final copper project regression and classfication (1).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2394b52f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "adbc8d65",
   "metadata": {},
   "outputs": [],
   "source": [
    "copper = pd.read_excel(\"C:\\\\Users\\\\User\\\\Downloads\\\\Copper_Set.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8cff96fb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <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",
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       "      <th>width</th>\n",
       "      <th>material_ref</th>\n",
       "      <th>product_ref</th>\n",
       "      <th>delivery date</th>\n",
       "      <th>selling_price</th>\n",
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       "      <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",
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       "      <td>854.00</td>\n",
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       "      <td>4E5F4B3D-DDDF-499D-AFDE-A3227EC49425</td>\n",
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       "      <td>768.024839</td>\n",
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       "      <td>20210401.0</td>\n",
       "      <td>1047.00</td>\n",
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       "      <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",
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       "      <td>28.0</td>\n",
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       "      <td>30349574.0</td>\n",
       "      <td>32.0</td>\n",
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       "      <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",
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       "    <tr>\n",
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       "      <td>785.526262</td>\n",
       "      <td>30211560.0</td>\n",
       "      <td>28.0</td>\n",
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       "      <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",
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       "      <td>406.686538</td>\n",
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       "      <td>25.0</td>\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",
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       "4       4E1C4E78-152B-430A-8094-ADD889C9D0AD  20210401.0    785.526262   \n",
       "...                                      ...         ...           ...   \n",
       "181668  DE633116-D1DF-4846-982E-55EFC3658A76  20200702.0    102.482422   \n",
       "181669  A48374B1-E6DB-45F2-889A-1F9C27C099EB  20200702.0    208.086469   \n",
       "181670  91643238-5C7B-4237-9A5F-63AE3D35F320  20200702.0      4.235594   \n",
       "181671  7AFFD323-01D9-4E15-B80D-7D1B03498FC8  20200702.0         -2000   \n",
       "181672  AD0CA853-AE3C-4B2F-9FBB-8B0B965F84BC  20200702.0    406.686538   \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  \\\n",
       "0                                    DEQ1 S460MC   1670798778     20210701.0   \n",
       "1       0000000000000000000000000000000000104991   1668701718     20210401.0   \n",
       "2                                       S0380700       628377     20210101.0   \n",
       "3                        DX51D+ZM310MAO 2.3X1317   1668701718     20210101.0   \n",
       "4                                2_S275JR+AR-CL1       640665     20210301.0   \n",
       "...                                          ...          ...            ...   \n",
       "181668  0000000000000000000000000000000001000777    164141591     20200701.0   \n",
       "181669  0000000000000000000000000000000001000227    164141591     20200701.0   \n",
       "181670  0000000000000000000000000000000001004216    164141591     20200701.0   \n",
       "181671  0000000000000000000000000000000001001149    164141591     20200701.0   \n",
       "181672  0000000000000000000000000000000001005439    164141591     20200701.0   \n",
       "\n",
       "        selling_price  \n",
       "0              854.00  \n",
       "1             1047.00  \n",
       "2              644.33  \n",
       "3              768.00  \n",
       "4              577.00  \n",
       "...               ...  \n",
       "181668         591.00  \n",
       "181669         589.00  \n",
       "181670         619.00  \n",
       "181671         601.00  \n",
       "181672         607.00  \n",
       "\n",
       "[181673 rows x 14 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "copper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "edc2f6d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id               181671\n",
       "item_date           252\n",
       "quantity tons    181673\n",
       "customer           1169\n",
       "country              17\n",
       "status                9\n",
       "item type             7\n",
       "application          30\n",
       "thickness           594\n",
       "width              1386\n",
       "material_ref      16563\n",
       "product_ref          33\n",
       "delivery date        28\n",
       "selling_price      9795\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#verfiy the number of unquie values in each feature\n",
    "copper.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9d92ca75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(181673, 14)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking the datashape and feature\n",
    "copper.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7c72ceb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                object\n",
       "item_date        float64\n",
       "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",
       "material_ref      object\n",
       "product_ref        int64\n",
       "delivery date    float64\n",
       "selling_price    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# verfiy the datatypes in all features\n",
    "copper.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "38da1be0",
   "metadata": {
    "scrolled": true
   },
   "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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the null values in dataset\n",
    "\n",
    "copper.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d9987118",
   "metadata": {},
   "outputs": [],
   "source": [
    "# converting the datatypes \n",
    "\n",
    "copper['quantity tons'] = pd.to_numeric(copper['quantity tons'],errors = 'coerce')\n",
    "copper['item_date_new']= pd.to_datetime(copper['item_date'],format = '%Y%m%d',errors = 'coerce').dt.date\n",
    "copper['delivery date_new']= pd.to_datetime(copper['delivery date'],format = '%Y%m%d',errors = 'coerce').dt.date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "21784d59",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>id</th>\n",
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       "      <th>delivery date_new</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <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",
       "      <td>2021-04-01</td>\n",
       "      <td>2021-07-01</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4E5F4B3D-DDDF-499D-AFDE-A3227EC49425</td>\n",
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       "      <td>0.80</td>\n",
       "      <td>1210.0</td>\n",
       "      <td>0000000000000000000000000000000000104991</td>\n",
       "      <td>1668701718</td>\n",
       "      <td>20210401.0</td>\n",
       "      <td>1047.00</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>2021-04-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <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",
       "      <td>628377</td>\n",
       "      <td>20210101.0</td>\n",
       "      <td>644.33</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>2021-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F8D507A0-9C62-4EFE-831E-33E1DA53BB50</td>\n",
       "      <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",
       "      <td>2021-04-01</td>\n",
       "      <td>2021-01-01</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",
       "      <td>2021-04-01</td>\n",
       "      <td>2021-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181668</th>\n",
       "      <td>DE633116-D1DF-4846-982E-55EFC3658A76</td>\n",
       "      <td>20200702.0</td>\n",
       "      <td>102.482422</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>0000000000000000000000000000000001000777</td>\n",
       "      <td>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>591.00</td>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>2020-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181669</th>\n",
       "      <td>A48374B1-E6DB-45F2-889A-1F9C27C099EB</td>\n",
       "      <td>20200702.0</td>\n",
       "      <td>208.086469</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>0.95</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>0000000000000000000000000000000001000227</td>\n",
       "      <td>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>589.00</td>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>2020-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181670</th>\n",
       "      <td>91643238-5C7B-4237-9A5F-63AE3D35F320</td>\n",
       "      <td>20200702.0</td>\n",
       "      <td>4.235594</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>1250.0</td>\n",
       "      <td>0000000000000000000000000000000001004216</td>\n",
       "      <td>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>619.00</td>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>2020-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181671</th>\n",
       "      <td>7AFFD323-01D9-4E15-B80D-7D1B03498FC8</td>\n",
       "      <td>20200702.0</td>\n",
       "      <td>-2000.000000</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>0000000000000000000000000000000001001149</td>\n",
       "      <td>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>601.00</td>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>2020-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181672</th>\n",
       "      <td>AD0CA853-AE3C-4B2F-9FBB-8B0B965F84BC</td>\n",
       "      <td>20200702.0</td>\n",
       "      <td>406.686538</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>0000000000000000000000000000000001005439</td>\n",
       "      <td>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>607.00</td>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>2020-07-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>181673 rows × 16 columns</p>\n",
       "</div>"
      ],
      "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     102.482422   \n",
       "181669  A48374B1-E6DB-45F2-889A-1F9C27C099EB  20200702.0     208.086469   \n",
       "181670  91643238-5C7B-4237-9A5F-63AE3D35F320  20200702.0       4.235594   \n",
       "181671  7AFFD323-01D9-4E15-B80D-7D1B03498FC8  20200702.0   -2000.000000   \n",
       "181672  AD0CA853-AE3C-4B2F-9FBB-8B0B965F84BC  20200702.0     406.686538   \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  \\\n",
       "0                                    DEQ1 S460MC   1670798778     20210701.0   \n",
       "1       0000000000000000000000000000000000104991   1668701718     20210401.0   \n",
       "2                                       S0380700       628377     20210101.0   \n",
       "3                        DX51D+ZM310MAO 2.3X1317   1668701718     20210101.0   \n",
       "4                                2_S275JR+AR-CL1       640665     20210301.0   \n",
       "...                                          ...          ...            ...   \n",
       "181668  0000000000000000000000000000000001000777    164141591     20200701.0   \n",
       "181669  0000000000000000000000000000000001000227    164141591     20200701.0   \n",
       "181670  0000000000000000000000000000000001004216    164141591     20200701.0   \n",
       "181671  0000000000000000000000000000000001001149    164141591     20200701.0   \n",
       "181672  0000000000000000000000000000000001005439    164141591     20200701.0   \n",
       "\n",
       "        selling_price item_date_new delivery date_new  \n",
       "0              854.00    2021-04-01        2021-07-01  \n",
       "1             1047.00    2021-04-01        2021-04-01  \n",
       "2              644.33    2021-04-01        2021-01-01  \n",
       "3              768.00    2021-04-01        2021-01-01  \n",
       "4              577.00    2021-04-01        2021-03-01  \n",
       "...               ...           ...               ...  \n",
       "181668         591.00    2020-07-02        2020-07-01  \n",
       "181669         589.00    2020-07-02        2020-07-01  \n",
       "181670         619.00    2020-07-02        2020-07-01  \n",
       "181671         601.00    2020-07-02        2020-07-01  \n",
       "181672         607.00    2020-07-02        2020-07-01  \n",
       "\n",
       "[181673 rows x 16 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "copper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "34f6d5fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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",
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       "      <th>product_ref</th>\n",
       "      <th>delivery date</th>\n",
       "      <th>selling_price</th>\n",
       "      <th>item_date_new</th>\n",
       "      <th>delivery date_new</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>1670798778</td>\n",
       "      <td>20210701.0</td>\n",
       "      <td>854.00</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>2021-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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>1668701718</td>\n",
       "      <td>20210401.0</td>\n",
       "      <td>1047.00</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>2021-04-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>628377</td>\n",
       "      <td>20210101.0</td>\n",
       "      <td>644.33</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>2021-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <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>1668701718</td>\n",
       "      <td>20210101.0</td>\n",
       "      <td>768.00</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>2021-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>640665</td>\n",
       "      <td>20210301.0</td>\n",
       "      <td>577.00</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>2021-03-01</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>181668</th>\n",
       "      <td>20200702.0</td>\n",
       "      <td>102.482422</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>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>591.00</td>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>2020-07-01</td>\n",
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       "    <tr>\n",
       "      <th>181669</th>\n",
       "      <td>20200702.0</td>\n",
       "      <td>208.086469</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>0.95</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>589.00</td>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>2020-07-01</td>\n",
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       "    <tr>\n",
       "      <th>181670</th>\n",
       "      <td>20200702.0</td>\n",
       "      <td>4.235594</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>1250.0</td>\n",
       "      <td>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>619.00</td>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>2020-07-01</td>\n",
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       "    <tr>\n",
       "      <th>181671</th>\n",
       "      <td>20200702.0</td>\n",
       "      <td>-2000.000000</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>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>601.00</td>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>2020-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181672</th>\n",
       "      <td>20200702.0</td>\n",
       "      <td>406.686538</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>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>607.00</td>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>2020-07-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>181673 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         item_date  quantity tons    customer  country status item type  \\\n",
       "0       20210401.0      54.151139  30156308.0     28.0    Won         W   \n",
       "1       20210401.0     768.024839  30202938.0     25.0    Won         W   \n",
       "2       20210401.0     386.127949  30153963.0     30.0    Won        WI   \n",
       "3       20210401.0     202.411065  30349574.0     32.0    Won         S   \n",
       "4       20210401.0     785.526262  30211560.0     28.0    Won         W   \n",
       "...            ...            ...         ...      ...    ...       ...   \n",
       "181668  20200702.0     102.482422  30200854.0     25.0    Won         W   \n",
       "181669  20200702.0     208.086469  30200854.0     25.0    Won         W   \n",
       "181670  20200702.0       4.235594  30200854.0     25.0    Won         W   \n",
       "181671  20200702.0   -2000.000000  30200854.0     25.0    Won         W   \n",
       "181672  20200702.0     406.686538  30200854.0     25.0    Won         W   \n",
       "\n",
       "        application  thickness   width  product_ref  delivery date  \\\n",
       "0              10.0       2.00  1500.0   1670798778     20210701.0   \n",
       "1              41.0       0.80  1210.0   1668701718     20210401.0   \n",
       "2              28.0       0.38   952.0       628377     20210101.0   \n",
       "3              59.0       2.30  1317.0   1668701718     20210101.0   \n",
       "4              10.0       4.00  2000.0       640665     20210301.0   \n",
       "...             ...        ...     ...          ...            ...   \n",
       "181668         41.0       0.96  1220.0    164141591     20200701.0   \n",
       "181669         41.0       0.95  1500.0    164141591     20200701.0   \n",
       "181670         41.0       0.71  1250.0    164141591     20200701.0   \n",
       "181671         41.0       0.85  1250.0    164141591     20200701.0   \n",
       "181672         41.0       0.71  1240.0    164141591     20200701.0   \n",
       "\n",
       "        selling_price item_date_new delivery date_new  \n",
       "0              854.00    2021-04-01        2021-07-01  \n",
       "1             1047.00    2021-04-01        2021-04-01  \n",
       "2              644.33    2021-04-01        2021-01-01  \n",
       "3              768.00    2021-04-01        2021-01-01  \n",
       "4              577.00    2021-04-01        2021-03-01  \n",
       "...               ...           ...               ...  \n",
       "181668         591.00    2020-07-02        2020-07-01  \n",
       "181669         589.00    2020-07-02        2020-07-01  \n",
       "181670         619.00    2020-07-02        2020-07-01  \n",
       "181671         601.00    2020-07-02        2020-07-01  \n",
       "181672         607.00    2020-07-02        2020-07-01  \n",
       "\n",
       "[181673 rows x 14 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dropping the irrelavent feature in dataset  \n",
    "\n",
    "copper.drop(columns=['id','material_ref'], inplace=True)\n",
    "copper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "325b6621",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        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>item_date</th>\n",
       "      <td>181672.0</td>\n",
       "      <td>2.020459e+07</td>\n",
       "      <td>4.551119e+03</td>\n",
       "      <td>19950000.00</td>\n",
       "      <td>2.020093e+07</td>\n",
       "      <td>2.020113e+07</td>\n",
       "      <td>2.021020e+07</td>\n",
       "      <td>2.021040e+07</td>\n",
       "    </tr>\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>delivery date</th>\n",
       "      <td>181672.0</td>\n",
       "      <td>2.020738e+07</td>\n",
       "      <td>2.411059e+04</td>\n",
       "      <td>20190401.00</td>\n",
       "      <td>2.020110e+07</td>\n",
       "      <td>2.021010e+07</td>\n",
       "      <td>2.021040e+07</td>\n",
       "      <td>3.031010e+07</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  \\\n",
       "item_date      181672.0  2.020459e+07  4.551119e+03  19950000.00   \n",
       "quantity tons  181672.0  5.874926e+03  2.349081e+06     -2000.00   \n",
       "customer       181672.0  3.051221e+07  2.433382e+07     12458.00   \n",
       "country        181645.0  4.489302e+01  2.440421e+01        25.00   \n",
       "application    181649.0  2.561581e+01  1.775417e+01         2.00   \n",
       "thickness      181672.0  2.564827e+00  6.572321e+00         0.18   \n",
       "width          181673.0  1.295287e+03  2.616318e+02         1.00   \n",
       "product_ref    181673.0  4.739679e+08  7.175101e+08    611728.00   \n",
       "delivery date  181672.0  2.020738e+07  2.411059e+04  20190401.00   \n",
       "selling_price  181672.0  1.918036e+03  3.317956e+05     -1160.00   \n",
       "\n",
       "                        25%           50%           75%           max  \n",
       "item_date      2.020093e+07  2.020113e+07  2.021020e+07  2.021040e+07  \n",
