MachineHack_GIH_LB.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# *`MachineHack GIH LB Notebook`*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This Notebook contains the Rank 26 solution to the The Great Indian Hiring Hackathon organized by MachineHack."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dataset Description:\n",
    " - Train.csv - 284780 rows x 8 columns (Inlcudes UnitPrice Columns as Target)\n",
    " - Test.csv - 122049 rows x 7 columns\n",
    " - Sample Submission.csv - Please check the Evaluation section for more details on how to generate a valid submission\n",
    " \n",
    "\n",
    "### Attribute Description:\n",
    " 1. Invoice No - Invoice ID, encoded as Label\n",
    " 2. StockCode - Unique code per stock, encoded as Label\n",
    " 3. Description - The Description, encoded as Label\n",
    " 4. Quantity - Quantity purchased\n",
    " 5. InvoiceDate - Date of purchase\n",
    " 6. UnitPrice - The *Target value*, price of every product\n",
    " 7. CustomerID - Unique Identifier for every Customer\n",
    " 8. Country - Country of sales, encoded as Label"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "from collections import Counter, defaultdict\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from xgboost import XGBRFRegressor\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.preprocessing import PowerTransformer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reading Train & Test csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6141</td>\n",
       "      <td>1583</td>\n",
       "      <td>144</td>\n",
       "      <td>3</td>\n",
       "      <td>2011-05-06 16:54:00</td>\n",
       "      <td>3.75</td>\n",
       "      <td>14056.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6349</td>\n",
       "      <td>1300</td>\n",
       "      <td>3682</td>\n",
       "      <td>6</td>\n",
       "      <td>2011-05-11 07:35:00</td>\n",
       "      <td>1.95</td>\n",
       "      <td>13098.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16783</td>\n",
       "      <td>2178</td>\n",
       "      <td>1939</td>\n",
       "      <td>4</td>\n",
       "      <td>2011-11-20 13:20:00</td>\n",
       "      <td>5.95</td>\n",
       "      <td>15044.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16971</td>\n",
       "      <td>2115</td>\n",
       "      <td>2983</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-11-22 12:07:00</td>\n",
       "      <td>0.83</td>\n",
       "      <td>15525.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6080</td>\n",
       "      <td>1210</td>\n",
       "      <td>2886</td>\n",
       "      <td>12</td>\n",
       "      <td>2011-05-06 09:00:00</td>\n",
       "      <td>1.65</td>\n",
       "      <td>13952.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   InvoiceNo  StockCode  Description  Quantity          InvoiceDate  \\\n",
       "0       6141       1583          144         3  2011-05-06 16:54:00   \n",
       "1       6349       1300         3682         6  2011-05-11 07:35:00   \n",
       "2      16783       2178         1939         4  2011-11-20 13:20:00   \n",
       "3      16971       2115         2983         1  2011-11-22 12:07:00   \n",
       "4       6080       1210         2886        12  2011-05-06 09:00:00   \n",
       "\n",
       "   UnitPrice  CustomerID  Country  \n",
       "0       3.75     14056.0       35  \n",
       "1       1.95     13098.0       35  \n",
       "2       5.95     15044.0       35  \n",
       "3       0.83     15525.0       35  \n",
       "4       1.65     13952.0       35  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = pd.read_csv(r\"Train.csv\")\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3139</td>\n",
       "      <td>1709</td>\n",
       "      <td>1079</td>\n",
       "      <td>3</td>\n",
       "      <td>2011-02-22 15:22:00</td>\n",
       "      <td>16161.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>617</td>\n",
       "      <td>510</td>\n",
       "      <td>3457</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-12-08 12:46:00</td>\n",
       "      <td>17341.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14653</td>\n",
       "      <td>604</td>\n",
       "      <td>694</td>\n",
       "      <td>36</td>\n",
       "      <td>2011-10-25 13:53:00</td>\n",
       "      <td>15158.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8634</td>\n",
       "      <td>1478</td>\n",
       "      <td>3473</td>\n",
       "      <td>2</td>\n",
       "      <td>2011-06-27 12:38:00</td>\n",
       "      <td>16033.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15546</td>\n",
       "      <td>3216</td>\n",
       "      <td>871</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-11-06 16:14:00</td>\n",
       "      <td>15351.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   InvoiceNo  StockCode  Description  Quantity          InvoiceDate  \\\n",
       "0       3139       1709         1079         3  2011-02-22 15:22:00   \n",
       "1        617        510         3457         1  2010-12-08 12:46:00   \n",
       "2      14653        604          694        36  2011-10-25 13:53:00   \n",
       "3       8634       1478         3473         2  2011-06-27 12:38:00   \n",
       "4      15546       3216          871         1  2011-11-06 16:14:00   \n",
       "\n",
       "   CustomerID  Country  \n",
       "0     16161.0       35  \n",
       "1     17341.0       35  \n",
       "2     15158.0       35  \n",
       "3     16033.0       35  \n",
       "4     15351.0       35  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df = pd.read_csv(r\"Test.csv\")\n",
    "test_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA & Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
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       "      <th>count</th>\n",
       "      <td>284780.000000</td>\n",
       "      <td>284780.000000</td>\n",
       "      <td>284780.000000</td>\n",
       "      <td>284780.000000</td>\n",
       "      <td>284780.000000</td>\n",
       "      <td>284780.000000</td>\n",
       "      <td>284780.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9955.394083</td>\n",
       "      <td>1573.386807</td>\n",
       "      <td>2023.955573</td>\n",
       "      <td>12.028468</td>\n",
       "      <td>3.451216</td>\n",
       "      <td>15288.224278</td>\n",
       "      <td>32.823846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5551.519138</td>\n",
       "      <td>843.604991</td>\n",
       "      <td>1089.812078</td>\n",
       "      <td>295.111588</td>\n",
       "      <td>78.399541</td>\n",
       "      <td>1714.336529</td>\n",
       "      <td>6.743602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-80995.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12346.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5069.000000</td>\n",
       "      <td>939.000000</td>\n",
       "      <td>1141.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>13953.000000</td>\n",
       "      <td>35.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>10310.000000</td>\n",
       "      <td>1521.000000</td>\n",
       "      <td>1987.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.950000</td>\n",
       "      <td>15152.000000</td>\n",
       "      <td>35.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>14657.000000</td>\n",
       "      <td>2106.000000</td>\n",
       "      <td>2945.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>3.750000</td>\n",
       "      <td>16794.000000</td>\n",
       "      <td>35.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>22188.000000</td>\n",
       "      <td>3683.000000</td>\n",
       "      <td>3895.000000</td>\n",
       "      <td>80995.000000</td>\n",
       "      <td>38970.000000</td>\n",
       "      <td>18287.000000</td>\n",
       "      <td>36.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           InvoiceNo      StockCode    Description       Quantity  \\\n",
       "count  284780.000000  284780.000000  284780.000000  284780.000000   \n",
       "mean     9955.394083    1573.386807    2023.955573      12.028468   \n",
       "std      5551.519138     843.604991    1089.812078     295.111588   \n",
       "min         0.000000       0.000000       0.000000  -80995.000000   \n",
       "25%      5069.000000     939.000000    1141.000000       2.000000   \n",
       "50%     10310.000000    1521.000000    1987.000000       5.000000   \n",
       "75%     14657.000000    2106.000000    2945.000000      12.000000   \n",
       "max     22188.000000    3683.000000    3895.000000   80995.000000   \n",
       "\n",
       "           UnitPrice     CustomerID        Country  \n",
       "count  284780.000000  284780.000000  284780.000000  \n",
       "mean        3.451216   15288.224278      32.823846  \n",
       "std        78.399541    1714.336529       6.743602  \n",
       "min         0.000000   12346.000000       0.000000  \n",
       "25%         1.250000   13953.000000      35.000000  \n",
       "50%         1.950000   15152.000000      35.000000  \n",
       "75%         3.750000   16794.000000      35.000000  \n",
       "max     38970.000000   18287.000000      36.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>CustomerID</th>\n",
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       "      <th>count</th>\n",
       "      <td>122049.000000</td>\n",
       "      <td>122049.000000</td>\n",
       "      <td>122049.000000</td>\n",
       "      <td>122049.000000</td>\n",
       "      <td>122049.000000</td>\n",
       "      <td>122049.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9940.419692</td>\n",
       "      <td>1572.955788</td>\n",
       "      <td>2026.412744</td>\n",
       "      <td>12.137920</td>\n",
       "      <td>15286.445256</td>\n",
       "      <td>32.836795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5554.116349</td>\n",
       "      <td>845.106082</td>\n",
       "      <td>1087.697388</td>\n",
       "      <td>54.311646</td>\n",
       "      <td>1711.887591</td>\n",
       "      <td>6.726816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-9360.000000</td>\n",
       "      <td>12347.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5018.000000</td>\n",
       "      <td>937.000000</td>\n",
       "      <td>1147.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>13953.000000</td>\n",
       "      <td>35.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>10311.000000</td>\n",
       "      <td>1523.000000</td>\n",
       "      <td>1987.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>15152.000000</td>\n",
       "      <td>35.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>14635.000000</td>\n",
       "      <td>2106.000000</td>\n",
       "      <td>2945.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>16782.000000</td>\n",
       "      <td>35.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>22189.000000</td>\n",
       "      <td>3683.000000</td>\n",
       "      <td>3895.000000</td>\n",
       "      <td>4800.000000</td>\n",
       "      <td>18287.000000</td>\n",
       "      <td>36.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "           InvoiceNo      StockCode    Description       Quantity  \\\n",
       "count  122049.000000  122049.000000  122049.000000  122049.000000   \n",
       "mean     9940.419692    1572.955788    2026.412744      12.137920   \n",
       "std      5554.116349     845.106082    1087.697388      54.311646   \n",
       "min         0.000000       0.000000       0.000000   -9360.000000   \n",
       "25%      5018.000000     937.000000    1147.000000       2.000000   \n",
       "50%     10311.000000    1523.000000    1987.000000       5.000000   \n",
       "75%     14635.000000    2106.000000    2945.000000      12.000000   \n",
       "max     22189.000000    3683.000000    3895.000000    4800.000000   \n",
       "\n",
       "          CustomerID        Country  \n",
       "count  122049.000000  122049.000000  \n",
       "mean    15286.445256      32.836795  \n",
       "std      1711.887591       6.726816  \n",
       "min     12347.000000       0.000000  \n",
       "25%     13953.000000      35.000000  \n",
       "50%     15152.000000      35.000000  \n",
       "75%     16782.000000      35.000000  \n",
       "max     18287.000000      36.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3847</th>\n",
       "      <td>14606</td>\n",
       "      <td>510</td>\n",
       "      <td>3457</td>\n",
       "      <td>2</td>\n",
       "      <td>2011-10-24 17:07:00</td>\n",
       "      <td>0.83</td>\n",
       "      <td>14096.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8530</th>\n",
       "      <td>139</td>\n",
       "      <td>510</td>\n",
       "      <td>3457</td>\n",
       "      <td>12</td>\n",
       "      <td>2010-12-02 10:17:00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>13941.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15351</th>\n",
       "      <td>7351</td>\n",
       "      <td>510</td>\n",
       "      <td>3457</td>\n",
       "      <td>2</td>\n",
       "      <td>2011-05-29 14:08:00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>16686.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16479</th>\n",
       "      <td>17524</td>\n",
       "      <td>510</td>\n",
       "      <td>3457</td>\n",
       "      <td>13</td>\n",
       "      <td>2011-11-28 15:54:00</td>\n",
       "      <td>0.83</td>\n",
       "      <td>14096.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25176</th>\n",
       "      <td>4638</td>\n",
       "      <td>510</td>\n",
       "      <td>3457</td>\n",
       "      <td>12</td>\n",
       "      <td>2011-03-30 15:38:00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>16092.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29943</th>\n",
       "      <td>11275</td>\n",
       "      <td>510</td>\n",
       "      <td>3457</td>\n",
       "      <td>12</td>\n",
       "      <td>2011-08-26 10:50:00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>14911.0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33532</th>\n",
       "      <td>15081</td>\n",
       "      <td>510</td>\n",
       "      <td>3457</td>\n",
       "      <td>2</td>\n",
       "      <td>2011-10-31 14:09:00</td>\n",
       "      <td>0.83</td>\n",
       "      <td>14096.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37525</th>\n",
       "      <td>15896</td>\n",
       "      <td>510</td>\n",
       "      <td>3457</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-11-10 12:37:00</td>\n",
       "      <td>0.83</td>\n",
       "      <td>14096.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41441</th>\n",
       "      <td>17243</td>\n",
       "      <td>510</td>\n",
       "      <td>3457</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-11-24 13:54:00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>14606.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42080</th>\n",
       "      <td>11813</td>\n",
       "      <td>510</td>\n",
       "      <td>3457</td>\n",
       "      <td>24</td>\n",
       "      <td>2011-09-08 12:47:00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>15306.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46761</th>\n",
       "      <td>374</td>\n",
       "      <td>510</td>\n",
       "      <td>3457</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-12-05 13:55:00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>15426.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55916</th>\n",
       "      <td>13917</td>\n",
       "      <td>510</td>\n",
       "      <td>3457</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-10-12 15:06:00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>15555.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71324</th>\n",
       "      <td>1157</td>\n",
       "      <td>510</td>\n",
       "      <td>3457</td>\n",
       "      <td>12</td>\n",
       "      <td>2010-12-16 13:02:00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>15379.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81301</th>\n",
       "      <td>466</td>\n",
       "      <td>510</td>\n",
       "      <td>3457</td>\n",
       "      <td>3</td>\n",
       "      <td>2010-12-06 13:53:00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>16550.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83076</th>\n",
       "      <td>1922</td>\n",
       "      <td>510</td>\n",
       "      <td>3457</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-18 13:05:00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>18118.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105561</th>\n",
       "      <td>3593</td>\n",
       "      <td>510</td>\n",
       "      <td>3457</td>\n",
       "      <td>4</td>\n",
