industrial_copper.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "37d4cdfd",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "\n",
    "## <center> INDUSTRIAL COPPER MODELLING"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4b3d566",
   "metadata": {},
   "source": [
    "### IMPORTING MODULE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a21c6abf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from xgboost import XGBRegressor\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler,LabelEncoder\n",
    "from sklearn.svm import SVC\n",
    "import xgboost as xgb\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9925a7ed",
   "metadata": {},
   "source": [
    "Reading file path using pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "93cf2f16",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_excel(r\"C:\\Users\\sridh\\Desktop\\Guvi Project\\Capstone project\\Industrial copper 5\\Copper_Set.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6dfda653",
   "metadata": {},
   "outputs": [
    {
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       "      <td>768.024839</td>\n",
       "      <td>386.127949</td>\n",
       "      <td>202.411065</td>\n",
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       "      <td>102.482422</td>\n",
       "      <td>208.086469</td>\n",
       "      <td>4.235594</td>\n",
       "      <td>-2000</td>\n",
       "      <td>406.686538</td>\n",
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       "      <td>Won</td>\n",
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       "      <td>Won</td>\n",
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       "      <td>Won</td>\n",
       "      <td>Won</td>\n",
       "      <td>Won</td>\n",
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       "      <td>Won</td>\n",
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       "    <tr>\n",
       "      <th>application</th>\n",
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       "    <tr>\n",
       "      <th>thickness</th>\n",
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       "      <td>0.96</td>\n",
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       "    <tr>\n",
       "      <th>width</th>\n",
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       "      <td>1240.0</td>\n",
       "      <td>1220.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>1250.0</td>\n",
       "      <td>1250.0</td>\n",
       "      <td>1240.0</td>\n",
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       "    <tr>\n",
       "      <th>material_ref</th>\n",
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       "    <tr>\n",
       "      <th>delivery date</th>\n",
       "      <td>20210701.0</td>\n",
       "      <td>20210401.0</td>\n",
       "      <td>20210101.0</td>\n",
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       "      <th>selling_price</th>\n",
       "      <td>854.0</td>\n",
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       "<p>14 rows × 181673 columns</p>\n",
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       "                                             0       \\\n",
       "id             EC06F063-9DF0-440C-8764-0B0C05A4F6AE   \n",
       "item_date                                20210401.0   \n",
       "quantity tons                             54.151139   \n",
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       "status                                          Won   \n",
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       "material_ref                            DEQ1 S460MC   \n",
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       "selling_price                                 854.0   \n",
       "\n",
       "                                                 1       \\\n",
       "id                 4E5F4B3D-DDDF-499D-AFDE-A3227EC49425   \n",
       "item_date                                    20210401.0   \n",
       "quantity tons                                768.024839   \n",
       "customer                                     30202938.0   \n",
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       "status                                              Won   \n",
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       "\n",
       "                                             2       \\\n",
       "id             E140FF1B-2407-4C02-A0DD-780A093B1158   \n",
       "item_date                                20210401.0   \n",
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       "status                                          Won   \n",
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       "\n",
       "                                             3       \\\n",
       "id             F8D507A0-9C62-4EFE-831E-33E1DA53BB50   \n",
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       "material_ref                DX51D+ZM310MAO 2.3X1317   \n",
       "product_ref                              1668701718   \n",
       "delivery date                            20210101.0   \n",
       "selling_price                                 768.0   \n",
       "\n",
       "                                             4       \\\n",
       "id             4E1C4E78-152B-430A-8094-ADD889C9D0AD   \n",
       "item_date                                20210401.0   \n",
       "quantity tons                            785.526262   \n",
       "customer                                 30211560.0   \n",
       "country                                        28.0   \n",
       "status                                          Won   \n",
       "item type                                         W   \n",
       "application                                    10.0   \n",
       "thickness                                       4.0   \n",
       "width                                        2000.0   \n",
       "material_ref                        2_S275JR+AR-CL1   \n",
       "product_ref                                  640665   \n",
       "delivery date                            20210301.0   \n",
       "selling_price                                 577.0   \n",
       "\n",
       "                                                 5       \\\n",
       "id                 FE372230-29B1-4A29-BB73-CB9D3078C286   \n",
       "item_date                                    20210401.0   \n",
       "quantity tons                                225.790676   \n",
       "customer                                     30202938.0   \n",
       "country                                            25.0   \n",
       "status                                              Won   \n",
       "item type                                             W   \n",
       "application                                        41.0   \n",
       "thickness                                           1.5   \n",
       "width                                            1265.0   \n",
       "material_ref   0000000000000000000000000000000000102325   \n",
       "product_ref                                  1668701718   \n",
       "delivery date                                20210401.0   \n",
       "selling_price                                     983.0   \n",
       "\n",
       "                                             6       \\\n",
       "id             E1B47219-42C8-411D-AE46-AD5256DC4757   \n",
       "item_date                                20210401.0   \n",
       "quantity tons                              3.522613   \n",
       "customer                                 30209509.0   \n",
       "country                                        30.0   \n",
       "status                                        Draft   \n",
       "item type                                         W   \n",
       "application                                    41.0   \n",
       "thickness                                      0.38   \n",
       "width                                        1125.0   \n",
       "material_ref                      NBW_L+_A_1125_0.4   \n",
       "product_ref                                  611993   \n",
       "delivery date                            20210701.0   \n",
       "selling_price                                  18.0   \n",
       "\n",
       "                                                 7       \\\n",
       "id                 AA3F18A2-A7D6-43DE-8644-F0632AE2CB68   \n",
       "item_date                                    20210401.0   \n",
       "quantity tons                                113.387124   \n",
       "customer                                     30342192.0   \n",
       "country                                            32.0   \n",
       "status                                              Won   \n",
       "item type                                             W   \n",
       "application                                        41.0   \n",
       "thickness                                          0.68   \n",
       "width                                            1220.0   \n",
       "material_ref   0000000000000000000000000000000001000944   \n",
       "product_ref                                      611993   \n",
       "delivery date                                20210101.0   \n",
       "selling_price                                     931.0   \n",
       "\n",
       "                                                 8       \\\n",
       "id                 2851F48B-DA32-4DF3-884B-17A3100DD65A   \n",
       "item_date                                    20210401.0   \n",
       "quantity tons                                 69.071853   \n",
       "customer                                     30341428.0   \n",
       "country                                            38.0   \n",
       "status                                              Won   \n",
