Data_Preprocessing_ICM_ (1).ipynb

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  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EnXZfn2NyP-C"
   },
   "source": [
    "# **Problem Statement [Click](https://docs.google.com/document/d/1nWKUnD6F76F1AtGFEjyfIVH9oxcBUU1N/edit)**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JNbNcQUFwf6c"
   },
   "source": [
    "# **Data Collection :**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "BtimP_BGwckC",
    "outputId": "d24edf5e-9a76-4061-f317-3939fa439340"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DINESH KUMAR\\AppData\\Local\\Temp\\ipykernel_15364\\888342677.py:3: DtypeWarning: Columns (2) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  df = pd.read_csv(\"C:\\\\Users\\\\DINESH KUMAR\\\\Downloads\\\\Copper_Set.csv\")\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(\"C:\\\\Users\\\\DINESH KUMAR\\\\Downloads\\\\Copper_Set.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "79SCzOoSwoAf",
    "outputId": "3556cc4f-ae1e-4a50-a2d8-62282badba98"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 181673 entries, 0 to 181672\n",
      "Data columns (total 14 columns):\n",
      " #   Column         Non-Null Count   Dtype  \n",
      "---  ------         --------------   -----  \n",
      " 0   id             181671 non-null  object \n",
      " 1   item_date      181672 non-null  float64\n",
      " 2   quantity tons  181673 non-null  object \n",
      " 3   customer       181672 non-null  float64\n",
      " 4   country        181645 non-null  float64\n",
      " 5   status         181671 non-null  object \n",
      " 6   item type      181673 non-null  object \n",
      " 7   application    181649 non-null  float64\n",
      " 8   thickness      181672 non-null  float64\n",
      " 9   width          181673 non-null  float64\n",
      " 10  material_ref   103754 non-null  object \n",
      " 11  product_ref    181673 non-null  int64  \n",
      " 12  delivery date  181672 non-null  float64\n",
      " 13  selling_price  181672 non-null  float64\n",
      "dtypes: float64(8), int64(1), object(5)\n",
      "memory usage: 19.4+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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    "id": "UhIebrlRwo5k",
    "outputId": "040b5c4a-8cfd-4027-a426-0fa9d248c627"
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    {
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       "      <th>id</th>\n",
       "      <th>item_date</th>\n",
       "      <th>quantity tons</th>\n",
       "      <th>customer</th>\n",
       "      <th>country</th>\n",
       "      <th>status</th>\n",
       "      <th>item type</th>\n",
       "      <th>application</th>\n",
       "      <th>thickness</th>\n",
       "      <th>width</th>\n",
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       "      <th>selling_price</th>\n",
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       "      <th>0</th>\n",
       "      <td>EC06F063-9DF0-440C-8764-0B0C05A4F6AE</td>\n",
       "      <td>20210401.0</td>\n",
       "      <td>54.151139</td>\n",
       "      <td>30156308.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>Won</td>\n",
       "      <td>W</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>DEQ1 S460MC</td>\n",
       "      <td>1670798778</td>\n",
       "      <td>20210701.0</td>\n",
       "      <td>854.00</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4E5F4B3D-DDDF-499D-AFDE-A3227EC49425</td>\n",
       "      <td>20210401.0</td>\n",
       "      <td>768.024839</td>\n",
       "      <td>30202938.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>Won</td>\n",
       "      <td>W</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1210.0</td>\n",
       "      <td>0000000000000000000000000000000000104991</td>\n",
       "      <td>1668701718</td>\n",
       "      <td>20210401.0</td>\n",
       "      <td>1047.00</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>E140FF1B-2407-4C02-A0DD-780A093B1158</td>\n",
       "      <td>20210401.0</td>\n",
       "      <td>386.127949</td>\n",
       "      <td>30153963.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Won</td>\n",
       "      <td>WI</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.38</td>\n",
       "      <td>952.0</td>\n",
       "      <td>S0380700</td>\n",
       "      <td>628377</td>\n",
       "      <td>20210101.0</td>\n",
       "      <td>644.33</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F8D507A0-9C62-4EFE-831E-33E1DA53BB50</td>\n",
       "      <td>20210401.0</td>\n",
       "      <td>202.411065</td>\n",
       "      <td>30349574.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>Won</td>\n",
       "      <td>S</td>\n",
       "      <td>59.0</td>\n",
       "      <td>2.30</td>\n",
       "      <td>1317.0</td>\n",
       "      <td>DX51D+ZM310MAO 2.3X1317</td>\n",
       "      <td>1668701718</td>\n",
       "      <td>20210101.0</td>\n",
       "      <td>768.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4E1C4E78-152B-430A-8094-ADD889C9D0AD</td>\n",
       "      <td>20210401.0</td>\n",
       "      <td>785.526262</td>\n",
       "      <td>30211560.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>Won</td>\n",
       "      <td>W</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>2_S275JR+AR-CL1</td>\n",
       "      <td>640665</td>\n",
       "      <td>20210301.0</td>\n",
       "      <td>577.00</td>\n",
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       "                                     id   item_date quantity tons    customer  \\\n",
       "0  EC06F063-9DF0-440C-8764-0B0C05A4F6AE  20210401.0     54.151139  30156308.0   \n",
       "1  4E5F4B3D-DDDF-499D-AFDE-A3227EC49425  20210401.0    768.024839  30202938.0   \n",
       "2  E140FF1B-2407-4C02-A0DD-780A093B1158  20210401.0    386.127949  30153963.0   \n",
       "3  F8D507A0-9C62-4EFE-831E-33E1DA53BB50  20210401.0    202.411065  30349574.0   \n",
       "4  4E1C4E78-152B-430A-8094-ADD889C9D0AD  20210401.0    785.526262  30211560.0   \n",
       "\n",
       "   country status item type  application  thickness   width  \\\n",
       "0     28.0    Won         W         10.0       2.00  1500.0   \n",
       "1     25.0    Won         W         41.0       0.80  1210.0   \n",
       "2     30.0    Won        WI         28.0       0.38   952.0   \n",
       "3     32.0    Won         S         59.0       2.30  1317.0   \n",
       "4     28.0    Won         W         10.0       4.00  2000.0   \n",
       "\n",
       "                               material_ref  product_ref  delivery date  \\\n",
       "0                               DEQ1 S460MC   1670798778     20210701.0   \n",
       "1  0000000000000000000000000000000000104991   1668701718     20210401.0   \n",
       "2                                  S0380700       628377     20210101.0   \n",
       "3                   DX51D+ZM310MAO 2.3X1317   1668701718     20210101.0   \n",
       "4                           2_S275JR+AR-CL1       640665     20210301.0   \n",
       "\n",
       "   selling_price  \n",
       "0         854.00  \n",
       "1        1047.00  \n",
       "2         644.33  \n",
       "3         768.00  \n",
       "4         577.00  "
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "jijf-wPEw6tI"
   },
   "outputs": [],
   "source": [
    "copydf = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
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    "id": "ZAKjn3oLw-Ac",
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       "      <td>EC06F063-9DF0-440C-8764-0B0C05A4F6AE</td>\n",
       "      <td>20210401.0</td>\n",
       "      <td>54.151139</td>\n",
       "      <td>30156308.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>Won</td>\n",
       "      <td>W</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>DEQ1 S460MC</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4E5F4B3D-DDDF-499D-AFDE-A3227EC49425</td>\n",
       "      <td>20210401.0</td>\n",
       "      <td>768.024839</td>\n",
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       "      <td>25.0</td>\n",
       "      <td>Won</td>\n",
       "      <td>W</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1210.0</td>\n",
       "      <td>0000000000000000000000000000000000104991</td>\n",
       "      <td>1668701718</td>\n",
       "      <td>20210401.0</td>\n",
       "      <td>1047.00</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>E140FF1B-2407-4C02-A0DD-780A093B1158</td>\n",
       "      <td>20210401.0</td>\n",
       "      <td>386.127949</td>\n",
       "      <td>30153963.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Won</td>\n",
       "      <td>WI</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.38</td>\n",
       "      <td>952.0</td>\n",
       "      <td>S0380700</td>\n",
       "      <td>628377</td>\n",
       "      <td>20210101.0</td>\n",
       "      <td>644.33</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F8D507A0-9C62-4EFE-831E-33E1DA53BB50</td>\n",
       "      <td>20210401.0</td>\n",
       "      <td>202.411065</td>\n",
       "      <td>30349574.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>Won</td>\n",
       "      <td>S</td>\n",
       "      <td>59.0</td>\n",
       "      <td>2.30</td>\n",
       "      <td>1317.0</td>\n",
       "      <td>DX51D+ZM310MAO 2.3X1317</td>\n",
       "      <td>1668701718</td>\n",
       "      <td>20210101.0</td>\n",
       "      <td>768.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4E1C4E78-152B-430A-8094-ADD889C9D0AD</td>\n",
       "      <td>20210401.0</td>\n",
       "      <td>785.526262</td>\n",
       "      <td>30211560.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>Won</td>\n",
       "      <td>W</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>2_S275JR+AR-CL1</td>\n",
       "      <td>640665</td>\n",
       "      <td>20210301.0</td>\n",
       "      <td>577.00</td>\n",
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      "text/plain": [
       "                                     id   item_date quantity tons    customer  \\\n",
       "0  EC06F063-9DF0-440C-8764-0B0C05A4F6AE  20210401.0     54.151139  30156308.0   \n",
       "1  4E5F4B3D-DDDF-499D-AFDE-A3227EC49425  20210401.0    768.024839  30202938.0   \n",
       "2  E140FF1B-2407-4C02-A0DD-780A093B1158  20210401.0    386.127949  30153963.0   \n",
       "3  F8D507A0-9C62-4EFE-831E-33E1DA53BB50  20210401.0    202.411065  30349574.0   \n",
       "4  4E1C4E78-152B-430A-8094-ADD889C9D0AD  20210401.0    785.526262  30211560.0   \n",
       "\n",
       "   country status item type  application  thickness   width  \\\n",
       "0     28.0    Won         W         10.0       2.00  1500.0   \n",
       "1     25.0    Won         W         41.0       0.80  1210.0   \n",
       "2     30.0    Won        WI         28.0       0.38   952.0   \n",
       "3     32.0    Won         S         59.0       2.30  1317.0   \n",
       "4     28.0    Won         W         10.0       4.00  2000.0   \n",
       "\n",
       "                               material_ref  product_ref  delivery date  \\\n",
       "0                               DEQ1 S460MC   1670798778     20210701.0   \n",
       "1  0000000000000000000000000000000000104991   1668701718     20210401.0   \n",
       "2                                  S0380700       628377     20210101.0   \n",
       "3                   DX51D+ZM310MAO 2.3X1317   1668701718     20210101.0   \n",
       "4                           2_S275JR+AR-CL1       640665     20210301.0   \n",
       "\n",
       "   selling_price  \n",
       "0         854.00  \n",
       "1        1047.00  \n",
       "2         644.33  \n",
       "3         768.00  \n",
       "4         577.00  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "copydf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WdNEfsXdxBm9"
   },
   "source": [
    "# **Data Preprocess :**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CTvRZwx2BmOF"
   },
   "source": [
    "**`Object Creation For Ordinal Encoder`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "SneIrj8GBsFI"
   },
   "outputs": [],
   "source": [
    "# Convert Categorical data into continous data\n",
    "\n",
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "\n",
    "encoder = OrdinalEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5rIWNZTIw_V2",
    "outputId": "8849d7f5-9a53-483a-8f15-5e6e65eac6c4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'item_date', 'quantity tons', 'customer', 'country', 'status',\n",
       "       'item type', 'application', 'thickness', 'width', 'material_ref',\n",
       "       'product_ref', 'delivery date', 'selling_price'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "copydf.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "guQqEbcD4G-u"
   },
   "outputs": [],
   "source": [
    "copydf.columns = ['id', 'item_date', 'quantity_tons', 'customer', 'country', 'status',\n",
    "       'item_type', 'application', 'thickness', 'width', 'material_ref',\n",
    "       'product_ref', 'delivery_date', 'selling_price']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZfWR01bnxRCe"
   },
   "source": [
    "**`Missing value Count`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "LnyYpm8exOWH",
    "outputId": "ad0732c6-18bf-4e1d-9740-c01d74f68683"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                0.001101\n",
       "item_date         0.000550\n",
       "quantity_tons     0.000000\n",
       "customer          0.000550\n",
       "country           0.015412\n",
       "status            0.001101\n",
       "item_type         0.000000\n",
       "application       0.013211\n",
       "thickness         0.000550\n",
       "width             0.000000\n",
       "material_ref     42.889697\n",
       "product_ref       0.000000\n",
       "delivery_date     0.000550\n",
       "selling_price     0.000550\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "copydf.isnull().mean()*100  # See Percentage of the missing values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lPC1Fq0_4ntP"
   },
   "source": [
    "**`Before Cleaning Process`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "nUdE6wnFxaUT",
    "outputId": "1adc41aa-5f05-45a9-c82f-94e9262f8864"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                   2\n",
       "item_date            1\n",
       "quantity_tons        0\n",
       "customer             1\n",
       "country             28\n",
       "status               2\n",
       "item_type            0\n",
       "application         24\n",
       "thickness            1\n",
       "width                0\n",
       "material_ref     77919\n",
       "product_ref          0\n",
       "delivery_date        1\n",
       "selling_price        1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "copydf.isnull().sum() # See total missing values for corresponding columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "F1UngfiQxo54",
    "outputId": "364a0ba0-6292-48ca-bdcd-8b394dfd41f0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total missing value : 77980\n"
     ]
    }
   ],
   "source": [
    "print(f'Total missing value : {copydf.isnull().sum().sum()}') # Overall missing value count"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "N_C0LEt04gVZ"
   },
   "source": [
    "**`Column Data Types`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5Zg55sXcxxOB",
    "outputId": "57483bad-8e79-44dc-d914-db2539bddbd8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                object\n",
       "item_date        float64\n",
       "quantity_tons     object\n",
       "customer         float64\n",
       "country          float64\n",
       "status            object\n",
       "item_type         object\n",
       "application      float64\n",
       "thickness        float64\n",
       "width            float64\n",
       "material_ref      object\n",
       "product_ref        int64\n",
       "delivery_date    float64\n",
       "selling_price    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "copydf.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cQa5n66f4t69"
   },
   "source": [
    "**`Data Cleaning :`**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "l5FRF51hyKi5"
   },
   "source": [
    "**Treat missing value for 'id' column**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "NF1czXtXyDCt",
    "outputId": "3e3ad439-2316-4dcb-83f8-2ab6eae69b68"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Missing Value : 0\n"
     ]
    }
   ],
   "source": [
    "copydf['id'] = copydf['id'].apply(lambda x : str(x))    # String conversion\n",
    "\n",
    "copydf['id'].fillna(method='bfill',inplace=True)\n",
    "\n",
    "print('Total Missing Value :',copydf['id'].isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iWeUGV56yndM"
   },
   "source": [
    "**Treat missing value for 'item_date' column**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "CE2R60F_yf3q",
    "outputId": "bd110646-467b-4afc-b85a-6be2eeaf3c3b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Missing Value : 0\n"
     ]
    }
   ],
   "source": [
    "copydf['item_date'].fillna(method='bfill',inplace=True) # Missing value Filled\n",
    "\n",
    "copydf['item_date'] = copydf['item_date'].apply(lambda x : str(float(x))) # String Conversion\n",
    "\n",
    "copydf['item_date'] = copydf['item_date'].apply(lambda x : f'{x[0:4]}-{x[4:6]}-{x[6:8]}')\n",
    "\n",
    "copydf['item_date_day'] = copydf['item_date'].apply(lambda x : x[8:])\n",
    "\n",
    "copydf['item_date'] = copydf['item_date'].apply(lambda x :  pd.to_datetime(f'{x[0:4]}-01-01',format='%Y-%m-%d')  if x[5:7] == '00' or x[8:] == '00' or int(x[5:7]) > 12 else pd.to_datetime(x,format='%Y-%m-%d')) # Date format Conversion\n",
    "\n",
    "copydf['item_date_year'] = pd.to_datetime(copydf['item_date']).dt.year\n",
    "\n",
    "copydf['item_date_month'] = pd.to_datetime(copydf['item_date']).dt.month\n",
    "\n",
    "print('Total Missing Value :',copydf['item_date'].isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qeIQvjQy37hz"
   },
   "source": [
    "**Treat Missing Value for \"quantity_tons\" column**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1NMVwQNY3Mzl",
    "outputId": "a66a3ce5-0a60-4959-b4ea-635eef262f03"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Missing Value : 0\n"
     ]
    }
   ],
   "source": [
    "copydf['quantity_tons'] = copydf['quantity_tons'].apply(lambda x : float('1000.00') if x == 'e' else float(x))\n",
    "\n",
    "print('Total Missing Value :',copydf['quantity_tons'].isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zZNKbd076d65"
   },
   "source": [
    "**'customer' column**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "BgqfStH97Jb9"
   },
   "outputs": [],
   "source": [
    "copydf['customer'] = copydf['customer'].apply(lambda x : str(x))\n",
    "\n",
    "copydf['customer'] = copydf['customer'].apply(lambda x : x.split('.'))\n",
    "\n",
    "copydf['customer'] = copydf['customer'].apply(lambda x : x[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0SHtVghD7KGZ",
    "outputId": "df7db1f4-9314-458a-95a8-74c9f5332bb7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                         object\n",
       "item_date          datetime64[ns]\n",
       "quantity_tons             float64\n",
       "customer                   object\n",
       "country                   float64\n",
       "status                     object\n",
       "item_type                  object\n",
       "application               float64\n",
       "thickness                 float64\n",
       "width                     float64\n",
       "material_ref               object\n",
       "product_ref                 int64\n",
       "delivery_date             float64\n",
       "selling_price             float64\n",
       "item_date_day              object\n",
       "item_date_year              int64\n",
       "item_date_month             int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "copydf.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bkRsWwmL7jmA"
   },
   "source": [
    "**Collecting only 'WON' and 'LOST' Status Feature**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "rADsbmU1546F",
    "outputId": "50246233-5660-4ef4-a655-d8385146f5dd"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>item_date</th>\n",
       "      <th>quantity_tons</th>\n",
       "      <th>customer</th>\n",
       "      <th>country</th>\n",
       "      <th>status</th>\n",
       "      <th>item_type</th>\n",
       "      <th>application</th>\n",
       "      <th>thickness</th>\n",
       "      <th>width</th>\n",
       "      <th>material_ref</th>\n",
       "      <th>product_ref</th>\n",
       "      <th>delivery_date</th>\n",
       "      <th>selling_price</th>\n",
       "      <th>item_date_day</th>\n",
       "      <th>item_date_year</th>\n",
       "      <th>item_date_month</th>\n",
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       "  </thead>\n",
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       "      <td>EC06F063-9DF0-440C-8764-0B0C05A4F6AE</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>54.151139</td>\n",
       "      <td>30156308</td>\n",
       "      <td>28.0</td>\n",
       "      <td>Won</td>\n",
       "      <td>W</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>DEQ1 S460MC</td>\n",
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       "      <td>854.00</td>\n",
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       "      <td>2021</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4E5F4B3D-DDDF-499D-AFDE-A3227EC49425</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>768.024839</td>\n",
       "      <td>30202938</td>\n",
       "      <td>25.0</td>\n",
       "      <td>Won</td>\n",
       "      <td>W</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1210.0</td>\n",
       "      <td>0000000000000000000000000000000000104991</td>\n",
       "      <td>1668701718</td>\n",
       "      <td>20210401.0</td>\n",
       "      <td>1047.00</td>\n",
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       "      <td>2021</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>E140FF1B-2407-4C02-A0DD-780A093B1158</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>386.127949</td>\n",
       "      <td>30153963</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Won</td>\n",
       "      <td>WI</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.38</td>\n",
       "      <td>952.0</td>\n",
       "      <td>S0380700</td>\n",
       "      <td>628377</td>\n",
       "      <td>20210101.0</td>\n",
       "      <td>644.33</td>\n",
       "      <td>01</td>\n",
       "      <td>2021</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F8D507A0-9C62-4EFE-831E-33E1DA53BB50</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>202.411065</td>\n",
       "      <td>30349574</td>\n",
       "      <td>32.0</td>\n",
       "      <td>Won</td>\n",
       "      <td>S</td>\n",
       "      <td>59.0</td>\n",
       "      <td>2.30</td>\n",
       "      <td>1317.0</td>\n",
       "      <td>DX51D+ZM310MAO 2.3X1317</td>\n",
       "      <td>1668701718</td>\n",
       "      <td>20210101.0</td>\n",
       "      <td>768.00</td>\n",
       "      <td>01</td>\n",
       "      <td>2021</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4E1C4E78-152B-430A-8094-ADD889C9D0AD</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>785.526262</td>\n",
       "      <td>30211560</td>\n",
       "      <td>28.0</td>\n",
       "      <td>Won</td>\n",
       "      <td>W</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>2_S275JR+AR-CL1</td>\n",
       "      <td>640665</td>\n",
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       "      <td>577.00</td>\n",
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       "      <th>181668</th>\n",
       "      <td>DE633116-D1DF-4846-982E-55EFC3658A76</td>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>102.482422</td>\n",
       "      <td>30200854</td>\n",
       "      <td>25.0</td>\n",
       "      <td>Won</td>\n",
       "      <td>W</td>\n",
       "      <td>41.0</td>\n",
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       "      <td>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>591.00</td>\n",
       "      <td>02</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>181669</th>\n",
       "      <td>A48374B1-E6DB-45F2-889A-1F9C27C099EB</td>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>208.086469</td>\n",
       "      <td>30200854</td>\n",
       "      <td>25.0</td>\n",
       "      <td>Won</td>\n",
       "      <td>W</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>0000000000000000000000000000000001000227</td>\n",
       "      <td>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>589.00</td>\n",
       "      <td>02</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181670</th>\n",
       "      <td>91643238-5C7B-4237-9A5F-63AE3D35F320</td>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>4.235594</td>\n",
       "      <td>30200854</td>\n",
       "      <td>25.0</td>\n",
       "      <td>Won</td>\n",
       "      <td>W</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.71</td>\n",
       "      <td>1250.0</td>\n",
       "      <td>0000000000000000000000000000000001004216</td>\n",
       "      <td>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>619.00</td>\n",
       "      <td>02</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181671</th>\n",
       "      <td>7AFFD323-01D9-4E15-B80D-7D1B03498FC8</td>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>-2000.000000</td>\n",
       "      <td>30200854</td>\n",
       "      <td>25.0</td>\n",
       "      <td>Won</td>\n",
       "      <td>W</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1250.0</td>\n",
       "      <td>0000000000000000000000000000000001001149</td>\n",
       "      <td>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>601.00</td>\n",
       "      <td>02</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181672</th>\n",
       "      <td>AD0CA853-AE3C-4B2F-9FBB-8B0B965F84BC</td>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>406.686538</td>\n",
       "      <td>30200854</td>\n",
       "      <td>25.0</td>\n",
       "      <td>Won</td>\n",
       "      <td>W</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.71</td>\n",
       "      <td>1240.0</td>\n",
       "      <td>0000000000000000000000000000000001005439</td>\n",
       "      <td>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>607.00</td>\n",
       "      <td>02</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>181673 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          id  item_date  quantity_tons  \\\n",
       "0       EC06F063-9DF0-440C-8764-0B0C05A4F6AE 2021-04-01      54.151139   \n",
       "1       4E5F4B3D-DDDF-499D-AFDE-A3227EC49425 2021-04-01     768.024839   \n",
       "2       E140FF1B-2407-4C02-A0DD-780A093B1158 2021-04-01     386.127949   \n",
       "3       F8D507A0-9C62-4EFE-831E-33E1DA53BB50 2021-04-01     202.411065   \n",
       "4       4E1C4E78-152B-430A-8094-ADD889C9D0AD 2021-04-01     785.526262   \n",
       "...                                      ...        ...            ...   \n",
       "181668  DE633116-D1DF-4846-982E-55EFC3658A76 2020-07-02     102.482422   \n",
       "181669  A48374B1-E6DB-45F2-889A-1F9C27C099EB 2020-07-02     208.086469   \n",
       "181670  91643238-5C7B-4237-9A5F-63AE3D35F320 2020-07-02       4.235594   \n",
       "181671  7AFFD323-01D9-4E15-B80D-7D1B03498FC8 2020-07-02   -2000.000000   \n",
       "181672  AD0CA853-AE3C-4B2F-9FBB-8B0B965F84BC 2020-07-02     406.686538   \n",
       "\n",
       "        customer  country status item_type  application  thickness   width  \\\n",
       "0       30156308     28.0    Won         W         10.0       2.00  1500.0   \n",
       "1       30202938     25.0    Won         W         41.0       0.80  1210.0   \n",
       "2       30153963     30.0    Won        WI         28.0       0.38   952.0   \n",
       "3       30349574     32.0    Won         S         59.0       2.30  1317.0   \n",
       "4       30211560     28.0    Won         W         10.0       4.00  2000.0   \n",
       "...          ...      ...    ...       ...          ...        ...     ...   \n",
       "181668  30200854     25.0    Won         W         41.0       0.96  1220.0   \n",
       "181669  30200854     25.0    Won         W         41.0       0.95  1500.0   \n",
       "181670  30200854     25.0    Won         W         41.0       0.71  1250.0   \n",
       "181671  30200854     25.0    Won         W         41.0       0.85  1250.0   \n",
       "181672  30200854     25.0    Won         W         41.0       0.71  1240.0   \n",
       "\n",
       "                                    material_ref  product_ref  delivery_date  \\\n",
       "0                                    DEQ1 S460MC   1670798778     20210701.0   \n",
       "1       0000000000000000000000000000000000104991   1668701718     20210401.0   \n",
       "2                                       S0380700       628377     20210101.0   \n",
       "3                        DX51D+ZM310MAO 2.3X1317   1668701718     20210101.0   \n",
       "4                                2_S275JR+AR-CL1       640665     20210301.0   \n",
       "...                                          ...          ...            ...   \n",
       "181668  0000000000000000000000000000000001000777    164141591     20200701.0   \n",
       "181669  0000000000000000000000000000000001000227    164141591     20200701.0   \n",
       "181670  0000000000000000000000000000000001004216    164141591     20200701.0   \n",
       "181671  0000000000000000000000000000000001001149    164141591     20200701.0   \n",
       "181672  0000000000000000000000000000000001005439    164141591     20200701.0   \n",
       "\n",
       "        selling_price item_date_day  item_date_year  item_date_month  \n",
       "0              854.00            01            2021                4  \n",
       "1             1047.00            01            2021                4  \n",
       "2              644.33            01            2021                4  \n",
       "3              768.00            01            2021                4  \n",
       "4              577.00            01            2021                4  \n",
       "...               ...           ...             ...              ...  \n",
       "181668         591.00            02            2020                7  \n",
       "181669         589.00            02            2020                7  \n",
       "181670         619.00            02            2020                7  \n",
       "181671         601.00            02            2020                7  \n",
       "181672         607.00            02            2020                7  \n",
       "\n",
       "[181673 rows x 17 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "copydf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "Gw2Cf-QN6nk_"
   },
   "outputs": [],
   "source": [
    "copydf = copydf.drop(['id','material_ref'],axis=1)\n",
    "\n",
    "new_df = copydf.query('status == \"Won\" or status == \"Lost\"')\n",
    "\n",
    "new_df = copydf.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "lFYsPCju_d8K",
    "outputId": "d711c3ac-7496-4279-d8af-500a0bb6eb37"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>item_date</th>\n",
       "      <th>quantity_tons</th>\n",
       "      <th>customer</th>\n",
       "      <th>country</th>\n",
       "      <th>status</th>\n",
       "      <th>item_type</th>\n",
       "      <th>application</th>\n",
       "      <th>thickness</th>\n",
       "      <th>width</th>\n",
       "      <th>product_ref</th>\n",
       "      <th>delivery_date</th>\n",
       "      <th>selling_price</th>\n",
       "      <th>item_date_day</th>\n",
       "      <th>item_date_year</th>\n",
       "      <th>item_date_month</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</td>\n",
       "      <td>28.0</td>\n",
       "      <td>Won</td>\n",
       "      <td>W</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>1670798778</td>\n",
       "      <td>20210701.0</td>\n",
       "      <td>854.00</td>\n",
       "      <td>01</td>\n",
       "      <td>2021</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>768.024839</td>\n",
       "      <td>30202938</td>\n",
       "      <td>25.0</td>\n",
       "      <td>Won</td>\n",
       "      <td>W</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1210.0</td>\n",
       "      <td>1668701718</td>\n",
       "      <td>20210401.0</td>\n",
       "      <td>1047.00</td>\n",
       "      <td>01</td>\n",
       "      <td>2021</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>386.127949</td>\n",
       "      <td>30153963</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Won</td>\n",
       "      <td>WI</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.38</td>\n",
       "      <td>952.0</td>\n",
       "      <td>628377</td>\n",
       "      <td>20210101.0</td>\n",
       "      <td>644.33</td>\n",
       "      <td>01</td>\n",