       "quantity tons  1.097030e+01  3.036464e+01  6.716061e+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",
       "delivery date  2.020110e+07  2.021010e+07  2.021040e+07  3.031010e+07  \n",
       "selling_price  6.690000e+02  8.120000e+02  9.530000e+02  1.000010e+08  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# descriptive stats analaysis\n",
    "copper.describe().T\n",
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "22af5af0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "status\n",
       "Won                116010\n",
       "Lost                34438\n",
       "Not lost for AM     19573\n",
       "Revised              4276\n",
       "To be approved       4170\n",
       "Draft                3140\n",
       "Offered                53\n",
       "Offerable              10\n",
       "Wonderful               1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking cat feature\n",
    "\n",
    "copper['status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4b435fce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "item type\n",
       "W         105615\n",
       "S          69236\n",
       "PL          5660\n",
       "Others       610\n",
       "WI           524\n",
       "IPL           27\n",
       "SLAWR          1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "copper['item type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "79cfe447",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "item_date             1\n",
       "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",
       "delivery date         1\n",
       "selling_price         1\n",
       "item_date_new         3\n",
       "delivery date_new     3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "copper.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "47034e9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# handling the null values using mean &mode& median  method\n",
    "# object datatype using mode\n",
    "copper['item_date'].fillna(copper['item_date'].mode().iloc[0],inplace = True)\n",
    "copper['item_date_new'].fillna(copper['item_date_new'].mode().iloc[0],inplace = True)\n",
    "copper['delivery date'].fillna(copper['delivery date'].mode().iloc[0],inplace = True)\n",
    "copper['delivery date_new'].fillna(copper['delivery date_new'].mode().iloc[0],inplace = True)\n",
    "copper['status'].fillna(copper['status'].mode().iloc[0], inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ac8eeed3",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#numerical datatype using median\n",
    "\n",
    "copper['customer'].fillna(copper['customer'].median(), inplace=True)\n",
    "copper['country'].fillna(copper['country'].median(), inplace=True)\n",
    "copper['application'].fillna(copper['application'].median(), inplace=True)\n",
    "copper['thickness'].fillna(copper['thickness'].median(), inplace=True)\n",
    "copper['selling_price'].fillna(copper['selling_price'].median(), inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b303340f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "item_date            0\n",
       "quantity tons        1\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",
       "delivery date        0\n",
       "selling_price        0\n",
       "item_date_new        0\n",
       "delivery date_new    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# final check null values in dataset\n",
    "copper.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "acdc169a",
   "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": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "copper['status'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "00382eda",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['W', 'WI', 'S', 'Others', 'PL', 'IPL', 'SLAWR'], dtype=object)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "copper['item type'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e5dbf12d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Handle NaN values before mapping\n",
    "copper['status'].fillna('Unknown', inplace=True)\n",
    "copper['status'] = copper['status'].map({\n",
    "    'Lost': 0,\n",
    "    'Won': 1,\n",
    "    'Draft': 2,\n",
    "    'To be approved': 3,\n",
    "    'Not lost for AM': 4,\n",
    "    'Wonderful': 5,\n",
    "    'Revised': 6,\n",
    "    'Offered': 7,\n",
    "    'Offerable': 8\n",
    "})\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "35e796bb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>item_date</th>\n",
       "      <th>quantity tons</th>\n",
       "      <th>customer</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20210401.0</td>\n",
       "      <td>54.151139</td>\n",
       "      <td>30156308.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1500.0</td>\n",
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       "      <td>20210701.0</td>\n",
       "      <td>854.00</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>2021-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20210401.0</td>\n",
       "      <td>768.024839</td>\n",
       "      <td>30202938.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1210.0</td>\n",
       "      <td>1668701718</td>\n",
       "      <td>20210401.0</td>\n",
       "      <td>1047.00</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>2021-04-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20210401.0</td>\n",
       "      <td>386.127949</td>\n",
       "      <td>30153963.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.38</td>\n",
       "      <td>952.0</td>\n",
       "      <td>628377</td>\n",
       "      <td>20210101.0</td>\n",
       "      <td>644.33</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>2021-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20210401.0</td>\n",
       "      <td>202.411065</td>\n",
       "      <td>30349574.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>2.30</td>\n",
       "      <td>1317.0</td>\n",
       "      <td>1668701718</td>\n",
       "      <td>20210101.0</td>\n",
       "      <td>768.00</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>2021-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20210401.0</td>\n",
       "      <td>785.526262</td>\n",
       "      <td>30211560.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>640665</td>\n",
       "      <td>20210301.0</td>\n",
       "      <td>577.00</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>2021-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181668</th>\n",
       "      <td>20200702.0</td>\n",
       "      <td>102.482422</td>\n",
       "      <td>30200854.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.96</td>\n",
       "      <td>1220.0</td>\n",
       "      <td>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>591.00</td>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>2020-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181669</th>\n",
       "      <td>20200702.0</td>\n",
       "      <td>208.086469</td>\n",
       "      <td>30200854.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>589.00</td>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>2020-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181670</th>\n",
       "      <td>20200702.0</td>\n",
       "      <td>4.235594</td>\n",
       "      <td>30200854.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.71</td>\n",
       "      <td>1250.0</td>\n",
       "      <td>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>619.00</td>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>2020-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181671</th>\n",
       "      <td>20200702.0</td>\n",
       "      <td>-2000.000000</td>\n",
       "      <td>30200854.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1250.0</td>\n",
       "      <td>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>601.00</td>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>2020-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181672</th>\n",
       "      <td>20200702.0</td>\n",
       "      <td>406.686538</td>\n",
       "      <td>30200854.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.71</td>\n",
       "      <td>1240.0</td>\n",
       "      <td>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>607.00</td>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>2020-07-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>181673 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         item_date  quantity tons    customer  country  status  item type  \\\n",
       "0       20210401.0      54.151139  30156308.0     28.0       1        5.0   \n",
       "1       20210401.0     768.024839  30202938.0     25.0       1        5.0   \n",
       "2       20210401.0     386.127949  30153963.0     30.0       1        6.0   \n",
       "3       20210401.0     202.411065  30349574.0     32.0       1        3.0   \n",
       "4       20210401.0     785.526262  30211560.0     28.0       1        5.0   \n",
       "...            ...            ...         ...      ...     ...        ...   \n",
       "181668  20200702.0     102.482422  30200854.0     25.0       1        5.0   \n",
       "181669  20200702.0     208.086469  30200854.0     25.0       1        5.0   \n",
       "181670  20200702.0       4.235594  30200854.0     25.0       1        5.0   \n",
       "181671  20200702.0   -2000.000000  30200854.0     25.0       1        5.0   \n",
       "181672  20200702.0     406.686538  30200854.0     25.0       1        5.0   \n",
       "\n",
       "        application  thickness   width  product_ref  delivery date  \\\n",
       "0              10.0       2.00  1500.0   1670798778     20210701.0   \n",
       "1              41.0       0.80  1210.0   1668701718     20210401.0   \n",
       "2              28.0       0.38   952.0       628377     20210101.0   \n",
       "3              59.0       2.30  1317.0   1668701718     20210101.0   \n",
       "4              10.0       4.00  2000.0       640665     20210301.0   \n",
       "...             ...        ...     ...          ...            ...   \n",
       "181668         41.0       0.96  1220.0    164141591     20200701.0   \n",
       "181669         41.0       0.95  1500.0    164141591     20200701.0   \n",
       "181670         41.0       0.71  1250.0    164141591     20200701.0   \n",
       "181671         41.0       0.85  1250.0    164141591     20200701.0   \n",
       "181672         41.0       0.71  1240.0    164141591     20200701.0   \n",
       "\n",
       "        selling_price item_date_new delivery date_new  \n",
       "0              854.00    2021-04-01        2021-07-01  \n",
       "1             1047.00    2021-04-01        2021-04-01  \n",
       "2              644.33    2021-04-01        2021-01-01  \n",
       "3              768.00    2021-04-01        2021-01-01  \n",
       "4              577.00    2021-04-01        2021-03-01  \n",
       "...               ...           ...               ...  \n",
       "181668         591.00    2020-07-02        2020-07-01  \n",
       "181669         589.00    2020-07-02        2020-07-01  \n",
       "181670         619.00    2020-07-02        2020-07-01  \n",
       "181671         601.00    2020-07-02        2020-07-01  \n",
       "181672         607.00    2020-07-02        2020-07-01  \n",
       "\n",
       "[181673 rows x 14 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# convert categorical data into numerical data - using  labelencoder methods\n",
    "encode = OrdinalEncoder()\n",
    "\n",
    "copper['item type'] = encode.fit_transform(copper[['item type']])\n",
    "copper\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2df82819",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "status\n",
       "1    116012\n",
       "0     34438\n",
       "4     19573\n",
       "6      4276\n",
       "3      4170\n",
       "2      3140\n",
       "7        53\n",
       "8        10\n",
       "5         1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "copper['status'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58aa8f9c",
   "metadata": {},
   "source": [
    "# Skewness Handling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "429d1337",
   "metadata": {},
   "outputs": [],
   "source": [
    "# find outliers - box plot & skewed data - hist plot and violin plot\n",
    "\n",
    "def plot(copper, column):\n",
    "    plt.figure(figsize=(20,5))\n",
    "    plt.subplot(1,3,1)\n",
    "    sns.boxplot(data=copper, x=column)\n",
    "    plt.title(f'Box Plot for {column}')\n",
    "\n",
    "    plt.subplot(1,3,2)\n",
    "    sns.histplot(data=copper, x=column, kde=True, bins=50)\n",
    "    plt.title(f'Distribution Plot for {column}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b6160740",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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tXLlSd999d8BZECV1Nr+npRlnnsmYyPN34eOPP1bNmjXVq1cvrzFRy5YtFR8fX2xM1Lx5c9sVtgY1A1DOmjRpojZt2qhNmza68cYb9Y9//EPdunXT8OHDrQqzBw4cUHx8fLF1WbGxsapevboOHDjgdfzPf/6z8vPzVb16dQ0ePPiM+jN+/HitX79eX3/9tXbu3Kmff/5Zt956q9/2WVlZMsZYU9w8JSQkWP0vK+7b8ne+wsJCZWVleR331daX/fv3l1mxJJfLpZUrV6ply5YaOXKkrrjiCiUkJOjFF18stj7NlwMHDpzRY1qrVi2vn8PCwgIeP378uCRp3759MsYoLi5OoaGhXl/r1q3Tb7/95nX9kj6WAICzc+TIER04cMD6/74vF198sZYsWaLY2FgNHDhQF198sS6++GL97W9/O6Nzncn/2+Pj4/0eK8v3e1/O9L2xdu3aXj+7p7YfO3asROdz/8Fm48aN2rNnj7799lt16NAhYP+kMxujnI3Tna/o41GjRg1rOWIgWVlZKigoKLMx0dn8npZmnHkmYyL3eEg6OSY6ePCgVbvL8ysjI4MxURVBzQAEXfPmzfXZZ59p27Ztuvrqq1W7dm198cUXMsZ4BQKZmZk6ceKE6tSpYx07cuSIkpOTddlll2nfvn16+OGH9X//938lPnejRo28qhqfTkxMjKpVq6a9e/cWu2zPnj2S5NW/s+V+Y/d3vmrVqikmJsbreEn3ZD7//POt4n7+hIeHS5JVPMet6BuEJDVr1kzz5s2TMUbffvutZsyYoTFjxigiIkLPPvtswPPUrl27XB7TOnXqyOFw6PPPP/e5/q/osTPd3xoAUDoLFy5UQUGBV2FZX6699lpde+21Kigo0FdffaUpU6ZoyJAhiouL0z333FOic53J/9szMjL8HnO/R5/Je+WZKK/3Rjf3H2xKqjRjlLNxuvMVfTxK+jzXqlVLISEhZzQm8hwv+HqeS/t7Wp7jzDp16qh27dpatGiRz8uLbuXImMiemBmAoNu0aZOkU4VTunTposOHDxfbj/7dd9+1Lnd79NFHtXPnTi1YsEBvv/22PvroI7366qvnrK+RkZFq27atFixY4JW0FxYWatasWbrwwgutKVRnmsj70rhxY11wwQWaM2eO1/TJI0eOaP78+Vb13tK46aabtHz58oDLGho0aCBJ+vbbb72Of/TRR36v43A41KJFC7366quqWbOm1zRKp9Pp8/Ho0qWLli1bZr3Rub377ruqUaNGmW231LNnTxlj9Ouvv1ozVDy/mjVrVibnAQCU3M6dOzVs2DC5XC4NGDCgRNcJCQlR27Zt9dprr0mS9V5TFu+9nr7//nt98803XsfmzJmjqKgoqzjwmbxX+nsf9KW83htL60zGKGXxvLRr104RERGaNWuW1/Hdu3dbSypKIyIiQklJSXr//fcDBjj+nuf//Oc/fq9zpr+nZzLOPFs9e/bUgQMHVFBQ4HNM1Lhx4zI5Dyo2ZgagXKWnp1vVXA8cOKAFCxZo8eLFuu2229SwYUNJ0gMPPKDXXntNffv21Y4dO9SsWTOtXr1aY8eO1c0336wbbrhBkvTPf/5Ts2bN0vTp03XFFVfoiiuu0BNPPKFnnnlGHTp0KLN9aYsaN26cunbtqs6dO2vYsGEKCwvTtGnTlJ6errlz51rJaWJioiTpzTffVFRUlMLDw9WwYcNi0/gCqVatmiZMmKD77rtPPXv21IABA5Sbm6tXXnlFBw8e1P/+7/+W+n6MGTNGn376qa677jqNHDlSzZo108GDB7Vo0SINHTpUl19+ua666io1btxYw4YN04kTJxQTE6PU1FStXr3a67Y+/vhjTZs2TbfeeqsaNWokY4wWLFiggwcPetVwaNasmVasWKH//Oc/qlu3rqKiotS4cWO9+OKL+vjjj9W5c2e98MILqlWrlmbPnq2FCxdqwoQJcrlcpb6fnjp06KBHHnlEDz74oL766itdd911ioyM1N69e7V69Wo1a9ZMjz32WJmcCwBQnHsccOLECWVmZurzzz/X9OnTFRISotTU1IBrtt944w0tW7ZMPXr00EUXXaTjx4/rnXfekSRrbBAVFaX69evr//7v/9SlSxfVqlVLderUsT7InamEhAT17t1bo0aNUt26dTVr1iwtXrxY48ePtz7olvS9Ujr5PrhgwQK9/vrrat26tapVq+b3r/Hl9d5YWmcyRnGH7X/729/Ut29fhYaGqnHjxsX++hxIzZo19fzzz2vkyJF64IEHdO+99+rAgQMaPXq0wsPD9eKLL5b6vqSkpKhjx45q27atnn32WV1yySXat2+fPvroI/3jH/9QVFSUbr75ZtWqVUsPPfSQxowZo+rVq2vGjBnatWuX122d7e9pSceZZ+uee+7R7NmzdfPNN+vJJ5/U1VdfrdDQUO3evVvLly/XLbfcottuu61MzoUKLFiVC1G1+NpNwOVymZYtW5qUlBRz/Phxr/YHDhwwjz76qKlbt66pXr26qV+/vhkxYoTV7ttvvzURERHFKtMfP37ctG7d2jRo0MBkZWX57Y+vyv2++NpNwBhjPv/8c3P99debyMhIExERYa655hrzn//8p9j1J0+ebBo2bGhCQkJ83k5J+/Thhx+atm3bmvDwcBMZGWm6dOli/t//+39ebdxVbvfv3x/wPnnatWuX+fOf/2zi4+NNaGioSUhIMHfffbfZt2+f1Wbbtm2mW7duJjo62px//vlm0KBBZuHChV4VhX/44Qdz7733mosvvthEREQYl8tlrr76ajNjxgyv823atMl06NDB1KhRw0gySUlJ1mXfffed6dWrl3G5XCYsLMy0aNGi2OPl7zHyV6Xa32PyzjvvmLZt21rP38UXX2weeOAB89VXX1ltkpKSzBVXXFHixxIA4F/RcUBYWJiJjY01SUlJZuzYsT6ruBet3r527Vpz2223mfr16xun02lq165tkpKSzEcffeR1vSVLlphWrVoZp9NpJFljhUDvk/52E+jRo4f54IMPzBVXXGHCwsJMgwYNilVpN6Zk75XGGPP777+bO++809SsWdM4HA6vc8pHdfmzeW/0N4YpqiQ7PXiep+guSSUZoxhjzIgRI0xCQoKpVq3aaXdbCtSnf/7zn6Z58+YmLCzMuFwuc8stt5jvv//eq03fvn1NZGRkwPtT1ObNm81dd91lateubcLCwsxFF11k+vXr5zVG/fLLL0379u1NZGSkueCCC8yLL75o/vnPf3rtJnC2v6fGlGyceaZjH1+PSX5+vpk4caJp0aKFCQ8PN+edd565/PLLzYABA8yPP/5otXO/FmA/DmNOU7oVAAAAAADYCjUDAAAAAACoYggDAAAAAACoYggDAAAAAACoYggDAAAAAACoYggDAAAAAACoYggDAAAAAACoYqqX9oqFhYXas2ePoqKi5HA4yrJPAABUWsYYHTp0SAkJCapWjcz9XGIsAgBAcSUdi5Q6DNizZ4/q1atX2qsDAGBru3bt0oUXXhjsbtgaYxEAAPw73Vik1GFAVFSUdYLo6OjS3gwAALaSk5OjevXqWe+TOHcYiwAAUFxJxyKlDgPc0/Gio6N5AwYAoAimrZ97jEUAAPDvdGMRFjMCAAAAAFDFEAYAAAAAAFDFEAYAAAAAAFDFEAYAAAAAAFDFEAYAAAAAAFDFEAYAAAAAAFDFEAYAAAAAAFDFEAYAAAAAAFDFEAYAAAAAAFDFEAYAAAAAAFDFEAYAAAAAAFDFEAYAAAAAAFDFEAYAAAAAAFDFEAYAAAAAAFDFEAYAAAAEkHeiUL2mrNaz878NdlcAACgzhAEAAAAB7Pz9qL77NVuffLc32F0BAKDMEAYAAAAEYIw5+W+Q+wEAQFkiDAAAAAjAFPsGAIDKjzAAAAAggMI/Zga4/wUAwA4IAwAAAAJwZwBEAQAAOyEMAAAACICZAQAAOyIMAAAACMCaGUAWAACwEcIAAACAAAgDAAB2RBgAAAAQgJHx+hcAADsgDAAAAAig0Hj/CwCAHRAGAAAABGD+WB9gWCcAALARwgAAAIAATJF/AQCwA8IAAACAAE7NDAhyRwAAKEOEAQAAAAF4hgAsFQAA2AVhAAAAQACehQMpIggAsAvCAAAAgAA8ZwMwMwAAYBeEAQAAAAEwMwAAYEeEAQAAAAEYj30EDHsKAABsonqwOxCIMUbHjx+XJIWHh8vhcAS5RwAAoKrxLiAYvH4AAFCWKvTMgOPHj+umm27STTfdZIUCAAAA5YkwAABgRxU+DPD1PQAAQHkp9EgACkkDAAA2UaHDAAAAgGAzfr4HAKAyIwwAAAAIgK0FAQB2RBgAAAAQgGFrQQCADREGAAAABOC1nSBhAADAJggDAAAAAigs9PieZQIAAJsgDAAAAAiAAoIAADsiDAAAAAiArQUBAHZEGAAAgA2cOHFCf/3rX9WwYUNFRESoUaNGGjNmjAo95rgbYzRq1CglJCQoIiJCnTp10vfff+91O7m5uRo0aJDq1KmjyMhI9e7dW7t37/Zqk5WVpeTkZLlcLrlcLiUnJ+vgwYNebXbu3KlevXopMjJSderU0eDBg5WXl+fV5rvvvlNSUpIiIiJ0wQUXaMyYMRWyWr9nlypg9wAAKBXCAAAAbGD8+PF64403NHXqVG3ZskUTJkzQK6+8oilTplhtJkyYoJSUFE2dOlXr169XfHy8unbtqkOHDllthgwZotTUVM2bN0+rV6/W4cOH1bNnTxUUFFht+vTpo02bNmnRokVatGiRNm3apOTkZOvygoIC9ejRQ0eOHNHq1as1b948zZ8/X08//bTVJicnR127dlVCQoLWr1+vKVOmaOLEiUpJSTnHj9SZY2tBAIAdVQ92BwAAwNlbu3atbrnlFvXo0UOS1KBBA82dO1dfffWVpJMfYidPnqznnntOt99+uyRp5syZiouL05w5czRgwABlZ2fr7bff1r/+9S/dcMMNkqRZs2apXr16WrJkibp3764tW7Zo0aJFWrdundq2bStJeuutt9SuXTtt3bpVjRs3VlpamjZv3qxdu3YpISFBkjRp0iT169dPL7/8sqKjozV79mwdP35cM2bMkNPpVGJiorZt26aUlBQNHTpUDoejvB9Cv6gZAACwI2YGAABgAx07dtTSpUu1bds2SdI333yj1atX6+abb5Ykbd++XRkZGerWrZt1HafTqaSkJK1Zs0aStGHDBuXn53u1SUhIUGJiotVm7dq1crlcVhAgSddcc41cLpdXm8TERCsIkKTu3bsrNzdXGzZssNokJSXJ6XR6tdmzZ4927Njh8z7m5uYqJyfH66s8UDMAAGBHzAwAAMAGnnnmGWVnZ+vyyy9XSEiICgoK9PLLL+vee++VJGVkZEiS4uLivK4XFxenX375xWoTFhammJiYYm3c18/IyFBsbGyx88fGxnq1KXqemJgYhYWFebVp0KBBsfO4L2vYsGGxc4wbN06jR48+/YNRxqgZAACwI2YGAABgA++9955mzZqlOXPm6Ouvv9bMmTM1ceJEzZw506td0en3xpjTTskv2sZX+7Jo416P768/I0aMUHZ2tvW1a9eugP0uKywTAADYETMDAACwgb/85S969tlndc8990iSmjVrpl9++UXjxo1T3759FR8fL+nkX93r1q1rXS8zM9P6i3x8fLzy8vKUlZXlNTsgMzNT7du3t9rs27ev2Pn379/vdTtffPGF1+VZWVnKz8/3auOeJeB5Hqn47AU3p9PptaygvHgWDSwsJA4AANgDMwMAALCBo0ePqlo177f1kJAQa2vBhg0bKj4+XosXL7Yuz8vL08qVK60P+q1bt1ZoaKhXm7179yo9Pd1q065dO2VnZ+vLL7+02nzxxRfKzs72apOenq69e/dabdLS0uR0OtW6dWurzapVq7y2G0xLS1NCQkKx5QPBxtIAAIAdEQYAAGADvXr10ssvv6yFCxdqx44dSk1NVUpKim677TZJJ6feDxkyRGPHjlVqaqrS09PVr18/1ahRQ3369JEkuVwuPfTQQ3r66ae1dOlSbdy4Uffff7+aNWtm7S7QpEkT3Xjjjerfv7/WrVundevWqX///urZs6caN24sSerWrZuaNm2q5ORkbdy4UUuXLtWwYcPUv39/RUdHSzq5PaHT6VS/fv2Unp6u1NRUjR07tsLtJCBRQBAAYE8sEwAAwAamTJmi559/Xo8//rgyMzOVkJCgAQMG6IUXXrDaDB8+XMeOHdPjjz+urKwstW3bVmlpaYqKirLavPrqq6pevbruvvtuHTt2TF26dNGMGTMUEhJitZk9e7YGDx5s7TrQu3dvTZ061bo8JCRECxcu1OOPP64OHTooIiJCffr00cSJE602LpdLixcv1sCBA9WmTRvFxMRo6NChGjp06Ll8mEqFAoIAADtyGFO6t7WcnBy5XC5lZ2dbKX9Zy8rKsv6ikZqaWqy6MQAAFU15vD/ipPJ6rN//apf+8sG3kqRlTyep0fnnnbNzAQBwtkr6/sgyAQAAgADYTQAAYEeEAQAAAAF4TqIs5YRKAAAqHMIAAACAAKgZAACwI8IAAACAAAqN7+8BAKjMCAMAAAACMB6VAgxVAwAANkEYAAAAEADLBAAAdkQYAAAAEIBn0cBC0gAAgE0QBgAAAATgtbUgWQAAwCYIAwAAAAIoLPTcWjCIHQEAoAwRBgAAAATgNTOAAoIAAJsgDAAAAAiArQUBAHZEGAAAABCAZwFBwzoBAIBNEAYAAAAEYJgZAACwIcIAAACAAEyRqgEAANgBYQAAAEAA1AwAANgRYQAAAEAAnssEKBkAALALwgAAAIAAPJcJUEAQAGAXhAEAAAABUEAQAGBHhAEAAAABeG0tSAFBAIBNEAYAAAAEUEjNAACADREGAAAABEABQQCAHREGAAAABFDokQAUkgYAAGyCMAAAACAA4+d7AAAqM8IAAACAAAwzAwAANkQYAAAAEIBhagAAwIYIAwAAAAKgZgAAwI4IAwAAAALwmhhAFgAAsAnCAAAAgAC8thYMXjcAAChThAEAAAABUEAQAGBHhAEAAAABsEwAAGBHhAEAAAABFBaeSgAMaQAAwCYIAwAAAAJgZ0EAgB0RBgAAAATA1oIAADsiDAAAAAjAazcBsgAAgE0QBgAAAATAbgIAADsiDAAAAAiAj/8AADsiDAAAAAiAmgEAADsiDAAAAAiAmgEAADsiDAAAAAjAa2tBwgAAgE0QBgAAAARAAUEAgB0RBgAAAATgtUwgeN0AAKBMEQYAAAAE4DkbwDAzAABgE4QBAAAAAVBAEABgR4QBAAAAARQa398DAFCZEQYAAAAEYDwqBRiqBgAAbIIwAAAAIADDzAAAgA0RBgAAAARgKBoAALAhwgAAAIAAqBkAALAjwgAAAIAAPD//s7UgAMAuCAMAAAACKDSeBQQBALAHwgAAAIBAWCYAALAhwgAAAIAAvLYWZJkAAMAmCAMAAAACKCw89T1ZAADALggDAAAAAvCaGUDVAACATRAGAAAABMDWggAAOyIMAAAACMBzaQDLBAAAdlGhw4BCj0V6nt8DAACUF8+igYWkAQAAm6jQYUBOTo7P7wEAAMoLH/8BAHZUocMAAACAYPOcDVBI0QAAgE0QBgAAAATgVTMgeN0AAKBMEQYAAAAE4DkzgJIBAAC7IAwAAAAoIQoIAgDsgjAAAAAgAJYJAADsiDAAAAAgAO9lAsQBAAB7IAwAAAAIwGtmAFkAAMAmCAMAAAAC8NpakDQAAGAThAEAAAABGD/fAwBQmREGAAAABGCYGQAAsCHCAAAAgAAMUwMAADZEGAAAABAANQMAAHZEGAAAABCA18QAsgAAgE0QBgAAAARQ6Lm1YPC6AQBAmSIMAAAACIRlAgAAGyIMAADAJn799Vfdf//9ql27tmrUqKGWLVtqw4YN1uXGGI0aNUoJCQmKiIhQp06d9P3333vdRm5urgYNGqQ6deooMjJSvXv31u7du73aZGVlKTk5WS6XSy6XS8nJyTp48KBXm507d6pXr16KjIxUnTp1NHjwYOXl5Xm1+e6775SUlKSIiAhdcMEFGjNmjFfl/oqCZQIAADsiDAAAwAaysrLUoUMHhYaG6tNPP9XmzZs1adIk1axZ02ozYcIEpaSkaOrUqVq/fr3i4+PVtWtXHTp0yGozZMgQpaamat68eVq9erUOHz6snj17qqCgwGrTp08fbdq0SYsWLdKiRYu0adMmJScnW5cXFBSoR48eOnLkiFavXq158+Zp/vz5evrpp602OTk56tq1qxISErR+/XpNmTJFEydOVEpKyrl9oErBczZARQwrAAAojerB7gAAADh748ePV7169TR9+nTrWIMGDazvjTGaPHmynnvuOd1+++2SpJkzZyouLk5z5szRgAEDlJ2drbffflv/+te/dMMNN0iSZs2apXr16mnJkiXq3r27tmzZokWLFmndunVq27atJOmtt95Su3bttHXrVjVu3FhpaWnavHmzdu3apYSEBEnSpEmT1K9fP7388suKjo7W7Nmzdfz4cc2YMUNOp1OJiYnatm2bUlJSNHToUDkcjnJ65E7PUDMAAGBDzAwAAMAGPvroI7Vp00Z33XWXYmNj1apVK7311lvW5du3b1dGRoa6detmHXM6nUpKStKaNWskSRs2bFB+fr5Xm4SEBCUmJlpt1q5dK5fLZQUBknTNNdfI5XJ5tUlMTLSCAEnq3r27cnNzrWULa9euVVJSkpxOp1ebPXv2aMeOHT7vY25urnJycry+yoNnAUFqBgAA7IIwAAAAG/j555/1+uuv69JLL9Vnn32mRx99VIMHD9a7774rScrIyJAkxcXFeV0vLi7OuiwjI0NhYWGKiYkJ2CY2NrbY+WNjY73aFD1PTEyMwsLCArZx/+xuU9S4ceOsOgUul0v16tU7zaNSNozXMoFyOSUAAOccYQAAADZQWFioK6+8UmPHjlWrVq00YMAA9e/fX6+//rpXu6LT740xp52SX7SNr/Zl0cb9odtff0aMGKHs7Gzra9euXQH7XVaM18yAcjklAADnHGEAAAA2ULduXTVt2tTrWJMmTbRz505JUnx8vKTif3XPzMy0/iIfHx+vvLw8ZWVlBWyzb9++Yuffv3+/V5ui58nKylJ+fn7ANpmZmZKKz15wczqdio6O9voqD8Z7P4FyOScAAOcaYQAAADbQoUMHbd261evYtm3bVL9+fUlSw4YNFR8fr8WLF1uX5+XlaeXKlWrfvr0kqXXr1goNDfVqs3fvXqWnp1tt2rVrp+zsbH355ZdWmy+++ELZ2dlebdLT07V3716rTVpampxOp1q3bm21WbVqldd2g2lpaUpISPAqfFgReNUMKAxePwAAKEuEAQAA2MBTTz2ldevWaezYsfrvf/+rOXPm6M0339TAgQMlnZx6P2TIEI0dO1apqalKT09Xv379VKNGDfXp00eS5HK59NBDD+npp5/W0qVLtXHjRt1///1q1qyZtbtAkyZNdOONN6p///5at26d1q1bp/79+6tnz55q3LixJKlbt25q2rSpkpOTtXHjRi1dulTDhg1T//79rb/m9+nTR06nU/369VN6erpSU1M1duzYCreTgFSkZgAzAwAANsHWggAA2MBVV12l1NRUjRgxQmPGjFHDhg01efJk3XfffVab4cOH69ixY3r88ceVlZWltm3bKi0tTVFRUVabV199VdWrV9fdd9+tY8eOqUuXLpoxY4ZCQkKsNrNnz9bgwYOtXQd69+6tqVOnWpeHhIRo4cKFevzxx9WhQwdFRESoT58+mjhxotXG5XJp8eLFGjhwoNq0aaOYmBgNHTpUQ4cOPZcPU6l4bS1IFgAAsAmHMaV7W8vJyZHL5VJ2dvY5W7O3fft2Pfjgg5Kk6dOnq2HDhufkPAAAlJXyeH/ESeX1WHeeuELbfzsiSbrjygs16e4W5+xcAACcrZK+P7JMAAAAIACWCQAA7IgwAAAAIIBClgkAAGyIMAAAACAAz9kApVxdCQBAhUMYAAAAEIDndoKFZAEAAJsgDAAAACghsgAAgF0QBgAAAARQ6LE0oJBlAgAAmyAMAAAACMDr8z9ZAADAJggDAAAAAmBmAADAjggDAAAAAvCaGEAWAACwCcIAAACAADy3EzSsEwAA2ARhAAAAQACeswHYWhAAYBeEAQAAAAGwTAAAYEeEAQAAAAF4Fg00pAEAAJsgDAAAAAjA8/M/UQAAwC4IAwAAAAJga0EAgB0RBgAAAATiOTOALAAAYBOEAQAAAAEwMwAAYEeEAQAAAAHw8R8AYEeEAQAAAAEwMwAAYEeEAQAAAAEYagYAAGyIMAAAACAAwgAAgB0RBgAAAARgxDIBAID9EAYAAAAE4DUzIHjdAACgTBEGAAAABOA5G8AwMwAAYBOEAQAAAAF4fvwnCwAA2AVhAAAAQACeAQA1AwAAdkEYAAAA4EfRZQFEAQAAuyAMAAAA8KPQBP4ZAIDKijAAAADAj2IFA1kmAACwCcIAAAAAP5gZAACwK8IAAAAAP4yK1gwgDQAA2ANhAAAAgB9FVwUUFganHwAAlDXCAAAAAD+KlQwITjcAAChzhAEAAARRo0aNdODAgWLHDx48qEaNGgWhR/BUbJkABQQBADZBGAAAQBDt2LFDBQUFxY7n5ubq119/DUKP4KlowUCyAACAXVQPdgcAAKiKPvroI+v7zz77TC6Xy/q5oKBAS5cuVYMGDYLQM3gqOhOAAoIAALsgDAAAIAhuvfVWSZLD4VDfvn29LgsNDVWDBg00adKkIPQMnthaEABgV4QBAAAEQeEfZekbNmyo9evXq06dOkHuEXwqtkyANAAAYA+EAQAABNH27duD3QUEUFh0mQBZAADAJggDAAAIsqVLl2rp0qXKzMy0Zgy4vfPOO0HqFaTiWwmSBQAA7IIwAACAIBo9erTGjBmjNm3aqG7dunI4HMHuEjwUnRlQ9GcAACorwgAAAILojTfe0IwZM5ScnBzsrsCHop/9yQIAAHZRLdgdAACgKsvLy1P79u2D3Q34UbRgIDMDAAB2QRgAAEAQPfzww5ozZ06wuwE/itUMIAsAANgEywQAAAii48eP680339SSJUvUvHlzhYaGel2ekpISpJ5B4sM/AMC+CAMAAAiib7/9Vi1btpQkpaene11GMcHgo4AgAMCuKlQYsGbNGv3tb3/Tk08+WWz95IMPPqgVK1YEp2MAAFvq1KmT9X2w3mOWL18elPOiZFgmAACwqwpTM+D48eNKSUnRvn37lJKSouPHj+vBBx/0auM5aAMA4GyMGTMm4M+AJBUWMjMAAGBPFWZmwOzZs3XgwAFJ0oEDByimBAA4p5YtW1bs5xdeeKHc+9G5c+eAywGK9hPBRRQAALCLCjEzYPfu3ZozZ461fY8xRu+++67PtswOAACcreuvv/6Mjp9LLVu2VIsWLayvpk2bKi8vT19//bWaNWtW7v2Bt6IzAYpuNQgAQGVV4pkBubm5ys3NtX7Oyckpkw4YY/S3v/3tjK7TqVMn6gcAAEolIyNDhYWFPi8rLCxURkaG4uPjy60/r776qs/jo0aN0uHDh8utH/Ct6Gd/sgAAgF2UeGbAuHHj5HK5rK969eqVSQd27typ9evXq6CgoExuDwCAQO65556zury83H///XrnnXeC3Y0qj90EAAB2VeIwYMSIEcrOzra+du3aVSYduOiii3TVVVcpJCSkTG4PAIBA5s2bd1aXl5e1a9cqPDw82N2o8ortJhCUXgAAUPZKvEzA6XTK6XSWeQccDoeefPJJ9e3bt8TXYYkAAKC04uPjVa1aNZ9LBapVq1auSwQk6fbbb/f62RijvXv36quvvtLzzz9frn1BcUVrBBTdXQAAgMqqQhQQvPDCC9WnTx+rmrLD4dADDzzgsy1BAADgbPmr0B+Myv2eS/BcLpdq1aqlTp066ZNPPtGLL75Y7v2Bt2I1A4LTDQAAylyF2Vrwvvvu06effqrffvtNderUUZ8+ffzuKAAAwNm6/vrrvT78B2MnAUmaPn16UM6Lkin24Z80AABgExViZoAkhYeHa+jQoYqLi9NTTz2l8PDwYgMkZgUAAMrKCy+8EPDn8rZhwwbNmjVLs2fP1saNG4PaF5xCAUEAgF1VmJkBktS+fXu1b9/e52X85QQAUNYqQsicmZmpe+65RytWrFDNmjVljFF2drY6d+6sefPm6fzzzw92F6s0lgkAAOyqwswMAACgKho0aJBycnL0/fff6/fff1dWVpbS09OVk5OjwYMHB7t7VR4zAwAAdlWhZgYAAFDVLFq0SEuWLFGTJk2sY02bNtVrr72mbt26BbFnkHzMDCALAADYBDMDAAAIosLCQoWGhhY7Hhoa6nP7Q5QvwgAAgF0RBgAAEETXX3+9nnzySe3Zs8c69uuvv+qpp55Sly5dgtgzSJL5o0pANYf3zwAAVHaEAQAABNHUqVN16NAhNWjQQBdffLEuueQSNWzYUIcOHdKUKVOC3b0qr/CPz/4hf6QBhWQBAACboGYAAABBVK9ePX399ddavHixfvjhBxlj1LRpU91www3B7hokGeOeGeCQZKyfAQCo7JgZAABAECxbtkxNmzZVTk6OJKlr164aNGiQBg8erKuuukpXXHGFPv/88yD3EswMAADYFWEAAABBMHnyZPXv31/R0dHFLnO5XBowYIBSUlKC0DN4O/npP8ThCHI/AAAoW4QBAAAEwTfffKMbb7zR7+XdunXThg0byrFH8MU9E6BatVNhAEsFAAB2QBgAAEAQ7Nu3z+eWgm7Vq1fX/v37y7FH8MUUWSYgsVQAAGAPhAEAAATBBRdcoO+++87v5d9++63q1q1bjj2CL94FBL2PAQBQmREGAAAQBDfffLNeeOEFHT9+vNhlx44d04svvqiePXsGoWfwZC0TcBQ/BgBAZcbWggAABMFf//pXLViwQJdddpmeeOIJNW7cWA6HQ1u2bNFrr72mgoICPffcc8HuZpVn3AUEPWsGiDQAAFD5EQYAABAEcXFxWrNmjR577DGNGDHCmnrucDjUvXt3TZs2TXFxcUHuJYw1M8BR7BgAAJUZYQAAAEFSv359ffLJJ8rKytJ///tfGWN06aWXKiYmJthdwx98FRAkDAAA2AE1AwAACLKYmBhdddVVuvrqq8ssCBg3bpwcDoeGDBliHTPGaNSoUUpISFBERIQ6deqk77//3ut6ubm5GjRokOrUqaPIyEj17t1bu3fv9mqTlZWl5ORkuVwuuVwuJScn6+DBg15tdu7cqV69eikyMlJ16tTR4MGDlZeX59Xmu+++U1JSkiIiInTBBRdozJgxFa44X6EpvkygsIL1EQCA0iAMAADAZtavX68333xTzZs39zo+YcIEpaSkaOrUqVq/fr3i4+PVtWtXHTp0yGozZMgQpaamat68eVq9erUOHz6snj17qqCgwGrTp08fbdq0SYsWLdKiRYu0adMmJScnW5cXFBSoR48eOnLkiFavXq158+Zp/vz5evrpp602OTk56tq1qxISErR+/XpNmTJFEydOVEpKyjl8ZM6c+2O/ZwFBogAAgB2wTAAAABs5fPiw7rvvPr311lt66aWXrOPGGE2ePFnPPfecbr/9dknSzJkzFRcXpzlz5mjAgAHKzs7W22+/rX/961+64YYbJEmzZs1SvXr1tGTJEnXv3l1btmzRokWLtG7dOrVt21aS9NZbb6ldu3baunWrGjdurLS0NG3evFm7du1SQkKCJGnSpEnq16+fXn75ZUVHR2v27Nk6fvy4ZsyYIafTqcTERG3btk0pKSkaOnSoHB5r9N1yc3OVm5tr/ZyTk3POHkc3ZgYAAOyKmQEAANjIwIED1aNHD+vDvNv27duVkZGhbt26WcecTqeSkpK0Zs0aSdKGDRuUn5/v1SYhIUGJiYlWm7Vr18rlcllBgCRdc801crlcXm0SExOtIECSunfvrtzcXG3YsMFqk5SUJKfT6dVmz5492rFjh8/7Nm7cOGtpgsvlUr169UrzEJ0ZCggCAGyKMAAAAJuYN2+evv76a40bN67YZRkZGZJUbIeCuLg467KMjAyFhYUVq1tQtE1sbGyx24+NjfVqU/Q8MTExCgsLC9jG/bO7TVEjRoxQdna29bVr1y6f7cqSr5kBrBMAANgBywQAALCBXbt26cknn1RaWprCw8P9tis6/d4Y43NKfqA2vtqXRRvP7RV9cTqdXjMJyoOvrQVZJgAAsANmBgAAYAMbNmxQZmamWrdurerVq6t69epauXKl/v73v6t69ep+/+qemZlpXRYfH6+8vDxlZWUFbLNv375i59+/f79Xm6LnycrKUn5+fsA2mZmZkorPXggmq4Cg59aCwekKAABlijAAAAAb6NKli7777jtt2rTJ+mrTpo3uu+8+bdq0SY0aNVJ8fLwWL15sXScvL08rV65U+/btJUmtW7dWaGioV5u9e/cqPT3datOuXTtlZ2fryy+/tNp88cUXys7O9mqTnp6uvXv3Wm3S0tLkdDrVunVrq82qVau8thtMS0tTQkKCGjRoUPYPUClZywQcxY8BAFCZsUwAAAAbiIqKUmJiotexyMhI1a5d2zo+ZMgQjR07VpdeeqkuvfRSjR07VjVq1FCfPn0kSS6XSw899JCefvpp1a5dW7Vq1dKwYcPUrFkzqyBhkyZNdOONN6p///76xz/+IUl65JFH1LNnTzVu3FiS1K1bNzVt2lTJycl65ZVX9Pvvv2vYsGHq37+/oqOjJZ3cnnD06NHq16+fRo4cqR9//FFjx47VCy+8cNplC+XJ/bnf4XDI4Tj5M1kAAMAOCAMAAKgihg8frmPHjunxxx9XVlaW2rZtq7S0NEVFRVltXn31VVWvXl133323jh07pi5dumjGjBkKCQmx2syePVuDBw+2dh3o3bu3pk6dal0eEhKihQsX6vHHH1eHDh0UERGhPn36aOLEiVYbl8ulxYsXa+DAgWrTpo1iYmI0dOhQDR06tBweiZKz6hj88WU8jgEAUJk5TCnf0XJycuRyuZSdnW2l/GVt+/btevDBByVJ06dPV8OGDc/JeQAAKCvl8f6Ik8rjsf7ku716fPbXurpBLX29M0snCo2+GNlFcdH+izQCABBMJX1/pGYAAACAH1Z9AIfkXr1AzQAAgB0QBgAAAPhxamvBU1sekgUAAOyAMAAAAMCPQqtmgEOOIscAAKjMCAMAAABOo1o1qRozAwAANkIYAAAA4IfXzICKs+MhAABnjTAAAADAD6t+oEMsEwAA2AphAAAAgB+nwgAHywQAALZCGAAAAOCHexZANYesqQHMDAAA2AFhAAAAgB/uj/0OeRQQDFpvAAAoO4QBAAAAfhh3AUHHqQKChpkBAAAbIAwAAADww/25v5qDrQUBAPZCGAAAAOBHofXB3+Gxm0CQOgMAQBkiDAAAAPDD6FQBQYdVM4A0AABQ+REGAAAA+FFobS0oq2ZAYWHw+gMAQFkhDAAAAPDH2lrQcXJ7QTEzAABgD4QBAAAAfnjNDBAFBAEA9kEYAAAA4IfvrQWD2CEAAMoIYQAAAIAf7s/9DnlsLcgyAQCADRAGAAAA+OFeJuAOAjyPAQBQmREGAAAA+HFqmYBUrZr3MQAAKjPCAAAAAD+Mx8wAdwFBZgYAAOyAMAAAAMAPd32AkzUDTh0FAKCyIwwAAADwo9CjgqDDwcwAAIB9EAYAAAD44bVMgK0FAQA2QhgAAADgR6E5tUzAUeQYAACVGWEAAADAaZycGXAyDiALAADYAWEAAACAH4WFHlsLupcJUEAQAGADhAEAAAB+WPUDPbYWZGYAAMAOCAMAAAD8cH/wdzhEAUEAgK0QBgAAAPjhLhZYzWtrQdIAAEDlRxgAAADgh7VMQA6PmgEAAFR+hAEAAAB+GK+ZASePMTMAAGAHhAEAAAB+nKoZ4FA1B1MDAAD2QRgAAADgh+csAIePYwAAVFaEAQAAAH64P/ZXczisAoJkAQAAOyAMAAAA8MM9C8BBzQAAgM0QBgAAAPjzx+f+ao5TywSIAgAAdkAYAAAA4MepmQGnCggyMQAAYAeEAQAAAH6c2k3g1DIBQxoAALABwgAAAAA/3B/7HfIoIBi87gAAUGYIAwAAAPxwLxPwrBlAAUEAgB0QBgAAAPjhuUyAmgEAADshDAAAAPDDWDMDHGwtCACwFcIAAAAAP07VDDg1MwAAADsgDAAAAPDDc2tBZgYAAOykQocB0dHRPr8HAAAoD541A4oeAwCgMqvQYUC1atV8fg8AAFAeCt1hgBzWMoFCwgAAgA3wCRsAAMAvj60F/5gdYJgaAACwAcIAAAAAPwoLT/7rtbVgEPsDAEBZIQwAAADww8ijgKD7GDMDAAA2QBgAAADgh2cBQYd7ZgBZAADABggDAAAA/HAXC6zmtbVg8PoDAEBZIQwAAADww1omoJNFBD2PAQBQmREGAAAA+GE8ZwaIrQUBAPZBGAAAAOCHu1igwyFVq2YdDF6HAAAoI4QBAAAAfhRaBQSZGQAAsBfCAAAAAD/cn/sd1n/YWhAAYA+EAQAAAH4Uei4TcDAzAABgH4QBAAAA/ngVEPQ6BABApUYYAAAA4If3zICTx1gmAACwA8IAAAAAP4xnAcE/lgmQBQAA7IAwAAAAwA/zx6IAh07ODvA8BgBAZUYYAAAA4EehV80ACggCAOyDMAAAAMCPU8sEPGsGBK8/AACUFcIAAAAAP9zFAqs5Ti0TKCQNAADYAGEAAACAH+6P/Q45VM2dBgAAYAOEAQAAAH54bi1ozQygaAAAwAYIAwAAAPzw3FpQfxQQJAoAANgBYQAAAIAfhR41A6pRMwAAYCOEAQAAAKfhuUyALAAAYAeEAQAAAH5YNQM8CgiSBQAA7IAwAAAAwI9TNQPcFQNObTcIAEBlRhgAAIANjBs3TldddZWioqIUGxurW2+9VVu3bvVqY4zRqFGjlJCQoIiICHXq1Enff/+9V5vc3FwNGjRIderUUWRkpHr37q3du3d7tcnKylJycrJcLpdcLpeSk5N18OBBrzY7d+5Ur169FBkZqTp16mjw4MHKy8vzavPdd98pKSlJERERuuCCCzRmzJgK90Hbs4Cgwz0zoGJ1EQCAUiEMAADABlauXKmBAwdq3bp1Wrx4sU6cOKFu3brpyJEjVpsJEyYoJSVFU6dO1fr16xUfH6+uXbvq0KFDVpshQ4YoNTVV8+bN0+rVq3X48GH17NlTBQUFVps+ffpo06ZNWrRokRYtWqRNmzYpOTnZurygoEA9evTQkSNHtHr1as2bN0/z58/X008/bbXJyclR165dlZCQoPXr12vKlCmaOHGiUlJSzvEjdWY8Cwg6KCAIALCR6sHuAAAAOHuLFi3y+nn69OmKjY3Vhg0bdN1118kYo8mTJ+u5557T7bffLkmaOXOm4uLiNGfOHA0YMEDZ2dl6++239a9//Us33HCDJGnWrFmqV6+elixZou7du2vLli1atGiR1q1bp7Zt20qS3nrrLbVr105bt25V48aNlZaWps2bN2vXrl1KSEiQJE2aNEn9+vXTyy+/rOjoaM2ePVvHjx/XjBkz5HQ6lZiYqG3btiklJUVDhw61/gofbO6P/dQMAADYDTMDAACwoezsbElSrVq1JEnbt29XRkaGunXrZrVxOp1KSkrSmjVrJEkbNmxQfn6+V5uEhAQlJiZabdauXSuXy2UFAZJ0zTXXyOVyebVJTEy0ggBJ6t69u3Jzc7VhwwarTVJSkpxOp1ebPXv2aMeOHT7vU25urnJycry+zjXjOTPgj2PMDAAA2AFhAAAANmOM0dChQ9WxY0clJiZKkjIyMiRJcXFxXm3j4uKsyzIyMhQWFqaYmJiAbWJjY4udMzY21qtN0fPExMQoLCwsYBv3z+42RY0bN86qU+ByuVSvXr3TPBJnz6uAoFVB8JyfFgCAc44wAAAAm3niiSf07bffau7cucUuKzr93hhz2in5Rdv4al8Wbdx/hffXnxEjRig7O9v62rVrV8B+l4VCjz65lwkwMwAAYAeEAQAA2MigQYP00Ucfafny5brwwgut4/Hx8ZKK/9U9MzPT+ot8fHy88vLylJWVFbDNvn37ip13//79Xm2KnicrK0v5+fkB22RmZkoqPnvBzel0Kjo62uvrXDtVM0DWOgGyAACAHRAGAABgA8YYPfHEE1qwYIGWLVumhg0bel3esGFDxcfHa/HixdaxvLw8rVy5Uu3bt5cktW7dWqGhoV5t9u7dq/T0dKtNu3btlJ2drS+//NJq88UXXyg7O9urTXp6uvbu3Wu1SUtLk9PpVOvWra02q1at8tpuMC0tTQkJCWrQoEEZPSpnr/CPD/7VvGYGBLFDAACUEcIAAABsYODAgZo1a5bmzJmjqKgoZWRkKCMjQ8eOHZN0cpr7kCFDNHbsWKWmpio9PV39+vVTjRo11KdPH0mSy+XSQw89pKefflpLly7Vxo0bdf/996tZs2bW7gJNmjTRjTfeqP79+2vdunVat26d+vfvr549e6px48aSpG7duqlp06ZKTk7Wxo0btXTpUg0bNkz9+/e3/prfp08fOZ1O9evXT+np6UpNTdXYsWMr1E4CkqxpAA6PAoKGogEAABtga0EAAGzg9ddflyR16tTJ6/j06dPVr18/SdLw4cN17NgxPf7448rKylLbtm2VlpamqKgoq/2rr76q6tWr6+6779axY8fUpUsXzZgxQyEhIVab2bNna/DgwdauA71799bUqVOty0NCQrRw4UI9/vjj6tChgyIiItSnTx9NnDjRauNyubR48WINHDhQbdq0UUxMjIYOHaqhQ4eW9UNzVgo9CghaWwuSBQAAbIAwAAAAGzAl+ITqcDg0atQojRo1ym+b8PBwTZkyRVOmTPHbplatWpo1a1bAc1100UX6+OOPA7Zp1qyZVq1aFbBNsLlnATgcDms3gZI81gAAVHQsEwAAAPCjsPDkvw6d2uWAKAAAYAeEAQAAAB7aX5ukRpc1UaPLmmjzli2SpAcf6q+/T31NkvTBgg+D2DsAAMoGywQAAAA8ZOzL1KDX/k+SNPuLX/Tb4Tzd8sQoZWQf17rtv+voH0UZAQCozJgZAAAA4IexCgh6bCdQkXY7AACglAgDAAAATsMhyXEqDQhmVwAAKBOEAQAAAH4Yj60FmRkAALATwgAAAAA/jMfeAUQAAAA7IQwAAADwwx0FODwWCcjB8AkAUPnxbgYAAODPqTSAqQEAAFshDAAAAPDDOwuggCAAwD4IAwAAAE7Dc2dBCggCAOyAMAAAAMAPY04VEGRCAADATggDAAAA/PBdQJBUAABQ+REGAAAAnI7D7w8AAFRKhAEAAAB+uFcJOCQ53DMCmBkAALABwgAAAIDT8N5ZkDAAAFD5EQYAAAD44S4g6PDcToCZAQAAGyAMAAAA8MNjLwHmAwAAbIUwAAAAwA+rZoBDp/YTYGYAAMAGCAMAAABKwlHsGwAAKi3CAAAAAD/MHwsFvAsIAgBQ+REGAAAA+GMtE/AsIMjwCQBQ+fFuBgAA4AcFBAEAdkUYAAAA4Ic7DKCAIADAbggDAAAA/PE5NYAwAABQ+REGAAAA+OGzgCAzAwAANkAYAAAAcBoOh4P5AAAAWyEMAAAA8MO4dxOw/iNmBgAAbIEwAAAAwA/vkgFWGhCMrgAAUKYIAwAAAPxxzwxweEwIYGYAAMAGCAMAAAD8MF5zA9wIAwAAlR9hAAAAgB/uKIACggAAuyEMAAAA8IcCggAAmyIMAAAA8IMCggAAuyIMAAAAOA0KCAIA7IYwAAAAwAdjfBUPBADAHggDAAAAfPBaIuBZQNDB8AkAUPnxbgYAAOCLRxrgVUAQAAAbqNBhQHh4uM/vAQAAzjXv4oEeBQSpGQAAsIHqwe5AIOHh4fr000+t7wEAAMqLKTI1wOH5AwAAlVyFDgMcDociIiKC3Q0AAFAVeWUBDnYTAADYSoVeJgAAABAs7CUAALAzwgAAAAAfPHcWdDhOzlj846eg9AcAgLJEGAAAAHAaXh//WSYAALABwgAAAAAfKCAIALAzwgAAAABfKCAIALAxwgAAAAAfKCAIALAzwgAAAAAfPMMAh+Pk7ADrBwAAKjnCAAAAAF+8SwZ4lAogDAAAVH6EAQAAAD6YIgsFTmUBhAEAgMqPMAAAAMAH4zkzwOEgAwAA2AphAAAAQADWJgJWzQCGTwCAyo93MwAAAB+siQHMCAAA2BBhAAAAgA/mj3UC1syAYt8AAFB5EQYAAAAE4Cg2NYAwAABQ+REGAAAA+GAVEHR4/UMWAACwBcIAAACAAE6tDnAUOQIAQOVFGAAAAOCD8XcBNQMAADZAGAAAAOCDVUDQvUyAdQIAABshDAAAAAiAAoIAADsiDAAAAPDBWiZQrIAgYQAAoPIjDAAAAPDljzSgeAFBAAAqP8IAAAAAH4rsLMjMAACArRAGAAAA+OAuIOgjDQhCbwAAKFuEAQAAAD6cmhlQ5MM/MwMAADZAGAAAAFACRAAAADshDAAAAPDBWiXg3k3g1DfB6RAAAGWIMAAAACCAYiUDmCMAALABwgAAAAAfzB9VA4ptKcjMAACADRAGAAAA+OBeJuDmYDcBAICNEAYAAACcCbIAAIANEAYAAAD44LeAIGkAAMAGCAMAAAACKBYBUDMAAGADhAEAACBopk2bpoYNGyo8PFytW7fW559/HuwuWfwXEGT4BACo/Hg3AwAAQfHee+9pyJAheu6557Rx40Zde+21uummm7Rz585gd01SoAKCkil6IQAAlUz1YHcAAABUTSkpKXrooYf08MMPS5ImT56szz77TK+//rrGjRtX7v3ZeeCovt+TLZPQTD/uO6R9h3Il+a4Q8Gl6RplWDijraKGsswpT5j08F30s49urBIFPlXyeq+DvTZnfYlX8vSnbmyvz/klS75YJOs9Zvh/PS3029y96Tk5OmXUGAIDKzv2+WBk+SARTXl6eNmzYoGeffdbreLdu3bRmzRqf18nNzVVubq71c3Z2tqSyG4ukfbNLY/6zWWp1lz7e8LN1vFpomI4fOaz8EwUqzD0qSXr0ndVlck4AACSpVfy1uiCmRpncVknHIqUOAw4dOiRJqlevXmlvAgAA2zp06JBcLlewu1Fh/fbbbyooKFBcXJzX8bi4OGVkZPi8zrhx4zR69Ohix8/1WGSXpI3n9AwAgKqu6eSyv83TjUVKHQYkJCRo165dioqKKl5Yp5RycnJUr1497dq1S9HR0WVymyg5Hv/g4zkILh7/4LPDc2CM0aFDh5SQkBDsrlQKRccQxhi/44oRI0Zo6NCh1s+FhYX6/fffVbt2bcYiNsHjH3w8B8HF4x98dngOSjoWKXUYUK1aNV144YWlvXpA0dHRlfaBtwMe/+DjOQguHv/gq+zPATMCTq9OnToKCQkpNgsgMzOz2GwBN6fTKafT6XWsZs2a56R/lf13sLLj8Q8+noPg4vEPvsr+HJRkLMJuAgAAoNyFhYWpdevWWrx4sdfxxYsXq3379kHqFQAAVQe7CQAAgKAYOnSokpOT1aZNG7Vr105vvvmmdu7cqUcffTTYXQMAwPYqVBjgdDr14osvFpsCiPLB4x98PAfBxeMffDwHVcuf/vQnHThwQGPGjNHevXuVmJioTz75RPXr1w9an/gdDC4e/+DjOQguHv/gq0rPgcOw9xEAAAAAAFUKNQMAAAAAAKhiCAMAAAAAAKhiCAMAAAAAAKhiCAMAAAAAAKhiyjUMmDZtmho2bKjw8HC1bt1an3/+ecD2K1euVOvWrRUeHq5GjRrpjTfeKKee2teZPAcrVqyQw+Eo9vXDDz+UY4/tY9WqVerVq5cSEhLkcDj04YcfnvY6vAbK1pk+B7wGys64ceN01VVXKSoqSrGxsbr11lu1devW016P1wDKGmOR4GMsEjyMRYKPsUhwMR7xVm5hwHvvvachQ4boueee08aNG3Xttdfqpptu0s6dO3223759u26++WZde+212rhxo0aOHKnBgwdr/vz55dVl2znT58Bt69at2rt3r/V16aWXllOP7eXIkSNq0aKFpk6dWqL2vAbK3pk+B268Bs7eypUrNXDgQK1bt06LFy/WiRMn1K1bNx05csTvdXgNoKwxFgk+xiLBxVgk+BiLBBfjkSJMObn66qvNo48+6nXs8ssvN88++6zP9sOHDzeXX36517EBAwaYa6655pz10e7O9DlYvny5kWSysrLKoXdViySTmpoasA2vgXOrJM8Br4FzJzMz00gyK1eu9NuG1wDKGmOR4GMsUnEwFgk+xiLBV9XHI+UyMyAvL08bNmxQt27dvI5369ZNa9as8XmdtWvXFmvfvXt3ffXVV8rPzz9nfbWr0jwHbq1atVLdunXVpUsXLV++/Fx2Ex54DVQcvAbKXnZ2tiSpVq1aftvwGkBZYiwSfIxFKh9eAxUHr4Fzo6qPR8olDPjtt99UUFCguLg4r+NxcXHKyMjweZ2MjAyf7U+cOKHffvvtnPXVrkrzHNStW1dvvvmm5s+frwULFqhx48bq0qWLVq1aVR5drvJ4DQQfr4FzwxijoUOHqmPHjkpMTPTbjtcAyhJjkeBjLFL58BoIPl4D5w7jEal6eZ7M4XB4/WyMKXbsdO19HUfJnclz0LhxYzVu3Nj6uV27dtq1a5cmTpyo66677pz2EyfxGgguXgPnxhNPPKFvv/1Wq1evPm1bXgMoa4xFgo+xSOXCayC4eA2cO4xHymlmQJ06dRQSElIs9c3MzCyWsrjFx8f7bF+9enXVrl37nPXVrkrzHPhyzTXX6Mcffyzr7sEHXgMVE6+BszNo0CB99NFHWr58uS688MKAbXkNoCwxFgk+xiKVD6+BionXwNljPHJSuYQBYWFhat26tRYvXux1fPHixWrfvr3P67Rr165Y+7S0NLVp00ahoaHnrK92VZrnwJeNGzeqbt26Zd09+MBroGLiNVA6xhg98cQTWrBggZYtW6aGDRue9jq8BlCWGIsEH2ORyofXQMXEa6D0GI8UUV6VCufNm2dCQ0PN22+/bTZv3myGDBliIiMjzY4dO4wxxjz77LMmOTnZav/zzz+bGjVqmKeeesps3rzZvP322yY0NNR88MEH5dVl2znT5+DVV181qampZtu2bSY9Pd08++yzRpKZP39+sO5CpXbo0CGzceNGs3HjRiPJpKSkmI0bN5pffvnFGMNroDyc6XPAa6DsPPbYY8blcpkVK1aYvXv3Wl9Hjx612vAawLnGWCT4GIsEF2OR4GMsElyMR7yVWxhgjDGvvfaaqV+/vgkLCzNXXnml1xYOffv2NUlJSV7tV6xYYVq1amXCwsJMgwYNzOuvv16e3bWlM3kOxo8fby6++GITHh5uYmJiTMeOHc3ChQuD0Gt7cG8NU/Srb9++xhheA+XhTJ8DXgNlx9fjLslMnz7dasNrAOWBsUjwMRYJHsYiwcdYJLgYj3hzGPNH9QMAAAAAAFAllEvNAAAAAAAAUHEQBgAAAAAAUMUQBgAAAAAAUMUQBgAAAAAAUMUQBgAAAAAAUMUQBgAAAAAAUMUQBgAAAAAAUMUQBgAAqpRVq1apV69eSkhIkMPh0IcffnjGt/Hvf/9bLVu2VI0aNVS/fn298sorZd9RAABgSxVlLEIYAACoUo4cOaIWLVpo6tSppbr+p59+qvvuu0+PPvqo0tPTNW3aNKWkpJT69gAAQNVSUcYiDmOMKVUPAJSbFStWqHPnzsrKylLNmjWD3R3ANhwOh1JTU3Xrrbdax/Ly8vTXv/5Vs2fP1sGDB5WYmKjx48erU6dOkqQ+ffooPz9f77//vnWdyZMna9KkSdq5c6ccDkc53wsAOPcYiwDnRjDHIswMAHDWjDE6ceJEsLsBlIkHH3xQ/+///T/NmzdP3377re666y7deOON+vHHHyVJubm5Cg8P97pORESEdu/erV9++SUYXQaAKo+xCOykvMYihAFAGSosLNT48eN1ySWXyOl06qKLLtLLL7+sFStWyOFw6ODBg1bbTZs2yeFwaMeOHZKkX375Rb169VJMTIwiIyN1xRVX6JNPPtGOHTvUuXNnSVJMTIwcDof69esn6eT/CAYPHqzY2FiFh4erY8eOWr9+vXUO93k/++wztWrVShEREbr++uuVmZmpTz/9VE2aNFF0dLTuvfdeHT161LqeMUYTJkxQo0aNFBERoRYtWuiDDz7webtt2rSR0+nU559/fu4eWKCc/PTTT5o7d67ef/99XXvttbr44os1bNgwdezYUdOnT5ckde/eXQsWLNDSpUtVWFiobdu2afLkyZKkvXv3BrH3AMBYBKjsynMsUv1c3AGgqhoxYoTeeustvfrqq+rYsaP27t2rH374oUTXHThwoPLy8rRq1SpFRkZq8+bNOu+881SvXj3Nnz9fd9xxh7Zu3aro6GhFRERIkoYPH6758+dr5syZql+/viZMmKDu3bvrv//9r2rVqmXd9qhRozR16lTVqFFDd999t+6++245nU7NmTNHhw8f1m233aYpU6bomWeekST99a9/1YIFC/T666/r0ksv1apVq3T//ffr/PPPV1JSknW7w4cP18SJE9WoUSOmDMIWvv76axljdNlll3kdz83NVe3atSVJ/fv3108//aSePXsqPz9f0dHRevLJJzVq1CiFhIQEo9sAYGEsAlRu5ToWMQDKRE5OjnE6neatt94qdtny5cuNJJOVlWUd27hxo5Fktm/fbowxplmzZmbUqFE+b9vX9Q8fPmxCQ0PN7NmzrWN5eXkmISHBTJgwwet6S5YssdqMGzfOSDI//fSTdWzAgAGme/fu1u2Gh4ebNWvWePXhoYceMvfee6/X7X744YcleGSAikuSSU1NtX6eN2+eCQkJMT/88IP58ccfvb727t3rdd0TJ06Y3bt3m9zcXPPJJ58YSWbfvn3lfA8A4BTGIkDlE8yxCDMDgDKyZcsW5ebmqkuXLqW6/uDBg/XYY48pLS1NN9xwg+644w41b97cb/uffvpJ+fn56tChg3UsNDRUV199tbZs2eLV1vN24uLiVKNGDTVq1Mjr2JdffilJ2rx5s44fP66uXbt63UZeXp5atWrldaxNmzZnfkeBCqxVq1YqKChQZmamrr322oBtQ0JCdMEFF0iS5s6dq3bt2ik2NrY8ugkAPjEWASq/8hyLEAYAZcQ9Xc6XatVOlucwHpt35Ofne7V5+OGH1b17dy1cuFBpaWkaN26cJk2apEGDBvm8TfdtFa0Waowpdiw0NNT63uFweP3sPlZYWChJ1r8LFy60/ufi5nQ6vX6OjIz02TegIjt8+LD++9//Wj9v375dmzZtUq1atXTZZZfpvvvu0wMPPKBJkyapVatW+u2337Rs2TI1a9ZMN998s3777Td98MEH6tSpk44fP67p06fr/fff18qVK4N4rwCAsQhQWVSUsQgFBIEycumllyoiIkJLly4tdtn5558vybugx6ZNm4q1q1evnh599FEtWLBATz/9tN566y1JUlhYmCSpoKDAanvJJZcoLCxMq1evto7l5+frq6++UpMmTUp9P5o2bSqn06mdO3fqkksu8fqqV69eqW8XqCi++uortWrVyvrr0tChQ9WqVSu98MILkqTp06frgQce0NNPP63GjRurd+/e+uKLL7x+/2fOnKk2bdqoQ4cO+v7777VixQpdffXVQbk/AODGWASoHCrKWISZAUAZCQ8P1zPPPKPhw4crLCxMHTp00P79+/X999/rgQceUL169TRq1Ci99NJL+vHHHzVp0iSv6w8ZMkQ33XSTLrvsMmVlZWnZsmXWG2n9+vXlcDj08ccf6+abb1ZERITOO+88PfbYY/rLX/6iWrVq6aKLLtKECRN09OhRPfTQQ6W+H1FRURo2bJieeuopFRYWqmPHjsrJydGaNWt03nnnqW/fvmf1OAHB1qlTJ6+/jBUVGhqq0aNHa/To0T4vr1OnjtauXXuuugcApcZYBKgcKspYhDAAKEPPP/+8qlevrhdeeEF79uxR3bp19eijjyo0NFRz587VY489phYtWuiqq67SSy+9pLvuusu6bkFBgQYOHKjdu3crOjpaN954o1599VVJ0gUXXKDRo0fr2Wef1YMPPqgHHnhAM2bM0P/+7/+qsLBQycnJOnTokNq0aaPPPvtMMTExZ3U//ud//kexsbEaN26cfv75Z9WsWVNXXnmlRo4ceVa3CwAAzi3GIgBKymECRRIAAAAAAMB2qBkAAAAAAEAVQxgAAAAAAEAVQxgAAAAAAEAVQxgAAAAAAEAVQxgAAAAAAEAVQxgAAAAAAEAVQxgAAAAAAEAVQxgAAAAAAEAVQxgAAAAAAEAVQxgAAAAAAEAVQxgAAAAAAEAV8/8BH+o2bgUsuNYAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 2000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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LF9Ztt92mtLQ0ffLJJ4qNjc10/zNnzmjNmjXq3LmzihQp4t5eoEABde/eXfv377+mKXmX27Fjh37//Xd1795ddvulf3pFihTR/fffr9WrV+vs2bNez7n//vuz9dqNGjXSl19+qUGDBmnp0qX6448/vB7/5Zdf9OOPP7qvE7x48aL7duedd+rAgQM++6wdOnTw+q2F6/i7pibu2LFDBw8e1IMPPuj1vIoVK6pZs2a5es+IiAjdfffdXtsefvhhpaenZ/jN1N13353hUpbLnT17VsuWLdODDz54xd+ifP7552rZsqXKli3rdUzbt28v6c+ZJwDy3vLly9WxY0eVLVtWNptNc+fOzfFrGGP02muvuS9fqlChgkaPHu37YoOEMeaKj998880KDQ3V3//+d33wwQfatWtXrt4nu+dJSapdu7bq1avnte3hhx9WcnKyNmzYkKv3z64lS5aodevWqlChgtf2Hj166OzZsxkuF7z8nHb5ufRqGjdurJCQEEVEROiuu+5S6dKl9eWXX7p7OFwuN2OUa7Fq1Sr98ccfGWYlVqhQQa1atcp0un5OxkRTp07VqFGjtHr1al24cMHr8ZUrV+r48eNKSEjwOnenp6erXbt2Wrt27VVnmWTX1f4eV69erdTU1AxjosaNG+f4skmXnHzPs3NMf/rpJ+3cuVM9e/ZUWFhYpvucO3dOixcv1r333qvChQtnGGeeO3cu00tGEXiEAci1m266SQ0bNlTDhg3Vrl07vfXWW2rbtq2ee+4595Ixx44dU+nSpTNcu1eyZEkVLFjQa8qWJD322GO6cOGCChYsqD59+uSonpdffllr167Vhg0btHfvXu3atUv33HNPlvufOHFCxhj3FDdPZcuWddfvK67Xyur90tPTdeLECa/tme2bmTfeeEP/+Mc/NHfuXLVs2VLR0dG655579PPPP0uSDh06JEkaOHCgQkJCvG5PPvmkJOno0aO5/myeihcv7nXf4XBIkjugcB2HzAYkWQ1Sriaz55UuXdrr/Vyyc0xPnDihtLS0qzagOnTokD777LMMx7R27dqSfHdMAVzZmTNnVK9evQyXBeVE37599e677+q1117Tjz/+qM8++8xnzc6CzZkzZ3Ts2DH3uTQzMTEx+vrrr1WyZEk99dRTiomJUUxMjP71r3/l6L2ye56ULp0XMtvmy/N9Zo4dO5aj8cbVzqVX4/qFzcaNG/X7779r8+bNVwzcczNGuRZXe7/Lj0fhwoWz3a1/1qxZSkhI0LvvvqsmTZooOjpajzzyiHv6u2tM1Llz5wzn75dfflnGGB0/fvxaPp5bIMZEOfmeZ+ffz5EjRyTpimOiY8eO6eLFixo/fnyGY3rnnXdKYkyUX9EzAD4VGxurhQsX6qefflKjRo1UvHhxrVmzRsYYr0Dg8OHDunjxokqUKOHedubMGXXv3l3Vq1fXoUOH1KtXL/33v//N9ntXrVrVq6vx1RQrVkx2u10HDhzI8Njvv/8uSV71XSvXCSGr97Pb7SpWrJjX9uyuyRweHq7ExEQlJibq0KFD7lkCHTt21I8//uj+HIMHD9Z9992X6WvUqFEjJx8n11zHwXUy9pTZdWrZcaXXuvxEnJ1jGh0drQIFCmj//v1X3K9EiRKKjY3VSy+9lOnjVxoIA/Cd9u3bu2fkZOb8+fN6/vnnNW3aNJ08eVJ16tTRyy+/7G7OtX37dk2aNElbt2712/+FVjZ//nylpaVdtflZ8+bN1bx5c6WlpWndunUaP368+vXrp1KlSumhhx7K1ntl9zwpZX6Oufxc4frN5+U9bK71B5nixYv7bbwhXfqFTXblZoxyLa72fpcfj5z8PZcoUULjxo3TuHHjtHfvXs2bN0+DBg3S4cOHtWDBAvdrjx8/Psuu+bn9QTynrjYmys3sgOx8z12yc1xdMySvNCYqVqyYe2btU089lek+VapUuep7wf+YGQCf2rRpk6RL/3G0bt1ap0+fzjBl88MPP3Q/7tK7d2/t3btXn376qd577z3NmzdPr7/+ep7VGh4erri4OH366adeSXt6ero+/vhjlS9f3t24JaeJfGZq1KihcuXKafr06V7TJ8+cOaP//Oc/7u6916pUqVLq0aOHunbtqh07dujs2bOqUaOGbrzxRn3//ffu2RyX3/y15m+NGjVUunTpDKsY7N27VytXrszVa6akpGjevHle26ZPny673a7bbrstx69XqFAhtWjRQrNnz77iAPCuu+7S1q1bFRMTk+kxJQwA8odHH31U3333nWbOnKnNmzfrgQceULt27dyzpz777DNVrVpVn3/+uapUqaLKlSu7G6UhZ/bu3auBAwcqKipKjz/+eLaeU6BAAcXFxbm7jrumMvvi3Ovphx9+0Pfff++1bfr06YqIiHA3B3b98LV582av/S4/x7jqy25trVu31pIlS9w//Lt8+OGHKly4cMCXcsvJGMUXfy9NmjRRoUKF9PHHH3tt379/v/uSCl+oWLGinn76abVp08b9vWrWrJmKFi2qbdu2ZTkmCg0N9cn7X01cXJwcDodmzZrltX316tXZviTkctn5nudE9erVFRMTo/fffz/LRs+FCxdWy5YttXHjRsXGxmZ6TC8PIpA/MDMAubZ161Z3N9djx47p008/1aJFi3Tvvfe6079HHnlEEyZMUEJCgvbs2aO6devq22+/1ejRo3XnnXfq9ttvlyS9++67+vjjjzVlyhTVrl1btWvX1tNPP61//OMfatasWZ5N1UxKSlKbNm3UsmVLDRw4UKGhoZo4caK2bt2qGTNmuBPTOnXqSPqzC35ERITCwsJUpUqVHP3HZrfb9corr6hbt26666679Pjjjys1NVWvvvqqTp48qX/+85+5/hxxcXG66667FBsbq2LFimn79u366KOPvE7eb731ltq3b6877rhDPXr0ULly5XT8+HFt375dGzZs0OzZs3P9/jlht9uVmJioxx9/XJ07d9Zjjz2mkydPKjExUWXKlPG6VjG7ihcvrieeeEJ79+5V9erV9cUXX+idd97RE088oYoVK+aqzrFjx+rWW29VXFycBg0apGrVqunQoUOaN2+e3nrrLUVERGjkyJFatGiRmjZtqj59+qhGjRo6d+6c9uzZoy+++EKTJ08O+FrXQLDbuXOnZsyYof3797sDuoEDB2rBggWaMmWKRo8erV27dunXX3/V7Nmz9eGHHyotLU39+/dX586dtWTJkgB/gvzLNQ64ePGiDh8+rBUrVmjKlCkqUKCA5syZc8WeK5MnT9aSJUvUoUMHVaxYUefOndP7778vSe6xQUREhCpVqqT//ve/at26taKjo1WiRIlcX0tdtmxZ3X333RoxYoTKlCmjjz/+WIsWLdLLL7/sPlf+5S9/UY0aNTRw4EBdvHhRxYoV05w5c/Ttt99meL26devq008/1aRJk9SgQQPZ7fYsfxs/fPhwd5+ZYcOGKTo6WtOmTdP8+fP1yiuvuFfbCZScjFHq1q0rSfrXv/6lhIQEhYSEqEaNGjn6pULRokX1wgsvaMiQIXrkkUfUtWtXHTt2TImJiQoLC8t15/lTp06pZcuWevjhh1WzZk1FRERo7dq1WrBggXtmZJEiRTR