       "item type                                             S   \n",
       "application                                        10.0   \n",
       "thickness                                           0.6   \n",
       "width                                            1275.0   \n",
       "material_ref   0000000000000000000000000000000000106786   \n",
       "product_ref                                  1668701376   \n",
       "delivery date                                20210701.0   \n",
       "selling_price                                    1363.0   \n",
       "\n",
       "                                                 9       ...  \\\n",
       "id                 6E4D638C-A820-4A40-836F-C9D48BC65528  ...   \n",
       "item_date                                    20210401.0  ...   \n",
       "quantity tons                                630.626917  ...   \n",
       "customer                                     30202938.0  ...   \n",
       "country                                            25.0  ...   \n",
       "status                                              Won  ...   \n",
       "item type                                             W  ...   \n",
       "application                                        41.0  ...   \n",
       "thickness                                           1.5  ...   \n",
       "width                                            1165.0  ...   \n",
       "material_ref   0000000000000000000000000000000000102325  ...   \n",
       "product_ref                                  1668701718  ...   \n",
       "delivery date                                20210401.0  ...   \n",
       "selling_price                                     983.0  ...   \n",
       "\n",
       "                                                 181663  \\\n",
       "id                 08F8F1D6-0DAC-42B5-BA8A-B6C99EF1700B   \n",
       "item_date                                    20200702.0   \n",
       "quantity tons                               1203.157826   \n",
       "customer                                     30200854.0   \n",
       "country                                            25.0   \n",
       "status                                              Won   \n",
       "item type                                             W   \n",
       "application                                        41.0   \n",
       "thickness                                          0.72   \n",
       "width                                            1240.0   \n",
       "material_ref   0000000000000000000000000000000001000856   \n",
       "product_ref                                   164141591   \n",
       "delivery date                                20200701.0   \n",
       "selling_price                                     622.0   \n",
       "\n",
       "                                                 181664  \\\n",
       "id                 A33557CD-93EF-4828-8235-E0A576C1A925   \n",
       "item_date                                    20200702.0   \n",
       "quantity tons                                 603.11494   \n",
       "customer                                     30200854.0   \n",
       "country                                            25.0   \n",
       "status                                              Won   \n",
       "item type                                             W   \n",
       "application                                        41.0   \n",
       "thickness                                          0.85   \n",
       "width                                            1240.0   \n",
       "material_ref   0000000000000000000000000000000001000221   \n",
       "product_ref                                   164141591   \n",
       "delivery date                                20200701.0   \n",
       "selling_price                                     609.0   \n",
       "\n",
       "                                                 181665  \\\n",
       "id                 F9B79E56-2EBF-417B-9802-7F575D1079F7   \n",
       "item_date                                    20200702.0   \n",
       "quantity tons                                  6.169226   \n",
       "customer                                     30200854.0   \n",
       "country                                            25.0   \n",
       "status                                              Won   \n",
       "item type                                             W   \n",
       "application                                        41.0   \n",
       "thickness                                          0.71   \n",
       "width                                            1300.0   \n",
       "material_ref   0000000000000000000000000000000001000224   \n",
       "product_ref                                   164141591   \n",
       "delivery date                                20200701.0   \n",
       "selling_price                                     608.0   \n",
       "\n",
       "                                                 181666  \\\n",
       "id                 C01DE4E8-BC5F-42AD-B35F-8FCBEE34C6ED   \n",
       "item_date                                    20200702.0   \n",
       "quantity tons                                 82.123794   \n",
       "customer                                     30200854.0   \n",
       "country                                            25.0   \n",
       "status                                              Won   \n",
       "item type                                             W   \n",
       "application                                        41.0   \n",
       "thickness                                          1.18   \n",
       "width                                            1220.0   \n",
       "material_ref   0000000000000000000000000000000001000812   \n",
       "product_ref                                   164141591   \n",
       "delivery date                                20200701.0   \n",
       "selling_price                                     590.0   \n",
       "\n",
       "                                                 181667  \\\n",
       "id                 0B812591-B501-4F34-859A-B06381FAEB44   \n",
       "item_date                                    20200702.0   \n",
       "quantity tons                                106.783299   \n",
       "customer                                     30200854.0   \n",
       "country                                            25.0   \n",
       "status                                              Won   \n",
       "item type                                             W   \n",
       "application                                        41.0   \n",
       "thickness                                          0.96   \n",
       "width                                            1240.0   \n",
       "material_ref   0000000000000000000000000000000001000222   \n",
       "product_ref                                   164141591   \n",
       "delivery date                                20200701.0   \n",
       "selling_price                                     602.0   \n",
       "\n",
       "                                                 181668  \\\n",
       "id                 DE633116-D1DF-4846-982E-55EFC3658A76   \n",
       "item_date                                    20200702.0   \n",
       "quantity tons                                102.482422   \n",
       "customer                                     30200854.0   \n",
       "country                                            25.0   \n",
       "status                                              Won   \n",
       "item type                                             W   \n",
       "application                                        41.0   \n",
       "thickness                                          0.96   \n",
       "width                                            1220.0   \n",
       "material_ref   0000000000000000000000000000000001000777   \n",
       "product_ref                                   164141591   \n",
       "delivery date                                20200701.0   \n",
       "selling_price                                     591.0   \n",
       "\n",
       "                                                 181669  \\\n",
       "id                 A48374B1-E6DB-45F2-889A-1F9C27C099EB   \n",
       "item_date                                    20200702.0   \n",
       "quantity tons                                208.086469   \n",
       "customer                                     30200854.0   \n",
       "country                                            25.0   \n",
       "status                                              Won   \n",
       "item type                                             W   \n",
       "application                                        41.0   \n",
       "thickness                                          0.95   \n",
       "width                                            1500.0   \n",
       "material_ref   0000000000000000000000000000000001000227   \n",
       "product_ref                                   164141591   \n",
       "delivery date                                20200701.0   \n",
       "selling_price                                     589.0   \n",
       "\n",
       "                                                 181670  \\\n",
       "id                 91643238-5C7B-4237-9A5F-63AE3D35F320   \n",
       "item_date                                    20200702.0   \n",
       "quantity tons                                  4.235594   \n",
       "customer                                     30200854.0   \n",
       "country                                            25.0   \n",
       "status                                              Won   \n",
       "item type                                             W   \n",