       "      <td>2011-03-06 14:15:00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>17050.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110051</th>\n",
       "      <td>2142</td>\n",
       "      <td>510</td>\n",
       "      <td>3457</td>\n",
       "      <td>12</td>\n",
       "      <td>2011-01-25 13:26:00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>12744.0</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110480</th>\n",
       "      <td>1399</td>\n",
       "      <td>510</td>\n",
       "      <td>3457</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-12-23 16:06:00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>18116.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114592</th>\n",
       "      <td>13035</td>\n",
       "      <td>510</td>\n",
       "      <td>3457</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-09-29 12:31:00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>14680.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125722</th>\n",
       "      <td>18165</td>\n",
       "      <td>510</td>\n",
       "      <td>3457</td>\n",
       "      <td>5</td>\n",
       "      <td>2011-12-05 17:17:00</td>\n",
       "      <td>0.83</td>\n",
       "      <td>14096.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131805</th>\n",
       "      <td>13764</td>\n",
       "      <td>510</td>\n",
       "      <td>3457</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-10-10 17:12:00</td>\n",
       "      <td>2.46</td>\n",
       "      <td>14096.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133762</th>\n",
       "      <td>17128</td>\n",
       "      <td>510</td>\n",
       "      <td>3457</td>\n",
       "      <td>2</td>\n",
       "      <td>2011-11-23 13:39:00</td>\n",
       "      <td>0.83</td>\n",
       "      <td>14096.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140939</th>\n",
       "      <td>3959</td>\n",
       "      <td>510</td>\n",
       "      <td>3457</td>\n",
       "      <td>12</td>\n",
       "      <td>2011-03-16 08:29:00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>16297.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152536</th>\n",
       "      <td>3091</td>\n",
       "      <td>510</td>\n",
       "      <td>3457</td>\n",
       "      <td>12</td>\n",
       "      <td>2011-02-21 13:51:00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>12365.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164036</th>\n",
       "      <td>5068</td>\n",
       "      <td>510</td>\n",
       "      <td>3457</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-04-10 12:52:00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>13606.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165710</th>\n",
       "      <td>11717</td>\n",
       "      <td>510</td>\n",
       "      <td>3457</td>\n",
       "      <td>12</td>\n",
       "      <td>2011-09-06 14:09:00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>14739.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170414</th>\n",
       "      <td>15598</td>\n",
       "      <td>510</td>\n",
       "      <td>3457</td>\n",
       "      <td>3</td>\n",
       "      <td>2011-11-07 13:56:00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>14623.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206745</th>\n",
       "      <td>5898</td>\n",
       "      <td>510</td>\n",
       "      <td>3457</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-05-03 11:40:00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>15048.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219174</th>\n",
       "      <td>13211</td>\n",
       "      <td>510</td>\n",
       "      <td>3457</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-10-03 09:57:00</td>\n",
       "      <td>0.83</td>\n",
       "      <td>14096.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221506</th>\n",
       "      <td>7216</td>\n",
       "      <td>510</td>\n",
       "      <td>3457</td>\n",
       "      <td>4</td>\n",
       "      <td>2011-05-25 17:12:00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>16161.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226380</th>\n",
       "      <td>7557</td>\n",
       "      <td>510</td>\n",
       "      <td>3457</td>\n",
       "      <td>3</td>\n",
       "      <td>2011-06-05 12:48:00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>13668.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227640</th>\n",
       "      <td>2613</td>\n",
       "      <td>510</td>\n",
       "      <td>3457</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-02-07 11:56:00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>16686.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238203</th>\n",
       "      <td>14160</td>\n",
       "      <td>510</td>\n",
       "      <td>3457</td>\n",
       "      <td>4</td>\n",
       "      <td>2011-10-17 13:31:00</td>\n",
       "      <td>0.83</td>\n",
       "      <td>14096.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248829</th>\n",
       "      <td>4683</td>\n",
       "      <td>510</td>\n",
       "      <td>3457</td>\n",
       "      <td>3</td>\n",
       "      <td>2011-03-31 13:49:00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>17071.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275370</th>\n",
       "      <td>8691</td>\n",
       "      <td>510</td>\n",
       "      <td>3457</td>\n",
       "      <td>3</td>\n",
       "      <td>2011-06-28 15:31:00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>16550.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275963</th>\n",
       "      <td>15357</td>\n",
       "      <td>510</td>\n",
       "      <td>3457</td>\n",
       "      <td>3</td>\n",
       "      <td>2011-11-03 19:28:00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>16016.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277619</th>\n",
       "      <td>1521</td>\n",
       "      <td>510</td>\n",
       "      <td>3457</td>\n",
       "      <td>24</td>\n",
       "      <td>2011-01-06 16:41:00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>13081.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283553</th>\n",
       "      <td>3139</td>\n",
       "      <td>510</td>\n",
       "      <td>3457</td>\n",
       "      <td>2</td>\n",
       "      <td>2011-02-22 15:22:00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>16161.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        InvoiceNo  StockCode  Description  Quantity          InvoiceDate  \\\n",
       "3847        14606        510         3457         2  2011-10-24 17:07:00   \n",
       "8530          139        510         3457        12  2010-12-02 10:17:00   \n",
       "15351        7351        510         3457         2  2011-05-29 14:08:00   \n",
       "16479       17524        510         3457        13  2011-11-28 15:54:00   \n",
       "25176        4638        510         3457        12  2011-03-30 15:38:00   \n",
       "29943       11275        510         3457        12  2011-08-26 10:50:00   \n",
       "33532       15081        510         3457         2  2011-10-31 14:09:00   \n",
       "37525       15896        510         3457         1  2011-11-10 12:37:00   \n",
       "41441       17243        510         3457         1  2011-11-24 13:54:00   \n",
       "42080       11813        510         3457        24  2011-09-08 12:47:00   \n",
       "46761         374        510         3457         1  2010-12-05 13:55:00   \n",
       "55916       13917        510         3457         1  2011-10-12 15:06:00   \n",
       "71324        1157        510         3457        12  2010-12-16 13:02:00   \n",
       "81301         466        510         3457         3  2010-12-06 13:53:00   \n",
       "83076        1922        510         3457         1  2011-01-18 13:05:00   \n",
       "105561       3593        510         3457         4  2011-03-06 14:15:00   \n",
       "110051       2142        510         3457        12  2011-01-25 13:26:00   \n",
       "110480       1399        510         3457         1  2010-12-23 16:06:00   \n",
       "114592      13035        510         3457         1  2011-09-29 12:31:00   \n",
       "125722      18165        510         3457         5  2011-12-05 17:17:00   \n",
       "131805      13764        510         3457         1  2011-10-10 17:12:00   \n",
       "133762      17128        510         3457         2  2011-11-23 13:39:00   \n",
       "140939       3959        510         3457        12  2011-03-16 08:29:00   \n",
       "152536       3091        510         3457        12  2011-02-21 13:51:00   \n",
       "164036       5068        510         3457         1  2011-04-10 12:52:00   \n",
       "165710      11717        510         3457        12  2011-09-06 14:09:00   \n",
       "170414      15598        510         3457         3  2011-11-07 13:56:00   \n",
       "206745       5898        510         3457         1  2011-05-03 11:40:00   \n",
       "219174      13211        510         3457         1  2011-10-03 09:57:00   \n",
       "221506       7216        510         3457         4  2011-05-25 17:12:00   \n",
       "226380       7557        510         3457         3  2011-06-05 12:48:00   \n",
       "227640       2613        510         3457         1  2011-02-07 11:56:00   \n",
       "238203      14160        510         3457         4  2011-10-17 13:31:00   \n",
       "248829       4683        510         3457         3  2011-03-31 13:49:00   \n",
       "275370       8691        510         3457         3  2011-06-28 15:31:00   \n",
       "275963      15357        510         3457         3  2011-11-03 19:28:00   \n",
       "277619       1521        510         3457        24  2011-01-06 16:41:00   \n",
       "283553       3139        510         3457         2  2011-02-22 15:22:00   \n",
       "\n",
       "        UnitPrice  CustomerID  Country  \n",
       "3847         0.83     14096.0       35  \n",
       "8530         1.25     13941.0       35  \n",
       "15351        1.25     16686.0       35  \n",
       "16479        0.83     14096.0       35  \n",
       "25176        1.25     16092.0       35  \n",
       "29943        1.25     14911.0       10  \n",
       "33532        0.83     14096.0       35  \n",
       "37525        0.83     14096.0       35  \n",
       "41441        1.25     14606.0       35  \n",
       "42080        1.25     15306.0       35  \n",
       "46761        1.25     15426.0       35  \n",
       "55916        1.25     15555.0       35  \n",
       "71324        1.25     15379.0       35  \n",
       "81301        1.25     16550.0       35  \n",
       "83076        1.25     18118.0       35  \n",
       "105561       1.25     17050.0       35  \n",
       "110051       1.25     12744.0       29  \n",
       "110480       1.25     18116.0       35  \n",
       "114592       1.25     14680.0       35  \n",
       "125722       0.83     14096.0       35  \n",
       "131805       2.46     14096.0       35  \n",
       "133762       0.83     14096.0       35  \n",
       "140939       1.25     16297.0       35  \n",
       "152536       1.25     12365.0        7  \n",
       "164036       1.25     13606.0       35  \n",
       "165710       1.25     14739.0       35  \n",
       "170414       1.25     14623.0       35  \n",
       "206745       1.25     15048.0       35  \n",
       "219174       0.83     14096.0       35  \n",
       "221506       1.25     16161.0       35  \n",
       "226380       1.25     13668.0       35  \n",
       "227640       1.25     16686.0       35  \n",
       "238203       0.83     14096.0       35  \n",
       "248829       1.25     17071.0       35  \n",
       "275370       1.25     16550.0       35  \n",
       "275963       1.25     16016.0       35  \n",
       "277619       1.25     13081.0       35  \n",
       "283553       1.25     16161.0       35  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[train_df[\"StockCode\"]==510]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
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       "      <td>2011-02-15 12:12:00</td>\n",
       "      <td>3.81</td>\n",
       "      <td>13941.0</td>\n",
       "      <td>35</td>\n",
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       "      <td>8374</td>\n",
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       "      <td>12</td>\n",
       "      <td>2011-06-21 09:44:00</td>\n",
       "      <td>1.65</td>\n",
       "      <td>13941.0</td>\n",
       "      <td>35</td>\n",
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       "      <th>151301</th>\n",
       "      <td>8374</td>\n",
       "      <td>1657</td>\n",
       "      <td>3095</td>\n",
       "      <td>6</td>\n",
       "      <td>2011-06-21 09:44:00</td>\n",
       "      <td>2.95</td>\n",
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       "      <td>35</td>\n",
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       "      <td>35</td>\n",
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       "    <tr>\n",
       "      <th>188908</th>\n",
       "      <td>11929</td>\n",
       "      <td>756</td>\n",
       "      <td>2772</td>\n",
       "      <td>144</td>\n",
       "      <td>2011-09-11 12:00:00</td>\n",
       "      <td>1.45</td>\n",
       "      <td>13941.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191281</th>\n",
       "      <td>2861</td>\n",
       "      <td>1334</td>\n",
       "      <td>253</td>\n",
       "      <td>6</td>\n",
       "      <td>2011-02-15 12:12:00</td>\n",
       "      <td>6.75</td>\n",
       "      <td>13941.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211013</th>\n",
       "      <td>139</td>\n",
       "      <td>3247</td>\n",
       "      <td>344</td>\n",
       "      <td>96</td>\n",
       "      <td>2010-12-02 10:17:00</td>\n",
       "      <td>0.65</td>\n",
       "      <td>13941.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231424</th>\n",
       "      <td>11929</td>\n",
       "      <td>1921</td>\n",
       "      <td>2974</td>\n",
       "      <td>48</td>\n",
       "      <td>2011-09-11 12:00:00</td>\n",
       "      <td>3.29</td>\n",
       "      <td>13941.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252971</th>\n",
       "      <td>2861</td>\n",
       "      <td>1633</td>\n",
       "      <td>1915</td>\n",
       "      <td>48</td>\n",
       "      <td>2011-02-15 12:12:00</td>\n",
       "      <td>3.75</td>\n",
       "      <td>13941.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253105</th>\n",
       "      <td>8374</td>\n",
       "      <td>3299</td>\n",
       "      <td>2913</td>\n",
       "      <td>16</td>\n",
       "      <td>2011-06-21 09:44:00</td>\n",
       "      <td>4.25</td>\n",
       "      <td>13941.0</td>\n",
       "      <td>35</td>\n",
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       "    <tr>\n",
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       "      <td>3298</td>\n",
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       "      <td>6</td>\n",
       "      <td>2011-09-11 12:00:00</td>\n",
       "      <td>2.55</td>\n",
       "      <td>13941.0</td>\n",
       "      <td>35</td>\n",
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       "    <tr>\n",
       "      <th>271271</th>\n",
       "      <td>2861</td>\n",
       "      <td>2755</td>\n",
       "      <td>725</td>\n",
       "      <td>72</td>\n",
       "      <td>2011-02-15 12:12:00</td>\n",
       "      <td>4.95</td>\n",
       "      <td>13941.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282358</th>\n",
       "      <td>139</td>\n",
       "      <td>253</td>\n",
       "      <td>1565</td>\n",
       "      <td>108</td>\n",
       "      <td>2010-12-02 10:17:00</td>\n",
       "      <td>2.10</td>\n",
       "      <td>13941.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        InvoiceNo  StockCode  Description  Quantity          InvoiceDate  \\\n",
       "458          2861        263          480        24  2011-02-15 12:12:00   \n",
       "3655          139       2753          205        20  2010-12-02 10:17:00   \n",
       "7482        11929       2753          205         6  2011-09-11 12:00:00   \n",
       "8530          139        510         3457        12  2010-12-02 10:17:00   \n",
       "21789        8374       3300          573       256  2011-06-21 09:44:00   \n",
       "29317       20595       1074         2941       -21  2011-07-19 15:47:00   \n",
       "31301       11929       2684         1480        72  2011-09-11 12:00:00   \n",
       "63578        2861       1074         2940         6  2011-02-15 12:12:00   \n",
       "73275       16636       2291         2056        96  2011-11-18 09:43:00   \n",
       "87707         139       2755          725        24  2010-12-02 10:17:00   \n",
