fj82bN2v+RqOiojBx4kS88cYbGDVqlMQRGp6srCwsX74cR44c0fk3ygYpSYGNK9ILmUyG+/fvlxj0ffv2bfj4+CArK0uiyKqm69evw9fXl/VGetOhQwc4OTlh27ZtsLOzAwA8fPgQ48aNQ1JSEs6cOSNxhIYnOjoa48ePx+nTp7W2cywpVYQGDRpg9+7daNWqldb20NBQvPrqq1xiQocRI0YgODgYo0ePhouLS4neM9OmTZMoMqrJ2BeE/pOZM2cCAARBwPz582Fubq55TalU4ty5c+zvXIYnFwouWtdk0aJFaNSokURRUXV04cIFhISEaBpWgHpSmqVLl6Jt27YSRma4XnvtNRgZGWHfvn06L9yI9CkhIQEFBQUltiuVSty/f1+CiAzfgQMH8Oeff6Jz585Sh0KkwcYV/Sfh4eEA1I2CS5cuwcTERPOaiYkJWrZsidmzZ0sVnsHTNcuiKIpwdXXFzp07JYqKqiMvLy/cv38fTZs21dqelJSEhg0bShSVYYuIiEBoaCiaNGkidShUA/Tq1QsTJ07E5s2b0aZNGwiCgJCQELzxxhvo3bu31OEZJDs7O83EWkSGgt0CSS9ee+01fP7551zPqpyCg4O1nheta9KwYUNOMkB6tX//fsydOxeLFi1Chw4dAABnz57FkiVLsHz5cnTp0kVTln/Ham3btsXq1au16oaooiQnJ2Ps2LH466+/YGxsDAAoLCxEv3798O2335YY00zA999/j71792Lbtm1aPWeIpMTGFZGEli1bhtq1ayMwMFBr+5YtW5CcnIx33nlHosioupHJZJp/F2VLi07/jz+v6WOJHu+qGxISgg8++AAff/wxmjdvrrngLcJGKFWE6OhoREZGAgC8vb25AG4ZWrVqhZs3b0IURbi7u5f4G+VadCQFNq5ILzhjz/Nxd3fHjh070KlTJ63t586dw/DhwzmAmfTmySxpWfz9/SswEsMmk8m0uuoWNTgfx0YoVbQnb3yQbosXLy7z9YULF1ZSJETF2O+I9GLChAllzthDuiUmJsLFxaXEdkdHRyQkJEgQEVVXNbnBVB6PL4h+69YtuLq6Qi6Xa5VRqVSIi4ur7NCoBvjuu++wYsUKXL9+HQDQuHFjzJkzB6NHj5Y4MsPExhMZIjauSC84Y8/zcXV1xalTp0osJHnq1CnUqVNHoqiounr48CE2b96stYjwa6+9xgHhj3m8EdqzZ89S1+/r3bs3xo4dW9nhUTW2atUqzJ8/H1OnTkXnzp0hiiJOnTqFSZMmISUlBTNmzJA6RCJ6BmxckV5wxp7nM2HCBEyfPh0FBQXo2bMnAODIkSOYO3cuZs2aJXF0VJ0EBwdj4MCBsLGxgZ+fHwDgiy++wJIlS/D7778zs6WDri6BAJCZmQlTU1MJIqLqbO3atdiwYQPGjBmj2RYQEICmTZti0aJFbFz9y97eHtHR0XBwcICdnV2ZPWVSU1MrMTIiNTauSC8+/PBDLFiwgDP2lNPcuXORmpqKKVOmID8/HwBgamqKd955B/PmzZM4OqpO3nzzTQwbNgwbNmzQdHNTKpWYMmUK3nzzTVy+fFniCA0H1+8jKSQkJJQYfwsAnTp1Yjfxx6xevRpWVlYAgDVr1kgbDJEOnNCC9IIz9vw3mZmZiIyMhJmZGRo1agSFQiF1SFTNmJmZISIiAl5eXlrbo6Ki4Ovri5ycHIkiMzw9evQAoM72dezYscT6fe7u7pg9ezYX+ia9atasGUaOHIn33ntPa/tHH32En376CZcuXZIoMiIqD2auSC8GDRokdQhVmqWlJdq2bSt1GFSNtW7dGpGRkSUaV5GRkczCPKFoUguu30eVafHixRg2bBiOHz+Ozp07QxAEnDx5EkeOHMGuXbukDs9gPL5cwtPwb5ekwMwVEVEN8NNPP2Hu3Ll46623tBYRXrduHZYvXw5vb29N2RYtWkgVJlGNFhYWhlWrViEyMhKiKMLHxwezZs1Cq1atpA7NYDy5XIIuXC6BpMTGFRFRDfD4IsK6CILACxIiiRQUFOD111/H/Pnz4enpKXU4Bo1r9pGhY+OK9EKpVGL16tXYtWsX4uLiNJMzFOGMPUTSun379jOXdXNzq8BIiEgXW1tbhIWFsXFFVMVxzBXpxeLFi/HNN99g5syZmD9/Pt5//33cunULe/bswYIFC6QOj6jGK2owXb16tcQNEEEQMGDAAKlCIyIAgwcPxp49ezSzVZJuFy9efOay7OJMUmDmivSiQYMG+OKLL9C/f39YWVkhIiJCs+3s2bPYsWOH1CES1WgxMTEYPHgwLl26pOkCCEAzdoFdAYmktXTpUnz22Wfo1asX2rRpAwsLC63X3377bYkiMyxFY66edvnKLs4kFTauSC8sLCwQGRmJ+vXrw8XFBX/++Sdat26NmJgYtGrVCo8ePZI6RKIabcCAAZDL5di0aRM8PT1x7tw5pKamYtasWfjss8/QtWtXqUMkqtE8PDxKfU0QBMTExFRiNIaLXZzJ0LFbIOlFvXr1kJCQgPr166Nhw4Y4ePAgWrdujX/++YdrNhEZgDNnzuDo0aNwdHSETCaDXC5Hly5dsGzZMrz99tsIDw+XOkSiGi02Nlbz7yczy1SMDSYydGVPH0X0jAYPHowjR44AAKZNm4b58+ejUaNGGDNmDAIDAyWOjoiUSiUsLS0BAA4ODrh37x4A9YVKVFSUlKER0b82b96MZs2awdTUFKampmjWrBm++eYbqcMyaNu3b0fnzp1Rp04dTVZrzZo12Lt3r8SRUU3FzBXpxfLlyzX/fvXVV1GvXj2cPn0aDRs2xMCBAyWMjIgAoFmzZrh48SI8PT3Rvn17fPrppzAxMcHXX3/N2cmIDMD8+fOxevVqvPXWW+jYsSMAdcZ5xowZuHXrFj766COJIzQ8GzZswIIFCzB9+nQsXbpUM8bK1tYWa9asQUBAgMQRUk3EMVdERDXA33//jaysLAwZMgQxMTF4+eWXce3aNdSqVQs//fQTevbsKXWIRDWag4MD1q5dixEjRmht37lzJ9566y2kpKRIFJnh8vHxwccff4xBgwbBysoKFy5cgKenJy5fvozu3buzzkgSzFyRXnz33Xdlvj5mzJhKioSIdOnXr5/m356enrh69SpSU1NhZ2fHcR1EBkCpVMLPz6/E9jZt2qCwsFCCiAxfbGwsWrVqVWK7QqFAVlaWBBERsXFFejJt2jSt5wUFBcjOzoaJiQnMzc3ZuCIyQPb29lKHQET/+r//+z9s2LABq1at0tr+9ddfY9SoURJFZdg8PDwQERFRYpKLAwcOwMfHR6KoqKZj44r04uHDhyW2Xb9+HZMnT8acOXMkiIiIiKhq2bx5Mw4ePIgOHToAAM6ePYv4+HiMGTNGa3HhJxtgNdWcOXPw5ptvIjc3F6Io4vz589i5cyeWLVvGiUBIMhxzRRUqJCQE//d//4dr165JHQoREZHB6tGjxzOVEwQBR48ereBoqo5Nmzbho48+Qnx8PAD10jALFy7E+PHjJY6Maio2rqhChYeHw9/fH+np6VKHQkRERNVITk4ORFGEubk5UlJSEBMTg1OnTsHHx0drnClRZWK3QNKL33//Xeu5KIpISEjAl19+ic6dO0sUFREREVVXAQEBGDJkCCZNmgQjIyMMHDgQxsbGSElJwapVqzB58mSpQ6QaiJkr0guZTHs9akEQ4OjoiJ49e2LlypVwcXGRKDIiIiKqjhwcHBAcHIymTZvim2++wdq1axEeHo5ffvkFCxYsQGRkpNQhUg3EzBXphUqlkjoEIiIiqkGys7NhZWUFADh48CCGDBkCmUyGDh064Pbt2xJHRzWV7OlFiIiIiIgMS8OGDbFnzx7Ex8fj77//Rt++fQEASUlJsLa2ljg6qqnYLZD04vEpYp+GU8gSERHRf7V7926MHDkSSqUSvXr1wsGDBwEAy5Ytw/Hjx3HgwAGJI6SaiI0r0osePXogLCwMhYWF8PLyAgBER0dDLpejdevWmnKcQpaIiIj0JTExEQkJCWjZsqVm/Pf58+dhbW2NJk2aSBwd1URsXJFerFq1CkFBQdi2bRvs7OwAqBcWfu2119C1a1fMmjVL4giJiIiIiCoWG1ekF3Xr1sXBgwfRtGlTre2XL19G3759ce/ePYkiIyIiIiKqHJzQgvQiPT0d9+/fL7E9KSkJGRkZEkRERERERFS52LgivRg8eDBee+017N69G3fu3MGdO3ewe/dujB8/HkOGDJE6PCIiIiKiCsdugaQX2dnZmD17NrZs2YKCggIAgJGREcaPH48VK1bAwsJC4giJiIiIiCoWG1ekV1lZWbh58yZEUUTDhg3ZqCIiIiKiGoONKyIiIiIiIj3gmCsiIiIiIiI9YOOKiIiIiIhID9i4IiIiIiIi0gM2roiIiIiIiPSAjSsiIiIiIiI9YOOKiIiIiIhID9i4IiIiIiIi0oP/Bzkgvf5L9kgDAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#get correlations of each features in dataset\n",
    "selected_columns = ['quantity tons', 'customer', 'country', 'application', 'thickness', 'width', 'product_ref', 'selling_price']\n",
    "subset_df = df[selected_columns]\n",
    "corr_matrix = subset_df.corr()\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', fmt=\".2f\", linewidths=.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0f15597",
   "metadata": {},
   "source": [
    "## Skewness Detection And Data Transformation(Log transformation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f53eed39",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'product_ref Boxplot')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=3,ncols=2)\n",
    "fig.set_size_inches(12, 10)\n",
    "plt.tight_layout()\n",
    "\n",
    "a = sns.distplot(df['quantity tons'] , ax=axes[0][0])\n",
    "a = sns.boxplot(df['quantity tons'] , orient = \"h\" , ax=axes[0][1])\n",
    "a.set_title(\"quantity tons Boxplot\",fontsize=12)\n",
    "\n",
    "\n",
    "a = sns.distplot(df['thickness'] , ax=axes[1][0])\n",
    "a = sns.boxplot(df['thickness'] , orient = \"h\" , ax=axes[1][1])\n",
    "a.set_title(\"thickness Boxplot\",fontsize=12)\n",
    "\n",
    "a = sns.distplot(df['product_ref'] , ax=axes[2][0])\n",
    "a = sns.boxplot(df['product_ref'] , orient = \"h\" , ax=axes[2][1])\n",
    "a.set_title(\"product_ref Boxplot\",fontsize=12)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7a980953",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['quantity tons', 'customer', 'country', 'status', 'item type',\n",
       "       'application', 'thickness', 'width', 'product_ref', 'selling_price'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1fd0b9d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['selling_price_log'] = np.log(df['selling_price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "6b2022e7",
   "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>quantity tons</th>\n",
       "      <th>customer</th>\n",
       "      <th>country</th>\n",
       "      <th>status</th>\n",
       "      <th>item type</th>\n",
       "      <th>application</th>\n",
       "      <th>thickness</th>\n",
       "      <th>width</th>\n",
       "      <th>product_ref</th>\n",
       "      <th>selling_price</th>\n",
       "      <th>selling_price_log</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>54.151139</td>\n",
       "      <td>30156308.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>Won</td>\n",
       "      <td>W</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>1670798778</td>\n",
       "      <td>854.00</td>\n",
       "      <td>6.749931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>768.024839</td>\n",
       "      <td>30202938.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>Won</td>\n",
       "      <td>W</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1210.0</td>\n",
       "      <td>1668701718</td>\n",
       "      <td>1047.00</td>\n",
       "      <td>6.953684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>386.127949</td>\n",
       "      <td>30153963.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Won</td>\n",
       "      <td>WI</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.38</td>\n",
       "      <td>952.0</td>\n",
       "      <td>628377</td>\n",
       "      <td>644.33</td>\n",
       "      <td>6.468211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>202.411065</td>\n",
       "      <td>30349574.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>Won</td>\n",
       "      <td>S</td>\n",
       "      <td>59.0</td>\n",
       "      <td>2.30</td>\n",
       "      <td>1317.0</td>\n",
       "      <td>1668701718</td>\n",
       "      <td>768.00</td>\n",
       "      <td>6.643790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>785.526262</td>\n",
       "      <td>30211560.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>Won</td>\n",
       "      <td>W</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>640665</td>\n",
       "      <td>577.00</td>\n",
       "      <td>6.357842</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   quantity tons    customer  country status item type  application  \\\n",
       "0      54.151139  30156308.0     28.0    Won         W         10.0   \n",
       "1     768.024839  30202938.0     25.0    Won         W         41.0   \n",
       "2     386.127949  30153963.0     30.0    Won        WI         28.0   \n",
       "3     202.411065  30349574.0     32.0    Won         S         59.0   \n",
       "4     785.526262  30211560.0     28.0    Won         W         10.0   \n",
       "\n",
       "   thickness   width  product_ref  selling_price  selling_price_log  \n",
       "0       2.00  1500.0   1670798778         854.00           6.749931  \n",
       "1       0.80  1210.0   1668701718        1047.00           6.953684  \n",
       "2       0.38   952.0       628377         644.33           6.468211  \n",
       "3       2.30  1317.0   1668701718         768.00           6.643790  \n",
       "4       4.00  2000.0       640665         577.00           6.357842  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "8a43075d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(df[df['selling_price_log'] <= 0].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "823bbf9c",
   "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>quantity tons</th>\n",
       "      <th>customer</th>\n",
       "      <th>country</th>\n",
       "      <th>status</th>\n",
       "      <th>item type</th>\n",
       "      <th>application</th>\n",
       "      <th>thickness</th>\n",
       "      <th>width</th>\n",
       "      <th>product_ref</th>\n",
       "      <th>selling_price</th>\n",
       "      <th>selling_price_log</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [quantity tons, customer, country, status, item type, application, thickness, width, product_ref, selling_price, selling_price_log]\n",
       "Index: []"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['selling_price_log'] <= 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b1c867ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "quantity tons        0\n",
       "customer             0\n",
       "country              0\n",