       "application                                        41.0   \n",
       "thickness                                          0.71   \n",
       "width                                            1250.0   \n",
       "material_ref   0000000000000000000000000000000001004216   \n",
       "product_ref                                   164141591   \n",
       "delivery date                                20200701.0   \n",
       "selling_price                                     619.0   \n",
       "\n",
       "                                                 181671  \\\n",
       "id                 7AFFD323-01D9-4E15-B80D-7D1B03498FC8   \n",
       "item_date                                    20200702.0   \n",
       "quantity tons                                     -2000   \n",
       "customer                                     30200854.0   \n",
       "country                                            25.0   \n",
       "status                                              Won   \n",
       "item type                                             W   \n",
       "application                                        41.0   \n",
       "thickness                                          0.85   \n",
       "width                                            1250.0   \n",
       "material_ref   0000000000000000000000000000000001001149   \n",
       "product_ref                                   164141591   \n",
       "delivery date                                20200701.0   \n",
       "selling_price                                     601.0   \n",
       "\n",
       "                                                 181672  \n",
       "id                 AD0CA853-AE3C-4B2F-9FBB-8B0B965F84BC  \n",
       "item_date                                    20200702.0  \n",
       "quantity tons                                406.686538  \n",
       "customer                                     30200854.0  \n",
       "country                                            25.0  \n",
       "status                                              Won  \n",
       "item type                                             W  \n",
       "application                                        41.0  \n",
       "thickness                                          0.71  \n",
       "width                                            1240.0  \n",
       "material_ref   0000000000000000000000000000000001005439  \n",
       "product_ref                                   164141591  \n",
       "delivery date                                20200701.0  \n",
       "selling_price                                     607.0  \n",
       "\n",
       "[14 rows x 181673 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=df.copy(True)\n",
    "data.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2185c10c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 181673 entries, 0 to 181672\n",
      "Data columns (total 14 columns):\n",
      " #   Column         Non-Null Count   Dtype  \n",
      "---  ------         --------------   -----  \n",
      " 0   id             181671 non-null  object \n",
      " 1   item_date      181672 non-null  float64\n",
      " 2   quantity tons  181673 non-null  object \n",
      " 3   customer       181672 non-null  float64\n",
      " 4   country        181645 non-null  float64\n",
      " 5   status         181671 non-null  object \n",
      " 6   item type      181673 non-null  object \n",
      " 7   application    181649 non-null  float64\n",
      " 8   thickness      181672 non-null  float64\n",
      " 9   width          181673 non-null  float64\n",
      " 10  material_ref   103754 non-null  object \n",
      " 11  product_ref    181673 non-null  int64  \n",
      " 12  delivery date  181672 non-null  float64\n",
      " 13  selling_price  181672 non-null  float64\n",
      "dtypes: float64(8), int64(1), object(5)\n",
      "memory usage: 19.4+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6f57a53d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                   2\n",
       "item_date            1\n",
       "quantity tons        0\n",
       "customer             1\n",
       "country             28\n",
       "status               2\n",
       "item type            0\n",
       "application         24\n",
       "thickness            1\n",
       "width                0\n",
       "material_ref     77919\n",
       "product_ref          0\n",
       "delivery date        1\n",
       "selling_price        1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4cd2bc45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(181673, 14)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7bc6f55e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>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>20200929.0</td>\n",
       "      <td>20201130.0</td>\n",
       "      <td>2.021020e+07</td>\n",
       "      <td>2.021040e+07</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>30196884.0</td>\n",
       "      <td>30205242.0</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>26.0</td>\n",
       "      <td>30.0</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>10.0</td>\n",
       "      <td>15.0</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>0.7</td>\n",
       "      <td>1.5</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>1180.0</td>\n",
       "      <td>1250.0</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>611993.0</td>\n",
       "      <td>640665.0</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>20201101.0</td>\n",
       "      <td>20210101.0</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>669.0</td>\n",
       "      <td>812.0</td>\n",
       "      <td>9.530000e+02</td>\n",
       "      <td>1.000010e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  count          mean           std          min         25%  \\\n",
       "item_date      181672.0  2.020459e+07  4.551119e+03  19950000.00  20200929.0   \n",
       "customer       181672.0  3.051221e+07  2.433382e+07     12458.00  30196884.0   \n",
       "country        181645.0  4.489302e+01  2.440421e+01        25.00        26.0   \n",
       "application    181649.0  2.561581e+01  1.775417e+01         2.00        10.0   \n",
       "thickness      181672.0  2.564827e+00  6.572321e+00         0.18         0.7   \n",
       "width          181673.0  1.295287e+03  2.616318e+02         1.00      1180.0   \n",
       "product_ref    181673.0  4.739679e+08  7.175101e+08    611728.00    611993.0   \n",
       "delivery date  181672.0  2.020738e+07  2.411059e+04  20190401.00  20201101.0   \n",
       "selling_price  181672.0  1.918036e+03  3.317956e+05     -1160.00       669.0   \n",
       "\n",
       "                      50%           75%           max  \n",
       "item_date      20201130.0  2.021020e+07  2.021040e+07  \n",
       "customer       30205242.0  3.028042e+07  2.147484e+09  \n",
       "country              30.0  7.800000e+01  1.130000e+02  \n",
       "application          15.0  4.100000e+01  9.900000e+01  \n",
       "thickness             1.5  3.000000e+00  2.500000e+03  \n",
       "width              1250.0  1.500000e+03  2.990000e+03  \n",
       "product_ref      640665.0  1.332077e+09  1.722208e+09  \n",
       "delivery date  20210101.0  2.021040e+07  3.031010e+07  \n",
       "selling_price       812.0  9.530000e+02  1.000010e+08  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe(exclude=object).T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8265c08",
   "metadata": {},
   "source": [
    "## CONVERTING DATAFRAME <br>\n",
    "- NUMERIC\n",
    "- DATETIME\n",
    "- CATEGORY\n",
    "\n",
    "## DATA PREPROCESSING <BR>\n",
    "- Handling missing values\n",
    "- Detecting and Treating outliers / skewness\n",
    "- Normalizing data\n",
    "- Feature selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "da51b927",
   "metadata": {},
   "outputs": [],
   "source": [
    "columns_to_convert_numeric = [\"quantity tons\", \"customer\", \"country\", \"application\", \"thickness\", \"width\", \"selling_price\"]\n",
    "data[columns_to_convert_numeric] = data[columns_to_convert_numeric].apply(pd.to_numeric, errors=\"coerce\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "15cb78d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "columns_to_convert_datetime = ['item_date', 'delivery date']\n",
    "for i in columns_to_convert_datetime:\n",
    "    data[i] = pd.to_datetime(data[i], format='%Y%m%d', errors='coerce').dt.date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9430e0ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.loc[data['material_ref'].str.contains('0{10,}', na=False), 'material_ref'] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2f3c3d15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                    2\n",
       "item_date             3\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",
       "material_ref     100610\n",
       "product_ref           0\n",
       "delivery date         3\n",
       "selling_price         1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "18f945a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "data=data.drop(['id'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "45fdf4da",
   "metadata": {},
   "outputs": [],
   "source": [
    "columns_fill_mean = [\"quantity tons\" , \"thickness\", \"width\", \"selling_price\"]\n",
    "column_means = data[columns_fill_mean].mean()\n",
    "data[columns_fill_mean] = data[columns_fill_mean].fillna(column_means)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1cad9f76",
   "metadata": {},
   "outputs": [],
   "source": [
    "columns_fill_mode = ['item_date', 'status', 'delivery date', 'customer', 'country', 'item type', 'application', 'material_ref', 'product_ref']\n",
