JM2_Regression_Project_v0.7.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6ddb527b",
   "metadata": {},
   "source": [
    "# Phase 0: Imports"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b538289e",
   "metadata": {},
   "source": [
    "Loading required python packages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2e836e27",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from statsmodels.graphics.correlation import plot_corr\n",
    "%matplotlib inline\n",
    "\n",
    "#models\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.ensemble import AdaBoostRegressor\n",
    "from sklearn.ensemble import GradientBoostingRegressor \n",
    "\n",
    "#metrics, procssing and model selection\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f13b3c5",
   "metadata": {},
   "source": [
    "# Phase 1: Data Collection"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "981d68ca",
   "metadata": {},
   "source": [
    "As kaggle already provided us with data in the form of csv files, all we have to do is load them into dataframes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c6b9a5a5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#loading csv's\n",
    "train=pd.read_csv('df - train_set.csv')\n",
    "test=pd.read_csv('df - test_set.csv')\n",
    "test_id=test['Index']\n",
    "test=test.drop('Index',axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24e77211",
   "metadata": {},
   "source": [
    "# Phase 2: Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71e2d5ec",
   "metadata": {},
   "source": [
    "Kaggle tries to provide us with clean data but it's always a good idea to check for ourselves."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "71c3ee77",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 64376 entries, 0 to 64375\n",
      "Data columns (total 13 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   Province          64376 non-null  object \n",
      " 1   Container         64376 non-null  object \n",
      " 2   Size_Grade        64376 non-null  object \n",
      " 3   Weight_Kg         64376 non-null  float64\n",
      " 4   Commodities       64376 non-null  object \n",
      " 5   Date              64376 non-null  object \n",
      " 6   Low_Price         64376 non-null  float64\n",
      " 7   High_Price        64376 non-null  float64\n",
      " 8   Sales_Total       64376 non-null  float64\n",
      " 9   Total_Qty_Sold    64376 non-null  int64  \n",
      " 10  Total_Kg_Sold     64376 non-null  float64\n",
      " 11  Stock_On_Hand     64376 non-null  int64  \n",
      " 12  avg_price_per_kg  64376 non-null  float64\n",
      "dtypes: float64(6), int64(2), object(5)\n",
      "memory usage: 6.4+ MB\n"
     ]
    }
   ],
   "source": [
    "#Checking for null values\n",
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "eeb06d9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Province            0\n",
       "Container           0\n",
       "Size_Grade          0\n",
       "Weight_Kg           0\n",
       "Commodities         0\n",
       "Date                0\n",
       "Low_Price           0\n",
       "High_Price          0\n",
       "Sales_Total         0\n",
       "Total_Qty_Sold      0\n",
       "Total_Kg_Sold       0\n",
       "Stock_On_Hand       0\n",
       "avg_price_per_kg    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e105d644",
   "metadata": {},
   "source": [
    "None of the columns contain any null values.\n",
    "\n",
    "Moreover, we note that the DataFrame consists of 13 columns.(12 predictors and 1 response)\n",
    "\n",
    "The predictor variables are broken down into the following types:\n",
    "\n",
    "    -object: Province, Container, Size_Grade, Commodities and Date\n",
    "\n",
    "    -float: Weight_Kg, Low_Price, High_Price and Sales_Total\n",
    "\n",
    "    -integer: Total_Qty_Sold, Total_Kg_Sold and Stock_On_Hand\n",
    "    \n",
    "with the response variable(avg_price_per_kg) being a float.\n",
    "    \n",