+/HglJCTo+PHj6ty5s0qWLKkjR47o+++/15EjRzRp0qRcvX9ORUdHa8CAAUpKSlKxYsV07733av/+/dc0JsrO9zynJkyYoI4dO6px48bq37+/KlasqL1792rhwoWaNm2apD+/D7feequaN2+uJ554QpUrV1ZKSop++eUXffbZZ/xfml8FsHkhrlOZrSYQFRVlbr75ZjN27Fhz7tw5r/2PHTtmevfubcqUKWMKFixoKlWqZAYPHuzeb/PmzaZQoUJeXViNMebcuXOmQYMGpnLlyubEiRNZ1pNZ5/7MZNYx3hhjVqxYYVq1amXCw8NNoUKFTOPGjc1nn32W4fnjxo0zVapUMQUKFMj0dbJb09y5c01cXJwJCwsz4eHhpnXr1ua7777z2sfVCfnIkSNX/EwugwYNMg0bNjTFihUzDofDVK1a1fTv398cPXrUa7/vv//ePPjgg6ZkyZImJCTElC5d2rRq1cpMnjw5Q+25XU3g1VdfzVCfMukG/fbbb5tq1aqZ0NBQU716dfP++++bTp06mfr162frM3vWUbt2bbN06VLTsGFD43A4TJkyZcyQIUO8Vk64Un1ZfTe2bdtmHnjgAVO8eHETGhpqKlasaHr06OH1HT9y5Ijp06ePqVKligkJCTHR0dGmQYMGZujQoeb06dM5+iwArp0kM2fOHPd9V/fu8PBwr1vBggXNgw8+aIz5c/URSWbHjh3u561fv95IMj/++KO/P0K+d/k4IDQ01JQsWdK0aNHCjB49OtNVWC4/j6xatcrce++9plKlSsbhcJjixYubFi1amHnz5nk97+uvvzb169c3DofDSHKPFa50nsxqNYEOHTqYTz75xNSuXduEhoaaypUrm7Fjx2Z4/k8//WTatm1rIiMjzQ033GCeeeYZM3/+/AznxuPHj5vOnTubokWLGpvN5vWemZ33tmzZYjp27GiioqJMaGioqVevXobzTlbjh6zOU5fLzkoPnu9z+SpJ2RmjGGPM4MGDTdmyZY3dbr/qaktXqundd981sbGxJjQ01ERFRZlOnTplWIknISHBhIeHX/HzuJw7d8707t3bxMbGmsjISFOoUCFTo0YNM3z4cHcnfZdly5aZDh06mOjoaBMSEmLKlStnOnTo4HXsr3U1gez8Paanp5tRo0aZ8uXLm9DQUBMbG2s+//xzU69ePXPvvfdm63N71pGd7/mVxqlZfTdWrVpl2rdvb6KioozD4TAxMTGmf//+GT7fY489ZsqVK2dCQkLMDTfcYJo2bWpGjRqVo88B/7EZc5V2rwDgBydPnlT16tV1zz336O2338728+Lj43X06FFt3bo1D6sDcL2w2WyaM2eOu4HsrFmz1K1bN/3www8Z1mcvUqSISpcureHDh2v06NFeXcf/+OMPFS5cWF999VWOVrYBgGu1e/du1axZU8OHD9eQIUOy/bzKlSurTp06+vzzz/OwOlgJlwkA8LuDBw/qpZdeUsuWLVW8eHH9+uuvev3115WSkqK+ffsGujwAFlK/fn2lpaXp8OHDat68eab7NGvWTBcvXtTOnTsVExMj6c/ltCSpUqVKfqsVQPD5/vvvNWPGDDVt2lSRkZHasWOHXnnlFUVGRqpnz56BLg8WRxgAwO8cDof27NmjJ598UsePH3c3T5o8ebJ7Wb60tLQrrlNts9ky/JYPQHA6ffq0fvnlF/f93bt3a9OmTYqOjlb16tXVrVs3PfLIIxozZozq16+vo0ePasmSJapbt667f80tt9yixx57TOPGjVN6erqeeuoptWnTxt1IFgDyQnh4uNatW6f33ntPJ0+eVFRUlOLj4/XSSy+5VzVgTIS8wmUCAPKl+Ph4LVu2LMvHK1WqpD179vivIAD51tKlS9WyZcsM2xMSEjR16lRduHBBo0aN0ocffqjffvtNxYsXV5MmTZSYmOhuhvb777/rmWee0VdffaXw8HC1b99eY8aMUXR0tL8/DgB4qVy58hVXF2jRooWWLl3qv4JgGYQBAPKlHTt2KCUlJcvHHQ6HexAPAABgVVu2bMlyWT/pz5U3atSo4ceKYBWEAQAAAAAABJmcL14JAAAAAACua7luIJienq7ff/9dERERstlsvqwJAIDrljFGKSkpKlu2rOx2Mve8xFgEAICMsjsWyXUY8Pvvv6tChQq5fToAAJa2b98+lS9fPtBlWBpjEQAAsna1sUiuw4CIiAj3G0RGRub2ZQAAsJTk5GRVqFDBfZ5E3mEsAgBARtkdi+Q6DHBNx4uMjOQEDADAZZi2nvcYiwAAkLWrjUW4mBEAAAAAgCBDGAAAAAAAQJAhDAAAAAAAIMgQBgAAAAAAEGQIAwAAAAAACDKEAQAAwO9GjBghm83mdStdunSgywIAIGjkemlBAACAa1G7dm19/fXX7vsFChQIYDUAAAQXwgAAABAQBQsWzNFsgNTUVKWmprrvJycn50VZAAAEBS4TAAAAAfHzzz+rbNmyqlKlih566CHt2rXrivsnJSUpKirKfatQoYKfKgUAwHoIAwAAgN/FxcXpww8/1MKFC/XOO+/o4MGDatq0qY4dO5blcwYPHqxTp065b/v27fNjxQAAWAuXCQAAAL9r3769+89169ZVkyZNFBMTow8++EADBgzI9DkOh0MOh8NfJQIAYGnMDAAAAAEXHh6uunXr6ueffw50KQAABAXCAAAAEHCpqanavn27ypQpE+hSAAAICoQBAADA7wYOHKhly5Zp9+7dWrNmjTp37qzk5GQlJCQErKbTqRe17/hZHUlJvfrOAABc5wgDAACA3+3fv19du3ZVjRo1dN999yk0NFSrV69WpUqVAlbT3I2/qfkr3+j5uVsCVgMAAP5CA0EAAOB3M2fODHQJGdhtNkmSMQEuBAAAP2BmAAAAgCRnFqB0wgAAQBAgDAAAAJBkc/+JNAAAYH2EAQAAALp0mQAzAwAAwYAwAAAAQHJPDTA0DQAABAHCAAAAAHk0EAxwHQAA+ANhAAAAgC71DOAyAQBAMCAMAAAAkGR3joq4TAAAEAwIAwAAACTZnHMDyAIAAMGAMAAAAECSzdVAkK4BAIAgQBgAAAAgyeZaWjA9wIUAAOAHhAEAAACS7MwMAAAEEcIAAAAAXeoZwGoCAIBgQBgAAACgSzMDmBgAAAgGhAEAAAC61EAwneUEAABBgDAAAABAklxLCwa4CgAA/IEwAAAAQB4NBJkZAAAIAoQBAAAA8lhakCwAABAECga6ABdjjM6dOydJCgsLc5+QAQAA/OHS0oIAAFhfvpkZcO7cObVv317t27d3hwIAAAD+YuMyAQBAEMlXYUBmfwYAAPAH16xEsgAAQDDIN2EAAABAILkuUGRpQQBAMCAMAAAAkGRnZgAAIIgQBgAAAOhSzwBmBgAAggFhAAAAgC7NDAAAIBgQBgAAAIieAQCA4EIYAAAAILnTALIAAEAwIAwAAACQRwPBANcBAIA/EAYAAACIywQAAMGFMAAAAECS3e66TiCwdQAA4A+EAQAAAGJmAAAguBAGAAAASLLRMwAAEEQIAwAAACQ5swBmBgAAggJhAAAAgDxWEyALAAAEAcIAAAAAXeoZQBgAAAgGhAEAAADynBlAGgAAsD7CAAAAAHn2DAhsHQAA+ANhAAAAgAfDegIAgCBAGAAAACAaCAIAggthAAAAgLhMAAAQXAgDAAAAdGlmgLhMAAAQBAgDAAAAxMwAAEBwIQwAAACQZHeGASwtCAAIBoQBAAAAkqQ/0wBmBgAAggFhAAAAgJgZAAAILoQBAAAAkmwsLQgACCKEAQAAAPKYGRDYMgAA8AvCAAAAAEk2d88A4gAAgPURBgAAAOjS0oJkAQCAYEAYAAAAoEthADMDAADBgDAAAABAHg0EA1wHAAD+QBgAAACgSw0ESQMAAMGAMAAAAEA0EAQABBfCAAAAALG0IAAguBAGAAAASBINBAEAQYQwAAAAQJLd1UCQLAAAEAQIAwAAAOSeGCBJMiQCAACLIwwAAADQpZkBErMDAADWRxgAAAAgySMLoG8AAMDyCAMAAAB0aWlBiRUFAADWRxgAAAAgyeYxKmJmAADA6ggDAAAAdHkDwYCVAQCAXxAGAAAAyLuBIAAAVkcYAAAAIBoIAgCCC2EAAAAIuKSkJNlsNvXr1y9gNbC0IAAgmBAGAACAgFq7dq3efvttxcbGBroUN2YGAACsjjAAAAAEzOnTp9WtWze98847KlasWEBr8ZoZEMA6AADwB8IAAAAQME899ZQ6dOig22+//ar7pqamKjk52evmS549A0y6T18aAIB8p2CgCwAAAMFp5syZ2rBhg9auXZut/ZOSkpSYmJhn9XjPDGBuAADA2pgZAAAA/G7fvn3q27evPv74Y4WFhWXrOYMHD9apU6fct3379vm0Js+FBdPJAgAAFsfMAAAA4Hfr16/X4cOH1aBBA/e2tLQ0LV++XG+++aZSU1NVoEABr+c4HA45HI48q8nrMgEaCAIALI4wAAAA+F3r1q21ZcsWr22PPvqoatasqX/84x8ZggB/sHmkAcwMAABYHWEAAADwu4iICNWpU8drW3h4uIoXL55huz/ZbJIx9AwAAFgfPQMAAACc3E0EyQIAABbHzAAAAJAvLF26NNAluJsIcpkAAMDqmBkAAADg5JoZwGUCAACrIwwAAABwcU4NYGYAAMDqCAMAAACc7K6WASwtCACwOMIAAAAAJ5tzagBZAADA6ggDAAAAnC7NDAhsHQAA5DXCAAAAACebs4FgOmkAAMDiCAMAAACcXEsLEgUAAKyOMAAAAMDJ5l5NgDgAAGBthAEAAABOrssEyAIAAFZHGAAAAODkaiDIhQIAAKsjDAAAAHC61EAwwIUAAJDHCAMAAACcWFoQABAsCAMAAADcWFoQABAcCAMAAACcmBkAAAgWhAEAAABOLC0IAAgWhAEAAABOdpvt6jsBAGABhAEAAABOriiAmQEAAKsjDAAAAHByLS1IFgAAsDrCAAAAACd6BgAAggVhAAAAgJMrDCAKAABYXb4JA9LT0zP9MwAAgL/Y3ZcJEAcAAKwt34QBycnJmf4ZAADAX1wNBMkCAABWl2/CAAAAgEBzzwwIcB0AAOQ1wgAAAAAXVwPBdOIAAIC1EQYAAAA4MTMAABAsCAMAAACcXD0DWFoQAGB1hAEAAABOdtYWBAAECcIAAAAAJ1cWQMsAAIDVEQYAAABcxjA1AABgcYQBAAAATq7LBJgZAACwOsIAAAAAJ3fLABoIAgAsjjAAAADAyb20IFkAAMDiCAMAAACcLi0mQBoAALA2wgAAAAAnGzMDAABBgjAAAADAyTkxgAaCAADLIwwAAABwstNAEAAQJAgDAAAAnGwsLQgACBKEAQAAAE6umQGigSAAwOIIAwAAAJxsYmYAACA4EAYAAAC4uHsGBLYMAADyGmEAAACAk+sygXTSAACAxREGAAAAOLkuEyAKAABYHWEAAACAk905MmJpQQCA1REGAAAAOLlnBpAFAAAsjjAAAADAyeZqIMiFAgAAiyMMAAAAcLI504D09AAXAgBAHiMMAAAAcLK7ZwYAAGBthAEAAABOziyApQUBAJZHGAAAAOBktzE1AAAQHAgDAAAAnFxZADMDAABWRxgAAADg5lxaMMBVAACQ1wgDAAAAnOzMDAAABAnCAAAAACd3ywCyAACAxREGAAAAOLkaCBrSAACAxREGAAAAOLGYAAAgWBAGAAAAONncMwMCXAgAAHmMMAAAAMDJOTGABoIAAMsjDAAAAHCyMzMAABAkCAMAAACcbCwtCAAIEoQBAAAATq6ZAQAAWB1hAAAAgBM9AwAAwYIwAAAAwMW1tCBZAADA4ggDAAAAnFyXCaQTBgAALI4wAAAAwMl1mYARaQAAwNoIAwAAgN9NmjRJsbGxioyMVGRkpJo0aaIvv/wy0GWxtCAAIGgQBgAAAL8rX768/vnPf2rdunVat26dWrVqpU6dOumHH34IaF02d88A0gAAgLUVDHQBAAAg+HTs2NHr/ksvvaRJkyZp9erVql27doCqkmzMDAAABAnCAAAAEFBpaWmaPXu2zpw5oyZNmmS5X2pqqlJTU933k5OTfV6La2YADQQBAFbHZQIAACAgtmzZoiJFisjhcKh3796aM2eOatWqleX+SUlJioqKct8qVKjg85rsrssEaCAIALA4wgAAABAQNWrU0KZNm7R69Wo98cQTSkhI0LZt27Lcf/DgwTp16pT7tm/fPp/XZBNLCwIAggOXCQAAgIAIDQ1VtWrVJEkNGzbU2rVr9a9//UtvvfVWpvs7HA45HI48rcnuXluQNAAAYG3MDAAAAPmCMcarJ0AguBoIMjMAAGB1zAwAAAB+N2TIELVv314VKlRQSkqKZs6cqaVLl2rBggWBLk0SPQMAANZHGAAAAPzu0KFD6t69uw4cOKCoqCjFxsZqwYIFatOmTUDrsjMzAAAQJAgDAACA37333nuBLiFTrqUFaRkAALA6egYAAAA4uZcWJA0AAFgcYQAAAICTq4EgUQAAwOoIAwAAAJxclwmk0zQAAGBxhAEAAABONjEzAAAQHAgDAAAAnOw0EAQABAnCAAAAACf3ZQKkAQAAiyMMAAAAOVK1alUdO3Ysw/aTJ0+qatWqAajId+yuNAAAAIsjDAAAADmyZ88epaWlZdiempqq3377LQAV+Y4rCmBmAADA6goGugAAAHB9mDdvnvvPCxcuVFRUlPt+WlqaFi9erMqVKwegMh9yLS1IFgAAsDjCAAAAkC333HOPJMlmsykhIcHrsZCQEFWuXFljxowJQGW+Y6dnAAAgSBAGAACAbElPT5ckValSRWvXrlWJEiUCXJHvsbQgACBYEAYAAIAc2b17d6BLyDOXlhYkDgAAWBthAAAAyLHFixdr8eLFOnz4sHvGgMv7778foKqunc0dBgS2DgAA8hphAAAAyJHExESNHDlSDRs2VJkyZWSz0HJ8rs9CzwAAgNURBgAAgByZPHmypk6dqu7duwe6FJ9jZgAAIFjYA10AAAC4vpw/f15NmzYNdBl5wm6jgSAAIDgQBgAAgBzp1auXpk+fHugy8oTrggcuEwAAWB2XCQAAgBw5d+6c3n77bX399deKjY1VSEiI1+Njx44NUGXXzu6+TiCwdQAAkNcIAwAAQI5s3rxZN998syRp69atXo9d780EXeUzMwAAYHX5Mgx49NFHtXTp0kCXAQCwuPfee0/Tpk1Tt27d1LNnz0CXc9345ptvAl1CniMKAABYXb7pGfDoo4963Y+Pjw9MIQCAoHDy5ElNmzZN6enpmjZtmk6ePBnokpAP2N1LCwa4EAAA8li+nBkAAEBee+GFF5Seni5JSk9P17Bhw/TGG28EuKrrQ8uWLa94OcCSJUv8WI1vXVpakDQAAGBt+WJmQFazAJgdAADIC+vWrdOWLVu8tm3evFnr1q0LUEXXl5tvvln16tVz32rVqqXz589rw4YNqlu3bqDLuybupQXJAgAAFpftmQGpqalKTU11309OTvZJAVf7gT8+Pp7+AQAAn0lPT9fIkSMzfWzkyJGaO3eu7PZ8kZXnW6+//nqm20eMGKHTp0/7uRrfurSYAGkAAMDasj3aSUpKUlRUlPtWoUKFvKwLAIA8sWbNmiwD7eTkZK1Zs8bPFVnHX//6V73//vuBLuOauC5/cF5BAgCAZWU7DBg8eLBOnTrlvu3bty8v6wIAIE/ExcUpMjIy08eioqIUFxfn54qsY9WqVQoLCwt0GdfE1QmBmQEAAKvL9mUCDodDDofD5wUsXbr0ipcKcIkAAMCX7Ha7hg0bpoEDB2Z4bPjw4VwikA333Xef131jjA4cOKB169bphRdeCFBVvkHPAABAsMgXI56sfuAnCAAA5IWGDRtmaHQXGxurW265JUAVXV88LxuMiopSdHS04uPj9cUXX2j48OGBLu+auHoGsLQgAMDqWFoQABCUXnzxRd13331KT0+X3W7PsqkgMpoyZUqgS8gzdveKiaQBAABryxczA6SMAwtmBQAA8lLRokXVrVs32e12devWTUWLFg10Sded9evX6+OPP9a0adO0cePGQJfjEzZn1wBmBgAArC5fzgyw8m8cAAD5R8+ePdWzZ89Al3HdOXz4sB566CEtXbpURYsWlTFGp06dUsuWLTVz5kzdcMMNgS4x91xLC9I0AABgcflmZgAAALg+PPPMM0pOTtYPP/yg48eP68SJE9q6dauSk5PVp0+fQJd3TVwNBJkZAACwunw5MwAAAORfCxYs0Ndff62bbrrJva1WrVqaMGGC2rZtG8DKrt2lpQUBALA2ZgYAAIAcSU9PV0hISIbtISEhSk9PD0BFvuNaWZLLBAAAVkcYAAAAcqRVq1bq27evfv/9d/e23377Tf3791fr1q0DWNm1czUQJAsAAFgdYQAAAMiRN998UykpKapcubJiYmJUrVo1ValSRSkpKRo/fnygy7smzpYBSicNAABYHD0DAABAjlSoUEEbNmzQokWL9OOPP8oYo1q1aun2228PdGnXzGZjZgAAIDgwMwAAAGTLkiVLVKtWLSUnJ0uS2rRpo2eeeUZ9+vTRX/7yF9WuXVsrVqwIcJXXxu5aWpAWggAAiyMMAAAA2TJu3Dj97W9/U2RkZIbHoqKi9Pjjj2vs2LEBqMx3XD0DWFoQAGB1hAEAACBbvv/+e7Vr1y7Lx9u2bav169f7sSLfs7O2IAAgSBAGAACAbDl06FCmSwq6FCxYUEeOHPFjRb5HA0EAQLAgDAAAANlSrlw5bdmyJcvHN2/erDJlyvixorzgbCAY4CoAAMhrhAEAACBb7rzzTg0bNkznzp3L8Ngff/yh4cOH66677gpAZb5jZ2YAACBIsLQgAADIlueff16ffvqpqlevrqefflo1atSQzWbT9u3bNWHCBKWlpWno0KGBLvOasLQgACBYEAYAAIBsKVWqlFauXKknnnhCgwcPlnH+xGyz2XTHHXdo4sSJKlWqVICrvDbupQVJAwAAFkcYAAAAsq1SpUr64osvdOLECf3yyy8yxujGG29UsWLFAl2aT7gaCBIFAACsjjAAAADkWLFixfSXv/wl0GX4nOsyAXoGAACsjgaCAAAATs6JAfQMAABYHmEAAACAk909MyDAhQAAkMcIAwAAAJxsNBAEAAQJwgAAAAAn18wAAACsjjAAAADAyRUF0EAQAGB1hAEAAAAu7ssEAlsGAAB5jTAAAADAyc7SggCAIEEYAAAA4OReWjCgVQAAkPcIAwAAAJzs9j/jACYGAACsjjAAAADAyT0zgDQAAGBxhAEAAABONnfPgAAXAgBAHiMMAAAAcLK5VhOgawAAwOIIAwAAAJzcqwmkB7gQAADyGGEAAACAk+3quwAAYAmEAQAAAE6umQE0EAQAWB1hAAAAgJOrZwANBAEAVkcYAAAAcBkaCAIArI4wAAAA+F1SUpL+8pe/KCIiQiVLltQ999yjHTt2BLqsSw0EyQIAABZHGAAAAPxu2bJleuqpp7R69WotWrRIFy9eVNu2bXXmzJmA1uVeWpAwAABgcQUDXQAAAAg+CxYs8Lo/ZcoUlSxZUuvXr9dtt92W6XNSU1OVmprqvp+cnOzzumggCAAIFswMAAAAAXfq1ClJUnR0dJb7JCUlKSoqyn2rUKGCz+twzwzw+SsDAJC/EAYAAICAMsZowIABuvXWW1WnTp0s9xs8eLBOnTrlvu3bt8/ntdjdqwkQBwAArI3LBAAAQEA9/fTT2rx5s7799tsr7udwOORwOPK4GtdlAnn8NgAABBhhAAAACJhnnnlG8+bN0/Lly1W+fPlAl8PMAABA0CAMAAAAfmeM0TPPPKM5c+Zo6dKlqlKlSqBLkiTZaBoAAAgShAEAAMDvnnrqKU2fPl3//e9/FRERoYMHD0qSoqKiVKhQoYDVZScLAAAECRoIAgAAv5s0aZJOnTql+Ph4lSlTxn2bNWtWQOuyOXsGcJkAAMDqmBkAAAD8zuTTH7bdVwnkz/IAAPAZZgYAAAA42WggCAAIEoQBAAAATq4GgkQBAACrIwwAAABwcjcQZGYAAMDiCAMAAACcXA0EyQIAAFZHGAAAAOBkp2cAACBIEAYAAAC4uC4TCGwVAADkOcIAAAAAJ7uNywQAAMGBMAAAAMDJ5vFnmggCAKyMMAAAAMDJNTNAYnYAAMDaCAMAAACcPLIAmggCACyNMAAAAMDJ5jkzIIB1AACQ1wgDAAAAnJgZAAAIFoQBAAAATt4NBANWBgAAeY4wAAAAwIkGggCAYEEYAAAA4OR5mYChawAAwMIIAwAAAJw8ZwakkwUAACyMMAAAACAThusEAAAWRhgAAADgxMwAAECwIAwAAABwsnktJxCwMgAAyHOEAQAAAE5eqwmQBgAALIwwAAAAwMlzYgCXCQAArIwwAAAAwMlraUEaCAIALIwwAAAAwMlGA0EAQJAgDAAAAPDgygPoGQAAsDLCAAAAAA+uJoJcJQAAsDLCAAAAAA+uCwUIAwAAVkYYAAAA4ME1MyCdNAAAYGH5JgyIjIzM9M8AAAB+5e4ZAACAdeWbMMBut2f6ZwAAAH+yO8OAdJYTAABYGD91AwAAeLDJdvWdAAC4zhEGAAAAeHDPDKBnAADAwggDAAAAPNhYWhAAEAQIAwAAADzYaCAIAAgChAEAAAAeXB0DuEwAAGBlhAEAAAAeuEwAABAMCAMAAAA8uBoIGtIAAICFEQYAAAB4cM8MCHAdAADkJcIAAAAADywtCAAIBoQBAAAAXugZAACwPsIAAAAAD8wMAAAEA8IAAAAADzZ3A8HA1gEAQF4iDAAAAPBgZ2lBAEAQIAwAAADw4JwYIMN6AgAACyMMAAAA8GBjZgAAIAgQBgAAAHiw0UAQABAECAMAAAA8uBsIBrYMAADyFGEAAACAh0sNBIkDAADWRRgAAADgwd1AkCwAAGBhhAEAAAAeXDMD0gkDAAAWRhgAAADgydUzgKkBAAALIwwAAADwwMwAAEAwIAwAAADw4O4ZwHoCAAALIwwAAADwcGk1gQAXAgBAHiIMAAAA8GBz9wwIbB0AAOQlwgAAAAAPNtfMAC4TAABYGGEAAACAB1fPABoIAgCsjDAAAADAg42lBQEAQYAwAAAAwAMNBAEAwYAwAAAAwIN7ZgA9AwAAFkYYAAAA4MHVQDA9PcCFAACQhwgDAAAAPLgaCDIvAABgZYQBAAAAHuzONCCdpgEAAAsjDAAAAPBgo4EgACAIEAYAAICAWL58uTp27KiyZcvKZrNp7ty5gS5J0qWZASwtCACwMsIAAAAQEGfOnFG9evX05ptvBroULzZn1wCiAACAlRUMdAEAACA4tW/fXu3btw90GRm4lxYkDQAAWBhhAAAAuC6kpqYqNTXVfT85OTlP3sdGA0EAQBDgMgEAAHBdSEpKUlRUlPtWoUKFPHkfLhMAAAQDwgAAAHBdGDx4sE6dOuW+7du3L0/ex+4cHdFAEABgZVwmAAAArgsOh0MOhyPP38c9M4AsAABgYcwMAAAA8EDPAABAMGBmAAAACIjTp0/rl19+cd/fvXu3Nm3apOjoaFWsWDFgddlszAwAAFgfYQAAAAiIdevWqWXLlu77AwYMkCQlJCRo6tSpAapKsjMzAAAQBAgDAABAQMTHx+fLJn3OLIDVBAAAlkbPAAAAAA9292UCxAEAAOsiDAAAAPDgaiBIFgAAsDLCAAAAAA+uBoLphAEAAAsjDAAAAPBwqWcAaQAAwLoIAwAAADxwmQAAIBgQBgAAAHiggSAAIBgQBgAAAHhwzwwIbBkAAOQpwgAAAAAP7gaCdBAEAFgYYQAAAICHSw0EAQCwLsIAAAAAD3aWFgQABAHCAAAAAA+XVhMgDQAAWBdhAAAAgIdLqwkEuBAAAPIQYQAAAICHSz0DSAMAANZFGAAAAODBRs8AAEAQIAwAAADwcKlnQGDrAAAgLxEGAAAAeOAyAQBAMCAMAAAA8EADQQBAMCAMAAAA8MDSggCAYEAYAAAA4IEGggCAYEAYAAAA4IEGggCAYEAYAAAA4MHuDAPSSQMAABZGGAAAAODB5lxPgCgAAGBlhAEAAAAe7DQQBAAEAcIAAAAADzaWFgQABAHCAAAAAA82egYAAIIAYQAAAIAHegYAAIIBYQAAAIAHlhYEAAQDwgAAAAAPNBAEAAQDwgAAAAAP7gaCAa4DAIC8lG/CgLCwsEz/DAAA4E/uBoLpxAEAAOsqGOgCXMLCwvTll1+6/wwAABAINBAEAASDfBMG2Gw2FSpUKNBlAACAIGdnaUEAQBDIN5cJAAAA5AesJgAACAaEAQAAAB7srgaCpAEAAAsjDAAAAPDgnBhAzwAAgKURBgAAAHhwLS1IzwAAgJURBgAAAHigZwAAIBgQBgAAAHhgaUEAQDAgDAAAAPBgd88MIA4AAFgXYQAAAIAHLhMAAAQDwgAAAAAPNBAEAAQDwgAAAAAPzAwAAAQDwgAAAAAPdvfMgAAXAgBAHiIMAAAA8OCcGCDDegIAAAsjDAAAAPDgmhnAZQIAACsjDAAAAPBgY2lBAEAQIAwAAADwYKNnAAAgCBAGAAAAeLjUMwAAAOsiDAAAAPDgukwgncsEAAAWRhgAAADgwe5uGhDYOgAAyEuEAQAAAB4uZQGkAQAA6yIMAAAA8OBuIJge4EIAAMhDhAEAAAAeLjUQZGYAAMC6CAMAAAA82FlaEAAQBAoGugAAAID85OVXX5WqtdOirxer6qjuGR4vXaqkVq5YFoDKAADwHcIAAAAAD8mnkiVJVWLjdHf3ezM8Pv6pTv4uCQAAn+MyAQAAAC9/Xh9gDNcJAACsizAAAADAkzMEIAoAAFgZYQAAAAAAAEGGMAAAAMCL6zKBAJcBAEAeIgwAAADwRAoAAAgChAEAAABeXD0DCAUAANZFGAAAAODJcJkAAMD6CAMAAAAAAAgyhAEAACBgJk6cqCpVqigsLEwNGjTQihUrAl0SMwMAAEGBMAAAAATErFmz1K9fPw0dOlQbN25U8+bN1b59e+3duzfAlZECAACsr2CgCwAAAMFp7Nix6tmzp3r16iVJGjdunBYuXKhJkyYpKSkpcIU5pwSknLugdXuOZ3y4ektNWrrT31UBACysW+OKigwL8et75joMMM4TZXJyss+KAQDgeuc6LxrmmF/R+fPntX79eg0aNMhre9u2bbVy5cpMn5OamqrU1FT3/VOnTkny/VgkPfWsTOpZnUyVVpzK5LVvbKmk/27w6XsCAIJbfJVwqVhhn7xWdsciuQ4DUlJSJEkVKlTI7UsAAGBZKSkpioqKCnQZ+dbRo0eVlpamUqVKeW0vVaqUDh48mOlzkpKSlJiYmGF7noxFVs/3/WsCAJCFWuN8/5pXG4vkOgwoW7as9u3bp4iICNlstty+jJfk5GRVqFBB+/btU2RkpE9eMxhxHH2HY+k7HEvf4Vj6Tl4cS2OMUlJSVLZsWZ+8ntVdPoYwxmQ5rhg8eLAGDBjgvp+enq7jx4+rePHijEXyIY6lb3AcfYdj6TscS98J5Fgk12GA3W5X+fLlc/v0K4qMjORL5QMcR9/hWPoOx9J3OJa+4+tjyYyAqytRooQKFCiQYRbA4cOHM8wWcHE4HHI4HF7bihYtmif18e/LdziWvsFx9B2Ope9wLH0nEGMRVhMAAAB+FxoaqgYNGmjRokVe2xctWqSmTZsGqCoAAIIHqwkAAICAGDBggLp3766GDRuqSZMmevvtt7V371717t070KUBAGB5+SoMcDgcGj58eIYpgMgZjqPvcCx9h2PpOxxL3+FYBlaXLl107NgxjRw5UgcOHFCdOnX0xRdfqFKlSgGrie+E73AsfYPj6DscS9/hWPpOII+lzbD2EQAAAAAAQYWeAQAAAAAABBnCAAAAAAAAggxhAAAAAAAAQYYwAAAAAACAIOPXMGDixImqUqWKwsLC1KBBA61YseKK+y9btkwNGjRQWFiYqlatqsmTJ/up0vwvJ8fy008/VZs2bXTDDTcoMjJSTZo00cKFC/1Ybf6W0++ly3fffaeCBQvq5ptvztsCryM5PZapqakaOnSoKlWqJIfDoZiYGL3//vt+qjZ/y+mxnDZtmurVq6fChQurTJkyevTRR3Xs2DE/VZt/LV++XB07dlTZsmVls9k0d+7cqz6Hc4+1MRbxHcYivsNYxHcYi/gOYxHfyNdjEeMnM2fONCEhIeadd94x27ZtM3379jXh4eHm119/zXT/Xbt2mcKFC5u+ffuabdu2mXfeeceEhISYTz75xF8l51s5PZZ9+/Y1L7/8svnf//5nfvrpJzN48GATEhJiNmzY4OfK85+cHkuXkydPmqpVq5q2bduaevXq+afYfC43x/Luu+82cXFxZtGiRWb37t1mzZo15rvvvvNj1flTTo/lihUrjN1uN//617/Mrl27zIoVK0zt2rXNPffc4+fK858vvvjCDB061PznP/8xksycOXOuuD/nHmtjLOI7jEV8h7GI7zAW8R3GIr6Tn8cifgsDGjVqZHr37u21rWbNmmbQoEGZ7v/cc8+ZmjVrem17/PHHTePGjfOsxutFTo9lZmrVqmUSExN9Xdp1J7fHskuXLub55583w4cP5wTslNNj+eWXX5qoqChz7Ngxf5R3XcnpsXz11VdN1apVvba98cYbpnz58nlW4/UoOydgzj3WxljEdxiL+A5jEd9hLOI7jEXyRn4bi/jlMoHz589r/fr1atu2rdf2tm3bauXKlZk+Z9WqVRn2v+OOO7Ru3TpduHAhz2rN73JzLC+Xnp6ulJQURUdH50WJ143cHsspU6Zo586dGj58eF6XeN3IzbGcN2+eGjZsqFdeeUXlypVT9erVNXDgQP3xxx/+KDnfys2xbNq0qfbv368vvvhCxhgdOnRIn3zyiTp06OCPki2Fc491MRbxHcYivsNYxHcYi/gOY5HA8ue5p6BPXy0LR48eVVpamkqVKuW1vVSpUjp48GCmzzl48GCm+1+8eFFHjx5VmTJl8qze/Cw3x/JyY8aM0ZkzZ/Tggw/mRYnXjdwcy59//lmDBg3SihUrVLCgX/75XBdycyx37dqlb7/9VmFhYZozZ46OHj2qJ598UsePHw/qa/VycyybNm2qadOmqUuXLjp37pwuXryou+++W+PHj/dHyZbCuce6GIv4DmMR32Es4juMRXyHsUhg+fPc49cGgjabzeu+MSbDtqvtn9n2YJTTY+kyY8YMjRgxQrNmzVLJkiXzqrzrSnaPZVpamh5++GElJiaqevXq/irvupKT72V6erpsNpumTZumRo0a6c4779TYsWM1derUoE/kpZwdy23btqlPnz4aNmyY1q9frwULFmj37t3q3bu3P0q1HM491sZYxHcYi/gOYxHfYSziO4xFAsdf5x6/xIklSpRQgQIFMiRJhw8fzpB6uJQuXTrT/QsWLKjixYvnWa35XW6OpcusWbPUs2dPzZ49W7fffntelnldyOmxTElJ0bp167Rx40Y9/fTTkv48iRhjVLBgQX311Vdq1aqVX2rPb3LzvSxTpozKlSunqKgo97abbrpJxhjt379fN954Y57WnF/l5lgmJSWpWbNmevbZZyVJsbGxCg8PV/PmzTVq1Kig/e1lbnDusS7GIr7DWMR3GIv4DmMR32EsElj+PPf4ZWZAaGioGjRooEWLFnltX7RokZo2bZrpc5o0aZJh/6+++koNGzZUSEhIntWa3+XmWEp/pvA9evTQ9OnTuXbHKafHMjIyUlu2bNGmTZvct969e6tGjRratGmT4uLi/FV6vpOb72WzZs30+++/6/Tp0+5tP/30k+x2u8qXL5+n9eZnuTmWZ8+eld3u/d95gQIFJF1KkpE9nHusi7GI7zAW8R3GIr7DWMR3GIsEll/PPT5vSZgF1/IU7733ntm2bZvp16+fCQ8PN3v27DHGGDNo0CDTvXt39/6uJRX69+9vtm3bZt577z2W83HK6bGcPn26KViwoJkwYYI5cOCA+3by5MlAfYR8I6fH8nJ08L0kp8cyJSXFlC9f3nTu3Nn88MMPZtmyZebGG280vXr1CtRHyDdyeiynTJliChYsaCZOnGh27txpvv32W9OwYUPTqFGjQH2EfCMlJcVs3LjRbNy40UgyY8eONRs3bnQvjcS5J7gwFvEdxiK+w1jEdxiL+A5jEd/Jz2MRv4UBxhgzYcIEU6lSJRMaGmpuueUWs2zZMvdjCQkJpkWLFl77L1261NSvX9+EhoaaypUrm0mTJvmz3HwtJ8eyRYsWRlKGW0JCgv8Lz4dy+r30xAnYW06P5fbt283tt99uChUqZMqXL28GDBhgzp496+eq86ecHss33njD1KpVyxQqVMiUKVPGdOvWzezfv9/PVec/33zzzRX//+PcE3wYi/gOYxHfYSziO4xFfIexiG/k57GIzRjmbQAAAAA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      "text/plain": [
       "<Figure size 2000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "for i in ['quantity tons', 'customer', 'country', 'item type', 'application', 'thickness', 'width', 'selling_price']:\n",
    "    plot(copper, i)\n",
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0450e46b",
   "metadata": {},
   "outputs": [],
   "source": [
    " #quantity tons, thickness and selling price this feature are skewd using log method \n",
    "\n",
    "copper['quantity tons_log'] = np.log(copper['quantity tons'])\n",
    "copper['thickness_log'] = np.log(copper['thickness'])\n",
    "copper['selling_price_log'] = np.log(copper['selling_price'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0ead3569",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Try visualizing outliers and skewness before and after treating skewness \n",
    "\n",
    "for i in ['quantity tons_log', 'thickness_log', 'selling_price_log']:\n",
    "    plot(copper,i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "625e2514",
   "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>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>product_ref</th>\n",
       "      <th>delivery date</th>\n",
       "      <th>selling_price</th>\n",
       "      <th>item_date_new</th>\n",
       "      <th>delivery date_new</th>\n",
       "      <th>quantity tons_log</th>\n",
       "      <th>thickness_log</th>\n",
       "      <th>selling_price_log</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20210401.0</td>\n",
       "      <td>54.151139</td>\n",
       "      <td>30156308.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>1670798778</td>\n",
       "      <td>20210701.0</td>\n",
       "      <td>854.00</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>2021-07-01</td>\n",
       "      <td>3.991779</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>6.749931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20210401.0</td>\n",
       "      <td>768.024839</td>\n",
       "      <td>30202938.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1210.0</td>\n",
       "      <td>1668701718</td>\n",
       "      <td>20210401.0</td>\n",
       "      <td>1047.00</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>6.643822</td>\n",
       "      <td>-0.223144</td>\n",
       "      <td>6.953684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20210401.0</td>\n",
       "      <td>386.127949</td>\n",
       "      <td>30153963.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.38</td>\n",
       "      <td>952.0</td>\n",
       "      <td>628377</td>\n",
       "      <td>20210101.0</td>\n",
       "      <td>644.33</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>5.956169</td>\n",
       "      <td>-0.967584</td>\n",
       "      <td>6.468211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20210401.0</td>\n",
       "      <td>202.411065</td>\n",
       "      <td>30349574.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>2.30</td>\n",
       "      <td>1317.0</td>\n",
       "      <td>1668701718</td>\n",
       "      <td>20210101.0</td>\n",
       "      <td>768.00</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>5.310301</td>\n",
       "      <td>0.832909</td>\n",
       "      <td>6.643790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20210401.0</td>\n",
       "      <td>785.526262</td>\n",
       "      <td>30211560.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>640665</td>\n",
       "      <td>20210301.0</td>\n",
       "      <td>577.00</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>2021-03-01</td>\n",
       "      <td>6.666354</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>6.357842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181668</th>\n",
       "      <td>20200702.0</td>\n",
       "      <td>102.482422</td>\n",
       "      <td>30200854.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.96</td>\n",
       "      <td>1220.0</td>\n",
       "      <td>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>591.00</td>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>4.629691</td>\n",
       "      <td>-0.040822</td>\n",
       "      <td>6.381816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181669</th>\n",
       "      <td>20200702.0</td>\n",
       "      <td>208.086469</td>\n",
       "      <td>30200854.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>589.00</td>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>5.337954</td>\n",
       "      <td>-0.051293</td>\n",
       "      <td>6.378426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181670</th>\n",
       "      <td>20200702.0</td>\n",
       "      <td>4.235594</td>\n",
       "      <td>30200854.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.71</td>\n",
       "      <td>1250.0</td>\n",
       "      <td>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>619.00</td>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>1.443523</td>\n",
       "      <td>-0.342490</td>\n",
       "      <td>6.428105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181671</th>\n",