       "106170       8374       3306         2716        24  2011-06-21 09:44:00   \n",
       "107333        139        508         3455        12  2010-12-02 10:17:00   \n",
       "108236      11929       1074         2941        24  2011-09-11 12:00:00   \n",
       "111419      16636       1074         2941        24  2011-11-18 09:43:00   \n",
       "112889      16636       3300          573       192  2011-11-18 09:43:00   \n",
       "117612        139        764         3353        96  2010-12-02 10:17:00   \n",
       "127708       2861       3306         2716        24  2011-02-15 12:12:00   \n",
       "132581       8374       2023         3556        12  2011-06-21 09:44:00   \n",
       "151301       8374       1657         3095         6  2011-06-21 09:44:00   \n",
       "176649        139       1074         2940        24  2010-12-02 10:17:00   \n",
       "182568      11929       2755          725        48  2011-09-11 12:00:00   \n",
       "184257      16636       3299         2913        16  2011-11-18 09:43:00   \n",
       "186395      16636       2753          205         6  2011-11-18 09:43:00   \n",
       "188908      11929        756         2772       144  2011-09-11 12:00:00   \n",
       "191281       2861       1334          253         6  2011-02-15 12:12:00   \n",
       "211013        139       3247          344        96  2010-12-02 10:17:00   \n",
       "231424      11929       1921         2974        48  2011-09-11 12:00:00   \n",
       "252971       2861       1633         1915        48  2011-02-15 12:12:00   \n",
       "253105       8374       3299         2913        16  2011-06-21 09:44:00   \n",
       "266117      11929       3298         3154         6  2011-09-11 12:00:00   \n",
       "271271       2861       2755          725        72  2011-02-15 12:12:00   \n",
       "282358        139        253         1565       108  2010-12-02 10:17:00   \n",
       "\n",
       "        UnitPrice  CustomerID  Country  \n",
       "458          2.95     13941.0       35  \n",
       "3655         2.55     13941.0       35  \n",
       "7482         2.95     13941.0       35  \n",
       "8530         1.25     13941.0       35  \n",
       "21789        0.36     13941.0       35  \n",
       "29317        5.95     13941.0       35  \n",
       "31301        1.25     13941.0       35  \n",
       "63578        6.75     13941.0       35  \n",
       "73275        3.29     13941.0       35  \n",
       "87707        4.25     13941.0       35  \n",
       "106170       3.81     13941.0       35  \n",
       "107333       1.25     13941.0       35  \n",
       "108236       5.95     13941.0       35  \n",
       "111419       5.95     13941.0       35  \n",
       "112889       0.36     13941.0       35  \n",
       "117612       2.55     13941.0       35  \n",
       "127708       3.81     13941.0       35  \n",
       "132581       1.65     13941.0       35  \n",
       "151301       2.95     13941.0       35  \n",
       "176649       5.95     13941.0       35  \n",
       "182568       4.95     13941.0       35  \n",
       "184257       4.25     13941.0       35  \n",
       "186395       2.95     13941.0       35  \n",
       "188908       1.45     13941.0       35  \n",
       "191281       6.75     13941.0       35  \n",
       "211013       0.65     13941.0       35  \n",
       "231424       3.29     13941.0       35  \n",
       "252971       3.75     13941.0       35  \n",
       "253105       4.25     13941.0       35  \n",
       "266117       2.55     13941.0       35  \n",
       "271271       4.95     13941.0       35  \n",
       "282358       2.10     13941.0       35  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[train_df[\"CustomerID\"]==13941.0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         1709\n",
       "1          510\n",
       "3         1478\n",
       "4         3216\n",
       "5         1817\n",
       "          ... \n",
       "122044    2487\n",
       "122045    3069\n",
       "122046    1419\n",
       "122047    1631\n",
       "122048    2088\n",
       "Name: StockCode, Length: 117038, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "common_stockcodes = test_df[\"StockCode\"][train_df[\"StockCode\"] & test_df[\"StockCode\"]]\n",
    "common_stockcodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [InvoiceNo, StockCode, Description, Quantity, InvoiceDate, UnitPrice, CustomerID, Country]\n",
       "Index: []"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[(train_df[\"InvoiceNo\"]==3139) & (train_df[\"StockCode\"]==1709)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       " '2011-09-16 11:51:00',\n",
       " '2011-03-22 09:43:00',\n",
       " '2011-04-26 11:19:00',\n",
       " '2011-02-25 11:33:00',\n",
       " '2011-10-13 15:05:00',\n",
       " '2011-11-27 12:10:00',\n",
       " '2011-06-13 13:13:00',\n",
       " '2011-10-16 15:56:00',\n",
       " '2011-11-17 11:29:00',\n",
       " '2011-11-02 14:43:00',\n",
       " '2010-12-15 13:49:00',\n",
       " '2011-02-16 15:43:00',\n",
       " '2011-08-02 12:59:00',\n",
       " '2011-08-19 17:02:00',\n",
       " '2011-03-02 09:24:00',\n",
       " '2011-10-06 14:43:00',\n",
       " '2011-09-11 14:51:00',\n",
       " '2011-08-15 13:48:00',\n",
       " '2011-09-18 13:31:00',\n",
       " '2011-05-29 15:44:00',\n",
       " '2011-10-24 12:06:00',\n",
       " '2011-05-26 11:44:00',\n",
       " '2011-09-20 09:40:00',\n",
       " '2011-05-12 12:34:00',\n",
       " '2010-12-14 11:57:00',\n",
       " '2010-12-07 09:57:00',\n",
       " '2011-10-23 12:16:00',\n",
       " '2011-02-20 13:06:00',\n",
       " '2010-12-13 14:55:00',\n",
       " '2011-10-07 10:37:00',\n",
       " '2011-11-27 13:03:00',\n",
       " '2011-09-15 14:56:00',\n",
       " '2011-09-30 12:52:00',\n",
       " '2011-09-18 11:13:00',\n",
       " '2011-10-26 12:39:00',\n",
       " '2011-01-09 12:46:00',\n",
       " '2011-11-10 15:09:00',\n",
       " '2011-05-23 15:29:00',\n",
       " '2011-10-27 12:09:00',\n",
       " '2011-10-14 17:13:00',\n",
       " '2011-10-16 14:35:00',\n",
       " '2011-03-14 11:04:00',\n",
       " '2011-12-08 10:24:00',\n",
       " '2011-05-24 16:25:00',\n",
       " '2011-04-12 11:32:00',\n",
       " '2011-05-11 16:28:00',\n",
       " '2011-04-01 12:19:00',\n",
       " '2010-12-03 15:25:00',\n",
       " '2011-11-28 16:47:00',\n",
       " '2011-07-20 15:33:00',\n",
       " '2011-08-09 13:43:00',\n",
       " '2011-02-18 13:29:00',\n",
       " '2011-12-01 08:12:00',\n",
       " '2011-11-30 14:33:00',\n",
       " '2011-09-02 10:20:00',\n",
       " '2011-03-30 09:15:00',\n",
       " '2011-09-13 16:33:00',\n",
       " '2011-03-27 12:04:00',\n",
       " '2011-01-30 15:40:00',\n",
       " '2011-09-20 12:12:00',\n",
       " '2011-03-18 13:27:00',\n",
       " '2011-05-05 15:44:00',\n",
       " '2011-12-05 16:26:00',\n",
       " '2011-09-20 16:33:00',\n",
       " '2011-06-10 10:38:00',\n",
       " '2011-02-21 10:36:00',\n",
       " '2011-06-09 12:21:00',\n",
       " '2011-09-04 10:51:00',\n",
       " '2011-07-22 14:41:00',\n",
       " '2011-03-27 14:34:00',\n",
       " '2011-09-05 15:56:00',\n",
       " '2011-12-08 13:13:00',\n",
       " '2011-07-11 12:01:00',\n",
       " '2010-12-15 16:43:00',\n",
       " '2011-09-06 15:30:00',\n",
       " '2011-07-21 17:12:00',\n",
       " '2011-08-08 12:34:00',\n",
       " '2011-02-22 10:59:00',\n",
       " '2011-01-20 12:16:00',\n",
       " '2011-07-05 11:30:00',\n",
       " '2011-11-17 14:22:00',\n",
       " '2011-02-21 13:51:00',\n",
       " '2011-11-20 15:00:00',\n",
       " '2011-06-09 09:16:00',\n",
       " '2011-05-31 14:18:00',\n",
       " '2011-12-02 13:11:00',\n",
       " '2011-12-01 11:51:00',\n",
       " '2011-10-09 12:04:00',\n",
       " '2011-04-28 15:23:00',\n",
       " '2011-04-11 11:45:00',\n",
       " '2011-11-28 11:54:00',\n",
       " '2010-12-08 09:56:00',\n",
       " '2011-01-24 13:06:00',\n",
       " '2011-06-07 12:34:00',\n",
       " '2011-05-26 10:41:00',\n",
       " '2011-08-01 13:29:00',\n",
       " '2011-04-06 10:21:00',\n",
       " '2011-01-06 17:38:00',\n",
       " '2011-07-20 14:59:00',\n",
       " '2011-02-16 14:56:00',\n",
       " '2011-03-10 11:45:00',\n",
       " '2011-09-26 12:48:00',\n",
       " '2011-10-21 11:00:00',\n",
       " '2011-10-06 18:12:00',\n",
       " '2011-04-01 12:56:00',\n",
       " '2011-10-04 13:35:00',\n",
       " '2011-05-18 14:08:00',\n",
       " '2011-07-05 12:44:00',\n",
       " '2011-12-06 13:02:00',\n",
       " '2011-03-30 12:05:00',\n",
       " '2011-11-17 12:56:00',\n",
       " '2011-08-02 11:34:00',\n",
       " '2011-04-11 13:22:00',\n",
       " '2011-06-21 11:19:00',\n",
       " '2011-02-01 10:00:00',\n",
       " '2010-12-16 15:59:00',\n",
       " '2011-09-28 16:35:00',\n",
       " '2011-03-23 12:44:00',\n",
       " '2010-12-10 13:48:00',\n",
       " '2011-05-27 12:47:00',\n",
       " '2011-11-29 09:47:00',\n",
       " '2011-05-05 09:34:00',\n",
       " '2011-05-10 12:08:00',\n",
       " '2011-05-03 10:43:00',\n",
       " '2011-11-14 13:13:00',\n",
       " '2011-09-21 12:55:00',\n",
       " '2011-03-23 12:15:00',\n",
       " '2011-10-18 13:19:00',\n",
       " '2011-09-16 17:05:00',\n",
       " '2011-11-27 13:26:00',\n",
       " '2010-12-10 09:51:00',\n",
       " '2011-10-27 14:24:00',\n",
       " '2011-11-23 10:50:00',\n",
       " '2011-07-17 11:59:00',\n",
       " '2011-09-02 15:48:00',\n",
       " '2011-11-23 16:22:00',\n",
       " '2011-10-06 19:21:00',\n",
       " '2011-05-27 11:02:00',\n",
       " '2011-10-25 12:38:00',\n",
       " '2011-09-18 15:55:00',\n",
       " '2011-10-04 17:22:00',\n",
       " '2011-01-28 10:28:00',\n",
       " '2011-01-24 12:00:00',\n",
       " '2011-10-28 16:13:00',\n",
       " '2011-02-28 11:58:00',\n",
       " '2011-06-17 14:46:00',\n",
       " '2011-09-09 13:47:00',\n",
       " '2011-06-27 13:00:00',\n",
       " '2011-10-30 10:58:00',\n",
       " '2011-04-12 13:01:00',\n",
       " '2011-11-29 17:23:00',\n",
       " '2011-09-26 11:02:00',\n",
       " '2011-10-27 15:24:00',\n",
       " '2011-11-28 12:54:00',\n",
       " '2011-07-18 16:05:00',\n",
       " '2011-11-22 12:52:00',\n",
       " '2011-07-31 12:43:00',\n",
       " '2011-09-15 16:30:00',\n",
       " '2011-08-02 16:19:00',\n",
       " '2011-12-01 16:38:00',\n",
       " ...]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "InvoiceNo:: match: 16985, mismatch: 1219\n",
      "StockCode:: match: 3345, mismatch: 65\n",
      "Description:: match: 3486, mismatch: 77\n",
      "Quantity:: match: 211, mismatch: 63\n",
      "InvoiceDate:: match: 16020, mismatch: 1009\n",
      "CustomerID:: match: 4193, mismatch: 33\n",
      "Country:: match: 37, mismatch: 0\n"
     ]
    }
   ],
   "source": [
    "for feature in test_df.columns:\n",
    "    match = 0\n",
    "    mismatch = 0\n",
    "    a = list(set(train_df[feature]))\n",
    "    b = list(set(test_df[feature]))\n",
    "    for i in b:\n",
    "        if i in a:\n",
    "            match+=1\n",
    "        else:\n",
    "    #         print(i)\n",
    "            mismatch+=1\n",
    "    print(\"{}:: match: {}, mismatch: {}\".format(feature, match, mismatch))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We can infer from the above cell results that using InvoiceNo & InvoiceDate will lead to poor results as the are incosistent over the Train and Test datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jEL1TaG6tnZfkvCRZt25dm5qamuMUJ8f09HT2x3kvVeoxWdRjsqjH0lBVud3tbpepqamsWbMmV199dZYtW3ZLbTdv3pw73vGO+da3vrVX9V6zZk2WL1++076bN2/OmjVrlvzvj9fIvrW73zX1mCzqMVmWYj0W8vTs/5vkTjPajkxy7bB80/Bze+Ye3l/SWju+tfaQ1to/j7XfMMu2vS9mVZL/OfRzfGvtx1trr5zj+ADADMuXL08yOtK8bt26fOc738nxxx+fZHSk+Xd+53fyjne8I4997GPzzW9+M4985CP3apyNGzfmtNNOy+bNm7Nt27Zs3rw5p512WjZu3DhfDwWS+F0DdrZgR5pba1ur6qtVdWJr7X1VdWSSRyf5qyRPmUMX1ye5w20Y/9tV9eWqOqW19vaqOjjJ8iTvSfInVfW6YY53S7Kttfa1vR0LAJaC1tpeXQxs+/btWbZsWY455phceumlOfTQQ3PwwQfnpJNOyoc+9KGcd955Oe+885IkJ510Ut7znvfs1fxOPfXUJMkZZ5yRLVu2ZM2aNTnzzDNvaYf54ncNGLfQ32l+YpK/rqq/GO6/oLX2uTl+IL8jyZur6rFJztjL8X8zyd9W1f9Isi3Jr7bWLq6qNUn+ZZjH1iRPSCI0A3DAa63l2POPvWV50px66qmCC/uE3zVghwUNza21TyZZP0v71NjytRm+09xam04yPSx/OslxY7uNn449a1/D/eePLX8mycNn2eevMjriDQAAAF0T9XeaAQAAYJIIzQAAANAhNAMAAECH0AwA3MokXgQMABaD0AwAAAAdQjMAAAB0CM0AAADQITQDAABAh9AMAAAAHUIzAAAAdAjNAAAA0CE0AwA7Oece5yz2FABgYgjNAAAA0CE0AwAAQIfQDAAAAB1CMwAAAHQIzQAAANAhNAMAAECH0AwAAAAdQjMAAAB0CM0AAADQITQDAABAh9AMAAAAHUIzAAAAdAjNAAAA0CE0AwAAQIfQDAAAAB1CMwAAAHQIzQAAANAhNAMAAECH0AwAAAAdQjMAAAB0CM0AAADQITQDAABAh9AMAAAAHUIzAAAAdAjNAAAA0CE0AwAAQIfQDAAAAB1CMwAAAHQIzQAAANAhNAMAO/m9992QY88/drGnAQATQWgGAHZyw7bFngEATA6hGQAAADqEZgAAAOgQmgEAAKBDaAYAAIAOoRkAAAA6hGYAAADoEJoBgJ188exfWOwpAMDEEJoBAACgQ2gGAACADqEZAAAAOhY8NFfV3avqwqr6TFV9vqpeVlUHz/MYU1X14LH7T6uqJw7LT66qH5nP8SbJBRdckLVr12b58uVZu3ZtLrjgggN6HgDMbvx9euXKlamq7m2HXW0z223ZsmU7LR933HFZu3Ztli1blpUrV2bZsmUT+xnhcwxYSiblPW1S5nFbLWhortEn71uTvL21du8k905ySJI/m+ehppLcEppba+e21l4z3H1ykiUZmi+44IJs3Lgx55xzTr773e/mnHPOycaNG/f5L+OkzAOA2Y2/Tx999NG56aabdgrH86W1lmXLluUVr3hFTjzxxFx11VX5zne+k3vc4x750z/906xevTqnnHLKxH1G+BwDlpJJeU+blHnMi9bagt2SnJjkAzPa7pDkm0mekeRlY+3vTDI1LP9NkkuTXJ3kBWPbXJPkBUk+luSqJD+ZZHWS/0jy70kuT/KQJM9P8qwkj0uyNcm/DetOTvK2sf4emeStu3sc97///dskOuaYY9oll1yyU9sll1zSjjnmmNZaa5s3b56IeTCyr+rB3KjHZFGPhTX+Pp2kPeYxj2lJdnlb++q1u91m5m358uUtSTvmmGPaMcccc8s4O8be8dkwaZ8R+8PnmNfIZFGPyaIeO1vs97Qd9VjseeypJJe2Th5ccRsz9+4ck+Sy8YbW2rer6ppkl2NvbK19o6qWJ3lfVR3XWrtyWHdta+1+VfW7SZ7VWvutqjo3ydbW2p8nSVWdOIz15qp6xrDdpcOR77+oqju31r6e5ClJXjXbBKrq9CSnJ8mqVasyPT29d8/AAtqyZUu2b9++09y2b9+eLVu2ZHp6Olu3bt0n897dPBjZV/VgbtRjsqjHwpr5Pv2UpzwlF1100byPs3379lvGS5IXvvCFueiii24Ze8dnw6R9RuwPn2NeI5NFPSaLeuxssd/TdtRjsecxnxY6NFdG//s8W/uu/NoQWlckuWuSo5PsCM1vHX5eluSX92QyrbVWVa9N8oSqelWSByV5Ymfb85KclyTr1q1rU1NTezLUPrFmzZosX74843PbvHlz1qxZk6mpqUxPT2dfzHt382BkX9WDuVGPyaIeC2vm+/SrXjXr/xffZsuXL8/27duzZs2ancbZMfaOz4bly5dP1GfE/vA55jUyWdRjsqjHzhb7PW1HPRZ7HvNpoS8EdnWSdeMNVXWHJKuS/N8Z468c1t8zo1OrT2ytHZdk0451g5uGn9uzd6H/VUmekOTUJG9qrX1/L/qYCBs3bsxpp52WzZs3Z9u2bdm8eXNOO+20bNy48YCcBwCzG3+fXrt2bS666KIF+U7z9u3bs2zZsjzzmc/MXe9611x00UW55z3vmac+9al58YtfnNNOOy2nnHLKxH1G+BwDlpJJeU+blHnMh4U+0vy+JGdV1RNba68ZTrf+iyQvS/KFJE+vqmVJ7pbkAcM+d0hyQ5LrqmpVkp9LMr2bca4f9uutO3zHndbaV6rqK0n+e0bfad5vnXrqqUmSM844I1u2bMmaNWty5pln3tJ+oM0DgNnNfJ8++OCDc9NNN+1mrz1XVbn55pvzW7/1W6mqHHvssbn55pvzyU9+Ms997nPzve99L29/+9sn7jPC5xiwlEzKe9qkzGM+LGhoHk6H/qUkf11V/3+SOyd5Q2vtzOH7xV/I6IJen8jo4l5prV1RVR/P6Cj155N8aA5DvSPJm6vqsUnOmLHu1UnOraobkzyotXZjktcluXNr7ZO3+UEuslNPPXUifvEmZR4AzG5P3qd3HIUeXRflwOBzDFhKJuU9bVLmcVst9JHmtNa+lOQxSTL8LeULqur+rbXLkvxGZ58nd9pXjy1fmtGfmkpr7dNJjhvb9J/HtntLkrfM6Opnk7x8zx4JAAAAB5oFD83jWmv/O8k99uWYM1XVZRmd/v3/LeY8AAAAmHz7NDRPgtba/Rd7DgAAAOwfFvrq2QAAALDfEpoBgJ3c44/fudhTAICJITQDAABAh9AMAAAAHUIzAAAAdAjNAAAA0CE0AwAAQIfQDAAAAB1CMwAAAHSsWOwJAACT5dWPPjRTU1ct9jQAYCI40gwAAAAdQjMAAAB0CM0AAADQITQDAABAh9AMAAAAHUIzAAAAdAjNAAAA0CE0AwAAQIfQDAAAAB1CMwAAAHQIzQAAANAhNAMAAECH0AwAAAAdQjMAAAB0CM0AAADQITQDAABAh9AMAAAAHUIzAAAAdAjNAAAA0CE0AwAAQIfQDAAAAB1CMwAAAHQIzQAAANAhNAMAAECH0AwAAAAdQjMAAAB0CM0AAADQITQDAABAh9AMAAAAHSsWewIAwP7hvi+4ONfduO2W+4ev2ZDrt5y10zZHHHJQrnjeSft6agCwYIRmAGBOrrtxW6456+Rb7h97/oad7ifJ6g2b9vW0AGBBOT0bAAAAOoRmAAAA6BCaAQAAoENoBgAAgA6hGQAAADqEZgAAAOgQmgGAnaxfv35e+6uqee0PAPYloRkAAAA6hGYAAADoEJoBAACgY86huaq2V9XlVXV1VV1RVf+tqhYsdFfVuqp66W62WV1V/3VP9gEAAIC5WrEH297YWjs+SarqLklen+SIJM+b70lV1YrW2qVJLt3NpquT/NdhLpnjPgAAADAne3WkuLX2tSSnJ3lGjSyvqhdV1Uer6sqq+p0kqaq7VtUHhiPUn6iqhwztj66qjw1HrN83tD2/qs6rqouTvKaqpqrqnWPrXltVl1TVZ6rqt4epnJXkIUP/z5yxz5FV9fZhPv9aVceN9fV3VTVdVZ+vqt+/Dc8fAAAAS9ieHGneSWvt88Pp2XdJ8tgk17XWTqiqg5N8aAi/v5zkPa21M6tqeZLbV9Wdk7w8yUNba1+oqiPHur1/kp9trd1YVVMzhjwuyQOTHJrk41W1KcmGJM9qrf1CkszY5wVJPt5aO6WqHp7kNUmOH9b9ZJL1SQ5P8m9V9TettW3jg1XV6Rn9x0BWrVqV6enpvXmaFtXWrVv3y3kvVeoxWdRjsqjH5Fm9YdOs7TPrNFvdZttXfW8br5HJoh6TRT0my1Ksx16H5sGOP7x4UpLjqupxw/0jktw7yUeT/F1VHZTk7a21y4dg+4HW2heSpLX2jbH+Lmqt3dgZ68Jh3Y1VtTnJA5J8axdz+9kkvzKMcUlV/VBVHTGs29RauynJTVX1tSSrknx5fOfW2nlJzkuSdevWtampqV0MNZmmp6ezP857qVKPyaIek0U9Js81Z518q7bVGzbtXKfzc+u6vXvTrfats2fZjj3iNTJZ1GOyqMdkWYr12OvQXFX3SrI9ydcyCs9ntNbeM8t2D01ycpLXVtWLMgq6rdPtDbsYcuY+vT5uGXoXfdw01rY9t/0/DwAAAFiC9uo7zcMp1ucmeVlrrSV5T5KnD0eUU1X3qapDq+oeSb7WWnt5klcmuV+Sf0nysKq657DtkbMOcmuPraqVVfVDSaYyOop9fUanWM/mA0l+YxhjKsm1rbVv7+ljBQAA4MC1J0dYD6mqy5MclOT7SV6b5MXDuldkdCXrj1VVJfl6klMyCrd/VFXbkmxN8sTW2teH7wu/dfhO9NeSPHIO438kyaYk/yXJn7TWvlJVX0/y/aq6Ismrk3x8bPvnJ3lVVV2Z5DtJnrQHjxUAAADmHppba8t3se7mJM8dbuPOH24zt39XknfNaHv+jPvTSabHmj7dWjt9xjbbkpw4o/vpYd03MrpA2cyxZ46zduY2AAAAkOzl6dkAAABwINgvLoA18+gwAAAA7AuONAMAAECH0AwA7GTz5s3z2t/oD20AwP5JaAYAAIAOoRkAAAA6hGYAAADoEJoBAACgQ2gGAACADqEZAAAAOlYs9gQAgP3H6g2bblk+fM3O95PkiEMO2tdTAoAFJTQDAHNyzVknz2iZeR8Alh6nZwMAAECH0AwAAAAdQjMAAAB0CM0AAADQITQDAABAh9AMAAAAHUIzAAAAdAjNAAAA0CE0AwAAQIfQDAAAAB1CMwAAAHQIzQAAANAhNAMAAECH0AwAAAAdQjMAAAB0CM0AAADQITQDAABAh9AMAAAAHUIzAAAAdAjNAAAA0CE0AwAAQIfQDAAAAB1CMwAAAHQIzQAAANAhNAMAAECH0AwAAAAdQjMAAAB0CM0AAADQITQDAABAx4rFngAAMPnu+4KLc92N25Ikh6/ZkOu3nJUkOeKQg3LF805azKkBwIJypBkA2K3rbtyWa846OdecdXKS3LK8I0gDwFIlNAMAAECH0AwAAAAdQjMAAAB0CM0AAADQITQDAABAh9AMAAAAHUIzALCT9evXz1tfVTVvfQHAYhCaAQAAoENoBgAAgA6hGQAAADpuU2iuqo1VdXVVXVlVl1fVT1fVH1bV7feyv+dX1bM6655VVZ+qqk9U1RVV9cQ96Heqqt65N3MCAADgwLVib3esqgcl+YUk92ut3VRVRyW5XZI3JPn7JN+ZnykmVfW0JI9M8oDW2rer6ogkp8xX/wAAADCb23Kk+a5Jrm2t3ZQkrbVrkzwuyY8k2VxVm5Okqk6tqquGI8Rn79i5qh5dVR8bjhq/b2bnVfXbVfWuqjokyXOT/G5r7dvDWNe11s4ftjuxqj4+jPF3VXXwWP+fqqoPJvnlsX4PHbb76LDfY2/DcwAAAMASdltC88VJfrSqPl1V/6uqHtZae2mSryRZ31pbX1U/kuTsJA9PcnySE6rqlKq6c5KXJ/mV1tp9k/zqeMdV9Ywkv5jR0eQVSQ5vrX1u5gSqamWSVyd5fGvt2GHbpw/tLx/6eEiSHx7bbWOSS1prJyRZn+RFVXXobXgeAAAAWKL2+vTs1trWqrp/RqF0fZI3VNWGGZudkGS6tfb1JKmq1yV5aJLtST7QWvvC0Nc3xvb5zSRfTnJKa23bcOS4dabxE0m+0Fr79HD//CS/l2R6aP/MMO7fJzl92OakJI8Z++70yiT/JcmW8Y6r6vQd+6xatSrT09O7fU4mzdatW/fLeS9V6jFZ1GOyqMfkWb1h063axms0vjzbtr392DteI5NFPSaLekyWpViPvQ7NSdJa255RQJ2uqquSPGnGJtXZtdIPwp/I6Kj03TMKvt+uqhuq6l6ttc/Psf/sov/K6Aj3v+1i37TWzktyXpKsW7euTU1N7WrziTQ9PZ39cd5LlXpMFvWYLOoxea456+Sd7q/esOkHNTo/P1h+96ZbbTuuzo7azgOvkcmiHpNFPSbLUqzHXp+eXVU/UVX3Hms6PskXk1yf5PCh7cNJHlZVR1XV8iSnJnl/kn8Z2u859HXkWD8fT/I7SS4aTu9Okv+Z5K+r6g7D9ncYjgR/KsnqqvrxYbvfHPr/VJJ7VtWPDe2njvX/niRnVFUNff3U3j4HAAAALG235UjzYUnOqao7Jvl+ks9mdDrzqUneVVVfHb7X/JwkmzM6wvuPrbULk1tOf35rVS1L8rWMro6dJGmtfXA4fXpTVT0yyd8M4320qrYl2ZbkL1pr362qpyR5U1WtSPLRJOcOV/M+fdj/2iQfTLJ26P5PkvxlkiuH4HxNRlcBBwAAgJ3clu80X5bkwbOsOme47dju9UleP8v+70ryrhltzx9bfk9GR4V3+LPhNrOf9yW51dHi1tq7k/zkLO03ZnQkGwAAAHbptlw9GwAAAJY0oRkAAAA6hGYAAADoEJoBgJ1s3rx53vpqrfcXIAFg/yA0AwAAQIfQDAAAAB1CMwAAAHQIzQAAANAhNAMAAECH0AwAAAAdKxZ7AgDA/mH1hk1JksPX/GD5iEMOWswpAcCCE5oBgN265qyTx+6d3N0OAJYap2cDAABAh9AMAAAAHUIzAAAAdAjNAAAA0CE0AwAAQIfQDAAAAB1CMwAAAHQIzQAAANAhNAMAAECH0AwAAAAdQjMAAAB0CM0AAADQITQDAABAh9AMAAAAHUIzAAAAdAjNAAAA0CE0AwAAQIfQDAAAAB1CMwAAAHQIzQAAANAhNAMAAECH0AwAAAAdQjMAAAB0CM0AAADQITQDAABAh9AMAAAAHUIzAAAAdAjNAAAA0CE0AwAAQMeKxZ4AAHBguu8LLs51N25b8HEOX7Mh1285a977PeKQg3LF806a934BmCxCMwCwKK67cVuuOevkBR/n2PM3LMg4qzdsmvc+AZg8Ts8GAACADqEZAAAAOoRmAAAA6BCaAQAAoENoBgAAgA6hGQAAADqEZgBgJ+vXr1/sKXAbVNViTwFgSRGaAQAAoENoBgAAgA6hGQAAADrmLTRX1db56musz3+sqjvuxX5Prqqbq+q4sbZPVNXq+ZwfAAAAS9tEH2lurf18a+1be7n7l5NsnMfpAAAAcICZ99BcVVNVNV1Vb66qT1XV62rk56rqjTO2e8ewfGpVXTUcDT57bJtrquqoYfmJVXVlVV1RVa8d2u5cVW+pqo8Ot58Zm8o7kxxTVT8xyxxnHQ8AAADGLdSR5p9K8odJjk5yryQ/k+S9SR5YVYcO2zw+yRuq6keSnJ3k4UmOT3JCVZ0y3llVHZPRUeOHt9bum+QPhlV/leQlrbUTkvxKkleM7XZzkj9L8twZfe12PAAAAEiSFQvU70daa19Okqq6PMnq1toHq+rdSX6xqt6c5OQkz84ovE631r4+bP+6JA9N8vax/h6e5M2ttWuTpLX2jaH9EUmOHvt7hHeoqsPH9nt9ko1Vdc+xthPmMF6q6vQkpyfJqlWrMj09vVdPxGLaunXrfjnvpUo9Jot6TBb1mDyrN2zaJ+Psq7ov1Dj76nlKkrx77mN5PS0s71mTRT0my1Ksx0KF5pvGlrePjfOGJL+X5BtJPtpau77GEu8uVJI2S/uyJA9qrd2408ZDl62171fVXyT54xl97VZr7bwk5yXJunXr2tTU1Fx2myjT09PZH+e9VKnHZFGPyaIek+eas05e8DFWb9i0b+p+fhZmnHdv2ifPU7Jnr5E6e4EeL7fwnjVZ1GOyLMV67OsLgU0nuV+S384oQCfJh5M8rKqOqqrlSU5N8v4Z+70vya9V1Q8lSVUdObRfnOQZOzaqquNnGfPVGR2RvvMejAcAAAD7NjS31rZndIGunxt+prX21STPSbI5yRVJPtZau3DGflcnOTPJ+6vqiiQvHlb9fpJ1wwXCPpnkabOM+b0kL01yl7mOBwAAAMk8np7dWjts+Dmd0RHlHe3PmLHdMzJ2dHhoe31G3z+e2efqseXzk5w/Y/21GV1QbOZ+r87oCPOO+y/NKDjvcjwAAAAYN9F/pxkAAAAWk9AMAAAAHUIzAAAAdAjNAAAA0CE0AwA72bx582JPgdugtbbYUwBYUoRmAAAA6BCaAQAAoENoBgAAgA6hGQAAADqEZgAAAOhYsdgTAAAOXKs3bFrwMQ5fszDjHHHIQfPeJwCTR2gGABbFNWedvI9G2lfjALAUOT0bAAAAOoRmAAAA6BCaAQAAoENoBgAAgA6hGQAAADqEZgAAAOgQmgEAAKBDaAYAAIAOoRkAAAA6hGYAAADoEJoBAACgQ2gGAACADqEZAAAAOoRmAAAA6KjW2mLPYeJV1deTfHGx57EXjkpy7WJPgluox2RRj8miHpNFPSaPmkwW9Zgs6jFZ9td63KO1dufZVgjNS1hVXdpaW7fY82BEPSaLekwW9Zgs6jF51GSyqMdkUY/JshTr4fRsAAAA6BCaAQAAoENoXtrOW+wJsBP1mCzqMVnUY7Kox+RRk8miHpNFPSbLkquH7zQDAABAhyPNAAAA0CE070eq6viq+tequryqLq2qB4yte05Vfbaq/q2qHjXWfv+qumpY99KqqqH94Kp6w9D+4apaPbbPk6rqM8PtSfv0Qe5nquqM4Tm/uqr+bKxdPRZJVT2rqlpVHTXWph77WFW9qKo+VVVXVtXbquqOY+vUY4JV1aOH2ny2qjYs9nyWiqr60araXFVbhs+MPxjaj6yq9w6/w++tqjuN7TNvrxVmV1XLq+rjVfXO4b56LKKqumNVvXn4/NhSVQ9Sk8VTVc8c3q8+UVUXVNXKA7YerTW3/eSW5OIkPzcs/3yS6WH56CRXJDk4yT2TfC7J8mHdR5I8KEkledfY/r+b5Nxh+deTvGFYPjLJ54efdxqW77TYj30Sb0nWJ/mnJAcP9++iHotekx9N8p6M/q76UeqxqLU4KcmKYfnsJGerx+TfkiwfanKvJLcbanX0Ys9rKdyS3DXJ/Yblw5N8eng9/FmSDUP7hoV4rbjtsi7/Lcnrk7xzuK8ei1uP85P81rB8uyR3VJNFq8XdknwhySHD/TcmefKBWg9HmvcvLckdhuUjknxlWH5skn9ord3UWvtCks8meUBV3TXJHVpr/9JGv42vSXLK2D7nD8tvTnLi8L8+j0ry3tbaN1pr30zy3iSPXuDHtb96epKzWms3JUlr7WtDu3osnpckeXZGr5Ud1GMRtNYubq19f7j7r0nuPiyrx2R7QJLPttY+31r7XpJ/yOj55zZqrX21tfaxYfn6JFsy+kfp+O/3+dn5936+XivMoqrunuTkJK8Ya1aPRVJVd0jy0CSvTJLW2vdaa9+KmiymFUkOqaoVSW6fUfY4IOshNO9f/jDJi6rqS0n+PMlzhva7JfnS2HZfHtruNizPbN9pn+Efttcl+aFd9MWt3SfJQ4bTSd5fVScM7eqxCKrqMUn+vbV2xYxV6rH4nprR/ywn6jHpPKf7wHAK4k8l+XCSVa21ryajYJ3kLsNm8/laYXZ/mdF/tN481qYei+deSb6e5FXDKfOvqKpDoyaLorX27xnljf+T5KtJrmutXZwDtB4rFnsC7Kyq/inJD8+yamOSE5M8s7X2lqr6tYz+J+4RGZ3qMFPbRXv2cp8Dzm7qsSKjU0IfmOSEJG+sqntFPRbMburx3IxOCb7VbrO0qcc82FU9WmsXDttsTPL9JK/bsdss26vH5PCcLrCqOizJW5L8YWvt27s4qDKfrxVmqKpfSPK11tplVTU1l11maVOP+bUiyf2SnNFa+3BV/VVGp//2qMkCGr6r/NiMTrX+VpI3VdUTdrXLLG1Lph5C84RprT2it66qXpPkD4a7b8oPTif6ckbf5dzh7hmdPvHl/OCUyPH28X2+PJxycUSSbwztUzP2md7zR7I07KYeT0/y1uFUk49U1c1Jjop6LJheParq2Ize1K8Y/gF69yQfq9HF8tRjgezq9ZGMLtKV5BeSnDi8ThL1mHS9+jAPquqgjALz61prbx2a/7Oq7tpa++pwGuOOr/rM52uFW/uZJI+pqp9PsjLJHarq76Mei+nLSb7cWvvwcP/NGYVmNVkcj0jyhdba15Okqt6a5ME5QOvh9Oz9y1eSPGxYfniSzwzLFyX59eEKdPdMcu8kHxlOmbi+qh44fD/giUkuHNvnScPy45JcMvyj9j1JTqqqOw3/w3TS0MatvT2jOqSq7pPRBSuujXrsc621q1prd2mtrW6trc7oTfh+rbX/iHosiqp6dJI/TvKY1tp3xlapx2T7aJJ7V9U9q+p2GV2Y5aJFntOSMPxevzLJltbai8dWjf9+Pyk7/97P12uFGVprz2mt3X34zPj1jJ6rJ0Q9Fs3wmf2lqvqJoenEJJ+MmiyW/5PkgVV1++F5PDGjazEcmPVoE3A1Mre53ZL8bJLLMroy3YeT3H9s3caMrlL3bxmuSDe0r0vyiWHdy5LU0L4yo6PVn83oinb3GtvnqUP7Z5M8ZbEf96TeMgrJfz88vx9L8nD1mIxbkmsyXD1bPRatBp/N6HtKlw+3c9Vj/7hl9NcZPj3UYeNiz2ep3IbP8JbkyrHXxc9n9P2992X0H+HvS3Lk2D7z9lpx22VtpvKDq2erx+LW4vgklw6vk7dn9DU4NVm8erwgyaeG5/K1GV0Z+4Csx44JAwAAADM4PRsAAAA6hGYAAADoEJoBAACgQ2gGAACADqEZAAAAOoRmAAAA6BCaAQAAoENoBgAAgI7/B+WJhg1v4IMlAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# construct box plot for continuous variables\n",
    "plt.figure(figsize=(16,8))\n",
    "train_df.boxplot(vert=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We can see that Quantity has negative values and has outliers of values around 80000, UnitPrice has outliers of values around 40000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace the negative value of Quantity with abs value\n",
    "train_df.loc[train_df[\"Quantity\"]<0,\"Quantity\"]= abs(train_df[\"Quantity\"])\n",
    "# Coverting CustomerID from float to int to reduce memory usage\n",
    "train_df[\"CustomerID\"] = train_df[\"CustomerID\"].astype(\"int\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# construct box plot for continuous variables\n",
    "plt.figure(figsize=(16,8))\n",
    "train_df.boxplot(vert=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Lets drop duplicates before processing for outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before Duplicates Dropped: 284780\n",
      "After Duplicates Dropped: 239387\n"
     ]
    }
   ],
   "source": [
    "print(\"Before Duplicates Dropped:\", len(train_df))\n",
    "train_df.drop_duplicates(subset=[\"StockCode\", \"Description\", \"Quantity\", \"CustomerID\"], inplace=True)\n",
    "print(\"After Duplicates Dropped:\", len(train_df))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Removing outliers using 2.5 times standard deviation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_outliers(df, column):\n",
    "#     for column in list(df.columns):\n",
    "    if df[column].dtype == \"int64\" or df[column].dtype == \"float64\":\n",
    "        mean = df[column].mean()\n",
    "        sd = df[column].std()\n",
    "        q = sd*2.5\n",
    "        print(\"{}: stdev: {}, +/-*2.5: {}\".format(column, sd, q))\n",
    "#             print(\"          q1: {}, q2: {}\".format(q1, q2))\n",
    "        df = df[(df[column]>=mean-q) & (df[column]<=mean+q)]\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before filter: 239387\n",
      "UnitPrice: stdev: 83.2918887484725, +/-*2.5: 208.22972187118125\n",
      "After filter: 239341\n"
     ]
    }
   ],
   "source": [
    "print(\"Before filter:\", len(train_df))\n",
    "train_df = remove_outliers(train_df, \"UnitPrice\")\n",
    "print(\"After filter:\", len(train_df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='UnitPrice', ylabel='Count'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# construct box plot for continuous variables\n",
    "plt.figure(figsize=(16,8))\n",
    "sns.histplot(x=train_df[\"UnitPrice\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='UnitPrice', ylabel='Count'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8))\n",
    "sns.histplot(train_df[\"UnitPrice\"][train_df[\"UnitPrice\"]>=150])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.934422284522919"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[\"UnitPrice\"].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We can see that unit price is centered around a mean of 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df.sort_values(by=['CustomerID'], inplace=True) #sorting so that UnitPrices are more randomized"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Coverting InvoiceDate to datetime column & extracting features from the same"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "      <th>InvoiceDate_year</th>\n",
       "      <th>InvoiceDate_month</th>\n",
       "      <th>InvoiceDate_Dayofweek</th>\n",
       "      <th>InvoiceDate_Day</th>\n",
       "      <th>InvoiceDate_Hour</th>\n",
       "      <th>diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>141379</th>\n",
       "      <td>1909</td>\n",
       "      <td>2001</td>\n",
       "      <td>2002</td>\n",
       "      <td>74215</td>\n",
       "      <td>2011-01-18 10:01:00</td>\n",
       "      <td>1.04</td>\n",
       "      <td>12346</td>\n",
       "      <td>35</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>10</td>\n",
       "      <td>326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117895</th>\n",
       "      <td>7844</td>\n",
       "      <td>2008</td>\n",
       "      <td>2790</td>\n",
       "      <td>2</td>\n",
       "      <td>2011-06-09 13:01:00</td>\n",
       "      <td>9.95</td>\n",
       "      <td>12347</td>\n",
       "      <td>16</td>\n",
       "      <td>2011</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84850</th>\n",
       "      <td>548</td>\n",
       "      <td>206</td>\n",
       "      <td>357</td>\n",
       "      <td>12</td>\n",
       "      <td>2010-12-07 14:57:00</td>\n",
       "      <td>4.65</td>\n",
       "      <td>12347</td>\n",
       "      <td>16</td>\n",
       "      <td>2010</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>14</td>\n",
       "      <td>367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214290</th>\n",
       "      <td>548</td>\n",
       "      <td>3287</td>\n",
       "      <td>366</td>\n",
       "      <td>30</td>\n",
       "      <td>2010-12-07 14:57:00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>12347</td>\n",
       "      <td>16</td>\n",
       "      <td>2010</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>14</td>\n",
       "      <td>367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257445</th>\n",
       "      <td>2205</td>\n",
       "      <td>1035</td>\n",
       "      <td>2055</td>\n",
       "      <td>12</td>\n",
       "      <td>2011-01-26 14:30:00</td>\n",
       "      <td>0.42</td>\n",
       "      <td>12347</td>\n",
       "      <td>16</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>26</td>\n",
       "      <td>14</td>\n",
       "      <td>317</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        InvoiceNo  StockCode  Description  Quantity         InvoiceDate  \\\n",
       "141379       1909       2001         2002     74215 2011-01-18 10:01:00   \n",
       "117895       7844       2008         2790         2 2011-06-09 13:01:00   \n",
       "84850         548        206          357        12 2010-12-07 14:57:00   \n",
       "214290        548       3287          366        30 2010-12-07 14:57:00   \n",
       "257445       2205       1035         2055        12 2011-01-26 14:30:00   \n",
       "\n",
       "        UnitPrice  CustomerID  Country  InvoiceDate_year  InvoiceDate_month  \\\n",
       "141379       1.04       12346       35              2011                  1   \n",
       "117895       9.95       12347       16              2011                  6   \n",
       "84850        4.65       12347       16              2010                 12   \n",
       "214290       1.25       12347       16              2010                 12   \n",
       "257445       0.42       12347       16              2011                  1   \n",
       "\n",
       "        InvoiceDate_Dayofweek  InvoiceDate_Day  InvoiceDate_Hour  diff  \n",
       "141379                      1               18                10   326  \n",
       "117895                      3                9                13   183  \n",
       "84850                       1                7                14   367  \n",
       "214290                      1                7                14   367  \n",
       "257445                      2               26                14   317  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[\"InvoiceDate\"] = pd.to_datetime(train_df[\"InvoiceDate\"])\n",
    "train_df[\"InvoiceDate_year\"] = pd.to_datetime(train_df[\"InvoiceDate\"]).dt.year\n",
    "train_df[\"InvoiceDate_month\"] = pd.to_datetime(train_df[\"InvoiceDate\"]).dt.month\n",
    "train_df[\"InvoiceDate_Dayofweek\"] = pd.to_datetime(train_df[\"InvoiceDate\"]).dt.dayofweek\n",
    "train_df[\"InvoiceDate_Day\"] = pd.to_datetime(train_df[\"InvoiceDate\"]).dt.day\n",
    "train_df[\"InvoiceDate_Hour\"] = pd.to_datetime(train_df[\"InvoiceDate\"]).dt.hour\n",
    "\n",
    "#Finding maximum date in the data\n",
    "maximum = max(train_df.InvoiceDate)\n",
    "\n",
    "#Adding one more day to the max date, so that the max date will have 1 as the difference and not zero.\n",
    "maximum = maximum + pd.DateOffset(days = 1)\n",
    "train_df['diff'] = maximum - train_df.InvoiceDate\n",
    "train_df[\"diff\"] = train_df[\"diff\"].dt.days\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "      <th>InvoiceDate_year</th>\n",
       "      <th>InvoiceDate_month</th>\n",
       "      <th>InvoiceDate_Dayofweek</th>\n",
       "      <th>InvoiceDate_Day</th>\n",
       "      <th>InvoiceDate_Hour</th>\n",
       "      <th>diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>InvoiceNo</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.090304</td>\n",
       "      <td>0.027127</td>\n",
       "      <td>-0.000237</td>\n",
       "      <td>-0.015554</td>\n",
       "      <td>-0.008070</td>\n",
       "      <td>0.010015</td>\n",
       "      <td>0.425090</td>\n",
       "      <td>0.646047</td>\n",
       "      <td>0.000527</td>\n",
       "      <td>0.086985</td>\n",
       "      <td>0.049190</td>\n",
       "      <td>-0.944104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StockCode</th>\n",
       "      <td>0.090304</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.010720</td>\n",
       "      <td>0.001308</td>\n",
       "      <td>0.081390</td>\n",
       "      <td>0.006085</td>\n",
       "      <td>0.013709</td>\n",
       "      <td>0.034812</td>\n",
       "      <td>0.068101</td>\n",
       "      <td>0.005176</td>\n",
       "      <td>0.006208</td>\n",
       "      <td>0.004893</td>\n",
       "      <td>-0.091243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Description</th>\n",
       "      <td>0.027127</td>\n",
       "      <td>-0.010720</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000814</td>\n",
       "      <td>-0.004063</td>\n",
       "      <td>-0.007283</td>\n",
       "      <td>-0.013210</td>\n",
       "      <td>0.025376</td>\n",
       "      <td>0.009320</td>\n",
       "      <td>0.002325</td>\n",
       "      <td>0.006123</td>\n",
       "      <td>-0.003331</td>\n",
       "      <td>-0.029435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Quantity</th>\n",
       "      <td>-0.000237</td>\n",
       "      <td>0.001308</td>\n",
       "      <td>0.000814</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.016397</td>\n",
       "      <td>-0.005372</td>\n",
       "      <td>-0.009808</td>\n",
       "      <td>0.001811</td>\n",
       "      <td>-0.002602</td>\n",
       "      <td>-0.006115</td>\n",
       "      <td>-0.000617</td>\n",
       "      <td>-0.012389</td>\n",
       "      <td>0.001027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UnitPrice</th>\n",
       "      <td>-0.015554</td>\n",
       "      <td>0.081390</td>\n",
       "      <td>-0.004063</td>\n",
       "      <td>-0.016397</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.023288</td>\n",
       "      <td>-0.033604</td>\n",
       "      <td>-0.013248</td>\n",
       "      <td>-0.027578</td>\n",
       "      <td>-0.012532</td>\n",
       "      <td>-0.002400</td>\n",
       "      <td>-0.007763</td>\n",
       "      <td>0.036272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CustomerID</th>\n",