       "status               0\n",
       "item type            0\n",
       "application          0\n",
       "thickness            0\n",
       "width                0\n",
       "product_ref          0\n",
       "selling_price        0\n",
       "selling_price_log    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c1d2da03",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "68eb8978",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(181627, 11)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "e182d452",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['selling_price_log'])\n",
    "plt.show()\n",
    "sns.boxplot(df['selling_price_log'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "345c8a19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['quantity tons', 'customer', 'country', 'status', 'item type',\n",
       "       'application', 'thickness', 'width', 'product_ref', 'selling_price',\n",
       "       'selling_price_log'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "9bfd0dbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "status\n",
       "Won                115999\n",
       "Lost                34433\n",
       "Not lost for AM     19566\n",
       "Revised              4276\n",
       "To be approved       4169\n",
       "Draft                3120\n",
       "Offered                53\n",
       "Offerable              10\n",
       "Wonderful               1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "26846848",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Won', 'Draft', 'To be approved', 'Lost', 'Not lost for AM',\n",
       "       'Wonderful', 'Revised', 'Offered', 'Offerable'], dtype=object)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['status'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "90067d89",
   "metadata": {},
   "outputs": [],
   "source": [
    "status_mapping = {'Won': 0, 'Draft': 1, 'To be approved': 2, 'Lost': 3, 'Not lost for AM': 4,'Wonderful': 5, 'Revised': 6, 'Offered': 7, 'Offerable': 8}\n",
    "df['status'].replace(status_mapping, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "2fe00b97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8], dtype=int64)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['status'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "fadf2fa2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "item type\n",
       "W         105601\n",
       "S          69205\n",
       "PL          5659\n",
       "Others       610\n",
       "WI           524\n",
       "IPL           27\n",
       "SLAWR          1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['item type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "e4848ecc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['W', 'WI', 'S', 'Others', 'PL', 'IPL', 'SLAWR'], dtype=object)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['item type'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "28c1e987",
   "metadata": {},
   "outputs": [],
   "source": [
    "itemtype_mapping = {'W' : 0, 'WI' : 1, 'S' : 2, 'Others': 3, 'PL': 4, 'IPL': 5 , 'SLAWR': 6}\n",
    "df['item type'].replace(itemtype_mapping, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "8883b6f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6], dtype=int64)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['item type'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "cfb40768",
   "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>quantity tons</th>\n",
       "      <th>customer</th>\n",
       "      <th>country</th>\n",
       "      <th>status</th>\n",
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       "      <th>selling_price</th>\n",
       "      <th>selling_price_log</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>54.151139</td>\n",
       "      <td>30156308.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>1670798778</td>\n",
       "      <td>854.00</td>\n",
       "      <td>6.749931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>768.024839</td>\n",
       "      <td>30202938.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1210.0</td>\n",
       "      <td>1668701718</td>\n",
       "      <td>1047.00</td>\n",
       "      <td>6.953684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>386.127949</td>\n",
       "      <td>30153963.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.38</td>\n",
       "      <td>952.0</td>\n",
       "      <td>628377</td>\n",
       "      <td>644.33</td>\n",
       "      <td>6.468211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>202.411065</td>\n",
       "      <td>30349574.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>59.0</td>\n",
       "      <td>2.30</td>\n",
       "      <td>1317.0</td>\n",
       "      <td>1668701718</td>\n",
       "      <td>768.00</td>\n",
       "      <td>6.643790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>785.526262</td>\n",
       "      <td>30211560.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>640665</td>\n",
       "      <td>577.00</td>\n",
       "      <td>6.357842</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   quantity tons    customer  country  status  item type  application  \\\n",
       "0      54.151139  30156308.0     28.0       0          0         10.0   \n",
       "1     768.024839  30202938.0     25.0       0          0         41.0   \n",
       "2     386.127949  30153963.0     30.0       0          1         28.0   \n",
       "3     202.411065  30349574.0     32.0       0          2         59.0   \n",
       "4     785.526262  30211560.0     28.0       0          0         10.0   \n",
       "\n",
       "   thickness   width  product_ref  selling_price  selling_price_log  \n",
       "0       2.00  1500.0   1670798778         854.00           6.749931  \n",
       "1       0.80  1210.0   1668701718        1047.00           6.953684  \n",
       "2       0.38   952.0       628377         644.33           6.468211  \n",
       "3       2.30  1317.0   1668701718         768.00           6.643790  \n",
       "4       4.00  2000.0       640665         577.00           6.357842  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "8472463b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>quantity tons</th>\n",
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       "      <td>-0.000010</td>\n",
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       "      <td>1.000000</td>\n",
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       "      <td>0.033077</td>\n",
       "      <td>0.081375</td>\n",
       "      <td>0.000899</td>\n",
       "      <td>0.086936</td>\n",
       "      <td>0.050522</td>\n",
       "      <td>-0.032338</td>\n",
       "      <td>0.001746</td>\n",
       "      <td>0.008785</td>\n",
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       "      <th>country</th>\n",
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       "      <td>-0.102477</td>\n",
       "      <td>-0.138032</td>\n",
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       "      <td>0.002993</td>\n",
       "      <td>-0.029195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>status</th>\n",
       "      <td>0.004003</td>\n",
       "      <td>0.033077</td>\n",
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       "      <td>1.000000</td>\n",
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       "      <td>0.067112</td>\n",
       "      <td>0.003473</td>\n",
       "      <td>0.017315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>item type</th>\n",
       "      <td>0.002236</td>\n",
       "      <td>0.081375</td>\n",
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       "      <td>0.213214</td>\n",
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       "      <td>0.006166</td>\n",
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       "      <th>application</th>\n",
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       "      <td>1.000000</td>\n",
       "      <td>-0.135323</td>\n",
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       "      <td>-0.131853</td>\n",
       "      <td>0.001462</td>\n",
       "      <td>0.022738</td>\n",
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       "    <tr>\n",
       "      <th>thickness</th>\n",
       "      <td>-0.001402</td>\n",
       "      <td>0.086936</td>\n",
       "      <td>-0.039477</td>\n",
       "      <td>0.083146</td>\n",
       "      <td>0.059498</td>\n",
       "      <td>-0.135323</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.357565</td>\n",
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       "      <td>-0.002467</td>\n",
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       "    <tr>\n",
       "      <th>width</th>\n",
       "      <td>-0.000428</td>\n",
       "      <td>0.050522</td>\n",
       "      <td>0.055299</td>\n",
       "      <td>0.066523</td>\n",
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       "      <td>-0.034409</td>\n",
       "      <td>0.000584</td>\n",
       "      <td>-0.069434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>product_ref</th>\n",
       "      <td>-0.001086</td>\n",
       "      <td>-0.032338</td>\n",
       "      <td>-0.147370</td>\n",
       "      <td>0.067112</td>\n",
       "      <td>0.086990</td>\n",
       "      <td>-0.131853</td>\n",
       "      <td>0.076531</td>\n",
       "      <td>-0.034409</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.002117</td>\n",
       "      <td>0.001118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>selling_price</th>\n",
       "      <td>-0.000010</td>\n",
       "      <td>0.001746</td>\n",
       "      <td>0.002993</td>\n",
       "      <td>0.003473</td>\n",
       "      <td>0.006166</td>\n",
       "      <td>0.001462</td>\n",
       "      <td>-0.002467</td>\n",
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       "      <td>0.002117</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.069989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>selling_price_log</th>\n",
       "      <td>-0.001041</td>\n",
       "      <td>0.008785</td>\n",
       "      <td>-0.029195</td>\n",
       "      <td>0.017315</td>\n",
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       "      <td>-0.104589</td>\n",
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       "      <td>0.001118</td>\n",
       "      <td>0.069989</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   quantity tons  customer   country    status  item type  \\\n",
       "quantity tons           1.000000 -0.000659 -0.001531  0.004003   0.002236   \n",
       "customer               -0.000659  1.000000  0.083558  0.033077   0.081375   \n",