    "As we can see in the next cell, the table contains information about 'Commodoties that we are not interested in, we filter only for 'APPLE GOLDEN DELICIOUS'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4abec184",
   "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>Province</th>\n",
       "      <th>Container</th>\n",
       "      <th>Size_Grade</th>\n",
       "      <th>Weight_Kg</th>\n",
       "      <th>Commodities</th>\n",
       "      <th>Date</th>\n",
       "      <th>Low_Price</th>\n",
       "      <th>High_Price</th>\n",
       "      <th>Sales_Total</th>\n",
       "      <th>Total_Qty_Sold</th>\n",
       "      <th>Total_Kg_Sold</th>\n",
       "      <th>Stock_On_Hand</th>\n",
       "      <th>avg_price_per_kg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CAPE</td>\n",
       "      <td>EC120</td>\n",
       "      <td>1L</td>\n",
       "      <td>12.0</td>\n",
       "      <td>APPLE GRANNY SMITH</td>\n",
       "      <td>2020-03-10</td>\n",
       "      <td>108.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>3236.0</td>\n",
       "      <td>29</td>\n",
       "      <td>348.0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CAPE</td>\n",
       "      <td>M4183</td>\n",
       "      <td>1L</td>\n",
       "      <td>18.3</td>\n",
       "      <td>APPLE GOLDEN DELICIOUS</td>\n",
       "      <td>2020-09-09</td>\n",
       "      <td>150.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>51710.0</td>\n",
       "      <td>332</td>\n",
       "      <td>6075.6</td>\n",
       "      <td>822</td>\n",
       "      <td>8.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GAUTENG</td>\n",
       "      <td>AT200</td>\n",
       "      <td>1L</td>\n",
       "      <td>20.0</td>\n",
       "      <td>AVOCADO PINKERTON</td>\n",
       "      <td>2020-05-05</td>\n",
       "      <td>70.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>4860.0</td>\n",
       "      <td>66</td>\n",
       "      <td>1320.0</td>\n",
       "      <td>50</td>\n",
       "      <td>3.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TRANSVAAL</td>\n",
       "      <td>BJ090</td>\n",
       "      <td>1L</td>\n",
       "      <td>9.0</td>\n",
       "      <td>TOMATOES-LONG LIFE</td>\n",
       "      <td>2020-01-20</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>10</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>WESTERN FREESTATE</td>\n",
       "      <td>PP100</td>\n",
       "      <td>1R</td>\n",
       "      <td>10.0</td>\n",
       "      <td>POTATO SIFRA (WASHED)</td>\n",
       "      <td>2020-07-14</td>\n",
       "      <td>40.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>41530.0</td>\n",
       "      <td>927</td>\n",
       "      <td>9270.0</td>\n",
       "      <td>393</td>\n",
       "      <td>4.48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Province Container Size_Grade  Weight_Kg             Commodities  \\\n",
       "0               CAPE     EC120         1L       12.0      APPLE GRANNY SMITH   \n",
       "1               CAPE     M4183         1L       18.3  APPLE GOLDEN DELICIOUS   \n",
       "2            GAUTENG     AT200         1L       20.0       AVOCADO PINKERTON   \n",
       "3          TRANSVAAL     BJ090         1L        9.0      TOMATOES-LONG LIFE   \n",
       "4  WESTERN FREESTATE     PP100         1R       10.0   POTATO SIFRA (WASHED)   \n",
       "\n",
       "         Date  Low_Price  High_Price  Sales_Total  Total_Qty_Sold  \\\n",
       "0  2020-03-10      108.0       112.0       3236.0              29   \n",
       "1  2020-09-09      150.0       170.0      51710.0             332   \n",
       "2  2020-05-05       70.0        80.0       4860.0              66   \n",
       "3  2020-01-20       60.0        60.0        600.0              10   \n",
       "4  2020-07-14       40.0        45.0      41530.0             927   \n",
       "\n",
       "   Total_Kg_Sold  Stock_On_Hand  avg_price_per_kg  \n",
       "0          348.0              0              9.30  \n",
       "1         6075.6            822              8.51  \n",
       "2         1320.0             50              3.68  \n",