       "      <td>2021</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>202.411065</td>\n",
       "      <td>30349574</td>\n",
       "      <td>32.0</td>\n",
       "      <td>Won</td>\n",
       "      <td>S</td>\n",
       "      <td>59.0</td>\n",
       "      <td>2.30</td>\n",
       "      <td>1317.0</td>\n",
       "      <td>1668701718</td>\n",
       "      <td>20210101.0</td>\n",
       "      <td>768.00</td>\n",
       "      <td>01</td>\n",
       "      <td>2021</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>785.526262</td>\n",
       "      <td>30211560</td>\n",
       "      <td>28.0</td>\n",
       "      <td>Won</td>\n",
       "      <td>W</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>640665</td>\n",
       "      <td>20210301.0</td>\n",
       "      <td>577.00</td>\n",
       "      <td>01</td>\n",
       "      <td>2021</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181668</th>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>102.482422</td>\n",
       "      <td>30200854</td>\n",
       "      <td>25.0</td>\n",
       "      <td>Won</td>\n",
       "      <td>W</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.96</td>\n",
       "      <td>1220.0</td>\n",
       "      <td>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>591.00</td>\n",
       "      <td>02</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181669</th>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>208.086469</td>\n",
       "      <td>30200854</td>\n",
       "      <td>25.0</td>\n",
       "      <td>Won</td>\n",
       "      <td>W</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>589.00</td>\n",
       "      <td>02</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181670</th>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>4.235594</td>\n",
       "      <td>30200854</td>\n",
       "      <td>25.0</td>\n",
       "      <td>Won</td>\n",
       "      <td>W</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.71</td>\n",
       "      <td>1250.0</td>\n",
       "      <td>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>619.00</td>\n",
       "      <td>02</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181671</th>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>-2000.000000</td>\n",
       "      <td>30200854</td>\n",
       "      <td>25.0</td>\n",
       "      <td>Won</td>\n",
       "      <td>W</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1250.0</td>\n",
       "      <td>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>601.00</td>\n",
       "      <td>02</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181672</th>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>406.686538</td>\n",
       "      <td>30200854</td>\n",
       "      <td>25.0</td>\n",
       "      <td>Won</td>\n",
       "      <td>W</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.71</td>\n",
       "      <td>1240.0</td>\n",
       "      <td>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>607.00</td>\n",
       "      <td>02</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>181673 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        item_date  quantity_tons  customer  country status item_type  \\\n",
       "0      2021-04-01      54.151139  30156308     28.0    Won         W   \n",
       "1      2021-04-01     768.024839  30202938     25.0    Won         W   \n",
       "2      2021-04-01     386.127949  30153963     30.0    Won        WI   \n",
       "3      2021-04-01     202.411065  30349574     32.0    Won         S   \n",
       "4      2021-04-01     785.526262  30211560     28.0    Won         W   \n",
       "...           ...            ...       ...      ...    ...       ...   \n",
       "181668 2020-07-02     102.482422  30200854     25.0    Won         W   \n",
       "181669 2020-07-02     208.086469  30200854     25.0    Won         W   \n",
       "181670 2020-07-02       4.235594  30200854     25.0    Won         W   \n",
       "181671 2020-07-02   -2000.000000  30200854     25.0    Won         W   \n",
       "181672 2020-07-02     406.686538  30200854     25.0    Won         W   \n",
       "\n",
       "        application  thickness   width  product_ref  delivery_date  \\\n",
       "0              10.0       2.00  1500.0   1670798778     20210701.0   \n",
       "1              41.0       0.80  1210.0   1668701718     20210401.0   \n",
       "2              28.0       0.38   952.0       628377     20210101.0   \n",
       "3              59.0       2.30  1317.0   1668701718     20210101.0   \n",
       "4              10.0       4.00  2000.0       640665     20210301.0   \n",
       "...             ...        ...     ...          ...            ...   \n",
       "181668         41.0       0.96  1220.0    164141591     20200701.0   \n",
       "181669         41.0       0.95  1500.0    164141591     20200701.0   \n",
       "181670         41.0       0.71  1250.0    164141591     20200701.0   \n",
       "181671         41.0       0.85  1250.0    164141591     20200701.0   \n",
       "181672         41.0       0.71  1240.0    164141591     20200701.0   \n",
       "\n",
       "        selling_price item_date_day  item_date_year  item_date_month  \n",
       "0              854.00            01            2021                4  \n",
       "1             1047.00            01            2021                4  \n",
       "2              644.33            01            2021                4  \n",
       "3              768.00            01            2021                4  \n",
       "4              577.00            01            2021                4  \n",
       "...               ...           ...             ...              ...  \n",
       "181668         591.00            02            2020                7  \n",
       "181669         589.00            02            2020                7  \n",
       "181670         619.00            02            2020                7  \n",
       "181671         601.00            02            2020                7  \n",
       "181672         607.00            02            2020                7  \n",
       "\n",
       "[181673 rows x 15 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gr4clUn79wy0"
   },
   "source": [
    "**'Country' Column**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "By_v1v_9_ue-",
    "outputId": "c8864bf4-07fa-474e-c5f7-91a355152a1d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "item_date           0\n",
       "quantity_tons       0\n",
       "customer            0\n",
       "country            28\n",
       "status              2\n",
       "item_type           0\n",
       "application        24\n",
       "thickness           1\n",
       "width               0\n",
       "product_ref         0\n",
       "delivery_date       1\n",
       "selling_price       1\n",
       "item_date_day       0\n",
       "item_date_year      0\n",
       "item_date_month     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "R0TZpdmZ9pCI"
   },
   "outputs": [],
   "source": [
    "new_df = new_df.dropna(subset=['country'])\n",
    "new_df = new_df.dropna(subset=['status'])\n",
    "new_df = new_df.dropna(subset=['thickness'])\n",
    "new_df = new_df.dropna(subset=['selling_price'])\n",
    "new_df = new_df.dropna(subset=['delivery_date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "eeBBzwLiGyJt",
    "outputId": "d0397a4f-813b-4cb6-dcf8-8652e550ef7e"
   },
   "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",
       "product_ref        0\n",
       "delivery_date      0\n",
       "selling_price      0\n",
       "item_date_day      0\n",
       "item_date_year     0\n",
       "item_date_month    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UIp-sChMAQK9"
   },
   "source": [
    "**'Status' Column**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "CGIIi39x_ElH",
    "outputId": "1fa2fa9c-7ce5-4bb4-dad6-e83aae00122d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Won', 'Draft', 'To be approved', 'Lost', 'Not lost for AM',\n",
       "       'Wonderful', 'Revised', 'Offered', 'Offerable'], dtype=object)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Before Conversion\n",
    "\n",
    "new_df['status'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "Svc_BzO7AmkT"
   },
   "outputs": [],
   "source": [
    "# Conversion Process\n",
    "\n",
    "new_df['status'] = new_df['status'].map({'Won':1, 'Draft':2, 'To be approved':3, 'Lost':0, 'Not lost for AM':5,\n",
    "       'Wonderful':6, 'Revised':7, 'Offered':8, 'Offerable':4})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6tSCVgg3CNKH",
    "outputId": "0d03c695-39c4-4438-dfba-49cb820b0b7b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 0, 5, 6, 7, 8, 4], dtype=int64)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# After Conversion\n",
    "\n",
    "new_df['status'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DRwCMia6B0S-"
   },
   "source": [
    "**'item_type' Column**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2N_JW3oRAJJc",
    "outputId": "fc961a11-5041-478b-dd0e-609ee76db39b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['W', 'WI', 'S', 'Others', 'PL', 'IPL', 'SLAWR'], dtype=object)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Before Converion\n",
    "\n",
    "new_df['item_type'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "id": "_dZjLML5Bbuj"
   },
   "outputs": [],
   "source": [
    "# Conversion Process\n",
    "\n",
    "new_df['item_type'] = encoder.fit_transform(new_df[['item_type']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yZ9IXsY8CGLA",
    "outputId": "3de7abf9-45c9-4ddc-a755-0ee26834997b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5., 6., 3., 1., 2., 0., 4.])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# After Conversion\n",
    "\n",
    "new_df['item_type'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gs3_CaQpDLcR"
   },
   "source": [
    "**'application' Column**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZJmrJzSgDCvP",
    "outputId": "247e5734-4c6a-4b43-e49f-b6e0fff6ad16"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10., 41., 28., 59., 15.,  4., 38., 56., 42., 26., 27., 19., 20.,\n",
       "       66., 29., 22., 40., 25., 67., 79.,  3., 99.,  2.,  5., 39., 69.,\n",
       "       70., 65., 58., 68.])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df['application'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "id": "ykCrCnBuDQI0"
   },
   "outputs": [],
   "source": [
    "new_df['application'].fillna(method='bfill',inplace=True)\n",
    "\n",
    "new_df['application'] = new_df['application'].apply(lambda x : float(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MII5TH6mDqKJ"
   },
   "source": [
    "**'thickness' Column**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "id": "aj7hcAo8DtW6"
   },
   "outputs": [],
   "source": [
    "new_df['thickness'] = new_df['thickness'].apply(lambda x : float(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fTzA7VTpEA4z"
   },
   "source": [
    "**'width' Column**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "id": "l44XQe-yEEkV"
   },
   "outputs": [],
   "source": [
    "new_df['width'] = new_df['width'].apply(lambda x : float(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "S2XEGXtDGn7I",
    "outputId": "2168e85b-9981-408d-b124-10cdbae5e873"
   },
   "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",
       "product_ref        0\n",
       "delivery_date      0\n",
       "selling_price      0\n",
       "item_date_day      0\n",
       "item_date_year     0\n",
       "item_date_month    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DbE3e0KCEwaw"
   },
   "source": [
    "**'product_ref' Column**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "id": "3LNoLnQrDnbL"
   },
   "outputs": [],
   "source": [
    "new_df['product_ref'] = new_df['product_ref'] .apply(lambda x : str(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SKehJjnCIWai"
   },
   "source": [
    "**'delivery date' Column**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "mcVE5c4f85Yb",
    "outputId": "a8b15644-9140-4ceb-fdbb-d70c13195102"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>item_date</th>\n",
       "      <th>quantity_tons</th>\n",
       "      <th>customer</th>\n",
       "      <th>country</th>\n",
       "      <th>status</th>\n",
       "      <th>item_type</th>\n",
       "      <th>application</th>\n",
       "      <th>thickness</th>\n",
       "      <th>width</th>\n",
       "      <th>product_ref</th>\n",
       "      <th>delivery_date</th>\n",
       "      <th>selling_price</th>\n",
       "      <th>item_date_day</th>\n",
       "      <th>item_date_year</th>\n",
       "      <th>item_date_month</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</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>1670798778</td>\n",
       "      <td>20210701.0</td>\n",
       "      <td>854.00</td>\n",
       "      <td>01</td>\n",
       "      <td>2021</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>768.024839</td>\n",
       "      <td>30202938</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1210.0</td>\n",
       "      <td>1668701718</td>\n",
       "      <td>20210401.0</td>\n",
       "      <td>1047.00</td>\n",
       "      <td>01</td>\n",
       "      <td>2021</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>386.127949</td>\n",
       "      <td>30153963</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.38</td>\n",
       "      <td>952.0</td>\n",
       "      <td>628377</td>\n",
       "      <td>20210101.0</td>\n",
       "      <td>644.33</td>\n",
       "      <td>01</td>\n",
       "      <td>2021</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>202.411065</td>\n",
       "      <td>30349574</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>2.30</td>\n",
       "      <td>1317.0</td>\n",
       "      <td>1668701718</td>\n",
       "      <td>20210101.0</td>\n",
       "      <td>768.00</td>\n",
       "      <td>01</td>\n",
       "      <td>2021</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>785.526262</td>\n",
       "      <td>30211560</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>640665</td>\n",
       "      <td>20210301.0</td>\n",
       "      <td>577.00</td>\n",
       "      <td>01</td>\n",
       "      <td>2021</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181668</th>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>102.482422</td>\n",
       "      <td>30200854</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.96</td>\n",
       "      <td>1220.0</td>\n",
       "      <td>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>591.00</td>\n",
       "      <td>02</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181669</th>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>208.086469</td>\n",
       "      <td>30200854</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>589.00</td>\n",
       "      <td>02</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181670</th>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>4.235594</td>\n",
       "      <td>30200854</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.71</td>\n",
       "      <td>1250.0</td>\n",
       "      <td>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>619.00</td>\n",
       "      <td>02</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181671</th>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>-2000.000000</td>\n",
       "      <td>30200854</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1250.0</td>\n",
       "      <td>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>601.00</td>\n",
       "      <td>02</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181672</th>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>406.686538</td>\n",
       "      <td>30200854</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.71</td>\n",
       "      <td>1240.0</td>\n",
       "      <td>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>607.00</td>\n",
       "      <td>02</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>181640 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        item_date  quantity_tons  customer  country  status  item_type  \\\n",
       "0      2021-04-01      54.151139  30156308     28.0       1        5.0   \n",
       "1      2021-04-01     768.024839  30202938     25.0       1        5.0   \n",
       "2      2021-04-01     386.127949  30153963     30.0       1        6.0   \n",
       "3      2021-04-01     202.411065  30349574     32.0       1        3.0   \n",
       "4      2021-04-01     785.526262  30211560     28.0       1        5.0   \n",
       "...           ...            ...       ...      ...     ...        ...   \n",
       "181668 2020-07-02     102.482422  30200854     25.0       1        5.0   \n",
       "181669 2020-07-02     208.086469  30200854     25.0       1        5.0   \n",
       "181670 2020-07-02       4.235594  30200854     25.0       1        5.0   \n",
       "181671 2020-07-02   -2000.000000  30200854     25.0       1        5.0   \n",
       "181672 2020-07-02     406.686538  30200854     25.0       1        5.0   \n",
       "\n",
       "        application  thickness   width product_ref  delivery_date  \\\n",
       "0              10.0       2.00  1500.0  1670798778     20210701.0   \n",
       "1              41.0       0.80  1210.0  1668701718     20210401.0   \n",
       "2              28.0       0.38   952.0      628377     20210101.0   \n",
       "3              59.0       2.30  1317.0  1668701718     20210101.0   \n",
       "4              10.0       4.00  2000.0      640665     20210301.0   \n",
       "...             ...        ...     ...         ...            ...   \n",
       "181668         41.0       0.96  1220.0   164141591     20200701.0   \n",
       "181669         41.0       0.95  1500.0   164141591     20200701.0   \n",
       "181670         41.0       0.71  1250.0   164141591     20200701.0   \n",
       "181671         41.0       0.85  1250.0   164141591     20200701.0   \n",
       "181672         41.0       0.71  1240.0   164141591     20200701.0   \n",
       "\n",
       "        selling_price item_date_day  item_date_year  item_date_month  \n",
       "0              854.00            01            2021                4  \n",
       "1             1047.00            01            2021                4  \n",
       "2              644.33            01            2021                4  \n",
       "3              768.00            01            2021                4  \n",
       "4              577.00            01            2021                4  \n",
       "...               ...           ...             ...              ...  \n",
       "181668         591.00            02            2020                7  \n",
       "181669         589.00            02            2020                7  \n",
       "181670         619.00            02            2020                7  \n",
       "181671         601.00            02            2020                7  \n",
       "181672         607.00            02            2020                7  \n",
       "\n",
       "[181640 rows x 15 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "id": "N6bdK8hLIfed"
   },
   "outputs": [],
   "source": [
    "new_df['delivery_date'].fillna(method='bfill',inplace=True) # Missing value Filled\n",
    "\n",
    "new_df['delivery_date'] = new_df['delivery_date'].apply(lambda x : str(x)) # String Conversion\n",
    "\n",
    "new_df['delivery_date'] = new_df['delivery_date'].apply(lambda x : f'{x[0:4]}-{x[4:6]}-{x[6:8]}')\n",
    "\n",
    "new_df['delivery_date'] = new_df['delivery_date'].apply(lambda x : '2023-01-01'   if  int(x[:4]) > 2023  else x)\n",
    "\n",
    "new_df['delivery_date_day'] = new_df['delivery_date'].apply(lambda x : int(x[8:]))\n",
    "\n",
    "new_df['delivery_date'] = new_df['delivery_date'].apply(lambda x : f'{x[:4]}-12-{x[8:]}'   if  int(x[5:7]) > 12  else x)\n",
    "\n",
    "new_df['delivery_date'] = pd.to_datetime(new_df['delivery_date']) # Date format Conversion\n",
    "\n",
    "new_df['delivery_date_month'] = pd.to_datetime(new_df['delivery_date']).dt.month\n",
    "\n",
    "new_df['delivery_date_year'] = pd.to_datetime(new_df['delivery_date']).dt.year\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "CDcAxI6AAdWH",
    "outputId": "03def484-0704-4998-d052-5f9c3f083378"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "item_date              datetime64[ns]\n",
       "quantity_tons                 float64\n",
       "customer                       object\n",
       "country                       float64\n",
       "status                          int64\n",
       "item_type                     float64\n",
       "application                   float64\n",
       "thickness                     float64\n",
       "width                         float64\n",
       "product_ref                    object\n",
       "delivery_date          datetime64[ns]\n",
       "selling_price                 float64\n",
       "item_date_day                  object\n",
       "item_date_year                  int64\n",
       "item_date_month                 int64\n",
       "delivery_date_day               int64\n",
       "delivery_date_month             int64\n",
       "delivery_date_year              int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VtwOwRLsSR_g"
   },
   "source": [
    "**Selling Price Column**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "id": "qeZL0zEUSU4R"
   },
   "outputs": [],
   "source": [
    "Mean = round(new_df['selling_price'].mean(),3)\n",
    "\n",
    "new_df['selling_price'].fillna(Mean,inplace=True)\n",
    "\n",
    "new_df['selling_price'] = new_df['selling_price'].apply(lambda x : float(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Ch05aOPYRJo9",
    "outputId": "d060cccc-044e-48ef-cbf3-ad2515ca53fe"
   },
   "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",
       "product_ref            0\n",
       "delivery_date          0\n",
       "selling_price          0\n",
       "item_date_day          0\n",
       "item_date_year         0\n",
       "item_date_month        0\n",
       "delivery_date_day      0\n",
       "delivery_date_month    0\n",
       "delivery_date_year     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.isnull().sum()"
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  },
  {
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    "id": "5NYI7z5wELyl",
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      "text/plain": [
       "        item_date  quantity_tons  customer  country  status  item_type  \\\n",
       "0      2021-04-01      54.151139  30156308     28.0       1        5.0   \n",
       "1      2021-04-01     768.024839  30202938     25.0       1        5.0   \n",
       "2      2021-04-01     386.127949  30153963     30.0       1        6.0   \n",
       "3      2021-04-01     202.411065  30349574     32.0       1        3.0   \n",
       "4      2021-04-01     785.526262  30211560     28.0       1        5.0   \n",
       "...           ...            ...       ...      ...     ...        ...   \n",
       "181668 2020-07-02     102.482422  30200854     25.0       1        5.0   \n",
       "181669 2020-07-02     208.086469  30200854     25.0       1        5.0   \n",
       "181670 2020-07-02       4.235594  30200854     25.0       1        5.0   \n",
       "181671 2020-07-02   -2000.000000  30200854     25.0       1        5.0   \n",
       "181672 2020-07-02     406.686538  30200854     25.0       1        5.0   \n",
       "\n",
       "        application  thickness   width product_ref delivery_date  \\\n",
       "0              10.0       2.00  1500.0  1670798778    2021-07-01   \n",
       "1              41.0       0.80  1210.0  1668701718    2021-04-01   \n",
       "2              28.0       0.38   952.0      628377    2021-01-01   \n",
       "3              59.0       2.30  1317.0  1668701718    2021-01-01   \n",
       "4              10.0       4.00  2000.0      640665    2021-03-01   \n",
       "...             ...        ...     ...         ...           ...   \n",
       "181668         41.0       0.96  1220.0   164141591    2020-07-01   \n",
       "181669         41.0       0.95  1500.0   164141591    2020-07-01   \n",
       "181670         41.0       0.71  1250.0   164141591    2020-07-01   \n",
       "181671         41.0       0.85  1250.0   164141591    2020-07-01   \n",
       "181672         41.0       0.71  1240.0   164141591    2020-07-01   \n",
       "\n",
       "        selling_price item_date_day  item_date_year  item_date_month  \\\n",
       "0              854.00            01            2021                4   \n",
       "1             1047.00            01            2021                4   \n",
       "2              644.33            01            2021                4   \n",
       "3              768.00            01            2021                4   \n",
       "4              577.00            01            2021                4   \n",
       "...               ...           ...             ...              ...   \n",
       "181668         591.00            02            2020                7   \n",
       "181669         589.00            02            2020                7   \n",
       "181670         619.00            02            2020                7   \n",
       "181671         601.00            02            2020                7   \n",
       "181672         607.00            02            2020                7   \n",
       "\n",
       "        delivery_date_day  delivery_date_month  delivery_date_year  \n",
       "0                       1                    7                2021  \n",
       "1                       1                    4                2021  \n",
       "2                       1                    1                2021  \n",
       "3                       1                    1                2021  \n",
       "4                       1                    3                2021  \n",
       "...                   ...                  ...                 ...  \n",
       "181668                  1                    7                2020  \n",
       "181669                  1                    7                2020  \n",
       "181670                  1                    7                2020  \n",
       "181671                  1                    7                2020  \n",
       "181672                  1                    7                2020  \n",
       "\n",
       "[181640 rows x 18 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "id": "HDURQ_adEcCX"
   },
   "outputs": [],
   "source": [
    "new_df['item_date_day'] = new_df['item_date_day'].apply(lambda x : int(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "id": "vSI-0_GF9a8o"
   },
   "outputs": [],
   "source": [
    "new_df['quantity_tons'] = new_df['quantity_tons'].apply(lambda x : 0.0  if x<0.0 else x)\n",
    "new_df['selling_price'] = new_df['selling_price'].apply(lambda x : 0.0  if x<0.0 else x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
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    },
    "id": "rQccK0dLEb-z",
    "outputId": "342f5d26-3d4b-4877-8073-d8af18da361b"
   },
   "outputs": [
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       "      <td>2000.0</td>\n",
       "      <td>640665</td>\n",
       "      <td>2021-03-01</td>\n",
       "      <td>577.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181668</th>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>102.482422</td>\n",
       "      <td>30200854</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.96</td>\n",
       "      <td>1220.0</td>\n",
       "      <td>164141591</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>591.00</td>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181669</th>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>208.086469</td>\n",
       "      <td>30200854</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>164141591</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>589.00</td>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181670</th>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>4.235594</td>\n",
       "      <td>30200854</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.71</td>\n",
       "      <td>1250.0</td>\n",
       "      <td>164141591</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>619.00</td>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181671</th>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>30200854</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1250.0</td>\n",
       "      <td>164141591</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>601.00</td>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181672</th>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>406.686538</td>\n",
       "      <td>30200854</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.71</td>\n",
       "      <td>1240.0</td>\n",
       "      <td>164141591</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>607.00</td>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>181640 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        item_date  quantity_tons  customer  country  status  item_type  \\\n",
       "0      2021-04-01      54.151139  30156308     28.0       1        5.0   \n",
       "1      2021-04-01     768.024839  30202938     25.0       1        5.0   \n",
       "2      2021-04-01     386.127949  30153963     30.0       1        6.0   \n",
       "3      2021-04-01     202.411065  30349574     32.0       1        3.0   \n",
       "4      2021-04-01     785.526262  30211560     28.0       1        5.0   \n",
       "...           ...            ...       ...      ...     ...        ...   \n",
       "181668 2020-07-02     102.482422  30200854     25.0       1        5.0   \n",
       "181669 2020-07-02     208.086469  30200854     25.0       1        5.0   \n",
       "181670 2020-07-02       4.235594  30200854     25.0       1        5.0   \n",
       "181671 2020-07-02       0.000000  30200854     25.0       1        5.0   \n",
       "181672 2020-07-02     406.686538  30200854     25.0       1        5.0   \n",
       "\n",
       "        application  thickness   width product_ref delivery_date  \\\n",
       "0              10.0       2.00  1500.0  1670798778    2021-07-01   \n",
       "1              41.0       0.80  1210.0  1668701718    2021-04-01   \n",
       "2              28.0       0.38   952.0      628377    2021-01-01   \n",
       "3              59.0       2.30  1317.0  1668701718    2021-01-01   \n",
       "4              10.0       4.00  2000.0      640665    2021-03-01   \n",
       "...             ...        ...     ...         ...           ...   \n",
       "181668         41.0       0.96  1220.0   164141591    2020-07-01   \n",
       "181669         41.0       0.95  1500.0   164141591    2020-07-01   \n",
       "181670         41.0       0.71  1250.0   164141591    2020-07-01   \n",
       "181671         41.0       0.85  1250.0   164141591    2020-07-01   \n",
       "181672         41.0       0.71  1240.0   164141591    2020-07-01   \n",
       "\n",
       "        selling_price  item_date_day  item_date_year  item_date_month  \\\n",
       "0              854.00              1            2021                4   \n",
       "1             1047.00              1            2021                4   \n",
       "2              644.33              1            2021                4   \n",
       "3              768.00              1            2021                4   \n",
       "4              577.00              1            2021                4   \n",
       "...               ...            ...             ...              ...   \n",
       "181668         591.00              2            2020                7   \n",
       "181669         589.00              2            2020                7   \n",
       "181670         619.00              2            2020                7   \n",
       "181671         601.00              2            2020                7   \n",
       "181672         607.00              2            2020                7   \n",
       "\n",
       "        delivery_date_day  delivery_date_month  delivery_date_year  \n",
       "0                       1                    7                2021  \n",
       "1                       1                    4                2021  \n",
       "2                       1                    1                2021  \n",
       "3                       1                    1                2021  \n",
       "4                       1                    3                2021  \n",
       "...                   ...                  ...                 ...  \n",
       "181668                  1                    7                2020  \n",
       "181669                  1                    7                2020  \n",
       "181670                  1                    7                2020  \n",
       "181671                  1                    7                2020  \n",
       "181672                  1                    7                2020  \n",