       "country                -0.001531  0.083558  1.000000 -0.102477  -0.138032   \n",
       "status                  0.004003  0.033077 -0.102477  1.000000   0.213214   \n",
       "item type               0.002236  0.081375 -0.138032  0.213214   1.000000   \n",
       "application            -0.001956  0.000899 -0.019356 -0.085291  -0.179962   \n",
       "thickness              -0.001402  0.086936 -0.039477  0.083146   0.059498   \n",
       "width                  -0.000428  0.050522  0.055299  0.066523   0.094756   \n",
       "product_ref            -0.001086 -0.032338 -0.147370  0.067112   0.086990   \n",
       "selling_price          -0.000010  0.001746  0.002993  0.003473   0.006166   \n",
       "selling_price_log      -0.001041  0.008785 -0.029195  0.017315  -0.046527   \n",
       "\n",
       "                   application  thickness     width  product_ref  \\\n",
       "quantity tons        -0.001956  -0.001402 -0.000428    -0.001086   \n",
       "customer              0.000899   0.086936  0.050522    -0.032338   \n",
       "country              -0.019356  -0.039477  0.055299    -0.147370   \n",
       "status               -0.085291   0.083146  0.066523     0.067112   \n",
       "item type            -0.179962   0.059498  0.094756     0.086990   \n",
       "application           1.000000  -0.135323 -0.204453    -0.131853   \n",
       "thickness            -0.135323   1.000000  0.357565     0.076531   \n",
       "width                -0.204453   0.357565  1.000000    -0.034409   \n",
       "product_ref          -0.131853   0.076531 -0.034409     1.000000   \n",
       "selling_price         0.001462  -0.002467  0.000584     0.002117   \n",
       "selling_price_log     0.022738  -0.104589 -0.069434     0.001118   \n",
       "\n",
       "                   selling_price  selling_price_log  \n",
       "quantity tons          -0.000010          -0.001041  \n",
       "customer                0.001746           0.008785  \n",
       "country                 0.002993          -0.029195  \n",
       "status                  0.003473           0.017315  \n",
       "item type               0.006166          -0.046527  \n",
       "application             0.001462           0.022738  \n",
       "thickness              -0.002467          -0.104589  \n",
       "width                   0.000584          -0.069434  \n",
       "product_ref             0.002117           0.001118  \n",
       "selling_price           1.000000           0.069989  \n",
       "selling_price_log       0.069989           1.000000  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "47828d82",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>customer</th>\n",
       "      <td>-1.557294e+08</td>\n",
       "      <td>1.011314e+10</td>\n",
       "      <td>2.050699e+05</td>\n",
       "      <td>5.711922e+03</td>\n",
       "      <td>9.145253e+03</td>\n",
       "      <td>1.605408e+03</td>\n",
       "      <td>2.602213e+04</td>\n",
       "      <td>1.329348e+06</td>\n",
       "      <td>-2.333476e+12</td>\n",
       "      <td>5.827951e+07</td>\n",
       "      <td>497.955030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <td>-8.775576e+04</td>\n",
       "      <td>2.050699e+05</td>\n",
       "      <td>5.955803e+02</td>\n",
       "      <td>-4.294485e+00</td>\n",
       "      <td>-3.764572e+00</td>\n",
       "      <td>-8.386507e+00</td>\n",
       "      <td>-2.867607e+00</td>\n",
       "      <td>3.531089e+02</td>\n",
       "      <td>-2.580606e+09</td>\n",
       "      <td>2.423513e+04</td>\n",
       "      <td>-0.401606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>status</th>\n",
       "      <td>1.614853e+04</td>\n",
       "      <td>5.711922e+03</td>\n",
       "      <td>-4.294485e+00</td>\n",
       "      <td>2.948713e+00</td>\n",
       "      <td>4.091637e-01</td>\n",
       "      <td>-2.600280e+00</td>\n",
       "      <td>4.249739e-01</td>\n",
       "      <td>2.988856e+01</td>\n",
       "      <td>8.269051e+07</td>\n",
       "      <td>1.978789e+03</td>\n",
       "      <td>0.016759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>item type</th>\n",
       "      <td>5.870923e+03</td>\n",
       "      <td>9.145253e+03</td>\n",
       "      <td>-3.764572e+00</td>\n",
       "      <td>4.091637e-01</td>\n",
       "      <td>1.248902e+00</td>\n",
       "      <td>-3.570647e+00</td>\n",
       "      <td>1.979119e-01</td>\n",
       "      <td>2.770699e+01</td>\n",
       "      <td>6.975485e+07</td>\n",
       "      <td>2.286595e+03</td>\n",
       "      <td>-0.029308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>application</th>\n",
       "      <td>-8.157410e+04</td>\n",
       "      <td>1.605408e+03</td>\n",
       "      <td>-8.386507e+00</td>\n",
       "      <td>-2.600280e+00</td>\n",
       "      <td>-3.570647e+00</td>\n",
       "      <td>3.152123e+02</td>\n",
       "      <td>-7.151145e+00</td>\n",
       "      <td>-9.497592e+02</td>\n",
       "      <td>-1.679700e+09</td>\n",
       "      <td>8.611889e+03</td>\n",
       "      <td>0.227549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thickness</th>\n",
       "      <td>-9.805882e+03</td>\n",
       "      <td>2.602213e+04</td>\n",
       "      <td>-2.867607e+00</td>\n",
       "      <td>4.249739e-01</td>\n",
       "      <td>1.979119e-01</td>\n",
       "      <td>-7.151145e+00</td>\n",
       "      <td>8.859404e+00</td>\n",
       "      <td>2.784674e+02</td>\n",
       "      <td>1.634476e+08</td>\n",
       "      <td>-2.436803e+03</td>\n",
       "      <td>-0.175471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>width</th>\n",
       "      <td>-2.631180e+05</td>\n",
       "      <td>1.329348e+06</td>\n",
       "      <td>3.531089e+02</td>\n",
       "      <td>2.988856e+01</td>\n",
       "      <td>2.770699e+01</td>\n",
       "      <td>-9.497592e+02</td>\n",
       "      <td>2.784674e+02</td>\n",
       "      <td>6.845976e+04</td>\n",
       "      <td>-6.459973e+09</td>\n",
       "      <td>5.069473e+04</td>\n",
       "      <td>-10.240194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>product_ref</th>\n",
       "      <td>-1.830641e+12</td>\n",
       "      <td>-2.333476e+12</td>\n",
       "      <td>-2.580606e+09</td>\n",
       "      <td>8.269051e+07</td>\n",
       "      <td>6.975485e+07</td>\n",
       "      <td>-1.679700e+09</td>\n",
       "      <td>1.634476e+08</td>\n",
       "      <td>-6.459973e+09</td>\n",
       "      <td>5.148523e+17</td>\n",
       "      <td>5.041663e+11</td>\n",
       "      <td>452096.865085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>selling_price</th>\n",
       "      <td>-7.716062e+06</td>\n",
       "      <td>5.827951e+07</td>\n",
       "      <td>2.423513e+04</td>\n",
       "      <td>1.978789e+03</td>\n",
       "      <td>2.286595e+03</td>\n",
       "      <td>8.611889e+03</td>\n",
       "      <td>-2.436803e+03</td>\n",
       "      <td>5.069473e+04</td>\n",
       "      <td>5.041663e+11</td>\n",
       "      <td>1.101156e+11</td>\n",
       "      <td>13090.957591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>selling_price_log</th>\n",
       "      <td>-1.378814e+03</td>\n",
       "      <td>4.979550e+02</td>\n",
       "      <td>-4.016059e-01</td>\n",
       "      <td>1.675904e-02</td>\n",
       "      <td>-2.930801e-02</td>\n",
       "      <td>2.275493e-01</td>\n",
       "      <td>-1.754711e-01</td>\n",
       "      <td>-1.024019e+01</td>\n",
       "      <td>4.520969e+05</td>\n",
       "      <td>1.309096e+04</td>\n",
       "      <td>0.317715</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   quantity tons      customer       country        status  \\\n",
       "quantity tons       5.519550e+12 -1.557294e+08 -8.775576e+04  1.614853e+04   \n",
       "customer           -1.557294e+08  1.011314e+10  2.050699e+05  5.711922e+03   \n",
       "country            -8.775576e+04  2.050699e+05  5.955803e+02 -4.294485e+00   \n",
       "status              1.614853e+04  5.711922e+03 -4.294485e+00  2.948713e+00   \n",
       "item type           5.870923e+03  9.145253e+03 -3.764572e+00  4.091637e-01   \n",
       "application        -8.157410e+04  1.605408e+03 -8.386507e+00 -2.600280e+00   \n",
       "thickness          -9.805882e+03  2.602213e+04 -2.867607e+00  4.249739e-01   \n",
       "width              -2.631180e+05  1.329348e+06  3.531089e+02  2.988856e+01   \n",
       "product_ref        -1.830641e+12 -2.333476e+12 -2.580606e+09  8.269051e+07   \n",
       "selling_price      -7.716062e+06  5.827951e+07  2.423513e+04  1.978789e+03   \n",
       "selling_price_log  -1.378814e+03  4.979550e+02 -4.016059e-01  1.675904e-02   \n",
       "\n",
       "                      item type   application     thickness         width  \\\n",
       "quantity tons      5.870923e+03 -8.157410e+04 -9.805882e+03 -2.631180e+05   \n",
       "customer           9.145253e+03  1.605408e+03  2.602213e+04  1.329348e+06   \n",
       "country           -3.764572e+00 -8.386507e+00 -2.867607e+00  3.531089e+02   \n",
       "status             4.091637e-01 -2.600280e+00  4.249739e-01  2.988856e+01   \n",
       "item type          1.248902e+00 -3.570647e+00  1.979119e-01  2.770699e+01   \n",
       "application       -3.570647e+00  3.152123e+02 -7.151145e+00 -9.497592e+02   \n",
       "thickness          1.979119e-01 -7.151145e+00  8.859404e+00  2.784674e+02   \n",
       "width              2.770699e+01 -9.497592e+02  2.784674e+02  6.845976e+04   \n",
       "product_ref        6.975485e+07 -1.679700e+09  1.634476e+08 -6.459973e+09   \n",
       "selling_price      2.286595e+03  8.611889e+03 -2.436803e+03  5.069473e+04   \n",
       "selling_price_log -2.930801e-02  2.275493e-01 -1.754711e-01 -1.024019e+01   \n",
       "\n",
       "                    product_ref  selling_price  selling_price_log  \n",
       "quantity tons     -1.830641e+12  -7.716062e+06       -1378.814205  \n",
       "customer          -2.333476e+12   5.827951e+07         497.955030  \n",
       "country           -2.580606e+09   2.423513e+04          -0.401606  \n",
       "status             8.269051e+07   1.978789e+03           0.016759  \n",
       "item type          6.975485e+07   2.286595e+03          -0.029308  \n",
       "application       -1.679700e+09   8.611889e+03           0.227549  \n",
       "thickness          1.634476e+08  -2.436803e+03          -0.175471  \n",
       "width             -6.459973e+09   5.069473e+04         -10.240194  \n",
       "product_ref        5.148523e+17   5.041663e+11      452096.865085  \n",
       "selling_price      5.041663e+11   1.101156e+11       13090.957591  \n",
       "selling_price_log  4.520969e+05   1.309096e+04           0.317715  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.cov()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fab6cd50",