    "columns_modes = data[columns_fill_mode].mode().iloc[0]\n",
    "data[columns_fill_mode] = data[columns_fill_mode].fillna(columns_modes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bb3e9799",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "item_date        0\n",
       "quantity tons    0\n",
       "customer         0\n",
       "country          0\n",
       "status           0\n",
       "item type        0\n",
       "application      0\n",
       "thickness        0\n",
       "width            0\n",
       "material_ref     0\n",
       "product_ref      0\n",
       "delivery date    0\n",
       "selling_price    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f1163eb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['country'] = data['country'].astype(np.int64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "92600d0a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 181673 entries, 0 to 181672\n",
      "Data columns (total 13 columns):\n",
      " #   Column         Non-Null Count   Dtype  \n",
      "---  ------         --------------   -----  \n",
      " 0   item_date      181673 non-null  object \n",
      " 1   quantity tons  181673 non-null  float64\n",
      " 2   customer       181673 non-null  float64\n",
      " 3   country        181673 non-null  int64  \n",
      " 4   status         181673 non-null  object \n",
      " 5   item type      181673 non-null  object \n",
      " 6   application    181673 non-null  float64\n",
      " 7   thickness      181673 non-null  float64\n",
      " 8   width          181673 non-null  float64\n",
      " 9   material_ref   181673 non-null  object \n",
      " 10  product_ref    181673 non-null  int64  \n",
      " 11  delivery date  181673 non-null  object \n",
      " 12  selling_price  181673 non-null  float64\n",
      "dtypes: float64(6), int64(2), object(5)\n",
      "memory usage: 18.0+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8358f6e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(181673, 13)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "093cab2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
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       "    .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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>181663</th>\n",
       "      <th>181664</th>\n",
       "      <th>181665</th>\n",
       "      <th>181666</th>\n",
       "      <th>181667</th>\n",
       "      <th>181668</th>\n",
       "      <th>181669</th>\n",
       "      <th>181670</th>\n",
       "      <th>181671</th>\n",
       "      <th>181672</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>item_date</th>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>...</td>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>2020-07-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>quantity tons</th>\n",
       "      <td>54.151139</td>\n",
       "      <td>768.024839</td>\n",
       "      <td>386.127949</td>\n",
       "      <td>202.411065</td>\n",
       "      <td>785.526262</td>\n",
       "      <td>225.790676</td>\n",
       "      <td>3.522613</td>\n",
       "      <td>113.387124</td>\n",
       "      <td>69.071853</td>\n",
       "      <td>630.626917</td>\n",
       "      <td>...</td>\n",
       "      <td>1203.157826</td>\n",
       "      <td>603.11494</td>\n",
       "      <td>6.169226</td>\n",
       "      <td>82.123794</td>\n",
       "      <td>106.783299</td>\n",
       "      <td>102.482422</td>\n",
       "      <td>208.086469</td>\n",
       "      <td>4.235594</td>\n",
       "      <td>-2000.0</td>\n",
       "      <td>406.686538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer</th>\n",
       "      <td>30156308.0</td>\n",
       "      <td>30202938.0</td>\n",
       "      <td>30153963.0</td>\n",
       "      <td>30349574.0</td>\n",
       "      <td>30211560.0</td>\n",
       "      <td>30202938.0</td>\n",
       "      <td>30209509.0</td>\n",
       "      <td>30342192.0</td>\n",
       "      <td>30341428.0</td>\n",
       "      <td>30202938.0</td>\n",
       "      <td>...</td>\n",
       "      <td>30200854.0</td>\n",
       "      <td>30200854.0</td>\n",
       "      <td>30200854.0</td>\n",
       "      <td>30200854.0</td>\n",
       "      <td>30200854.0</td>\n",
       "      <td>30200854.0</td>\n",
       "      <td>30200854.0</td>\n",
       "      <td>30200854.0</td>\n",
       "      <td>30200854.0</td>\n",
       "      <td>30200854.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <td>28</td>\n",
       "      <td>25</td>\n",
       "      <td>30</td>\n",
       "      <td>32</td>\n",
       "      <td>28</td>\n",
       "      <td>25</td>\n",
       "      <td>30</td>\n",
       "      <td>32</td>\n",
       "      <td>38</td>\n",
       "      <td>25</td>\n",
       "      <td>...</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>status</th>\n",
       "      <td>Won</td>\n",
       "      <td>Won</td>\n",
       "      <td>Won</td>\n",
       "      <td>Won</td>\n",
       "      <td>Won</td>\n",
       "      <td>Won</td>\n",
       "      <td>Draft</td>\n",
       "      <td>Won</td>\n",
       "      <td>Won</td>\n",
       "      <td>Won</td>\n",
       "      <td>...</td>\n",
       "      <td>Won</td>\n",
       "      <td>Won</td>\n",
       "      <td>Won</td>\n",
       "      <td>Won</td>\n",
       "      <td>Won</td>\n",
       "      <td>Won</td>\n",
       "      <td>Won</td>\n",
       "      <td>Won</td>\n",
       "      <td>Won</td>\n",
       "      <td>Won</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>item type</th>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "      <td>WI</td>\n",
       "      <td>S</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "      <td>S</td>\n",
       "      <td>W</td>\n",
       "      <td>...</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>application</th>\n",
       "      <td>10.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>...</td>\n",
       "      <td>41.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thickness</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.38</td>\n",
       "      <td>2.3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.71</td>\n",
       "      <td>1.18</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>width</th>\n",
       "      <td>1500.0</td>\n",
       "      <td>1210.0</td>\n",
       "      <td>952.0</td>\n",
       "      <td>1317.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>1265.0</td>\n",
       "      <td>1125.0</td>\n",
       "      <td>1220.0</td>\n",
       "      <td>1275.0</td>\n",
       "      <td>1165.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1240.0</td>\n",
       "      <td>1240.0</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>1220.0</td>\n",
       "      <td>1240.0</td>\n",
       "      <td>1220.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>1250.0</td>\n",
       "      <td>1250.0</td>\n",
       "      <td>1240.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>material_ref</th>\n",
       "      <td>DEQ1 S460MC</td>\n",
       "      <td>DX51D+Z</td>\n",
       "      <td>S0380700</td>\n",
       "      <td>DX51D+ZM310MAO 2.3X1317</td>\n",
       "      <td>2_S275JR+AR-CL1</td>\n",
       "      <td>DX51D+Z</td>\n",
       "      <td>NBW_L+_A_1125_0.4</td>\n",
       "      <td>DX51D+Z</td>\n",
       "      <td>DX51D+Z</td>\n",
       "      <td>DX51D+Z</td>\n",
       "      <td>...</td>\n",
       "      <td>DX51D+Z</td>\n",
       "      <td>DX51D+Z</td>\n",
       "      <td>DX51D+Z</td>\n",
       "      <td>DX51D+Z</td>\n",
       "      <td>DX51D+Z</td>\n",
       "      <td>DX51D+Z</td>\n",
       "      <td>DX51D+Z</td>\n",
       "      <td>DX51D+Z</td>\n",
       "      <td>DX51D+Z</td>\n",
       "      <td>DX51D+Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>product_ref</th>\n",
       "      <td>1670798778</td>\n",
       "      <td>1668701718</td>\n",
       "      <td>628377</td>\n",
       "      <td>1668701718</td>\n",
       "      <td>640665</td>\n",
       "      <td>1668701718</td>\n",
       "      <td>611993</td>\n",
       "      <td>611993</td>\n",
       "      <td>1668701376</td>\n",
       "      <td>1668701718</td>\n",
       "      <td>...</td>\n",
       "      <td>164141591</td>\n",
       "      <td>164141591</td>\n",
       "      <td>164141591</td>\n",
       "      <td>164141591</td>\n",
       "      <td>164141591</td>\n",
       "      <td>164141591</td>\n",
       "      <td>164141591</td>\n",
       "      <td>164141591</td>\n",
       "      <td>164141591</td>\n",
       "      <td>164141591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>delivery date</th>\n",
       "      <td>2021-07-01</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>2021-03-01</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>2021-07-01</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>2021-07-01</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>...</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>2020-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>selling_price</th>\n",
       "      <td>854.0</td>\n",
       "      <td>1047.0</td>\n",
       "      <td>644.33</td>\n",
       "      <td>768.0</td>\n",
       "      <td>577.0</td>\n",
       "      <td>983.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>931.0</td>\n",
       "      <td>1363.0</td>\n",
       "      <td>983.0</td>\n",
       "      <td>...</td>\n",
       "      <td>622.0</td>\n",
       "      <td>609.0</td>\n",
       "      <td>608.0</td>\n",
       "      <td>590.0</td>\n",
       "      <td>602.0</td>\n",
       "      <td>591.0</td>\n",
       "      <td>589.0</td>\n",
       "      <td>619.0</td>\n",
       "      <td>601.0</td>\n",