       "\n",
       "[181640 rows x 18 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "id": "01VY8F9sPP-8"
   },
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ui8WF7VgPMFM",
    "outputId": "37ca7ac0-5b4a-4846-e507-d1739090b427"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DINESH KUMAR\\AppData\\Local\\Temp\\ipykernel_15364\\3981604865.py:2: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  sns.heatmap(new_df.corr(),annot=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1300x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(13,8))\n",
    "sns.heatmap(new_df.corr(),annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bqPPVvSdfD2X"
   },
   "source": [
    "# **Treating Skewness**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ygZ8ORpaaHzn",
    "outputId": "014c4298-9fa1-4f47-bb00-5e5b985683da"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>item_date</th>\n",
       "      <th>quantity_tons</th>\n",
       "      <th>customer</th>\n",
       "      <th>country</th>\n",
       "      <th>status</th>\n",
       "      <th>item_type</th>\n",
       "      <th>application</th>\n",
       "      <th>thickness</th>\n",
       "      <th>width</th>\n",
       "      <th>product_ref</th>\n",
       "      <th>delivery_date</th>\n",
       "      <th>selling_price</th>\n",
       "      <th>item_date_day</th>\n",
       "      <th>item_date_year</th>\n",
       "      <th>item_date_month</th>\n",
       "      <th>delivery_date_day</th>\n",
       "      <th>delivery_date_month</th>\n",
       "      <th>delivery_date_year</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</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>1670798778</td>\n",
       "      <td>2021-07-01</td>\n",
       "      <td>854.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>768.024839</td>\n",
       "      <td>30202938</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1210.0</td>\n",
       "      <td>1668701718</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>1047.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>386.127949</td>\n",
       "      <td>30153963</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.38</td>\n",
       "      <td>952.0</td>\n",
       "      <td>628377</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>644.33</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>202.411065</td>\n",
       "      <td>30349574</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>2.30</td>\n",
       "      <td>1317.0</td>\n",
       "      <td>1668701718</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>768.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>785.526262</td>\n",
       "      <td>30211560</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>640665</td>\n",
       "      <td>2021-03-01</td>\n",
       "      <td>577.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181668</th>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>102.482422</td>\n",
       "      <td>30200854</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.96</td>\n",
       "      <td>1220.0</td>\n",
       "      <td>164141591</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>591.00</td>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181669</th>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>208.086469</td>\n",
       "      <td>30200854</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>164141591</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>589.00</td>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181670</th>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>4.235594</td>\n",
       "      <td>30200854</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.71</td>\n",
       "      <td>1250.0</td>\n",
       "      <td>164141591</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>619.00</td>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181671</th>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>30200854</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1250.0</td>\n",
       "      <td>164141591</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>601.00</td>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181672</th>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>406.686538</td>\n",
       "      <td>30200854</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.71</td>\n",
       "      <td>1240.0</td>\n",
       "      <td>164141591</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>607.00</td>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>181640 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        item_date  quantity_tons  customer  country  status  item_type  \\\n",
       "0      2021-04-01      54.151139  30156308     28.0       1        5.0   \n",
       "1      2021-04-01     768.024839  30202938     25.0       1        5.0   \n",
       "2      2021-04-01     386.127949  30153963     30.0       1        6.0   \n",
       "3      2021-04-01     202.411065  30349574     32.0       1        3.0   \n",
       "4      2021-04-01     785.526262  30211560     28.0       1        5.0   \n",
       "...           ...            ...       ...      ...     ...        ...   \n",
       "181668 2020-07-02     102.482422  30200854     25.0       1        5.0   \n",
       "181669 2020-07-02     208.086469  30200854     25.0       1        5.0   \n",
       "181670 2020-07-02       4.235594  30200854     25.0       1        5.0   \n",
       "181671 2020-07-02       0.000000  30200854     25.0       1        5.0   \n",
       "181672 2020-07-02     406.686538  30200854     25.0       1        5.0   \n",
       "\n",
       "        application  thickness   width product_ref delivery_date  \\\n",
       "0              10.0       2.00  1500.0  1670798778    2021-07-01   \n",
       "1              41.0       0.80  1210.0  1668701718    2021-04-01   \n",
       "2              28.0       0.38   952.0      628377    2021-01-01   \n",
       "3              59.0       2.30  1317.0  1668701718    2021-01-01   \n",
       "4              10.0       4.00  2000.0      640665    2021-03-01   \n",
       "...             ...        ...     ...         ...           ...   \n",
       "181668         41.0       0.96  1220.0   164141591    2020-07-01   \n",
       "181669         41.0       0.95  1500.0   164141591    2020-07-01   \n",
       "181670         41.0       0.71  1250.0   164141591    2020-07-01   \n",
       "181671         41.0       0.85  1250.0   164141591    2020-07-01   \n",
       "181672         41.0       0.71  1240.0   164141591    2020-07-01   \n",
       "\n",
       "        selling_price  item_date_day  item_date_year  item_date_month  \\\n",
       "0              854.00              1            2021                4   \n",
       "1             1047.00              1            2021                4   \n",
       "2              644.33              1            2021                4   \n",
       "3              768.00              1            2021                4   \n",
       "4              577.00              1            2021                4   \n",
       "...               ...            ...             ...              ...   \n",
       "181668         591.00              2            2020                7   \n",
       "181669         589.00              2            2020                7   \n",
       "181670         619.00              2            2020                7   \n",
       "181671         601.00              2            2020                7   \n",
       "181672         607.00              2            2020                7   \n",
       "\n",
       "        delivery_date_day  delivery_date_month  delivery_date_year  \n",
       "0                       1                    7                2021  \n",
       "1                       1                    4                2021  \n",
       "2                       1                    1                2021  \n",
       "3                       1                    1                2021  \n",
       "4                       1                    3                2021  \n",
       "...                   ...                  ...                 ...  \n",
       "181668                  1                    7                2020  \n",
       "181669                  1                    7                2020  \n",
       "181670                  1                    7                2020  \n",
       "181671                  1                    7                2020  \n",
       "181672                  1                    7                2020  \n",
       "\n",
       "[181640 rows x 18 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "id": "SotiT7i9feRA"
   },
   "outputs": [],
   "source": [
    "columns = [ 'quantity_tons',  'application', 'thickness', 'width', 'selling_price', ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OPqfFRZwl4tR"
   },
   "source": [
    "# **Before Treating Skewness**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "id": "MFshWdzkfeOr"
   },
   "outputs": [],
   "source": [
    "# from scipy.stats import norm\n",
    "# def before_skewness(column):\n",
    "#   for i in columns:\n",
    "#     sns.set_style(\"darkgrid\")\n",
    "#     sns.histplot(new_df[i],kde=True,color='r')\n",
    "#     plt.title(f'Skewness In {i}')\n",
    "#     plt.show()\n",
    "\n",
    "# before_skewness(columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ifYGpL4DlwFQ"
   },
   "source": [
    "# **After Treated The Skewness**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "id": "w4xlXasSfeKM"
   },
   "outputs": [],
   "source": [
    "new_df_1 = new_df.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PPg4emW9nBUF"
   },
   "source": [
    "**`Log Transoformation`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "rQul_aO7nK27",
    "outputId": "e60ae1d4-b496-4372-830f-61d9590a3909"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['quantity_tons', 'application', 'thickness', 'width', 'selling_price']"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "p98iDkVcfeH6",
    "outputId": "0133f1d6-cbd3-4070-f522-9bd406bfafac"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DINESH KUMAR\\anaconda3\\lib\\site-packages\\pandas\\core\\arraylike.py:402: RuntimeWarning: divide by zero encountered in log\n",
      "  result = getattr(ufunc, method)(*inputs, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "def log_transformation(column):\n",
    "  for i in column:\n",
    "    new_df_1[i] = np.log(new_df_1[i])\n",
    "\n",
    "log_transformation(columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "JvNRhqj5feFY",
    "outputId": "ffce10bd-903d-4191-9097-5f9a38487659"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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QPvnkE+espua477776NatG2+88QbLly8nKiqKlJQU/vCHP9C5c2egesr1M888w3PPPcfx48fp0aMH99xzD1OnTgWqB3mHhISwZs0a/vKXv9C5c2dGjx7NH//4x2bH0xKLFy/m6aefZsmSJVRUVDBmzBiuvvpqdu/e7Txn4sSJ/PDDD6xdu5a33nqLpKQknnvuOW666SbnOQ899BA2m41FixZhtVo588wzmTZtGjk5OXzyySd1usoakpaWxldffcWUKVOYP39+nTFHycnJDBs2jKeffpotW7a4LHY5atQojh07Rmpqaovq47e//S0vvPACU6ZMYf369aSkpPDKK6+wePFi/vCHPxASEsKQIUNYuHAhCQkJTb7v+eefz9KlS/nLX/7CH/7wB6qqqhgwYAArVqzg3HPPbVGsIm3BZGh3OhEJcA899BDbtm3jk08+8XYozWIYBmPHjiU5OZlHHnnE2+GI+AW1+IiI+Jni4mIyMzPZs2cPBw4c4IUXXnApr6qqanTWmMlkchngLRIolPiIiPiZsLAw3nzzTefqyo4FDh1SU1Mb3cn9jDPO8LsWLpG2oK4uEZEOZv/+/Vit1gbPCQkJcdmfSyRQKPERERGRgKF1fERERCRgKPERERGRgKHER0RERAKGEh8REREJGJrO7kZ+fhH+MOTbZIKuXTv7TbyeonpxT/XinurFPdWLe6oX97xdL47nN4USHzcMA7/6QPtbvJ6ienFP9eKe6sU91Yt7qhf3/KFe1NUlIiIiAUOJj4iIiAQMJT4iIiISMJT4iIiISMBQ4iMiIiIBQ4mPiIiIBAwlPiIiIhIwlPiIiIhIwFDiIyIiIgFDiY+IiIgEDCU+IiIiEjCU+IiIiEjAUOIjIiIiAUOJj4iIiAQMJT4i4vOW7cnwdggi0kEo8RERn2etsno7BBHpIJT4iIiISMBQ4iMiIiIBQ4mPiIiIBAwlPiIiIhIwPJ74/P3vfycxMdHla8CAAQwYMACAXbt2MXnyZBITExk9ejSrVq1yuX7t2rWkpqYyaNAgJkyYwM6dO51lVVVVLFy4kGHDhpGYmMi0adM4evSoR99PREREfJfHE5/rrruOnTt3Or8++OADoqOjSU9P58SJE0ydOpXx48ezfft20tPTmT9/Prt37wZg69atPP744yxYsIDt27dz3XXXMW3aNMrKygDIyMhg06ZNrFmzho0bNxIWFsbs2bM9/YoiIiLio7za1WUYBjNnzmTUqFGMGzeODRs2EB0dTVpaGsHBwaSkpDB27FhWrlwJwKpVqxgzZgyDBw/GYrFw2223ERMTw/r1653lU6ZMoUePHkRGRjJr1iw+//xzcnNzvfmaIiIi4iOCvfnwd999l5ycHF544QUAsrOz6dOnj8s58fHxrF69GoCcnBwmTpxYp3zfvn0UFRVx+PBhl+vj4uKIiopi//799OrVq8lxmUwtfSPPcsTpL/F6iurFPX+uF5Op/eL253ppT6oX91Qv7nm7XprzXK8lPna7nYyMDH77298SGRkJQElJCeHh4S7nhYWFUVpa2mh5SUkJABEREXXKHWVN1bVr52ad723+Fq+nqF7c88d6iQgPJS6ufeP2x3rxBNWLe6oX9/yhXryW+GzdupWjR48yadIk57Hw8HCKiopczisvL6dTp07O8vLy8jrlMTExzoTIMd7H3fVNlZ9fhGE06xKvMJmqP2T+Eq+nqF7c8+d6KS2rIC+vqPETW8Cf66U9qV7cU7245+16cTy/KbyW+Hz44Yekpqa6tND06dOHTZs2uZyXk5NDQkICAAkJCWRnZ9cpHzlyJFFRUXTv3p2cnBxnd9exY8coLCys033WGMPArz7Q/havp6he3PPHevFEzP5YL56genFP9eKeP9SL1wY3f/HFF1xyySUux1JTU8nLyyMzMxObzUZWVhbr1q1zjuuZNGkS69atIysrC5vNRmZmJvn5+aSmpgIwYcIEMjIyyM3Npbi4mHnz5pGUlETv3r09/n4i0nYsZguZe5drs1IRaTWvtfgcPHiQ0047zeVYTEwMK1asID09ncWLFxMbG8vs2bMZOnQoACkpKcyZM4e5c+dy5MgR4uPjWbZsGdHR0QBMnz6dyspK0tLSKCkpITk5mUWLFnn4zUSkPVirrNjsNm+HISJ+zmQYvt4o5Xl5ef7Rd2syQVxcZ7+J11NUL+75c728tLt65qfNbmP6oHvb9N7+XC/tSfXinurFPW/Xi+P5TaEtK0RERCRgKPERERGRgKHER0RERAKGEh8REREJGEp8RMSnZe5d7u0QRKQDUeIjIj7NWmX1dggi0oEo8REREZGAocRHREREAoYSHxEREQkYSnxEREQkYCjxERERkYChxEdEREQChhIfERERCRhKfERERCRgKPERERGRgKHER0RERAKGEh8REREJGEp8REREJGAo8REREZGAocRHREREAoYSHxEREQkYSnxEREQkYCjxERERkYChxEdEREQChhIfERERCRhKfERERCRgKPERERGRgKHER0RERAKGEh8REREJGEp8REREJGAo8REREZGAocRHREREAoYSHxEREQkYSnxEREQkYCjxERERkYChxEdEREQChhIfERERCRhKfETE51VUVvBj8UEMw/B2KCLi55T4iIjP+9+cNWT8+3kOFud6OxQR8XNeSXwKCwt54IEHSE5O5pJLLuHuu+/m6NGjAOzatYvJkyeTmJjI6NGjWbVqlcu1a9euJTU1lUGDBjFhwgR27tzpLKuqqmLhwoUMGzaMxMREpk2b5ryviPivgvICAArLC70biIj4Pa8kPr/73e8oLS3ln//8J59++ilBQUE88sgjnDhxgqlTpzJ+/Hi2b99Oeno68+fPZ/fu3QBs3bqVxx9/nAULFrB9+3auu+46pk2bRllZGQAZGRls2rSJNWvWsHHjRsLCwpg9e7Y3XlFE2pDNbgWg0l7p5UhExN95PPH56quv2LVrFwsWLKBLly5ERkby+OOPM2PGDDZs2EB0dDRpaWkEBweTkpLC2LFjWblyJQCrVq1izJgxDB48GIvFwm233UZMTAzr1693lk+ZMoUePXoQGRnJrFmz+Pzzz8nNVfO4iD+zVVUnPFWGEh8RaZ1gTz9w9+7dxMfH8/bbb/O3v/2NsrIyLr30Uh588EGys7Pp06ePy/nx8fGsXr0agJycHCZOnFinfN++fRQVFXH48GGX6+Pi4oiKimL//v306tWryTGaTK14QQ9yxOkv8XqK6sU9f60Xk6lWi49R2ebx+2u9tDfVi3uqF/e8XS/Nea7HE58TJ06wf/9+BgwYwNq1aykvL+eBBx7gwQcfJC4ujvDwcJfzw8LCKC0tBaCkpKTe8pKSEgAiIiLqlDvKmqpr187NfS2v8rd4PUX14p6/1UtEeCg2uw0AcxDExbVP/P5WL56ienFP9eKeP9SLxxOfkJAQAGbNmkVoaCiRkZHcd999/PKXv2TChAmUl5e7nF9eXk6nTp0ACA8Pd1seExPjTIgc433cXd9U+flF+MOsWZOp+kPmL/F6iurFPX+tl5LScmxV1YlPaUU5eXlFbXp/f62X9qZ6cU/14p6368Xx/KbweOITHx+P3W7HZrMRGhoKgN1uB+CCCy7gjTfecDk/JyeHhIQEABISEsjOzq5TPnLkSKKioujevTs5OTnO7q5jx45RWFhYp/usMYaBX32g/S1eT1G9uOdv9VJpr8KO/eT3le0Wu7/Vi6eoXtxTvbjnD/Xi8cHNw4YNo1evXvzpT3+ipKSEgoICnn32Wa644gquvfZa8vLyyMzMxGazkZWVxbp165zjeiZNmsS6devIysrCZrORmZlJfn4+qampAEyYMIGMjAxyc3MpLi5m3rx5JCUl0bt3b0+/poi0EUc3F2hWl4i0nsdbfCwWC6+99hoLFizgqquuoqKigtGjRzNr1iy6dOnCihUrSE9PZ/HixcTGxjJ79myGDh0KQEpKCnPmzGHu3LkcOXKE+Ph4li1bRnR0NADTp0+nsrKStLQ0SkpKSE5OZtGiRZ5+RRFpQ45uLlDiIyKtZzK0BnwdeXn+0XdrMlUP9PSXeD1F9eKev9bL/K2P8ewXTwEwqFsiGyb/q03v76/10t5UL+6pXtzzdr04nt8U2rJCRHyaS1eX1vERkVZS4iMiPk1jfESkLSnxERGfpjE+ItKWlPiIiE9Ti4+ItCUlPiLi0xzbVYDG+IhI6ynxERGfZq3V1VVlr/JiJCLSESjxERGf5trVZWvgTBGRxinxERGfZquq1dWlMT4i0kpKfETEp2kdHxFpS0p8RMSnuXZ1aYyPiLSOEh8R8Wmazi4ibUmJj4j4NC1gKCJtSYmPiPi02uv4GNiV/IhIqyjxERGfZjtlCntFVYWXIhGRjkCJj4j4tNpdXQAVVeVeikREOgIlPiLi06y1uroAKirV4iMiLafER0R82qldXeVq8RGRVlDiIyI+7dSuLmuVtZ4zRUQap8RHRHya7dSuLrX4iEgrKPEREZ9mO2X6enkDY3yW7clo73BExM8p8RERn+Zo8bGYLUDDLT7qBhORxijxERGfZRiGc4xPWHA4oK4uEWkdJT4i4rOsdisGBgARzsRHrToi0nJKfETEZ5XZSp3fhwdHAGrxEZHWUeIjIj6rrLIMALPJTGhQKKAFDEWkdZT4iIjPKqusbvGxmC0EmYMALWAoIq2jxEdEfFbpyRYfi9lC8MlZXVZtUioiraDER0R8Vk2LTwjB5mBAu7OLSOso8RERn1VeWd2tZQmyYDmZ+DiOiYi0hBIfEfFZLmN8TGrxEZHWU+IjIj6rzGWMT3XiozE+ItIaSnxExGc5Ep+QoJoxPuVKfESkFZT4iIjPKq3V1dWUvbpERBqjxEdEfFaZraarK8hUvY5PhQY3i0grKPEREZ/lHNwcFOJcx0d7dYlIayjxERGf5W5ws7q6RKQ1lPiIiM+qafGpWcenvunsmXuXeywuEfFfSnxExGc5FzA0WwhqZAFDq7rARKQJlPiIiM+qPavLuY6PXQmOiLScVxKf9evX069fPxITE51fM2fOBGDXrl1MnjyZxMRERo8ezapVq1yuXbt2LampqQwaNIgJEyawc+dOZ1lVVRULFy5k2LBhJCYmMm3aNI4ePerRdxORtlMzxieEYMfKzZrVJSKt4JXEZ8+ePYwbN46dO3c6v5588klOnDjB1KlTGT9+PNu3byc9PZ358+eze/duALZu3crjjz/OggUL2L59O9dddx3Tpk2jrKz6l2NGRgabNm1izZo1bNy4kbCwMGbPnu2NVxSRNuA6xqd6Vle5BjeLSCt4LfEZMGBAneMbNmwgOjqatLQ0goODSUlJYezYsaxcuRKAVatWMWbMGAYPHozFYuG2224jJiaG9evXO8unTJlCjx49iIyMZNasWXz++efk5uZ69P1EpG04V242Wwgyn1zHRys3i0grBHv6gXa7nb179xIeHs7y5cupqqrisssuY8aMGWRnZ9OnTx+X8+Pj41m9ejUAOTk5TJw4sU75vn37KCoq4vDhwy7Xx8XFERUVxf79++nVq1eTYzSZWvGCHuSI01/i9RTVi3v+WC/Orq6gECxBJ9fxqSx3+w4mU81XfZbtyWDKwGl1rqv9p1RTvbinenHP2/XSnOd6PPEpKCigX79+XHXVVSxevJjjx4/z4IMPMnPmTLp160Z4eLjL+WFhYZSWVjd3l5SU1FteUlICQERERJ1yR1lTde3aubmv5VX+Fq+nqF7c86d6sRrV3VqdwyPoHFb9d9tqtxIXV/cdIsJDsdnNbsscgkNM9Zb7U714kurFPdWLe/5QLx5PfOLi4pxdVwDh4eHMnDmTX/7yl0yYMIHyctf++/Lycjp16uQ81115TEyMMyFyjPdxd31T5ecXYRjNusQrTKbqD5m/xOspqhf3/LFeisur/9FirwSbzQ5UT2fPyyuqc25pWQU2u81tWe1zTi33x3rxBNWLe6oX97xdL47nN4XHE599+/bxj3/8gz/+8Y+YTrZNWa1WzGYzF154IX/9619dzs/JySEhIQGAhIQEsrOz65SPHDmSqKgounfvTk5OjrO769ixYxQWFtbpPmuMYeBXH2h/i9dTVC/u+VO9OAY3B5tCCDr566rKqMJWVemc3u7geK+G3q2hcn+qF09SvbinenHPH+rF44Obo6OjWblyJcuXL6eyspJDhw7x5JNPcv3113PVVVeRl5dHZmYmNpuNrKws1q1b5xzXM2nSJNatW0dWVhY2m43MzEzy8/NJTU0FYMKECWRkZJCbm0txcTHz5s0jKSmJ3r17e/o1RaQNlDkWMAyyuCQ6mtklIi3l8Raf008/nRdffJFnnnmGjIwMQkNDGTNmDDNnziQ0NJQVK1aQnp7O4sWLiY2NZfbs2QwdOhSAlJQU5syZw9y5czly5Ajx8fEsW7aM6OhoAKZPn05lZSVpaWmUlJSQnJzMokWLPP2KItIGDMNwtviEmF0TH2tVBVgivRWaiPgxjyc+AElJSbz55ptuywYOHFhvGcC4ceMYN26c2zKLxcKMGTOYMWNGm8QpIt5jtVsxqG4zDzZbMJvMmE1B2I0qKio1pV1EWkZbVoiIT7LV2nvLsYZPsKn6T3V1iUhLKfEREZ9Ue0+uoJMJT3AjO7SLiDRGiY+I+CRblQ0AEybMpupfVc6NSpX4iEgLKfEREZ/kaPFxdHNBTeJTrjE+ItJCSnxExCfZ7NUtPo5ururvHV1dGuMjIi2jxEdEfJKjq8uR7EDtMT5KfESkZZT4iIhPqunqqvk15dyotNaMLxGR5lDiIyI+yTGdvXZXV7DJkfioxUdEWkaJj4j4JJu9Eji1q6s6CdIChiLSUkp8RMQn2Rqa1aUWHxFpISU+IuKTrM6urppfU8FmdXWJSOso8RERn1Qznb3urC6rBjeLSAsp8RERn+Sczl67q8vkWMBQLT4i0jJKfETEJznH+JjqjvHRXl0i0lJKfETEJ1ndTWfXAoYi0kpKfETEJznH+GivLhFpQ0p8RMQnWd10dTkGOju6wRwy9y73XGAi4teU+IiIT6p0N7jZsYDhKWN8NMtLRJpKiY+I+CTrya4us9sWH5tXYhIR/6fER0R8ktu9uk62+Fg1q0tEWkiJj4j4JMcYn2Bz7RYf911dIiJNpcRHRHxSpXOT0rqzuhyLG4qINJcSHxHxSY4Byy5jfLSAoYi0khIfEfFJ7nZnd7T+aHCziLSUEh8R8UlWx3R2N11dGtwsIi2lxEdEfFKlm93ZHd+rq0tEWkqJj4j4pJqVm2t+TTlmeKmrS0RaSomPiPgkm5uVmzWdXURaS4mPiPikmhafWl1dzuns2qJCRFpGiY+I+KRKt7uzO1p8lPiISMso8RERn2R1t2XFydYfq11dXSLSMkp8RMQn2ex1p7M7Wn8q7ZXYDbtX4hIR/6bER0R8krPFx1x3d/ba5SIizaHER0R8UmUDLT6gRQxFpGWU+IiIT7K6GdxcOwmyai0fEWkBJT4i4pNsbgY3m01m56alavERkZZQ4iMiPqlmHZ8gl+NBzsRHY3xEpPnaLPEpLi5uq1uJiFBprwRcu7qgZi0fJT4i0hLNTnySkpLcHh81alRrYxERcapZxyfY5bhz2wqt5SMiLRDc+Cnw/fff8+c//xnDMCguLubXv/61S3lxcTFdunRp9sOrqqq47bbbOOOMM1iwYAEAu3bt4oknniAnJ4eYmBimTZvG5MmTndesXbuWF154gWPHjnHuuefyyCOPkJiY6LzfU089xbvvvktZWRlDhw7l0Ucf5bTTTmt2bCLiXTY3m5SCtq0QkdZpUovPWWedxZVXXklSUhJms5mkpCSXr6uvvpqXXnqp2Q9//vnn2bFjh/PnEydOMHXqVMaPH8/27dtJT09n/vz57N69G4CtW7fy+OOPs2DBArZv3851113HtGnTKCsrAyAjI4NNmzaxZs0aNm7cSFhYGLNnz252XCLifVY3m5QCBGuMj4i0QpNafADS0tIAOPPMMxk/fnyrH7xlyxY2bNjAlVde6Ty2YcMGoqOjnc9KSUlh7NixrFy5kgsvvJBVq1YxZswYBg8eDMBtt93GW2+9xfr165k4cSKrVq1ixowZ9OjRA4BZs2YxYsQIcnNz6dWrV6tjFhHPsbnZpBRqEiHt0C4iLdHkxMdh/Pjx7N69m//+978YhlGnrCny8/OZNWsWL7zwApmZmc7j2dnZ9OnTx+Xc+Ph4Vq9eDUBOTg4TJ06sU75v3z6Kioo4fPiwy/VxcXFERUWxf//+ZiU+JlOTT/UqR5z+Eq+nqF7c86d6MQzDuWVFsDnIJWZHImSzW+u8k8nU8Pu5K/enevEk1Yt7qhf3vF0vzXlusxOfZ555hmXLltGtWzeCg2suN5lMTUp87HY7M2fO5Pbbb+f88893KSspKSE8PNzlWFhYGKWlpY2Wl5SUABAREVGn3FHWVF27dm7W+d7mb/F6iurFPX+oF1tVzeKEkRHhRFhCnT+HBFsACOsURFxc9btEhFeX2+xm5zF3IsJD6y33h3rxBtWLe6oX9/yhXpqd+Lz77rssXbqUyy67rEUPfPHFFwkJCeGWW26pUxYeHk5RUZHLsfLycjp16uQsLy8vr1MeExPjTIgc433cXd9U+flFnNKY5ZNMpuoPmb/E6ymqF/f8qV5KbDX/WLFWVEFlTbeWyagemphXeIK8vOrfF6Vl1eU2u815zJ3Ssoo65f5UL56kenFP9eKet+vF8fymaHbiU1paysiRI5sdlMO7777L0aNHGTJkCIAzkfnoo4944IEH2LRpk8v5OTk5JCQkAJCQkEB2dnad8pEjRxIVFUX37t3JyclxdncdO3aMwsLCOt1njTEM/OoD7W/xeorqxT1/qBdrZc3AZTNBLvE6x/hUVjiP1/6zoXdrqNwf6sUbVC/uqV7c84d6afY6PqNGjWLdunUtfuAHH3zAl19+yY4dO9ixYwfXXnst1157LTt27CA1NZW8vDwyMzOx2WxkZWWxbt0657ieSZMmsW7dOrKysrDZbGRmZpKfn09qaioAEyZMICMjg9zcXIqLi5k3bx5JSUn07t27xfGKiOfZTi5eCNXbVNTmXLnZrlldItJ8zW7xqaio4KGHHmLp0qXExcW5lL366qutCiYmJoYVK1aQnp7O4sWLiY2NZfbs2QwdOhSonuU1Z84c5s6dy5EjR4iPj2fZsmVER0cDMH36dCorK0lLS6OkpITk5GQWLVrUqphExPMcM7osZgumU0YtBp9cx0fT2UWkJZqd+PTp06fZXUcNcSxc6DBw4EDefPPNes8fN24c48aNc1tmsViYMWMGM2bMaLP4RMTzHEmNxRxSp0x7dYlIazQ78bnnnnvaIw4RESfHVPaQIEudMufKzerqEpEWaHbi8/DDD9dbNn/+/FYFIyICNYlPQy0+WsBQRFqi1buzHz9+nPfff7/O+jkiIi1lq6oZ43OqIO3OLiKt0OwWH3etOps3b+aNN95ok4BERBwztixuurqCTRrcLCIt1+oWH4Bhw4aRlZXVFrcSEXGu3BzirqvL0eJjV1eXiDRfs1t8TlVZWck//vEPYmNj2yIeEZGaMT5BmtUlIm2r2YnP+eefX2ddjaCgIGbNmtVmQYlIYHPM2ApxM8ZH6/iISGs0O/E5dZFCs9nMWWedRbdu3dosKBEJbNYqx87sbgY3O1t81NUlIs3X7DE+SUlJDBkyhLCwMPLy8gDo2rVrmwcmIoHL2eLjrqvL0eJjt7kctxt2iq31b1AqIgItaPE5duwYv/3tb9m3bx/R0dEcP36cs88+mxUrVnD66ae3R4wiEmBq1vFxN6vLfYvPhwfeZ9OPGxl55uWk9Bze/kGKiF9qdovPwoULOfvss9m2bRubNm1i69atXHDBBVq8UETajHNWl9sWH8cChq5jfA4W5QLwdf7edo5ORPxZs1t8srKy+OCDD+jUqRMAnTt3Zu7cufziF79o8+BEJDA51vFxP8bH/ZYVJbbik3+WtHN0IuLPmt3iY7fb68zqMplMWCx1f0GJiLSEY+XmBtfxOaWrq9hanfiUnkyARETcaXbik5yczNy5cyktLQWgpKSEuXPnkpSU1ObBiUhgstkrgYZXbq7d1VVpr6S8qhxQi4+INKzZXV0zZ87k9ttvJykpiejoaAoLCznvvPN46aWX2iM+EQlANev41N/iY6uV+NROdpT4iEhDmpX4GIZBZWUl7733Hjt27CA/P58ff/yRO+64g6CgoPaKUUQCjGNxwobW8am9O3tJre6t0kolPiJSvyZ3dZWWlnLTTTfx//7f/yM4OJihQ4cydOhQnn/+eW655RZn15eISGvVrONT/8rNtlrr+DjG94BafESkYU1OfDIyMrBYLDz66KPOY127duXTTz+lsrKSF198sV0CFJHA41i52eKuq8tNi0+xTYmPiDRNkxOfDz/8kCeeeKLOKs1du3bl0Ucf5YMPPmjz4EQkMFXaG1/Hx+oyxqfY7fciIqdqcuKTn5/PWWed5bbsggsu4NixY20WlIgEtpp1fOoOQwx2s46PWnxEpKmanPhERkZy/Phxt2WFhYWEh4e3WVAi0vEs25PR5HOdKzc3MKvLZXCzxviISBM1OfFJSUlh5cqVbsveeOMNBg0a1FYxiUgHZD1li4kGzz3ZmmNx19V1ssXHbtipPLnej+t0dnV1iUj9mjyd/a677mLChAkcP36ca665hm7dunH06FHef/991qxZw+uvv96ecYpIAHGO8XG3Sam5ZukMa5WVYHMwxbaaXdnV4iMiDWly4nPOOefw8ssvM2fOHFauXInJZMIwDPr06cOyZcsYMGBAe8YpIgHEMasrOMjiTIIcHLO6qs+rIMISQXGtZMdmt2GtsrodGC0i0qwFDC+++GLWrVtHbm4uBQUFdOvWjZ49e7ZXbCISoGqv3Hxq4mOunfjYbRiGUad7q8RWTEhQbPsHKiJ+p9lbVgD06tWLXr16tXUsIiJAzXggi5uuLpPJRIg5BKvdirWqgsKK49gNe3UZZgzslNpKiQlT4iMidbUo8RERaU+OQcv1dVeFBIU6E5+yyjIAwoLCMJlMlFWWaZyPiNSr2buzi4i0t5p1fOq2+ACEnkyIrHYbx0qPAhAZEumc/q6ZXSJSHyU+IuJzHDuv19fi45jmbq2qIK+sevHUTpZI5/kl2qhUROqhri4R8TlWu2OvLvctPiFBodXnVVk5djLxibREul3XR0SkNrX4iIjPaWivLqhZ36c68anV1RWkri4RaZgSHxHxOTWzuuof3AzV21bkleYBJ7u6nGN81OIjIu4p8RERn2NzdnW57413DG622a3ux/jU0+JjMVuatWeYiHQ8SnxExOc4W3zqG9x8smWnonZXlyXSeX5DLT7N2TNMRDoeJT4i4nNq9upyn/iEOgc3nzKrS11dItIIJT4i4nOcs7qC3M/qchy32W0cKz05qyukk7Orq1SJj4jUQ4mPiPgcWwNbVkDN4OZjZcecO7N3CYmqNcZHiY+IuKfER0R8jrXWJqXuhJqrE5/vCnMA6GSpbu1RV5eINEaJj4j4lCp7lXPT0ca6ur49mfhEh8YAaB0fEWmUVxKfLVu2MHnyZC6++GKGDx/O448/Tnl5OQC7du1i8uTJJCYmMnr0aFatWuVy7dq1a0lNTWXQoEFMmDCBnTt3OsuqqqpYuHAhw4YNIzExkWnTpnH06FGPvpuI1JW5d3mTz3VMZYfGBzfnFGYD7hIftfiIiHseT3wKCgq46667uOmmm9ixYwdr165l27ZtvPTSS5w4cYKpU6cyfvx4tm/fTnp6OvPnz2f37t0AbN26lccff5wFCxawfft2rrvuOqZNm0ZZWfXuzBkZGWzatIk1a9awceNGwsLCmD17tqdfUURO0Zwp5DZ7zbn1bVLqSHAcM7qiw6JPHq9OiJT4iEh9PJ74xMbGsnnzZiZMmIDJZKKwsJCKigpiY2PZsGED0dHRpKWlERwcTEpKCmPHjmXlypUArFq1ijFjxjB48GAsFgu33XYbMTExrF+/3lk+ZcoUevToQWRkJLNmzeLzzz8nNzfX068pIi1krapp8al3cPMpLUExjhYf7c4uIo3wyialkZGRAFx22WUcOXKEIUOGMGHCBBYtWkSfPn1czo2Pj2f16tUA5OTkMHHixDrl+/bto6ioiMOHD7tcHxcXR1RUFPv376dXr15Njs9kaumbeZYjTn+J11NUL+55s15MppqvxlQa1S0+QaYggoOC6lxjMkFIcKjLseiw6JPHa7q63D3LXRz6vLinenFP9eKet+ulOc/16u7sGzZs4MSJE8yYMYPf//73dO/enfDwcJdzwsLCKC0tBaCkpKTe8pKS6qbtiIiIOuWOsqbq2rVzc1/Fq/wtXk9RvbjnjXqJCA/FZjcTF9f4s0uCC4Dq7qy4uM5EhLsmOTa7maqgSJdjPaK6ExEeSlRV9fHSyhK3z2ooDn1e3FO9uKd6cc8f6sWriU9YWBhhYWHMnDmTyZMnc8stt1BUVORyTnl5OZ06dQIgPDzcOQi6dnlMTIwzIXKM93F3fVPl5xdhGM19G88zmao/ZP4Sr6eoXtzzZr2UllVgs9vIyytq9NzDhdWJT7DZwnMbl1Bqq3Apt9ltVFa6XhNGJ0rLKnD0kpXYSjh67ARmk2tvvrs49HlxT/XinurFPW/Xi+P5TeHxxOfLL7/kT3/6E3//+98JCalulrZarVgsFuLj49m0aZPL+Tk5OSQkJACQkJBAdnZ2nfKRI0cSFRVF9+7dycnJcXZ3HTt2jMLCwjrdZ40xDPzqA+1v8XqK6sU9b9SL45lNea5jjE+I2UJFpa3ONYYBIeaaVqDo0GhCg8JOHq8Z+1NqK6OTpVOda+uLQ58X91Qv7qle3POHevH44Oa+fftSXl7O008/jdVq5ccff2ThwoVMmjSJq666iry8PDIzM7HZbGRlZbFu3TrnuJ5Jkyaxbt06srKysNlsZGZmkp+fT2pqKgATJkwgIyOD3NxciouLmTdvHklJSfTu3dvTrykiLWRrZINSgJBa6/uc2bnm73ew2YKJ6s5+zewSEXc83uLTqVMnli9fzrx58xg+fDidO3dm7NixTJ8+nZCQEFasWEF6ejqLFy8mNjaW2bNnM3ToUABSUlKYM2cOc+fO5ciRI8THx7Ns2TKio6MBmD59OpWVlaSlpVFSUkJycjKLFi3y9CuKSCvYGtmgFGqmrQP0qpX4mEwmIiydKLEVn5zZdVq7xSki/skrY3zi4+NZsWKF27KBAwfy5ptv1nvtuHHjGDdunNsyi8XCjBkzmDFjRpvEKSKe59igNNhc/6+n2klRr86uMzY7ORMftfiISF3askJEfIqjqyukwa6umrIz3SQ+oK4uEXFPiY+I+BTHys2WJnd1neVS1slSPaVdixiKiDtKfETEp5RXVk9fb2qLj7uuLlCLj4i4p8RHRHyKo6Um0hJZ7zmuY3xcZ23WJD5q8RGRupT4iEi7as7O7AClldUrtUdY6l941NHi08kS6dyZ3aGmq0stPiJSlxIfEWlXzdmZHWoSllMXH6zt7C7nEGQK4uLuQzCdskmPurpEpCFKfETEpzi6qCKC6098zuh8Jjtu3sNr/1N36QtH4lNaWZ34LNuT0Q5Rioi/8upeXSIipyq1VXd1NdTiA9XJjzudgl27uprb4iQiHZtafETEpzhafLIL97fo+ghLBACl6uoSETeU+IiIT3G01ASZglp0vWM2WJH15zaLSUQ6DiU+ItLurFVWcot+wG7YGz3XMaur9iKFzRETFgtAfnlBi64XkY5NY3xEpN0YhsFXeXtY/90/+Nl6gkHdLubG89MavMbR1dXQAoYNiQvvBkB+WV6LrheRjk2Jj4i0m8nrxvH5wc+cP+ccz270Gsfg5oZ2Z29IXER14pNXdqxF14tIx6auLhFpN9t+ygKg28lWmILy/EavcYzxaXGLT1gcUN3i05SuNREJLEp8RKRdlFWWUV5VDkBi98EA5Dcp8WldV1fX8OrEp8qo4kRFYYvuISIdlxIfEWkXR0oOA2AxW4g9OeC4KS0+zsHNLezqCgkKISo0GoA8jfMRkVMo8RGRdnG4tDrx6RzSxbkKc0FZc7q6WjarCyDuZKuPxvmIyKmU+IhIuzhS8hMAnUM6O1dhbqzFxzAM58KDLe3qAugapsRHRNxT4iMi7eLwycSnS0gX507rxyuOU2WvqveassoyDAygdYmPY0q7urpE5FRKfESkXRwuqd3VVb2NhN2wc8JaWO81tXdUt5gtLX52TeKjFh8RcaXER0TaRe0WnyBzEKFBYQAUlNW/orJjR3WL2YLZ1PJfT3HhXQElPiJSlxIfEWkXR2oNboaazUPzyuvvfmqLgc2gri4RqZ8SHxFpF4drDW4GnN1dDc3scq7h08Kp7A7atkJE6qPER0TahWOMT5dQ1xafhmZ2OberaMXAZtC2FSJSPyU+ItLmim3FFNuKAOhsqU58OgU3PqW9tdtVOGg6u4jUR4mPiLS5oydbe0KDQgkNrh6vE36yxSe/KV1dDSQ+FrOFZXsyGny+o6vrePlx7Eb90+fr09j9RcR/KfERkTZXM5W9s/OYc4xPQ11dTdyuwlplbbA8NiwWEyYMDEptZc7jVUYVh0t+wjCMVt1fRPyXEh8RaXOHSx0Dm7s4j0U0YfXmturqCjIHOfcHc7QiAXzw3Xu8uOsF/vHd31t1fxHxX0p8RKTN1V680KFpg5vbZjo71HR3OZKpElsJXxzdAcDX+V+1+v4i4p+U+IhIm6u9eKGDo6ur4TE+bdPiAzWJj2NRxH98+66zC6uw4nir7y8i/kmJj4i0uSOnrOEDtVt86l+5ua3W8YG6LT5v7lvpLCssL2z1/UXEPynxEZE2d/jkqs2uLT7VY3x+tp6od/Cwc3BzG7T4dD25bUWJrYTvfz7ApkMbnWUnKgpbfX8R8U9KfESkzdWs2lyT+IQFhzn33zpeT6tPa7u6ak91r93i89a+NwAINgdXP19dXSIBS4mPiLQpwzA44mZws9lkJiY0BoD8egY4t0VXl6M1yZH4HC7+icy9ywEYdNrFgFp8RAKZEh8RaVPFtiJnl1XtMT4AsWHV3U/1zeyq2bKi9bO6uoZXr958qORH8sry6Nd1AEO6XwJAoRIfkYClxEdE2tRPxdXdXGFBYXW6rBzJSH0blbblrK5uJ1t8AM6M7MWb165xtkAVVhxvdBFDEemYlPiISJs6VPIjANGh0S7HLWYLP1tPAPV3dTmmnrdF4nNO9HmYTWbCgsP527VrOL1TD8KDwwGotFdScvJZIhJYgr0dgIh0LIeKqxOfLqckPgBhJxOP+rq6nC0+bTCdvXtEd+4e9DtCgkLpG3s+UJ18mU1B2I0qTpQXEmmJbPVzRMS/qMVHRNqUI/GJCo2qU+bcr8sDXV0Ap3fq4TLOyGQyOVt9NLNLJDB5JfHZt28ft99+O0lJSQwfPpwHHniAgoLq6a27du1i8uTJJCYmMnr0aFatWuVy7dq1a0lNTWXQoEFMmDCBnTt3OsuqqqpYuHAhw4YNIzExkWnTpnH06FGPvptIoHMkPqd2dUHNIobuuroMw2jTLSvqEx4cBmhml0ig8njiU15ezp133kliYiL/93//xz/+8Q8KCwv505/+xIkTJ5g6dSrjx49n+/btpKenM3/+fHbv3g3A1q1befzxx1mwYAHbt2/nuuuuY9q0aZSVVe++nJGRwaZNm1izZg0bN24kLCyM2bNne/oVRQKaY4xPQy0+x8qO1SkrqyzDoHrAcWNdXbXX62lOGdR0tx0vV4uPSCDyeOJz6NAhzj//fKZPn05ISAgxMTHccMMNbN++nQ0bNhAdHU1aWhrBwcGkpKQwduxYVq6sXmp+1apVjBkzhsGDB2OxWLjtttuIiYlh/fr1zvIpU6bQo0cPIiMjmTVrFp9//jm5ubmefk2RgFXT1RVdp8xx7Meiun8nHd1cAJYgS6PPqW/158bKHF1davERCUweH9x87rnnsnz5cpdjH374If379yc7O5s+ffq4lMXHx7N69WoAcnJymDhxYp3yffv2UVRUxOHDh12uj4uLIyoqiv3799OrV68mx2gyNfetvMMRp7/E6ymqF/c8VS+Hig8B1V1dpz4rOiwagINFuRjYnSs5A5RVVSc+EcERBJnNzljri7d22annOMpMJggJsric50x8rIX13qOh5wYK/T1yT/XinrfrpTnP9eqsLsMwWLRoEZ9++imvv/46r776KuHh4S7nhIWFUVpavahZSUlJveUlJSd/aUZE1Cl3lDVV166dGz/Jh/hbvJ6ienGvPeulqKLIOWX99Ohuzi0iHE63dCPIFITVbsUaWsSZXc50lv1UVf1np5BORIRXj/EJNgdjsbtvmLbZzcTFdWbpjqXO808tcxyPi6t+54jwUDqHVc/kqjCXOI+Da71EhIe6lAUy/T1yT/Xinj/Ui9cSn+LiYh5++GH27t3L66+/Tt++fQkPD6eoqMjlvPLycjp1qt7cMDw8nPLy8jrlMTExzoTIMd7H3fVNlZ9fhD+sbWYyVX/I/CVeT1G9uOeJevmmYD8AXUKiMGxmSu0VLuU2eyVndu7F9z8fYOeBvYT1rBkH9OOx6okIEcGdKC2rvs5itmOz29w+y2a3kZdXRFFxaZ1zHGWO++TlVf9eKS2rIJjq8UM/HT9KXl6R23opLatwXhOo9PfIPdWLe96uF8fzm8Iric8PP/zAlClT6NmzJ6tXryY2NhaAPn36sGnTJpdzc3JySEhIACAhIYHs7Ow65SNHjiQqKoru3buTk5Pj7O46duwYhYWFdbrPGmMY+NUH2t/i9RTVi3vtWS8Hiw4C0DOyp9vnGAb07nwW3/98gB9+/p6hPYY5y4qtNV1djusaijXYZOGVr5bX+5zax2v/GRZU/Y+k6tWb617T2HMDjerCPdWLe/5QLx4f3HzixAluvfVWLr74Yl5++WVn0gOQmppKXl4emZmZ2Gw2srKyWLdunXNcz6RJk1i3bh1ZWVnYbDYyMzPJz88nNTUVgAkTJpCRkUFubi7FxcXMmzePpKQkevfu7enXFAlIP5VUj+9paDuI3l3OAuCHn793Oe4Y3BxhaXoLbUODmOsT7mZW16KsRc2+j4j4J4+3+LzzzjscOnSI999/nw8++MClbOfOnaxYsYL09HQWL15MbGwss2fPZujQoQCkpKQwZ84c5s6dy5EjR4iPj2fZsmVER0cDMH36dCorK0lLS6OkpITk5GQWLVrk4TcUCVw/Fle3+ESG1N/k3Ktz9T9EfihyTXwc21V0aufVlN3N6rJVue9OE5GOx+OJz+23387tt99eb/nAgQN588036y0fN24c48aNc1tmsViYMWMGM2bMaHWcItJ8P52c0eVuDR+Hxlt8Iupc05bCTi5gqJWbRQKTtqwQkTbjaPGJCmkg8el8NgC5RT+4HC+1Vc/e7BTcvMkIzaV1fEQCmxIfEWkzjjE+7hYvdDjrZIvPj8UHXbqYSmzFgCe7uk5gN+zt+iwR8T1KfESkzfzo3JndfYuPxWzhf79dQ2hQKHbD7mwhAig+mfi0f1dXdeJjYPBzxYl2fZaI+B4lPiLSJoqsP1Nk/RlouKur0l7pdoDz/oL/AHBm5Jlur2srweZg555hheruEgk4SnxEpE04tqoICwojNLjh3dUdA5xzf64e51Nlr2Lb4a0ADO05rN7r2oqjK07jfEQCjxIfEWkThxrp5qqtd+eTM7uKDgDwdf5XFFl/JtLSmf5dB7ZbjA7RJxMfzewSCTxKfESkTdTsyt6ExKfL2QB8f3JKe9ZPmwFI6pFMkDmofQKsJTosBlCLj0ggUuIjIm3iwM//BSAmNKbRc3s7xvicTHy2HKpOfGpvYdGeHF1dGuMjEniU+IhIm/juxLcAdA2Pa/Rc5yKGRd9jGIazxWdoz+HtF2AtjuSssFxdXSKBRomPiLSJ/574DoCuYV0bPffcqPOwmC0cLT1Cetaj5JUdIzQolMTTLiZz7/L2DlUtPiIBTImPiLSaYRh8V9j0Fp8uoVE8cMmfAFi88xkALu4+hNCg0BZtPNpc0c7ERy0+IoFGiY+ItNrR0iOUVpZgwkRMWGyD51rMFpbtyeCexPu49IzLnMeH9khps3gsZkuDLUeOwc1q8REJPEp8RKTVHON7okNjCDY3vvexYRis2PsSf7niJedigpeeOapNY2qo5SgurLpV6kjJ4TZ9poj4Po/vzi4iHU9NN1fj43scDMPg4x8+ZMqF0zhS+hPDe17aXuHVcdbJ6fS1V44WkcCgxEdEWq05M7pqs1bZ6BbRjeiwaEwmU3uE5pYj8TlaesS5K7yIBAZ1dYlIqzkTnybM6PIF0WExzs1KHWsJiUhgUOIjIq3WnBldvsKxls/3Px/wbiAi4lFKfESkVeyGnQM/n1zDx58Sn5MzuxwrTotIYFDiIyKtcrjkJ8oqyzBjbtJ2Fb4iWi0+IgFJiY+ItIpjfE9MWEyLNxh1rO3jSY71hpT4iAQWJT4i0iptNb7HMAyPbFfh4Ojq+v7EAY89U0S8T4mPiLRKW87o8sR2FQ61W3wMw/DYc0XEu5T4iEirfFeYA/jXwGaAqJAozCYz5VXlFFmLvB2OiHiIEh8RaZX/FHwNwGkR3b0cSfMEmYM4M7IXAAVlBY2e7+kxSCLSPpT4iEiLldhKnIODu3fyr8QHalZwzi/Lb/RcT3bDiUj7UeIjIi22v+A/AERaIulkifRyNM3nSHya0uIjIh2DEh8RabF9JxOf7p1O93IkLeNIfI6XHfduICLiMUp8RKTF/pO/F4Dufja+x6E5XV0i0jEo8RGRFvuPo8Unwr9bfPJK8+o9R4OaRToWJT4i0mLOFh8/7eo6LzqeIFMQRdYivinY7/YcDWoW6ViU+IhIi+SV5XGs7Cjgf1PZHbqERvGLs1IBeHP/G16ORkQ8QYmPiLTIvpPr98SExRISFOLlaFruxvPTAFi1/02q7FVejkZE2psSHxFpEUc31+l+Or7HsTHqlWdfTURwBIdLfuLzg595OywRaWdKfESkRRxT2U/zw4ULHaxVVkKDQknskQjAW/tXejkiEWlvSnxEpEW+9vMWn9qSeiYBsP67f/DBf9djN9TlJdJRKfERkWarslfVtPh0gMSnV5deXNhtEOVV5fz6/Rt57otn2XV0p7fDEpF2oMRHRJrtPwVfU2IrJjQolG4R3bwdTquZTCbeGLOKuwf9ntiwWH62nmDlf171dlgi0g6U+IhIs20/vBWAMzv3xmzqGL9GunfqztxhT5A+4v8BsDf/Ky9HJCLtoWP8xhIRj3IkPr079/ZyJG2vf9xAAPbmfYXdsHs5GhFpa15NfAoKCkhNTWXr1q3OY7t27WLy5MkkJiYyevRoVq1a5XLN2rVrSU1NZdCgQUyYMIGdO2v64auqqli4cCHDhg0jMTGRadOmcfToUY+9j0ig2HYy8XFs+dCRxEcnEGQKprSyhAM//9fb4YhIG/Na4vPFF19www038MMPPziPnThxgqlTpzJ+/Hi2b99Oeno68+fPZ/fu3QBs3bqVxx9/nAULFrB9+3auu+46pk2bRllZGQAZGRls2rSJNWvWsHHjRsLCwpg9e7ZX3k+kozpScpgffj6ACRNndu7l7XAa5Vivp6le/08mp0WcBlS3+ohIx+KVxGft2rXMmDGD+++/3+X4hg0biI6OJi0tjeDgYFJSUhg7diwrV1avrbFq1SrGjBnD4MGDsVgs3HbbbcTExLB+/Xpn+ZQpU+jRoweRkZHMmjWLzz//nNzc3GbFZzL5z5e/xat68f962XFkGwA9InsQbgnz+js15ctmtzq/Dwmy8Nevl9dbLza7ldNP7j32df4er8fu75+XjvilevHNemmq4GZlBG1kxIgRjB07luDgYJfkJzs7mz59+ricGx8fz+rVqwHIyclh4sSJdcr37dtHUVERhw8fdrk+Li6OqKgo9u/fT69eTf+XadeunVvyWl7jb/F6iurFvdbWy54vvgTg3JhziQgPrfe8YHMwFnvD/7Zq7Jy2ugeVVcTFdXaJN5hQbHazsz4cf0aEh9Ir+kx2Hv2S7KJ9jIge4bw2Lq7+uluUtYj7ht7XYKz+SH+P3FO9uOcP9eKVxKdbN/fTX0tKSggPD3c5FhYWRmlpaaPlJSUlAERERNQpd5Q1VX5+EYbRrEu8wmSq/pD5S7yeonpxr63q5fP/bgTgzE69KS2rqPc8i9mOzW5r8F6NndNW9yitrCAvr6hOvDa7jfz8IqDm731pWQWxIXEAfPnjTi6Ou8R5bV5eUb3POVFc3GC5v9HfI/dUL+55u14cz28KryQ+9QkPD6eoyPUXR3l5OZ06dXKWl5eX1ymPiYlxJkSO8T7urm8qw8CvPtD+Fq+nqF7ca029lFeWs+vYvwHo3fnsBu/TlOc0dk5b3sPdebWP1T7HsSjjweJcSm1l9V7f3Fj9UUd9r9ZSvbjnD/XiU9PZ+/TpQ3Z2tsuxnJwcEhISAEhISKi3PCoqiu7du5OTk+MsO3bsGIWFhXW6z0SkZb44sh2b3Ua38NOIDYv1djjtJjw4nF4np+ofKTns5WhEpC35VOKTmppKXl4emZmZ2Gw2srKyWLdunXNcz6RJk1i3bh1ZWVnYbDYyMzPJz88nNTUVgAkTJpCRkUFubi7FxcXMmzePpKQkevfueGuNiHjDR99vAKBXl96YmjOa0MssZguZe5c365r+XQcAcKRUiY9IR+JTXV0xMTGsWLGC9PR0Fi9eTGxsLLNnz2bo0KEApKSkMGfOHObOncuRI0eIj49n2bJlREdHAzB9+nQqKytJS0ujpKSE5ORkFi1a5L0XEulgPvr+QwASohO8HEnzWauszTq/X9wAPjiwnsNq8RHpULye+Ozfv9/l54EDB/Lmm2/We/64ceMYN26c2zKLxcKMGTOYMWNGm8YoIvDDz9+z//g+zJiJj/G/xKe5BnS9EIAfi5q3HIaI+Daf6uoSEd/1z5OtPb269CY8OKKRs/3fiDMuJcgUxLGyY3z/8wFvhyMibUSJj4g0iaObq2/s+V6OxDOiw2JI7pECwIYD73s5GhFpK0p8RKRRpbZSNv1YvX5P35iOm/icOgD6yrP/B4APD3zgjXBEpB0o8RGRRv3fj/+ivKqc6NAYTovo7u1w2syps71OHQB95VlXA7Dl0P9RUem6hpiI+CclPiLSqPXf/QOAvrF9/Woae1M0NNsrPiaB2LCu2Ow2vi3McSlrzsanIuI7lPiISIOsVVbe++86APp3HejlaDyvT0xfAL457joDtbnT40XENyjxEZEGfZb7MScqCukc0pmzo87xdjge5xjM/c3xb7BVNbxvmIj4PiU+ItKgtdlrABjQdSBmU+D9yujdpTdx4d0oqyzlQ83uEvF7gfdbTESarKyyjA8OrAdgYLeLvByNd5hNQdx8wa0AvNLMbS9ExPco8RGRen30/QZKbMVEh0Y7N+0MRLf0vw0TJjYe/Izs4994OxwRaQUlPiJSr7XZqwEYEHdhh5vN1RCL2eIya6tX597OQc5/3fuyt8ISkTagxEdE3Mory+PDk91cF3Ub5N1gvODUWVtDTk8C4M19b1BiK/FGSCLSBpT4iIhbb+17A5vdxhmRZ9Ijsqe3w2lXwebgOqs2n+q86PM4J+pcfrae4M19r3soMhFpa0p8RKQOwzBY+Z+/AjDk9Eu8HI1nNLYuj8lk5q6LpgPwl52LqbJXeSIsEWljSnxEpI6tP20hpzCbEHMIF8YN8nY4PuOm828m0hLJweJcvsrb7e1wRKQFlPiISB2vfZ0JwMBuFxIaHOrdYHxIeHA4Q3sOA+D/fvwcu2H3ckQi0lxKfETERX5ZPuu+/V+gZkCv1BjS/RKiQqPJK8vj7f1/83Y4ItJMSnxExMVf975MeVU5PTudwZmRvbwdjs8JDQ5jysDfAnD/p/eQ+VXTp7drY1MR7wv2dgAi4jsqqip4ec9LAAw/49KAWrvnVBazpd6ZXvcPnslHP2zg30e/5IHP7ye/PI+w4LBG76mNTUW8Ty0+IuL0zjerOFZ2lKiQKAbEBd5O7KeqL1GxBFm47rzxPJQ0G4Ant8/np+JDngxNRFpIiY+IANVT2Jfueh6obu0JMgd5OSLfZjKZ+MOQB7g+fiJ2w8767/6B3bCrO0vExynxEREA3vtuHf8p+JqI4E4k9Rjq7XD8xtxh6XQ6OcX9b/95Xd1ZIj5OiY+IUGwrZvb/PQjA0J4phAeHezki31PfmJ8ekT154JI/AfB41p8pryz3dGgi0gxKfESEJ7fN51DJj8SGxTLijJHeDsdn1deac+fAu4gL70ZBeQFZhzZ7OCoRaQ4lPiIBbk/ebl7c9RcArj1vHCFBIV6OyL8s25OBJcjCqF6jAcj6aTMF5flejkpE6qPERySAFZTn85sPbsaOnbHnjadPTF9vh+RzLGYLK/+T6fZ45t7lzlagfl370b/rQCqqKnhh5xIPRykiTaXERyRAWaus/OaDW/j+5wPEhMZycfeLvR2Sz7JW2Zzf1x7rU7vry2Qy82DSLACW71nKUzsWeDZIEWkSJT4iAcgwDGb+6z42H/o/QoNCuaX/rVjM6uJqqvrG+lx19v/QM/IMSitLeWlXBjnHszW9XcTHKPERCUDztj7G3/a