       "      <td>20200702.0</td>\n",
       "      <td>-2000.000000</td>\n",
       "      <td>30200854.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1250.0</td>\n",
       "      <td>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>601.00</td>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.162519</td>\n",
       "      <td>6.398595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181672</th>\n",
       "      <td>20200702.0</td>\n",
       "      <td>406.686538</td>\n",
       "      <td>30200854.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.71</td>\n",
       "      <td>1240.0</td>\n",
       "      <td>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>607.00</td>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>6.008043</td>\n",
       "      <td>-0.342490</td>\n",
       "      <td>6.408529</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>181673 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         item_date  quantity tons    customer  country  status  item type  \\\n",
       "0       20210401.0      54.151139  30156308.0     28.0       1        5.0   \n",
       "1       20210401.0     768.024839  30202938.0     25.0       1        5.0   \n",
       "2       20210401.0     386.127949  30153963.0     30.0       1        6.0   \n",
       "3       20210401.0     202.411065  30349574.0     32.0       1        3.0   \n",
       "4       20210401.0     785.526262  30211560.0     28.0       1        5.0   \n",
       "...            ...            ...         ...      ...     ...        ...   \n",
       "181668  20200702.0     102.482422  30200854.0     25.0       1        5.0   \n",
       "181669  20200702.0     208.086469  30200854.0     25.0       1        5.0   \n",
       "181670  20200702.0       4.235594  30200854.0     25.0       1        5.0   \n",
       "181671  20200702.0   -2000.000000  30200854.0     25.0       1        5.0   \n",
       "181672  20200702.0     406.686538  30200854.0     25.0       1        5.0   \n",
       "\n",
       "        application  thickness   width  product_ref  delivery date  \\\n",
       "0              10.0       2.00  1500.0   1670798778     20210701.0   \n",
       "1              41.0       0.80  1210.0   1668701718     20210401.0   \n",
       "2              28.0       0.38   952.0       628377     20210101.0   \n",
       "3              59.0       2.30  1317.0   1668701718     20210101.0   \n",
       "4              10.0       4.00  2000.0       640665     20210301.0   \n",
       "...             ...        ...     ...          ...            ...   \n",
       "181668         41.0       0.96  1220.0    164141591     20200701.0   \n",
       "181669         41.0       0.95  1500.0    164141591     20200701.0   \n",
       "181670         41.0       0.71  1250.0    164141591     20200701.0   \n",
       "181671         41.0       0.85  1250.0    164141591     20200701.0   \n",
       "181672         41.0       0.71  1240.0    164141591     20200701.0   \n",
       "\n",
       "        selling_price item_date_new delivery date_new  quantity tons_log  \\\n",
       "0              854.00    2021-04-01        2021-07-01           3.991779   \n",
       "1             1047.00    2021-04-01        2021-04-01           6.643822   \n",
       "2              644.33    2021-04-01        2021-01-01           5.956169   \n",
       "3              768.00    2021-04-01        2021-01-01           5.310301   \n",
       "4              577.00    2021-04-01        2021-03-01           6.666354   \n",
       "...               ...           ...               ...                ...   \n",
       "181668         591.00    2020-07-02        2020-07-01           4.629691   \n",
       "181669         589.00    2020-07-02        2020-07-01           5.337954   \n",
       "181670         619.00    2020-07-02        2020-07-01           1.443523   \n",
       "181671         601.00    2020-07-02        2020-07-01                NaN   \n",
       "181672         607.00    2020-07-02        2020-07-01           6.008043   \n",
       "\n",
       "        thickness_log  selling_price_log  \n",
       "0            0.693147           6.749931  \n",
       "1           -0.223144           6.953684  \n",
       "2           -0.967584           6.468211  \n",
       "3            0.832909           6.643790  \n",
       "4            1.386294           6.357842  \n",
       "...               ...                ...  \n",
       "181668      -0.040822           6.381816  \n",
       "181669      -0.051293           6.378426  \n",
       "181670      -0.342490           6.428105  \n",
       "181671      -0.162519           6.398595  \n",
       "181672      -0.342490           6.408529  \n",
       "\n",
       "[181673 rows x 17 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "copper_new = copper.copy()\n",
    "copper_new\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "425bdcf1",
   "metadata": {},
   "source": [
    "# Treat Outliers using IQR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e005c141",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>product_ref</th>\n",
       "      <th>delivery date</th>\n",
       "      <th>selling_price</th>\n",
       "      <th>item_date_new</th>\n",
       "      <th>delivery date_new</th>\n",
       "      <th>quantity tons_log</th>\n",
       "      <th>thickness_log</th>\n",
       "      <th>selling_price_log</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20210401.0</td>\n",
       "      <td>54.151139</td>\n",
       "      <td>30156308.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>1670798778</td>\n",
       "      <td>20210701.0</td>\n",
       "      <td>854.00</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>2021-07-01</td>\n",
       "      <td>3.991779</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>6.749931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20210401.0</td>\n",
       "      <td>768.024839</td>\n",
       "      <td>30202938.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1210.0</td>\n",
       "      <td>1668701718</td>\n",
       "      <td>20210401.0</td>\n",
       "      <td>1047.00</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>6.643822</td>\n",
       "      <td>-0.223144</td>\n",
       "      <td>6.953684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20210401.0</td>\n",
       "      <td>386.127949</td>\n",
       "      <td>30153963.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.38</td>\n",
       "      <td>952.0</td>\n",
       "      <td>628377</td>\n",
       "      <td>20210101.0</td>\n",
       "      <td>644.33</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>5.956169</td>\n",
       "      <td>-0.967584</td>\n",
       "      <td>6.468211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20210401.0</td>\n",
       "      <td>202.411065</td>\n",
       "      <td>30349574.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>2.30</td>\n",
       "      <td>1317.0</td>\n",
       "      <td>1668701718</td>\n",
       "      <td>20210101.0</td>\n",
       "      <td>768.00</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>5.310301</td>\n",
       "      <td>0.832909</td>\n",
       "      <td>6.643790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20210401.0</td>\n",
       "      <td>785.526262</td>\n",
       "      <td>30211560.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1980.0</td>\n",
       "      <td>640665</td>\n",
       "      <td>20210301.0</td>\n",
       "      <td>577.00</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>2021-03-01</td>\n",
       "      <td>6.666354</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>6.357842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181668</th>\n",
       "      <td>20200702.0</td>\n",
       "      <td>102.482422</td>\n",
       "      <td>30200854.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.96</td>\n",
       "      <td>1220.0</td>\n",
       "      <td>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>591.00</td>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>4.629691</td>\n",
       "      <td>-0.040822</td>\n",
       "      <td>6.381816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181669</th>\n",
       "      <td>20200702.0</td>\n",
       "      <td>208.086469</td>\n",
       "      <td>30200854.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>589.00</td>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>5.337954</td>\n",
       "      <td>-0.051293</td>\n",
       "      <td>6.378426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181670</th>\n",
       "      <td>20200702.0</td>\n",
       "      <td>4.235594</td>\n",
       "      <td>30200854.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.71</td>\n",
       "      <td>1250.0</td>\n",
       "      <td>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>619.00</td>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>1.443523</td>\n",
       "      <td>-0.342490</td>\n",
       "      <td>6.428105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181671</th>\n",
       "      <td>20200702.0</td>\n",
       "      <td>-2000.000000</td>\n",
       "      <td>30200854.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1250.0</td>\n",
       "      <td>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>601.00</td>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.162519</td>\n",
       "      <td>6.398595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181672</th>\n",
       "      <td>20200702.0</td>\n",
       "      <td>406.686538</td>\n",
       "      <td>30200854.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.71</td>\n",
       "      <td>1240.0</td>\n",
       "      <td>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>607.00</td>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>6.008043</td>\n",
       "      <td>-0.342490</td>\n",
       "      <td>6.408529</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>181673 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         item_date  quantity tons    customer  country  status  item type  \\\n",
       "0       20210401.0      54.151139  30156308.0     28.0       1        5.0   \n",
       "1       20210401.0     768.024839  30202938.0     25.0       1        5.0   \n",
       "2       20210401.0     386.127949  30153963.0     30.0       1        6.0   \n",
       "3       20210401.0     202.411065  30349574.0     32.0       1        3.0   \n",
       "4       20210401.0     785.526262  30211560.0     28.0       1        5.0   \n",
       "...            ...            ...         ...      ...     ...        ...   \n",
       "181668  20200702.0     102.482422  30200854.0     25.0       1        5.0   \n",
       "181669  20200702.0     208.086469  30200854.0     25.0       1        5.0   \n",
       "181670  20200702.0       4.235594  30200854.0     25.0       1        5.0   \n",
       "181671  20200702.0   -2000.000000  30200854.0     25.0       1        5.0   \n",
       "181672  20200702.0     406.686538  30200854.0     25.0       1        5.0   \n",
       "\n",
       "        application  thickness   width  product_ref  delivery date  \\\n",
       "0              10.0       2.00  1500.0   1670798778     20210701.0   \n",
       "1              41.0       0.80  1210.0   1668701718     20210401.0   \n",
       "2              28.0       0.38   952.0       628377     20210101.0   \n",
       "3              59.0       2.30  1317.0   1668701718     20210101.0   \n",
       "4              10.0       4.00  1980.0       640665     20210301.0   \n",
       "...             ...        ...     ...          ...            ...   \n",
       "181668         41.0       0.96  1220.0    164141591     20200701.0   \n",
       "181669         41.0       0.95  1500.0    164141591     20200701.0   \n",
       "181670         41.0       0.71  1250.0    164141591     20200701.0   \n",
       "181671         41.0       0.85  1250.0    164141591     20200701.0   \n",
       "181672         41.0       0.71  1240.0    164141591     20200701.0   \n",
       "\n",
       "        selling_price item_date_new delivery date_new  quantity tons_log  \\\n",
       "0              854.00    2021-04-01        2021-07-01           3.991779   \n",
       "1             1047.00    2021-04-01        2021-04-01           6.643822   \n",
       "2              644.33    2021-04-01        2021-01-01           5.956169   \n",
       "3              768.00    2021-04-01        2021-01-01           5.310301   \n",
       "4              577.00    2021-04-01        2021-03-01           6.666354   \n",
       "...               ...           ...               ...                ...   \n",
       "181668         591.00    2020-07-02        2020-07-01           4.629691   \n",
       "181669         589.00    2020-07-02        2020-07-01           5.337954   \n",
       "181670         619.00    2020-07-02        2020-07-01           1.443523   \n",
       "181671         601.00    2020-07-02        2020-07-01                NaN   \n",
       "181672         607.00    2020-07-02        2020-07-01           6.008043   \n",
       "\n",
       "        thickness_log  selling_price_log  \n",
       "0            0.693147           6.749931  \n",
       "1           -0.223144           6.953684  \n",
       "2           -0.967584           6.468211  \n",
       "3            0.832909           6.643790  \n",
       "4            1.386294           6.357842  \n",
       "...               ...                ...  \n",
       "181668      -0.040822           6.381816  \n",
       "181669      -0.051293           6.378426  \n",
       "181670      -0.342490           6.428105  \n",
       "181671      -0.162519           6.398595  \n",
       "181672      -0.342490           6.408529  \n",
       "\n",
       "[181673 rows x 17 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#using the IQR & Clips  method removing the outliars :\n",
    "def outlier(copper_new, column):\n",
    "    iqr = copper_new[column].quantile(0.75) - copper_new[column].quantile(0.25)\n",
    "    upper_value = copper_new[column].quantile(0.75) + (1.5*iqr)\n",
    "    lower_value = copper_new[column].quantile(0.25) - (1.5*iqr)\n",
    "    copper_new[column] = copper_new[column].clip(lower_value, upper_value)\n",
    "    \n",
    "outlier(copper_new, 'quantity tons_log')\n",
    "outlier(copper_new, 'thickness_log')\n",
    "outlier(copper_new, 'selling_price_log')\n",
    "outlier(copper_new, 'width')\n",
    "copper_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "63021996",
   "metadata": {},
   "outputs": [],
   "source": [
    "#df =copper_new[(copper_new.thickness_log <lower_value)|(copper_new.thickness_log>upper_value)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "182f38d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# after IQR method checking outliars using boxplot\n",
    "for i in ['quantity tons_log', 'thickness_log','width' ,'selling_price_log']:\n",
    "    plot(copper_new,i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "2ec89f8a",
   "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>item_date</th>\n",
       "      <th>customer</th>\n",
       "      <th>country</th>\n",
       "      <th>status</th>\n",
       "      <th>item type</th>\n",
       "      <th>application</th>\n",
       "      <th>width</th>\n",
       "      <th>product_ref</th>\n",
       "      <th>delivery date</th>\n",
       "      <th>item_date_new</th>\n",
       "      <th>delivery date_new</th>\n",
       "      <th>quantity tons_log</th>\n",
       "      <th>thickness_log</th>\n",
       "      <th>selling_price_log</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20210401.0</td>\n",
       "      <td>30156308.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>1670798778</td>\n",
       "      <td>20210701.0</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>2021-07-01</td>\n",
       "      <td>3.991779</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>6.749931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20210401.0</td>\n",
       "      <td>30202938.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1210.0</td>\n",
       "      <td>1668701718</td>\n",
       "      <td>20210401.0</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>6.643822</td>\n",
       "      <td>-0.223144</td>\n",
       "      <td>6.953684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20210401.0</td>\n",
       "      <td>30153963.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>952.0</td>\n",
       "      <td>628377</td>\n",
       "      <td>20210101.0</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>5.956169</td>\n",
       "      <td>-0.967584</td>\n",
       "      <td>6.468211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20210401.0</td>\n",
       "      <td>30349574.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>1317.0</td>\n",
       "      <td>1668701718</td>\n",
       "      <td>20210101.0</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>5.310301</td>\n",
       "      <td>0.832909</td>\n",
       "      <td>6.643790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20210401.0</td>\n",
       "      <td>30211560.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1980.0</td>\n",
       "      <td>640665</td>\n",
       "      <td>20210301.0</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>2021-03-01</td>\n",
       "      <td>6.666354</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>6.357842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181668</th>\n",
       "      <td>20200702.0</td>\n",
       "      <td>30200854.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1220.0</td>\n",
       "      <td>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>4.629691</td>\n",
       "      <td>-0.040822</td>\n",
       "      <td>6.381816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181669</th>\n",
       "      <td>20200702.0</td>\n",
       "      <td>30200854.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>5.337954</td>\n",
       "      <td>-0.051293</td>\n",
       "      <td>6.378426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181670</th>\n",
       "      <td>20200702.0</td>\n",
       "      <td>30200854.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1250.0</td>\n",
       "      <td>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>1.443523</td>\n",
       "      <td>-0.342490</td>\n",
       "      <td>6.428105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181671</th>\n",
       "      <td>20200702.0</td>\n",
       "      <td>30200854.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1250.0</td>\n",
       "      <td>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.162519</td>\n",
       "      <td>6.398595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181672</th>\n",
       "      <td>20200702.0</td>\n",
       "      <td>30200854.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1240.0</td>\n",
       "      <td>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>6.008043</td>\n",
       "      <td>-0.342490</td>\n",
       "      <td>6.408529</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>181673 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         item_date    customer  country  status  item type  application  \\\n",
       "0       20210401.0  30156308.0     28.0       1        5.0         10.0   \n",
       "1       20210401.0  30202938.0     25.0       1        5.0         41.0   \n",
       "2       20210401.0  30153963.0     30.0       1        6.0         28.0   \n",
       "3       20210401.0  30349574.0     32.0       1        3.0         59.0   \n",
       "4       20210401.0  30211560.0     28.0       1        5.0         10.0   \n",
       "...            ...         ...      ...     ...        ...          ...   \n",
       "181668  20200702.0  30200854.0     25.0       1        5.0         41.0   \n",
       "181669  20200702.0  30200854.0     25.0       1        5.0         41.0   \n",
       "181670  20200702.0  30200854.0     25.0       1        5.0         41.0   \n",
       "181671  20200702.0  30200854.0     25.0       1        5.0         41.0   \n",
       "181672  20200702.0  30200854.0     25.0       1        5.0         41.0   \n",
       "\n",
       "         width  product_ref  delivery date item_date_new delivery date_new  \\\n",
       "0       1500.0   1670798778     20210701.0    2021-04-01        2021-07-01   \n",
       "1       1210.0   1668701718     20210401.0    2021-04-01        2021-04-01   \n",
       "2        952.0       628377     20210101.0    2021-04-01        2021-01-01   \n",
       "3       1317.0   1668701718     20210101.0    2021-04-01        2021-01-01   \n",
       "4       1980.0       640665     20210301.0    2021-04-01        2021-03-01   \n",
       "...        ...          ...            ...           ...               ...   \n",
       "181668  1220.0    164141591     20200701.0    2020-07-02        2020-07-01   \n",
       "181669  1500.0    164141591     20200701.0    2020-07-02        2020-07-01   \n",
       "181670  1250.0    164141591     20200701.0    2020-07-02        2020-07-01   \n",
       "181671  1250.0    164141591     20200701.0    2020-07-02        2020-07-01   \n",