       "      <td>607.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13 rows × 181673 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    0           1           2                        3       \\\n",
       "item_date       2021-04-01  2021-04-01  2021-04-01               2021-04-01   \n",
       "quantity tons    54.151139  768.024839  386.127949               202.411065   \n",
       "customer        30156308.0  30202938.0  30153963.0               30349574.0   \n",
       "country                 28          25          30                       32   \n",
       "status                 Won         Won         Won                      Won   \n",
       "item type                W           W          WI                        S   \n",
       "application           10.0        41.0        28.0                     59.0   \n",
       "thickness              2.0         0.8        0.38                      2.3   \n",
       "width               1500.0      1210.0       952.0                   1317.0   \n",
       "material_ref   DEQ1 S460MC     DX51D+Z    S0380700  DX51D+ZM310MAO 2.3X1317   \n",
       "product_ref     1670798778  1668701718      628377               1668701718   \n",
       "delivery date   2021-07-01  2021-04-01  2021-01-01               2021-01-01   \n",
       "selling_price        854.0      1047.0      644.33                    768.0   \n",
       "\n",
       "                        4           5                  6           7       \\\n",
       "item_date           2021-04-01  2021-04-01         2021-04-01  2021-04-01   \n",
       "quantity tons       785.526262  225.790676           3.522613  113.387124   \n",
       "customer            30211560.0  30202938.0         30209509.0  30342192.0   \n",
       "country                     28          25                 30          32   \n",
       "status                     Won         Won              Draft         Won   \n",
       "item type                    W           W                  W           W   \n",
       "application               10.0        41.0               41.0        41.0   \n",
       "thickness                  4.0         1.5               0.38        0.68   \n",
       "width                   2000.0      1265.0             1125.0      1220.0   \n",
       "material_ref   2_S275JR+AR-CL1     DX51D+Z  NBW_L+_A_1125_0.4     DX51D+Z   \n",
       "product_ref             640665  1668701718             611993      611993   \n",
       "delivery date       2021-03-01  2021-04-01         2021-07-01  2021-01-01   \n",
       "selling_price            577.0       983.0               18.0       931.0   \n",
       "\n",
       "                   8           9       ...       181663      181664  \\\n",
       "item_date      2021-04-01  2021-04-01  ...   2020-07-02  2020-07-02   \n",
       "quantity tons   69.071853  630.626917  ...  1203.157826   603.11494   \n",
       "customer       30341428.0  30202938.0  ...   30200854.0  30200854.0   \n",
       "country                38          25  ...           25          25   \n",
       "status                Won         Won  ...          Won         Won   \n",
       "item type               S           W  ...            W           W   \n",
       "application          10.0        41.0  ...         41.0        41.0   \n",
       "thickness             0.6         1.5  ...         0.72        0.85   \n",
       "width              1275.0      1165.0  ...       1240.0      1240.0   \n",
       "material_ref      DX51D+Z     DX51D+Z  ...      DX51D+Z     DX51D+Z   \n",
       "product_ref    1668701376  1668701718  ...    164141591   164141591   \n",
       "delivery date  2021-07-01  2021-04-01  ...   2020-07-01  2020-07-01   \n",
       "selling_price      1363.0       983.0  ...        622.0       609.0   \n",
       "\n",
       "                   181665      181666      181667      181668      181669  \\\n",
       "item_date      2020-07-02  2020-07-02  2020-07-02  2020-07-02  2020-07-02   \n",
       "quantity tons    6.169226   82.123794  106.783299  102.482422  208.086469   \n",
       "customer       30200854.0  30200854.0  30200854.0  30200854.0  30200854.0   \n",
       "country                25          25          25          25          25   \n",
       "status                Won         Won         Won         Won         Won   \n",
       "item type               W           W           W           W           W   \n",
       "application          41.0        41.0        41.0        41.0        41.0   \n",
       "thickness            0.71        1.18        0.96        0.96        0.95   \n",
       "width              1300.0      1220.0      1240.0      1220.0      1500.0   \n",
       "material_ref      DX51D+Z     DX51D+Z     DX51D+Z     DX51D+Z     DX51D+Z   \n",
       "product_ref     164141591   164141591   164141591   164141591   164141591   \n",
       "delivery date  2020-07-01  2020-07-01  2020-07-01  2020-07-01  2020-07-01   \n",
       "selling_price       608.0       590.0       602.0       591.0       589.0   \n",
       "\n",
       "                   181670      181671      181672  \n",
       "item_date      2020-07-02  2020-07-02  2020-07-02  \n",
       "quantity tons    4.235594     -2000.0  406.686538  \n",
       "customer       30200854.0  30200854.0  30200854.0  \n",
       "country                25          25          25  \n",
       "status                Won         Won         Won  \n",
       "item type               W           W           W  \n",
       "application          41.0        41.0        41.0  \n",
       "thickness            0.71        0.85        0.71  \n",
       "width              1250.0      1250.0      1240.0  \n",
       "material_ref      DX51D+Z     DX51D+Z     DX51D+Z  \n",
       "product_ref     164141591   164141591   164141591  \n",
       "delivery date  2020-07-01  2020-07-01  2020-07-01  \n",
       "selling_price       619.0       601.0       607.0  \n",
       "\n",
       "[13 rows x 181673 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1cee841",
   "metadata": {},
   "source": [
    "## EXPLORATORY DATA ANALYSIS \n",
    "\n",
    "TO VISUALIZE OUTLIERS AND SKEWNESS"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1759a782",
   "metadata": {},
   "source": [
    "#### BEFORE TREATING OUTLIERS AND  SKEWNESS "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4e2ed7c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "eda_data=data.copy(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c30f7e42",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x2000 with 14 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a figure with 6 subplots\n",
    "fig, axes = plt.subplots(7, 2, figsize=(10,20))\n",
    " \n",
    "sns.boxplot(ax=axes[0, 0], data=eda_data['quantity tons'])      #quantity tons\n",
    "axes[0, 0].set_title('quantity tons - Outliers')\n",
    "sns.distplot(ax=axes[0, 1], a=eda_data['quantity tons'])\n",
    "axes[0, 1].set_title('quantity tons - Skewness')\n",
    " \n",
    "sns.boxplot(ax=axes[1, 0], data=eda_data['customer'])          #customer\n",
    "axes[1, 0].set_title('customer - Outliers')\n",
    "sns.distplot(ax=axes[1, 1], a=eda_data['customer'])\n",
    "axes[1, 1].set_title('customer - Skewness')\n",
    "\n",
    "sns.boxplot(ax=axes[2, 0], data=eda_data['country'])           #country\n",
    "axes[2, 0].set_title('country - Outliers')\n",
    "sns.distplot(ax=axes[2, 1], a=eda_data['country'])\n",
    "axes[2, 1].set_title('country - Skewness')\n",
    "\n",
    "sns.boxplot(ax=axes[3, 0], data=eda_data['application'])       #application\n",
    "axes[3, 0].set_title('application - Outliers')\n",
    "sns.distplot(ax=axes[3, 1], a=eda_data['application'])\n",
    "axes[3, 1].set_title('application - Skewness')\n",
    "\n",
    "sns.boxplot(ax=axes[4, 0], data=eda_data['thickness'])        #thickness\n",
    "axes[4, 0].set_title('thickness - Outliers')\n",
    "sns.distplot(ax=axes[4, 1], a=eda_data['thickness'])\n",
    "axes[4, 1].set_title('thickness - Skewness')\n",
    "\n",
    "sns.boxplot(ax=axes[5, 0], data=eda_data['width'])            #width\n",
    "axes[5, 0].set_title('width - Outliers')\n",
    "sns.distplot(ax=axes[5, 1], a=eda_data['width'])\n",
    "axes[5, 1].set_title('width - Skewness')\n",
    "\n",
    "sns.boxplot(ax=axes[6, 0], data=eda_data['selling_price'])   #selling_price\n",
    "axes[6, 0].set_title('selling_price - Outliers')\n",
    "sns.distplot(ax=axes[6, 1], a=eda_data['selling_price'])\n",
    "axes[6, 1].set_title('selling_price - Skewness')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9d2d6305",
   "metadata": {},
   "outputs": [],
   "source": [
    "out_skew_data=eda_data.copy(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "19627e77",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "quantity tons : 4 \n",
      "thickness : 0 \n",
      "width : 0 \n",
      "selling_price : 7 \n"
     ]
    }
   ],
   "source": [
    "skewed_columns = ['quantity tons', 'thickness', 'width', 'selling_price']\n",
    "for column in skewed_columns:\n",
    "    neg = (out_skew_data[column] <= 0)\n",
    "    print(f'{column} : {neg.sum()} ')\n",
    "    out_skew_data.loc[neg,column] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "621a2a51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "item_date        0\n",
       "quantity tons    4\n",
       "customer         0\n",
       "country          0\n",
       "status           0\n",
       "item type        0\n",
       "application      0\n",
       "thickness        0\n",
       "width            0\n",
       "material_ref     0\n",
       "product_ref      0\n",
       "delivery date    0\n",
       "selling_price    7\n",
       "dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out_skew_data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0d943742",