9jgkTN/T9FadFdPd2SB2CyWRiYsJkzu5yDoUVxxnzzhV8nbfX22GJSC1KfEQCzNJ/P89zXz4NwLj4CfSJ1biethQb3pX3JnzEGZFncrziOCv/8yq//edvOFJ6xNuhiQhKfEQCyuu7X+eRTdVrzqSedTVDTr/EyxF1PBazhff++y53DJzKiDMuxYSJd7JXM/yNIew4vA27Yfd2iCIBTYmPSID454EPuf3d2wEYccZIRp55mZcj6risVVZCgkK4+pwx3HnhXVzULZGfrSd477t1XLf2agrLj3s7RJGApcRHJAD888AH3PHhr6m0VzLotETGnDs2oHdeb0uOWV4Ws8Vtec/IM/hg4ifMG/H/CAkKZdvhLG5efwOltlIPRyoioMRHpEMzDIPndz7HzetvoKyyjDEJY5iQMAmzSX/121Jj+3MFmYMIDgrmNwPuJCo0mm2Hs5i64TbndZr5JeI5+u0n0kEdLMrl1+/fyGNbHsHAYGiPFK449wrtuu4l1ior3TudzmvXvEWwOZgN33/AZW8N5aPvP9TGpiIepMRHpIMptZWy+MtnGfG3JD488D5BpiCuPfc6rj3vOjC8HV3HVd8mpqceH9ojhRv6/oq48G58W5jDr96bzBtfv0bO8WxArT8i7S3Y2wGISNs4UnqENd+8zV92PsexsqMAnNXlbMbFX89pEd3RkJ72V1/LzanHL+jaj76x57PhwAds/WkL2YXfMPKtZO4c+FuiQqM8EapIwFLiI+KnbFU2dh3byeZD/8e/Dn7Gph8/d06VjgmNZXTvX3DRaYkaz+NDarf+mE1mrj7nGlJ6DuPdnLV8c3w/S3c9T0RwJ7qFn8aN56dpw1iRdqDER8QPlFeW821hDt8c38f+4/v48sgOtv20ldLKEpfzzuzci2E9h9O/60CN5fExFrMFm91Wp/UnKjSamy64mQti+/G7j3/LsbJjzPjXvTy9YyF3DLyLoT2GER8TT2xYVy9FLtKxdLjEJz8/n0ceeYRt27YRFBTEddddx4MPPkhwcId7VelgDMOgoLyA707kkHM8m2+O7+fjHzZQUVXB9z8fcLvwXXhwBOdGncu50ecRH92HruFdnf+DFf9hMVv4oeh77h88k38d/Ix/H/2Sn0oO8UTWHOc5sWGxRFoiGXbGpcRHJ3BedALx0QmcHXUOoUGhXoxexL90uGzgvvvuo3v37mzcuJG8vDymTZtGZmYmd955p7dDkw7GMAyqjCqqjCoq7ZXYjSqq7FXY7JWUVZZSVllW68/qrxJbMUXWIuefJyoK+bH4ILlFP/DDzz/UacGpLTw4nNMiup/8Oo1zos7ltIju6srqIKxVVixmC0N7prD8qr/yx89+x7+P7uRo6REKKwopKC+goLyAH/atdLnObDLTq3Nv4k8mQudFJ9Ajsich5hBCg0IJCXL86fp9aFBI9TFziNZ0koBiMgyjw8zz+P7777nyyiv5/PPP6d69etPF9evX8+STT/Lpp582+T4FBUW0Za1U2auYs2k2B37+b9vd9CSLJQibrcr5swnP/AIzak0PcnyEHMecf556vFal1lxft8zdtQ0/hzrHg4PMVFbZa64xTnlerefasVcnLYadKnuVM5mpsldhN6qwG/aTZZUuZUY7TZGKCo2iW0Q34sKrv3pEnk5saFciQzpjMpmwmIOw2avqvb6h8tCwYCrKK1t1j6ae40/36BQRRklpudfjcJxjCQrCVlVzrrXKyglrIcdKjnKk9CgF5fkcLT3KifITFNuKGrxnU4SYTyZBQRZnUmQJCiEiJJwgIxhLUIjHfrc0h7cStlN/79bma/XksXhMteqlgV+NQeYgbut/B6PP+kXbPt4EsbGdm3Ruh2rxyc7OJjo62pn0AJx33nkcOnSIn3/+mS5dujTpPk2tvObIGP98m99TREREmqdDtZGXlJQQHh7ucszxc2mplocXEREJdB0q8YmIiKCsrMzlmOPnTp06eSMkERER8SEdKvFJSEigsLCQvLw857Fvv/2W008/nc6d2777SkRERPxLh0p8zj77bAYPHsy8efMoLi4mNzeXF154gUmTJnk7NBEREfEBHWpWF0BeXh6PPfYYW7duxWw2M378eGbMmEFQkBZzExERCXQdLvERERERqU+H6uoSERERaYgSHxEREQkYSnxEREQkYCjxERERkYChxMePlZWVccMNN/DOO++4HP/vf//LrbfeSmJiIiNGjGDp0qVeitC7du3axfnnn09iYqLzKy0tzdtheUV+fj533303Q4YMITk5mfT0dCorK70dltetX7+efv36uXxGZs6c6e2wvKagoIDU1FS2bt3qPLZr1y4mT55MYmIio0ePZtWqVV6M0PPc1cmcOXMYMGCAy+fmrbfe8mKUnrNv3z5uv/12kpKSGD58OA888AAFBQWAH31WDPFL33zzjXH99dcbffr0MdasWeM8brVajSuvvNJ48sknjYqKCmPv3r3GiBEjjPXr13sxWu947bXXjJtvvtnbYfiEm2++2fjjH/9olJaWGj/88IMxZswYY9myZd4Oy+sWLFhgPPTQQ94Owyfs2LHDuOKKK4w+ffoYWVlZhmEYRmFhoZGUlGS8/vrrhs1mMzZv3mwkJiYau3bt8nK0nuGuTgzDMK6//nrjnXfe8WJk3lFWVmYMHz7ceO6554yKigqjoKDAmDJlinHXXXf51WdFLT5+aMuWLdx6661cf/319OzZ06Vs+/btHD16lN///veEhITQr18/brnlFlauXOmlaL1nz549DBgwwNtheN3333/Ptm3bmDlzJuHh4fTq1Yu77747ID8Tp9JnpNratWuZMWMG999/v8vxDRs2EB0dTVpaGsHBwaSkpDB27NiA+OzUVydWq5VvvvkmID83hw4d4vzzz2f69OmEhIQQExPDDTfcwPbt2/3qs9KhdmfvKMrLyzly5Ijbsm7dunH++efz6aefEhoayiuvvOJSnp2dzTnnnENISIjzWHx8PC+99FK7xuwNjdXTnj17iIuL48orr6S4uJikpCQeeughTj/9dA9H6l3Z2dlER0fTvXt357HzzjuPQ4cO8fPPP9OlSxcvRuc9drudvXv3Eh4ezvLly6mqquKyyy5jxowZREVFeTs8jxoxYgRjx44lODjY5X/02dnZ9OnTx+Xc+Ph4Vq9e7ekQPa6+Otm3bx+VlZUsXryYL774gs6dOzNx4kTuvPNOzOaO3ZZw7rnnsnz5cpdjH374If379/erz4oSHx+0a9cufv3rX7st+8tf/sIVV1xR77X17VDfEXenb6ieFi9ezGmnncawYcO46aabsNlsPP7440ydOpW1a9cG1Ere9X0mAEpLSwM28SkoKKBfv35cddVVLF68mOPHj/Pggw8yc+bMDvkPhYZ069bN7XF3n52wsLAO+fvkVPXVSVFREUlJSdxyyy0888wz/Oc//2H69OmYzWbuvPNOD0fpPYZhsGjRIj799FNef/11Xn31Vb/5rCjx8UHJycns37+/RdfWt0N9R9ydvrF6uuqqq1x+fuSRR0hJSeHbb7+t8y+Tjqy+zwTQIT8XTRUXF+fSDB8eHs7MmTP55S9/SXFxMZGRkV6MzjeEh4dTVFTkcqy8vDygPzfDhw9n+PDhzp8vvPBCbr31VtavXx8wiU9xcTEPP/wwe/fu5fXXX6dv375+9Vnp2O1yASghIYEDBw64zNjJyckhISHBi1F53k8//cT8+fMpKSlxHrNarUD1v0ICSUJCAoWFheTl5TmPffvtt5x++ul07tzZi5F51759+3jqqacwau3aY7VaMZvNLl3FgaxPnz5kZ2e7HAvE3ye1ffTRR7z55psux6xWa8D8Xvnhhx+YOHEixcXFrF69mr59+wL+9VlR4tPBJCcnExMTw9NPP01FRQX79u3jtddeC7gd6mNiYnjvvfd49tlnqaiooKCggEcffZSUlBR69+7t7fA86uyzz2bw4MHMmzeP4uJicnNzeeGFFwLuM3Gq6OhoVq5cyfLly6msrOTQoUM8+eSTXH/99Up8TkpNTSUvL4/MzExsNhtZWVmsW7eOiRMnejs0rzEMg/nz57NlyxYMw2Dnzp28+uqr3HDDDd4Ord2dOHGCW2+9lYsvvpiXX36Z2NhYZ5k/fVa0SamfGz16NPfccw8TJkxwHvv+++957LHH2LVrFxEREdx8881MnTrVi1F6x759+1i4cCFfffUVAKNGjWLWrFlER0d7NzAvyMvL47HHHmPr1q2YzWbGjx/PjBkzAmqskzvbtm3jmWee4ZtvviE0NJQxY8Ywc+ZMQkNDvR2a1/Tt25dXX32V5ORkoHrmW3p6Ot988w2xsbHcfffdLr9vAsGpdfLmm2/yyiuvcOTIEeLi4rj99tsDYo2wV155hQULFhAeHo7JZHIp27lzp998VpT4iIiISMBQV5eIiIgEDCU+IiIiEjCU+IiIiEjAUOIjIiIiAUOJj4iIiAQMJT4iIiISMJT4iIiISMBQ4iMifq+iooLDhw83u6y5Dhw40Cb3ERHvUeIjIn7vV7/6FZs3bwZgx44dJCYmui1rja+//pprr7221fcREe9S4iMifu/48ePO74cMGcLOnTvdlrVGUVERNputTe4lIt6jxEdE2swXX3zBxIkTGTRoEJMnT+bpp5/mlltu4Z133mH06NEu595yyy0sWbIEgOLiYmbPns2VV17JoEGDuPTSS1m6dKnz3NGjR/Piiy8yfvx4EhMTGT9+PFlZWQD85je/4dChQ8yZM8e5H5ljx+hTy+644w4eeeQRlzjuuusunnvuuQbfKzc3lylTpgCQmJjIzp07sdvtvPTSS1xxxRUMHjyYSZMmsXHjxibFDLBkyRIuu+wykpKSmDhxIh9//HFzq1tEWsIQEWkD+fn5xpAhQ4wXX3zRsFqtRlZWlnHxxRcbN998s7FmzRrj8ssvdzn/5ptvNhYvXmwYhmHMmTPHuPXWW40TJ04Ydrvd+OCDD4w+ffoYBw4cMAzDMC6//HIjNTXVOHDggFFaWmo8+OCDxlVXXeW81+WXX26sWbPGMAzDyMrKMvr06eO27L333jOGDBliVFRUGIZhGMeOHTP69+9v/PDDD42+36n3Xbx4sTFy5Ejjq6++Mmw2m/Hee+8ZAwYMMHbt2tVozFu2bDGGDx9uHDlyxLDb7cbf/vY3Izk52bBarc2rdBFpNrX4iEib+PTTTwkPD2fKlClYLBaSk5OZOHFik6793e9+x6JFi4iMjOTw4cPO3dGPHj3qPGfSpEmcddZZhIeHM3bs2BYNNL7iiiswm8188sknAKxbt47ExER69erV7HutWbOGqVOn0r9/f4KDg7nmmmsYPXo0q1evbjTm0NBQTpw4wdtvv83XX3/N5MmT2bJlCxaLpdlxiEjzKPERkTaRn59Pjx49MJlMzmPnnHNOk6+99957SU5O5u6773Z2+9jtduc5cXFxzu+Dg4MxDKPZMYaEhHDttdfy7rvvArB27domJ2enysvLq5MwnXnmmfz444+NxpyYmMiSJUvYuXMnaWlpDB8+nBdeeMHlfUWkfQR7OwAR6Rh69erFjz/+iN1ux2yu/jeVYxq52WzGarW6nF970PG9997L6NGjefnllwkODub48eO8/fbb7RLnxIkT+eUvf8nOnTs5ePAgV111VYvuc8YZZ5Cbm+tyLDc3l9NOO63Raw8dOkTXrl15+eWXsVqtbNmyhXvuuYf+/fszatSoFsUjIk2jFh8RaROjRo0iJCSExYsXY7Va2bt3L2+99RYA5513Hnl5eWRlZWEYBu+++y7ffvut89qioiLCwsIICgqioKCAJ554AqDJs6hCQkIoKipqUlm/fv2Ij4/nscce45prriE8PLxJz3B0vznuNXnyZF566SX27t1LVVUV77//Pp988gnXX399o/fas2cPd955J/v27SMkJISuXbsCEBMT06RYRKTl1OIjIm0iPDycFStW8NhjjzF8+HDOPvtshg4dSn5+PgMHDmTatGk89NBDlJSUcMUVV7i0tMyfP5958+axYsUKoqKiuOaaa+jXrx/ffPMNI0aMaPTZkyZN4tlnn2XPnj1Mnjy53rKnnnoKgAkTJpCens6f//znJr9fnz59GDx4MJdeeinPPfcct99+O3a7nfvvv59jx45x1lln8cwzz5CUlNTova666ioOHDjAtGnTOH78OF27duVPf/oTF110UZPjEZGWMRkt6SgXEWmCJUuWsG3bNl577TVvh+Li448/5qmnnuL999/3digi4mFq8RGRgHH8+HEOHz5MRkYGN910k7fDEREvUOIjIgHjq6++4p577mHYsGHceOONzuMffvghDz30UL3XDR48mOXLl3siRBFpZ+rqEhERkYChWV0iIiISMJT4iIiISMBQ4iMiIiIBQ4mPiIiIBAwlPiIiIhIwlPiIiIhIwFDiIyIiIgFDiY+IiIgEjP8PlLt2BnHGHXgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "def before_skewness(column):\n",
    "  for i in columns:\n",
    "    sns.set_style(\"darkgrid\")\n",
    "    sns.histplot(new_df_1[i],kde=True,color='g')\n",
    "    plt.title(f'Skewness In {i}')\n",
    "    plt.show()\n",
    "\n",
    "before_skewness(columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GTZmrAG_gmzf"
   },
   "source": [
    "# **Treating Outliers With IQR Method**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "YTNkjH3Jhdu2",
    "outputId": "78e84314-ec82-4e6c-cc9b-f85fd4e9cfb5"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>item_date</th>\n",
       "      <th>quantity_tons</th>\n",
       "      <th>customer</th>\n",
       "      <th>country</th>\n",
       "      <th>status</th>\n",
       "      <th>item_type</th>\n",
       "      <th>application</th>\n",
       "      <th>thickness</th>\n",
       "      <th>width</th>\n",
       "      <th>product_ref</th>\n",
       "      <th>delivery_date</th>\n",
       "      <th>selling_price</th>\n",
       "      <th>item_date_day</th>\n",
       "      <th>item_date_year</th>\n",
       "      <th>item_date_month</th>\n",
       "      <th>delivery_date_day</th>\n",
       "      <th>delivery_date_month</th>\n",
       "      <th>delivery_date_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>3.991779</td>\n",
       "      <td>30156308</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.302585</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>7.313220</td>\n",
       "      <td>1670798778</td>\n",
       "      <td>2021-07-01</td>\n",
       "      <td>6.749931</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>6.643822</td>\n",
       "      <td>30202938</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.713572</td>\n",
       "      <td>-0.223144</td>\n",
       "      <td>7.098376</td>\n",
       "      <td>1668701718</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>6.953684</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>5.956169</td>\n",
       "      <td>30153963</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.332205</td>\n",
       "      <td>-0.967584</td>\n",
       "      <td>6.858565</td>\n",
       "      <td>628377</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>6.468211</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>5.310301</td>\n",
       "      <td>30349574</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.077537</td>\n",
       "      <td>0.832909</td>\n",
       "      <td>7.183112</td>\n",
       "      <td>1668701718</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>6.643790</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>6.666354</td>\n",
       "      <td>30211560</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.302585</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>7.600902</td>\n",
       "      <td>640665</td>\n",
       "      <td>2021-03-01</td>\n",
       "      <td>6.357842</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181668</th>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>4.629691</td>\n",
       "      <td>30200854</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.713572</td>\n",
       "      <td>-0.040822</td>\n",
       "      <td>7.106606</td>\n",
       "      <td>164141591</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>6.381816</td>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181669</th>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>5.337954</td>\n",
       "      <td>30200854</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.713572</td>\n",
       "      <td>-0.051293</td>\n",
       "      <td>7.313220</td>\n",
       "      <td>164141591</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>6.378426</td>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181670</th>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>1.443523</td>\n",
       "      <td>30200854</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.713572</td>\n",
       "      <td>-0.342490</td>\n",
       "      <td>7.130899</td>\n",
       "      <td>164141591</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>6.428105</td>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181671</th>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>-inf</td>\n",
       "      <td>30200854</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.713572</td>\n",
       "      <td>-0.162519</td>\n",
       "      <td>7.130899</td>\n",
       "      <td>164141591</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>6.398595</td>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181672</th>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>6.008043</td>\n",
       "      <td>30200854</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.713572</td>\n",
       "      <td>-0.342490</td>\n",
       "      <td>7.122867</td>\n",
       "      <td>164141591</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>6.408529</td>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>181640 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        item_date  quantity_tons  customer  country  status  item_type  \\\n",
       "0      2021-04-01       3.991779  30156308     28.0       1        5.0   \n",
       "1      2021-04-01       6.643822  30202938     25.0       1        5.0   \n",
       "2      2021-04-01       5.956169  30153963     30.0       1        6.0   \n",
       "3      2021-04-01       5.310301  30349574     32.0       1        3.0   \n",
       "4      2021-04-01       6.666354  30211560     28.0       1        5.0   \n",
       "...           ...            ...       ...      ...     ...        ...   \n",
       "181668 2020-07-02       4.629691  30200854     25.0       1        5.0   \n",
       "181669 2020-07-02       5.337954  30200854     25.0       1        5.0   \n",
       "181670 2020-07-02       1.443523  30200854     25.0       1        5.0   \n",
       "181671 2020-07-02           -inf  30200854     25.0       1        5.0   \n",
       "181672 2020-07-02       6.008043  30200854     25.0       1        5.0   \n",
       "\n",
       "        application  thickness     width product_ref delivery_date  \\\n",
       "0          2.302585   0.693147  7.313220  1670798778    2021-07-01   \n",
       "1          3.713572  -0.223144  7.098376  1668701718    2021-04-01   \n",
       "2          3.332205  -0.967584  6.858565      628377    2021-01-01   \n",
       "3          4.077537   0.832909  7.183112  1668701718    2021-01-01   \n",
       "4          2.302585   1.386294  7.600902      640665    2021-03-01   \n",
       "...             ...        ...       ...         ...           ...   \n",
       "181668     3.713572  -0.040822  7.106606   164141591    2020-07-01   \n",
       "181669     3.713572  -0.051293  7.313220   164141591    2020-07-01   \n",
       "181670     3.713572  -0.342490  7.130899   164141591    2020-07-01   \n",
       "181671     3.713572  -0.162519  7.130899   164141591    2020-07-01   \n",
       "181672     3.713572  -0.342490  7.122867   164141591    2020-07-01   \n",
       "\n",
       "        selling_price  item_date_day  item_date_year  item_date_month  \\\n",
       "0            6.749931              1            2021                4   \n",
       "1            6.953684              1            2021                4   \n",
       "2            6.468211              1            2021                4   \n",
       "3            6.643790              1            2021                4   \n",
       "4            6.357842              1            2021                4   \n",
       "...               ...            ...             ...              ...   \n",
       "181668       6.381816              2            2020                7   \n",
       "181669       6.378426              2            2020                7   \n",
       "181670       6.428105              2            2020                7   \n",
       "181671       6.398595              2            2020                7   \n",
       "181672       6.408529              2            2020                7   \n",
       "\n",
       "        delivery_date_day  delivery_date_month  delivery_date_year  \n",
       "0                       1                    7                2021  \n",
       "1                       1                    4                2021  \n",
       "2                       1                    1                2021  \n",
       "3                       1                    1                2021  \n",
       "4                       1                    3                2021  \n",
       "...                   ...                  ...                 ...  \n",
       "181668                  1                    7                2020  \n",
       "181669                  1                    7                2020  \n",
       "181670                  1                    7                2020  \n",
       "181671                  1                    7                2020  \n",
       "181672                  1                    7                2020  \n",
       "\n",
       "[181640 rows x 18 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df_1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bweBrd89FSZ2"
   },
   "source": [
    "# **Before Treating Outliers**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "oTUG7NTjQU44",
    "outputId": "8f34e9bb-724d-49eb-eb3d-260ece10c037"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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v4vjjjy96pDbtrrvuio4dO8aoUaOKHiWdjh07RkREbW1tVFZWRpcuXeKqq66K888/P9555x2POQfJggULYv78+TFv3ryIiOjfv3+MHTs2Jk+eHFdddVWL7qtFXzMQEbF58+a47LLLYvv27VFXVxf9+vVr6V0Q790otm7dGhs3boxjjjkmIiJefvnl6N27d3Tt2rXg6dq+119/PcaMGRN9+vSJurq66N69e9EjtXmPPPJIvPnmm1FdXR0R752yjIh44oknvJjwIDvuuOOiubk5duzYEZWVlRHx3rPWiNh9LpuW98Ybb+z1PwcqKiqiQ4cOLb6vFv3fBDt27IjRo0dHly5dYvbs2ULgIPrkJz8Zp5xyStx6663R0NAQq1evjhkzZsTIkSOLHq3Nq6+vj+985zvxmc98JmbOnCkEDpF58+bFn/70p1i2bFksW7Yshg8fHsOHDxcCh8DnPve56NevX0ycODHeeeed2Lx5c0ybNi2+9KUv7X5BIS3v9NNPjw0bNsSdd94Zu3btitWrV8cdd9wRX/va11p8X6VyC2bd448/HpdffnlUVlZG+/bt9/jc73//++jTp09L7YqI2LhxY9x0002xZMmSaNeuXYwYMSK+//3v77X2tKyf//znMWXKlOjUqVOUSqU9Prd8+fKCpsrn/d8xMGXKlIInyWH9+vUxZcqUePbZZ6OpqSm++MUvRm1trRcrH2SLFi2Kn/zkJ/H3v/89unbtGl//+tdj7Nixu0/dtJQWjQEA4PDj1xEDQHJiAACSEwMAkJwYAIDkxAAAJCcGACA5MQAAyYkBaIWampriH//4R9FjAEmIAWiFLrzwwli0aFEsWbLkQ98A6c4774zRo0fv8/qmT5/uDX6Af6nF36gIOHBbtmz5SNtdeumlB3kSIANHBqCV+e53vxvr1q2LH/zgB3HzzTdHRMTMmTPjy1/+cpx88slxxRVXRENDQ0Ts/Yz/d7/7XQwfPjwGDhwYX/nKV+LRRx/d6/rr6+vjm9/8Zlx55ZWxY8eOuPbaa+P666+PSy+9NAYOHBhnnXVW/PKXv9y9fUNDQ9x0000xdOjQ+OxnPxtXX311bNy4cffnp0+fHkOHDo1BgwbFueeeG08++WREROzcuTNuuOGGGDJkSAwePDguvPDCeO655w7KmgEHRgxAKzNr1qzo06dP3HjjjTFp0qSIiFi7dm3MnTs35s+fH88//3zcf//9e33dkiVLYuLEiXHNNdfEc889F9ddd12MHz8+Xnrppd3bbNmyJS6++OI4/vjj4/bbb9/9VqgPPfRQjBo1Kp599tkYM2ZMTJkyJdavXx8RERMnTozXXnstHnrooXjiiSeiS5cuMW7cuCiXy/HHP/4xfvOb38SDDz4YS5YsifPOOy9qa2tjx44d8cgjj8Ty5cvjsccei0WLFsWpp54aN9544yFYQWB/iQE4DLz/bqC9evWKU089NV5//fW9tpkzZ04MGzYshg4dGu3atYszzzwzfv3rX0evXr0i4v/eerlnz55x66237vHuloMHD44hQ4ZERUVFnHvuubFr1654/fXXY9OmTTF//vyora2NHj16xBFHHBETJ06MF154IVauXBmVlZVRX18fDzzwQPz1r3+N8847LxYvXhwdOnSIqqqqWLNmTdTV1cUrr7wSV155Zfz2t789ZGsGfHRiAA4DRx999O6PO3ToELt27dprmzfffHOvtwk/8cQTo2vXrhERsWrVqujdu3c8++yzsXr16j2269mz5x7XHxHR3Nwca9eujYiI888/P6qrq6O6ujrOOOOMaN++faxZsyYGDhwY06dPj+XLl8dFF10UQ4YMiRkzZkRzc3N89atfjUmTJsWTTz4ZI0aMiC984Qsxe/bsllkQoEV5ASG0Eccee2ysW7duj8tmzZoVJ598ckREDBw4MO6+++644oorYsKECXH//fdHu3Yf/nzg/aMKjz322B7B8NJLL0W/fv1i3bp10aNHj5g5c2Zs3749Fi9eHOPGjYsBAwbEJz7xiRgwYECMGDEiGhsbY968eTFhwoSorq6O/v37t+w/HjggjgxAK9SxY8d4++239+trzjnnnFiwYEE888wz0dzcHE8//XRMnz5995GB95/x33DDDfHKK6/Evffeu8/r7NWrV3z+85+PyZMnx5YtW2LHjh1xxx13xMiRI+Ott96KF154IUaPHh0vvvhidOzYMXr06BER7x3JWLhwYYwbNy7WrFkTVVVV0a1bt6ioqNg9D9B6ODIArdDIkSNj2rRpceSRR37krznllFNi6tSpMXXq1Fi7dm307ds3br/99ujfv3/Mmzdv93bdu3eP66+/PsaPHx9nnnnmPq/3tttuix//+McxYsSIaGhoiP79+8e9994bPXv2jLPPPjteffXVuOyyy2LLli3Ro0ePmDhxYpx00kkxYMCAWL9+fXzrW9+KhoaG6Nu3b0ybNi169+79sdYEOHhK5XK5XPQQAEBxnCYAgOTEAAAkJwYAIDkxAADJiQEASE4MAEByYgAAkhMDAJCcGACA5MQAACQnBgAguf8Bv6xBLwmKC9oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "def box_plot(columns):\n",
    "\n",
    "  for i in columns:\n",
    "    sns.boxplot(data=new_df_1,x=i)\n",
    "    plt.show()\n",
    "\n",
    "box_plot(['quantity_tons','thickness','width','selling_price'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZILQyEqEG9gR"
   },
   "source": [
    "# **After Treating Outliers**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "AMGVVmiLQUsf",
    "outputId": "7d0c955f-76e1-4c39-a8b7-81b56380ff57"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done !!!!\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "def treat_outliers(column):\n",
    "  for i in column:\n",
    "    iqr = np.percentile(new_df_1[i],75) - np.percentile(new_df_1[i],25)  # Q3 - Q1\n",
    "    lower_bound =  np.percentile(new_df_1[i],25) - (1.5 * iqr)\n",
    "    upper_bound = np.percentile(new_df_1[i],75) + (1.5 * iqr)\n",
    "    new_df_1[i] = new_df_1[i].clip(lower_bound,upper_bound)\n",
    "  print('Done !!!!')\n",
    "\n",
    "treat_outliers(['quantity_tons','thickness','width','selling_price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "qmeu09r7QUp-",
    "outputId": "273bc162-23e3-4517-d1a8-0b502a8a8dc8"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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x9NNPR0TEoUOH4q677or29vaYPHlyXHfddbF58+YBWTPg3REDUGMeeeSRGDduXNx9992xaNGiiIjYuXNnPPXUU7Fu3brYunVrrFy58riv27hxY3R2dsb8+fNj8+bNcfvtt8eCBQvilVde6d9m79698bnPfS4mTJgQ999/fzQ2NkbEP94AZ9asWbFp06a48cYb4+tf/3rs2rUrIiI6Ozvjtddei9WrV8dvfvObGDlyZMydOzeq1Wr89re/jR//+MfxxBNPxMaNG6OjoyPuuOOOOHjwYPzsZz+LLVu2xJo1a2LDhg1xySWXxN133z0IKwicLDEAp4Evf/nL0dTUFGPGjIlLLrkktm/fftw2P/3pT+PjH/94TJkyJYYMGRKXXXZZ/PCHP4wxY8ZExL/e/ralpSWWLFkSQ4cO7f/ayZMnR3t7ezQ0NMT06dPj8OHDsX379ti9e3esW7cu7rjjjmhubo4RI0ZEZ2dnvPDCC/Hiiy9GU1NT9PT0xE9+8pP4wx/+EB0dHdHV1RWNjY1RLpdjx44dsWrVqnj11VfjlltuiZ///OeDtmbAiRMDcBo4++yz+y83NjbG4cOHj9vmL3/5S4wbN+6Y6z7wgQ/EqFGjIiJi27ZtMXbs2Ni0aVO8/vrrx2zX0tJyzO1HRBw5ciR27twZERGf+cxnoq2tLdra2uLDH/5wDB06NHbs2BGtra2xbNmy2LJlS1x//fXR3t4eDz74YBw5ciQ+/elPx6JFi+Lpp5+OqVOnxkc/+tF4/PHHT82CAKeUFxBCnXjve98b3d3dx1z3yCOPxKRJkyIiorW1NR5++OGYN29eLFy4MFauXBlDhrz984GjRxXWrFlzTDC88sorce6550Z3d3c0NzfHihUr4sCBA9HV1RVz586NiRMnxvve976YOHFiTJ06NSqVSqxduzYWLlwYbW1tMX78+FP7wwPviiMDUIOGDRsWb7755kl9zdVXXx2//vWv49lnn40jR47EM888E8uWLes/MnD0Gf9dd90Vr776aixfvvwdb3PMmDHxkY98JO69997Yu3dvHDx4MB566KGYMWNGvPHGG/HCCy/E7Nmz46WXXophw4ZFc3NzRPzjSMb69etj7ty5sWPHjiiXy3HWWWdFQ0ND/zxA7XBkAGrQjBkz4oEHHogzzzzzhL/m4osvjqVLl8bSpUtj586dcc4558T9998f48ePj7Vr1/ZvN3r06LjzzjtjwYIFcdlll73j7X7jG9+I++67L6ZOnRq9vb0xfvz4WL58ebS0tMQnPvGJ+NOf/hRf+tKXYu/evdHc3BydnZ1x4YUXxsSJE2PXrl1xzTXXRG9vb5xzzjnxwAMPxNixY/+rNQEGTqlarVaLHgIAKI7TBACQnBgAgOTEAAAkJwYAIDkxAADJiQEASE4MAEByYgAAkhMDAJCcGACA5MQAACT3f5jx2yDl2B3jAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def box_plot(columns):\n",
    "\n",
    "  for i in columns:\n",
    "    sns.boxplot(data=new_df_1,x=i)\n",
    "    plt.show()\n",
    "\n",
    "box_plot(['quantity_tons','thickness','width','selling_price'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ak01iRCsrB4t"
   },
   "source": [
    "\n",
    "\n",
    "```\n",
    "# Note : We cleared all the outliers values !!!\n",
    "```\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7NmSGh0Ax_jm"
   },
   "source": [
    "# **Handling Wrong Delivery Dates**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "z5ZcFloaQYgV",
    "outputId": "448beaa5-b779-4f3e-b170-e7b6d6dee035"
   },
   "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>id</th>\n",
       "      <th>item_date</th>\n",
       "      <th>quantity tons</th>\n",
       "      <th>customer</th>\n",
       "      <th>country</th>\n",
       "      <th>status</th>\n",
       "      <th>item type</th>\n",
       "      <th>application</th>\n",
       "      <th>thickness</th>\n",
       "      <th>width</th>\n",
       "      <th>material_ref</th>\n",
       "      <th>product_ref</th>\n",
       "      <th>delivery date</th>\n",
       "      <th>selling_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EC06F063-9DF0-440C-8764-0B0C05A4F6AE</td>\n",
       "      <td>20210401.0</td>\n",
       "      <td>54.151139</td>\n",
       "      <td>30156308.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>Won</td>\n",
       "      <td>W</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>DEQ1 S460MC</td>\n",
       "      <td>1670798778</td>\n",
       "      <td>20210701.0</td>\n",
       "      <td>854.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4E5F4B3D-DDDF-499D-AFDE-A3227EC49425</td>\n",
       "      <td>20210401.0</td>\n",
       "      <td>768.024839</td>\n",
       "      <td>30202938.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>Won</td>\n",
       "      <td>W</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1210.0</td>\n",
       "      <td>0000000000000000000000000000000000104991</td>\n",
       "      <td>1668701718</td>\n",
       "      <td>20210401.0</td>\n",
       "      <td>1047.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>E140FF1B-2407-4C02-A0DD-780A093B1158</td>\n",
       "      <td>20210401.0</td>\n",
       "      <td>386.127949</td>\n",
       "      <td>30153963.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Won</td>\n",
       "      <td>WI</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.38</td>\n",
       "      <td>952.0</td>\n",
       "      <td>S0380700</td>\n",
       "      <td>628377</td>\n",
       "      <td>20210101.0</td>\n",
       "      <td>644.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F8D507A0-9C62-4EFE-831E-33E1DA53BB50</td>\n",
       "      <td>20210401.0</td>\n",
       "      <td>202.411065</td>\n",
       "      <td>30349574.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>Won</td>\n",
       "      <td>S</td>\n",
       "      <td>59.0</td>\n",
       "      <td>2.30</td>\n",
       "      <td>1317.0</td>\n",
       "      <td>DX51D+ZM310MAO 2.3X1317</td>\n",
       "      <td>1668701718</td>\n",
       "      <td>20210101.0</td>\n",
       "      <td>768.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4E1C4E78-152B-430A-8094-ADD889C9D0AD</td>\n",
       "      <td>20210401.0</td>\n",
       "      <td>785.526262</td>\n",
       "      <td>30211560.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>Won</td>\n",
       "      <td>W</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>2_S275JR+AR-CL1</td>\n",
       "      <td>640665</td>\n",
       "      <td>20210301.0</td>\n",
       "      <td>577.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181668</th>\n",
       "      <td>DE633116-D1DF-4846-982E-55EFC3658A76</td>\n",
       "      <td>20200702.0</td>\n",
       "      <td>102.4824216</td>\n",
       "      <td>30200854.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>Won</td>\n",
       "      <td>W</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.96</td>\n",
       "      <td>1220.0</td>\n",
       "      <td>0000000000000000000000000000000001000777</td>\n",
       "      <td>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>591.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181669</th>\n",
       "      <td>A48374B1-E6DB-45F2-889A-1F9C27C099EB</td>\n",
       "      <td>20200702.0</td>\n",
       "      <td>208.0864686</td>\n",
       "      <td>30200854.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>Won</td>\n",
       "      <td>W</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>0000000000000000000000000000000001000227</td>\n",
       "      <td>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>589.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181670</th>\n",
       "      <td>91643238-5C7B-4237-9A5F-63AE3D35F320</td>\n",
       "      <td>20200702.0</td>\n",
       "      <td>4.235593568</td>\n",
       "      <td>30200854.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>Won</td>\n",
       "      <td>W</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.71</td>\n",
       "      <td>1250.0</td>\n",
       "      <td>0000000000000000000000000000000001004216</td>\n",
       "      <td>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>619.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181671</th>\n",
       "      <td>7AFFD323-01D9-4E15-B80D-7D1B03498FC8</td>\n",
       "      <td>20200702.0</td>\n",
       "      <td>-2000</td>\n",
       "      <td>30200854.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>Won</td>\n",
       "      <td>W</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1250.0</td>\n",
       "      <td>0000000000000000000000000000000001001149</td>\n",
       "      <td>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>601.00</td>\n",
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       "    <tr>\n",
       "      <th>181672</th>\n",
       "      <td>AD0CA853-AE3C-4B2F-9FBB-8B0B965F84BC</td>\n",
       "      <td>20200702.0</td>\n",
       "      <td>406.6865384</td>\n",
       "      <td>30200854.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>Won</td>\n",
       "      <td>W</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.71</td>\n",
       "      <td>1240.0</td>\n",
       "      <td>0000000000000000000000000000000001005439</td>\n",
       "      <td>164141591</td>\n",
       "      <td>20200701.0</td>\n",
       "      <td>607.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>181673 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          id   item_date quantity tons  \\\n",
       "0       EC06F063-9DF0-440C-8764-0B0C05A4F6AE  20210401.0     54.151139   \n",
       "1       4E5F4B3D-DDDF-499D-AFDE-A3227EC49425  20210401.0    768.024839   \n",
       "2       E140FF1B-2407-4C02-A0DD-780A093B1158  20210401.0    386.127949   \n",
       "3       F8D507A0-9C62-4EFE-831E-33E1DA53BB50  20210401.0    202.411065   \n",
       "4       4E1C4E78-152B-430A-8094-ADD889C9D0AD  20210401.0    785.526262   \n",
       "...                                      ...         ...           ...   \n",
       "181668  DE633116-D1DF-4846-982E-55EFC3658A76  20200702.0   102.4824216   \n",
       "181669  A48374B1-E6DB-45F2-889A-1F9C27C099EB  20200702.0   208.0864686   \n",
       "181670  91643238-5C7B-4237-9A5F-63AE3D35F320  20200702.0   4.235593568   \n",
       "181671  7AFFD323-01D9-4E15-B80D-7D1B03498FC8  20200702.0         -2000   \n",
       "181672  AD0CA853-AE3C-4B2F-9FBB-8B0B965F84BC  20200702.0   406.6865384   \n",
       "\n",
       "          customer  country status item type  application  thickness   width  \\\n",
       "0       30156308.0     28.0    Won         W         10.0       2.00  1500.0   \n",
       "1       30202938.0     25.0    Won         W         41.0       0.80  1210.0   \n",
       "2       30153963.0     30.0    Won        WI         28.0       0.38   952.0   \n",
       "3       30349574.0     32.0    Won         S         59.0       2.30  1317.0   \n",
       "4       30211560.0     28.0    Won         W         10.0       4.00  2000.0   \n",
       "...            ...      ...    ...       ...          ...        ...     ...   \n",
       "181668  30200854.0     25.0    Won         W         41.0       0.96  1220.0   \n",
       "181669  30200854.0     25.0    Won         W         41.0       0.95  1500.0   \n",
       "181670  30200854.0     25.0    Won         W         41.0       0.71  1250.0   \n",
       "181671  30200854.0     25.0    Won         W         41.0       0.85  1250.0   \n",
       "181672  30200854.0     25.0    Won         W         41.0       0.71  1240.0   \n",
       "\n",
       "                                    material_ref  product_ref  delivery date  \\\n",
       "0                                    DEQ1 S460MC   1670798778     20210701.0   \n",
       "1       0000000000000000000000000000000000104991   1668701718     20210401.0   \n",
       "2                                       S0380700       628377     20210101.0   \n",
       "3                        DX51D+ZM310MAO 2.3X1317   1668701718     20210101.0   \n",
       "4                                2_S275JR+AR-CL1       640665     20210301.0   \n",
       "...                                          ...          ...            ...   \n",
       "181668  0000000000000000000000000000000001000777    164141591     20200701.0   \n",
       "181669  0000000000000000000000000000000001000227    164141591     20200701.0   \n",
       "181670  0000000000000000000000000000000001004216    164141591     20200701.0   \n",
       "181671  0000000000000000000000000000000001001149    164141591     20200701.0   \n",
       "181672  0000000000000000000000000000000001005439    164141591     20200701.0   \n",
       "\n",
       "        selling_price  \n",
       "0              854.00  \n",
       "1             1047.00  \n",
       "2              644.33  \n",
       "3              768.00  \n",
       "4              577.00  \n",
       "...               ...  \n",
       "181668         591.00  \n",
       "181669         589.00  \n",
       "181670         619.00  \n",
       "181671         601.00  \n",
       "181672         607.00  \n",
       "\n",
       "[181673 rows x 14 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "GYwU2LzbtrIo",
    "outputId": "442cfbc6-9de2-49b9-e793-8dd15c222d55"
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>item_date</th>\n",
       "      <th>quantity_tons</th>\n",
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       "      <td>2021-04-01</td>\n",
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       "      <th>3</th>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>5.310301</td>\n",
       "      <td>30349574</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-04-01</td>\n",
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       "      <td>30211560</td>\n",
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       "      <td>2021-03-01</td>\n",
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       "      <td>7</td>\n",
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       "      <td>7</td>\n",
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       "      <th>181670</th>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>1.443523</td>\n",
       "      <td>30200854</td>\n",
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       "      <td>164141591</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>6.428105</td>\n",
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       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2020</td>\n",
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       "      <td>7</td>\n",
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       "    <tr>\n",
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       "      <td>2020-07-01</td>\n",
       "      <td>6.408529</td>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2020</td>\n",