       "181672  1240.0    164141591     20200701.0    2020-07-02        2020-07-01   \n",
       "\n",
       "        quantity tons_log  thickness_log  selling_price_log  \n",
       "0                3.991779       0.693147           6.749931  \n",
       "1                6.643822      -0.223144           6.953684  \n",
       "2                5.956169      -0.967584           6.468211  \n",
       "3                5.310301       0.832909           6.643790  \n",
       "4                6.666354       1.386294           6.357842  \n",
       "...                   ...            ...                ...  \n",
       "181668           4.629691      -0.040822           6.381816  \n",
       "181669           5.337954      -0.051293           6.378426  \n",
       "181670           1.443523      -0.342490           6.428105  \n",
       "181671                NaN      -0.162519           6.398595  \n",
       "181672           6.008043      -0.342490           6.408529  \n",
       "\n",
       "[181673 rows x 14 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dropping the existing feature\n",
    "copper_new1=copper_new.drop(columns = ['quantity tons', 'thickness', 'selling_price'])\n",
    "copper_new1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "aec2f912",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "item_date            float64\n",
       "customer             float64\n",
       "country              float64\n",
       "status                 int64\n",
       "item type            float64\n",
       "application          float64\n",
       "width                float64\n",
       "product_ref            int64\n",
       "delivery date        float64\n",
       "item_date_new         object\n",
       "delivery date_new     object\n",
       "quantity tons_log    float64\n",
       "thickness_log        float64\n",
       "selling_price_log    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "copper_new1.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "5cbb4b82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Need to verify any columns are highly correlated using Heatmap.\n",
    "\n",
    "heat_feat = ['quantity tons_log','customer','country','status','application','width','product_ref','thickness_log','selling_price_log']\n",
    "\n",
    "heat_feature = copper_new1[heat_feat].corr()\n",
    "\n",
    "sns.heatmap(heat_feature,annot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "1daad0fa",
   "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>item_date</th>\n",
       "      <th>customer</th>\n",
       "      <th>country</th>\n",
       "      <th>status</th>\n",
       "      <th>item type</th>\n",
       "      <th>application</th>\n",
       "      <th>width</th>\n",
       "      <th>product_ref</th>\n",
       "      <th>delivery date</th>\n",
       "      <th>item_date_new</th>\n",
       "      <th>delivery date_new</th>\n",
       "      <th>quantity tons_log</th>\n",
       "      <th>thickness_log</th>\n",
       "      <th>selling_price_log</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20210401.0</td>\n",
       "      <td>30156308.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>1670798778</td>\n",
       "      <td>20210701.0</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>2021-07-01</td>\n",
       "      <td>3.991779</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>6.749931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20210401.0</td>\n",
       "      <td>30202938.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1210.0</td>\n",
       "      <td>1668701718</td>\n",
       "      <td>20210401.0</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>6.643822</td>\n",
       "      <td>-0.223144</td>\n",
       "      <td>6.953684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20210401.0</td>\n",
       "      <td>30153963.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>952.0</td>\n",
       "      <td>628377</td>\n",
       "      <td>20210101.0</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>5.956169</td>\n",
       "      <td>-0.967584</td>\n",
       "      <td>6.468211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20210401.0</td>\n",
       "      <td>30349574.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>1317.0</td>\n",
       "      <td>1668701718</td>\n",
       "      <td>20210101.0</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>5.310301</td>\n",
       "      <td>0.832909</td>\n",
       "      <td>6.643790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20210401.0</td>\n",
       "      <td>30211560.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1980.0</td>\n",
       "      <td>640665</td>\n",
       "      <td>20210301.0</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>2021-03-01</td>\n",
       "      <td>6.666354</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>6.357842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181668</th>\n",
       "      <td>20200702.0</td>\n",
       "      <td>30200854.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1220.0</td>\n",
       "      <td>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>4.629691</td>\n",
       "      <td>-0.040822</td>\n",
       "      <td>6.381816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181669</th>\n",
       "      <td>20200702.0</td>\n",
       "      <td>30200854.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>5.337954</td>\n",
       "      <td>-0.051293</td>\n",
       "      <td>6.378426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181670</th>\n",
       "      <td>20200702.0</td>\n",
       "      <td>30200854.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1250.0</td>\n",
       "      <td>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>1.443523</td>\n",
       "      <td>-0.342490</td>\n",
       "      <td>6.428105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181671</th>\n",
       "      <td>20200702.0</td>\n",
       "      <td>30200854.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1250.0</td>\n",
       "      <td>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.162519</td>\n",
       "      <td>6.398595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181672</th>\n",
       "      <td>20200702.0</td>\n",
       "      <td>30200854.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1240.0</td>\n",
       "      <td>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>6.008043</td>\n",
       "      <td>-0.342490</td>\n",
       "      <td>6.408529</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>181673 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         item_date    customer  country  status  item type  application  \\\n",
       "0       20210401.0  30156308.0     28.0       1        5.0         10.0   \n",
       "1       20210401.0  30202938.0     25.0       1        5.0         41.0   \n",
       "2       20210401.0  30153963.0     30.0       1        6.0         28.0   \n",
       "3       20210401.0  30349574.0     32.0       1        3.0         59.0   \n",
       "4       20210401.0  30211560.0     28.0       1        5.0         10.0   \n",
       "...            ...         ...      ...     ...        ...          ...   \n",
       "181668  20200702.0  30200854.0     25.0       1        5.0         41.0   \n",
       "181669  20200702.0  30200854.0     25.0       1        5.0         41.0   \n",
       "181670  20200702.0  30200854.0     25.0       1        5.0         41.0   \n",
       "181671  20200702.0  30200854.0     25.0       1        5.0         41.0   \n",
       "181672  20200702.0  30200854.0     25.0       1        5.0         41.0   \n",
       "\n",
       "         width  product_ref  delivery date item_date_new delivery date_new  \\\n",
       "0       1500.0   1670798778     20210701.0    2021-04-01        2021-07-01   \n",
       "1       1210.0   1668701718     20210401.0    2021-04-01        2021-04-01   \n",
       "2        952.0       628377     20210101.0    2021-04-01        2021-01-01   \n",
       "3       1317.0   1668701718     20210101.0    2021-04-01        2021-01-01   \n",
       "4       1980.0       640665     20210301.0    2021-04-01        2021-03-01   \n",
       "...        ...          ...            ...           ...               ...   \n",
       "181668  1220.0    164141591     20200701.0    2020-07-02        2020-07-01   \n",
       "181669  1500.0    164141591     20200701.0    2020-07-02        2020-07-01   \n",
       "181670  1250.0    164141591     20200701.0    2020-07-02        2020-07-01   \n",
       "181671  1250.0    164141591     20200701.0    2020-07-02        2020-07-01   \n",
       "181672  1240.0    164141591     20200701.0    2020-07-02        2020-07-01   \n",
       "\n",
       "        quantity tons_log  thickness_log  selling_price_log  \n",
       "0                3.991779       0.693147           6.749931  \n",
       "1                6.643822      -0.223144           6.953684  \n",
       "2                5.956169      -0.967584           6.468211  \n",
       "3                5.310301       0.832909           6.643790  \n",
       "4                6.666354       1.386294           6.357842  \n",
       "...                   ...            ...                ...  \n",
       "181668           4.629691      -0.040822           6.381816  \n",
       "181669           5.337954      -0.051293           6.378426  \n",
       "181670           1.443523      -0.342490           6.428105  \n",
       "181671                NaN      -0.162519           6.398595  \n",
       "181672           6.008043      -0.342490           6.408529  \n",
       "\n",
       "[181673 rows x 14 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "copper_new2 = copper_new1.copy()\n",
    "copper_new2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97842167",
   "metadata": {},
   "source": [
    "# Wrong Delivery Date Handling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "6cd2e977",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "item_date            float64\n",
       "customer             float64\n",
       "country              float64\n",
       "status                 int64\n",
       "item type            float64\n",
       "application          float64\n",
       "width                float64\n",
       "product_ref            int64\n",
       "delivery date        float64\n",
       "item_date_new         object\n",
       "delivery date_new     object\n",
       "quantity tons_log    float64\n",
       "thickness_log        float64\n",
       "selling_price_log    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "copper_new2.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "88216819",
   "metadata": {},
   "outputs": [],
   "source": [
    "# converting datatypes item_date_new delivery date_new this features\n",
    "copper_new2['delivery date_new'] = pd.to_datetime(copper_new2['delivery date_new'], errors='coerce')\n",
    "\n",
    "copper_new2['item_date_new'] = pd.to_datetime(copper_new2['delivery date_new'], errors='coerce')\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f5eeed4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "item_date                   float64\n",
       "customer                    float64\n",
       "country                     float64\n",
       "status                        int64\n",
       "item type                   float64\n",
       "application                 float64\n",
       "width                       float64\n",
       "product_ref                   int64\n",
       "delivery date               float64\n",
       "item_date_new        datetime64[ns]\n",
       "delivery date_new    datetime64[ns]\n",
       "quantity tons_log           float64\n",
       "thickness_log               float64\n",
       "selling_price_log           float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "copper_new2.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "1e966884",
   "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>item_date</th>\n",
       "      <th>customer</th>\n",
       "      <th>country</th>\n",
       "      <th>status</th>\n",
       "      <th>item type</th>\n",
       "      <th>application</th>\n",
       "      <th>width</th>\n",
       "      <th>product_ref</th>\n",
       "      <th>delivery date</th>\n",
       "      <th>item_date_new</th>\n",
       "      <th>delivery date_new</th>\n",
       "      <th>quantity tons_log</th>\n",
       "      <th>thickness_log</th>\n",
       "      <th>selling_price_log</th>\n",
       "      <th>Date_difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20210401.0</td>\n",
       "      <td>30156308.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>1670798778</td>\n",
       "      <td>20210701.0</td>\n",
       "      <td>2021-07-01</td>\n",
       "      <td>2021-07-01</td>\n",
       "      <td>3.991779</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>6.749931</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20210401.0</td>\n",
       "      <td>30202938.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1210.0</td>\n",
       "      <td>1668701718</td>\n",
       "      <td>20210401.0</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>6.643822</td>\n",
       "      <td>-0.223144</td>\n",
       "      <td>6.953684</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20210401.0</td>\n",
       "      <td>30153963.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>952.0</td>\n",
       "      <td>628377</td>\n",
       "      <td>20210101.0</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>5.956169</td>\n",
       "      <td>-0.967584</td>\n",
       "      <td>6.468211</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20210401.0</td>\n",
       "      <td>30349574.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>1317.0</td>\n",
       "      <td>1668701718</td>\n",
       "      <td>20210101.0</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>5.310301</td>\n",
       "      <td>0.832909</td>\n",
       "      <td>6.643790</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20210401.0</td>\n",
       "      <td>30211560.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1980.0</td>\n",
       "      <td>640665</td>\n",
       "      <td>20210301.0</td>\n",
       "      <td>2021-03-01</td>\n",
       "      <td>2021-03-01</td>\n",
       "      <td>6.666354</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>6.357842</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181668</th>\n",
       "      <td>20200702.0</td>\n",
       "      <td>30200854.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1220.0</td>\n",
       "      <td>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>4.629691</td>\n",
       "      <td>-0.040822</td>\n",
       "      <td>6.381816</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181669</th>\n",
       "      <td>20200702.0</td>\n",
       "      <td>30200854.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>5.337954</td>\n",
       "      <td>-0.051293</td>\n",
       "      <td>6.378426</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181670</th>\n",
       "      <td>20200702.0</td>\n",
       "      <td>30200854.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1250.0</td>\n",
       "      <td>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>1.443523</td>\n",
       "      <td>-0.342490</td>\n",
       "      <td>6.428105</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181671</th>\n",
       "      <td>20200702.0</td>\n",
       "      <td>30200854.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1250.0</td>\n",
       "      <td>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.162519</td>\n",
       "      <td>6.398595</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181672</th>\n",
       "      <td>20200702.0</td>\n",
       "      <td>30200854.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1240.0</td>\n",
       "      <td>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>6.008043</td>\n",
       "      <td>-0.342490</td>\n",
       "      <td>6.408529</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>181673 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         item_date    customer  country  status  item type  application  \\\n",
       "0       20210401.0  30156308.0     28.0       1        5.0         10.0   \n",
       "1       20210401.0  30202938.0     25.0       1        5.0         41.0   \n",
       "2       20210401.0  30153963.0     30.0       1        6.0         28.0   \n",
       "3       20210401.0  30349574.0     32.0       1        3.0         59.0   \n",
       "4       20210401.0  30211560.0     28.0       1        5.0         10.0   \n",
       "...            ...         ...      ...     ...        ...          ...   \n",
       "181668  20200702.0  30200854.0     25.0       1        5.0         41.0   \n",
       "181669  20200702.0  30200854.0     25.0       1        5.0         41.0   \n",
       "181670  20200702.0  30200854.0     25.0       1        5.0         41.0   \n",
       "181671  20200702.0  30200854.0     25.0       1        5.0         41.0   \n",
       "181672  20200702.0  30200854.0     25.0       1        5.0         41.0   \n",
       "\n",
       "         width  product_ref  delivery date item_date_new delivery date_new  \\\n",
       "0       1500.0   1670798778     20210701.0    2021-07-01        2021-07-01   \n",
       "1       1210.0   1668701718     20210401.0    2021-04-01        2021-04-01   \n",
       "2        952.0       628377     20210101.0    2021-01-01        2021-01-01   \n",
       "3       1317.0   1668701718     20210101.0    2021-01-01        2021-01-01   \n",
       "4       1980.0       640665     20210301.0    2021-03-01        2021-03-01   \n",
       "...        ...          ...            ...           ...               ...   \n",
       "181668  1220.0    164141591     20200701.0    2020-07-01        2020-07-01   \n",
       "181669  1500.0    164141591     20200701.0    2020-07-01        2020-07-01   \n",
       "181670  1250.0    164141591     20200701.0    2020-07-01        2020-07-01   \n",
       "181671  1250.0    164141591     20200701.0    2020-07-01        2020-07-01   \n",
       "181672  1240.0    164141591     20200701.0    2020-07-01        2020-07-01   \n",
       "\n",
       "        quantity tons_log  thickness_log  selling_price_log  Date_difference  \n",
       "0                3.991779       0.693147           6.749931                0  \n",
       "1                6.643822      -0.223144           6.953684                0  \n",
       "2                5.956169      -0.967584           6.468211                0  \n",
       "3                5.310301       0.832909           6.643790                0  \n",
       "4                6.666354       1.386294           6.357842                0  \n",
       "...                   ...            ...                ...              ...  \n",
       "181668           4.629691      -0.040822           6.381816                0  \n",
       "181669           5.337954      -0.051293           6.378426                0  \n",
       "181670           1.443523      -0.342490           6.428105                0  \n",
       "181671                NaN      -0.162519           6.398595                0  \n",
       "181672           6.008043      -0.342490           6.408529                0  \n",
       "\n",
       "[181673 rows x 15 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# Assuming copper_new2 already exists and has the structure as mentioned before\n",
    "copper_new2['item_date_new'] = pd.to_datetime(copper_new2['item_date_new'])\n",
    "\n",
    "copper_new2['Date_difference'] = (copper_new2['delivery date_new'] - copper_new2['item_date_new']).dt.days\n",
    "\n",
    "copper_new2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "26445b9b",
   "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>customer</th>\n",
       "      <th>country</th>\n",
       "      <th>status</th>\n",
       "      <th>item type</th>\n",
       "      <th>application</th>\n",
       "      <th>width</th>\n",
       "      <th>product_ref</th>\n",
       "      <th>delivery date</th>\n",
       "      <th>item_date_new</th>\n",
       "      <th>delivery date_new</th>\n",
       "      <th>quantity tons_log</th>\n",
       "      <th>thickness_log</th>\n",
       "      <th>selling_price_log</th>\n",
       "      <th>Date_difference</th>\n",
       "      <th>item_date_day</th>\n",
       "      <th>item_date_month</th>\n",
       "      <th>item_date_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30156308.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>1670798778</td>\n",
       "      <td>20210701.0</td>\n",
       "      <td>2021-07-01</td>\n",
       "      <td>2021-07-01</td>\n",
       "      <td>3.991779</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>6.749931</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30202938.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1210.0</td>\n",
       "      <td>1668701718</td>\n",
       "      <td>20210401.0</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>6.643822</td>\n",
       "      <td>-0.223144</td>\n",
       "      <td>6.953684</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30153963.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>952.0</td>\n",