       "      <td>-0.008070</td>\n",
       "      <td>0.006085</td>\n",
       "      <td>-0.007283</td>\n",
       "      <td>-0.005372</td>\n",
       "      <td>-0.023288</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.401581</td>\n",
       "      <td>-0.040105</td>\n",
       "      <td>0.030676</td>\n",
       "      <td>0.018767</td>\n",
       "      <td>0.000623</td>\n",
       "      <td>0.069426</td>\n",
       "      <td>0.003764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <td>0.010015</td>\n",
       "      <td>0.013709</td>\n",
       "      <td>-0.013210</td>\n",
       "      <td>-0.009808</td>\n",
       "      <td>-0.033604</td>\n",
       "      <td>0.401581</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.017851</td>\n",
       "      <td>0.028361</td>\n",
       "      <td>0.038709</td>\n",
       "      <td>0.001345</td>\n",
       "      <td>0.105732</td>\n",
       "      <td>-0.012180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>InvoiceDate_year</th>\n",
       "      <td>0.425090</td>\n",
       "      <td>0.034812</td>\n",
       "      <td>0.025376</td>\n",
       "      <td>0.001811</td>\n",
       "      <td>-0.013248</td>\n",
       "      <td>-0.040105</td>\n",
       "      <td>-0.017851</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.341305</td>\n",
       "      <td>-0.015769</td>\n",
       "      <td>0.168874</td>\n",
       "      <td>-0.021373</td>\n",
       "      <td>-0.503572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>InvoiceDate_month</th>\n",
       "      <td>0.646047</td>\n",
       "      <td>0.068101</td>\n",
       "      <td>0.009320</td>\n",
       "      <td>-0.002602</td>\n",
       "      <td>-0.027578</td>\n",
       "      <td>0.030676</td>\n",
       "      <td>0.028361</td>\n",
       "      <td>-0.341305</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.025065</td>\n",
       "      <td>-0.119316</td>\n",
       "      <td>0.055186</td>\n",
       "      <td>-0.637091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>InvoiceDate_Dayofweek</th>\n",
       "      <td>0.000527</td>\n",
       "      <td>0.005176</td>\n",
       "      <td>0.002325</td>\n",
       "      <td>-0.006115</td>\n",
       "      <td>-0.012532</td>\n",
       "      <td>0.018767</td>\n",
       "      <td>0.038709</td>\n",
       "      <td>-0.015769</td>\n",
       "      <td>0.025065</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.002510</td>\n",
       "      <td>0.036400</td>\n",
       "      <td>-0.010253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>InvoiceDate_Day</th>\n",
       "      <td>0.086985</td>\n",
       "      <td>0.006208</td>\n",
       "      <td>0.006123</td>\n",
       "      <td>-0.000617</td>\n",
       "      <td>-0.002400</td>\n",
       "      <td>0.000623</td>\n",
       "      <td>0.001345</td>\n",
       "      <td>0.168874</td>\n",
       "      <td>-0.119316</td>\n",
       "      <td>-0.002510</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.006039</td>\n",
       "      <td>-0.102563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>InvoiceDate_Hour</th>\n",
       "      <td>0.049190</td>\n",
       "      <td>0.004893</td>\n",
       "      <td>-0.003331</td>\n",
       "      <td>-0.012389</td>\n",
       "      <td>-0.007763</td>\n",
       "      <td>0.069426</td>\n",
       "      <td>0.105732</td>\n",
       "      <td>-0.021373</td>\n",
       "      <td>0.055186</td>\n",
       "      <td>0.036400</td>\n",
       "      <td>0.006039</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.037630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>diff</th>\n",
       "      <td>-0.944104</td>\n",
       "      <td>-0.091243</td>\n",
       "      <td>-0.029435</td>\n",
       "      <td>0.001027</td>\n",
       "      <td>0.036272</td>\n",
       "      <td>0.003764</td>\n",
       "      <td>-0.012180</td>\n",
       "      <td>-0.503572</td>\n",
       "      <td>-0.637091</td>\n",
       "      <td>-0.010253</td>\n",
       "      <td>-0.102563</td>\n",
       "      <td>-0.037630</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       InvoiceNo  StockCode  Description  Quantity  UnitPrice  \\\n",
       "InvoiceNo               1.000000   0.090304     0.027127 -0.000237  -0.015554   \n",
       "StockCode               0.090304   1.000000    -0.010720  0.001308   0.081390   \n",
       "Description             0.027127  -0.010720     1.000000  0.000814  -0.004063   \n",
       "Quantity               -0.000237   0.001308     0.000814  1.000000  -0.016397   \n",
       "UnitPrice              -0.015554   0.081390    -0.004063 -0.016397   1.000000   \n",
       "CustomerID             -0.008070   0.006085    -0.007283 -0.005372  -0.023288   \n",
       "Country                 0.010015   0.013709    -0.013210 -0.009808  -0.033604   \n",
       "InvoiceDate_year        0.425090   0.034812     0.025376  0.001811  -0.013248   \n",
       "InvoiceDate_month       0.646047   0.068101     0.009320 -0.002602  -0.027578   \n",
       "InvoiceDate_Dayofweek   0.000527   0.005176     0.002325 -0.006115  -0.012532   \n",
       "InvoiceDate_Day         0.086985   0.006208     0.006123 -0.000617  -0.002400   \n",
       "InvoiceDate_Hour        0.049190   0.004893    -0.003331 -0.012389  -0.007763   \n",
       "diff                   -0.944104  -0.091243    -0.029435  0.001027   0.036272   \n",
       "\n",
       "                       CustomerID   Country  InvoiceDate_year  \\\n",
       "InvoiceNo               -0.008070  0.010015          0.425090   \n",
       "StockCode                0.006085  0.013709          0.034812   \n",
       "Description             -0.007283 -0.013210          0.025376   \n",
       "Quantity                -0.005372 -0.009808          0.001811   \n",
       "UnitPrice               -0.023288 -0.033604         -0.013248   \n",
       "CustomerID               1.000000  0.401581         -0.040105   \n",
       "Country                  0.401581  1.000000         -0.017851   \n",
       "InvoiceDate_year        -0.040105 -0.017851          1.000000   \n",
       "InvoiceDate_month        0.030676  0.028361         -0.341305   \n",
       "InvoiceDate_Dayofweek    0.018767  0.038709         -0.015769   \n",
       "InvoiceDate_Day          0.000623  0.001345          0.168874   \n",
       "InvoiceDate_Hour         0.069426  0.105732         -0.021373   \n",
       "diff                     0.003764 -0.012180         -0.503572   \n",
       "\n",
       "                       InvoiceDate_month  InvoiceDate_Dayofweek  \\\n",
       "InvoiceNo                       0.646047               0.000527   \n",
       "StockCode                       0.068101               0.005176   \n",
       "Description                     0.009320               0.002325   \n",
       "Quantity                       -0.002602              -0.006115   \n",
       "UnitPrice                      -0.027578              -0.012532   \n",
       "CustomerID                      0.030676               0.018767   \n",
       "Country                         0.028361               0.038709   \n",
       "InvoiceDate_year               -0.341305              -0.015769   \n",
       "InvoiceDate_month               1.000000               0.025065   \n",
       "InvoiceDate_Dayofweek           0.025065               1.000000   \n",
       "InvoiceDate_Day                -0.119316              -0.002510   \n",
       "InvoiceDate_Hour                0.055186               0.036400   \n",
       "diff                           -0.637091              -0.010253   \n",
       "\n",
       "                       InvoiceDate_Day  InvoiceDate_Hour      diff  \n",
       "InvoiceNo                     0.086985          0.049190 -0.944104  \n",
       "StockCode                     0.006208          0.004893 -0.091243  \n",
       "Description                   0.006123         -0.003331 -0.029435  \n",
       "Quantity                     -0.000617         -0.012389  0.001027  \n",
       "UnitPrice                    -0.002400         -0.007763  0.036272  \n",
       "CustomerID                    0.000623          0.069426  0.003764  \n",
       "Country                       0.001345          0.105732 -0.012180  \n",
       "InvoiceDate_year              0.168874         -0.021373 -0.503572  \n",
       "InvoiceDate_month            -0.119316          0.055186 -0.637091  \n",
       "InvoiceDate_Dayofweek        -0.002510          0.036400 -0.010253  \n",
       "InvoiceDate_Day               1.000000          0.006039 -0.102563  \n",
       "InvoiceDate_Hour              0.006039          1.000000 -0.037630  \n",
       "diff                         -0.102563         -0.037630  1.000000  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#finding correalation in df\n",
    "train_df.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### As expected Stockcode has the most corealation with UnitPrice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "F:\\Anaconda\\envs\\opencv\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='StockCode', ylabel='UnitPrice'>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8))\n",
    "sns.scatterplot(train_df[\"StockCode\"], train_df[\"UnitPrice\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### From the above plot it can be infered that the UnitPrices are mostly concentrated towards the mean value and has higher values for a few StockCodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='UnitPrice', ylabel='Density'>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#checking for the distribution in the UnitPrice\n",
    "# not checking normal distribution for other features except UnitPrice as it is the only continious value other than quantity\n",
    "plt.figure(figsize=(16,8))\n",
    "sns.kdeplot(train_df[\"UnitPrice\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### After ploting KDE we can see that the UnitPrice distribution is not normal and hence it needs to be normalized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we need to seprate train_df into train and test datasets before we start normalizing the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting Data into Train & Test sets for Training model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random State: 91336\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "F:\\Anaconda\\envs\\opencv\\lib\\site-packages\\ipykernel_launcher.py:1: DeprecationWarning: This function is deprecated. Please call randint(0, 99999 + 1) instead\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "seed = np.random.random_integers(0, 99999)\n",
    "print(\"Random State:\", seed)\n",
    "\n",
    "x = train_df.loc[:, ['StockCode', 'Description', 'Quantity', 'CustomerID', 'Country']] #Random State: 57528 LB: 23.04894\n",
    "y = train_df[\"UnitPrice\"]\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, shuffle=True, random_state=seed, test_size=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "pt = PowerTransformer()\n",
    "#transform data\n",
    "x_train_pt = pt.fit_transform(x_train)\n",
    "x_test_pt = pt.transform(x_test)\n",
    "\n",
    "pt_y = PowerTransformer() \n",
    "#PowerTransformer requires a matrix/DataFrame hence using .to_frame() to convert 1D data to 2D\n",
    "y_train_pt = pt_y.fit_transform(y_train.to_frame()) \n",
    "y_test_pt = pt_y.transform(y_test.to_frame())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UnitPrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.068337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.938095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.365723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.798259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.149277</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   UnitPrice\n",
       "0  -1.068337\n",
       "1  -0.938095\n",
       "2  -1.365723\n",
       "3   0.798259\n",
       "4  -0.149277"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pt = pd.DataFrame(y_train_pt, columns=[\"UnitPrice\"])\n",
    "y_pt.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='UnitPrice', ylabel='Density'>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lx47H9KrAKgOchBALf/2ghxVYIiIigzHAEhHVsWhaaf0NequrwM4k9W0hHl1FBbatuVyB1SHAFoolHB2LLziBWNUd8HAKMRERkcEYYImI6thcgF31HthygNW7AhvNIOBxwOtyVPwxLocNLT4XxnVoIT45lUS2UMIFPYsH2K4gAywREZHRGGCJiOqYGmBXuwd2roVY7zuwsQy6y6tpVqLD79alAnt4JAYA2Nl9/godVVfQg3i2gES2oPnXJyIiooUxwBIR1bFqK7Buhx0+lx2zaf2nEHeuoH1Y1RHw6DLE6fBoDC6HDRvbfYs+Rr2vyyosERGRcRhgiYjqWCKjVAcDnspbc88V8roMGeLUvYIBTqpOnSqwh0ai2N7lh9O++P8mzw6RYoAlIiIyCgMsEVEdS5bbW1dyt/RcIa9T1z2w+WIJU4nsKiuwbkwmsiiWpKZnGphKYVN785KP6Q6yAktERGQ0BlgiojqWzBUBAD736gNs2OvCrI4V2Il4FlKeDYQr0RnwoFiSmu6pLZYkxmOZZc+jTkweYwWWiIjIMAywRER1TK3A+lz2VX+OoM4V2LFoGsDKVuioOvzlVToa3oONJLIolOSyAdbjtCPkdbICS0REZCAGWCKiOpbMFeB22OBY4i7ncsJep65DnOZ2wK7iDmxH+WO0vAernqeSqchdAc/c44mIiEh/DLBERHUsmS1U1T4MAKEmpYW4pPE9U9XYXGC0RgV2LlBXcJ7OgIdDnIiIiAzEAEtEVMdS2SK8VbQPA8oQp5IE4hl99p2OxzJwOWyrWvXTXg6w45pWYJWW5koCdXfQwzuwREREBmKAJSKqY8lcAc1VVmDDXhcAYDatzyCnqUQO7c1uCCFW/LFuhx1hr1PTCuxYVAnULT7Xso/tDHgwlcgiXyxp9vWJiIhocQywRER1LKlRBRYAZnQa5DSVyKKtefmwuJgOv0fjCqwygbiSQN0d9EBKZZIyERER6Y8BloiojiVzGtyBVSuwOq3SmUrk0NbsXvXHdwTcmgbIsWim4oFSnXO7YNOafX0iIiJaHAMsEVEdS2YL8LmqDbBKBVavVTpKBbaKAOv3YELDe6gj0XTFA6W65wIsK7BERERGYIAlIqpjyWyx6gqsegd2RocKbKkkMZ3MobWKFuLOgBuT8awmU5JLJYnxWAbdoeVX6ABnV/9wkBMREZExGGCJiOqY0kJc3R1YdTqwHhXY2XQexZKssgLrRqEkNQnYkWQO+aKsuAIbbHLC7bCxhZiIiMggDLBERHUspUEF1m4TCHgcutyBnUoorbdt/tUH2M5yFVSLQU7qCp1K78AKIcqrdNhCTEREZAQGWCKiOpUrlJArluCrcgoxAIR9Lsymta/ATpWHL1U1hTighF8tVumMRpXP0R2srIUYUAI0K7BERETGYIAlIqpTqVwBAKquwAJAqMmpyxqdyXIFtr3KIU4AMKFBFXRMDbChyiqwAMoVWN6BJSIiMgIDLBFRnUpkywG2yinEgLJKR48W4khC+ZytVQTYdr+2FViX3YYWb+UV4c6gB+PRLKSsfogUERERLY0BloioTqVyRQCAt8ohTgAQ9jp1GeI0lcjCbhMIlQdFrYbHaUfI69TsDmxn0A2bTVT8MV0BD3LFEqaT+uzJJSIiorMYYImI6lQyq2ELsdelyxqdqUQWrT7XigLjQjr8bs0qsCu5/wrM2wXLNmIiIiLdMcASEdWpZFapwGrTQuxEPFNAoViq+nPNN5XIVbVCR9Xh92hSgR2LZipeoaNSpyCr92eJiIhIPwywRER1Kjk3xKn6FmK1xTeq8STiSCKL1iomEKs6Am5MxqsLsKWSxFg0g64VBli1YssKLBERkf4YYImI6lRSwyFOYZ8SMrWeRDyVyFU1gVjV4fdgIp6papDSdCqHXLGENStsIW5rdsEmWIElIiIyAgMsEVGdSpaHOGl1BxYAomnt7sFKKTGZyKLNX32A7Qy4kS/KqgK2GkBXWoF12G3o8HsYYImIiAzAAEtEVKfODnHSroV4JqldBTaeLSBXKKFNixZidRdsFYOcRtUdsCsMsICySoctxERERPpjgCUiqlOpbAFCAE1OLdboKCFzVsM7sHM7YH3aVGABVDXIaTSaBrDyCiwAdAXcrMASEREZgAGWiKhOJbJF+FwOCFHdihoACPmUCuyshqt0phJK2NSihXiuAltFFXQ0moHTLtC2ikDdGfBgosohUkRERLQ8BlgiojqVyhU0aR8GAL/bAbtNaLoLdqoc+DRpIS5XYKsJkWPRDDoDnlXtpA17XYhl8iiWVj9EioiIiJbHAEtEVKcS2YImE4gBQAiBUJMTsxpOIVYrsFpMIfY47Qh4HFVVYEdm06u6/woAYa8TUmq/ZoiIiIheiAGWiKhOpXJFeDWqwAJA0Kt1gFWqueqKnmp1BDxV3YEdi2Xmdrqu1Nk1Q9pVqImIiOh8DLBERHUqqWEFFlDaZGc1XKMzlcgi7HXCadfmf0WdAfeqpxBLKTEazay6AquuGdLyjjARERGdjwGWiKhOJXMFTXbAqsJep6ZrdKYSWbRp0D6s6vCvvgI7k8ojVyitagIxoM+aISIiIjofAywRUZ1KZYuaBthgk0vjKcQ5bQNswI3JeBZSrnyQ0sisskJn9Xdg2UJMRERkBAZYIqI6pQxx0u4ObNjr1HQP7FQii1YNJhCrOvwe5IqlVQ1SUne4rvYO7Nk1Q6zAEhER6YkBloioTqVy2lZgQ14nUrkisoWiJp8vonEFtrO8Smc1bcSjMTXArq4C63c74NB4zRARERGdjwGWiGgRyWwB//rT40jntAlsRpJSKndgNazAqoOKohpUGTP5IhLZAtr92t6BBbCqQU6js2k4bAKtqwzUQgiEvE7MsAJLRESkKwZYIqJF/O+zI/jEz5/HL45OmH2UFUvni5ASGg9xUu95Vh/SJuNKlbRNwxbiaiqwY9EMOgMe2G1i1V8/5NX2jjARERGdjwGWiGgRDxybBAAcGI6afJKVS2QLAACvxi3EgDaDiqYSSshs9VmkAlvFCh1V2OtkCzEREZHOGGCJiBaQK5Tw8IkpAMDBGgywqazS9tzs1rKFWLtBRZGEEvTaNGwhbnLZ4fc4MLGaO7DR9KpX6KiUCixbiImIiPTEAEtEtICnTs8gkS2gK+DBgeHoqlazmGmuAuvSsgKrtPtq0SarVmC1bCEGgA6/e8UV2FJJYmQ2g57Q6iYQq8JeJwMsERGRznQNsEKIlwohjgkhTgghPrTE4y4XQhSFEK/R8zxERJV64PgEHDaB376uD9F0HkMzabOPtCKpnFqB1fIObLkCq8EqnbMBVrsKLKC0Ea/0DuxEPItcsYS1Ld6qvnbY62ILMRERkc50C7BCCDuATwG4HcBOAK8XQuxc5HF/D+DHep2FiGilHjw2iT19YVy1sRVA7d2DTc5VYLVrIW5y2uFy2DS6A5tDs9sBj1O78wHKIKeVVmDPTKcAoOoAG/K6kC2UanJqNRERUa3QswJ7BYATUsqTUsocgG8AuHOBx70HwL0Aam/MJxHVpdFoGkfH4rhxWwe2dfnhtIvaC7A5JcBqOYVYCIFQk1OTNTpTiazm7cMA0BHwYCKWXVHL96AaYMPVtxAD2gy5IiIiooXpGWB7AAzO+/VQ+W1zhBA9AF4F4O6lPpEQ4h1CiH1CiH2Tk5OaH5SIaL4Hy9OHb9zWDrfDjq2d/pob5KQOcdIywALatckqAVbb9mFAuQObLZQQSxcq/pjBmRSEAHqqDLChuTVDDLBERER60TPALrRM79yXxP8NwAellEv2W0kpPyel3COl3NPe3q7V+YiIFvTAsUl0BTzY1ukHAFzYE6y5QU7qECefhi3EABD0OjXZAzuVyOkTYAMrX6VzZjqFroAHbkd1v1dhDac0ExER0cL0DLBDANbO+3UvgJFzHrMHwDeEEAMAXgPg00KIV+p4JiKiJeWLJTxyYgo3bmuHEMrrcLt6gphN1dYgp1RO+ynEgBLStGohbtWhhbizvJZnJYOchqbTWBuu7v4rAIR9rMASERHpTc8A+ySALUKIDUIIF4DXAbhv/gOklBuklH1Syj4A3wbwLinl93Q8ExHRkp46PYN4toAbt3XMve3CniCA2toHm8gW4bLb4HJo+9e8Fi3E+WIJs6m8ZSqwgzOpqgc4AUCoSb0DywosERGRXnQLsFLKAoB3Q5kufATAPVLKQ0KIu4QQd+n1dYmIqvHAsUk4bALXbm6de9u2Lj8cttoa5JTKFeBza9s+DCgtxLPpfFXt1NNJJQC3+fW5AwtUXoHNFooYi2WwtqW6+6/AvD25SVZgiYiI9KJtb9k5pJT3A7j/nLctOLBJSvlWPc9CRFSJB45NYE9fGH6Pc+5tHqcyyKmWAmwiW9C8fRhQKrC5QgnpfHHVn38yroTLdh1aiH1uB5rdjoorsMMzaUgJTVqIXQ4bfC47K7BEREQ60rOFmIiopoxFM3Prc851YU8QB2tokFMqW0SzxhOIgfmrYlYf0qYSSoBt1aGFGFCqsBMVVmAHy/eatWghBpQq7CzvwBIREemGAZaIqGzf6WkAwHWb2857367eIGZSeQzP1sYgp2SuAK8eLcRN5TbZKkJaJFFuIdYrwAbcFVdgz5R3wK7TKMCGfU4OcSIiItIRAywRUdlYVAk9C7WT1togp2S2oGsFtppVMWoFtk2HFmIA6PB7MBGvrAI7NJ2Cy2GbuztbLWXIFVuIiYiI9MIAS0RUNhnPwuWwIdB0fvDbXmODnJLZIrwa74AF5g0qqjLAuh02XQI2AHQG3BiPZSpq9x6cSaE31ASbbaHV5SvHFmIiIiJ9McASEZVNxrPo8Lvn9r/Od3aQU8yEk61cMleAT5chTuod2NWHtMl4Fm3NC/8+a6HD70EmX0I8W1j2sWemtVmhowp7nazAEhER6YgBloiobCKeRfsSraS1NMgplSvCp0OFMzjXQrz6ADsSzWBNyKPVkc7TEVC+hxOx5e/BDk6nNVmhowp5XYhl8iiWrP9nhIiIqBYxwBIRlU3EM0vehdzVG8R0MoeRaGUDgsyUyOozxMntsMNb5aqYsWgG3UHtQuO5OvxKOF5uEnE0nUc0nddkhY4q7HVCSiCWrp0qbKkk8eNDY3jNZ/binV97yuzjEBERLUnXPbBERLVkMp7FlRtaF33/rjUBAMDhkRh6QvoFsGrliyXkCiU069BCDCjTgycrHJJ0rlJJKgF2l34V2K6g8rmXe6FhsDyBWNsWYuWO8Ewqh7BPnyFVWskWivj+MyP47K/60T+ZhNMuIKXydrdD+xc/iIiItMAKLBERgFyhhJlUfskWYrVqWOmKFrOkskUAgFenIUldAQ/GKmjPXch0KodcsYTuoH4Bdm24CS6HDcfGlr6vPDSj7QodAAhpsCfXKH/23YP4k3ufg9thxydfvxv/9BsXo1CSeH48YfbRiIiIFsUKLBERgMnyapelWohbyhW16YS1p8wmc8rwomYdWogBoDPowXNDs6v62NFZJfh26dhC7LDbsL3Lj8OjSwfYwWllp6+2LcTV78k1yiMnpnD7ri58+g2XQgiBk5NKcD0yGsOu8tooIiIiq2EFlogImGuJXaoC63LYEPA4EElaO5wky9N3vTq1EHcHPRiLVram5lyj0fTc59DTBWsCODwSW/KMgzMp+D2OucFUWjjbQmztCux0MofRaAaXrgvPTYNe3+pDk9O+bPAnIiIyEwMsERHOTqxVBwAtprXZbf0Am1NaiPXbs+pBtlBCdBWDitTW424dpxADwM7uAGZSeYwucQ/2zHRK0/ZhAAj5qp/SbIQj5ZC6s3yvGwDsNoFtXf659xEREVkRAywREZQVOsDZFSyLafW5EEmsboCRUc5WYPVpIe4KKOFzNfdgR2YzcNoF2nxL/z5Xa+e8gVuLGZxOado+DAB+twN2m6hqT64R1N+XHd2BF7x9R3cAR0bjNbEqioiIGhMDLBERlBZiIZSAupQWnwvTVq/AlgOsHntgAaArqITPpaqbixmLptEZ8MBmE1of6wW2dwUgBBZthy2VJIZmtN0BCwBCCISanJZvIT48GkN30DN3r1u1s9uPaHrpyjUREZGZGGCJiKBUYFt9LjjsS/+12NrsxlSNDHHSK8B2liuw46sIOSPRjO73XwHl331Dqw+HRqILvn8ykUW2UNJ0hY4q5HVavoX40EgUO8+pvgKVVa6JiIjMxABLRARgMp5B+zL3XwGgrdmFmVQOpZJ1WyyT5TU6Pp1aiNV7wqtpIR6LZubWEeltx5rAohVYPXbAqsJeF2aS1q3AZvJF9E8mX3D/VbWtS3kb78ESEZFVMcASEUFpIV5qArGqxedCsSRXNcDIKCmdK7Auhw1tzW6MrzDAlkqyHGD1r8ACyiTiwen0gt+rwfIOWK3vwAJAyOuy9B3Y4+NxFEtywQpss9uB9a1eHFlmhy4REZFZGGCJiKC0EC+1A1bV2qw8JpK07iCnRLkC2+TUpwILKPdgx1bYQjydyiFXLBkWYNWAtlA18UxEWefTG9a+Ghz2OjFr4TuwanvwQhVYANjRpQxyIiIisiIGWCJqeKWSxGSlAbY89CZi4XuwqWwBPpdd10FJXQHPigf9qIG3y6AW4qXucw7OpNAZcMOjQ8gP+6xdgT08GkOz27Fo9XlHdwADkeTcMDAiIiIrYYAlooY3m86jUJIVtRC3NpcDrIUnESdzBXh1ah9WdQY8K24hHplVqp5rdN4Bq+rwe9Dudy94D1aPFTqqkNeJbKGEdHkfr9UcHolhR7d/0Rc4dq4JQErg6BirsEREZD0MsETU8CbiShDrqGCIU8tcBda6LcTJbBHNOgfYroAHM6k8MvnKQ5o69KnLoBZiQGkjPrRQBXY6pcsAJ0AZ4gQAs2nrvchRKkkcGY0teP9VtaPbD4CDnIiIyJoYYImo4U3ElDDaEahgiJO3Biqw2QK8Ok0gVnWWQ6j6e1eJkdkMnHaBNt/yv89a2bkmgBMTceQKpbm3HRmNYSSawbYuvy5fM+x1AoAlJxGfmU4hmSsuev8VAHpCTQh4HAywRERkSQywRNTwJuNKCGtvXj5YOew2hL1OS9+BTeYKuk0gVqmDmFaySmcsmkZnwKPr3dxzXbAmgHxR4vmJs+2w//LT4/B7HHj95et0+ZohtQJrwXuwajv1zu7goo8RQmB79+IriIiIiMzEAEtEDW8iXnkFFlDaiKctXYEt6rYDVtUVUALsaDRd8ceMGLhCR6W2yqptxM8OzuKnh8fxjhdtRLBcKdWa2kI8Y8FJxIdHYrDbBLZ0Ni/5uJ3dARwbi1t63zERETUmBlgiangT8Qya3Q54XZVVLVub3Ziy8h1YAyqwagvxSgY5KTtgjZlArOpr9cHrss9NIv7nnx5H2OvE267boNvXnGshtmgFdnN787LTl3d2B5DKFXF6OmXQyYiIiCrDAEtEDW8ynq1oArGq1eey/B1YX4VhfLX8bge8LjvGopUFeSllOcAaW4G12QR2lNthnzg1jV8dn8Q7b9yk65ArS7cQj8SWvP+q2tG9+AoiIiIiMzHAElHDm1hpgG22dgtxKlvUvQIrhEDXClbpRJI55IolwwMsoFQTj4zE8E8/OYZ2vxtvuqpP16/nctjgc9kt10IcSWQxFsssOYFYtaWzGXab4CAnIiKyHAZYImp4k/EsOlYQYFt8bsykciha8H6glLLcQqzvHVhA2QVb6RCnsai6QsfYFmJAmUQczxbwxKlp/P6Nm9Ck8/1gQKnCWq2F+MioMsiqkgqsx2nHxjYfAywREVkOAywRNbyVthC3NbsgpTXvOGbyJZQkKr7PW42uoGcumC5nZFYZ9rQmZHwF9oJyYFsT9OD1V+ozefhcIa8TsxarwB4ejQI42x68nB3dAQZYIiKyHAZYojrxyIkpDEwlzT5GzUnlCkhkC+jwVx6sWst7TK24SieZKwAAmg2owHYFlRbiSibVqpXaLhNaiLd2+rG9y48/vWMH3A79f18AZRKx1V7gODwSQ3fQgxafq6LH97X5MBrLIF8sLf9gIiIigzDAEtWBYknid7+6D+/9xjOQ0nptrVY2ESuv0FlRC7ESACIWnESczCoB1pAKbMCDQklWNNBqNJqB0y7Q5qv891krHqcd//e+6/FrF68x7GtasQJ7dCyO7V3+ih/fFfBAyrN7komIiKyAAZaoDvRPJpDKFfHcUBS/ODph9nFqymQ5hK60hRiAJScRJ7NFANB9iBOg3IEFKlulMzqbRmfAA5tN6H0sS7BaBVZKiTPTKfS1+Sr+mK6g8t9EpfeciYiIjMAAS1QHDgwpd9v8Hgf+7WfPswq7AnMV2ECdVGDLLcRGDHFS24EruQc7asIKHTOFvU5E03nLDPqaSuSQyhWxvsVb8cfMvUBR4T1nIiIiIzDAEtWBA8NRNDnt+PAdO3BgmFXYlZiIK0/OV3IHNuR1wSZgyVU6aguxERXYrnLAGa2kAhvNoNuECcRmCXmVQV+xtDXaiM9MK/fj17dWXoFVAywrsEREZCUMsER14OBwFDvXBPCay3qxrsXLKuwKTMazcNgEQk3Oij/GbhMIe12YsmSALbcQG3AHtq1ZCfLLVeiklBhrtAqsT/nzZJU24tORFABgXWvlFdgWrwtOu2CAJSIiS2GAJapxxZLEoZEYLuwJwmm34T03b8aB4Sh+foRV2EpMlFforPRuZmuzq+FbiB12Gzr8y++CjSRzyBVLDRVgQ16lzXzGIoOczkynIATQG668Cm6zCXT4PWwhJiIiS2GAJapxJycTSOeL2NUTBAC8ancP1rd68W8/P84qbAUm4tkVTSBWtfrc1m4hNqACCwCd5VU6S1HvyHY1UAtxuBxgo2lr/Bk5E0mhO+BZ8RqhruDyL1AQEREZiQGWqMYdGFYGOF1YDrAOuw3vuXkLDg7H8DNWYZc1Wa7ArlRLs8uSe2BTOeOmEANAV8C97BCn0fL714QapwIb9pZbiJPWqMCenk6tqH1Y1RXwzA06IyIisgIGWKIad2A4Co/Thk3tZ4ezvPKSNVgT9OBb+wZNPFltmIxn0L6CAU6qNp/Lkmt0EtkCnHYBl8OYv967AstX6EajaeWxDdlCbI0/I6cjKaxvqXyAk6qz/P1lNwcREVkFAyxRjTswFMXO7gAc9rP/OTvsNmzvDmBwJm3iyayvUCwhksytqoW4xedGNJ1HvljS4WSrl8oW4DWofRhQWojjmcJc6/JCRqMZOO0Cbb6V/z7XqoDHAbtNYNYCd2CT2QKmEtnVVWCDbqRyRcSX+P4SEREZiQGWqIapA5wu6g2d977ecBOGZ1LGH6qGRJI5SIlVtRC3NpcrbBarwiZzRTQb1D4MnF2ls1QVdnQ2jc6AZ8WDsmqZEMpkaytUYM9MlycQr2AHrIq7YImIyGoYYIlq2LkDnObrCTUhlikgljG/AmRV6t2+1Q1xUgLslMXuwSazBXhd+k8gVqltwUsFnGeHotjW6TfqSJYR8jotUYFVA+z6VVRguQuWiIishgGWqIadO8Bpvp7yuoxhthEvaiKuPCnvCKz8bmZrsxJ6I0lrDbhJ5oqGDXAClq/AjsymcWoqiWs2txl2JqsIe13WqMCWd8Cu5g7s3PeXFVgiIrIIBliiGrbQACdVT4gBdjmTcSV8VtNCbLVVOslswZAdsCq1ArtYgN3bHwEAXLOp1bAzWUXI67TEHtjT00kEm5wIlicjr4T6/Z2IW+uFGiIialwMsEQ17ODw+QOcVL1hpV1weJYBdjHqFGG1HXglrNxCbNQOWADwuhzwexyLVuj29k+hxedq0BZiF2YtUIE9HUmtqn0YADxOO4JNTlZgiYjIMhhgiWqUOsBpofZhAGhrdsHtsDHALmE2lUOT0w6Pc+UVy4DHCYdNYNpyLcQFQ1uIAeVu5cFyO/t8UkrsPRHB1ZtaG2qAkyrstc4Qp9UMcFJVsiqJiIjIKAywRDXq1FQCqdzCA5wAZQpqT6gJQ5xEvKiZVB7hVbRVAoDNJhD2uRCxWAU2lS0a2kIMALfv6sbTZ2bn7lqqTk0lMRbLNGT7MKBUYDP5EjL5omlnKBRLGJ5JVxVgO4MejFskwJZK3EdLRNToGGCJatTcAKfehQMsoAxy4h3Yxc0kcwh5V94+rGr1uSzXQpwwuIUYAO68ZA0A4Pv7h1/w9rP3XxtvgBOgDHECYGoVdjSaQaEkV91CDABdAbclWoizhSJe+elH8CffftbsoxARkYkYYIlq1HNDygCnze3Niz6mN9zEFuIlzKRyaFnF/VdVa7PLUi3EhWIJ2ULJ8Bbi3rAXV2xowff2D0PKsxWyvf1TWBP0oK+K8FTL1Or+TNK8QU6nI+oO2JVPIFZ1BjyYSmRRKJa0OtaqfP6hU3huKIp79g3hZ4fHTT0LERGZhwGWqEYtNcBJ1RNqwlQiZ2oLo5XNpvIIrbKFGABafe65QVBWkMwp32cj98CqXrW7B/2TSRwcjgFQWj0f7Y/g6k1tEKLx7r8CmKvumznI6fR0EsDqdsCqOgMelCQwmTDvxZrB6RT+/RfP47adndje5ceff/8g4txxTUTUkBhgiWqQlBKHR2KL3n9Vqbtgh9hGvKBpLSqwFmohTuUKAGB4BRYA7tjVDZfdhu8+o7QRHxmLYSaVb9j7rwAQ9pUrsCau0jkTScFlt83tc10N9WPHY+YF2L/+wWEICPzVKy7A3/76hRiLZfCPPz5m2nmIiMg8DLBENSiaziOZKy47mKUnxFU6iymWJKLpfNV3YOPZgmUq3Mmscg4zAmzQ68TN2ztw37MjKBRLeFS9/7q5gQOsWoFNm1iBjaTQ29JU1RTouV2/Jt2D/cXRcfz08Djee8sWrAk1Yfe6MN5ydR/+67HTeOr0jClnIiIi81QUYIUQ9wohXiaEYOAlsoCJuFIJ6VimqtJbrsBykNP5ouk8pMSqpxADQGuzGwAwbZE24mS2XIE1oYUYAF65uwdTiSwe6Y9gb38EG9t86A42mXIWK1Db02fNrMBOp7C+ignEgNJCDMCUScSZfBF/cd8hbO5oxu9ct2Hu7X/8km3oDnjwp995DrmCuXdziYjIWJUG0s8A+C0Azwsh/k4IsV3HMxHRMtQnkh1+95KP6wx44LAJDM9ylc651Mmw1bQQqx9rmQBrYgsxANy0vR3BJie+tW8Qj59U9r82MrfDDq/LjhmT/nxIKZUA27r6AU6A0mngtAtTdsF++pcnMDidxl/feQFcjrNPWZrdDnzsVbtwfDyBz/2q3/BzERGReSoKsFLKn0kp3wDgUgADAH4qhNgrhHibEGL15QsiWpWJ8l20zmUqsHabQFfQwzuwC1AH61TTQtzWrHzslInDbeabayE2eI2Oyu2w42UXdeMHz40imSvi2s2NuT5nvrDXZdod2OlkDolsoaodsICy87jD78G4wS3ExZLEFx4+hTsu7FpwFdPN2zvxoi1t+NZTQ4aei4iIzFVxS7AQohXAWwG8HcAzAD4BJdD+VJeTEdGi5lqIl6nAAsokYrYQn2+6vNqkmhbiFp/y+x+xyCCns0OczGkhBpRpxKqrNjZ2BRZQ2ojNmkJ8elrpvKhmArGqI+A2vALbP5lAMlfErTs6F33MLds7cDqSwuA0u0yIiBpFpXdgvwPgIQBeAL8mpXyFlPKbUsr3AFh8CSUR6WI8loHPZa+oVbSHu2AXpLYQh6uowLaUP3bGxDUp8yWy5rYQA8Bl68LoDTdhZ3egqvbseqFUYM3583Emol2A7Qp4DL8D+9xQFABwUe/i09av26JUZh85MWXImYiIyHyVPsv5vJTy/vlvEEK4pZRZKeUeHc5FREuYjGeXbR9W9Ya9GI8NI1coveAOWaNTq2LhKkKW3+OAEEAsbY19lCkTpxCrbDaBz71pD+xVTL2tJyGvEyMmvYB0plyV7A1XH2A7Ax489LyxIfHA0Cy8Ljs2tC3+Ovmm9mZ0Btx4+MQUXnfFOgNPR0REZqn02ezHFnjbo1oehIgqNxHPoL2C9mEA6A01oSTNW4FhVdPJPJx2UdXEXptNIOBxImqRAKtWYL1O81qIAWDnmgC2dflNPYNVhLxO0yqwpyMpdAU88Gjw56Er6EEiW5j7M2aEA8NR7FoTXPLFECEErt3chr39EZRK0rCzERGReZYMsEKILiHEZQCahBC7hRCXlv+5EUo7MRGZYDyWXXaFjqqnvEpniJOIX2A2lUPY64IQ1VUKg01OzFokwKZyBXhd9qp2fpK2wl4Xoum8KeHqzHQS6zRoHwaUFmLAuBfCCsUSDo3EcOES7cOq6za3YTqZw+HRmAEnIyIisy3XZ/YSKIObegH8y7y3xwF8WKczEdESpJSYiGfQWWEFtifEXbALmU7mqrr/qgo2WakCW4TXpAnEtLCQ14WSBGKZfFUTr1fjdCSFG7a2a/K55u+C3dyh/+iL5ycSyBZKS95/VV23+ew92F09yz+eiIhq25LPdKSUXwHwFSHEq6WU9xp0JiJaQjxbQCZfQkegsgDbHfJACHCQ0zlmU3mEfdVvAQt5rRNgU7kCmk2cQEznU6dcz6SMDbCJbAET8Sw2tFe3A1bVFTS2AnugPMDpwgoCaUfAg62dzXj4xBR+74ZNeh+NiIhMtmSAFUK8UUr5NQB9Qog/PPf9Usp/WeDDiEhHE+VJoB3+ylqI3Q47Ovxu7oI9x0wqp0klKdDktEx1O8kKrOWE502q3gBtwmQlBqaSAIANrdp8zc7yC2bjcWMC7HPDs2h2O9BX4fmv3dyG/378DDL5oiZ3fomIyLqWG+Kk/p+jGYB/gX+IyGATsfIO2AorsAB3wS5kJpXTpCJmpRbiZLZg6g5YOl+oXIE1ehfsKTXAalSB9boc8HscGDeqAjscw66eQMX3ua/b3IZsoYSnT8/ofDIiIjLbci3Eny3/+Fer+eRCiJcC+AQAO5RVPH93zvvvBPBRACUABQDvk1I+vJqvRdQoJuLlAFthBRYAesJePDs4q9OJao+UErOpPFq0aCEuB1gpZdUDoaqVyhWqWgtE2purwCaNfZFDDbCVVjAr0RXwYMyAXbC5QglHRmN46zV9FX/MlRtb4bAJPHxiCteU78QSEVF9qmiNjhDiH4QQASGEUwjxcyHElBDijct8jB3ApwDcDmAngNcLIXae87CfA7hYSnkJgN8G8PkV/xsQNZjx8hPIzhVUYHvDTRiNplHkmgkAyj3iQklqNsSpUJJI5ooanKw6iWwBPrYQW8r8FmIjnZpKYk1QmxU6qq6gB2PlDhA9HR+PI1coVXT/VdXsdmD3uhAePmHsrloiIjJepXtgXyyljAF4OYAhAFsBfGCZj7kCwAkp5UkpZQ7ANwDcOf8BUsqElFJ9Ru0DwGfXRMuYiGfR5LSj2V15UOkJNSFfVKYXEzCTVMKEVi3EACzRRpzKFdlCbDF+jwM2Yfyfj1NTSc3ah1WdAY8hLcQHhisf4DTftZvbcGA4ani7NhERGavSAKv22d0B4H+klNMVfEwPgMF5vx4qv+0FhBCvEkIcBfBDKFXY8wgh3iGE2CeE2Dc5OVnhkYnq03gsg46Ae0XtquouWN6DVcyklDChSQtx+Y5jNGV+gE1kCxziZDE2m0DI6zK0AiulxMnJBDa0aRtguwIeTCayundyPDcUhd/jwPoV7rC9bnMbpAQe7Y/odDIiIrKCSgPs/5ZD5h4APxdCtANY7mXYhZ5dn/d/PSnld6WU2wG8Esp92PM/SMrPSSn3SCn3tLdrs9OOqFZNxLPoXMH9VwDoVXfBcpUOAG0rsIFyBXY2bW7VR0qJVK64oso8GSPkdc69aGKEmVQesUxB0/uvgHJtoViSiCT1bSM+OBzFRb3BFd8pv3htCM1uB9uIiYjqXEUBVkr5IQBXA9gjpcwDSOKcduAFDAFYO+/XvQBGlvgavwKwSQjB6QtES5iMZ9G+gvuvwNkKLFfpKNRqmFZ3YAEgZnILcbZQQrEk4WULseWEvS5D21rVAU4bNW4h7ggoL5xN6HgPNlso4uhYDBf2hFb8sU67DVduaGEFloiozlVagQWAHQBeK4R4M4DXAHjxMo9/EsAWIcQGIYQLwOsA3Df/AUKIzaL8EqsQ4lIALgD8Pw/REsZjGXT4VxZgvS4HWnwuBtiyuRZiDQKsWsU1+w5sMlsAAFZgLSjsdRo6hXhuhU5b9XuO51P/3tHzLv2xsTjyRYmLeld2/1W1p68FJ6eSmE7yHiwRUb2q6JmOEOK/AGwCsB+AOmpTAvjqYh8jpSwIId4N4MdQ1uh8UUp5SAhxV/n9dwN4NYA3CyHyANIAXjtvqBMRnSORLSCVK6IzsLIWYqC8C5YtxACUFmKbUAbsVEutwM6afAc2mVX+auYdWOsJeV04PBIz7OudmkrAbhPoLXdeaEX9e2dcxwrsc0OrG+Ckumx9GADw9OkZ3LqzU7NzERGRdVT6TGcPgJ0rDZdSyvsB3H/O2+6e9/O/B/D3K/mcRI1sorxCZ6UVWEC5v8YKrGImlUPI64LNVv3eVp/LDrtNmF+BzakVWLYQW03Y4DuwA1MprGvxwmlfSZPV8tqalb93xnXcBXtwOIqQ17nq8H1RbxAOm8BTZxhgiYjqVaX/dzsIoEvPgxDR8tTKR8cKhzgBypPPCNvqACjV0rC3+gnEACCEQKjJaX6ALbcQswJrPSGvC+l8EZm8MbuCT04lNZ9ADAAuhw2tPhcm4vpWYC/sWfkAJ5XHaccFPUE8NTCj8cmIiMgqKg2wbQAOCyF+LIS4T/1Hz4MR0fnUu2edKxziBCgBdjqZQ0nnFRi1YDqZ02SAkyrY5MSs2QE2p4Qj7oG1HnXVkhFt5lJKDEwlNZ9ArGr3u+c6QbSWK5RwfDyOXatsH1btWR/Gs0OzyBVKGp2MiIispNKX6v9Sz0MQUWUm49VUYF0oliRmUjm0Nq88ANeTmVQOa1tWtmNyKYEmp+lTiFPlCqyPQ5wsR32xZCaVQ1dw5f/trsR4LIt0vogNGk8gVnUGPLpVYAdnUiiUJDa3Vzd86rL1YXzh4VM4PBrDJWtD2hyOiIgso9I1Og8CGADgLP/8SQBP63guIlrAeCwDt8OGQNPKQ4oaWqcSbCOeSeU0ayEGlAqb2S3ECTXAsoXYctQK7IwBq3ROTiUAABt1aCEGlO4Pve7ADpSnJ/dVeXZ1kNNTp9lGTERUjyoKsEKI3wXwbQCfLb+pB8D3dDoTES1iIp5FR8C9qvth6gCWSEK/+2u1QEqJmVRe+xZik6cQp+ZaiBlgrUb9s2bEn5FTGoXAxXT4PZhK5FDU4SrC2fU/1Z29M+BBT6gJT52e1uJYRERkMZXegf19ANcCiAGAlPJ5AB16HYqIFjYRy66qfRgA2v3Kk+jJBg+w6XwRuUIJYZ+2AdYqFVivi3dgrWZ+C7HeBqaScDts6F7Fqq1KdAbcKJYkIknt/x4ZiCQR8Dg06Y7Y0xfGU6dnwM18RET1p9IAm5VSzv2fVwjhgLIHlogMNB7PrGqAEwC0+thCDCgDnABo20Lc5EQskzd1QFYqV4DDJuB2aLs6hapn5BCnU+UJxFqsiFpIe/kFtAkddsEOTKXQ1+Zb9QTi+S5bH8Z4LMvd10REdajSZzoPCiE+DKBJCHEbgG8B+F/9jkVEC5msogIbbHLCYRMN30KshoiQhi3EgSYnpATimYJmn3OlktkivC67Jk/+SVsepx1NTjtmDFhjdVLHCcTA2Qno6kR0LQ1EtDv7pet4D5aIqF5VGmA/BGASwAEAvwfgfgD/T69DEdH5UrkC4tkC2v2rq8DabAKtzS5MNXiAVds4WzRsIVbDsJltxMlsAc28/2pZYa/+q5YKxRLORFK6TSAGgI5ya/K4xhXYbKGIkdm0Znd3t3f54XXZGWCJiOpQRc92pJQlIcT3AHxPSjmp75GIaCFqy15nFXfb2prdbCHWoYU42KR8LlMDbK4ALwOsZYW8LszqfAd2eDaNQklWPQRpKe3lYXBatxAPTqdQksCGNm3WWznsNuxeF2KAJSKqQ0tWYIXiL4UQUwCOAjgmhJgUQnzEmOMRkWpibgfs6ne4tja7G74Cq0cLsRpgZ9PmvTiQzBY5gdjCwj4nZnS+A3tSoym+S3E5bGj1uTCucQvxqakUAGja/nzZujCOjMaQzJrX2k9ERNpbroX4fVCmD18upWyVUrYAuBLAtUKI9+t9OCI6S9292LHKIU4A0NbsQoQVWADK4CWtqEN6zG4h9nECsWWFvC7dpxCfmtQ/wAJAu9+teQV2QIfwfen6MEoSeHZwVrPPSURE5lsuwL4ZwOullKfUN0gpTwJ4Y/l9RGQQtQLbucohToDS/jeZyDb0aonZVA4BjwMOu3bTeq3RQlyE18UKrFWFvfrvCh6IJOH3ONCq4f3uhXQGPJoPcRqIJBFscmraGbF7XRhCAPvYRkxEVFeWewbnlFJOnfvG8j1Y7coXRLSsiXgGLrttrtq3Gm3NbuQKJcQbuKVuJpXXdAcsMK+F2IA1KYtJ5QpodrMCa1Xh8h1YPVctqSt09J5E3eF3z3WEaGUgktRsgJMq2OTE1g4/78ESEdWZ5QLsUv1Ojd2HSGSwiVgW7X53VU9OW5uV4DYVb9x7sDOpHMIaVnkAZU2K22FDzOQWYg5xsq6Q14WSBGIZ/f6MnJxM6t4+DCgV2KlEDkUNw/jAVAobWrUZ4DTfnr4w9g1MI1coaf65iYjIHMsF2IuFELEF/okDuNCIAxKRYiKeqer+K6BUYAEgYsA+SqtSAqz2DSTBJqfJd2CLXKNjYer+VK3Xz6gS2QJGomlsam/W5fPP1xFwo1iSiCS1+XfJ5IsYiWq3Qme+G7a2I5kr4smBac0/NxERmWPJACultEspAwv845dSsoWYyEATsWxVE4iBswG2oSuwSe1biAElwJrVQlwsSaTzRXg5xMmyuoPK3fXRaFqXz390NAYpgQvWBHT5/PN1lO/hazXI6cx0ClLqM3zq2s1tcNlt+OXRCc0/NxERmUO7KSZEpKvxWKaqHbAA0OYvtxA38CodPVqIAWUSsVkV2FROudPMCqx1qf/tan13VHV4NAYA2GlAgFWryVoNclInEK/XcIWOyud24MqNLfjFMQZYIqJ6wQBLVAMy+SJimULVFdgWrwtCAFMNukonky8ilSvWXQtxMlsEAE4htrAOvwdCAKNRfQLsoeEYwl4nuqp8kasSHQFtK7ADkfIKHR0CLADcvL0DJyeTOF3+OkREVNsYYIlqgFoxba8ywDrsNoS9roatwKotvnq0EAfMDLDlCqyPU4gty+Wwoa3ZjTGdAuzh0Rh2rgnoPoEYUNZxAdrd5z01lULY60RQhxeWACXAAsAv2EZMRFQXGGCJakCkXDFt9VUXYAGgrblxA+xMSvl91KWFuMllYgW2HGBZgbW0roBHlwpsvljCsfE4LlgT1PxzL8TlsKHF59K0hViPAU6q9a0+bGz3aRZgoyauyyIiIgZYopowXZ4a3NJcffBq9bkbtoVYDbDV7NJdTLDJiUS2gHzR+HUdaguxj3dgLa0r6NHlDuzJySRyhRJ2dut//1Wl7ILV5oWw05Gkbu3Dqpu3deDxk9NzL/as1Gwqh6/sHcDLPvkQLv7rn+AB3qklIjINAyxRDVArpm1aVGD9bkQatQKbVConLbpMIVbCoxm7YOcqsGwhtrTuoD4V2EMjUQDGDHBSdQQ8mlRglRU6GV0GOM130/YO5Iol7O2PrOjjppM5vPd/nsEVH/85/uK+QwCUqxyf/mW/HsckIqIKMMAS1QB1b2urBhVYpYW4sSuw+kwhVj6nGW3E6h1YDnGyts6AB9F0HulcUdPPe3gkBrfDho06tuGeq9Pv1mSI0+lICgDQ1+at+nMt5fK+FjS7HStuI/7oDw7jRwdH8VtXrsMP33sdfvjeF+GdN2zCEwPTeOr0jE6nJSKipTDAEtWASCILj9OmyZ7PtmY3EtkCMnltn0TXglmdW4gBcwJsqhyIuEbH2tRdsGMatxEfHo1he5cfDrtx/0vvDHgwmciiWJJVfZ5T5RU6euyAnc/lsOG6zW144NgEpKzszI+djOC7zwzj967fhL98xQVzd4xfe/laBJuc+NyvWIUlIjIDAyxRDYgkcmj1uTWZMNrW3Li7YKcSOfjdDrgd2rfaBsoBdtbEFmIvW4gtrascYEejac0+p5QSh0ZihrYPA0BHwI1iSc7dz18tdYWOnkOcVDdv78BoNIMjo/FlH5svlvDn3zuInlATfv+mzS94n8/twJuvXo+fHB5H/2RCr+MSEdEiGGCJasBUMjcXPKvVVl6B0YhtxNPJnCaDsBaiVnXNuQNbHuLEFmJLU3e0arlKZySaQTSdN3SAE6DstQVQ9VCq05EkWn0uBDz6rNCZ78bt7QCAX1YwgOmLD5/C8xMJ/NUrLkDTAp0vb7mmDy67DZ9/6KTm5yQioqUxwBLVgEgii9bm6gc4AfMCbLzxKrCRZBatOgxwAsxtIU7mCvA4bbDb9N8BSqt3tgKrXYA9PBIDAOw0aIWOqiOg/D0yWeXfI6emkljfqu/9V1WH34MLe4LL3oMdjabxiZ8/j1t3dODWnZ0LPqat2Y3f2NOLe58a1mydEBERVYYBlqgGTCdzmk3ObfOrFdgGDLCJnGYvBJxLDbCzJuyITGYLvP9aA7wuB4JNTk1X6RweiUEIYHuXX7PPWYnOgDYV2IGplCHtw6o7LuzGU6dn8P39w4s+5qM/OIxiSeIvfu2CJT/X26/biEKphC8/MqDxKYmIaCkMsEQWJ6UsBy9tAqxagYxUeXetFkWSOd0qsE67DT6X3ZwKbLbACcQ1oiug7SqdQyNRbGj1Gb4DuL38QlA1u2DTuSLGYhndd8DO9/YXbcDlfWF86N4DODZ2/l3Ye58awv0HxvDumzZjbcvSleG+Nh9u39WN/3rsNBKr3C9LREQrxwBLZHHxbAG5YkmTHbAA4HHa4Xc7qm79qzWl8sAZrV4IWEiwyWlSC3HR8ABDq9MV9Gh6B/bwqPEDnABlqm+Lz1VV++zcBOJ24wKs027Dp37rUjR7HLjra08hllH+e5VS4tMPnMAffetZXLWxBe+4YWNFn+/tL9qAeKaA+w+M6nlsIiKahwGWyOIiCe12wKra/O6GayGOZfIoliRaNXohYCGBJqdpLcQ+DVYskf66gx7N1uhE03kMzaRNCbAA0OF3V1WBPTmlTPDd2Nas1ZEq0hHw4NNvuBSD0yn80T3PKhOHv38Q//B/x/CKi9fgK799RcWTyi/uDSHkdWLfwLTOpyYiIhVfsieyuEg5aGp5d7Ot2dVwAXZKhxcCzhXyOs2ZQpwrItSk/xRXql5nwIOpRBa5QgkuR3WvIc8NcDJ4ArGqI+DBZBUV2JOTxuyAXcjlfS34s5ftwF/972G8+F9/hVNTSdx1wyb8yUu2wbaCYWg2m8Bl68LYd3pGx9MSEdF8rMASWdxc8NLw7marzz1X2W0Ucy8E6FiBNa2FOFuAjztga0J30AMpocnk2sOj6gRicwJsZ5UV2P7JBHpCTQuuqTHCW6/pw52XrMHpSBIfvfMCfOj27SsKr6rL+sI4OZmseicuERFVhgGWyOLUJ0VtWlZg/Y1XgVV/H7Wa5ryQYJMTs2njn8SmsgXugK0R6iodLe7BHh6Jod3vntvJarSOgBuTiSxKJbmqjz85mcRGA++/nksIgX/5zUvw0Advxpuu7lv159mzvgUA8BSrsEREhmCAJbI4tXIY9mnXItrW7MZMKo98saTZ57S6qbkXAvRsIXaZUoFNZAsc4lQjuoNNAKDJPdhDI1HT2ocBpR26WJKrejFMSomTkwlsNKF9eD67TaAn1FTV57ioNwinXWDfad6DJSIyAgMskcVFkjn4PY6Kh4pUQr1PO9NALW9nXwjQtwKbyZeQyRd1+xrnklIilSvCyyFONaEroE0FNpMv4sREwrT2YQBYV14zMxBJrfhjJ+JZJHNFbOowdoCTHjxOO3b1BPHUACuwRERGYIAlsripRFbT9mEAaC9XIScbqI14OplDsMkJp12/v/YC5UFKRg5yyhVLKJQkK7A1ItDkQJPTXvUu2GcHZ1EoSVy6LqzRyVZOHb40UF6HsxL9k+ZMINbLnvVhPDccRbZg3ItXRESNigGWyOIiiZymA5yAs/dppxpokFMkoe8OWABzk4CNbCNOZpUnzFyjUxuEEJqs0nn81DSEAK7oa9HoZCvXE2qC0y5wKrLyAKtOIDbzDqyWLlvfglyhhIPDUbOPQkRU9xhgiSwuksxqHrzmAmy8cSqwU4ks2nScQAwoLcQAMGtogC0AACuwNaQz4Km6hfiJU9PY1ulH0Gve+iSH3Ya1LV6cmlxdBbbJaZ9rqa51l61XKuH72EZMRKQ7Blgii5tO5jTdAQuc3YUaSTZOgJ1O5nSdQAycDbDRlIEBNscAW2u6g9UF2HyxhKdOz+DKDeZVX1Ub23wYWGUFdkObb1Vra6yo3e9GX6uX+2CJiAzAAEtkYcWSxHQyhzaNg1ez2wG3w9ZYLcRJA1qIvSa2EDPA1oyuoAfjscyq188cGI4inS/iyo2tGp9s5fpalQC70n+Xk1OJumkfVl22vgVPn56BlKv7vhIRUWUYYIksbDaVQ0lC8wqsEAJtze6GaSEuliRmUtpXss9lagsx78DWjK6gB4WSxNQqOyCeOKWsa7ncxPuvqr42HzL50oru9GbyRQzNpLGpvT4GOKn29IURSeZwahVDrYiIqHIMsEQWFimvudGj9bXN757bjVrvZlI5SAnNh2Gdy+9xQgggmjLu9zXFFuKaU+0qncdPRrCp3Yd2v74vyFRi4yomEZ+OpCBl/QxwUu1R78GyjZiISFcMsEQWNlVec6NH62ubz4XJBqnARsqt0nq3ENttAgGP09AKbGJuCjEDbK3oDjYBwKpW6RRLEvsGZnDFBvPbhwGlAgtgRZOIT5ZX6NRbBXZTezOCTU7ugyUi0hkDLJGFqcFL6z2wgDJ0ZKpB9sCqw6padZ5CDABhrxMzBg5xOluBZQtxregMKn8Ox1exSufIaAzxbMESA5wApZrsdthWNIlY3QGr7pGtFzabwGXrw9h3etrsoxAR1TUGWCILi6gVWB1aXzv8bkQSWRRXOUimlhhVgQWAkNeFWQNbiBNco1Nz2nxuOGxiVRXYx8v3X6+wSIC12QQ2rHAS8cnJJLoCnrr8M3vZ+jD6J5OYaZDrGUREZmCAJbKw6WQONqGEIq21+90oycZYpaPnCwHnUiqwxj15jaULcNltcDv413mtsNnEqnfBPnEqgrUtTVgTatLhZKvT1+pb0eCi/qlk3d1/Van3YJ/iPVgiIt3wGQ+RhU2Vd5faddiVqA6AaYR7sNPJHIROLwScK+x1YSZpXAtxLJNHoMkBIepjn2aj6FrFLthSSeKJU9O40iL3X1V9bT6cmU6hUCwt+1gpJU5O1t8KHdXFa0Nw2AT2D86afRQiorrFAEtkYZFEVrd7m40UYKeSObR49Xkh4Fwhr8vQPbDRdB6B8voeqh1dQc+KVs8AwInJBGZSecu0D6s2tvmQL0qMzC7/7zOVyCGeKdTdACeVx2nH1k4/nh2aNfsoRER1iwGWyMIiiZwuK3QAoMOvrPKYaIAAG0lkDbn/CgAhrxOJbAG5wvLVKC3E0nkEPAywtaY74MFoNA0pK7+Drt5/vcqCFVigsknE6gTijXUaYAHgot4gDgxHV/S9JSKiyjHAEllYJJnTLXipk40boQI7ndTvhYBzhb1KmJxNG3MPNpYpIMgKbM3pCnqQyZdWVK1//GQEXQEP1rZY5/4rAPS1eQEAp8rhdCn95WnFG+tsAvF8F/WGMJvK48x0yuyjEBHVJQZYIgubSmR1WaEDAE0uO/xuR0ME2Egih1adfh/Ppd6znTVolU6MLcQ1aW2LEvpOTCwf+gDl7ugTp6ZxxYYWy913bm92o9ntwEBk+cB2cjIBt8OGHgsNodLaRb1BAMBzQ1GTT0JEVJ8YYIksKlcoIZ4p6Do5tz3gxmQD7IKdSmTRZlgFVvk6Rq3RUFqI628dSb27oq8FQgB7+yMVPf75iQQm4llctdFa7cMAIIRAX5u3oknEJ6eS2NDmg82A++hm2dblh8thw3O8B0tEpAsGWCKLmk6qu0v1qxy2N7sxGavvAJsrlBDLFNCi0zCsc4XKLcQzBlRgpZSIZfJsIa5BYZ8LF6wJ4OETUxU9/gfPjcImgFt3duh8stWpdJVOPU8gVjntNuzsDuBZVmCJiHTBAEtkUVPq7lIdhw+1++u/AqvuZDVqiFPYp7YQ61+BTeeLyBclW4hr1LWb2vDMmRmkcoUlHyelxA+eG8GVG1rnhq9ZzcY2H4ZmUksOL8sVShicSWNjW/0OcFJd3BvEoeEoiqX6G+SUyhXwu1/dh+8+M2T2UYioQTHAEllUpFyBbdMxeHX4PXV/BzaS0P/3cb5QkzrESf8KbCytBB9OIa5N125uQ74o8eTAzJKPOzIax8nJJF52UbdBJ1u5vjYfShIYnFn8Hmz/ZALFksSmjvquwALAhb0hJHPFuanL9UJKiQ/dewA/PTyOP/3Ogbr79yOi2sAAS2RREbUCq2Pra7vfjUS2sGwFqJZFksrvo1EtxF6XHS67ba7yq6dYRgnJbCGuTZf3tcBlt+GRZdqIf/DcCOw2gdt3dRl0spXboK7SmVy8jfjB45MAYMl7vFq7uDzIqd7aiL+ydwD3PTuC3752A9wOO/7oW8/WZZWZiKyNAZbIotTKYYvOLcRAfa/SUX8fjWohFkIg5HViNql/BVZdwRJo4hCnWtTksmP3utCSAVZKiR8eGMU1m1oNm6S9GmqAHVhiF+wvjkxgZ3cA3cH6nUCs2tjeDJ/LjgN1NMjpqdPT+NgPj+DWHR34fy/bgb++8wI8c2YWn/vVSbOPRkQNhgGWyKKmklm47Db43fqFk4YIsGortkEVWECZRGxIBVYNsGwhrlnXbW7D4dHY3NC2cx0cjuF0JIWXW7h9GFDWR4W8zkUHOc2mcth3ehq37LDmECqt2W0Cu3qCdVOBnYxn8a6vP42ecBP++Tcvgc0m8IqL1+COC7vwrz89jqNjMbOPSEQNhAGWyKKmEzm0Nrt03fnYUQ6wE/UcYBNZOGzC0CplyOs0ZA+sWoFlC3HtumZzG6QEHl1knc4PnhuBwybwkgus2z6s2tC2+CTiB49PoiSBm7c3RoAFlH2wh0djSw62qgVSSvzBN55BNJ3HZ95w2dzfN0IIfPTOXQg0OfBH9zxb8/+eRFQ7GGCJLCqSzOne9toQFdhEDi0+fV8IOJfhFVgG2Jp1cW8QzW4HHuk/v41YmT48iuu2tCHkNaYFvhobWn0YWCTA/uLoBFp9LlzcGzL2UCa6qDeEXKGE4+Nxs49SlaNjceztj+ADL9mOnWsCL3hfa7MbH3/VhTg0EsPnH2YrMREZgwGWyKIiiayuA5wAoMXrgt0m6jvAJpUAa6SQ12nMFOKMOoWYd2BrlcNuw1UbW7B3gXuw+wdnMTybxssvWmPCyVaur82HkWgG6VzxBW8vFEt44NgkbtzWAZvNuBeSzKaG9edqvI34oeeV4Vsvu3DhNvaXXNCFKza04L79I0Yei4gamK4BVgjxUiHEMSHECSHEhxZ4/xuEEM+V/9krhLhYz/MQ1ZKphP4VWJtNoK3ZhYl4RtevY6ZIMos2g4ffhLwuzKZykFLf6ZzRdB4+lx0OO1+LrGXXbGrDQCSFoXNW0PzguVG47Da8+IJOk062Mps7lP2uj558YRh/+swsoul8w9x/Va1taULI68RzNT7I6VfHp7C1sxldwcV3EN+8vQNHx+IYi9bv/0uIyDp0e9YjhLAD+BSA2wHsBPB6IcTOcx52CsANUsqLAHwUwOf0Og9RLZFSGha82v3u+q7AGvBCwLnCXifyRYnkOZUorcXSebYP14FrN7cBAPaeOHsPtlSSuP/AKK7f2l4zQ7pu2dGBDW0+fOyHR15wH/LnR8fhsAm8aEubiacznhACF9b4IKd0rognBqbxoi3tSz7uxm3K+x88PmHEsYiowen5sv0VAE5IKU9KKXMAvgHgzvkPkFLulVKqG9wfA9Cr43mIakYqV0QmXzKk9bW92Y3JRP0G2GkTWojD5fuKM4tMltVKLJPnAKc6sLWzGW3NbjxcbiM+OBzF6/7zMYxGM7jzktpoHwYAt8OOv/i1nTg5mcQXHzk19/ZfHp3AlRtb4K+RIK6li3tDOD4eRyav74tZenliYBq5QgnXb106wG7r9KMr4MEDxyYNOhkRNTI9A2wPgMF5vx4qv20xvwPgRzqeh6hmzO0uNSB4dfg9mIjVZ4DN5ItIZAsmtBArT9T1nkQcTedrpjpHixNC4NrNrdjbP4UPfOtZ/Np/PIz+iQQ+/qpdll+fc64bt3Xgtp2d+OTPn8doNI3B6RSOjydw8/baaIPW2oW9QRRLEodGanPNzK+OT8LlsOGKvpYlHyeEwI3b2vHw81PIFzmNmIj0pWeAXWhSw4IXwoQQN0EJsB9c5P3vEELsE0Lsm5zkq3tU/9Q7qeqUYD21+92IJHMolvS9r2kGdbemES8EzBcufz29JxHH0gVD1wORfq7d1IapRA7f3z+Cd7xoI375gRvxhivXGzo9WysfeflOFEoSf3P/UfziqNJSeksDrc+Z7+wgp1lTz7FaDz0/iSv6WtDksi/72Bu3tSOeLeDp0zPLPpaIqBp6PvMZArB23q97AZw3ok4IcRGAzwO4XUq54CI8KeXnUL4fu2fPnvp7lk10DnUva2dg8aEZWmn3u1EsScykcoZXKvWmVrINn0JcbuvVexJxNJ3H9m6/rl+DjPFrF6/BZCKLl1/UjfWtPrOPU5W1LV6884ZN+MTPn8fTp2ewsc2Hvrba/ndara6gBz2hJjx+chpvu3aD2cdZkbFoBsfHE3jNZZXd7rp2cxscNoEHjk/iyo2tOp+OiBqZnhXYJwFsEUJsEEK4ALwOwH3zHyCEWAfgOwDeJKU8ruNZiGrKREypwHYYVIFVvmb9tRFPJZV/p1YTphADwKzeFdgMW4jrRZPLjt+/aXPNh1fVO2/chN5wE4Zn07i5Qauvqms3t+LRk5Ga63L5VXl9znIDnFR+jxOXrQ/zHiwR6U63ACulLAB4N4AfAzgC4B4p5SEhxF1CiLvKD/sIgFYAnxZC7BdC7NPrPES1ZCKehcMm5oYB6UkNyfU4yGm6XIFtM3gKsXoHdiapXwW2VJJIZAsc4kSW5HHa8Ze/dgFsArh9kf2hjeLazW2IpvM4NFJb04gfen4Kbc1ubO+qvMvjxm0dODIaw3iM63SISD+6Lg+UUt4vpdwqpdwkpfx4+W13SynvLv/87VLKsJTykvI/e/Q8D1GtmIhn0e53w2bT//6bWoGtx1U6kXIF1ugWYqfdBr/boesd2HimACnBNTpkWbfu7MQzH3kxLlsfNvsoprpmk7I+SJ0yXQuKJYmHn5/E9VvaVnQPe26dDquwRKQjXQMsEa3OeCxjSPswMK+FOF5/r5hHEjm47DY0u40fdBTyOXVtIY5llOpuwMMhTmRd7BBQ/o7d3uV/wZ5fqzs0EsVMKr/s+pxzbe8qr9PhPlgi0hEDLJEFTcaz6DBggBMAeF0ONLsddVmBHYlm0BX0mDLJNex1YUbHNTrR8oAoBgQi67tmUxueHJiumX2wDz2vVIuv3dy2oo8TQuCGre146PkpFLhOh4h0wgBLZEET8axhFVhAqRDUY4AdnE5hbUuTKV872OTUdQpxrPy52UJMZH3XbWlFtlCqmRUzDx6fxM7uwKpWud24rR3xTAFPn5nV/mBERGCAJbKcXKGE6WQOHX5jKrAA0N7snlvdU0+GZtLoDXlN+dphr8ugFmIGWCKru2JDKxw2URP3YBPlXa4rbR9WXbulvE7nGNuIiUgfDLBEFqNOA+4IGFiBDbgxVWcBNp0rYiqRNa0CG/Y6MZPUMcCmCwCAoJcBlsjqmt0OXLI2hEdqIMA+OziLQkniqo0tq/r4gMeJ3etC2NtfO3d+iai2MMASWYyRO2BV7c3110I8NJMCAKxtMacCG/K6EMsUdLsHpt6B5RAnotpw7eY2HBiOIqrj3Xgt7B+cBQDsXrv66dGX97Xg4HAU6Vxt3PklotrCAEtkMWorb6dBQ5wA5Q5sPFuoqycbQzNpAEBv2LwKLHA2aGotlsnDJmDKhGUiWrnrtrShJIFHT1q7MvnMmVlsbPNV1d1xeV8LCiU5F4aJiLTEAEtkMWqANXqIE1Bfu2AH1Qps2KQ7sOXds3pNIo6m8wg0OU2ZsExEK3dxbwhelx17+63bRiylEjovWRuq6vNcuj4MIYAnB6a1ORgR0TwMsEQWMxnLwCaA1mbjAqwalicT9bMLdnA6BbfDtqopmlpQ19tE0/rcg42l8xzgRFRDXA4brtzQYulBTkMzaUwlsrhkXaiqzxNscmJbp58Bloh0wQBLZDHjsSxam92w24yrrNVjBXZoJo3ecJNpFcqwt1yBTerVQlzgDliiGnPt5jacnExiNJo2+ygL0uL+q+ryvhY8fXqG+2CJSHMMsEQWMxHPoNPACcTA2QBbT6t0BmdS6DWpfRiYF2B1WqWjtBDz/itRLbl2cxsA4JET1rwHu39wFm6HDdu7/VV/rj19YSRzRRwdi2twMiKisxhgiSxmIp41dAcsALT63LCJ+qrADk6nTVuhAwAhn1IdndXpDixbiIlqz7ZOPzr8btx/YNTsoyxo/+AsdvUE4bRX//Tw8j5lDQ/biIlIawywRBajBFhjK7B2m0BrHa3SiWXyiKbzpg1wAgC/2wG7TehWgY1l8mwhJqoxNpvAb125Dr84OoETEwmzj/MC+WIJB4ejVQ9wUq0JNaEn1IR9AzOafD4iIhUDLJGFFIolRBLGB1hA2QVbLy3EQ9PqCh3zAqwQAqEmp+5TiImotrzpqvVwO2z4wsOnzD7KCxwdjSNbKGkWYAHg8r4wnhyYhpRSs89JRMQAS2QhkWQOJQm0G7gDVtURqJ8K7NwKHRNbiAEg5HXqMoU4Wygiky+xAktUg1qb3fj1S3vxnaeHEElY5+/c/YNKpXR3lROI59vT14KJeBaD09YcWkVEtYkBlshCJmLKk5lOkyqw47H6WKMzNKM8WTKzhRhQBjnpMYU4nikAAAIeDnEiqkW/c90GZAsl/Ndjp80+ypxnzsyirdmNnpB2L/yp92Cf4D1YItIQAyyRhUzElQDZYUIFtjfsxUQ8i0y+aPjX1trgdAo+lx0hr7kVypDXpcsd2GhaCcVsISaqTZs7mnHL9g7816OnLfN37v7BWVyyNqTp6rEtHc0IeBzYxwBLRBpigCWyEPUOqhl3YNe3KtXKwemU4V9ba0MzKaxt8Zq2A1YV9jp1mUIcY4Alqnlvf9FGRJI5fPeZYbOPgmgqj5NTSU3bhwFlaNWevhZOIiYiTTHAElmI2kLc1mxegB2I1EOATZs6wEkV9ulcgeUaHaKaddXGFuzqCeDzD51EqWTukKP9Q7MAoOkAJ9XlfS3on0xa6r4vEdU2BlgiCxmPZ9Dic8HlMP4/zb5WHwDgdCRp+NfWkpQSg9Mp9IbNHeAEAMEmJ7KFEtI5bVsEY+U7sMEm3oElqlVCCPzuizaifzKJB45PmHqW/WdmIQRwUW9Q8899eV8YALDvNNfpEJE2GGCJLGQiZs4KHUCZmOv3OHC6xiuwM6k8krki1rZYoALrdQEAZjWeRMwWYqL6cMeF3egJNeHvfnRU8xe6VmL/4Ay2dDTDr0NXx4W9QbgcNt6DJSLNMMASWchkPGPKACdAqQb0tfpwusbvwA6pK3QsUIENl4dIaT2JmC3ERPXBabfhb3/9Qjw/kcBf3HfQlDNIKecGOOnB7bDj4t4gnjjFAEtE2mCAJbKQibh5FVgAWNfqrfkWYnXfoBXuwIbUCqzG92BjmTzcDhs8Trumn5eIjHf91na8+6bNuGffEO59asjwr386ksJMKo9L1oZ1+xpXbWzFwZEY4hnth9oRUeNhgCWyiFJJYtLkANvX6sXwTBr5Ysm0M1RrUK3AtligAusrV2A1nkQcSxfYPkxUR95361ZctbEF/+97B/H8eNzQr63uaN3Tp1+AvXpjK4olyWnERKQJBlgii5hO5VAoSVMD7PpWHwoliZHZtGlnqNbQTKp8n9f8gKfegdV6EnEsnUfAwwFORPXCbhP45Ot2w+e2411ffxqpXMGwr/34yWm0+FzY0tGs29e4dH0YLrsNj/ZHdPsaRNQ4GGCJLEJdodNp0h1YAFjfUvurdAan05aYQAwoU4gBfVqIg6zAEtWVjoAH//ba3TgxmcBHvn/IsK/7+KkIruhr0XVvtsdpx+51ITx6kgGWiKrHAEtkERPxDACgI2BiC3GbskrnTA3fgx2cSWGtBe6/AsqTNq/LjmkdhjixhZio/ly3pQ3vuXkLvv3UEL61b1D3rzc8m8bQTBpXbmzR/WtdvakVh0ZiiGp8pYKIGg8DLJFFTMSVCmyH37wKbIffDY/TVrMVWCklhmfSlliho2r3uzGZyGr6OZUWYgZYonr0B7dswdUbW/Hn3z+I4zrfh328XBG9ckOrrl8HUO7BSqlUfImIqsEAS2QREzGlAttu4h1YIQTWt/hqdhfsZDyLbKFkmRZiAOj0e+a+t1qJZQpsISaqU3abwCdefwma3U686+tPI5nV7z7s4yenEWxyYnuXX7evobpkXQhuh41txERUNQZYIouYiGcRbHKavhplfQ2v0pmbQGyRFmJAaQlXq+takFKWW4g5xImoXnX4Pfjk6y5B/2QCf/69g5BS6vJ1Hj8VweV9LbDZ9Lv/qnI77NjTF+YgJyKqGgMskUVMxMxdoaNa3+rFmekUSiV9njDpaWhGmZ5shRU6qs6AB+OxjGZPQFO5IoolyRZiojp3zeY2/MEtW/CdZ4Zxjw73YceiGQxEUrjKgPuvqqs3tuLoWBzTSW0H2xFRY2GAJbKIiXjG1AFOqvWtPmQLJYzHtW17NcLgtFKB7QlZpwLbGXAjlSsioVEbYCyjDEBhCzFR/XvPzVtwzaZWfPQHRzCl8V169S6qEfdfVVdvUr7W42wjJqIqMMASWcREPGvqACfV+lYl/NXiPdgz0ym0NbvR5DK3DXs+9Xs6HtPmyWc0rQRYTiEmqn92m8Bf37kL6XwR//az45p+7sdOTsPvdmDnmoCmn3cpF/WG4HXZeQ+WiKrCAEtkAVJKy7QQ97Uqq3Rq8R7sE6emcWGPcU/GKqFW1bUa5BRLK5VcVmCJGsPmjma88cp1+O/Hz+B5DacSP34qgss3tMBuwP1XldNuw56+Ft6DJaKqMMASWUA0nUeuWEJHwPwKbHfQA4dN1NwqnYGpJAYiKdy4rcPso7xAZ/l7qlVLdkytwPIOLFHD+INbt8LnduBv7j+iyeebiGdwcjKJKzcYd/9VdfXGVjw/kcCkhsPtiKixMMASWcDZHbDmV2AddhvWtnhxpsYC7IPHJwEAN25rN/kkLzQXYDVvIeYUYqJG0eJz4d03bcYvj03i4eenqv58T5yaBgBcudG4+68q9R7sY2wjJqJVYoAlsoDh8vTcNSHzK7CAcg92oMZaiB88Pom+Vi/Wl1ugraLZ7YDPZceERgGWQ5yIGtNbrunD2pYmfOyHh1Gsckr84yen4XPZscvA+6+qXWsCaHY7eA+WiFaNAZbIAk5NKWGxzyLha325AqvX7kGtZfJFPNofwQ1brVV9VXUGPJq1EKsV2GY3K7BEjcTjtOODL92Oo2NxfPup6tbqPH4qgsv6WuCwG/800GG34YoNLXjkxFTN/D+GiKyFAZbIAgYiSfg9DrT4XGYfBYCySieeLdTMrr59AzNI54u4wWLtw6qOgFuzIU6zqTz8HocpTzyJyFwvu7Abl64L4Z9+chyp3OpWc00nczg+njDl/qvqpm3tOB1JoX+ytjp9iMga+AyIyAJOTSXR1+qDEMZNg1zK3Cqd6dq4B/vAsQm4HDZcZcJ9rkp0Bjya3YEdj2Xm7tUSUWMRQuDDd+zAZDyLLz0ysKrP8fMj4wDO3kU1wy07OgEAPyufhYhoJRhgiSxgIJJEX5s12ocBzN0jrZVVOg8en8SVG1rgdVmzrVYJsBlN2uXGYhl0McASNaw9fS24dUcH7n6wH7OplXfJfO2x09jc0Yzda0PaH65Ca0JN2NUTwE8PM8AS0coxwBKZLFcoYXgmjQ3lqqcVrG1pghDAwJT1K7DDs2k8P5Gw7P1XQJkunS2U5na4VmM8ygosUaP745dsQyJbwGce6F/Rx+0fnMWzQ1G86ar1pnf83LqjE0+fmcFUgut0iGhlGGCJTHZmOoWShKUqsG6HHWuCTThTAy3Ev7Lo+pz5OjTaBVsqSUzEs+gKmr9uiYjMs70rgFft7sGX9w5gNJqu+OO++ugAfC47fv3SHh1PV5lbd3RCSuAXRyfMPgoR1RgGWCKTqW26VgqwQO2s0nng2AR6Qk3Y1N5s9lEW1Vne7zte5SCnqWQWhZJkCzER4f23boWUwCd+9nxFj59O5vCD50bxqkt74PeYv4brgjUBrAl68DO2ERPRCjHAEplMXaGzwSIrdFTrW704HbF2BTZfLOGRExFcv7Xd9Ha4pagtv9UOchqPZl/w+Yioca1t8eINV63DPfsGcWIisezjv/nkIHKFEt58dZ/+h6uAEAK37uzEQ89PIZMvmn0cIqohDLBEJhuIJBHwOBDymv+K+Hw7uwOYTubQP7n8E6NqjEbT+N9nR/CX9x3Cqz79CP7wnv3Y2z+FUmn5gUdPn55BIluw9P1XQFmjAwATVbYQj5UruF1BBlgiAt5902Y0Oe34xx8fXfJxxZLE1x47jas2tmBrp9+g0y3v1h2dSOeLeOTElNlHIaIawgBLZLKBqRQ2tFlnhY7qpu0dAM6uXNDaYycjuPVfHsTVf/sLvOd/nsE3nxyEXQj89NA4fus/H8f1//hL/OtPj2Msunjo+9mRcThsAtdutub6HJXX5YDf48BElRXYuQDLCiwRAWhtduNdN23Gjw+N4/MPnVz0cb88OoHh2bRlqq+qqza2otnt4DodIloRa+6cIGogp6aS2NMXNvsY5+kNe7G9y4+fHZnAO67fpNnnzeSL+If/O4YvPnIKfa1efOTlO7GnL4wd3QE47TZk8kX8+NAYvv3UED75i+fx2V/14+3XbcTv3bBx7t7W0EwKf3P/Edx/YAy37uiwxH2u5airdKoxHs3AbhNobeYQJyJS3HXDJhwYiuLj9x/BmlAT7riw+7zHfOXRAXQG3LhtZ6cJJ1ycy2HDDdva8bMjE/h4ScJms9YLuURkTQywRCbK5IsYiabR19pr9lEWdOuOTnymvGsw5HVV/fn2D87iD+/Zj5OTSbz56vX40O3bz9vd6nHaceclPbjzkh6ciaTwTz85hv/45Qn8zxNn8L5bt2AqkcPdD/ZDCOAPb9uKd1y/sepzGaEz4K46wI7FMujwu2HnkzwiKrPbBP7tdZfgDZ9/HO/75n60+924vK8FACClxAPHJvHQ81N4/61b4bRbr/Huth2d+OFzo3h2aBa711nvxVwish7r/U1G1ECGZlKQEthgsQnEqlt2dKBYUp4AVes7Tw/hNZ/Zi3SuiK/9zpX46zt3nRdez7Wu1YtPvn43vv/712JTRzP+/PuH8ImfP48XX9CFX/zRjXjvLVvgcdqrPpsROvye6oc4xTJzK3mIiFQepx3/+eY96A014Xe/ug/PDc3ivx4dwG3/+iu87ctPojPgxuuvXGv2MRd047Z22G2CbcREVDFWYIlMdGpKmfK7vtVr8kkWdnFvCG3Nbvz0yDheuXv1ewM//9BJfOyHR3DNplZ85o2XIdi0spbfi9eG8M13XIVH+yPwuh24ZG1o1WcxS0fAjYl4BlLKVd93HotmsLHdmi92EJG5WnwufPltV+DXP/MIXvEfjwAALuoN4p9+42K8/KJuy77YF/K6cEVfC35yaBwfeMl2s49DRDWAAZbIRAPqCh2LVmBtNoGbt7fjRwfGkCuU4HKsrGlDSom//79juPvBftxxYRf+9bWXwO1Y3ZMoIQSu2dy2qo+1gk6/B/mixEwqjxbf6tqxx2IZXLPJ2gOriMg861q9+PLbrsC3nxrCK3f31MyLfbdf2IWPfP8QDo/EsHNNwOzjEJHFsYWYyESnIkmEvE5N7pfq5dYdnYhnC3hyYHpFH1csSXzw3udw94P9eMOV6/Dvr7901eG1HpzdBbu6e7CpXAHxTAGdXKFDREvY1RPEX77igpoJrwDw8ovWwGkX+O4zQ2YfhYhqAAMskYkGppLoa7Vm9VV13ZY2uBy2Fd1PKpYkPvCtZ3HPviG895Yt+NgrdzX84KHOuV2wq7sHq64T4godIqo3LT4XbtrWge/tH0GhWDL7OERkcQywRCYamEpatn1Y5XU5cO2mVvzsyDiklMs+vlSS+PB3DuA7zwzjj27bij+8bavldtyaodoKLHfAElE9+/VLezEZz+LhE1NmH4WILI4BlsgkygqdjOUrsABwy45ODE6n8fxEYsnHSSnxkfsO4pv7BvGemzfjPbdsMeiE1tfuL1dgVxlg1eDLFmIiqkc3bW9HyOvEd54eNvsoRGRxDLBEJjkzrUwg7muz5gTi+W7Z0QEAS7YRSynx0R8cwdceO4Pfu34j/vC2rUYdryZ4nHaEvM5Vr9IZiyofxwosEdUjt8OOV1y8Bj8+NIZ4Jm/2cYjIwhhgiUxyqjyBuBYqsN3BJlywJoCfH5lY8P3JbAHv+Z9n8MVHTuFt1/bhQ7dvZ9vwAjr87lW3EI/HMvC7HfC5OTyeiOrTr1/ai2yhhB8dGDP7KERkYQywRCZRV+j0WfwOrOrFO7vw1OkZvP+b++fODgAnJuK481OP4P4Do/jAS7bhIy/fyfC6iM6AB+NVDHFi+zAR1bOLe4PY2O7DvU9zGjERLY4v5ROZZCCSRIvPhWCT0+yjVOQd129EMlfAVx8dwH3PjuA1l/bi4rUhfPyHh+Fx2vFfv3Mlrq3hPa1G6PB7cGJidQNKxmIZtg8TUV0TQuDVl/biH398DIPTKaxtsf4VGyIyHiuwRCY5NZVEX2vt/M+5yWXHh+/YgV994Ca86ar1+O4zw/jwdw9gW5cfP3jvdQyvFegMuDERz6JUWn6a87nGY5m5ScZERPXqlbt7AADfe4bDnIhoYazAEplkYCqFaza1mn2MFesIePCXr7gA77h+Ix7tj+DXLl4Dl4OvhVWiM+BBsSQRSebmphJXoliSmIhn0RWs/GOIiGpRT6gJV29sxXeeGca7b97MKylEdB4+6yQyQTpXxFgsUzP3XxeyJtSEV1/Wy/C6Ap2B8iqd+MoGOUUSWRRLki3ERNQQfmNPL05NJbkTlogWxGeeRCY4PV1bA5xIGx3lADqxwlU6Y+oOWAZYImoAL7uoG23Nbnz5kQGzj0JEFqRrgBVCvFQIcUwIcUII8aEF3r9dCPGoECIrhPhjPc9CZCXHxuIAgI0MsA1FDaArXaUzFlUe38UpxETUANwOO95w5Tr84tjEC6beExEBOgZYIYQdwKcA3A5gJ4DXCyF2nvOwaQDvBfBPep2DyIoeOxmB3+PA9i6/2UchA7U3Ky3E4yuswKqBly3ERNQo3nDVOjhsAl/eO2D2UYjIYvSswF4B4ISU8qSUMgfgGwDunP8AKeWElPJJAHkdz0FkOY/2R3DlhhY47OzibyQuhw0tPtdcS3ClxmNZ2G0Crc0c4kREjaHD78HLL1qDbz81hHiGTxOJ6Cw9nz33ABic9+uh8tuIGtrIbBoDkRSu3sS1M41oXYsXJycTK/qYsVgGHX437DZO4ySixvHWa/qQyBbwrX1DZh+FiCxEzwC70DOtlS8/BCCEeIcQYp8QYt/k5GSVxyIy16P9EQDA1Rtrb4UOVW9HdwBHRmOQsvK/DrkDloga0cVrQ7h0XQhfeXRgVfuziag+6RlghwCsnffrXgAjq/lEUsrPSSn3SCn3tLe3a3I4IrPs7Y8g7HXy/muD2rkmgFimgJFo5W3EY9EM778SUUN627UbcDqSwi+PTZh9FCKyCD0D7JMAtgghNgghXABeB+A+Hb8ekeVJKfHYyQiu2tgKG9tBG9LObuWFi8MjsYo/ZiyW4QRiImpIL93Vha6AB1/iSh0iKtMtwEopCwDeDeDHAI4AuEdKeUgIcZcQ4i4AEEJ0CSGGAPwhgP8nhBgSQgT0OhOR2c5MpzA8m8Y1m9g+3Ki2dQUgBHBktLIAm8oVEM8U2EJMRA3JabfhTVevx8MnpvDMmRmzj0NEFqDrCFQp5f1Syq1Syk1Syo+X33a3lPLu8s/HpJS9UsqAlDJU/nnlZQmiGjN3/5UBtmE1ux1Y3+KtOMCe3QHLCcRE1Jjeck0f2ppd+Jv7j6xofgAR1Sfu8CAy0N7+CNr9bmxqbzb7KGSiHd0BHK40wJZX7rACS0SNqtntwPtv24onB2bw40PjZh+HiEzGAEtkECklHj0ZwdUbWyEE7782sp3dAZyOpJDIFpZ97Hg5wHKIExE1stfuWYstHc34ux8dQa5QMvs4RGQiBlgig/RPJjAZz/L+K2FHt3LV/2gFVdixaBYAK7BE1Ngcdhs+fMcODERS+Prjp80+DhGZiAGWyCDq/ddrNrWZfBIy2841SoCt5B7seCwDv9sBn9uh97GIiCztxm3tuHZzKz7x8+cRTefNPg4RmYQBlsgge/sj6Ak1YW1Lk9lHIZN1Bz0INjlxeDS+7GMPjUSxsYN3pomIhBD48B07EE3n8elfnjD7OERkEgZYIgOUSsr+16s38f4rKU/CdnT7lx3klMwW8MyZWbadExGVXbAmiFdf2osvPTKAg8NRs49DRCZggCUywNGxOGZSeVy9kUGEFDu7gzg2FkOxtPhKiCcHplEoSQZYIqJ5PvjS7Wj3u/HWLz2B05Gk2cchIoMxwBIZ4PvPDsMmgGs38/4rKXZ0+5HJl3BqavEnX3v7I3DZbdizvsXAkxERWVu7342v/PblKJQk3vLFJzCVyJp9JCIyEAMskc6mkzn816On8WsXr0FXkJNkSVHJIKe9/VPYvS6EJpfdqGMREdWEzR1+fOEtl2MslsHvfPlJJCtYS0ZE9YEBlkhnX3z4FNL5It5902azj0IWsrmjGQ6bWDTAzqZyODQS49RqIqJFXLY+jP94/aU4MBzFu77+NOIZTiYmagQMsEQ6iqbz+MreAdy+qwtbOv1mH4csxO2wY3NH86KDnB47GYGUwLWbef+ViGgxt+7sxN+86kI8eHwSL/qHX+LTD5xgNZaozjHAEunoy48MIJ4t4N03bTH7KGRBO7sDi1Zg9/ZH4HXZcVFvyNhDERHVmNddsQ73vftaXLI2hH/4v2O4/h9+if/81UlEeDeWqC4xwBLpJJ7J4wsPn8RtOzvn7jsSzbejO4DxWHbBJ1mPnJjCFRta4HLwr2kiouVc1BvCl992Be595zXY0R3Ax+8/gj0f/xle85m9+OyD/eifTJh9RCLSCJ8ZEenkq4+eRixTwHtvZvWVFrajWx3kFH/B28djGfRPJrk+h4hohS5bH8bX3n4lfvCe6/Dem7cgnS/ib390FLf884N425eewPHx+PKfhIgszWH2AYjqUTJbwBcePoWbtrXjwt6g2cchi9rRrdyLPjIaw3Vbzg5r2ts/BQAc4EREtEq7eoLY1RPE+2/biuHZNL6/fxifeaAfL/23X+G1l6/F+2/big4/NwMQ1SJWYIl0cPeD/ZhO5vCeW1h9pcW1NrvRGXDj8VMRSCnn3r73RAQhrxM7u9l6TkRUrZ5QE95142b86gM34S3X9OHbTw3hxn98AN97ZtjsoxHRKjDAEmnsiVPT+NQvT+DXd/fg0nVhs49DFvfqS3vxsyMT+LPvHUSxJCGlxN7+CK7e2AqbTZh9PCKiuhH2ufAXv3YBfvr+G5Tq7D378e2nhsw+FhGtEFuIiTQUTeXxvm88g96wF3/9yl1mH4dqwAdesg0SwGce6Ec0ncf7b92C4dk07rpho9lHIyKqS31tPnzlbVfgd7+6Dx/49rMoSYnf3LPW7GMRUYUYYIk0IqXEh797ABPxLL79zmvQ7OZ/XrQ8IQQ++NLtCHud+Jv7j+Lxk9MAgGs28/4rEZFemlx2fP4te/C7X92HD977HKSUeO3l68w+FhFVgC3ERBq5Z98gfnhgFH/44q24ZG3I7ONQjXnH9ZvwD6+5CNPJLDoDbmxs85l9JCKiuuZx2vGfb96DF21pxwfvPYB72U5MVBNYIiLSQP9kAn9532Fcs6kVd12/yezjUI36zT1r0dfqg5QSQvD+KxGR3jxOOz73psvw219+En/6nQPY2O7Dbs6vILI0VmCJqpQtFPHe/3kGHqcN//Kbl3DwDlXlig0tuHIj978SERnF47TjU791KToCbtz1tacwEc+YfSQiWgIDLFGV/unHx3BoJIa/f/VF6ApypxwREVGtCftc+Nyb9iCazuNdX3sauULJ7CMR0SIYYImq8ODxSfznQ6fwpqvW48UXdJl9HCIiIlqlnWsC+IfXXIx9p2fw1z84ZPZxiGgRvANLtEpTiSz+6J5nsbWzGX/2sh1mH4eIiIiq9IqL1+DgcBSf+9VJXNgT5GRiIgtiBZZoFaSU+ONvPYtYJo9Pvn43PE672UciIiIiDfzJS7bhRVva8OffO4Snz8yYfRwiOgcDLNEqfPGRATxwbBL/72U7sL0rYPZxiIiISCMOuw3//vrd6Ay68U4OdSKyHAZYohV6cmAaf3v/Edy2sxNvumq92cchIiIijYW8ylCnWLrAoU5EFsMAS7QCE7EM3vX1p7G2xYt//s2LuauTiIioTu3oDuAff+Mi7Ds9g7/6Xw51IrIKDnEiqlCuUMK7vv40EpkCvvY7VyLgcZp9JCIiItLRyy9agwPDUXz2wZPY0R3AG9l5RWQ6BliiCv3N/Uew7/QMPvn63djW5Tf7OERERGSAP3nJdhwbi+PPv38QbocNv7FnrdlHImpobCEmqsC39g3iy3sH8NvXbsArLl5j9nGIiIjIIHabwN1vvAzXbW7Dn9z7HO7ZN2j2kYgaGgMs0RKklLj7wX584NvP4eqNrfjTO7abfSQiIiIymMdpx3++eQ+u29yGD977HO55kiGWyCwMsESLyBdL+PB3D+DvfnQUL7+oG1962+Vw2vmfDBERUSNSQ+yLtrTjg995Dv/9+Bmzj0TUkPhsnGgBsUwev/3lJ/E/Twzi92/ahE++bjc8TrvZxyIiIiITeZx2fO5Nl+GGre348HcP4CPfP8gVO0QG4xAnorJMvoiHnp/C/x0cw8+OjCOZLeAfXnMRfpPDGoiIiKjM47Tj82/eg7//v6P4z4dO4fBIDJ9+w6XoCHjMPhpRQ2CApYYQTeVxYjKO05EUBiIpDE6nEM8UUCiVUChK5IolHByOIpUrIuBx4NYdnXjj1etx6bqw2UcnIiIii3HYbfizl+3Ehb0hfPDbz+Hl//4wPvPGS3HZ+hazj0ZU9xhgqa7FMnl8+pf9+OIjp+ZafIQA1gSbEGhywmkXcNgEHHYbXrm7B7fv6sJVG1t515WIiIiW9YqL12BLRzPu+tpTeN3nHsNHXr4Tb7xqPYQQZh+NqG4xwFJdyhdL+Ppjp/HJX5zAdDKHX9/dg5df3I31rT70hpvgdvA+KxEREVVvR3cA9/3+dXjfN5/Bn3//EJ4diuJjr9zF2RlEOmGApbpzJpLC2778BPonk7hmUys+fMcO7OoJmn0sIiIiqlNBrxNfeMvl+MTPn8cnfv48jo7FcPcbL0Nv2Gv20YjqDvskqa70TybwG5/di0gyhy+8ZQ++/vYrGV6JiIhIdzabwPtv24rPv3kPTk+lcOd/PIIDQ1Gzj0VUdxhgqW4cHYvhtZ99FMWSxDfecRVu2dHJOyhERERkqFt3duJ7774WHqcdr/vco9h7YsrsIxHVFQZYqgsHh6N43eceg90m8I13XI3tXQGzj0REREQNalN7M+595zXoCTfhrV96Ev93cNTsIxHVDQZYqnmHR2J4/X8+Bp/LgXt+72ps7mg2+0hERETU4LqCHtzze1djV08A7/r60/jGE2fMPhJRXWCApZo2PJvG2778BHwuB775e1dhfavP7CMRERERAQBCXhe+9vYrcf3WdnzoOwdwz75Bs49EVPMYYKlmRVN5vPWLTyCVLeLLv305J/0RERGR5XhdDnz2TZfhRVva8KF7n8OPD42ZfSSimsYASzUpky/id/9rH05HUvjsmy/jnVciIiKyLLfDjrvfeBkuXhvCe/77GTzCwU5Eq8YASzWnVJL4o289iydOTeMff+MiXLOpzewjERERES3J53bgS2+9HBvafPjdr+7D/sFZs49EVJMYYKmmlEoSH/7uAfzwuVH86e3bceclPWYfiYiIiKgiIa8LX/2dK9Da7MJbv/QETkwkzD4SUc1hgKWaIaXER+47iG88OYh337QZ77h+o9lHIiIiIlqRzoAHX/udK+GwCbzli09gIpYx+0hENYUBlmqClBJ/9b+H8bXHzuCuGzbhj168FUIIs49FREREtGLrW3340luvwEwqh7d+6UnEM3mzj0RUMxhgyfKklPjoD47gy3sH8PbrNuCDL93G8EpEREQ17cLeID79hktxfDyOd37taeQKJbOPRFQTGGDJ0pLZAv7gG/vxxUdO4a3X9OHPXraD4ZWIiIjqwo3bOvB3r74ID5+Ywp98+1mUStLsIxFZnsPsAxAt5vnxON759adxcjKBP37xVvz+TZsZXomIiKiuvOayXozHMvjHHx+D1+3Ax+7cBZuNz3eIFsMAS5b0vWeG8affOQCf246vvf1KrsohIiKiuvWuGzchkS3gMw/0wyaAj965