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       "<p>181640 rows × 18 columns</p>\n",
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      ],
      "text/plain": [
       "        item_date  quantity_tons  customer  country  status  item_type  \\\n",
       "0      2021-04-01       3.991779  30156308     28.0       1        5.0   \n",
       "1      2021-04-01       6.643822  30202938     25.0       1        5.0   \n",
       "2      2021-04-01       5.956169  30153963     30.0       1        6.0   \n",
       "3      2021-04-01       5.310301  30349574     32.0       1        3.0   \n",
       "4      2021-04-01       6.666354  30211560     28.0       1        5.0   \n",
       "...           ...            ...       ...      ...     ...        ...   \n",
       "181668 2020-07-02       4.629691  30200854     25.0       1        5.0   \n",
       "181669 2020-07-02       5.337954  30200854     25.0       1        5.0   \n",
       "181670 2020-07-02       1.443523  30200854     25.0       1        5.0   \n",
       "181671 2020-07-02      -0.323075  30200854     25.0       1        5.0   \n",
       "181672 2020-07-02       6.008043  30200854     25.0       1        5.0   \n",
       "\n",
       "        application  thickness     width product_ref delivery_date  \\\n",
       "0          2.302585   0.693147  7.313220  1670798778    2021-07-01   \n",
       "1          3.713572  -0.223144  7.098376  1668701718    2021-04-01   \n",
       "2          3.332205  -0.967584  6.858565      628377    2021-01-01   \n",
       "3          4.077537   0.832909  7.183112  1668701718    2021-01-01   \n",
       "4          2.302585   1.386294  7.600902      640665    2021-03-01   \n",
       "...             ...        ...       ...         ...           ...   \n",
       "181668     3.713572  -0.040822  7.106606   164141591    2020-07-01   \n",
       "181669     3.713572  -0.051293  7.313220   164141591    2020-07-01   \n",
       "181670     3.713572  -0.342490  7.130899   164141591    2020-07-01   \n",
       "181671     3.713572  -0.162519  7.130899   164141591    2020-07-01   \n",
       "181672     3.713572  -0.342490  7.122867   164141591    2020-07-01   \n",
       "\n",
       "        selling_price  item_date_day  item_date_year  item_date_month  \\\n",
       "0            6.749931              1            2021                4   \n",
       "1            6.953684              1            2021                4   \n",
       "2            6.468211              1            2021                4   \n",
       "3            6.643790              1            2021                4   \n",
       "4            6.357842              1            2021                4   \n",
       "...               ...            ...             ...              ...   \n",
       "181668       6.381816              2            2020                7   \n",
       "181669       6.378426              2            2020                7   \n",
       "181670       6.428105              2            2020                7   \n",
       "181671       6.398595              2            2020                7   \n",
       "181672       6.408529              2            2020                7   \n",
       "\n",
       "        delivery_date_day  delivery_date_month  delivery_date_year  \n",
       "0                       1                    7                2021  \n",
       "1                       1                    4                2021  \n",
       "2                       1                    1                2021  \n",
       "3                       1                    1                2021  \n",
       "4                       1                    3                2021  \n",
       "...                   ...                  ...                 ...  \n",
       "181668                  1                    7                2020  \n",
       "181669                  1                    7                2020  \n",
       "181670                  1                    7                2020  \n",
       "181671                  1                    7                2020  \n",
       "181672                  1                    7                2020  \n",
       "\n",
       "[181640 rows x 18 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "id": "tdQmwwhmxuVy"
   },
   "outputs": [],
   "source": [
    "new_df_1['delivery_item_date_difference_num'] = (new_df_1['delivery_date']-new_df_1['item_date']).dt.days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "id": "1N5uyn8H0rfR"
   },
   "outputs": [],
   "source": [
    "positive = new_df_1[new_df_1['delivery_item_date_difference_num']>0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "NauAhEef03Fr",
    "outputId": "cb77756f-ee3a-41c0-c401-ce056d919e62"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "165073"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(positive)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 555
    },
    "id": "VVMNDoyN03CP",
    "outputId": "01e3cef8-8cb6-4e64-f349-2fe239bf2491"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DINESH KUMAR\\AppData\\Local\\Temp\\ipykernel_15364\\683216786.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  new_df_1.corr()\n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>quantity_tons</th>\n",
       "      <th>country</th>\n",
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       "      <td>0.053367</td>\n",
       "      <td>-0.025606</td>\n",
       "      <td>-0.002520</td>\n",
       "      <td>0.007013</td>\n",
       "      <td>-0.000377</td>\n",
       "      <td>-0.106267</td>\n",
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       "    <tr>\n",
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       "      <td>-0.000833</td>\n",
       "      <td>0.055971</td>\n",
       "      <td>-0.018668</td>\n",
       "      <td>0.012186</td>\n",
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       "      <td>-0.016378</td>\n",
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       "      <td>0.053367</td>\n",
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       "      <td>-0.102203</td>\n",
       "      <td>0.548514</td>\n",
       "      <td>0.281210</td>\n",
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       "      <td>0.004281</td>\n",
       "      <td>0.001628</td>\n",
       "      <td>0.015568</td>\n",
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       "    <tr>\n",
       "      <th>delivery_date_month</th>\n",
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       "      <td>0.014619</td>\n",
       "      <td>-0.163299</td>\n",
       "      <td>0.055971</td>\n",
       "      <td>-0.102203</td>\n",
       "      <td>-0.070816</td>\n",
       "      <td>0.004281</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.797631</td>\n",
       "      <td>-0.111216</td>\n",
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       "    <tr>\n",
       "      <th>delivery_date_year</th>\n",
       "      <td>0.022001</td>\n",
       "      <td>-0.072048</td>\n",
       "      <td>-0.010619</td>\n",
       "      <td>-0.000377</td>\n",
       "      <td>-0.060764</td>\n",
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       "      <td>0.007975</td>\n",
       "      <td>0.446844</td>\n",
       "      <td>-0.018668</td>\n",
       "      <td>0.548514</td>\n",
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       "      <td>0.001628</td>\n",
       "      <td>-0.797631</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.495844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>delivery_item_date_difference_num</th>\n",
       "      <td>0.060859</td>\n",
       "      <td>-0.178263</td>\n",
       "      <td>0.041400</td>\n",
       "      <td>-0.106267</td>\n",
       "      <td>-0.150918</td>\n",
       "      <td>0.091102</td>\n",
       "      <td>0.044685</td>\n",
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       "      <td>0.015568</td>\n",
       "      <td>-0.111216</td>\n",
       "      <td>0.495844</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   quantity_tons   country    status  \\\n",
       "quantity_tons                           1.000000 -0.090553  0.034578   \n",
       "country                                -0.090553  1.000000 -0.035932   \n",
       "status                                  0.034578 -0.035932  1.000000   \n",
       "item_type                              -0.324754  0.129954 -0.082258   \n",
       "application                             0.055706 -0.033796  0.067847   \n",
       "thickness                               0.041169 -0.065251 -0.070415   \n",
       "width                                   0.034730  0.052758 -0.043021   \n",
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       "item_date_day                           0.037436 -0.058935  0.003612   \n",
       "item_date_year                         -0.008942 -0.016378 -0.001194   \n",
       "item_date_month                        -0.001017  0.022838 -0.004364   \n",
       "delivery_date_day                       0.000054 -0.001624 -0.000642   \n",
       "delivery_date_month                    -0.019381  0.022657  0.031334   \n",
       "delivery_date_year                      0.022001 -0.072048 -0.010619   \n",
       "delivery_item_date_difference_num       0.060859 -0.178263  0.041400   \n",
       "\n",
       "                                   item_type  application  thickness  \\\n",
       "quantity_tons                      -0.324754     0.055706   0.041169   \n",
       "country                             0.129954    -0.033796  -0.065251   \n",
       "status                             -0.082258     0.067847  -0.070415   \n",
       "item_type                           1.000000     0.176207  -0.101228   \n",
       "application                         0.176207     1.000000  -0.225863   \n",
       "thickness                          -0.101228    -0.225863   1.000000   \n",
       "width                              -0.093742    -0.186293   0.378392   \n",
       "selling_price                       0.095118     0.145437  -0.420918   \n",
       "item_date_day                      -0.038792    -0.006602  -0.004677   \n",
       "item_date_year                      0.053367    -0.012629   0.007379   \n",
       "item_date_month                    -0.025606     0.015625  -0.007492   \n",
       "delivery_date_day                  -0.002520     0.004122  -0.002491   \n",
       "delivery_date_month                 0.007013     0.020163  -0.016801   \n",
       "delivery_date_year                 -0.000377    -0.060764   0.039947   \n",
       "delivery_item_date_difference_num  -0.106267    -0.150918   0.091102   \n",
       "\n",
       "                                      width  selling_price  item_date_day  \\\n",
       "quantity_tons                      0.034730      -0.038993       0.037436   \n",
       "country                            0.052758      -0.020073      -0.058935   \n",
       "status                            -0.043021       0.036356       0.003612   \n",
       "item_type                         -0.093742       0.095118      -0.038792   \n",
       "application                       -0.186293       0.145437      -0.006602   \n",
       "thickness                          0.378392      -0.420918      -0.004677   \n",
       "width                              1.000000      -0.160883      -0.001389   \n",
       "selling_price                     -0.160883       1.000000      -0.001866   \n",
       "item_date_day                     -0.001389      -0.001866       1.000000   \n",
       "item_date_year                     0.019201       0.516128      -0.000461   \n",
       "item_date_month                   -0.023778      -0.392326      -0.055407   \n",
       "delivery_date_day                 -0.000275       0.000318      -0.000833   \n",
       "delivery_date_month                0.014619      -0.163299       0.055971   \n",
       "delivery_date_year                 0.007975       0.446844      -0.018668   \n",
       "delivery_item_date_difference_num  0.044685       0.265650       0.012186   \n",
       "\n",
       "                                   item_date_year  item_date_month  \\\n",
       "quantity_tons                           -0.008942        -0.001017   \n",
       "country                                 -0.016378         0.022838   \n",
       "status                                  -0.001194        -0.004364   \n",
       "item_type                                0.053367        -0.025606   \n",
       "application                             -0.012629         0.015625   \n",
       "thickness                                0.007379        -0.007492   \n",
       "width                                    0.019201        -0.023778   \n",
       "selling_price                            0.516128        -0.392326   \n",
       "item_date_day                           -0.000461        -0.055407   \n",
       "item_date_year                           1.000000        -0.924102   \n",
       "item_date_month                         -0.924102         1.000000   \n",
       "delivery_date_day                       -0.001863         0.002536   \n",
       "delivery_date_month                     -0.102203        -0.070816   \n",
       "delivery_date_year                       0.548514        -0.301945   \n",
       "delivery_item_date_difference_num        0.281210        -0.245255   \n",
       "\n",
       "                                   delivery_date_day  delivery_date_month  \\\n",
       "quantity_tons                               0.000054            -0.019381   \n",
       "country                                    -0.001624             0.022657   \n",
       "status                                     -0.000642             0.031334   \n",
       "item_type                                  -0.002520             0.007013   \n",
       "application                                 0.004122             0.020163   \n",
       "thickness                                  -0.002491            -0.016801   \n",
       "width                                      -0.000275             0.014619   \n",
       "selling_price                               0.000318            -0.163299   \n",
       "item_date_day                              -0.000833             0.055971   \n",
       "item_date_year                             -0.001863            -0.102203   \n",
       "item_date_month                             0.002536            -0.070816   \n",
       "delivery_date_day                           1.000000             0.004281   \n",
       "delivery_date_month                         0.004281             1.000000   \n",
       "delivery_date_year                          0.001628            -0.797631   \n",
       "delivery_item_date_difference_num           0.015568            -0.111216   \n",
       "\n",
       "                                   delivery_date_year  \\\n",
       "quantity_tons                                0.022001   \n",
       "country                                     -0.072048   \n",
       "status                                      -0.010619   \n",
       "item_type                                   -0.000377   \n",
       "application                                 -0.060764   \n",
       "thickness                                    0.039947   \n",
       "width                                        0.007975   \n",
       "selling_price                                0.446844   \n",
       "item_date_day                               -0.018668   \n",
       "item_date_year                               0.548514   \n",
       "item_date_month                             -0.301945   \n",
       "delivery_date_day                            0.001628   \n",
       "delivery_date_month                         -0.797631   \n",
       "delivery_date_year                           1.000000   \n",
       "delivery_item_date_difference_num            0.495844   \n",
       "\n",
       "                                   delivery_item_date_difference_num  \n",
       "quantity_tons                                               0.060859  \n",
       "country                                                    -0.178263  \n",
       "status                                                      0.041400  \n",
       "item_type                                                  -0.106267  \n",
       "application                                                -0.150918  \n",
       "thickness                                                   0.091102  \n",
       "width                                                       0.044685  \n",
       "selling_price                                               0.265650  \n",
       "item_date_day                                               0.012186  \n",
       "item_date_year                                              0.281210  \n",
       "item_date_month                                            -0.245255  \n",
       "delivery_date_day                                           0.015568  \n",
       "delivery_date_month                                        -0.111216  \n",
       "delivery_date_year                                          0.495844  \n",
       "delivery_item_date_difference_num                           1.000000  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df_1.corr()"
   ]
  },
  {
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   "execution_count": 67,
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    "id": "Rv03DuhF02_9",
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      "text/plain": [
       "   item_date  quantity_tons  customer  country  status  item_type  \\\n",
       "0 2021-04-01       3.991779  30156308     28.0       1        5.0   \n",
       "1 2021-04-01       6.643822  30202938     25.0       1        5.0   \n",
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       "   application  thickness     width product_ref delivery_date  selling_price  \\\n",
       "0     2.302585   0.693147  7.313220  1670798778    2021-07-01       6.749931   \n",
       "1     3.713572  -0.223144  7.098376  1668701718    2021-04-01       6.953684   \n",
       "\n",
       "   item_date_day  item_date_year  item_date_month  delivery_date_day  \\\n",
       "0              1            2021                4                  1   \n",
       "1              1            2021                4                  1   \n",
       "\n",
       "   delivery_date_month  delivery_date_year  delivery_item_date_difference_num  \n",
       "0                    7                2021                                 91  \n",
       "1                    4                2021                                  0  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "new_df_1.head(2)"
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  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "H1ViYEHe4CYz"
   },
   "source": [
    "**Model To Predict Delivery Date**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "id": "clsTNQW740wG"
   },
   "outputs": [],
   "source": [
    "correct_delivery_dates_df = new_df_1.query('delivery_item_date_difference_num>0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 617
    },
    "id": "AFyPU_hx7UWG",
    "outputId": "7e474596-dd5e-46c8-e10c-643a9e939b01"
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       "      <td>30165529</td>\n",
       "      <td>78.0</td>\n",
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       "      <td>2021-07-01</td>\n",
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       "      <td>1</td>\n",
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       "      <td>3.472467</td>\n",
       "      <td>30227718</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.708050</td>\n",
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       "      <td>7.600902</td>\n",
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       "      <td>2020-08-01</td>\n",
       "      <td>6.313548</td>\n",
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       "      <td>7</td>\n",
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       "      <td>2020</td>\n",
       "      <td>30</td>\n",
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       "    <tr>\n",
       "      <th>181525</th>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>4.196171</td>\n",
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       "      <td>0</td>\n",
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       "      <td>6.126869</td>\n",
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       "      <td>30</td>\n",
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       "    <tr>\n",
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       "      <td>2020-07-02</td>\n",
       "      <td>4.156686</td>\n",
       "      <td>30161559</td>\n",
       "      <td>30.0</td>\n",
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       "      <td>3.0</td>\n",
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       "      <td>1.766442</td>\n",
       "      <td>7.329750</td>\n",
       "      <td>640665</td>\n",
       "      <td>2020-08-01</td>\n",
       "      <td>6.124683</td>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
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       "    <tr>\n",
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       "      <td>2020-07-02</td>\n",
       "      <td>4.025862</td>\n",
       "      <td>30161559</td>\n",
       "      <td>30.0</td>\n",
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       "      <td>640665</td>\n",
       "      <td>2020-08-01</td>\n",
       "      <td>6.126869</td>\n",
       "      <td>2</td>\n",
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       "      <td>2020-07-02</td>\n",
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      "text/plain": [
       "        item_date  quantity_tons  customer  country  status  item_type  \\\n",
       "0      2021-04-01       3.991779  30156308     28.0       1        5.0   \n",
       "6      2021-04-01       1.259203  30209509     30.0       2        5.0   \n",
       "8      2021-04-01       4.235147  30341428     38.0       1        3.0   \n",
       "10     2021-04-01       2.216566  30209509     30.0       2        5.0   \n",
       "12     2021-04-01       3.314642  30165529     78.0       1        5.0   \n",
       "...           ...            ...       ...      ...     ...        ...   \n",
       "181433 2020-07-02       3.472467  30227718     25.0       0        3.0   \n",
       "181525 2020-07-02       4.196171  30161559     30.0       0        3.0   \n",
       "181527 2020-07-02       4.156686  30161559     30.0       0        3.0   \n",
       "181529 2020-07-02       4.025862  30161559     30.0       0        3.0   \n",
       "181531 2020-07-02       5.328963  30161559     30.0       0        3.0   \n",
       "\n",
       "        application  thickness     width product_ref delivery_date  \\\n",
       "0          2.302585   0.693147  7.313220  1670798778    2021-07-01   \n",
       "6          3.713572  -0.967584  7.025538      611993    2021-07-01   \n",
       "8          2.302585  -0.510826  7.150701  1668701376    2021-07-01   \n",
       "10         3.713572  -0.967584  7.025538      611993    2021-07-01   \n",
       "12         2.302585  -0.287682  7.130899   164141591    2021-07-01   \n",
       "...             ...        ...       ...         ...           ...   \n",
       "181433     2.708050   1.791759  7.600902      640665    2020-08-01   \n",
       "181525     2.302585   1.348073  7.329750      640665    2020-08-01   \n",
       "181527     2.302585   1.766442  7.329750      640665    2020-08-01   \n",
       "181529     2.302585   2.060514  7.313220      640665    2020-08-01   \n",
       "181531     2.302585   1.348073  7.512071      640665    2020-08-01   \n",
       "\n",
       "        selling_price  item_date_day  item_date_year  item_date_month  \\\n",
       "0            6.749931              1            2021                4   \n",
       "6            5.975038              1            2021                4   \n",
       "8            7.217443              1            2021                4   \n",
       "10           5.975038              1            2021                4   \n",
       "12           7.001246              1            2021                4   \n",
       "...               ...            ...             ...              ...   \n",
       "181433       6.313548              2            2020                7   \n",
       "181525       6.126869              2            2020                7   \n",
       "181527       6.124683              2            2020                7   \n",
       "181529       6.126869              2            2020                7   \n",
       "181531       6.131226              2            2020                7   \n",
       "\n",
       "        delivery_date_day  delivery_date_month  delivery_date_year  \\\n",
       "0                       1                    7                2021   \n",
       "6                       1                    7                2021   \n",
       "8                       1                    7                2021   \n",
       "10                      1                    7                2021   \n",
       "12                      1                    7                2021   \n",
       "...                   ...                  ...                 ...   \n",
       "181433                  1                    8                2020   \n",
       "181525                  1                    8                2020   \n",
       "181527                  1                    8                2020   \n",
       "181529                  1                    8                2020   \n",
       "181531                  1                    8                2020   \n",
       "\n",
       "        delivery_item_date_difference_num  \n",
       "0                                      91  \n",
       "6                                      91  \n",
       "8                                      91  \n",
       "10                                     91  \n",
       "12                                     91  \n",
       "...                                   ...  \n",
       "181433                                 30  \n",
       "181525                                 30  \n",
       "181527                                 30  \n",
       "181529                                 30  \n",
       "181531                                 30  \n",
       "\n",
       "[165073 rows x 19 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
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    }
   ],
   "source": [
    "correct_delivery_dates_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "id": "E4KFJaUe5XIW"
   },
   "outputs": [],
   "source": [
    "wrong_delivery_dates_df = new_df_1.query('delivery_item_date_difference_num<=0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "colab": {
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    {
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       "      <td>2021-01-01</td>\n",
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      "text/plain": [
       "        item_date  quantity_tons  customer  country  status  item_type  \\\n",
       "1      2021-04-01       6.643822  30202938     25.0       1        5.0   \n",
       "2      2021-04-01       5.956169  30153963     30.0       1        6.0   \n",
       "3      2021-04-01       5.310301  30349574     32.0       1        3.0   \n",
       "4      2021-04-01       6.666354  30211560     28.0       1        5.0   \n",
       "5      2021-04-01       5.419608  30202938     25.0       1        5.0   \n",
       "...           ...            ...       ...      ...     ...        ...   \n",
       "181668 2020-07-02       4.629691  30200854     25.0       1        5.0   \n",
       "181669 2020-07-02       5.337954  30200854     25.0       1        5.0   \n",
       "181670 2020-07-02       1.443523  30200854     25.0       1        5.0   \n",
       "181671 2020-07-02      -0.323075  30200854     25.0       1        5.0   \n",
       "181672 2020-07-02       6.008043  30200854     25.0       1        5.0   \n",
       "\n",
       "        application  thickness     width product_ref delivery_date  \\\n",
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       "2          3.332205  -0.967584  6.858565      628377    2021-01-01   \n",
       "3          4.077537   0.832909  7.183112  1668701718    2021-01-01   \n",
       "4          2.302585   1.386294  7.600902      640665    2021-03-01   \n",
       "5          3.713572   0.405465  7.142827  1668701718    2021-04-01   \n",
       "...             ...        ...       ...         ...           ...   \n",
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       "181669     3.713572  -0.051293  7.313220   164141591    2020-07-01   \n",
       "181670     3.713572  -0.342490  7.130899   164141591    2020-07-01   \n",
       "181671     3.713572  -0.162519  7.130899   164141591    2020-07-01   \n",
       "181672     3.713572  -0.342490  7.122867   164141591    2020-07-01   \n",
       "\n",
       "        selling_price  item_date_day  item_date_year  item_date_month  \\\n",
       "1            6.953684              1            2021                4   \n",
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       "3            6.643790              1            2021                4   \n",
       "4            6.357842              1            2021                4   \n",
       "5            6.890609              1            2021                4   \n",
       "...               ...            ...             ...              ...   \n",
       "181668       6.381816              2            2020                7   \n",
       "181669       6.378426              2            2020                7   \n",
       "181670       6.428105              2            2020                7   \n",
       "181671       6.398595              2            2020                7   \n",
       "181672       6.408529              2            2020                7   \n",
       "\n",
       "        delivery_date_day  delivery_date_month  delivery_date_year  \\\n",
       "1                       1                    4                2021   \n",
       "2                       1                    1                2021   \n",
       "3                       1                    1                2021   \n",
       "4                       1                    3                2021   \n",
       "5                       1                    4                2021   \n",
       "...                   ...                  ...                 ...   \n",
       "181668                  1                    7                2020   \n",
       "181669                  1                    7                2020   \n",
       "181670                  1                    7                2020   \n",
       "181671                  1                    7                2020   \n",
       "181672                  1                    7                2020   \n",
       "\n",
       "        delivery_item_date_difference_num  \n",
       "1                                       0  \n",
       "2                                     -90  \n",
       "3                                     -90  \n",
       "4                                     -31  \n",
       "5                                       0  \n",
       "...                                   ...  \n",
       "181668                                 -1  \n",
       "181669                                 -1  \n",
       "181670                                 -1  \n",
       "181671                                 -1  \n",
       "181672                                 -1  \n",
       "\n",
       "[16567 rows x 19 columns]"
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     },
     "execution_count": 71,
     "metadata": {},
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    }
   ],
   "source": [
    "wrong_delivery_dates_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "id": "2bp7EOox7IZs"
   },
   "outputs": [],
   "source": [
    "correct_delivery_dates_df =  correct_delivery_dates_df.dropna()"
   ]
  },
  {
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   "execution_count": 73,
   "metadata": {
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     "base_uri": "https://localhost:8080/",
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    "id": "zZp6xYBp7pbD",
    "outputId": "278d1f97-03d3-48f9-d48b-e5e497b9eef5"
   },
   "outputs": [
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       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2021</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>1.259203</td>\n",
       "      <td>30209509</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.713572</td>\n",
       "      <td>-0.967584</td>\n",
       "      <td>7.025538</td>\n",
       "      <td>611993</td>\n",
       "      <td>2021-07-01</td>\n",
       "      <td>5.975038</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2021</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>4.235147</td>\n",