   "metadata": {},
   "source": [
    "## Outlier Handling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "1f91ba29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1500 with 11 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,15))\n",
    "for i, col in enumerate(df.columns, 0):\n",
    "    plt.subplot(5, 3, i + 1)\n",
    "    sns.boxplot(x=df[col])\n",
    "    plt.title(col)  \n",
    "\n",
    "plt.tight_layout() \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "f297a996",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using IQR method to handle the outliers.\n",
    "def outlier(df, column):\n",
    "    iqr = df[column].quantile(0.75) - df[column].quantile(0.25)\n",
    "    upper_threshold = df[column].quantile(0.75) + (1.5*iqr)\n",
    "    lower_threshold = df[column].quantile(0.25) - (1.5*iqr)\n",
    "    df[column] = df[column].clip(lower_threshold, upper_threshold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "2373409a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['quantity tons', 'customer', 'country', 'status', 'item type',\n",
       "       'application', 'thickness', 'width', 'product_ref', 'selling_price',\n",
       "       'selling_price_log'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "f544e72a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([30156308., 30202938., 30153963., ..., 30349515., 30284694.,\n",
       "       30161559.])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['customer'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "c0480d12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12458.0, 30408185.0)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min(df['customer']),max(df['customer'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "a3a8ec47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1e-05, 1000000000.0)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min(df['quantity tons']),max(df['quantity tons'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "e598d53c",
   "metadata": {},
   "outputs": [],
   "source": [
    "column = ['quantity tons', 'thickness', 'width']\n",
    "for i in column:\n",
    "    outlier(df, i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "f5634dea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1500 with 11 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,15))\n",
    "for i, col in enumerate(df.columns, 0):\n",
    "    plt.subplot(5, 3, i + 1)\n",
    "    sns.boxplot(x=df[col])\n",
    "    plt.title(col)  \n",
    "\n",
    "plt.tight_layout() \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "708c7e1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "quantity tons        0\n",
       "customer             0\n",
       "country              0\n",
       "status               0\n",
       "item type            0\n",
       "application          0\n",
       "thickness            0\n",
       "width                0\n",
       "product_ref          0\n",
       "selling_price        0\n",
       "selling_price_log    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "9ac461f5",
   "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>quantity tons</th>\n",
       "      <th>customer</th>\n",
       "      <th>country</th>\n",
       "      <th>status</th>\n",
       "      <th>item type</th>\n",
       "      <th>application</th>\n",
       "      <th>thickness</th>\n",
       "      <th>width</th>\n",
       "      <th>product_ref</th>\n",
       "      <th>selling_price</th>\n",
       "      <th>selling_price_log</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>54.151139</td>\n",
       "      <td>30156308.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1500.00</td>\n",
       "      <td>1670798778</td>\n",
       "      <td>854.00</td>\n",
       "      <td>6.749931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>151.450919</td>\n",
       "      <td>30202938.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1210.00</td>\n",
       "      <td>1668701718</td>\n",
       "      <td>1047.00</td>\n",
       "      <td>6.953684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>151.450919</td>\n",
       "      <td>30153963.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.38</td>\n",
       "      <td>952.00</td>\n",
       "      <td>628377</td>\n",
       "      <td>644.33</td>\n",
       "      <td>6.468211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>151.450919</td>\n",
       "      <td>30349574.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>59.0</td>\n",
       "      <td>2.30</td>\n",
       "      <td>1317.00</td>\n",
       "      <td>1668701718</td>\n",
       "      <td>768.00</td>\n",
       "      <td>6.643790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>151.450919</td>\n",
       "      <td>30211560.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1980.75</td>\n",
       "      <td>640665</td>\n",
       "      <td>577.00</td>\n",
       "      <td>6.357842</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   quantity tons    customer  country  status  item type  application  \\\n",
       "0      54.151139  30156308.0     28.0       0          0         10.0   \n",
       "1     151.450919  30202938.0     25.0       0          0         41.0   \n",
       "2     151.450919  30153963.0     30.0       0          1         28.0   \n",
       "3     151.450919  30349574.0     32.0       0          2         59.0   \n",
       "4     151.450919  30211560.0     28.0       0          0         10.0   \n",
       "\n",
       "   thickness    width  product_ref  selling_price  selling_price_log  \n",
       "0       2.00  1500.00   1670798778         854.00           6.749931  \n",
       "1       0.80  1210.00   1668701718        1047.00           6.953684  \n",
       "2       0.38   952.00       628377         644.33           6.468211  \n",
       "3       2.30  1317.00   1668701718         768.00           6.643790  \n",
       "4       4.00  1980.75       640665         577.00           6.357842  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "0ba2fa8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "5d6cc9fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['quantity tons', 'customer', 'country', 'status', 'item type',\n",
       "       'application', 'thickness', 'width', 'product_ref', 'selling_price',\n",
       "       'selling_price_log'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "e545e45a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.drop(['selling_price'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "b2f7aa13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>quantity tons</th>\n",
       "      <th>customer</th>\n",
       "      <th>country</th>\n",
       "      <th>status</th>\n",
       "      <th>item type</th>\n",
       "      <th>application</th>\n",
       "      <th>thickness</th>\n",
       "      <th>width</th>\n",
       "      <th>product_ref</th>\n",
       "      <th>selling_price_log</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>54.151139</td>\n",
       "      <td>30156308.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1500.00</td>\n",
       "      <td>1670798778</td>\n",
       "      <td>6.749931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>151.450919</td>\n",
       "      <td>30202938.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1210.00</td>\n",
       "      <td>1668701718</td>\n",
       "      <td>6.953684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>151.450919</td>\n",
       "      <td>30153963.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.38</td>\n",
       "      <td>952.00</td>\n",
       "      <td>628377</td>\n",
       "      <td>6.468211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>151.450919</td>\n",
       "      <td>30349574.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>59.0</td>\n",
       "      <td>2.30</td>\n",
       "      <td>1317.00</td>\n",
       "      <td>1668701718</td>\n",
       "      <td>6.643790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>151.450919</td>\n",
       "      <td>30211560.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1980.75</td>\n",
       "      <td>640665</td>\n",
       "      <td>6.357842</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   quantity tons    customer  country  status  item type  application  \\\n",
       "0      54.151139  30156308.0     28.0       0          0         10.0   \n",
       "1     151.450919  30202938.0     25.0       0          0         41.0   \n",
       "2     151.450919  30153963.0     30.0       0          1         28.0   \n",
       "3     151.450919  30349574.0     32.0       0          2         59.0   \n",
       "4     151.450919  30211560.0     28.0       0          0         10.0   \n",
       "\n",
       "   thickness    width  product_ref  selling_price_log  \n",
       "0       2.00  1500.00   1670798778           6.749931  \n",
       "1       0.80  1210.00   1668701718           6.953684  \n",
       "2       0.38   952.00       628377           6.468211  \n",
       "3       2.30  1317.00   1668701718           6.643790  \n",
       "4       4.00  1980.75       640665           6.357842  "
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c326ccf",
   "metadata": {},
   "source": [
    "# Regression Model Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "d88208e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "69ccf3e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df1.drop('selling_price_log',axis=1)\n",
    "y = df1['selling_price_log']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "defd850c",
   "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>quantity tons</th>\n",
       "      <th>customer</th>\n",
       "      <th>country</th>\n",
       "      <th>status</th>\n",
       "      <th>item type</th>\n",
       "      <th>application</th>\n",
       "      <th>thickness</th>\n",
       "      <th>width</th>\n",
       "      <th>product_ref</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>54.151139</td>\n",
       "      <td>30156308.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1500.00</td>\n",
       "      <td>1670798778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>151.450919</td>\n",
       "      <td>30202938.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1210.00</td>\n",
       "      <td>1668701718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>151.450919</td>\n",
       "      <td>30153963.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.38</td>\n",
       "      <td>952.00</td>\n",
       "      <td>628377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>151.450919</td>\n",