   "metadata": {},
   "outputs": [],
   "source": [
    "out_skew_data.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2974b706",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "item_date        0\n",
       "quantity tons    0\n",
       "customer         0\n",
       "country          0\n",
       "status           0\n",
       "item type        0\n",
       "application      0\n",
       "thickness        0\n",
       "width            0\n",
       "material_ref     0\n",
       "product_ref      0\n",
       "delivery date    0\n",
       "selling_price    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out_skew_data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "21c83936",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "quantity tons    424.677959\n",
       "customer          86.982674\n",
       "country            0.753023\n",
       "application        0.724614\n",
       "thickness         15.291371\n",
       "width              0.374564\n",
       "product_ref        1.015276\n",
       "selling_price    301.378964\n",
       "dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out_skew_data.skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e5c8829e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary statistics before removing outliers:\n",
      "       quantity tons      thickness          width  selling_price\n",
      "count   1.816620e+05  181662.000000  181662.000000   1.816620e+05\n",
      "mean    5.875291e+03       2.551133    1295.289042   1.918156e+03\n",
      "std     2.349146e+06       2.977116     261.638547   3.318048e+05\n",
      "min     1.000000e-05       0.180000       1.000000   1.000000e-01\n",
      "25%     1.097088e+01       0.700000    1180.000000   6.690000e+02\n",
      "50%     3.036501e+01       1.500000    1250.000000   8.120000e+02\n",
      "75%     6.716141e+01       3.000000    1500.000000   9.530000e+02\n",
      "max     1.000000e+09     400.000000    2990.000000   1.000010e+08\n",
      "\n",
      "Summary statistics after removing outliers:\n",
      "       quantity tons      thickness          width  selling_price\n",
      "count  132631.000000  132631.000000  132631.000000  132631.000000\n",
      "mean       35.586278       1.803089    1265.155654     841.050729\n",
      "std        31.885531       1.441616     174.815790     189.197046\n",
      "min         0.000010       0.180000     855.000000     396.000000\n",
      "25%         9.564901       0.700000    1180.000000     692.000000\n",
      "50%        27.650488       1.250000    1250.000000     832.000000\n",
      "75%        51.948853       2.500000    1410.000000     971.000000\n",
      "max       144.399648       6.300000    1810.000000    1365.000000\n"
     ]
    }
   ],
   "source": [
    "outlier_columns = ['quantity tons', 'thickness', 'width', 'selling_price']\n",
    "before_stats = out_skew_data[outlier_columns].describe()\n",
    "\n",
    "# Iterate over outlier columns and remove outliers\n",
    "for column in outlier_columns:\n",
    "    Q1 = out_skew_data[column].quantile(0.30)\n",
    "    Q3 = out_skew_data[column].quantile(0.75)\n",
    "    IQR = Q3 - Q1\n",
    "    lower_bound = Q1 - 1.5 * IQR\n",
    "    upper_bound = Q3 + 1.5 * IQR\n",
    "    out_skew_data = out_skew_data[(out_skew_data[column] >= lower_bound) & (out_skew_data[column] <= upper_bound)]\n",
    "\n",
    "# Calculate summary statistics after removing outliers\n",
    "after_stats = out_skew_data[outlier_columns].describe()\n",
    "\n",
    "# Compare the statistics before and after outlier removal\n",
    "print(\"Summary statistics before removing outliers:\")\n",
    "print(before_stats)\n",
    "print(\"\\nSummary statistics after removing outliers:\")\n",
    "print(after_stats)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f879be2",
   "metadata": {},
   "source": [
    "#### AFTER TREATING OUTLIERS AND SKEWNESS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "91460efb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1800x1500 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_skew_data.plot(kind='box',subplots=True,layout=(4,4),figsize=(18,15))\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0552a0ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "outlier_columns = ['quantity tons', 'thickness', 'width', 'selling_price']\n",
    "\n",
    "# Plot the histograms of the transformed data\n",
    "fig, axes = plt.subplots(1, 4,figsize=(15, 5))\n",
    "axes = axes.flatten()\n",
    "for i, column in enumerate(outlier_columns):\n",
    "    sns.distplot(out_skew_data[column], kde=True, ax=axes[i])\n",
    "    axes[i].set_title(f\"Transformed {column}\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8a50ad7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "quantity tons      1.239223\n",
       "customer         210.220811\n",
       "country            0.700979\n",
       "application        0.684824\n",
       "thickness          1.276129\n",
       "width              0.057992\n",
       "product_ref        0.983758\n",
       "selling_price      0.310072\n",
       "dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out_skew_data.skew()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db56c63e",
   "metadata": {},
   "source": [
    "Here are some guidelines for interpreting skewness values:   <br>\n",
    "\n",
    "Skewness value close to 0: The distribution is approximately symmetrical.<br>\n",
    "Skewness value between -0.5 and -1 or between 0.5 and 1: The distribution is moderately skewed.<br>\n",
    "Skewness value less than -1 or greater than 1: The distribution is highly skewed.<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e3cf84d",
   "metadata": {},
   "source": [
    "### FEATURE ENGINEERING\n",
    "- Lable encoding\n",
    "- Feature selection\n",
    "- Standardize / normalize data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8d75ebd9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 132631 entries, 0 to 181670\n",
      "Data columns (total 13 columns):\n",
      " #   Column         Non-Null Count   Dtype  \n",
      "---  ------         --------------   -----  \n",
      " 0   item_date      132631 non-null  object \n",
      " 1   quantity tons  132631 non-null  float64\n",
      " 2   customer       132631 non-null  float64\n",
      " 3   country        132631 non-null  int64  \n",
      " 4   status         132631 non-null  object \n",
      " 5   item type      132631 non-null  object \n",
      " 6   application    132631 non-null  float64\n",
      " 7   thickness      132631 non-null  float64\n",
      " 8   width          132631 non-null  float64\n",
      " 9   material_ref   132631 non-null  object \n",
      " 10  product_ref    132631 non-null  int64  \n",
      " 11  delivery date  132631 non-null  object \n",
      " 12  selling_price  132631 non-null  float64\n",
      "dtypes: float64(6), int64(2), object(5)\n",
      "memory usage: 14.2+ MB\n"
     ]
    }
   ],
   "source": [
    "model_data=out_skew_data.copy(True)\n",
    "model_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3ef874a2",
   "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>material_ref</th>\n",
       "      <th>product_ref</th>\n",
       "      <th>delivery date</th>\n",
       "      <th>selling_price</th>\n",
       "      <th>delivery_time_taken</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>54.151139</td>\n",
       "      <td>30156308.0</td>\n",
       "      <td>28</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>2021-07-01</td>\n",
       "      <td>854.0</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>113.387124</td>\n",
       "      <td>30342192.0</td>\n",
       "      <td>32</td>\n",
       "      <td>Won</td>\n",
       "      <td>W</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.68</td>\n",
       "      <td>1220.0</td>\n",
       "      <td>DX51D+Z</td>\n",
       "      <td>611993</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>931.0</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>69.071853</td>\n",
       "      <td>30341428.0</td>\n",
       "      <td>38</td>\n",
       "      <td>Won</td>\n",
       "      <td>S</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.60</td>\n",
       "      <td>1275.0</td>\n",
       "      <td>DX51D+Z</td>\n",
       "      <td>1668701376</td>\n",
       "      <td>2021-07-01</td>\n",
       "      <td>1363.0</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>113.995666</td>\n",
       "      <td>30342192.0</td>\n",
       "      <td>32</td>\n",
       "      <td>Won</td>\n",
       "      <td>W</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.55</td>\n",
       "      <td>1220.0</td>\n",
       "      <td>DX51D+Z</td>\n",
       "      <td>611993</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>954.0</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>27.512545</td>\n",
       "      <td>30165529.0</td>\n",
       "      <td>78</td>\n",
       "      <td>Won</td>\n",
       "      <td>W</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1250.0</td>\n",
       "      <td>DX51D+Z</td>\n",
       "      <td>164141591</td>\n",
       "      <td>2021-07-01</td>\n",
       "      <td>1098.0</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     item_date  quantity tons    customer  country status item type  \\\n",
       "0   2021-04-01      54.151139  30156308.0       28    Won         W   \n",
       "7   2021-04-01     113.387124  30342192.0       32    Won         W   \n",
       "8   2021-04-01      69.071853  30341428.0       38    Won         S   \n",
       "11  2021-04-01     113.995666  30342192.0       32    Won         W   \n",
       "12  2021-04-01      27.512545  30165529.0       78    Won         W   \n",