       "      <td>30341428</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.302585</td>\n",
       "      <td>-0.510826</td>\n",
       "      <td>7.150701</td>\n",
       "      <td>1668701376</td>\n",
       "      <td>2021-07-01</td>\n",
       "      <td>7.217443</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2021</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>2.216566</td>\n",
       "      <td>30209509</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.713572</td>\n",
       "      <td>-0.967584</td>\n",
       "      <td>7.025538</td>\n",
       "      <td>611993</td>\n",
       "      <td>2021-07-01</td>\n",
       "      <td>5.975038</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2021</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>3.314642</td>\n",
       "      <td>30165529</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.302585</td>\n",
       "      <td>-0.287682</td>\n",
       "      <td>7.130899</td>\n",
       "      <td>164141591</td>\n",
       "      <td>2021-07-01</td>\n",
       "      <td>7.001246</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2021</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181433</th>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>3.472467</td>\n",
       "      <td>30227718</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.708050</td>\n",
       "      <td>1.791759</td>\n",
       "      <td>7.600902</td>\n",
       "      <td>640665</td>\n",
       "      <td>2020-08-01</td>\n",
       "      <td>6.313548</td>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>2020</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181525</th>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>4.196171</td>\n",
       "      <td>30161559</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.302585</td>\n",
       "      <td>1.348073</td>\n",
       "      <td>7.329750</td>\n",
       "      <td>640665</td>\n",
       "      <td>2020-08-01</td>\n",
       "      <td>6.126869</td>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>2020</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181527</th>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>4.156686</td>\n",
       "      <td>30161559</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.302585</td>\n",
       "      <td>1.766442</td>\n",
       "      <td>7.329750</td>\n",
       "      <td>640665</td>\n",
       "      <td>2020-08-01</td>\n",
       "      <td>6.124683</td>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>2020</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181529</th>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>4.025862</td>\n",
       "      <td>30161559</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.302585</td>\n",
       "      <td>2.060514</td>\n",
       "      <td>7.313220</td>\n",
       "      <td>640665</td>\n",
       "      <td>2020-08-01</td>\n",
       "      <td>6.126869</td>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>2020</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181531</th>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>5.328963</td>\n",
       "      <td>30161559</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.302585</td>\n",
       "      <td>1.348073</td>\n",
       "      <td>7.512071</td>\n",
       "      <td>640665</td>\n",
       "      <td>2020-08-01</td>\n",
       "      <td>6.131226</td>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>2020</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>165073 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        item_date  quantity_tons  customer  country  status  item_type  \\\n",
       "0      2021-04-01       3.991779  30156308     28.0       1        5.0   \n",
       "6      2021-04-01       1.259203  30209509     30.0       2        5.0   \n",
       "8      2021-04-01       4.235147  30341428     38.0       1        3.0   \n",
       "10     2021-04-01       2.216566  30209509     30.0       2        5.0   \n",
       "12     2021-04-01       3.314642  30165529     78.0       1        5.0   \n",
       "...           ...            ...       ...      ...     ...        ...   \n",
       "181433 2020-07-02       3.472467  30227718     25.0       0        3.0   \n",
       "181525 2020-07-02       4.196171  30161559     30.0       0        3.0   \n",
       "181527 2020-07-02       4.156686  30161559     30.0       0        3.0   \n",
       "181529 2020-07-02       4.025862  30161559     30.0       0        3.0   \n",
       "181531 2020-07-02       5.328963  30161559     30.0       0        3.0   \n",
       "\n",
       "        application  thickness     width product_ref delivery_date  \\\n",
       "0          2.302585   0.693147  7.313220  1670798778    2021-07-01   \n",
       "6          3.713572  -0.967584  7.025538      611993    2021-07-01   \n",
       "8          2.302585  -0.510826  7.150701  1668701376    2021-07-01   \n",
       "10         3.713572  -0.967584  7.025538      611993    2021-07-01   \n",
       "12         2.302585  -0.287682  7.130899   164141591    2021-07-01   \n",
       "...             ...        ...       ...         ...           ...   \n",
       "181433     2.708050   1.791759  7.600902      640665    2020-08-01   \n",
       "181525     2.302585   1.348073  7.329750      640665    2020-08-01   \n",
       "181527     2.302585   1.766442  7.329750      640665    2020-08-01   \n",
       "181529     2.302585   2.060514  7.313220      640665    2020-08-01   \n",
       "181531     2.302585   1.348073  7.512071      640665    2020-08-01   \n",
       "\n",
       "        selling_price  item_date_day  item_date_year  item_date_month  \\\n",
       "0            6.749931              1            2021                4   \n",
       "6            5.975038              1            2021                4   \n",
       "8            7.217443              1            2021                4   \n",
       "10           5.975038              1            2021                4   \n",
       "12           7.001246              1            2021                4   \n",
       "...               ...            ...             ...              ...   \n",
       "181433       6.313548              2            2020                7   \n",
       "181525       6.126869              2            2020                7   \n",
       "181527       6.124683              2            2020                7   \n",
       "181529       6.126869              2            2020                7   \n",
       "181531       6.131226              2            2020                7   \n",
       "\n",
       "        delivery_date_day  delivery_date_month  delivery_date_year  \\\n",
       "0                       1                    7                2021   \n",
       "6                       1                    7                2021   \n",
       "8                       1                    7                2021   \n",
       "10                      1                    7                2021   \n",
       "12                      1                    7                2021   \n",
       "...                   ...                  ...                 ...   \n",
       "181433                  1                    8                2020   \n",
       "181525                  1                    8                2020   \n",
       "181527                  1                    8                2020   \n",
       "181529                  1                    8                2020   \n",
       "181531                  1                    8                2020   \n",
       "\n",
       "        delivery_item_date_difference_num  \n",
       "0                                      91  \n",
       "6                                      91  \n",
       "8                                      91  \n",
       "10                                     91  \n",
       "12                                     91  \n",
       "...                                   ...  \n",
       "181433                                 30  \n",
       "181525                                 30  \n",
       "181527                                 30  \n",
       "181529                                 30  \n",
       "181531                                 30  \n",
       "\n",
       "[165073 rows x 19 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correct_delivery_dates_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "id": "gKaEJavb8RTK"
   },
   "outputs": [],
   "source": [
    "x = correct_delivery_dates_df[[ 'quantity_tons', 'customer', 'country', 'status',\n",
    "       'item_type', 'application', 'thickness', 'width', 'product_ref',\n",
    "        'selling_price', 'item_date_day', 'item_date_year',\n",
    "       'item_date_month', 'delivery_date_day', 'delivery_date_month',\n",
    "       'delivery_date_year',]]\n",
    "\n",
    "y = correct_delivery_dates_df['delivery_item_date_difference_num']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "id": "2gzD0jlG0263"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "xtrain, xtest, ytrain, ytest = train_test_split(x,y,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "id": "pwVpCotEn3u6"
   },
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "\n",
    "imputer = SimpleImputer(strategy='mean')\n",
    "\n",
    "\n",
    "xtrain = imputer.fit_transform(xtrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "id": "OwBrEf4a024T"
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "model =  RandomForestRegressor().fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "id": "MSnPpYHF-PVt"
   },
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "\n",
    "imputer = SimpleImputer(strategy='mean')\n",
    "\n",
    "\n",
    "xtest = imputer.fit_transform(xtest)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "FQFg1NwP021-",
    "outputId": "cf1bbd2e-9382-4b93-f933-706b9ce53e94"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DINESH KUMAR\\anaconda3\\lib\\site-packages\\sklearn\\base.py:432: UserWarning: X has feature names, but RandomForestRegressor was fitted without feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9952367740404259"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# predict\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "xtest = pd.DataFrame(xtest,columns=[ 'quantity_tons', 'customer', 'country', 'status',\n",
    "       'item_type', 'application', 'thickness', 'width', 'product_ref',\n",
    "        'selling_price', 'item_date_day', 'item_date_year',\n",
    "       'item_date_month', 'delivery_date_day', 'delivery_date_month',\n",
    "       'delivery_date_year',])\n",
    "\n",
    "ypred = model.predict(xtest)\n",
    "\n",
    "\n",
    "score = r2_score(ytest,ypred)\n",
    "score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-q9SqBYQ_4IW"
   },
   "source": [
    "\n",
    "\n",
    "```\n",
    "# Note : Now time to find delivery date for wrong_delivery_date_df\n",
    "```\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "id": "DVS2wnW202zp"
   },
   "outputs": [],
   "source": [
    "dates_predict = wrong_delivery_dates_df[[ 'quantity_tons', 'customer', 'country', 'status',\n",
    "       'item_type', 'application', 'thickness', 'width', 'product_ref',\n",
    "        'selling_price', 'item_date_day', 'item_date_year',\n",
    "       'item_date_month', 'delivery_date_day', 'delivery_date_month',\n",
    "       'delivery_date_year',]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "BrhOdruu02xU",
    "outputId": "eb648130-d7e9-4acd-9343-2b43685bd95f"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>quantity_tons</th>\n",
       "      <th>customer</th>\n",
       "      <th>country</th>\n",
       "      <th>status</th>\n",
       "      <th>item_type</th>\n",
       "      <th>application</th>\n",
       "      <th>thickness</th>\n",
       "      <th>width</th>\n",
       "      <th>product_ref</th>\n",
       "      <th>selling_price</th>\n",
       "      <th>item_date_day</th>\n",
       "      <th>item_date_year</th>\n",
       "      <th>item_date_month</th>\n",
       "      <th>delivery_date_day</th>\n",
       "      <th>delivery_date_month</th>\n",
       "      <th>delivery_date_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.643822</td>\n",
       "      <td>30202938</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.713572</td>\n",
       "      <td>-0.223144</td>\n",
       "      <td>7.098376</td>\n",
       "      <td>1668701718</td>\n",
       "      <td>6.953684</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.956169</td>\n",
       "      <td>30153963</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.332205</td>\n",
       "      <td>-0.967584</td>\n",
       "      <td>6.858565</td>\n",
       "      <td>628377</td>\n",
       "      <td>6.468211</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.310301</td>\n",
       "      <td>30349574</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.077537</td>\n",
       "      <td>0.832909</td>\n",
       "      <td>7.183112</td>\n",
       "      <td>1668701718</td>\n",
       "      <td>6.643790</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.666354</td>\n",
       "      <td>30211560</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.302585</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>7.600902</td>\n",
       "      <td>640665</td>\n",
       "      <td>6.357842</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.419608</td>\n",
       "      <td>30202938</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.713572</td>\n",
       "      <td>0.405465</td>\n",
       "      <td>7.142827</td>\n",
       "      <td>1668701718</td>\n",
       "      <td>6.890609</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181668</th>\n",
       "      <td>4.629691</td>\n",
       "      <td>30200854</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.713572</td>\n",
       "      <td>-0.040822</td>\n",
       "      <td>7.106606</td>\n",
       "      <td>164141591</td>\n",
       "      <td>6.381816</td>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181669</th>\n",
       "      <td>5.337954</td>\n",
       "      <td>30200854</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.713572</td>\n",
       "      <td>-0.051293</td>\n",
       "      <td>7.313220</td>\n",
       "      <td>164141591</td>\n",
       "      <td>6.378426</td>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181670</th>\n",
       "      <td>1.443523</td>\n",
       "      <td>30200854</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.713572</td>\n",
       "      <td>-0.342490</td>\n",
       "      <td>7.130899</td>\n",
       "      <td>164141591</td>\n",
       "      <td>6.428105</td>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181671</th>\n",
       "      <td>-0.323075</td>\n",
       "      <td>30200854</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.713572</td>\n",
       "      <td>-0.162519</td>\n",
       "      <td>7.130899</td>\n",
       "      <td>164141591</td>\n",
       "      <td>6.398595</td>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181672</th>\n",
       "      <td>6.008043</td>\n",
       "      <td>30200854</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.713572</td>\n",
       "      <td>-0.342490</td>\n",
       "      <td>7.122867</td>\n",
       "      <td>164141591</td>\n",
       "      <td>6.408529</td>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16567 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        quantity_tons  customer  country  status  item_type  application  \\\n",
       "1            6.643822  30202938     25.0       1        5.0     3.713572   \n",
       "2            5.956169  30153963     30.0       1        6.0     3.332205   \n",
       "3            5.310301  30349574     32.0       1        3.0     4.077537   \n",
       "4            6.666354  30211560     28.0       1        5.0     2.302585   \n",
       "5            5.419608  30202938     25.0       1        5.0     3.713572   \n",
       "...               ...       ...      ...     ...        ...          ...   \n",
       "181668       4.629691  30200854     25.0       1        5.0     3.713572   \n",
       "181669       5.337954  30200854     25.0       1        5.0     3.713572   \n",
       "181670       1.443523  30200854     25.0       1        5.0     3.713572   \n",
       "181671      -0.323075  30200854     25.0       1        5.0     3.713572   \n",
       "181672       6.008043  30200854     25.0       1        5.0     3.713572   \n",
       "\n",
       "        thickness     width product_ref  selling_price  item_date_day  \\\n",
       "1       -0.223144  7.098376  1668701718       6.953684              1   \n",
       "2       -0.967584  6.858565      628377       6.468211              1   \n",
       "3        0.832909  7.183112  1668701718       6.643790              1   \n",
       "4        1.386294  7.600902      640665       6.357842              1   \n",
       "5        0.405465  7.142827  1668701718       6.890609              1   \n",
       "...           ...       ...         ...            ...            ...   \n",
       "181668  -0.040822  7.106606   164141591       6.381816              2   \n",
       "181669  -0.051293  7.313220   164141591       6.378426              2   \n",
       "181670  -0.342490  7.130899   164141591       6.428105              2   \n",
       "181671  -0.162519  7.130899   164141591       6.398595              2   \n",
       "181672  -0.342490  7.122867   164141591       6.408529              2   \n",
       "\n",
       "        item_date_year  item_date_month  delivery_date_day  \\\n",
       "1                 2021                4                  1   \n",
       "2                 2021                4                  1   \n",
       "3                 2021                4                  1   \n",
       "4                 2021                4                  1   \n",
       "5                 2021                4                  1   \n",
       "...                ...              ...                ...   \n",
       "181668            2020                7                  1   \n",
       "181669            2020                7                  1   \n",
       "181670            2020                7                  1   \n",
       "181671            2020                7                  1   \n",
       "181672            2020                7                  1   \n",
       "\n",
       "        delivery_date_month  delivery_date_year  \n",
       "1                         4                2021  \n",
       "2                         1                2021  \n",
       "3                         1                2021  \n",
       "4                         3                2021  \n",
       "5                         4                2021  \n",
       "...                     ...                 ...  \n",
       "181668                    7                2020  \n",
       "181669                    7                2020  \n",
       "181670                    7                2020  \n",
       "181671                    7                2020  \n",
       "181672                    7                2020  \n",
       "\n",
       "[16567 rows x 16 columns]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "CtLEO8rO02vI",
    "outputId": "f1db165c-3bec-4883-c3e5-6f1b44fa89ce"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DINESH KUMAR\\anaconda3\\lib\\site-packages\\sklearn\\base.py:432: UserWarning: X has feature names, but RandomForestRegressor was fitted without feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "yhat_dates = model.predict(dates_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Rqs1Uti_02st",
    "outputId": "7f2e60a8-5fed-4def-ba8e-fedce774f42e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 31.  , 156.96, 177.78, ...,  30.  ,  30.  ,  30.  ])"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yhat_dates  # We found correct dates for the wrong_delivery_dates_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7fpV5a1kAiEM",
    "outputId": "be231d42-c2c1-4ef2-f0c9-c4b3a33aa296"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DINESH KUMAR\\AppData\\Local\\Temp\\ipykernel_15364\\1257841389.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  wrong_delivery_dates_df.drop('delivery_item_date_difference_num',axis=1,inplace=True)\n",
      "C:\\Users\\DINESH KUMAR\\AppData\\Local\\Temp\\ipykernel_15364\\1257841389.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  wrong_delivery_dates_df['delivery_item_date_difference_num'] = yhat_dates\n"
     ]
    }
   ],
   "source": [
    "wrong_delivery_dates_df.drop('delivery_item_date_difference_num',axis=1,inplace=True)\n",
    "\n",
    "wrong_delivery_dates_df['delivery_item_date_difference_num'] = yhat_dates\n",
    "\n",
    "# wrong_delivery_dates_df.drop('delivery_item_date_difference',axis=1,inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "tKIKATSsBAuL",
    "outputId": "2bf92951-9e4c-4795-9d53-ffde76c2efc0"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DINESH KUMAR\\AppData\\Local\\Temp\\ipykernel_15364\\778405921.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  wrong_delivery_dates_df['delivery_item_date_difference_num'] = wrong_delivery_dates_df['delivery_item_date_difference_num'].apply(lambda x : int(x))\n"
     ]
    }
   ],
   "source": [
    "wrong_delivery_dates_df['delivery_item_date_difference_num'] = wrong_delivery_dates_df['delivery_item_date_difference_num'].apply(lambda x : int(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 617
    },
    "id": "NlM_4iryBoYa",
    "outputId": "c6c01569-b67f-4720-f02f-090f914c1d4f"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>item_date</th>\n",
       "      <th>quantity_tons</th>\n",
       "      <th>customer</th>\n",
       "      <th>country</th>\n",
       "      <th>status</th>\n",
       "      <th>item_type</th>\n",
       "      <th>application</th>\n",
       "      <th>thickness</th>\n",
       "      <th>width</th>\n",
       "      <th>product_ref</th>\n",
       "      <th>delivery_date</th>\n",
       "      <th>selling_price</th>\n",
       "      <th>item_date_day</th>\n",
       "      <th>item_date_year</th>\n",
       "      <th>item_date_month</th>\n",
       "      <th>delivery_date_day</th>\n",
       "      <th>delivery_date_month</th>\n",
       "      <th>delivery_date_year</th>\n",
       "      <th>delivery_item_date_difference_num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>1</th>\n",
       "      <td>2021-04-01</td>\n",
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       "      <td>7.098376</td>\n",
       "      <td>1668701718</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>6.953684</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2021</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>5.956169</td>\n",
       "      <td>30153963</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.332205</td>\n",
       "      <td>-0.967584</td>\n",
       "      <td>6.858565</td>\n",
       "      <td>628377</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>6.468211</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>5.310301</td>\n",
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       "      <td>32.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.077537</td>\n",
       "      <td>0.832909</td>\n",
       "      <td>7.183112</td>\n",
       "      <td>1668701718</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>6.643790</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>6.666354</td>\n",
       "      <td>30211560</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.302585</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>7.600902</td>\n",
       "      <td>640665</td>\n",
       "      <td>2021-03-01</td>\n",
       "      <td>6.357842</td>\n",
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       "      <td>2021</td>\n",
       "      <td>4</td>\n",
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       "      <td>3</td>\n",
       "      <td>2021</td>\n",
       "      <td>31</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>5.419608</td>\n",
       "      <td>30202938</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
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       "      <td>0.405465</td>\n",
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       "      <td>1668701718</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>6.890609</td>\n",
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       "      <td>2021</td>\n",
       "      <td>4</td>\n",
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       "      <td>31</td>\n",
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       "      <td>2020-07-02</td>\n",
       "      <td>4.629691</td>\n",
       "      <td>30200854</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.713572</td>\n",
       "      <td>-0.040822</td>\n",
       "      <td>7.106606</td>\n",
       "      <td>164141591</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>6.381816</td>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2020</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181669</th>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>5.337954</td>\n",
       "      <td>30200854</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.713572</td>\n",
       "      <td>-0.051293</td>\n",
       "      <td>7.313220</td>\n",
       "      <td>164141591</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>6.378426</td>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2020</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181670</th>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>1.443523</td>\n",
       "      <td>30200854</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.713572</td>\n",
       "      <td>-0.342490</td>\n",
       "      <td>7.130899</td>\n",
       "      <td>164141591</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>6.428105</td>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2020</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181671</th>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>-0.323075</td>\n",
       "      <td>30200854</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.713572</td>\n",
       "      <td>-0.162519</td>\n",
       "      <td>7.130899</td>\n",
       "      <td>164141591</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>6.398595</td>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2020</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181672</th>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>6.008043</td>\n",
       "      <td>30200854</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.713572</td>\n",
       "      <td>-0.342490</td>\n",
       "      <td>7.122867</td>\n",
       "      <td>164141591</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>6.408529</td>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2020</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16567 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        item_date  quantity_tons  customer  country  status  item_type  \\\n",
       "1      2021-04-01       6.643822  30202938     25.0       1        5.0   \n",
       "2      2021-04-01       5.956169  30153963     30.0       1        6.0   \n",
       "3      2021-04-01       5.310301  30349574     32.0       1        3.0   \n",
       "4      2021-04-01       6.666354  30211560     28.0       1        5.0   \n",
       "5      2021-04-01       5.419608  30202938     25.0       1        5.0   \n",
       "...           ...            ...       ...      ...     ...        ...   \n",
       "181668 2020-07-02       4.629691  30200854     25.0       1        5.0   \n",
       "181669 2020-07-02       5.337954  30200854     25.0       1        5.0   \n",
       "181670 2020-07-02       1.443523  30200854     25.0       1        5.0   \n",
       "181671 2020-07-02      -0.323075  30200854     25.0       1        5.0   \n",
       "181672 2020-07-02       6.008043  30200854     25.0       1        5.0   \n",
       "\n",
       "        application  thickness     width product_ref delivery_date  \\\n",
       "1          3.713572  -0.223144  7.098376  1668701718    2021-04-01   \n",
       "2          3.332205  -0.967584  6.858565      628377    2021-01-01   \n",
       "3          4.077537   0.832909  7.183112  1668701718    2021-01-01   \n",
       "4          2.302585   1.386294  7.600902      640665    2021-03-01   \n",
       "5          3.713572   0.405465  7.142827  1668701718    2021-04-01   \n",
       "...             ...        ...       ...         ...           ...   \n",
       "181668     3.713572  -0.040822  7.106606   164141591    2020-07-01   \n",
       "181669     3.713572  -0.051293  7.313220   164141591    2020-07-01   \n",
       "181670     3.713572  -0.342490  7.130899   164141591    2020-07-01   \n",
       "181671     3.713572  -0.162519  7.130899   164141591    2020-07-01   \n",
       "181672     3.713572  -0.342490  7.122867   164141591    2020-07-01   \n",
       "\n",
       "        selling_price  item_date_day  item_date_year  item_date_month  \\\n",
       "1            6.953684              1            2021                4   \n",
       "2            6.468211              1            2021                4   \n",
       "3            6.643790              1            2021                4   \n",
       "4            6.357842              1            2021                4   \n",
       "5            6.890609              1            2021                4   \n",
       "...               ...            ...             ...              ...   \n",
       "181668       6.381816              2            2020                7   \n",
       "181669       6.378426              2            2020                7   \n",
       "181670       6.428105              2            2020                7   \n",
       "181671       6.398595              2            2020                7   \n",
       "181672       6.408529              2            2020                7   \n",
       "\n",
       "        delivery_date_day  delivery_date_month  delivery_date_year  \\\n",
       "1                       1                    4                2021   \n",
       "2                       1                    1                2021   \n",
       "3                       1                    1                2021   \n",
       "4                       1                    3                2021   \n",
       "5                       1                    4                2021   \n",
       "...                   ...                  ...                 ...   \n",
       "181668                  1                    7                2020   \n",
       "181669                  1                    7                2020   \n",
       "181670                  1                    7                2020   \n",
       "181671                  1                    7                2020   \n",
       "181672                  1                    7                2020   \n",
       "\n",
       "        delivery_item_date_difference_num  \n",
       "1                                      31  \n",
       "2                                     156  \n",
       "3                                     177  \n",
       "4                                      31  \n",
       "5                                      31  \n",
       "...                                   ...  \n",
       "181668                                 30  \n",
       "181669                                 30  \n",
       "181670                                 30  \n",
       "181671                                 30  \n",
       "181672                                 30  \n",
       "\n",
       "[16567 rows x 19 columns]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wrong_delivery_dates_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 617
    },
    "id": "VuJkosDHBrZt",
    "outputId": "4bca4df3-7d01-46c8-801c-640a55c444e1"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>item_date</th>\n",
       "      <th>quantity_tons</th>\n",
       "      <th>customer</th>\n",
       "      <th>country</th>\n",
       "      <th>status</th>\n",
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       "      <th>delivery_date</th>\n",
       "      <th>selling_price</th>\n",
       "      <th>item_date_day</th>\n",
       "      <th>item_date_year</th>\n",
       "      <th>item_date_month</th>\n",
       "      <th>delivery_date_day</th>\n",
       "      <th>delivery_date_month</th>\n",
       "      <th>delivery_date_year</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>3.991779</td>\n",
       "      <td>30156308</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.302585</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>7.313220</td>\n",
       "      <td>1670798778</td>\n",
       "      <td>2021-07-01</td>\n",
       "      <td>6.749931</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2021</td>\n",
       "      <td>91</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>1.259203</td>\n",