       "      <td>30349574.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>59.0</td>\n",
       "      <td>2.30</td>\n",
       "      <td>1317.00</td>\n",
       "      <td>1668701718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>151.450919</td>\n",
       "      <td>30211560.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1980.75</td>\n",
       "      <td>640665</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   quantity tons    customer  country  status  item type  application  \\\n",
       "0      54.151139  30156308.0     28.0       0          0         10.0   \n",
       "1     151.450919  30202938.0     25.0       0          0         41.0   \n",
       "2     151.450919  30153963.0     30.0       0          1         28.0   \n",
       "3     151.450919  30349574.0     32.0       0          2         59.0   \n",
       "4     151.450919  30211560.0     28.0       0          0         10.0   \n",
       "\n",
       "   thickness    width  product_ref  \n",
       "0       2.00  1500.00   1670798778  \n",
       "1       0.80  1210.00   1668701718  \n",
       "2       0.38   952.00       628377  \n",
       "3       2.30  1317.00   1668701718  \n",
       "4       4.00  1980.75       640665  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "c933993d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    6.749931\n",
       "1    6.953684\n",
       "2    6.468211\n",
       "3    6.643790\n",
       "4    6.357842\n",
       "Name: selling_price_log, dtype: float64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "99bc5d4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(181627, 9)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "bcc1eb40",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_rg, x_test_rg, y_train_rg, y_test_rg = train_test_split( x, y, test_size=0.30, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "925a3506",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(127138, 9)\n",
      "(54489, 9)\n",
      "(127138,)\n",
      "(54489,)\n"
     ]
    }
   ],
   "source": [
    "print(x_train_rg.shape)\n",
    "print(x_test_rg.shape)\n",
    "print(y_train_rg.shape)\n",
    "print(y_test_rg.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "0ae33678",
   "metadata": {},
   "outputs": [],
   "source": [
    "rf =  RandomForestRegressor()\n",
    "dtr = DecisionTreeRegressor()\n",
    "knn = KNeighborsRegressor()\n",
    "\n",
    "models = [rf, dtr, knn]\n",
    "metrics_list = []\n",
    "\n",
    "for model in models:\n",
    "    model.fit(x_train_rg, y_train_rg)\n",
    "    y_train_pred_rg = model.predict(x_train_rg)\n",
    "    y_pred_rg = model.predict(x_test_rg)\n",
    "    mse_test = metrics.mean_squared_error(y_test_rg, y_pred_rg) * 100\n",
    "    mae_test = metrics.mean_absolute_error(y_test_rg, y_pred_rg) * 100\n",
    "    r2_test = metrics.r2_score(y_test_rg, y_pred_rg)\n",
    "    rmse_test = np.sqrt(mse_test)  # Fixing the variable name here\n",
    "    mse_train = metrics.mean_squared_error(y_train_rg, y_train_pred_rg) * 100\n",
    "    mae_train = metrics.mean_absolute_error(y_train_rg, y_train_pred_rg) * 100\n",
    "    r2_train = metrics.r2_score(y_train_rg, y_train_pred_rg)\n",
    "    rmse_train = np.sqrt(mse_train)  # Fixing the variable name here\n",
    "\n",
    "    model_metrics = {'Algorithm': str(model).split('(')[0],\n",
    "                     'R2_train': r2_train,\n",
    "                     'R2_test': r2_test,\n",
    "                      'Mean Absolute Error_train': mae_train,\n",
    "                     'Mean Absolute Error_test': mae_test,\n",
    "                     'Mean Squared Error_train': mse_train,\n",
    "                     'Mean Squared Error_test': mse_test,\n",
    "                     'Root Mean Squared Error_train': rmse_train,\n",
    "                     'Root Mean Squared Error_test': rmse_test}\n",
    "                     \n",
    "\n",
    "    metrics_list.append(model_metrics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "241f1295",
   "metadata": {},
   "outputs": [],
   "source": [
    "best_models = pd.DataFrame(metrics_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "0bb1a66f",
   "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>Algorithm</th>\n",
       "      <th>R2_train</th>\n",
       "      <th>R2_test</th>\n",
       "      <th>Mean Absolute Error_train</th>\n",
       "      <th>Mean Absolute Error_test</th>\n",
       "      <th>Mean Squared Error_train</th>\n",
       "      <th>Mean Squared Error_test</th>\n",
       "      <th>Root Mean Squared Error_train</th>\n",
       "      <th>Root Mean Squared Error_test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RandomForestRegressor</td>\n",
       "      <td>0.986823</td>\n",
       "      <td>0.934667</td>\n",
       "      <td>4.325213</td>\n",
       "      <td>10.715369</td>\n",
       "      <td>0.413416</td>\n",
       "      <td>2.136047</td>\n",
       "      <td>0.642974</td>\n",
       "      <td>1.461522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DecisionTreeRegressor</td>\n",
       "      <td>0.996674</td>\n",
       "      <td>0.886416</td>\n",
       "      <td>0.632009</td>\n",
       "      <td>12.580241</td>\n",
       "      <td>0.104367</td>\n",
       "      <td>3.713600</td>\n",
       "      <td>0.323059</td>\n",
       "      <td>1.927070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KNeighborsRegressor</td>\n",
       "      <td>0.665893</td>\n",
       "      <td>0.494579</td>\n",
       "      <td>13.820724</td>\n",
       "      <td>17.510095</td>\n",
       "      <td>10.482492</td>\n",
       "      <td>16.524640</td>\n",
       "      <td>3.237668</td>\n",
       "      <td>4.065051</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Algorithm  R2_train   R2_test  Mean Absolute Error_train  \\\n",
       "0  RandomForestRegressor  0.986823  0.934667                   4.325213   \n",
       "1  DecisionTreeRegressor  0.996674  0.886416                   0.632009   \n",
       "2    KNeighborsRegressor  0.665893  0.494579                  13.820724   \n",
       "\n",
       "   Mean Absolute Error_test  Mean Squared Error_train  \\\n",
       "0                 10.715369                  0.413416   \n",
       "1                 12.580241                  0.104367   \n",
       "2                 17.510095                 10.482492   \n",
       "\n",
       "   Mean Squared Error_test  Root Mean Squared Error_train  \\\n",
       "0                 2.136047                       0.642974   \n",
       "1                 3.713600                       0.323059   \n",
       "2                16.524640                       3.237668   \n",
       "\n",
       "   Root Mean Squared Error_test  \n",
       "0                      1.461522  \n",
       "1                      1.927070  \n",
       "2                      4.065051  "
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54b922c1",
   "metadata": {},
   "source": [
    "#### Hyperparameter Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "3c44cc1b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 81 candidates, totalling 405 fits\n",
      "Best Cross-validated Parameters:  {'max_depth': 20, 'max_features': None, 'min_samples_split': 8, 'n_estimators': 200}\n",
      "Best Cross-validated Score:  0.926513099020152\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "param_grid = {\n",
    "    'n_estimators': [50, 100, 200],\n",
    "    'max_features': [None, 'sqrt', 'log2'],\n",
    "    'max_depth': [None, 10, 20],\n",
    "    'min_samples_split': [2, 4, 8],\n",
    "}\n",
    "\n",
    "rf = RandomForestRegressor()\n",
    "x_train_rg, x_test_rg, y_train_rg, y_test_rg = train_test_split( x, y, test_size=0.30, random_state=42)\n",
    "\n",
    "grid_search = GridSearchCV(estimator=rf, param_grid=param_grid, cv=5 , n_jobs=-1, verbose=3)\n",
    "grid_search.fit(x_train_rg, y_train_rg)\n",
    "\n",
    "\n",
    "print(\"Best Cross-validated Parameters: \", grid_search.best_params_)\n",
    "print(\"Best Cross-validated Score: \", grid_search.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "ea394eaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(max_depth=20, max_features=None, min_samples_split=8,\n",
       "                      n_estimators=200)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(max_depth=20, max_features=None, min_samples_split=8,\n",
       "                      n_estimators=200)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor(max_depth=20, max_features=None, min_samples_split=8,\n",
       "                      n_estimators=200)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_rg = RandomForestRegressor(n_estimators = 200, min_samples_split = 8, max_depth = 20, max_features = None)\n",
    "rf_rg.fit(x_train_rg,y_train_rg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "ede30987",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_pred_rg=rf_rg.predict(x_train_rg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "79ee0231",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_rg=rf_rg.predict(x_test_rg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "4eac61a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.02002712149113177"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.mean_squared_error(y_test_rg,y_pred_rg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "c6418cba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.10788788851759709"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.mean_absolute_error(y_test_rg, y_pred_rg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "0d29a827",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9620606172254446\n"
     ]
    }
   ],
   "source": [
    "print(metrics.r2_score(y_train_rg, x_pred_rg))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "c4769677",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9387452179880557\n"
     ]
    }
   ],
   "source": [
    "print(metrics.r2_score(y_test_rg,y_pred_rg))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "abaa70be",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Saving the model in pickel file\n",
    "\n",
    "import pickle\n",
    "with open('RegressorModel_Selling_Price_Predt.pkl', 'wb') as file:\n",
    "    pickle.dump(rf_rg, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "4368d5ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading the  regressor model \n",
    "with open('RegressorModel_selling_Price_Predt.pkl','rb') as file:\n",
    "    reg_mod= pickle.load(file)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "34c706cf",
   "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>quantity tons</th>\n",
       "      <th>customer</th>\n",
       "      <th>country</th>\n",
       "      <th>status</th>\n",
       "      <th>item type</th>\n",
       "      <th>application</th>\n",
       "      <th>thickness</th>\n",
       "      <th>width</th>\n",
       "      <th>product_ref</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>54.151139</td>\n",
       "      <td>30156308.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>1670798778</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   quantity tons    customer  country  status  item type  application  \\\n",
       "0      54.151139  30156308.0     28.0       0          0         10.0   \n",
       "\n",
       "   thickness   width  product_ref  \n",
       "0        2.0  1500.0   1670798778  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "b578112f",
   "metadata": {},
   "outputs": [],
   "source": [
    "array=[[54.151139,30156308.0,28.0,0,0,10.0,2.0,1500.0,1670798778]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "c857d2f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[54.151139, 30156308.0, 28.0, 0, 0, 10.0, 2.0, 1500.0, 1670798778]]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "a7c99ef4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.74993119378857"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[0]  #Actuall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "1be16c64",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6.58880242]\n"
     ]
    }
   ],
   "source": [
    "# Predicted\n",
    "result=reg_mod.predict(array)\n",
    "print(result) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4cbba75",
   "metadata": {},
   "source": [
    "#### Actual Value Is 6.74993119378857 Random Foreset Predict it as 6.59852939"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c1df028",
   "metadata": {},
   "source": [
    "# Classification Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "4dbd49b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, precision_score, recall_score, f1_score,classification_report,roc_curve,roc_auc_score\n",
    "from imblearn.over_sampling import SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "776108f3",
   "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>quantity tons</th>\n",
       "      <th>customer</th>\n",
       "      <th>country</th>\n",
       "      <th>status</th>\n",
       "      <th>item type</th>\n",
       "      <th>application</th>\n",
       "      <th>thickness</th>\n",
       "      <th>width</th>\n",
       "      <th>product_ref</th>\n",
       "      <th>selling_price</th>\n",
       "      <th>selling_price_log</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>54.151139</td>\n",
       "      <td>30156308.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1500.00</td>\n",
       "      <td>1670798778</td>\n",
       "      <td>854.00</td>\n",
       "      <td>6.749931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>151.450919</td>\n",
       "      <td>30202938.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1210.00</td>\n",
       "      <td>1668701718</td>\n",
       "      <td>1047.00</td>\n",
       "      <td>6.953684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>151.450919</td>\n",
       "      <td>30153963.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.38</td>\n",
       "      <td>952.00</td>\n",
       "      <td>628377</td>\n",
       "      <td>644.33</td>\n",
       "      <td>6.468211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>151.450919</td>\n",
       "      <td>30349574.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>59.0</td>\n",
       "      <td>2.30</td>\n",
       "      <td>1317.00</td>\n",
       "      <td>1668701718</td>\n",
       "      <td>768.00</td>\n",
       "      <td>6.643790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>151.450919</td>\n",
       "      <td>30211560.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1980.75</td>\n",
       "      <td>640665</td>\n",
       "      <td>577.00</td>\n",
       "      <td>6.357842</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   quantity tons    customer  country  status  item type  application  \\\n",
       "0      54.151139  30156308.0     28.0       0          0         10.0   \n",
       "1     151.450919  30202938.0     25.0       0          0         41.0   \n",
       "2     151.450919  30153963.0     30.0       0          1         28.0   \n",
       "3     151.450919  30349574.0     32.0       0          2         59.0   \n",
       "4     151.450919  30211560.0     28.0       0          0         10.0   \n",
       "\n",
       "   thickness    width  product_ref  selling_price  selling_price_log  \n",
       "0       2.00  1500.00   1670798778         854.00           6.749931  \n",
       "1       0.80  1210.00   1668701718        1047.00           6.953684  \n",
       "2       0.38   952.00       628377         644.33           6.468211  \n",
       "3       2.30  1317.00   1668701718         768.00           6.643790  \n",
       "4       4.00  1980.75       640665         577.00           6.357842  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "ffd24e2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(181627, 11)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "4245055d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8], dtype=int64)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['status'].unique()"
   ]
  },
  {
   "cell_type": "raw",
   "id": "75c389cd",
   "metadata": {},
   "source": [
    "# Catgorical data\n",
    "# 'Won': 0\n",
    "# 'Draft': 1\n",
    "# 'To be approved': 2\n",
    "# 'Lost': 3\n",
    "# 'Not lost for AM': 4\n",
    "# 'Wonderful': 5\n",
    "# 'Revised': 6\n",
    "# 'Offered': 7\n",
    "# 'Offerable': 8"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a2d4fca",
   "metadata": {},
   "source": [
    "#### 'Won': 0 ; 'Lost': 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "48cd570c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "150432"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cls = df[df['status'].isin([0,3])]\n",
    "len(df_cls)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "e65d9677",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "status\n",
       "0    115999\n",
       "3     34433\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cls['status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "86fbf587",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "status\n",
       "0    0.771106\n",
       "3    0.228894\n",
       "Name: proportion, dtype: float64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cls['status'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "94193ce6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cls['status'].replace(3 , 1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "94b5325b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "status\n",
       "0    115999\n",
       "1     34433\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cls['status'].value_counts() # 0 --> WON, 1 --> LOST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "e74011f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df_cls.drop('status', axis=1)\n",
    "y = df_cls['status']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "cb9505e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "sm = SMOTE(sampling_strategy=.45,random_state=42)\n",
    "X_train_resample, y_train_resample = sm.fit_resample(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "6440f77e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "status\n",
       "0    115999\n",
       "1     52199\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_resample.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "f67cf65b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\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>quantity tons</th>\n",
       "      <th>customer</th>\n",
       "      <th>country</th>\n",
       "      <th>item type</th>\n",
       "      <th>application</th>\n",
       "      <th>thickness</th>\n",
       "      <th>width</th>\n",
       "      <th>product_ref</th>\n",
       "      <th>selling_price</th>\n",
       "      <th>selling_price_log</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>54.151139</td>\n",
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       "      <td>6.749931</td>\n",
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       "      <td>41.0</td>\n",
       "      <td>0.800000</td>\n",
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       "      <td>1047.000000</td>\n",
       "      <td>6.953684</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>151.450919</td>\n",
       "      <td>30153963.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0.380000</td>\n",
       "      <td>952.00</td>\n",
       "      <td>628377</td>\n",
       "      <td>644.330000</td>\n",
       "      <td>6.468211</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>151.450919</td>\n",
       "      <td>30349574.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>2</td>\n",
       "      <td>59.0</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>1317.00</td>\n",
       "      <td>1668701718</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>6.643790</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>151.450919</td>\n",
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       "      <td>4.000000</td>\n",
       "      <td>1980.75</td>\n",
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       "      <td>577.000000</td>\n",
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       "    <tr>\n",
       "      <th>168193</th>\n",
       "      <td>151.450919</td>\n",
       "      <td>30403833.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1.150000</td>\n",
       "      <td>911.00</td>\n",
       "      <td>628377</td>\n",
       "      <td>635.387175</td>\n",
       "      <td>6.454222</td>\n",
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       "    <tr>\n",
       "      <th>168194</th>\n",
       "      <td>39.507692</td>\n",
       "      <td>30278678.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>6.450000</td>\n",
       "      <td>1500.00</td>\n",
       "      <td>640405</td>\n",
       "      <td>980.628413</td>\n",
       "      <td>6.887558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168195</th>\n",
       "      <td>3.110952</td>\n",
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       "      <td>6.253079</td>\n",
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       "    <tr>\n",
       "      <th>168196</th>\n",
       "      <td>108.895675</td>\n",
       "      <td>30156308.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.905670</td>\n",
       "      <td>1980.75</td>\n",
       "      <td>640665</td>\n",
       "      <td>547.000000</td>\n",