       "      <td>628377</td>\n",
       "      <td>20210101.0</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>5.956169</td>\n",
       "      <td>-0.967584</td>\n",
       "      <td>6.468211</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30349574.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>1317.0</td>\n",
       "      <td>1668701718</td>\n",
       "      <td>20210101.0</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>5.310301</td>\n",
       "      <td>0.832909</td>\n",
       "      <td>6.643790</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30211560.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1980.0</td>\n",
       "      <td>640665</td>\n",
       "      <td>20210301.0</td>\n",
       "      <td>2021-03-01</td>\n",
       "      <td>2021-03-01</td>\n",
       "      <td>6.666354</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>6.357842</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181668</th>\n",
       "      <td>30200854.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1220.0</td>\n",
       "      <td>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>4.629691</td>\n",
       "      <td>-0.040822</td>\n",
       "      <td>6.381816</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181669</th>\n",
       "      <td>30200854.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>5.337954</td>\n",
       "      <td>-0.051293</td>\n",
       "      <td>6.378426</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181670</th>\n",
       "      <td>30200854.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1250.0</td>\n",
       "      <td>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>1.443523</td>\n",
       "      <td>-0.342490</td>\n",
       "      <td>6.428105</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181671</th>\n",
       "      <td>30200854.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1250.0</td>\n",
       "      <td>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>3.392949</td>\n",
       "      <td>-0.162519</td>\n",
       "      <td>6.398595</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181672</th>\n",
       "      <td>30200854.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1240.0</td>\n",
       "      <td>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>6.008043</td>\n",
       "      <td>-0.342490</td>\n",
       "      <td>6.408529</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150450 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          customer  country  status  item type  application   width  \\\n",
       "0       30156308.0     28.0       1        5.0         10.0  1500.0   \n",
       "1       30202938.0     25.0       1        5.0         41.0  1210.0   \n",
       "2       30153963.0     30.0       1        6.0         28.0   952.0   \n",
       "3       30349574.0     32.0       1        3.0         59.0  1317.0   \n",
       "4       30211560.0     28.0       1        5.0         10.0  1980.0   \n",
       "...            ...      ...     ...        ...          ...     ...   \n",
       "181668  30200854.0     25.0       1        5.0         41.0  1220.0   \n",
       "181669  30200854.0     25.0       1        5.0         41.0  1500.0   \n",
       "181670  30200854.0     25.0       1        5.0         41.0  1250.0   \n",
       "181671  30200854.0     25.0       1        5.0         41.0  1250.0   \n",
       "181672  30200854.0     25.0       1        5.0         41.0  1240.0   \n",
       "\n",
       "        product_ref  delivery date item_date_new delivery date_new  \\\n",
       "0        1670798778     20210701.0    2021-07-01        2021-07-01   \n",
       "1        1668701718     20210401.0    2021-04-01        2021-04-01   \n",
       "2            628377     20210101.0    2021-01-01        2021-01-01   \n",
       "3        1668701718     20210101.0    2021-01-01        2021-01-01   \n",
       "4            640665     20210301.0    2021-03-01        2021-03-01   \n",
       "...             ...            ...           ...               ...   \n",
       "181668    164141591     20200701.0    2020-07-01        2020-07-01   \n",
       "181669    164141591     20200701.0    2020-07-01        2020-07-01   \n",
       "181670    164141591     20200701.0    2020-07-01        2020-07-01   \n",
       "181671    164141591     20200701.0    2020-07-01        2020-07-01   \n",
       "181672    164141591     20200701.0    2020-07-01        2020-07-01   \n",
       "\n",
       "        quantity tons_log  thickness_log  selling_price_log  Date_difference  \\\n",
       "0                3.991779       0.693147           6.749931                0   \n",
       "1                6.643822      -0.223144           6.953684                0   \n",
       "2                5.956169      -0.967584           6.468211                0   \n",
       "3                5.310301       0.832909           6.643790                0   \n",
       "4                6.666354       1.386294           6.357842                0   \n",
       "...                   ...            ...                ...              ...   \n",
       "181668           4.629691      -0.040822           6.381816                0   \n",
       "181669           5.337954      -0.051293           6.378426                0   \n",
       "181670           1.443523      -0.342490           6.428105                0   \n",
       "181671           3.392949      -0.162519           6.398595                0   \n",
       "181672           6.008043      -0.342490           6.408529                0   \n",
       "\n",
       "        item_date_day  item_date_month  item_date_year  \n",
       "0                   1                7            2021  \n",
       "1                   1                4            2021  \n",
       "2                   1                1            2021  \n",
       "3                   1                1            2021  \n",
       "4                   1                3            2021  \n",
       "...               ...              ...             ...  \n",
       "181668              1                7            2020  \n",
       "181669              1                7            2020  \n",
       "181670              1                7            2020  \n",
       "181671              1                7            2020  \n",
       "181672              1                7            2020  \n",
       "\n",
       "[150450 rows x 17 columns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert the data type using pandas\n",
    "copper_new2['item_date_new'] = pd.to_datetime(copper_new2['item_date_new'])\n",
    "\n",
    "# split the day, month, and year from 'item_date_1' column and add dataframe (This data also help us to prediction)\n",
    "copper_new2['item_date_day'] = copper_new2['item_date_new'].dt.day\n",
    "copper_new2['item_date_month'] = copper_new2['item_date_new'].dt.month\n",
    "copper_new2['item_date_year'] = copper_new2['item_date_new'].dt.year\n",
    "\n",
    "copper_new2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "aec9f29e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique Dates in 'item_date_new': <DatetimeArray>\n",
      "['2021-07-01 00:00:00', '2021-04-01 00:00:00', '2021-01-01 00:00:00',\n",
      " '2021-03-01 00:00:00', '2021-06-01 00:00:00', '2021-05-01 00:00:00',\n",
      " '2021-08-01 00:00:00', '2021-09-01 00:00:00', '2021-10-01 00:00:00',\n",
      " '2021-11-01 00:00:00', '2022-01-01 00:00:00', '2020-07-01 00:00:00',\n",
      " '2020-10-01 00:00:00', '2021-02-01 00:00:00', '2019-07-01 00:00:00',\n",
      " '2020-08-01 00:00:00', '2020-11-01 00:00:00', '2020-12-01 00:00:00',\n",
      " '2019-04-01 00:00:00', '2020-01-01 00:00:00', '2020-06-01 00:00:00',\n",
      " '2020-04-01 00:00:00', '2020-09-01 00:00:00', '2020-05-01 00:00:00',\n",
      " '2021-12-01 00:00:00']\n",
      "Length: 25, dtype: datetime64[ns]\n",
      "Unique Dates in 'delivery date_new': <DatetimeArray>\n",
      "['2021-07-01 00:00:00', '2021-04-01 00:00:00', '2021-01-01 00:00:00',\n",
      " '2021-03-01 00:00:00', '2021-06-01 00:00:00', '2021-05-01 00:00:00',\n",
      " '2021-08-01 00:00:00', '2021-09-01 00:00:00', '2021-10-01 00:00:00',\n",
      " '2021-11-01 00:00:00', '2022-01-01 00:00:00', '2020-07-01 00:00:00',\n",
      " '2020-10-01 00:00:00', '2021-02-01 00:00:00', '2019-07-01 00:00:00',\n",
      " '2020-08-01 00:00:00', '2020-11-01 00:00:00', '2020-12-01 00:00:00',\n",
      " '2019-04-01 00:00:00', '2020-01-01 00:00:00', '2020-06-01 00:00:00',\n",
      " '2020-04-01 00:00:00', '2020-09-01 00:00:00', '2020-05-01 00:00:00',\n",
      " '2021-12-01 00:00:00']\n",
      "Length: 25, dtype: datetime64[ns]\n"
     ]
    }
   ],
   "source": [
    "print(\"Unique Dates in 'item_date_new':\", copper_new2['item_date_new'].unique())\n",
    "print(\"Unique Dates in 'delivery date_new':\", copper_new2['delivery date_new'].unique())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "137c5f0a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "customer                    float64\n",
      "country                     float64\n",
      "status                        int64\n",
      "item type                   float64\n",
      "application                 float64\n",
      "width                       float64\n",
      "product_ref                   int64\n",
      "delivery date               float64\n",
      "item_date_new        datetime64[ns]\n",
      "delivery date_new    datetime64[ns]\n",
      "quantity tons_log           float64\n",
      "thickness_log               float64\n",
      "selling_price_log           float64\n",
      "Date_difference               int64\n",
      "item_date_day                 int32\n",
      "item_date_month               int32\n",
      "item_date_year                int32\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(copper_new2.dtypes)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "72cb6f1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "copper_new2.drop('item_date', axis=1, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "e087eeb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#copper_new2.isnull().sum()\n",
    "\n",
    "copper_new2['selling_price_log'].fillna(copper_new2['selling_price_log'].mean(),inplace=True)\n",
    "copper_new2['quantity tons_log'].fillna(copper_new2['quantity tons_log'].mean(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "235c7387",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customer             0\n",
       "country              0\n",
       "status               0\n",
       "item type            0\n",
       "application          0\n",
       "width                0\n",
       "product_ref          0\n",
       "delivery date        0\n",
       "item_date_new        0\n",
       "delivery date_new    0\n",
       "quantity tons_log    0\n",
       "thickness_log        0\n",
       "selling_price_log    0\n",
       "Date_difference      0\n",
       "item_date_day        0\n",
       "item_date_month      0\n",
       "item_date_year       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "copper_new2.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "a513bd49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['customer', 'country', 'status', 'item type', 'application', 'width',\n",
       "       'product_ref', 'delivery date', 'item_date_new', 'delivery date_new',\n",
       "       'quantity tons_log', 'thickness_log', 'selling_price_log',\n",
       "       'Date_difference', 'item_date_day', 'item_date_month',\n",
       "       'item_date_year'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "copper_new2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "0a9df5a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customer                    float64\n",
       "country                     float64\n",
       "status                        int64\n",
       "item type                   float64\n",
       "application                 float64\n",
       "width                       float64\n",
       "product_ref                   int64\n",
       "delivery date               float64\n",
       "item_date_new        datetime64[ns]\n",
       "delivery date_new    datetime64[ns]\n",
       "quantity tons_log           float64\n",
       "thickness_log               float64\n",
       "selling_price_log           float64\n",
       "Date_difference               int64\n",
       "item_date_day                 int32\n",
       "item_date_month               int32\n",
       "item_date_year                int32\n",
       "dtype: object"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "copper_new2.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7541589",
   "metadata": {},
   "source": [
    "# Model Building and Evaluation:\n",
    "\n",
    "1 .Regression Method - predicts continuous variable ‘Selling_Price’. \n",
    "2 .Classification Method - Predict Status "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "3b5dc47d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "import pickle\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "14ed172f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# chooseing the x and y in dataset\n",
    "X = copper_new2[['customer', 'country', 'status', 'item type', 'application', 'width',\n",
    "       'product_ref', \n",
    "       'quantity tons_log', 'thickness_log',\n",
    "        'item_date_day', 'item_date_month',\n",
    "       'item_date_year']]\n",
    "y = copper_new2[['selling_price_log']]\n",
    "# Data Pre-Processing with Standard scaler  \n",
    "scaler = StandardScaler()  \n",
    "X = scaler.fit_transform(X)\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y, test_size=(0.3),random_state = 42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "afed141b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45135"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "288addb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "105315"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "023a80e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9384920506438417, 0.9909495472815406)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fitting the model using RandomForestRegressor \n",
    "RFR = RandomForestRegressor() \n",
    "RFR = RandomForestRegressor(n_estimators= 50,random_state= 0)\n",
    "RFR.fit(X_test,y_test)\n",
    "y_train_pred = RFR.predict(X_train)\n",
    "y_test_pred =  RFR.predict(X_test)\n",
    "\n",
    "r2_train = r2_score(y_train, y_train_pred)\n",
    "r2_test = r2_score(y_test,y_test_pred)\n",
    "r2_train, r2_test\n",
    "     \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "3f68075b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# we got good accuracy after oversampling\n",
    "# RandomForestRegressor have good testing accuracy, but in training accuracy is overfitting.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "ec372557",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9441894302210081"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# GridsearchCV is a cross validation function\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size = 0.25,random_state = 42)\n",
    "\n",
    "param = {'max_depth'        : [20],\n",
    "              'min_samples_split': [ 5, ],\n",
    "              'min_samples_leaf' : [ 2, ],\n",
    "              'max_features'     : ['log2']}\n",
    "grid_searchcv = GridSearchCV(RandomForestRegressor(),param_grid = param,  cv = 5)\n",
    "grid_searchcv.fit(X_train, y_train)\n",
    "grid_searchcv.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "5a52d97c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# now the training accuracy overfitting reduced. so now model will predict effectively for unseen data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "86a437c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9692842072585235, 0.944851031093911)"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size = 0.25,random_state = 42)\n",
    "X_train.shape,X_test.shape\n",
    "\n",
    "RFR = RandomForestRegressor()\n",
    "\n",
    "Hyper_model= RandomForestRegressor(max_depth= 20 ,max_features='log2' ,min_samples_leaf=2, min_samples_split=5)\n",
    "\n",
    "# fitting the model: \n",
    "\n",
    "Hyper_model.fit(X_train,y_train)\n",
    "\n",
    "y_pred_train = Hyper_model.predict(X_train)\n",
    "y_pred_test = Hyper_model.predict(X_test)\n",
    "\n",
    "r2_train = r2_score(y_train,y_pred_train)\n",
    "r2_test = r2_score(y_test,y_pred_test)\n",
    "\n",
    "r2_train,r2_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "45e655d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 0.0031203790147568433\n",
      "Mean Absolute Error: 0.03834558221249196\n",
      "Root Mean Squared Error: 0.0558603527983564\n",
      "0.9450081157243594\n"
     ]
    }
   ],
   "source": [
    " # predict the selling price with hypertuning parameters and calculate the accuracy using metrics\n",
    "\n",
    "X_train,x_test,y_train,y_test = train_test_split(X,y,test_size = 0.25,random_state = 42)\n",
    "X_train.shape,x_test.shape\n",
    "\n",
    "RFR = RandomForestRegressor()\n",
    "\n",
    "Hyper_model= RandomForestRegressor(max_depth= 20 ,max_features='log2' ,min_samples_leaf=2, min_samples_split=5)\n",
    "\n",
    "# fitting the model: \n",
    "\n",
    "Hyper_model.fit(X_train,y_train)\n",
    "\n",
    "y_pred_train = Hyper_model.predict(X_train)\n",
    "y_pred_test = Hyper_model.predict(X_test)                      \n",
    "print('Mean Squared Error:' ,mean_squared_error(y_test,y_pred_test))\n",
    "print('Mean Absolute Error:',mean_absolute_error(y_test,y_pred_test))\n",
    "print('Root Mean Squared Error:',np.sqrt(mean_squared_error(y_test,y_pred_test)))\n",
    "print(r2_score(y_test,y_pred_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "e2fbf2ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the model \n",
    "import pickle\n",
    "\n",
    "with open(\"C:\\\\Users\\\\User\\\\Downloads\\\\copper_model_regression_final.pkl\", 'wb') as f:\n",
    "    pickle.dump(Hyper_model, f)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "dc480ea7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.797671352703596"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load pickle model to predict the status (like Trained brain).\n",
    "\n",
    "with open(\"C:\\\\Users\\\\User\\\\Downloads\\\\copper_model_regression_final.pkl\", 'rb') as f:\n",
    "    model = pickle.load(f)\n",
    "\n",
    "user_data = np.array([[30156308.0,25.0,1,5.0,10.0,1500.0,167079877841,3.991779,0.693147,1,4,2021]])\n",
    "y_pred = model.predict(user_data)\n",
    "y_pred[0]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a4975a7",
   "metadata": {},
   "source": [
    "# 2 .Classification Method - Predict Status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "7ddbefbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>customer</th>\n",
       "      <th>country</th>\n",
       "      <th>status</th>\n",
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       "      <th>width</th>\n",
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       "      <th>delivery date</th>\n",
       "      <th>item_date_new</th>\n",
       "      <th>delivery date_new</th>\n",
       "      <th>quantity tons_log</th>\n",
       "      <th>thickness_log</th>\n",
       "      <th>selling_price_log</th>\n",
       "      <th>Date_difference</th>\n",
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       "      <th>item_date_year</th>\n",
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       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
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       "      <td>1317.0</td>\n",
       "      <td>1668701718</td>\n",
       "      <td>20210101.0</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>5.310301</td>\n",
       "      <td>0.832909</td>\n",
       "      <td>6.643790</td>\n",
       "      <td>0</td>\n",
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       "      <td>2021</td>\n",
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       "      <td>2020</td>\n",
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       "      <td>30200854.0</td>\n",
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       "      <td>20200701.0</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>6.008043</td>\n",
       "      <td>-0.342490</td>\n",
       "      <td>6.408529</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150450 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          customer  country  status  item type  application   width  \\\n",
       "0       30156308.0     28.0       1        5.0         10.0  1500.0   \n",
       "1       30202938.0     25.0       1        5.0         41.0  1210.0   \n",
       "2       30153963.0     30.0       1        6.0         28.0   952.0   \n",
       "3       30349574.0     32.0       1        3.0         59.0  1317.0   \n",
       "4       30211560.0     28.0       1        5.0         10.0  1980.0   \n",
       "...            ...      ...     ...        ...          ...     ...   \n",
       "181668  30200854.0     25.0       1        5.0         41.0  1220.0   \n",
       "181669  30200854.0     25.0       1        5.0         41.0  1500.0   \n",
       "181670  30200854.0     25.0       1        5.0         41.0  1250.0   \n",