       "      <td>30209509</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.713572</td>\n",
       "      <td>-0.967584</td>\n",
       "      <td>7.025538</td>\n",
       "      <td>611993</td>\n",
       "      <td>2021-07-01</td>\n",
       "      <td>5.975038</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2021</td>\n",
       "      <td>91</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>4.235147</td>\n",
       "      <td>30341428</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.302585</td>\n",
       "      <td>-0.510826</td>\n",
       "      <td>7.150701</td>\n",
       "      <td>1668701376</td>\n",
       "      <td>2021-07-01</td>\n",
       "      <td>7.217443</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2021</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>2.216566</td>\n",
       "      <td>30209509</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.713572</td>\n",
       "      <td>-0.967584</td>\n",
       "      <td>7.025538</td>\n",
       "      <td>611993</td>\n",
       "      <td>2021-07-01</td>\n",
       "      <td>5.975038</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2021</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>3.314642</td>\n",
       "      <td>30165529</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.302585</td>\n",
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       "      <td>7.130899</td>\n",
       "      <td>164141591</td>\n",
       "      <td>2021-07-01</td>\n",
       "      <td>7.001246</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2021</td>\n",
       "      <td>91</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>181433</th>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>3.472467</td>\n",
       "      <td>30227718</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.708050</td>\n",
       "      <td>1.791759</td>\n",
       "      <td>7.600902</td>\n",
       "      <td>640665</td>\n",
       "      <td>2020-08-01</td>\n",
       "      <td>6.313548</td>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>2020</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181525</th>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>4.196171</td>\n",
       "      <td>30161559</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.302585</td>\n",
       "      <td>1.348073</td>\n",
       "      <td>7.329750</td>\n",
       "      <td>640665</td>\n",
       "      <td>2020-08-01</td>\n",
       "      <td>6.126869</td>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>2020</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181527</th>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>4.156686</td>\n",
       "      <td>30161559</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.302585</td>\n",
       "      <td>1.766442</td>\n",
       "      <td>7.329750</td>\n",
       "      <td>640665</td>\n",
       "      <td>2020-08-01</td>\n",
       "      <td>6.124683</td>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>2020</td>\n",
       "      <td>30</td>\n",
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       "    <tr>\n",
       "      <th>181529</th>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>4.025862</td>\n",
       "      <td>30161559</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.302585</td>\n",
       "      <td>2.060514</td>\n",
       "      <td>7.313220</td>\n",
       "      <td>640665</td>\n",
       "      <td>2020-08-01</td>\n",
       "      <td>6.126869</td>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>2020</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181531</th>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>5.328963</td>\n",
       "      <td>30161559</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.302585</td>\n",
       "      <td>1.348073</td>\n",
       "      <td>7.512071</td>\n",
       "      <td>640665</td>\n",
       "      <td>2020-08-01</td>\n",
       "      <td>6.131226</td>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>2020</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>165073 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        item_date  quantity_tons  customer  country  status  item_type  \\\n",
       "0      2021-04-01       3.991779  30156308     28.0       1        5.0   \n",
       "6      2021-04-01       1.259203  30209509     30.0       2        5.0   \n",
       "8      2021-04-01       4.235147  30341428     38.0       1        3.0   \n",
       "10     2021-04-01       2.216566  30209509     30.0       2        5.0   \n",
       "12     2021-04-01       3.314642  30165529     78.0       1        5.0   \n",
       "...           ...            ...       ...      ...     ...        ...   \n",
       "181433 2020-07-02       3.472467  30227718     25.0       0        3.0   \n",
       "181525 2020-07-02       4.196171  30161559     30.0       0        3.0   \n",
       "181527 2020-07-02       4.156686  30161559     30.0       0        3.0   \n",
       "181529 2020-07-02       4.025862  30161559     30.0       0        3.0   \n",
       "181531 2020-07-02       5.328963  30161559     30.0       0        3.0   \n",
       "\n",
       "        application  thickness     width product_ref delivery_date  \\\n",
       "0          2.302585   0.693147  7.313220  1670798778    2021-07-01   \n",
       "6          3.713572  -0.967584  7.025538      611993    2021-07-01   \n",
       "8          2.302585  -0.510826  7.150701  1668701376    2021-07-01   \n",
       "10         3.713572  -0.967584  7.025538      611993    2021-07-01   \n",
       "12         2.302585  -0.287682  7.130899   164141591    2021-07-01   \n",
       "...             ...        ...       ...         ...           ...   \n",
       "181433     2.708050   1.791759  7.600902      640665    2020-08-01   \n",
       "181525     2.302585   1.348073  7.329750      640665    2020-08-01   \n",
       "181527     2.302585   1.766442  7.329750      640665    2020-08-01   \n",
       "181529     2.302585   2.060514  7.313220      640665    2020-08-01   \n",
       "181531     2.302585   1.348073  7.512071      640665    2020-08-01   \n",
       "\n",
       "        selling_price  item_date_day  item_date_year  item_date_month  \\\n",
       "0            6.749931              1            2021                4   \n",
       "6            5.975038              1            2021                4   \n",
       "8            7.217443              1            2021                4   \n",
       "10           5.975038              1            2021                4   \n",
       "12           7.001246              1            2021                4   \n",
       "...               ...            ...             ...              ...   \n",
       "181433       6.313548              2            2020                7   \n",
       "181525       6.126869              2            2020                7   \n",
       "181527       6.124683              2            2020                7   \n",
       "181529       6.126869              2            2020                7   \n",
       "181531       6.131226              2            2020                7   \n",
       "\n",
       "        delivery_date_day  delivery_date_month  delivery_date_year  \\\n",
       "0                       1                    7                2021   \n",
       "6                       1                    7                2021   \n",
       "8                       1                    7                2021   \n",
       "10                      1                    7                2021   \n",
       "12                      1                    7                2021   \n",
       "...                   ...                  ...                 ...   \n",
       "181433                  1                    8                2020   \n",
       "181525                  1                    8                2020   \n",
       "181527                  1                    8                2020   \n",
       "181529                  1                    8                2020   \n",
       "181531                  1                    8                2020   \n",
       "\n",
       "        delivery_item_date_difference_num  \n",
       "0                                      91  \n",
       "6                                      91  \n",
       "8                                      91  \n",
       "10                                     91  \n",
       "12                                     91  \n",
       "...                                   ...  \n",
       "181433                                 30  \n",
       "181525                                 30  \n",
       "181527                                 30  \n",
       "181529                                 30  \n",
       "181531                                 30  \n",
       "\n",
       "[165073 rows x 19 columns]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# correct_delivery_dates_df.drop('delivery_item_date_difference',axis=1,inplace=True)\n",
    "\n",
    "correct_delivery_dates_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "id": "n4HWrxytB5Y4"
   },
   "outputs": [],
   "source": [
    "cleaned_data = pd.concat([correct_delivery_dates_df,wrong_delivery_dates_df],axis=0,ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 617
    },
    "id": "N4EcUqbKCg5S",
    "outputId": "bd7e231e-d441-47b2-ff5a-84735cf4786f"
   },
   "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",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>item_date</th>\n",
       "      <th>quantity_tons</th>\n",
       "      <th>customer</th>\n",
       "      <th>country</th>\n",
       "      <th>status</th>\n",
       "      <th>item_type</th>\n",
       "      <th>application</th>\n",
       "      <th>thickness</th>\n",
       "      <th>width</th>\n",
       "      <th>product_ref</th>\n",
       "      <th>delivery_date</th>\n",
       "      <th>selling_price</th>\n",
       "      <th>item_date_day</th>\n",
       "      <th>item_date_year</th>\n",
       "      <th>item_date_month</th>\n",
       "      <th>delivery_date_day</th>\n",
       "      <th>delivery_date_month</th>\n",
       "      <th>delivery_date_year</th>\n",
       "      <th>delivery_item_date_difference_num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>3.991779</td>\n",
       "      <td>30156308</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.302585</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>7.313220</td>\n",
       "      <td>1670798778</td>\n",
       "      <td>2021-07-01</td>\n",
       "      <td>6.749931</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2021</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>1.259203</td>\n",
       "      <td>30209509</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.713572</td>\n",
       "      <td>-0.967584</td>\n",
       "      <td>7.025538</td>\n",
       "      <td>611993</td>\n",
       "      <td>2021-07-01</td>\n",
       "      <td>5.975038</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2021</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>4.235147</td>\n",
       "      <td>30341428</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.302585</td>\n",
       "      <td>-0.510826</td>\n",
       "      <td>7.150701</td>\n",
       "      <td>1668701376</td>\n",
       "      <td>2021-07-01</td>\n",
       "      <td>7.217443</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2021</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>2.216566</td>\n",
       "      <td>30209509</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.713572</td>\n",
       "      <td>-0.967584</td>\n",
       "      <td>7.025538</td>\n",
       "      <td>611993</td>\n",
       "      <td>2021-07-01</td>\n",
       "      <td>5.975038</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2021</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>3.314642</td>\n",
       "      <td>30165529</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.302585</td>\n",
       "      <td>-0.287682</td>\n",
       "      <td>7.130899</td>\n",
       "      <td>164141591</td>\n",
       "      <td>2021-07-01</td>\n",
       "      <td>7.001246</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2021</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181635</th>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>4.629691</td>\n",
       "      <td>30200854</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.713572</td>\n",
       "      <td>-0.040822</td>\n",
       "      <td>7.106606</td>\n",
       "      <td>164141591</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>6.381816</td>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2020</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181636</th>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>5.337954</td>\n",
       "      <td>30200854</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.713572</td>\n",
       "      <td>-0.051293</td>\n",
       "      <td>7.313220</td>\n",
       "      <td>164141591</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>6.378426</td>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2020</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181637</th>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>1.443523</td>\n",
       "      <td>30200854</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.713572</td>\n",
       "      <td>-0.342490</td>\n",
       "      <td>7.130899</td>\n",
       "      <td>164141591</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>6.428105</td>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2020</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181638</th>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>-0.323075</td>\n",
       "      <td>30200854</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.713572</td>\n",
       "      <td>-0.162519</td>\n",
       "      <td>7.130899</td>\n",
       "      <td>164141591</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>6.398595</td>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2020</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181639</th>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>6.008043</td>\n",
       "      <td>30200854</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.713572</td>\n",
       "      <td>-0.342490</td>\n",
       "      <td>7.122867</td>\n",
       "      <td>164141591</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>6.408529</td>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2020</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>181640 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        item_date  quantity_tons  customer  country  status  item_type  \\\n",
       "0      2021-04-01       3.991779  30156308     28.0       1        5.0   \n",
       "1      2021-04-01       1.259203  30209509     30.0       2        5.0   \n",
       "2      2021-04-01       4.235147  30341428     38.0       1        3.0   \n",
       "3      2021-04-01       2.216566  30209509     30.0       2        5.0   \n",
       "4      2021-04-01       3.314642  30165529     78.0       1        5.0   \n",
       "...           ...            ...       ...      ...     ...        ...   \n",
       "181635 2020-07-02       4.629691  30200854     25.0       1        5.0   \n",
       "181636 2020-07-02       5.337954  30200854     25.0       1        5.0   \n",
       "181637 2020-07-02       1.443523  30200854     25.0       1        5.0   \n",
       "181638 2020-07-02      -0.323075  30200854     25.0       1        5.0   \n",
       "181639 2020-07-02       6.008043  30200854     25.0       1        5.0   \n",
       "\n",
       "        application  thickness     width product_ref delivery_date  \\\n",
       "0          2.302585   0.693147  7.313220  1670798778    2021-07-01   \n",
       "1          3.713572  -0.967584  7.025538      611993    2021-07-01   \n",
       "2          2.302585  -0.510826  7.150701  1668701376    2021-07-01   \n",
       "3          3.713572  -0.967584  7.025538      611993    2021-07-01   \n",
       "4          2.302585  -0.287682  7.130899   164141591    2021-07-01   \n",
       "...             ...        ...       ...         ...           ...   \n",
       "181635     3.713572  -0.040822  7.106606   164141591    2020-07-01   \n",
       "181636     3.713572  -0.051293  7.313220   164141591    2020-07-01   \n",
       "181637     3.713572  -0.342490  7.130899   164141591    2020-07-01   \n",
       "181638     3.713572  -0.162519  7.130899   164141591    2020-07-01   \n",
       "181639     3.713572  -0.342490  7.122867   164141591    2020-07-01   \n",
       "\n",
       "        selling_price  item_date_day  item_date_year  item_date_month  \\\n",
       "0            6.749931              1            2021                4   \n",
       "1            5.975038              1            2021                4   \n",
       "2            7.217443              1            2021                4   \n",
       "3            5.975038              1            2021                4   \n",
       "4            7.001246              1            2021                4   \n",
       "...               ...            ...             ...              ...   \n",
       "181635       6.381816              2            2020                7   \n",
       "181636       6.378426              2            2020                7   \n",
       "181637       6.428105              2            2020                7   \n",
       "181638       6.398595              2            2020                7   \n",
       "181639       6.408529              2            2020                7   \n",
       "\n",
       "        delivery_date_day  delivery_date_month  delivery_date_year  \\\n",
       "0                       1                    7                2021   \n",
       "1                       1                    7                2021   \n",
       "2                       1                    7                2021   \n",
       "3                       1                    7                2021   \n",
       "4                       1                    7                2021   \n",
       "...                   ...                  ...                 ...   \n",
       "181635                  1                    7                2020   \n",
       "181636                  1                    7                2020   \n",
       "181637                  1                    7                2020   \n",
       "181638                  1                    7                2020   \n",
       "181639                  1                    7                2020   \n",
       "\n",
       "        delivery_item_date_difference_num  \n",
       "0                                      91  \n",
       "1                                      91  \n",
       "2                                      91  \n",
       "3                                      91  \n",
       "4                                      91  \n",
       "...                                   ...  \n",
       "181635                                 30  \n",
       "181636                                 30  \n",
       "181637                                 30  \n",
       "181638                                 30  \n",
       "181639                                 30  \n",
       "\n",
       "[181640 rows x 19 columns]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "m5uneyHdSiJE",
    "outputId": "04fcac4a-2a45-434d-f777-b4aa40c718e5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['item_date', 'quantity_tons', 'customer', 'country', 'status',\n",
       "       'item_type', 'application', 'thickness', 'width', 'product_ref',\n",
       "       'delivery_date', 'selling_price', 'item_date_day', 'item_date_year',\n",
       "       'item_date_month', 'delivery_date_day', 'delivery_date_month',\n",
       "       'delivery_date_year', 'delivery_item_date_difference_num'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "g5zov4p5SRw0",
    "outputId": "3cd5ef54-a103-42aa-c08b-ccdd7a807289"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "item_date                            datetime64[ns]\n",
       "quantity_tons                               float64\n",
       "customer                                     object\n",
       "country                                     float64\n",
       "status                                        int64\n",
       "item_type                                   float64\n",
       "application                                 float64\n",
       "thickness                                   float64\n",
       "width                                       float64\n",
       "product_ref                                  object\n",
       "delivery_date                        datetime64[ns]\n",
       "selling_price                               float64\n",
       "item_date_day                                 int64\n",
       "item_date_year                                int64\n",
       "item_date_month                               int64\n",
       "delivery_date_day                             int64\n",
       "delivery_date_month                           int64\n",
       "delivery_date_year                            int64\n",
       "delivery_item_date_difference_num             int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned_data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 617
    },
    "id": "2mIRTl3ADEHU",
    "outputId": "b1d3ad48-e270-4131-e16d-8013806b6a0c"
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    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>item_date</th>\n",
       "      <th>quantity_tons</th>\n",
       "      <th>customer</th>\n",
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       "      <th>delivery_date</th>\n",
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       "      <th>item_date_day</th>\n",
       "      <th>item_date_year</th>\n",
       "      <th>item_date_month</th>\n",
       "      <th>delivery_date_day</th>\n",
       "      <th>delivery_date_month</th>\n",
       "      <th>delivery_date_year</th>\n",
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       "      <th>0</th>\n",
       "      <td>2021-04-01</td>\n",
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       "      <td>2021-07-01</td>\n",
       "      <td>6.749931</td>\n",
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       "      <td>2021</td>\n",
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       "      <td>7</td>\n",
       "      <td>2021</td>\n",
       "      <td>91</td>\n",
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       "      <td>2021-04-01</td>\n",
       "      <td>1.259203</td>\n",
       "      <td>30209509</td>\n",
       "      <td>30.0</td>\n",
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       "      <td>5.0</td>\n",
       "      <td>3.713572</td>\n",
       "      <td>-0.967584</td>\n",
       "      <td>7.025538</td>\n",
       "      <td>611993</td>\n",
       "      <td>2021-07-01</td>\n",
       "      <td>5.975038</td>\n",
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       "      <td>2021</td>\n",
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       "      <td>2021-04-01</td>\n",
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       "      <td>2021-07-01</td>\n",
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       "      <td>2021</td>\n",
       "      <td>91</td>\n",
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       "      <th>3</th>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>2.216566</td>\n",
       "      <td>30209509</td>\n",
       "      <td>30.0</td>\n",
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       "      <td>611993</td>\n",
       "      <td>2021-07-01</td>\n",
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       "      <td>2021</td>\n",
       "      <td>4</td>\n",
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       "      <td>7</td>\n",
       "      <td>2021</td>\n",
       "      <td>91</td>\n",
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       "      <th>4</th>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>3.314642</td>\n",
       "      <td>30165529</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>7.130899</td>\n",
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       "      <td>2021-07-01</td>\n",
       "      <td>7.001246</td>\n",
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       "      <th>181635</th>\n",
       "      <td>2020-07-02</td>\n",
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       "      <td>25.0</td>\n",
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       "      <td>2020-07-01</td>\n",
       "      <td>6.381816</td>\n",
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       "      <td>2020</td>\n",
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       "      <td>2020</td>\n",
       "      <td>30</td>\n",
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       "    <tr>\n",
       "      <th>181636</th>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>5.337954</td>\n",
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       "      <td>25.0</td>\n",
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       "      <td>2020-07-01</td>\n",
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       "      <td>2020</td>\n",
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       "      <td>2020</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181637</th>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>1.443523</td>\n",
       "      <td>30200854</td>\n",
       "      <td>25.0</td>\n",
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       "      <td>5.0</td>\n",
       "      <td>3.713572</td>\n",
       "      <td>-0.342490</td>\n",
       "      <td>7.130899</td>\n",
       "      <td>164141591</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>6.428105</td>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
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       "      <td>2020</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>2020-07-02</td>\n",
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       "      <td>164141591</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>6.398595</td>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
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       "      <td>2020</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181639</th>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>6.008043</td>\n",
       "      <td>30200854</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.713572</td>\n",
       "      <td>-0.342490</td>\n",
       "      <td>7.122867</td>\n",
       "      <td>164141591</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>6.408529</td>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2020</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>181640 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        item_date  quantity_tons  customer  country  status  item_type  \\\n",
       "0      2021-04-01       3.991779  30156308     28.0       1        5.0   \n",
       "1      2021-04-01       1.259203  30209509     30.0       2        5.0   \n",
       "2      2021-04-01       4.235147  30341428     38.0       1        3.0   \n",
       "3      2021-04-01       2.216566  30209509     30.0       2        5.0   \n",
       "4      2021-04-01       3.314642  30165529     78.0       1        5.0   \n",
       "...           ...            ...       ...      ...     ...        ...   \n",
       "181635 2020-07-02       4.629691  30200854     25.0       1        5.0   \n",
       "181636 2020-07-02       5.337954  30200854     25.0       1        5.0   \n",
       "181637 2020-07-02       1.443523  30200854     25.0       1        5.0   \n",
       "181638 2020-07-02      -0.323075  30200854     25.0       1        5.0   \n",
       "181639 2020-07-02       6.008043  30200854     25.0       1        5.0   \n",
       "\n",
       "        application  thickness     width product_ref delivery_date  \\\n",
       "0          2.302585   0.693147  7.313220  1670798778    2021-07-01   \n",
       "1          3.713572  -0.967584  7.025538      611993    2021-07-01   \n",
       "2          2.302585  -0.510826  7.150701  1668701376    2021-07-01   \n",
       "3          3.713572  -0.967584  7.025538      611993    2021-07-01   \n",
       "4          2.302585  -0.287682  7.130899   164141591    2021-07-01   \n",
       "...             ...        ...       ...         ...           ...   \n",
       "181635     3.713572  -0.040822  7.106606   164141591    2020-07-01   \n",
       "181636     3.713572  -0.051293  7.313220   164141591    2020-07-01   \n",
       "181637     3.713572  -0.342490  7.130899   164141591    2020-07-01   \n",
       "181638     3.713572  -0.162519  7.130899   164141591    2020-07-01   \n",
       "181639     3.713572  -0.342490  7.122867   164141591    2020-07-01   \n",
       "\n",
       "        selling_price  item_date_day  item_date_year  item_date_month  \\\n",
       "0            6.749931              1            2021                4   \n",
       "1            5.975038              1            2021                4   \n",
       "2            7.217443              1            2021                4   \n",
       "3            5.975038              1            2021                4   \n",
       "4            7.001246              1            2021                4   \n",
       "...               ...            ...             ...              ...   \n",
       "181635       6.381816              2            2020                7   \n",
       "181636       6.378426              2            2020                7   \n",
       "181637       6.428105              2            2020                7   \n",
       "181638       6.398595              2            2020                7   \n",
       "181639       6.408529              2            2020                7   \n",
       "\n",
       "        delivery_date_day  delivery_date_month  delivery_date_year  \\\n",
       "0                       1                    7                2021   \n",
       "1                       1                    7                2021   \n",
       "2                       1                    7                2021   \n",
       "3                       1                    7                2021   \n",
       "4                       1                    7                2021   \n",
       "...                   ...                  ...                 ...   \n",
       "181635                  1                    7                2020   \n",
       "181636                  1                    7                2020   \n",
       "181637                  1                    7                2020   \n",
       "181638                  1                    7                2020   \n",
       "181639                  1                    7                2020   \n",
       "\n",
       "        delivery_item_date_difference_num  \n",
       "0                                      91  \n",
       "1                                      91  \n",
       "2                                      91  \n",
       "3                                      91  \n",
       "4                                      91  \n",
       "...                                   ...  \n",
       "181635                                 30  \n",
       "181636                                 30  \n",
       "181637                                 30  \n",
       "181638                                 30  \n",
       "181639                                 30  \n",
       "\n",
       "[181640 rows x 19 columns]"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
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    },
    "id": "HvKk9XYrDRp0",
    "outputId": "71d96fcd-9125-4f5a-826e-2a5671d845a0"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['item_date', 'quantity_tons', 'customer', 'country', 'status',\n",
       "       'item_type', 'application', 'thickness', 'width', 'product_ref',\n",
       "       'delivery_date', 'selling_price', 'item_date_day', 'item_date_year',\n",
       "       'item_date_month', 'delivery_date_day', 'delivery_date_month',\n",
       "       'delivery_date_year', 'delivery_item_date_difference_num'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "id": "Q8GXNEuwS8OL"
   },
   "outputs": [],
   "source": [
    "def delivery_date(date,days_x):\n",
    "\n",
    "  delivery_date = []\n",
    "\n",
    "  for i,j in zip(date,days_x):\n",
    "\n",
    "      result_date = i + pd.DateOffset(days=j)\n",
    "\n",
    "      result_date_str = result_date.strftime('%Y-%m-%d')\n",
    "\n",
    "      delivery_date.append(result_date_str)\n",
    "  return delivery_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "HmOslJuPS8Ls",
    "outputId": "8db21d77-838a-4c2c-b9e3-e093fe1c628c"
   },
   "outputs": [
    {
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       " ...]"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = delivery_date(cleaned_data['item_date'],cleaned_data['delivery_item_date_difference_num'])\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "id": "4AlONUT7S8Je"