       "      <td>6.304449</td>\n",
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       "      <th>168197</th>\n",
       "      <td>30.995227</td>\n",
       "      <td>30292440.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>2</td>\n",
       "      <td>15.0</td>\n",
       "      <td>6.340797</td>\n",
       "      <td>1500.00</td>\n",
       "      <td>640405</td>\n",
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       "      <td>6.478932</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>168198 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        quantity tons    customer  country  item type  application  thickness  \\\n",
       "0           54.151139  30156308.0     28.0          0         10.0   2.000000   \n",
       "1          151.450919  30202938.0     25.0          0         41.0   0.800000   \n",
       "2          151.450919  30153963.0     30.0          1         28.0   0.380000   \n",
       "3          151.450919  30349574.0     32.0          2         59.0   2.300000   \n",
       "4          151.450919  30211560.0     28.0          0         10.0   4.000000   \n",
       "...               ...         ...      ...        ...          ...        ...   \n",
       "168193     151.450919  30403833.0     26.0          0         28.0   1.150000   \n",
       "168194      39.507692  30278678.0     77.0          0         15.0   6.450000   \n",
       "168195       3.110952  30211560.0     28.0          0         10.0   6.175399   \n",
       "168196     108.895675  30156308.0     28.0          0         10.0   4.905670   \n",
       "168197      30.995227  30292440.0     84.0          2         15.0   6.340797   \n",
       "\n",
       "          width  product_ref  selling_price  selling_price_log  \n",
       "0       1500.00   1670798778     854.000000           6.749931  \n",
       "1       1210.00   1668701718    1047.000000           6.953684  \n",
       "2        952.00       628377     644.330000           6.468211  \n",
       "3       1317.00   1668701718     768.000000           6.643790  \n",
       "4       1980.75       640665     577.000000           6.357842  \n",
       "...         ...          ...            ...                ...  \n",
       "168193   911.00       628377     635.387175           6.454222  \n",
       "168194  1500.00       640405     980.628413           6.887558  \n",
       "168195  1980.75       640665     519.610223           6.253079  \n",
       "168196  1980.75       640665     547.000000           6.304449  \n",
       "168197  1500.00       640405     651.594367           6.478932  \n",
       "\n",
       "[168198 rows x 10 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_resample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "751026c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "status\n",
       "0    0.689657\n",
       "1    0.310343\n",
       "Name: proportion, dtype: float64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_resample.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "6e4138a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(150432, 10)\n",
      "(150432,)\n",
      "(168198, 10)\n",
      "(168198,)\n"
     ]
    }
   ],
   "source": [
    "# Orginal Shape of DataSet\n",
    "print(x.shape)\n",
    "print(y.shape)\n",
    "# After Resampling with Somte\n",
    "print(X_train_resample.shape)\n",
    "print(y_train_resample.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "3215538c",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split( X_train_resample, y_train_resample, test_size=0.30, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "a4ec9471",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((117738, 10), (50460, 10))"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape,x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "fbccfa9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>84.0</td>\n",
       "      <td>0</td>\n",
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       "      <th>146867</th>\n",
       "      <td>104.561775</td>\n",
       "      <td>30397632.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1250.0</td>\n",
       "      <td>164141591</td>\n",
       "      <td>621.00</td>\n",
       "      <td>6.431331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121958</th>\n",
       "      <td>108.172476</td>\n",
       "      <td>30162405.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>0.60</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>164141591</td>\n",
       "      <td>741.00</td>\n",
       "      <td>6.608001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>117738 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        quantity tons    customer  country  item type  application  thickness  \\\n",
       "137093      25.937878  30206203.0     26.0          2         41.0       0.30   \n",
       "10849        9.697097  30160005.0     78.0          0         41.0       1.82   \n",
       "117163      60.611597  30299349.0     25.0          0         25.0       0.60   \n",
       "143801      49.821416  30405913.0     26.0          2         38.0       2.00   \n",
       "129314      21.049418  30165992.0     77.0          2         10.0       0.75   \n",
       "...               ...         ...      ...        ...          ...        ...   \n",
       "119879       6.146746  30196884.0     84.0          0         10.0       1.50   \n",
       "103694      32.567761  30267268.0     28.0          0         41.0       0.43   \n",
       "131932      61.262653  30206248.0     26.0          2         15.0       4.00   \n",
       "146867     104.561775  30397632.0     78.0          2         10.0       0.50   \n",
       "121958     108.172476  30162405.0     84.0          0         79.0       0.60   \n",
       "\n",
       "         width  product_ref  selling_price  selling_price_log  \n",
       "137093  1240.0       611993        1198.00           7.088409  \n",
       "10849   1120.0    164141591         929.00           6.834109  \n",
       "117163  1202.0       628377         646.00           6.470800  \n",
       "143801  1236.0    164141591         618.00           6.426488  \n",
       "129314  1000.0    164141591         620.00           6.429719  \n",
       "...        ...          ...            ...                ...  \n",
       "119879  1147.0    164141591         713.41           6.570056  \n",
       "103694  1486.0       611993        1101.00           7.003974  \n",
       "131932  1500.0   1670798778         562.00           6.331502  \n",
       "146867  1250.0    164141591         621.00           6.431331  \n",
       "121958  1500.0    164141591         741.00           6.608001  \n",
       "\n",
       "[117738 rows x 10 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "abfbdc66",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>quantity tons</th>\n",
       "      <th>customer</th>\n",
       "      <th>country</th>\n",
       "      <th>item type</th>\n",
       "      <th>application</th>\n",
       "      <th>thickness</th>\n",
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       "      <th>selling_price_log</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>146439</th>\n",
       "      <td>22.072053</td>\n",
       "      <td>30205825.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>1705.00</td>\n",
       "      <td>1693867550</td>\n",
       "      <td>595.000000</td>\n",
       "      <td>6.388561</td>\n",
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       "    <tr>\n",
       "      <th>44033</th>\n",
       "      <td>65.368039</td>\n",
       "      <td>30157111.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.880000</td>\n",
       "      <td>1500.00</td>\n",
       "      <td>611993</td>\n",
       "      <td>986.000000</td>\n",
       "      <td>6.893656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49435</th>\n",
       "      <td>4.977580</td>\n",
       "      <td>30232042.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1250.00</td>\n",
       "      <td>611993</td>\n",
       "      <td>1075.000000</td>\n",
       "      <td>6.980076</td>\n",
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       "    <tr>\n",
       "      <th>152661</th>\n",
       "      <td>151.450919</td>\n",
       "      <td>30202645.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.291464</td>\n",
       "      <td>1295.00</td>\n",
       "      <td>628377</td>\n",
       "      <td>861.447147</td>\n",
       "      <td>6.758572</td>\n",
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       "    <tr>\n",
       "      <th>97396</th>\n",
       "      <td>26.558580</td>\n",
       "      <td>30272192.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>2</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>1250.00</td>\n",
       "      <td>611993</td>\n",
       "      <td>1018.000000</td>\n",
       "      <td>6.925595</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78909</th>\n",
       "      <td>2.018245</td>\n",
       "      <td>30206006.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>800.00</td>\n",
       "      <td>1670798778</td>\n",
       "      <td>646.000000</td>\n",
       "      <td>6.470800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100589</th>\n",
       "      <td>12.080353</td>\n",
       "      <td>30342192.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.970000</td>\n",
       "      <td>1268.00</td>\n",
       "      <td>611993</td>\n",
       "      <td>951.000000</td>\n",
       "      <td>6.857514</td>\n",
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       "    <tr>\n",
       "      <th>129949</th>\n",
       "      <td>50.354646</td>\n",
       "      <td>30199065.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1000.00</td>\n",
       "      <td>1332077137</td>\n",
       "      <td>602.000000</td>\n",
       "      <td>6.400257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73330</th>\n",
       "      <td>1.534746</td>\n",
       "      <td>30213686.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1980.75</td>\n",
       "      <td>640665</td>\n",
       "      <td>751.000000</td>\n",
       "      <td>6.621406</td>\n",
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       "    <tr>\n",
       "      <th>140620</th>\n",
       "      <td>54.309698</td>\n",
       "      <td>30209332.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>2</td>\n",
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       "      <td>2.500000</td>\n",
       "      <td>1250.00</td>\n",
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       "      <td>623.000000</td>\n",