iy/aEy2CAZYs5fh4HP/wf8fwsyPjuKKvBf/+W7vRGfCYfSwiIiIi3Qgh8Ccv2YaSlPjsgydhEwJ/9YoLGGKJFsAAS5YwMpvGv/70OO59egg+lwMfeMk2/N71G+Gw85o2ERER1T8hBD700u2QEvjcr05CAPhLhlii8zDAkqkODkfx9cdP496nhwEJ/M51G/CuGzcj7HOZfTQiIiIiQwkh8Ke3b4eUEv/50CmMxTL4x9+4GAGP0+yjEVkGAywZLp0r4gfPjeBrj5/Bs4Oz8DhtePWlPXj3zVvQE2oy+3hEREREphFC4MN37EBnwIO/+9FRvOLfH8Zn3ngZdnQHzD4akSUIKWtrXPeePXvkvn37zD4GrVCxJPFofwTfeWYIPz44hmSuiE3tPrzhyvV49aW9CHr5yiIRERHRfE8OTOP3v/40Ypk8PvbKC/Gay3rNPhKRIYQQT0kp9yz4PgZY0kupJPHUmRn86MAYfvDcCCbiWfjdDtxxYTdedWkPrtzQwnsdREREREuYjGfx3v95Bo+ejOCaTa14361bccWGFrOPRaQrBlgyTCZfxJMD0/jJoXH8+NAYJuJZuBw23LC1Ha/a3YObt3fA47SbfUwiIiKimlEolvCVR0/jMw/0YyqRxbWbW/EHtzDIUv1igCXdFEsSz0/EsfdEBA8en8TjpyLI5EvwOG24aVsHbr+wGzdv70Czm9etiYiIiKqRzhXx9cdP4+4HT2IqkcW6Fi+u29KG67e04epNbQg28UoW1QcGWNJEqSQxNJPGsfE4nhuaxTNnZrF/cBaJbAEAsLHNh+u3tuOGre24amMrmlystBIRERFpLZ0r4jvPDOGXRyfxaP8UkrkibALoDXvRG27C2rAXa1uaEPK64HXZ4XU5yj++8Ocuhw02m4BdCNhtAkJg3s8FpJQoliQKJYl8sYR8UaJQLCFfKv9YVN5eKErkS8qPhWIJufLbSlKi2eNAsMmJYJMTYa8LPhY1qAKmBVghxEsBfAKAHcDnpZR/d877Rfn9dwBIAXirlPLppT4nA6y+coUSRqNpDM2kMTSTwvBMGoMzaZyYSODERALpfBEAYLcJbO/yY/e6EHavDeOKDS1Y2+I1+fREREREjSVfLGH/4Cwefn4KJ6eSGJxOYWgmjalEturPLQSgdVRodjvQHfSgK+jBmmCT8mPIg65gE9aU3+7n2qCGt1SA1e0lECGEHcCnANwGYAjAk0KI+6SUh+c97HYAW8r/XAngM+UfSWPpXBFTiSwiyRwiiSwiiRymkuUfE1mMzCqhdSyWecFfVDYBdAU82NTRjNdfsQ5bO5uxpdOP7V1+voJGREREZDKn3YbL+1pwed8L78Nm8kXE0nkkc0WkcgWkc0Ukc0WkcwWkyj/PF0oolausJYl5P5colZQnhA67DQ67gMtug8Mm4LDb4LQLOGw2OB02OMtvW+gxNiEQy+QRS+cRTecxncxjPJbBaDSNsWgGR8fimEpkzwvJfrcDXUEPukNN6A540B3yoDvoQXewCS0+F/weB/weJ/weB5x2W9W/h0qVuVxBLsm5X5dKeOGPUvl9ctgEnHYbXA7bvB8FnDalok360jOBXAHghJTyJAAIIb4B4E4A8wPsnQC+KpUy8GNCiJAQoltKOarjuUwh5dm/GEpSQs79XPlRls6+r1CSyOSLyBZKL/wxX0KmcP6PmVwR8WwBiUwBiWwB8Uyh/Os8EtkCYunCXOX0XD6XHS3NLqwJNuGaTW3oDTeV/1FaULqCHk3+YiAiIiIi43ic9poYnJkrlDAey2AslsHIrBJsR6NKyB2NZnBkNIbJ+OLVZCUwK+HRrgZr+9m3QQCFohJK88XSXDt0sSTnWp+1rDI77QJNTjt8bqVVu9ntgNflgM/tgM+ttHA3z/3ogNdth099v8sOr9sBj9MGh82mvCBgV8Ky8u+k/GgXSou3gFIlb7StHnoG2B4Ag/N+PYTzq6sLPaYHQM0G2Ev++idI54rzAqoSUvXmdzvQ7FH+Q1DvGvSGmtDsdsDvcaC12Y3WZhfaml1o9Sk/b/W5eU+ViIiIiEzjctiwtsW75FU0NeSORjOYSeWUYk0mj3imgEy+OBdK1QpqYV5QlcDZKnE57Koh1247W01W3q7c/3XYBOzlx6t3gtV/hFAqtrmCctc3X1DuAueKpbm3pXNFJLMFJHMFJLNKBXx4No1U+dfJ7OLFpWoIgXKonRduobxR/TWgvO2+d1+LLZ1+zc9gBD0D7EIvBZwb5Sp5DIQQ7wDwjvIvE0KIY1WebTFtAKZ0+txkffz+Ny5+7xsbv/+Njd//xsXvfWNr6O//1o+ZfYJlrV/sHXoG2CEAa+f9uhfAyCoeAynl5wB8TusDnksIsW+xy8JU//j9b1z83jc2fv8bG7//jYvf+8bG73/t0vNy45MAtgghNgghXABeB+C+cx5zH4A3C8VVAKL1eP+ViIiIiIiIqqdbBVZKWRBCvBvAj6Gs0fmilPKQEOKu8vvvBnA/lBU6J6Cs0XmbXuchIiIiIiKi2qbrHhQp5f1QQur8t9097+cSwO/reYYV0r1NmSyN3//Gxe99Y+P3v7Hx+9+4+L1vbPz+1yghtd5OTERERERERKQDLvgkIiIiIiKimsAAew4hxEeFEM8JIfYLIX4ihFhj9pnIGEKIfxRCHC1//78rhAiZfSYyjhDiN4QQh4QQJSEEpxI2CCHES4UQx4QQJ4QQHzL7PGQcIcQXhRATQoiDZp+FjCWEWCuE+KUQ4kj57/0/MPtMZBwhhEcI8YQQ4tny9/+vzD4TrQxbiM8hhAhIKWPln78XwE4p5V0mH4sMIIR4MYBflAeQ/T0ASCk/aPKxyCBCiB0ASgA+C+CPpZT7TD4S6UwIYQdwHMBtUNa6PQng9VLKw6YejAwhhLgeQALAV6WUu8w+DxlHCNENoFtK+bQQwg/gKQCv5H/7jUEIIQD4pJQJIYQTwMMA/kBK+ZjJR6MKsQJ7DjW8lvkAMOE3CCnlT6SUhfIvH4Oyl5gahJTyiJTymNnnIENdAeCElPKklDIH4BsA7jT5TGQQKeWvAEybfQ4ynpRyVEr5dPnncQBHAPSYeyoyilQkyr90lv/h8/0awgC7ACHEx4UQgwDeAOAjZp+HTPHbAH5k9iGISFc9AAbn/XoIfBJL1FCEEH0AdgN43OSjkIGEEHYhxH4AEwB+KqXk97+GNGSAFUL8TAhxcIF/7gQAKeWfSSnXAvg6gHebe1rS0nLf+/Jj/gxAAcr3n+pIJd9/aihigbfxVXiiBiGEaAZwL4D3ndOBR3VOSlmUUl4CpdvuCiEErxHUEF33wFqVlPLWCh/63wB+COAvdDwOGWi5770Q4i0AXg7gFskL4nVnBf/tU2MYArB23q97AYyYdBYiMlD57uO9AL4upfyO2echc0gpZ4UQDwB4KQAOdKsRDVmBXYoQYsu8X74CwFGzzkLGEkK8FMAHAbxCSpky+zxEpLsnAWwRQmwQQrgAvA7AfSafiYh0Vh7i8wUAR6SU/2L2echYQoh2ddOEEKIJwK3g8/2awinE5xBC3AtgG5RppKcB3CWlHDb3VGQEIcQJAG4AkfKbHuME6sYhhHgVgH8H0A5gFsB+KeVLTD0U6U4IcQeAfwNgB/BFKeXHzT0RGUUI8T8AbgTQBmAcwF9IKb9g6qHIEEKI6wA8BOAAlOd7APBhKeX95p2KjCKEuAjAV6D8vW8DcI+U8q/NPRWtBAMsERERERER1QS2EBMREREREVFNYIAlIiIiIiKimsAAS0RERERERDWBAZaIiIiIiIhqAgMsERERERER1QQGWCIiIo0IIfqEEAfPedtfCiH+eImP2SOE+GT55zcKIa4552OHhRD7hRAHhRCvWORz3CWEeLNW/x5ERERW5TD7AERERI1MSrkPwL7yL28EkACwd95D/lVK+U9CiB0AHhJCdEgp1d2VEEI4pJR3G3ZgIiIiE7ECS0REZAAhxANCiL8XQjwhhDguhHhR+e03CiF+IIToA3AXgPeXK64vmv/xUsojAAoA2sqf62+EEA8C+IP5VV4hxGYhxM+EEM8KIZ4WQmwqv/0DQognhRDPCSH+ysh/dyIiIq0wwBIRERnHIaW8AsD7APzF/HdIKQcA3A2l4nqJlPKh+e8XQlwJoARgsvymkJTyBinlP5/zNb4O4FNSyosBXANgVAjxYgBbAFwB4BIAlwkhrtfyX4yIiMgIbCEmIiLSjlzm7d8p//gUgL4KP+f7hRBvBBAH8FoppRRCAMA3z32gEMIPoEdK+V0AkFJmym9/MYAXA3im/NBmKIH2VxWegYiIyBIYYImIiLQTARA+520tAE79/3buF6WCKIoD8O8kQbQYRHA5FqMuQIMLcAUWk0UwuwGTzaTBJFhFs80V+CcY5Bic+ISn74EOfF+Z4cDcEy8/7pk7vL8Pz49MvwefdPfxhPrbhFp9s0YlOeru0yl7AsC/ZIQYAOaku1/zNbK7kSRVtZJkM8nNlEu8JFmeof9zkqeq2hr6L1TVYpLLJHtVtTTU16tq9bd9AOCvCLAAMF+7SQ6q6i7JdZLD7n6c8tuLJNuTLnH6gZ0k+1V1n6/bjNe6+yrJWZLbqnpIcp4ZgjIA/JXq/u53HQAAAPg/nMACAAAwCgIsAAAAoyDAAgAAMAoCLAAAAKMgwAIAADAKAiwAAACjIMACAAAwCgIsAAAAo/AJZHpI3WCxdLgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualizing Normalized UnitPrice Data distribution\n",
    "plt.figure(figsize=(16,8))\n",
    "sns.kdeplot(y_pt[\"UnitPrice\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### As we can see from the above graph the UnitPrice data is now much better normalized than before"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb = XGBRFRegressor(max_depth=25,\n",
    "    learning_rate=1,\n",
    "    n_estimators=1000,\n",
    "    verbosity=1,\n",
    "    silent=None,\n",
    "    objective='reg:squarederror',\n",
    "    n_jobs=-1,\n",
    "    nthread=None,\n",
    "    gamma=0,\n",
    "    min_child_weight=1,\n",
    "    max_delta_step=0,\n",
    "    subsample=0.8,\n",
    "    colsample_bytree=1,\n",
    "    colsample_bylevel=1,\n",
    "    colsample_bynode=0.8,\n",
    "    reg_alpha=0,\n",
    "    reg_lambda=1,\n",
    "    scale_pos_weight=1,\n",
    "    base_score=0.5,\n",
    "    random_state=0,\n",
    "    seed=None,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBRFRegressor(max_depth=25, n_estimators=1000, n_jobs=-1,\n",
       "               objective='reg:squarederror')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb.fit(x_train_pt, y_train_pt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rmse: 0.23502033732469116, r2: 0.9377971565448132\n"
     ]
    }
   ],
   "source": [
    "y_pred_pt = xgb.predict(x_test_pt)\n",
    "rmse = np.sqrt(mean_squared_error(y_pred_pt, y_test_pt)) #oob True\n",
    "r2 = r2_score(y_pred_pt, y_test_pt)\n",
    "print(\"rmse: {}, r2: {}\".format(rmse, r2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Note: Here im not inverse transforming the output as i have transformed y_test as well for final submission must do inverse transform on output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   5 | elapsed: 26.3min remaining: 39.4min\n",
      "[Parallel(n_jobs=-1)]: Done   5 out of   5 | elapsed: 26.6min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([0.81634141, 0.83038938, 0.87689754, 0.87901574, 0.756182  ]),\n",
       " 0.8317652147826807)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cval_T = cross_val_score(xgb,x,y, cv=5, n_jobs=-1, verbose=1)\n",
    "cval_T, cval_T.sum()/5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   5 | elapsed:  2.2min remaining:  3.2min\n",
      "[Parallel(n_jobs=-1)]: Done   5 out of   5 | elapsed:  2.2min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([0.7650079 , 0.53672904, 0.68916919, 0.58734993, 0.80793603]),\n",
       " 0.6772384187735453)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cval = cross_val_score(xgb,x_test,y_test, cv=5, n_jobs=-1, verbose=1)\n",
    "cval, cval.sum()/5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.772509083890565"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(cval_T.sum()+cval.sum())/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test DF & Output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3139</td>\n",
       "      <td>1709</td>\n",
       "      <td>1079</td>\n",
       "      <td>3</td>\n",
       "      <td>2011-02-22 15:22:00</td>\n",
       "      <td>16161.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>617</td>\n",
       "      <td>510</td>\n",
       "      <td>3457</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-12-08 12:46:00</td>\n",
       "      <td>17341.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14653</td>\n",
       "      <td>604</td>\n",
       "      <td>694</td>\n",
       "      <td>36</td>\n",
       "      <td>2011-10-25 13:53:00</td>\n",
       "      <td>15158.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8634</td>\n",
       "      <td>1478</td>\n",
       "      <td>3473</td>\n",
       "      <td>2</td>\n",
       "      <td>2011-06-27 12:38:00</td>\n",
       "      <td>16033.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15546</td>\n",
       "      <td>3216</td>\n",
       "      <td>871</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-11-06 16:14:00</td>\n",
       "      <td>15351.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   InvoiceNo  StockCode  Description  Quantity          InvoiceDate  \\\n",
       "0       3139       1709         1079         3  2011-02-22 15:22:00   \n",
       "1        617        510         3457         1  2010-12-08 12:46:00   \n",
       "2      14653        604          694        36  2011-10-25 13:53:00   \n",
       "3       8634       1478         3473         2  2011-06-27 12:38:00   \n",
       "4      15546       3216          871         1  2011-11-06 16:14:00   \n",
       "\n",
       "   CustomerID  Country  \n",
       "0     16161.0       35  \n",
       "1     17341.0       35  \n",
       "2     15158.0       35  \n",
       "3     16033.0       35  \n",
       "4     15351.0       35  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df = pd.read_csv(r\"Test.csv\")\n",
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "      <th>InvoiceDate_year</th>\n",
       "      <th>InvoiceDate_month</th>\n",
       "      <th>InvoiceDate_Dayofweek</th>\n",
       "      <th>InvoiceDate_Day</th>\n",
       "      <th>InvoiceDate_Hour</th>\n",
       "      <th>diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3139</td>\n",
       "      <td>1709</td>\n",
       "      <td>1079</td>\n",
       "      <td>3</td>\n",
       "      <td>2011-02-22 15:22:00</td>\n",
       "      <td>16161.0</td>\n",
       "      <td>35</td>\n",
       "      <td>2011</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>15</td>\n",
       "      <td>290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>617</td>\n",
       "      <td>510</td>\n",
       "      <td>3457</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-12-08 12:46:00</td>\n",
       "      <td>17341.0</td>\n",
       "      <td>35</td>\n",
       "      <td>2010</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14653</td>\n",
       "      <td>604</td>\n",
       "      <td>694</td>\n",
       "      <td>36</td>\n",
       "      <td>2011-10-25 13:53:00</td>\n",
       "      <td>15158.0</td>\n",
       "      <td>35</td>\n",
       "      <td>2011</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>13</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8634</td>\n",
       "      <td>1478</td>\n",
       "      <td>3473</td>\n",
       "      <td>2</td>\n",
       "      <td>2011-06-27 12:38:00</td>\n",
       "      <td>16033.0</td>\n",
       "      <td>35</td>\n",
       "      <td>2011</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>12</td>\n",
       "      <td>166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15546</td>\n",
       "      <td>3216</td>\n",
       "      <td>871</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-11-06 16:14:00</td>\n",
       "      <td>15351.0</td>\n",
       "      <td>35</td>\n",
       "      <td>2011</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   InvoiceNo  StockCode  Description  Quantity         InvoiceDate  \\\n",
       "0       3139       1709         1079         3 2011-02-22 15:22:00   \n",
       "1        617        510         3457         1 2010-12-08 12:46:00   \n",
       "2      14653        604          694        36 2011-10-25 13:53:00   \n",
       "3       8634       1478         3473         2 2011-06-27 12:38:00   \n",
       "4      15546       3216          871         1 2011-11-06 16:14:00   \n",
       "\n",
       "   CustomerID  Country  InvoiceDate_year  InvoiceDate_month  \\\n",
       "0     16161.0       35              2011                  2   \n",
       "1     17341.0       35              2010                 12   \n",
       "2     15158.0       35              2011                 10   \n",
       "3     16033.0       35              2011                  6   \n",
       "4     15351.0       35              2011                 11   \n",
       "\n",
       "   InvoiceDate_Dayofweek  InvoiceDate_Day  InvoiceDate_Hour  diff  \n",
       "0                      1               22                15   290  \n",
       "1                      2                8                12   367  \n",
       "2                      1               25                13    45  \n",
       "3                      0               27                12   166  \n",
       "4                      6                6                16    33  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df[\"InvoiceDate\"] = pd.to_datetime(test_df[\"InvoiceDate\"])\n",
    "test_df[\"InvoiceDate_year\"] = pd.to_datetime(test_df[\"InvoiceDate\"]).dt.year\n",
    "test_df[\"InvoiceDate_month\"] = pd.to_datetime(test_df[\"InvoiceDate\"]).dt.month\n",
    "test_df[\"InvoiceDate_Dayofweek\"] = pd.to_datetime(test_df[\"InvoiceDate\"]).dt.dayofweek\n",
    "test_df[\"InvoiceDate_Day\"] = pd.to_datetime(test_df[\"InvoiceDate\"]).dt.day\n",
    "test_df[\"InvoiceDate_Hour\"] = pd.to_datetime(test_df[\"InvoiceDate\"]).dt.hour\n",
    "\n",
    "#Finding maximum date in the data\n",
    "maximum = max(test_df.InvoiceDate)\n",
    "\n",
    "#Adding one more day to the max date, so that the max date will have 1 as the difference and not zero.\n",
    "maximum = maximum + pd.DateOffset(days = 1)\n",
    "test_df['diff'] = maximum - test_df.InvoiceDate\n",
    "test_df[\"diff\"] = test_df[\"diff\"].dt.days\n",
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = test_df.loc[:, ['StockCode', 'Description', 'Quantity', 'CustomerID', 'Country']] #Random State: 57528 LB: 23.04894\n",
    "X_pt = pt.transform(X)\n",
    "Y_pred = xgb.predict(X_pt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UnitPrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.654675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.252705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.010334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.628124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12.672167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.617283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2.806538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2.434206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2.934873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.198978</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   UnitPrice\n",
       "0   1.654675\n",
       "1   1.252705\n",
       "2   3.010334\n",
       "3   1.628124\n",
       "4  12.672167\n",
       "5   1.617283\n",
       "6   2.806538\n",
       "7   2.434206\n",
       "8   2.934873\n",
       "9   4.198978"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output = pd.DataFrame(Y_pred, columns=[\"UnitPrice\"])\n",
    "output[\"UnitPrice\"] = pt_y.inverse_transform(pd.DataFrame(Y_pred))\n",
    "output.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Checking for any missing values before wiriting to submission files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    },
    {
     "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>UnitPrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [UnitPrice]\n",
       "Index: []"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = output[output[\"UnitPrice\"].isna()]\n",
    "print(len(temp))\n",
    "temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "#writing to csv file for submission\n",
    "output.to_csv(\"MachineHack_GIH_Submission_LB.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparing output data distribution to the original training dataset before commiting to submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "F:\\Anaconda\\envs\\opencv\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n",
      "F:\\Anaconda\\envs\\opencv\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='StockCode', ylabel='UnitPrice'>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8))\n",
    "sns.scatterplot(train_df[\"StockCode\"][train_df[\"UnitPrice\"]<=10000], train_df[\"UnitPrice\"][train_df[\"UnitPrice\"]<=10000], color=\"green\")\n",
    "sns.scatterplot(test_df[\"StockCode\"], output[\"UnitPrice\"],  color=\"orange\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Output distribution closly matches the train data sistribution hence commiting to submission."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}