       "\n",
       "    application  thickness   width material_ref  product_ref delivery date  \\\n",
       "0          10.0       2.00  1500.0  DEQ1 S460MC   1670798778    2021-07-01   \n",
       "7          41.0       0.68  1220.0      DX51D+Z       611993    2021-01-01   \n",
       "8          10.0       0.60  1275.0      DX51D+Z   1668701376    2021-07-01   \n",
       "11         41.0       0.55  1220.0      DX51D+Z       611993    2021-01-01   \n",
       "12         10.0       0.75  1250.0      DX51D+Z    164141591    2021-07-01   \n",
       "\n",
       "    selling_price  delivery_time_taken  \n",
       "0           854.0                   91  \n",
       "7           931.0                   90  \n",
       "8          1363.0                   91  \n",
       "11          954.0                   90  \n",
       "12         1098.0                   91  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_data['delivery_time_taken']=(model_data['item_date']-model_data['delivery date']).abs().dt.days\n",
    "model_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a548872c",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=model_data[['application','thickness','width','selling_price','quantity tons','country','customer','product_ref','delivery_time_taken']].corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d075f63e",
   "metadata": {},
   "source": [
    "#### HEATMAP FOR FIND THE CORRELATON BETWEEN THE DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c6a7e640",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(x, annot=True, cmap=\"viridis\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa5c5eab",
   "metadata": {},
   "source": [
    "#### FINDING UNIQUE VALUE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "0379a927",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['28' '32' '38' '78' '27' '30' '25' '77' '39' '40' '26' '84' '80' '79'\n",
      " '113' '89']\n",
      "['Won' 'To be approved' 'Lost' 'Not lost for AM' 'Wonderful' 'Revised'\n",
      " 'Offered' 'Offerable']\n",
      "['W' 'S' 'Others' 'PL' 'WI' 'IPL']\n"
     ]
    }
   ],
   "source": [
    "model_data['country'] = model_data['country'].astype(str)\n",
    "country=model_data['country'].unique()\n",
    "status=model_data['status'].unique()\n",
    "item=model_data['item type'].unique()\n",
    "print(country)\n",
    "print(status)\n",
    "print(item)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26878f99",
   "metadata": {},
   "source": [
    "#### ENCODE THE CATEGORICAL COLUMN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "0ab75df1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 4  6  7 11  3  5  1 10  8  9  2 14 13 12  0 15]\n",
      "[6 5 0 1 7 4 3 2]\n",
      "[4 3 1 2 5 0]\n"
     ]
    }
   ],
   "source": [
    "encode=LabelEncoder()\n",
    "encode_country=encode.fit(country)\n",
    "transformed_country = encode_country.transform(country)\n",
    "\n",
    "encode=LabelEncoder()\n",
    "encode_status=encode.fit(status)\n",
    "transformed_status = encode_status.transform(status)\n",
    "\n",
    "encode=LabelEncoder()\n",
    "encode_item=encode.fit(item)\n",
    "transformed_item = encode_item.transform(item)\n",
    "\n",
    "print(transformed_country)\n",
    "print(transformed_status)\n",
    "print(transformed_item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "800c64ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "before encoding : Won --- encoded : 6\n",
      "before encoding : To be approved --- encoded : 5\n",
      "before encoding : Lost --- encoded : 0\n",
      "before encoding : Not lost for AM --- encoded : 1\n",
      "before encoding : Wonderful --- encoded : 7\n",
      "before encoding : Revised --- encoded : 4\n",
      "before encoding : Offered --- encoded : 3\n",
      "before encoding : Offerable --- encoded : 2\n"
     ]
    }
   ],
   "source": [
    "for i, j in zip(status, transformed_status):\n",
    "    print('before encoding :',i,'--- encoded :',j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "deb6dd8e",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pickle file created: country.pkl, status.pkl,item type.pkl\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "\n",
    "file_path='country.pkl'\n",
    "file_path2='status.pkl'\n",
    "file_path3='item type.pkl'\n",
    "\n",
    "with open(file_path, 'wb') as file:\n",
    "    pickle.dump(encode_country, file)\n",
    "with open(file_path2, 'wb') as file:\n",
    "    pickle.dump(encode_status, file)\n",
    "with open(file_path3, 'wb') as file:\n",
    "    pickle.dump(encode_item, file)\n",
    "\n",
    "print(f'Pickle file created: {file_path}, {file_path2},{file_path3}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "e9da6295",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6, 5, 0, 1, 7, 4, 3, 2])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open('status.pkl', 'rb') as file:\n",
    "    encode_status = pickle.load(file)\n",
    "    \n",
    "transformed_status = encode_status.transform(status)\n",
    "transformed_status"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f74b81e0",
   "metadata": {},
   "source": [
    "## REGRESSION MODEL FOR FINDING SELLING PRICE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "bcd0def6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "status\n",
      "[6 5 0 1 7 4 3 2]\n",
      "item type\n",
      "[4 3 1 2 5 0]\n",
      "country\n",
      "[ 4  6  7 11  3  5  1 10  8  9  2 14 13 12  0 15]\n"
     ]
    }
   ],
   "source": [
    "category_col=['status', 'item type','country']\n",
    "encoder= LabelEncoder()\n",
    "for i in category_col:\n",
    "    print(i)\n",
    "    model_data[i] = encoder.fit_transform(model_data[i])\n",
    "    print(model_data[i].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "9fc4e474",
   "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>material_ref</th>\n",
       "      <th>product_ref</th>\n",
       "      <th>delivery date</th>\n",
       "      <th>selling_price</th>\n",
       "      <th>delivery_time_taken</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>54.151139</td>\n",
       "      <td>30156308.0</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>4</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>2021-07-01</td>\n",
       "      <td>854.0</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>113.387124</td>\n",
       "      <td>30342192.0</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.68</td>\n",
       "      <td>1220.0</td>\n",
       "      <td>DX51D+Z</td>\n",
       "      <td>611993</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>931.0</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>69.071853</td>\n",
       "      <td>30341428.0</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.60</td>\n",
       "      <td>1275.0</td>\n",
       "      <td>DX51D+Z</td>\n",
       "      <td>1668701376</td>\n",
       "      <td>2021-07-01</td>\n",
       "      <td>1363.0</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>113.995666</td>\n",
       "      <td>30342192.0</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.55</td>\n",
       "      <td>1220.0</td>\n",
       "      <td>DX51D+Z</td>\n",
       "      <td>611993</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>954.0</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>27.512545</td>\n",
       "      <td>30165529.0</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1250.0</td>\n",
       "      <td>DX51D+Z</td>\n",
       "      <td>164141591</td>\n",
       "      <td>2021-07-01</td>\n",
       "      <td>1098.0</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     item_date  quantity tons    customer  country  status  item type  \\\n",
       "0   2021-04-01      54.151139  30156308.0        4       6          4   \n",
       "7   2021-04-01     113.387124  30342192.0        6       6          4   \n",
       "8   2021-04-01      69.071853  30341428.0        7       6          3   \n",
       "11  2021-04-01     113.995666  30342192.0        6       6          4   \n",
       "12  2021-04-01      27.512545  30165529.0       11       6          4   \n",
       "\n",
       "    application  thickness   width material_ref  product_ref delivery date  \\\n",
       "0          10.0       2.00  1500.0  DEQ1 S460MC   1670798778    2021-07-01   \n",
       "7          41.0       0.68  1220.0      DX51D+Z       611993    2021-01-01   \n",
       "8          10.0       0.60  1275.0      DX51D+Z   1668701376    2021-07-01   \n",
       "11         41.0       0.55  1220.0      DX51D+Z       611993    2021-01-01   \n",
       "12         10.0       0.75  1250.0      DX51D+Z    164141591    2021-07-01   \n",
       "\n",
       "    selling_price  delivery_time_taken  \n",
       "0           854.0                   91  \n",
       "7           931.0                   90  \n",
       "8          1363.0                   91  \n",
       "11          954.0                   90  \n",
       "12         1098.0                   91  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "f035b30f",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = model_data[[ 'quantity tons','thickness','width','country','status','item type', 'application','product_ref','delivery_time_taken']].values\n",
    "y = model_data[['selling_price']].values\n",
    "\n",
    "scaler = StandardScaler().fit(X)\n",
    "X = scaler.transform(X)\n",
    "\n",
    "# Split the dataset into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "dfba1a82",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pickle file created: scaling.pkl\n"
     ]
    }
   ],
   "source": [
    "file_path6='scaling.pkl'\n",
    "with open(file_path6, 'wb') as file:\n",
    "    pickle.dump(scaler, file)\n",