       "      <td>6.434547</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>50460 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        quantity tons    customer  country  item type  application  thickness  \\\n",
       "146439      22.072053  30205825.0     25.0          0         10.0   1.250000   \n",
       "44033       65.368039  30157111.0     78.0          0         41.0   0.880000   \n",
       "49435        4.977580  30232042.0     28.0          0         41.0   0.500000   \n",
       "152661     151.450919  30202645.0     32.0          0         10.0   1.291464   \n",
       "97396       26.558580  30272192.0     28.0          2         41.0   0.550000   \n",
       "...               ...         ...      ...        ...          ...        ...   \n",
       "78909        2.018245  30206006.0     26.0          0         56.0   3.000000   \n",
       "100589      12.080353  30342192.0     32.0          0         41.0   0.970000   \n",
       "129949      50.354646  30199065.0     26.0          2         10.0   2.000000   \n",
       "73330        1.534746  30213686.0     78.0          0         10.0   6.000000   \n",
       "140620      54.309698  30209332.0     27.0          2         10.0   2.500000   \n",
       "\n",
       "          width  product_ref  selling_price  selling_price_log  \n",
       "146439  1705.00   1693867550     595.000000           6.388561  \n",
       "44033   1500.00       611993     986.000000           6.893656  \n",
       "49435   1250.00       611993    1075.000000           6.980076  \n",
       "152661  1295.00       628377     861.447147           6.758572  \n",
       "97396   1250.00       611993    1018.000000           6.925595  \n",
       "...         ...          ...            ...                ...  \n",
       "78909    800.00   1670798778     646.000000           6.470800  \n",
       "100589  1268.00       611993     951.000000           6.857514  \n",
       "129949  1000.00   1332077137     602.000000           6.400257  \n",
       "73330   1980.75       640665     751.000000           6.621406  \n",
       "140620  1250.00    164141591     623.000000           6.434547  \n",
       "\n",
       "[50460 rows x 10 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "359913e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "rf =  RandomForestClassifier()\n",
    "dtr = DecisionTreeClassifier()\n",
    "knn = KNeighborsClassifier()\n",
    "\n",
    "models = [rf, dtr, knn]\n",
    "metrics_list = []\n",
    "\n",
    "for model in models:\n",
    "    model.fit(x_train, y_train)\n",
    "    y_pred_train = model.predict(x_train)\n",
    "    y_pred_test = model.predict(x_test)\n",
    "\n",
    "    accuracy_train = metrics.accuracy_score(y_train, y_pred_train)\n",
    "    accuracy_test = metrics.accuracy_score(y_test, y_pred_test)\n",
    "    accuracy_metrics = {'Algorithm'    : str(model).split('(')[0],\n",
    "                        'Accuracy_Train': accuracy_train,\n",
    "                        'Accuracy_Test' : accuracy_test}\n",
    "\n",
    "    metrics_list.append(accuracy_metrics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "3c9f11cb",
   "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>Algorithm</th>\n",
       "      <th>Accuracy_Train</th>\n",
       "      <th>Accuracy_Test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.999932</td>\n",
       "      <td>0.939160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DecisionTreeClassifier</td>\n",
       "      <td>0.999932</td>\n",
       "      <td>0.912802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KNeighborsClassifier</td>\n",
       "      <td>0.935458</td>\n",
       "      <td>0.898197</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Algorithm  Accuracy_Train  Accuracy_Test\n",
       "0  RandomForestClassifier        0.999932       0.939160\n",
       "1  DecisionTreeClassifier        0.999932       0.912802\n",
       "2    KNeighborsClassifier        0.935458       0.898197"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Best_cls_model =pd.DataFrame(metrics_list)\n",
    "Best_cls_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "09f38fd9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 81 candidates, totalling 405 fits\n",
      "Best Cross-validated Parameters:  {'max_depth': None, 'max_features': 'sqrt', 'min_samples_split': 2, 'n_estimators': 200}\n",
      "Best Cross-validated Score:  0.9354159474854887\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "param_grid = {\n",
    "    'n_estimators': [50, 100, 200],\n",
    "    'max_features': [None, 'sqrt', 'log2'],\n",
    "    'max_depth': [None, 10, 20],\n",
    "    'min_samples_split': [2, 4, 8],\n",
    "}\n",
    "\n",
    "rf = RandomForestClassifier()\n",
    "x_train, x_test, y_train, y_test = train_test_split( X_train_resample, y_train_resample, test_size=0.30, random_state=42)\n",
    "\n",
    "grid_search = GridSearchCV(estimator=rf, param_grid=param_grid, cv=5,n_jobs=-1, verbose=3)\n",
    "grid_search.fit(x_train, y_train)\n",
    "\n",
    "print(\"Best Cross-validated Parameters: \", grid_search.best_params_)\n",
    "print(\"Best Cross-validated Score: \", grid_search.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "b6494da3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(n_estimators=200)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(n_estimators=200)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(n_estimators=200)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_cls = RandomForestClassifier(max_depth = None, max_features = 'sqrt', min_samples_split = 2, n_estimators = 200)\n",
    "rf_cls.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "8c9a460b",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_pred=rf_cls.predict(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "a5962248",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=rf_cls.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "e8b13cab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9999320525233995"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Training Accuracy\n",
    "metrics.accuracy_score(y_train, x_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "fd19208b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9402497027348394"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Testing Accuracy\n",
    "metrics.accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "79a5d5a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n",
      "[[33346  1430]\n",
      " [ 1585 14099]]\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.96      0.96     34776\n",
      "           1       0.91      0.90      0.90     15684\n",
      "\n",
      "    accuracy                           0.94     50460\n",
      "   macro avg       0.93      0.93      0.93     50460\n",
      "weighted avg       0.94      0.94      0.94     50460\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Testing Confution Matrix\n",
    "print(\"Confusion Matrix:\")\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print(\"Classification Report:\")\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "9af6c18c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n",
      "[[81217     6]\n",
      " [    2 36513]]\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     81223\n",
      "           1       1.00      1.00      1.00     36515\n",
      "\n",
      "    accuracy                           1.00    117738\n",
      "   macro avg       1.00      1.00      1.00    117738\n",
      "weighted avg       1.00      1.00      1.00    117738\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Training Confusion matrix\n",
    "print(\"Confusion Matrix:\")\n",
    "print(confusion_matrix(y_train, x_pred))\n",
    "print(\"Classification Report:\")\n",
    "print(classification_report(y_train, x_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "872608d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "with open('ClassifierModel_Stauts_Predt.pkl', 'wb') as file:\n",
    "    pickle.dump(rf_cls, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "35a2382e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading the  regressor model \n",
    "with open('ClassifierModel_Stauts_Predt.pkl','rb') as file:\n",
    "    reg_cls= pickle.load(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "b622657a",
   "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>quantity tons</th>\n",
       "      <th>customer</th>\n",
       "      <th>country</th>\n",
       "      <th>item type</th>\n",
       "      <th>application</th>\n",
       "      <th>thickness</th>\n",
       "      <th>width</th>\n",
       "      <th>product_ref</th>\n",
       "      <th>selling_price</th>\n",
       "      <th>selling_price_log</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>54.151139</td>\n",
       "      <td>30156308.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>1670798778</td>\n",
       "      <td>854.0</td>\n",
       "      <td>6.749931</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   quantity tons    customer  country  item type  application  thickness  \\\n",
       "0      54.151139  30156308.0     28.0          0         10.0        2.0   \n",
       "\n",
       "    width  product_ref  selling_price  selling_price_log  \n",
       "0  1500.0   1670798778          854.0           6.749931  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "cd7533cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "array=[[54.151139, 30156308.0, 28.0 , 0 , 10.0,2.00,1500.00,1670798778,854.00,6.749931]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "a8b55216",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[54.151139,\n",
       "  30156308.0,\n",
       "  28.0,\n",
       "  0,\n",
       "  10.0,\n",
       "  2.0,\n",
       "  1500.0,\n",
       "  1670798778,\n",
       "  854.0,\n",
       "  6.749931]]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "d4c9f9b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "Name: status, dtype: int64"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Actual\n",
    "y.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "e3eefb4c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\n"
     ]
    }
   ],
   "source": [
    "# Predicted\n",
    "result=reg_cls.predict(array)\n",
    "print(result) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "1d6b83c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WON\n"
     ]
    }
   ],
   "source": [
    "if result==0:\n",
    "    print(\"WON\")\n",
    "else:\n",
    "    print(\"LOST\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c75685f4",
   "metadata": {},
   "source": [
    "#### Actual Value Is 0(Won) Random Foreset Predict it has 0(Won)"
   ]
  }
 ],
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