       "181671  30200854.0     25.0       1        5.0         41.0  1250.0   \n",
       "181672  30200854.0     25.0       1        5.0         41.0  1240.0   \n",
       "\n",
       "        product_ref  delivery date item_date_new delivery date_new  \\\n",
       "0        1670798778     20210701.0    2021-07-01        2021-07-01   \n",
       "1        1668701718     20210401.0    2021-04-01        2021-04-01   \n",
       "2            628377     20210101.0    2021-01-01        2021-01-01   \n",
       "3        1668701718     20210101.0    2021-01-01        2021-01-01   \n",
       "4            640665     20210301.0    2021-03-01        2021-03-01   \n",
       "...             ...            ...           ...               ...   \n",
       "181668    164141591     20200701.0    2020-07-01        2020-07-01   \n",
       "181669    164141591     20200701.0    2020-07-01        2020-07-01   \n",
       "181670    164141591     20200701.0    2020-07-01        2020-07-01   \n",
       "181671    164141591     20200701.0    2020-07-01        2020-07-01   \n",
       "181672    164141591     20200701.0    2020-07-01        2020-07-01   \n",
       "\n",
       "        quantity tons_log  thickness_log  selling_price_log  Date_difference  \\\n",
       "0                3.991779       0.693147           6.749931                0   \n",
       "1                6.643822      -0.223144           6.953684                0   \n",
       "2                5.956169      -0.967584           6.468211                0   \n",
       "3                5.310301       0.832909           6.643790                0   \n",
       "4                6.666354       1.386294           6.357842                0   \n",
       "...                   ...            ...                ...              ...   \n",
       "181668           4.629691      -0.040822           6.381816                0   \n",
       "181669           5.337954      -0.051293           6.378426                0   \n",
       "181670           1.443523      -0.342490           6.428105                0   \n",
       "181671           3.392949      -0.162519           6.398595                0   \n",
       "181672           6.008043      -0.342490           6.408529                0   \n",
       "\n",
       "        item_date_day  item_date_month  item_date_year  \n",
       "0                   1                7            2021  \n",
       "1                   1                4            2021  \n",
       "2                   1                1            2021  \n",
       "3                   1                1            2021  \n",
       "4                   1                3            2021  \n",
       "...               ...              ...             ...  \n",
       "181668              1                7            2020  \n",
       "181669              1                7            2020  \n",
       "181670              1                7            2020  \n",
       "181671              1                7            2020  \n",
       "181672              1                7            2020  \n",
       "\n",
       "[150450 rows x 17 columns]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "copper_new2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "69542b4a",
   "metadata": {
    "scrolled": true
   },
   "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>customer</th>\n",
       "      <th>country</th>\n",
       "      <th>status</th>\n",
       "      <th>item type</th>\n",
       "      <th>application</th>\n",
       "      <th>width</th>\n",
       "      <th>product_ref</th>\n",
       "      <th>delivery date</th>\n",
       "      <th>item_date_new</th>\n",
       "      <th>delivery date_new</th>\n",
       "      <th>quantity tons_log</th>\n",
       "      <th>thickness_log</th>\n",
       "      <th>selling_price_log</th>\n",
       "      <th>Date_difference</th>\n",
       "      <th>item_date_day</th>\n",
       "      <th>item_date_month</th>\n",
       "      <th>item_date_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30156308.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>1670798778</td>\n",
       "      <td>20210701.0</td>\n",
       "      <td>2021-07-01</td>\n",
       "      <td>2021-07-01</td>\n",
       "      <td>3.991779</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>6.749931</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30202938.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1210.0</td>\n",
       "      <td>1668701718</td>\n",
       "      <td>20210401.0</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>6.643822</td>\n",
       "      <td>-0.223144</td>\n",
       "      <td>6.953684</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30153963.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>952.0</td>\n",
       "      <td>628377</td>\n",
       "      <td>20210101.0</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>5.956169</td>\n",
       "      <td>-0.967584</td>\n",
       "      <td>6.468211</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30349574.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>1317.0</td>\n",
       "      <td>1668701718</td>\n",
       "      <td>20210101.0</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>5.310301</td>\n",
       "      <td>0.832909</td>\n",
       "      <td>6.643790</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30211560.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1980.0</td>\n",
       "      <td>640665</td>\n",
       "      <td>20210301.0</td>\n",
       "      <td>2021-03-01</td>\n",
       "      <td>2021-03-01</td>\n",
       "      <td>6.666354</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>6.357842</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2021</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\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>30200854.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1220.0</td>\n",
       "      <td>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>4.629691</td>\n",
       "      <td>-0.040822</td>\n",
       "      <td>6.381816</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181669</th>\n",
       "      <td>30200854.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>5.337954</td>\n",
       "      <td>-0.051293</td>\n",
       "      <td>6.378426</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181670</th>\n",
       "      <td>30200854.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1250.0</td>\n",
       "      <td>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>1.443523</td>\n",
       "      <td>-0.342490</td>\n",
       "      <td>6.428105</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181671</th>\n",
       "      <td>30200854.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1250.0</td>\n",
       "      <td>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>3.392949</td>\n",
       "      <td>-0.162519</td>\n",
       "      <td>6.398595</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181672</th>\n",
       "      <td>30200854.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1240.0</td>\n",
       "      <td>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>6.008043</td>\n",
       "      <td>-0.342490</td>\n",
       "      <td>6.408529</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150450 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          customer  country  status  item type  application   width  \\\n",
       "0       30156308.0     28.0       1        5.0         10.0  1500.0   \n",
       "1       30202938.0     25.0       1        5.0         41.0  1210.0   \n",
       "2       30153963.0     30.0       1        6.0         28.0   952.0   \n",
       "3       30349574.0     32.0       1        3.0         59.0  1317.0   \n",
       "4       30211560.0     28.0       1        5.0         10.0  1980.0   \n",
       "...            ...      ...     ...        ...          ...     ...   \n",
       "181668  30200854.0     25.0       1        5.0         41.0  1220.0   \n",
       "181669  30200854.0     25.0       1        5.0         41.0  1500.0   \n",
       "181670  30200854.0     25.0       1        5.0         41.0  1250.0   \n",
       "181671  30200854.0     25.0       1        5.0         41.0  1250.0   \n",
       "181672  30200854.0     25.0       1        5.0         41.0  1240.0   \n",
       "\n",
       "        product_ref  delivery date item_date_new delivery date_new  \\\n",
       "0        1670798778     20210701.0    2021-07-01        2021-07-01   \n",
       "1        1668701718     20210401.0    2021-04-01        2021-04-01   \n",
       "2            628377     20210101.0    2021-01-01        2021-01-01   \n",
       "3        1668701718     20210101.0    2021-01-01        2021-01-01   \n",
       "4            640665     20210301.0    2021-03-01        2021-03-01   \n",
       "...             ...            ...           ...               ...   \n",
       "181668    164141591     20200701.0    2020-07-01        2020-07-01   \n",
       "181669    164141591     20200701.0    2020-07-01        2020-07-01   \n",
       "181670    164141591     20200701.0    2020-07-01        2020-07-01   \n",
       "181671    164141591     20200701.0    2020-07-01        2020-07-01   \n",
       "181672    164141591     20200701.0    2020-07-01        2020-07-01   \n",
       "\n",
       "        quantity tons_log  thickness_log  selling_price_log  Date_difference  \\\n",
       "0                3.991779       0.693147           6.749931                0   \n",
       "1                6.643822      -0.223144           6.953684                0   \n",
       "2                5.956169      -0.967584           6.468211                0   \n",
       "3                5.310301       0.832909           6.643790                0   \n",
       "4                6.666354       1.386294           6.357842                0   \n",
       "...                   ...            ...                ...              ...   \n",
       "181668           4.629691      -0.040822           6.381816                0   \n",
       "181669           5.337954      -0.051293           6.378426                0   \n",
       "181670           1.443523      -0.342490           6.428105                0   \n",
       "181671           3.392949      -0.162519           6.398595                0   \n",
       "181672           6.008043      -0.342490           6.408529                0   \n",
       "\n",
       "        item_date_day  item_date_month  item_date_year  \n",
       "0                   1                7            2021  \n",
       "1                   1                4            2021  \n",
       "2                   1                1            2021  \n",
       "3                   1                1            2021  \n",
       "4                   1                3            2021  \n",
       "...               ...              ...             ...  \n",
       "181668              1                7            2020  \n",
       "181669              1                7            2020  \n",
       "181670              1                7            2020  \n",
       "181671              1                7            2020  \n",
       "181672              1                7            2020  \n",
       "\n",
       "[150450 rows x 17 columns]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filter the status column values only 7 & 1 rows in a new dataframe ['Won':7 & 'Lost':1]\n",
    "copper_new2= copper_new2[(copper_new2.status == 0) | (copper_new2.status == 1)]\n",
    "copper_new2\n",
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "3818f38e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "status\n",
       "1    116012\n",
       "0     34438\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "copper_new2[\"status\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "0f0c8bab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['customer', 'country', 'status', 'item type', 'application', 'width',\n",
       "       'product_ref', 'delivery date', 'item_date_new', 'delivery date_new',\n",
       "       'quantity tons_log', 'thickness_log', 'selling_price_log',\n",
       "       'Date_difference', 'item_date_day', 'item_date_month',\n",
       "       'item_date_year'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "copper_new2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "deff9a94",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import  RandomForestClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import confusion_matrix, classification_report, roc_curve, auc\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "bcc1f17f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.01256427, -0.71016603,  0.76983752, ...,  0.        ,\n",
       "         0.37675436,  0.69841068],\n",
       "       [-0.00763246, -0.83267579,  0.76983752, ...,  0.        ,\n",
       "        -0.50012565,  0.69841068],\n",
       "       [-0.01281229, -0.62849286,  1.72299563, ...,  0.        ,\n",
       "        -1.37700566,  0.69841068],\n",
       "       ...,\n",
       "       [-0.00785288, -0.83267579,  0.76983752, ...,  0.        ,\n",
       "         0.37675436, -1.43104552],\n",
       "       [-0.00785288, -0.83267579,  0.76983752, ...,  0.        ,\n",
       "         0.37675436, -1.43104552],\n",
       "       [-0.00785288, -0.83267579,  0.76983752, ...,  0.        ,\n",
       "         0.37675436, -1.43104552]])"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = copper_new2[['customer', 'country', 'item type', 'application', 'width',\n",
    "       'product_ref',\n",
    "       'quantity tons_log', 'thickness_log','selling_price_log',\n",
    "        'item_date_day', 'item_date_month',\n",
    "       'item_date_year']]\n",
    "y = copper_new2[['status']]\n",
    "\n",
    "\n",
    "encode = StandardScaler()\n",
    "encode.fit_transform(X)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "bda0d5b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((150450, 12), (150450, 1))"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape,y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "0aeee8e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.7717264872050515, 0.7685942173479561)"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the accuracy of training and testing using metric  LogisticRegression alogorithm \n",
    "\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size = 0.2 ,random_state = 42)\n",
    "\n",
    "Log =LogisticRegression()\n",
    "\n",
    "\n",
    "\n",
    "Log.fit(X_train,y_train)\n",
    "\n",
    "y_pred_train = Log.predict(X_train)\n",
    "y_pred_test= Log.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_train,accuracy_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "8e23611e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "120360"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "19a235cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30090"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "a416bfe4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.8151213027583915, 0.810601528747092)"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the accuracy of training and testing using metric  DecisionTreeClassifie alogorithm \n",
    "\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size = 0.2 ,random_state = 42)\n",
    "\n",
    "DTC = DecisionTreeClassifier()\n",
    "\n",
    "DTC = DecisionTreeClassifier(criterion = 'gini',max_depth=5)\n",
    "\n",
    "DTC.fit(X_train,y_train)\n",
    "\n",
    "y_pred_train = DTC.predict(X_train)\n",
    "y_pred_test= DTC.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_train,accuracy_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "87796897",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.999950149551346, 0.9480890661349285)"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the accuracy of training and testing using metric  RandomForestClassifier alogorithm \n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size = 0.2 ,random_state = 42)\n",
    "\n",
    "RFR =RandomForestClassifier()\n",
    "\n",
    "RFR =RandomForestClassifier(n_estimators = 50 , random_state = 0)\n",
    "\n",
    "RFR.fit(X_train,y_train)\n",
    "\n",
    "y_pred_train = RFR.predict(X_train)\n",
    "y_pred_test= RFR.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_train,accuracy_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "6ad85242",
   "metadata": {},
   "outputs": [],
   "source": [
    "# DecisionTreeClassifierTreesClassifier and RandomForestClassifier both have good testing accuracy, but in training accuracy is overfitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "499aa4bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9910518444666002, 0.9445330674642738)"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# GridsearchCV is a cross validation function\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size = 0.2 ,random_state = 42)\n",
    "\n",
    "model = RandomForestClassifier(max_depth=20, max_features='sqrt', min_samples_leaf=1, min_samples_split=2)\n",
    "model.fit(X_train,y_train)\n",
    "y_pred_train = model.predict(X_train)\n",
    "y_pred_test = model.predict(X_test)\n",
    "accuracy_train = metrics.accuracy_score(y_train, y_pred_train)\n",
    "accuracy_test = metrics.accuracy_score(y_test, y_pred_test)\n",
    "accuracy_train, accuracy_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "f22fdb63",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# now the training accuracy overfitting reduced. so now model will predict effectively for unseen data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "b8a3a19a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 5899  1064]\n",
      " [  668 22459]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.85      0.87      6963\n",
      "           1       0.95      0.97      0.96     23127\n",
      "\n",
      "    accuracy                           0.94     30090\n",
      "   macro avg       0.93      0.91      0.92     30090\n",
      "weighted avg       0.94      0.94      0.94     30090\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# predict the status and check the accuracy using metrics\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size = 0.2 ,random_state = 42)\n",
    "\n",
    "model = RandomForestClassifier(max_depth=20, max_features='sqrt', min_samples_leaf=1, min_samples_split=2)\n",
    "model.fit(X_train,y_train)\n",
    "\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "print(confusion_matrix(y_true=y_test, y_pred=y_pred))\n",
    "print(classification_report(y_true=y_test, y_pred=y_pred))\n",
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "432e4193",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9091541568783962\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Receiver Operating Characteristic (ROC) Curve and Area Under the Curve (AUC)\n",
    "\n",
    "FP,TP,threshold = roc_curve(y_true=y_test, y_score=y_pred)\n",
    "auc_curve = auc(x=FP, y=TP)\n",
    "print(auc_curve)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "b8f17a99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.plot(FP, TP, label=f\"ROC Curve (area={round(auc_curve, 2)}) \")\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.10])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()\n",
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "732eb082",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Won\n"
     ]
    }
   ],
   "source": [
    "#manually passed the user input and predict the status\n",
    "\n",
    "user_data = np.array([[30156308.0,25.0,5.0,10.0,1500.0,167079877841,3.991779,0.693147,6.749931,1,4,2021]])\n",
    "y_p = model.predict(user_data)\n",
    "if y_p[0] == 1:\n",
    "    print('Won')\n",
    "else:\n",
    "    print('Lose')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "160ac244",
   "metadata": {},
   "outputs": [],
   "source": [
    "#save the classification model by using pickle\n",
    "\n",
    "import pickle\n",
    "\n",
    "with open(\"C:\\\\Users\\\\User\\\\Downloads\\\\copper_model_class_final.pkl\", 'wb') as f:\n",
    "    pickle.dump(model, f)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "83143d31",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Won\n"
     ]
    }
   ],
   "source": [
    "# load pickle model to predict the status (like Trained brain).\n",
    "\n",
    "with open(\"C:\\\\Users\\\\User\\\\Downloads\\\\copper_model_class_final.pkl\", 'rb') as f:\n",
    "    model = pickle.load(f)\n",
    "\n",
    "user_data = np.array([[30156308.0,25.0,5.0,10.0,1500.0,167079877841,3.991779,0.693147,6.749931,1,4,2021]])\n",
    "y_p = model.predict(user_data)\n",
    "if y_p[0] == 1:\n",
    "    print('Won')\n",
    "else:\n",
    "    print('Lose')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "394eb7c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb8a0f65",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "179834aa",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "284bba60",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3caf663e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2ae6d41e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7572654e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35919320",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}