    "print(f'Pickle file created: {file_path6}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "4bf78a5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create an instance of each regression model\n",
    "et_model = ExtraTreesRegressor()\n",
    "xgb_model = XGBRegressor()\n",
    "dt_model = DecisionTreeRegressor()\n",
    "\n",
    "# Train the models\n",
    "et_model.fit(X_train, y_train)\n",
    "xgb_model.fit(X_train, y_train)\n",
    "dt_model.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions on the test set\n",
    "et_preds = et_model.predict(X_test)\n",
    "xgb_preds = xgb_model.predict(X_test)\n",
    "dt_preds = dt_model.predict(X_test)\n",
    "\n",
    "# Evaluate the models\n",
    "\n",
    "et_mse = mean_squared_error(y_test, et_preds)\n",
    "et_mae = mean_absolute_error(y_test, et_preds)\n",
    "et_r2 = r2_score(y_test, et_preds)\n",
    "\n",
    "xgb_mse = mean_squared_error(y_test, xgb_preds)\n",
    "xgb_mae = mean_absolute_error(y_test, xgb_preds)\n",
    "xgb_r2 = r2_score(y_test, xgb_preds)\n",
    "\n",
    "dt_mse = mean_squared_error(y_test, dt_preds)\n",
    "dt_mae = mean_absolute_error(y_test, dt_preds)\n",
    "dt_r2 = r2_score(y_test, dt_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "79a69778",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FINDING BEST FIT MODEL FOR EVALUATION BASED ON R_2 SCORE\n",
      "\n",
      "ExtraTreesRegressor 0.8710370466652599\n",
      "DecisionTreeRegressor 0.7728700351386455\n",
      "XGBRegressor 0.7816785387753782\n",
      "\n",
      "Model: ExtraTreesRegressor contain Maximum R2_Score: 0.8710370466652599 \n"
     ]
    }
   ],
   "source": [
    "models = ['ExtraTreesRegressor', 'DecisionTreeRegressor', 'XGBRegressor']\n",
    "score = [et_r2, dt_r2, xgb_r2]\n",
    "\n",
    "plt.barh(models, score, color='red',hatch='//')\n",
    "plt.xlabel('Models')\n",
    "plt.ylabel('Score')\n",
    "plt.title('R_2 of Regression Models')\n",
    "plt.show()\n",
    "\n",
    "print(\"FINDING BEST FIT MODEL FOR EVALUATION BASED ON R_2 SCORE\\n\")\n",
    "max_score = max(score)  # Initialize max_score with the maximum value in the score list\n",
    "max_model = \"\"\n",
    "\n",
    "for model, score_value in zip(models, score):\n",
    "    print(model, score_value)\n",
    "    if score_value == max_score:\n",
    "        max_model = model\n",
    "\n",
    "print(f\"\\nModel: {max_model} contain Maximum R2_Score: {max_score} \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "a8425637",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=et_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "99e23ff1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pickle file created: Extratreeregressor.pkl\n"
     ]
    }
   ],
   "source": [
    "file_path7='Extratreeregressor.pkl'\n",
    "with open(file_path7, 'wb') as file:\n",
    "    pickle.dump(model, file)\n",
    "print(f'Pickle file created: {file_path7}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "649babf4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter quantity :6987532\n",
      "Enter thickness :0.65\n",
      "Enter width :1500.36\n",
      "Enter a country: 28\n",
      "Enter a status: Won\n",
      "Enter an item type: W\n",
      "Predicted selling price: [835.7388]\n"
     ]
    }
   ],
   "source": [
    "quantity=input('Enter quantity :')\n",
    "thickness=input('Enter thickness :')\n",
    "width=input('Enter width :')\n",
    "\n",
    "new_sample_co = input(\"Enter a country: \")\n",
    "c = None\n",
    "for i, j in zip(country, transformed_country):\n",
    "    if new_sample_co == i:\n",
    "        c = j\n",
    "        break\n",
    "else:\n",
    "    print(\"Country not found.\")\n",
    "    exit()\n",
    "\n",
    "new_sample_st = input(\"Enter a status: \")\n",
    "s = None\n",
    "for i, j in zip(status, transformed_status):\n",
    "    if new_sample_st == i:\n",
    "        s = j\n",
    "        break\n",
    "else:\n",
    "    print(\"Status not found.\")\n",
    "    exit()\n",
    "\n",
    "new_sample_it = input(\"Enter an item type: \")\n",
    "it = None\n",
    "for i, j in zip(item, transformed_item):\n",
    "    if new_sample_it == i:\n",
    "        it = j\n",
    "        break\n",
    "else:\n",
    "    print(\"Item type not found.\")\n",
    "    exit()\n",
    "\n",
    "\n",
    "x = [quantity,thickness,width,c, s, it, 41.0,611993, 28]\n",
    "x = np.array(x).reshape(1, -1)  # Reshape to 2D array\n",
    "new_sample_scaled = scaler.transform(x)\n",
    "new_pred = et_model.predict(new_sample_scaled)\n",
    "print('Predicted selling price:',new_pred )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e3fab6e",
   "metadata": {},
   "source": [
    "## CLASSIFICATION MODEL FOR PREDICT THE STATUS WON/LOST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "4f81438f",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = model_data[[ 'quantity tons','thickness','width','selling_price','country','item type','application','product_ref','delivery_time_taken']].values\n",
    "y = model_data[['status']].values\n",
    "\n",
    "scaler_classify = StandardScaler().fit(X)\n",
    "X = scaler_classify.transform(X)\n",
    "\n",
    "# Split the dataset into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "fcae5632",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pickle file created: scaling_classify.pkl\n"
     ]
    }
   ],
   "source": [
    "file_path8='scaling_classify.pkl'\n",
    "with open(file_path8, 'wb') as file:\n",
    "    pickle.dump(scaler_classify, file)\n",
    "print(f'Pickle file created: {file_path8}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "76377a1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create an instance of each regression model\n",
    "rf_model = RandomForestClassifier()\n",
    "xgb_cls_model = xgb.XGBClassifier()\n",
    "svm_model = SVC()\n",
    "\n",
    "# Train the models\n",
    "rf_model.fit(X_train, y_train)\n",
    "xgb_cls_model.fit(X_train, y_train)\n",
    "svm_model.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions on the test set\n",
    "rf_preds = rf_model.predict(X_test)\n",
    "xgb_cls_preds = xgb_cls_model.predict(X_test)\n",
    "svm_preds = svm_model.predict(X_test)\n",
    "\n",
    "# Evaluate the models\n",
    "rf_accuracy = accuracy_score(y_test, rf_preds)\n",
    "xgb_cls_accuracy = accuracy_score(y_test, xgb_cls_preds)\n",
    "svm_accuracy = accuracy_score(y_test, svm_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "1259c658",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FINDING BEST FIT MODEL FOR EVALUATION BASED ON  ACCURACY\n",
      "\n",
      "RandomForestClassifier 0.914878110077909\n",
      "XGBClassifier 0.8434531289268661\n",
      "SVM 0.7034430761497864\n",
      "\n",
      "Model: RandomForestClassifier contain Maximum accuracy: 0.914878110077909 \n"
     ]
    }
   ],
   "source": [
    "models = ['RandomForestClassifier', 'XGBClassifier', 'SVM']\n",
    "score = [rf_accuracy, xgb_cls_accuracy, svm_accuracy]\n",
    "\n",
    "plt.barh(models, score, color='green',height=0.5,hatch='*',edgecolor='red')\n",
    "plt.xlabel('Models')\n",
    "plt.ylabel('Score')\n",
    "plt.title('Accuracy of Classification Models')\n",
    "plt.show()\n",
    "\n",
    "print(\"FINDING BEST FIT MODEL FOR EVALUATION BASED ON  ACCURACY\\n\")\n",
    "max_score = max(score)  # Initialize max_score with the maximum value in the score list\n",
    "max_model = \"\"\n",
    "\n",
    "for model, score_value in zip(models, score):\n",
    "    print(model, score_value)\n",
    "    if score_value == max_score:\n",
    "        max_model = model\n",
    "\n",
    "print(f\"\\nModel: {max_model} contain Maximum accuracy: {max_score} \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "03c291ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_cls=rf_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "e1790369",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pickle file created: randomforest_classification.pkl\n"
     ]
    }
   ],
   "source": [
    "file_path9='randomforest_classification.pkl'\n",
    "with open(file_path9, 'wb') as file:\n",
    "    pickle.dump(model_cls, file)\n",
    "print(f'Pickle file created: {file_path9}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "55a990c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter quantity :7589654\n",
      "Enter thickness :0.98\n",
      "Enter width :1200.37\n",
      "Enter selling price :632548\n",
      "Enter a country: 28\n",
      "Enter an item type: W\n",
      "WON\n"
     ]
    }
   ],
   "source": [
    "quantity=input('Enter quantity :')\n",
    "thickness=input('Enter thickness :')\n",
    "width=input('Enter width :')\n",
    "selling_price=input('Enter selling price :')\n",
    "\n",
    "new_sample_co = input(\"Enter a country: \")\n",
    "c = None\n",
    "for i, j in zip(country, transformed_country):\n",
    "    if new_sample_co == i:\n",
    "        c = j\n",
    "        break\n",
    "else:\n",
    "    print(\"Country not found.\")\n",
    "    exit()\n",
    "\n",
    "new_sample_it = input(\"Enter an item type: \")\n",
    "it = None\n",
    "for i, j in zip(item, transformed_item):\n",
    "    if new_sample_it == i:\n",
    "        it = j\n",
    "        break\n",
    "else:\n",
    "    print(\"Item type not found.\")\n",
    "    exit()\n",
    "\n",
    "x = [quantity,thickness,width,selling_price,c,it, 10.0,1670798778, 91]\n",
    "x = np.array(x).reshape(1, -1)  # Reshape to 2D array\n",
    "new_sample_scaled = scaler_classify.transform(x)\n",
    "new_pred = rf_model.predict(new_sample_scaled)\n",
    "\n",
    "if new_pred==6:\n",
    "    print('WON' )\n",
    "else:\n",
    "    print('LOST')"
   ]
  }
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