   },
   "outputs": [],
   "source": [
    "cleaned_data['delivery_date_pred'] = x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 617
    },
    "id": "2GHjT-evS78J",
    "outputId": "aa99f737-af36-47a8-8785-601ec5ee8dba"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>item_date</th>\n",
       "      <th>quantity_tons</th>\n",
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       "      <th>delivery_date_pred</th>\n",
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       "      <td>2021-07-01</td>\n",
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       "      <th>181635</th>\n",
       "      <td>2020-07-02</td>\n",
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       "      <td>2020-07-01</td>\n",
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       "      <td>2020-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181637</th>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>1.443523</td>\n",
       "      <td>30200854</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.713572</td>\n",
       "      <td>-0.342490</td>\n",
       "      <td>7.130899</td>\n",
       "      <td>164141591</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>6.428105</td>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2020</td>\n",
       "      <td>30</td>\n",
       "      <td>2020-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181638</th>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>-0.323075</td>\n",
       "      <td>30200854</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.713572</td>\n",
       "      <td>-0.162519</td>\n",
       "      <td>7.130899</td>\n",
       "      <td>164141591</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>6.398595</td>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2020</td>\n",
       "      <td>30</td>\n",
       "      <td>2020-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181639</th>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>6.008043</td>\n",
       "      <td>30200854</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.713572</td>\n",
       "      <td>-0.342490</td>\n",
       "      <td>7.122867</td>\n",
       "      <td>164141591</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>6.408529</td>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2020</td>\n",
       "      <td>30</td>\n",
       "      <td>2020-08-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>181640 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        item_date  quantity_tons  customer  country  status  item_type  \\\n",
       "0      2021-04-01       3.991779  30156308     28.0       1        5.0   \n",
       "1      2021-04-01       1.259203  30209509     30.0       2        5.0   \n",
       "2      2021-04-01       4.235147  30341428     38.0       1        3.0   \n",
       "3      2021-04-01       2.216566  30209509     30.0       2        5.0   \n",
       "4      2021-04-01       3.314642  30165529     78.0       1        5.0   \n",
       "...           ...            ...       ...      ...     ...        ...   \n",
       "181635 2020-07-02       4.629691  30200854     25.0       1        5.0   \n",
       "181636 2020-07-02       5.337954  30200854     25.0       1        5.0   \n",
       "181637 2020-07-02       1.443523  30200854     25.0       1        5.0   \n",
       "181638 2020-07-02      -0.323075  30200854     25.0       1        5.0   \n",
       "181639 2020-07-02       6.008043  30200854     25.0       1        5.0   \n",
       "\n",
       "        application  thickness     width product_ref delivery_date  \\\n",
       "0          2.302585   0.693147  7.313220  1670798778    2021-07-01   \n",
       "1          3.713572  -0.967584  7.025538      611993    2021-07-01   \n",
       "2          2.302585  -0.510826  7.150701  1668701376    2021-07-01   \n",
       "3          3.713572  -0.967584  7.025538      611993    2021-07-01   \n",
       "4          2.302585  -0.287682  7.130899   164141591    2021-07-01   \n",
       "...             ...        ...       ...         ...           ...   \n",
       "181635     3.713572  -0.040822  7.106606   164141591    2020-07-01   \n",
       "181636     3.713572  -0.051293  7.313220   164141591    2020-07-01   \n",
       "181637     3.713572  -0.342490  7.130899   164141591    2020-07-01   \n",
       "181638     3.713572  -0.162519  7.130899   164141591    2020-07-01   \n",
       "181639     3.713572  -0.342490  7.122867   164141591    2020-07-01   \n",
       "\n",
       "        selling_price  item_date_day  item_date_year  item_date_month  \\\n",
       "0            6.749931              1            2021                4   \n",
       "1            5.975038              1            2021                4   \n",
       "2            7.217443              1            2021                4   \n",
       "3            5.975038              1            2021                4   \n",
       "4            7.001246              1            2021                4   \n",
       "...               ...            ...             ...              ...   \n",
       "181635       6.381816              2            2020                7   \n",
       "181636       6.378426              2            2020                7   \n",
       "181637       6.428105              2            2020                7   \n",
       "181638       6.398595              2            2020                7   \n",
       "181639       6.408529              2            2020                7   \n",
       "\n",
       "        delivery_date_day  delivery_date_month  delivery_date_year  \\\n",
       "0                       1                    7                2021   \n",
       "1                       1                    7                2021   \n",
       "2                       1                    7                2021   \n",
       "3                       1                    7                2021   \n",
       "4                       1                    7                2021   \n",
       "...                   ...                  ...                 ...   \n",
       "181635                  1                    7                2020   \n",
       "181636                  1                    7                2020   \n",
       "181637                  1                    7                2020   \n",
       "181638                  1                    7                2020   \n",
       "181639                  1                    7                2020   \n",
       "\n",
       "        delivery_item_date_difference_num delivery_date_pred  \n",
       "0                                      91         2021-07-01  \n",
       "1                                      91         2021-07-01  \n",
       "2                                      91         2021-07-01  \n",
       "3                                      91         2021-07-01  \n",
       "4                                      91         2021-07-01  \n",
       "...                                   ...                ...  \n",
       "181635                                 30         2020-08-01  \n",
       "181636                                 30         2020-08-01  \n",
       "181637                                 30         2020-08-01  \n",
       "181638                                 30         2020-08-01  \n",
       "181639                                 30         2020-08-01  \n",
       "\n",
       "[181640 rows x 20 columns]"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "dmMkO0Z5DLth",
    "outputId": "bab20c5a-840a-4f4e-84ae-93bebdc89558"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['item_date', 'quantity_tons', 'customer', 'country', 'status',\n",
       "       'item_type', 'application', 'thickness', 'width', 'product_ref',\n",
       "       'delivery_date', 'selling_price', 'item_date_day', 'item_date_year',\n",
       "       'item_date_month', 'delivery_date_day', 'delivery_date_month',\n",
       "       'delivery_date_year', 'delivery_item_date_difference_num',\n",
       "       'delivery_date_pred'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "id": "8tcvvnfeVpjx"
   },
   "outputs": [],
   "source": [
    "cleaned_data = cleaned_data[[ 'quantity_tons', 'customer', 'country', 'status',\n",
    "       'item_type', 'application', 'thickness', 'width', 'product_ref'\n",
    "       , 'selling_price', 'item_date_day', 'item_date_year',\n",
    "       'item_date_month','item_date',\n",
    "       'delivery_date_pred']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JGOY1PlOWY7w",
    "outputId": "eb6815df-a3ad-43fd-e8c8-520952711f9b"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DINESH KUMAR\\AppData\\Local\\Temp\\ipykernel_15364\\815200478.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  cleaned_data['delivery_date_pred'] = pd.to_datetime(cleaned_data['delivery_date_pred'])\n"
     ]
    }
   ],
   "source": [
    "cleaned_data['delivery_date_pred'] = pd.to_datetime(cleaned_data['delivery_date_pred'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "uKgT-JT_V_gx",
    "outputId": "7e1f8e9a-2d5a-490f-96e8-460b003d6756"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DINESH KUMAR\\AppData\\Local\\Temp\\ipykernel_15364\\120759387.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  cleaned_data['delivery_date_day'] = cleaned_data['delivery_date_pred'].dt.day\n",
      "C:\\Users\\DINESH KUMAR\\AppData\\Local\\Temp\\ipykernel_15364\\120759387.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  cleaned_data['delivery_date_month'] = cleaned_data['delivery_date_pred'].dt.month\n",
      "C:\\Users\\DINESH KUMAR\\AppData\\Local\\Temp\\ipykernel_15364\\120759387.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  cleaned_data['delivery_date_year'] = cleaned_data['delivery_date_pred'].dt.year\n"
     ]
    }
   ],
   "source": [
    "cleaned_data['delivery_date_day'] = cleaned_data['delivery_date_pred'].dt.day\n",
    "cleaned_data['delivery_date_month'] = cleaned_data['delivery_date_pred'].dt.month\n",
    "cleaned_data['delivery_date_year'] = cleaned_data['delivery_date_pred'].dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Z-rtRLjtDgpI",
    "outputId": "e7cf9e11-8e24-45c8-d57f-111d2e49599a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "quantity_tons                 float64\n",
       "customer                       object\n",
       "country                       float64\n",
       "status                          int64\n",
       "item_type                     float64\n",
       "application                   float64\n",
       "thickness                     float64\n",
       "width                         float64\n",
       "product_ref                    object\n",
       "selling_price                 float64\n",
       "item_date_day                   int64\n",
       "item_date_year                  int64\n",
       "item_date_month                 int64\n",
       "item_date              datetime64[ns]\n",
       "delivery_date_pred     datetime64[ns]\n",
       "delivery_date_day               int64\n",
       "delivery_date_month             int64\n",
       "delivery_date_year              int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned_data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "CTTghT4OW1Yd",
    "outputId": "0e8d2a98-12c0-43bf-8715-33ced002fb6f"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>quantity_tons</th>\n",
       "      <th>customer</th>\n",
       "      <th>country</th>\n",
       "      <th>status</th>\n",
       "      <th>item_type</th>\n",
       "      <th>application</th>\n",
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       "      <th>width</th>\n",
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       "      <th>item_date_day</th>\n",
       "      <th>item_date_year</th>\n",
       "      <th>item_date_month</th>\n",
       "      <th>item_date</th>\n",
       "      <th>delivery_date_pred</th>\n",
       "      <th>delivery_date_day</th>\n",
       "      <th>delivery_date_month</th>\n",
       "      <th>delivery_date_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.991779</td>\n",
       "      <td>30156308</td>\n",
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       "      <td>2021-04-01</td>\n",
       "      <td>2021-07-01</td>\n",
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       "      <td>30.0</td>\n",
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       "      <td>-0.967584</td>\n",
       "      <td>7.025538</td>\n",
       "      <td>611993</td>\n",
       "      <td>5.975038</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>4</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>2021-07-01</td>\n",
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       "      <td>7</td>\n",
       "      <td>2021</td>\n",
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       "      <td>2021-07-01</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2021</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.216566</td>\n",
       "      <td>30209509</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.713572</td>\n",
       "      <td>-0.967584</td>\n",
       "      <td>7.025538</td>\n",
       "      <td>611993</td>\n",
       "      <td>5.975038</td>\n",
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       "      <td>2021-04-01</td>\n",
       "      <td>2021-07-01</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2021</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.314642</td>\n",
       "      <td>30165529</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>2021-07-01</td>\n",
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       "      <td>2020-08-01</td>\n",
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       "      <td>164141591</td>\n",
       "      <td>6.378426</td>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>2020-08-01</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181637</th>\n",
       "      <td>1.443523</td>\n",
       "      <td>30200854</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.713572</td>\n",
       "      <td>-0.342490</td>\n",
       "      <td>7.130899</td>\n",
       "      <td>164141591</td>\n",
       "      <td>6.428105</td>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>2020-08-01</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181638</th>\n",
       "      <td>-0.323075</td>\n",
       "      <td>30200854</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.713572</td>\n",
       "      <td>-0.162519</td>\n",
       "      <td>7.130899</td>\n",
       "      <td>164141591</td>\n",
       "      <td>6.398595</td>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>2020-08-01</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181639</th>\n",
       "      <td>6.008043</td>\n",
       "      <td>30200854</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.713572</td>\n",
       "      <td>-0.342490</td>\n",
       "      <td>7.122867</td>\n",
       "      <td>164141591</td>\n",
       "      <td>6.408529</td>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>2020-08-01</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>181640 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        quantity_tons  customer  country  status  item_type  application  \\\n",
       "0            3.991779  30156308     28.0       1        5.0     2.302585   \n",
       "1            1.259203  30209509     30.0       2        5.0     3.713572   \n",
       "2            4.235147  30341428     38.0       1        3.0     2.302585   \n",
       "3            2.216566  30209509     30.0       2        5.0     3.713572   \n",
       "4            3.314642  30165529     78.0       1        5.0     2.302585   \n",
       "...               ...       ...      ...     ...        ...          ...   \n",
       "181635       4.629691  30200854     25.0       1        5.0     3.713572   \n",
       "181636       5.337954  30200854     25.0       1        5.0     3.713572   \n",
       "181637       1.443523  30200854     25.0       1        5.0     3.713572   \n",
       "181638      -0.323075  30200854     25.0       1        5.0     3.713572   \n",
       "181639       6.008043  30200854     25.0       1        5.0     3.713572   \n",
       "\n",
       "        thickness     width product_ref  selling_price  item_date_day  \\\n",
       "0        0.693147  7.313220  1670798778       6.749931              1   \n",
       "1       -0.967584  7.025538      611993       5.975038              1   \n",
       "2       -0.510826  7.150701  1668701376       7.217443              1   \n",
       "3       -0.967584  7.025538      611993       5.975038              1   \n",
       "4       -0.287682  7.130899   164141591       7.001246              1   \n",
       "...           ...       ...         ...            ...            ...   \n",
       "181635  -0.040822  7.106606   164141591       6.381816              2   \n",
       "181636  -0.051293  7.313220   164141591       6.378426              2   \n",
       "181637  -0.342490  7.130899   164141591       6.428105              2   \n",
       "181638  -0.162519  7.130899   164141591       6.398595              2   \n",
       "181639  -0.342490  7.122867   164141591       6.408529              2   \n",
       "\n",
       "        item_date_year  item_date_month  item_date delivery_date_pred  \\\n",
       "0                 2021                4 2021-04-01         2021-07-01   \n",
       "1                 2021                4 2021-04-01         2021-07-01   \n",
       "2                 2021                4 2021-04-01         2021-07-01   \n",
       "3                 2021                4 2021-04-01         2021-07-01   \n",
       "4                 2021                4 2021-04-01         2021-07-01   \n",
       "...                ...              ...        ...                ...   \n",
       "181635            2020                7 2020-07-02         2020-08-01   \n",
       "181636            2020                7 2020-07-02         2020-08-01   \n",
       "181637            2020                7 2020-07-02         2020-08-01   \n",
       "181638            2020                7 2020-07-02         2020-08-01   \n",
       "181639            2020                7 2020-07-02         2020-08-01   \n",
       "\n",
       "        delivery_date_day  delivery_date_month  delivery_date_year  \n",
       "0                       1                    7                2021  \n",
       "1                       1                    7                2021  \n",
       "2                       1                    7                2021  \n",
       "3                       1                    7                2021  \n",
       "4                       1                    7                2021  \n",
       "...                   ...                  ...                 ...  \n",
       "181635                  1                    8                2020  \n",
       "181636                  1                    8                2020  \n",
       "181637                  1                    8                2020  \n",
       "181638                  1                    8                2020  \n",
       "181639                  1                    8                2020  \n",
       "\n",
       "[181640 rows x 18 columns]"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZmOWWnhHW1U7",
    "outputId": "308dfc92-d2a3-4987-be1f-3eccd8310a76"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "quantity_tons          0\n",
       "customer               0\n",
       "country                0\n",
       "status                 0\n",
       "item_type              0\n",
       "application            0\n",
       "thickness              0\n",
       "width                  0\n",
       "product_ref            0\n",
       "selling_price          0\n",
       "item_date_day          0\n",
       "item_date_year         0\n",
       "item_date_month        0\n",
       "item_date              0\n",
       "delivery_date_pred     0\n",
       "delivery_date_day      0\n",
       "delivery_date_month    0\n",
       "delivery_date_year     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "id": "Sfi9sDJ4W1St"
   },
   "outputs": [],
   "source": [
    "cleaned_data = cleaned_data.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "id": "P9vy5GFL_J0Z"
   },
   "outputs": [],
   "source": [
    "cleaned_data['customer'] = pd.to_numeric(cleaned_data['customer'],errors='coerce').fillna(method='bfill').astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "id": "e2KRhGkRWluX"
   },
   "outputs": [],
   "source": [
    "cleaned_data['customer'] = cleaned_data['customer'].apply(lambda x : int(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "cicydNIjXkG2",
    "outputId": "04d0b071-d79f-47e3-db94-9a3e42daabf7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['quantity_tons', 'customer', 'country', 'status', 'item_type',\n",
       "       'application', 'thickness', 'width', 'product_ref', 'selling_price',\n",
       "       'item_date_day', 'item_date_year', 'item_date_month', 'item_date',\n",
       "       'delivery_date_pred', 'delivery_date_day', 'delivery_date_month',\n",
       "       'delivery_date_year'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "id": "ujUK1IRJD40s"
   },
   "outputs": [],
   "source": [
    "cleaned_data = cleaned_data[['quantity_tons', 'customer', 'country', 'status', 'item_type',\n",
    "       'application', 'thickness', 'width', 'product_ref', 'selling_price','item_date', 'delivery_date_pred',\n",
    "       'item_date_day', 'item_date_month',  'item_date_year',\n",
    "       'delivery_date_day', 'delivery_date_month',\n",
    "       'delivery_date_year']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "cClnUpeMYS-R",
    "outputId": "d96e9565-7434-427c-c2a5-31e461e185d7"
   },
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>quantity_tons</th>\n",
       "      <th>customer</th>\n",
       "      <th>country</th>\n",
       "      <th>status</th>\n",
       "      <th>item_type</th>\n",
       "      <th>application</th>\n",
       "      <th>thickness</th>\n",
       "      <th>width</th>\n",
       "      <th>product_ref</th>\n",
       "      <th>selling_price</th>\n",
       "      <th>item_date</th>\n",
       "      <th>delivery_date_pred</th>\n",
       "      <th>item_date_day</th>\n",
       "      <th>item_date_month</th>\n",
       "      <th>item_date_year</th>\n",
       "      <th>delivery_date_day</th>\n",
       "      <th>delivery_date_month</th>\n",
       "      <th>delivery_date_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.991779</td>\n",
       "      <td>30156308</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.302585</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>7.313220</td>\n",
       "      <td>1670798778</td>\n",
       "      <td>6.749931</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>2021-07-01</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2021</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.259203</td>\n",
       "      <td>30209509</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.713572</td>\n",
       "      <td>-0.967584</td>\n",
       "      <td>7.025538</td>\n",
       "      <td>611993</td>\n",
       "      <td>5.975038</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>2021-07-01</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2021</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.235147</td>\n",
       "      <td>30341428</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.302585</td>\n",
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       "      <td>7.150701</td>\n",
       "      <td>1668701376</td>\n",
       "      <td>7.217443</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>2021-07-01</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2021</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.216566</td>\n",
       "      <td>30209509</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.713572</td>\n",
       "      <td>-0.967584</td>\n",
       "      <td>7.025538</td>\n",
       "      <td>611993</td>\n",
       "      <td>5.975038</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>2021-07-01</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2021</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.314642</td>\n",
       "      <td>30165529</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.302585</td>\n",
       "      <td>-0.287682</td>\n",
       "      <td>7.130899</td>\n",
       "      <td>164141591</td>\n",
       "      <td>7.001246</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>2021-07-01</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2021</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2021</td>\n",
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       "    <tr>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>181635</th>\n",
       "      <td>4.629691</td>\n",
       "      <td>30200854</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.713572</td>\n",
       "      <td>-0.040822</td>\n",
       "      <td>7.106606</td>\n",
       "      <td>164141591</td>\n",
       "      <td>6.381816</td>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>2020-08-01</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181636</th>\n",
       "      <td>5.337954</td>\n",
       "      <td>30200854</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.713572</td>\n",
       "      <td>-0.051293</td>\n",
       "      <td>7.313220</td>\n",
       "      <td>164141591</td>\n",
       "      <td>6.378426</td>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>2020-08-01</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181637</th>\n",
       "      <td>1.443523</td>\n",
       "      <td>30200854</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.713572</td>\n",
       "      <td>-0.342490</td>\n",
       "      <td>7.130899</td>\n",
       "      <td>164141591</td>\n",
       "      <td>6.428105</td>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>2020-08-01</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181638</th>\n",
       "      <td>-0.323075</td>\n",
       "      <td>30200854</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.713572</td>\n",
       "      <td>-0.162519</td>\n",
       "      <td>7.130899</td>\n",
       "      <td>164141591</td>\n",
       "      <td>6.398595</td>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>2020-08-01</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181639</th>\n",
       "      <td>6.008043</td>\n",
       "      <td>30200854</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.713572</td>\n",
       "      <td>-0.342490</td>\n",
       "      <td>7.122867</td>\n",
       "      <td>164141591</td>\n",
       "      <td>6.408529</td>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>2020-08-01</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>181640 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        quantity_tons  customer  country  status  item_type  application  \\\n",
       "0            3.991779  30156308     28.0       1        5.0     2.302585   \n",
       "1            1.259203  30209509     30.0       2        5.0     3.713572   \n",
       "2            4.235147  30341428     38.0       1        3.0     2.302585   \n",
       "3            2.216566  30209509     30.0       2        5.0     3.713572   \n",
       "4            3.314642  30165529     78.0       1        5.0     2.302585   \n",
       "...               ...       ...      ...     ...        ...          ...   \n",
       "181635       4.629691  30200854     25.0       1        5.0     3.713572   \n",
       "181636       5.337954  30200854     25.0       1        5.0     3.713572   \n",
       "181637       1.443523  30200854     25.0       1        5.0     3.713572   \n",
       "181638      -0.323075  30200854     25.0       1        5.0     3.713572   \n",
       "181639       6.008043  30200854     25.0       1        5.0     3.713572   \n",
       "\n",
       "        thickness     width product_ref  selling_price  item_date  \\\n",
       "0        0.693147  7.313220  1670798778       6.749931 2021-04-01   \n",
       "1       -0.967584  7.025538      611993       5.975038 2021-04-01   \n",
       "2       -0.510826  7.150701  1668701376       7.217443 2021-04-01   \n",
       "3       -0.967584  7.025538      611993       5.975038 2021-04-01   \n",
       "4       -0.287682  7.130899   164141591       7.001246 2021-04-01   \n",
       "...           ...       ...         ...            ...        ...   \n",
       "181635  -0.040822  7.106606   164141591       6.381816 2020-07-02   \n",
       "181636  -0.051293  7.313220   164141591       6.378426 2020-07-02   \n",
       "181637  -0.342490  7.130899   164141591       6.428105 2020-07-02   \n",
       "181638  -0.162519  7.130899   164141591       6.398595 2020-07-02   \n",
       "181639  -0.342490  7.122867   164141591       6.408529 2020-07-02   \n",
       "\n",
       "       delivery_date_pred  item_date_day  item_date_month  item_date_year  \\\n",
       "0              2021-07-01              1                4            2021   \n",
       "1              2021-07-01              1                4            2021   \n",
       "2              2021-07-01              1                4            2021   \n",
       "3              2021-07-01              1                4            2021   \n",
       "4              2021-07-01              1                4            2021   \n",
       "...                   ...            ...              ...             ...   \n",
       "181635         2020-08-01              2                7            2020   \n",
       "181636         2020-08-01              2                7            2020   \n",
       "181637         2020-08-01              2                7            2020   \n",
       "181638         2020-08-01              2                7            2020   \n",
       "181639         2020-08-01              2                7            2020   \n",
       "\n",
       "        delivery_date_day  delivery_date_month  delivery_date_year  \n",
       "0                       1                    7                2021  \n",
       "1                       1                    7                2021  \n",
       "2                       1                    7                2021  \n",
       "3                       1                    7                2021  \n",
       "4                       1                    7                2021  \n",
       "...                   ...                  ...                 ...  \n",
       "181635                  1                    8                2020  \n",
       "181636                  1                    8                2020  \n",
       "181637                  1                    8                2020  \n",
       "181638                  1                    8                2020  \n",
       "181639                  1                    8                2020  \n",
       "\n",
       "[181640 rows x 18 columns]"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "id": "rrYxI-3gE_Ru"
   },
   "outputs": [],
   "source": [
    "cleaned_data['product_ref'] = pd.to_numeric(cleaned_data['product_ref'] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "bWpeySMqE40Q",
    "outputId": "aeb6e162-cbf2-40b6-c6a2-e0e0a334b723"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "quantity_tons                 float64\n",
       "customer                        int64\n",
       "country                       float64\n",
       "status                          int64\n",
       "item_type                     float64\n",
       "application                   float64\n",
       "thickness                     float64\n",
       "width                         float64\n",
       "product_ref                     int64\n",
       "selling_price                 float64\n",
       "item_date              datetime64[ns]\n",
       "delivery_date_pred     datetime64[ns]\n",
       "item_date_day                   int64\n",
       "item_date_month                 int64\n",
       "item_date_year                  int64\n",
       "delivery_date_day               int64\n",
       "delivery_date_month             int64\n",
       "delivery_date_year              int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned_data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oXca61xyFHeO"
   },
   "source": [
    "#**Cleaned Dataset**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YEgTdpMlELvN"
   },
   "source": [
    "\n",
    "\n",
    "```\n",
    "# Here you can download the cleaned dataset\n",
    "```\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "X3OiGCW3FKOs"
   },
   "outputs": [],
   "source": [
    "cleaned_data.to_csv('icm_final_cd.csv',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CnvhvqoLpQ-a"
   },
   "source": [
    "\n",
    "\n",
    "```\n",
    "                                                         Finished\n",
    "```\n",
    "\n"
   ]
  }
 ],
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