       "3           90.0              0              6.67  \n",
       "4         9270.0            393              4.48  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3d556f31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Province</th>\n",
       "      <th>Container</th>\n",
       "      <th>Size_Grade</th>\n",
       "      <th>Weight_Kg</th>\n",
       "      <th>Commodities</th>\n",
       "      <th>Date</th>\n",
       "      <th>Low_Price</th>\n",
       "      <th>High_Price</th>\n",
       "      <th>Sales_Total</th>\n",
       "      <th>Total_Qty_Sold</th>\n",
       "      <th>Total_Kg_Sold</th>\n",
       "      <th>Stock_On_Hand</th>\n",
       "      <th>avg_price_per_kg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CAPE</td>\n",
       "      <td>M4183</td>\n",
       "      <td>1L</td>\n",
       "      <td>18.3</td>\n",
       "      <td>APPLE GOLDEN DELICIOUS</td>\n",
       "      <td>2020-09-09</td>\n",
       "      <td>150.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>51710.0</td>\n",
       "      <td>332</td>\n",
       "      <td>6075.6</td>\n",
       "      <td>822</td>\n",
       "      <td>8.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CAPE</td>\n",
       "      <td>JG110</td>\n",
       "      <td>2M</td>\n",
       "      <td>11.0</td>\n",
       "      <td>APPLE GOLDEN DELICIOUS</td>\n",
       "      <td>2020-04-14</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>16000.0</td>\n",
       "      <td>320</td>\n",
       "      <td>3520.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>W.CAPE-BERGRIVER ETC</td>\n",
       "      <td>JE090</td>\n",
       "      <td>2S</td>\n",
       "      <td>9.0</td>\n",
       "      <td>APPLE GOLDEN DELICIOUS</td>\n",
       "      <td>2020-04-16</td>\n",
       "      <td>55.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>990.0</td>\n",
       "      <td>18</td>\n",
       "      <td>162.0</td>\n",
       "      <td>1506</td>\n",
       "      <td>6.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>CAPE</td>\n",
       "      <td>M4183</td>\n",
       "      <td>1S</td>\n",
       "      <td>18.3</td>\n",
       "      <td>APPLE GOLDEN DELICIOUS</td>\n",
       "      <td>2020-05-04</td>\n",
       "      <td>80.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>32020.0</td>\n",
       "      <td>388</td>\n",
       "      <td>7100.4</td>\n",
       "      <td>443</td>\n",
       "      <td>4.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>EASTERN CAPE</td>\n",
       "      <td>IA400</td>\n",
       "      <td>1S</td>\n",
       "      <td>400.0</td>\n",
       "      <td>APPLE GOLDEN DELICIOUS</td>\n",
       "      <td>2020-09-28</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>1</td>\n",
       "      <td>400.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Province Container Size_Grade  Weight_Kg  \\\n",
       "1                   CAPE     M4183         1L       18.3   \n",
       "7                   CAPE     JG110         2M       11.0   \n",
       "24  W.CAPE-BERGRIVER ETC     JE090         2S        9.0   \n",
       "40                  CAPE     M4183         1S       18.3   \n",
       "69          EASTERN CAPE     IA400         1S      400.0   \n",
       "\n",
       "               Commodities        Date  Low_Price  High_Price  Sales_Total  \\\n",
       "1   APPLE GOLDEN DELICIOUS  2020-09-09      150.0       170.0      51710.0   \n",
       "7   APPLE GOLDEN DELICIOUS  2020-04-14       50.0        50.0      16000.0   \n",
       "24  APPLE GOLDEN DELICIOUS  2020-04-16       55.0        55.0        990.0   \n",
       "40  APPLE GOLDEN DELICIOUS  2020-05-04       80.0       120.0      32020.0   \n",
       "69  APPLE GOLDEN DELICIOUS  2020-09-28     1800.0      1800.0       1800.0   \n",
       "\n",
       "    Total_Qty_Sold  Total_Kg_Sold  Stock_On_Hand  avg_price_per_kg  \n",
       "1              332         6075.6            822              8.51  \n",
       "7              320         3520.0              0              4.55  \n",
       "24              18          162.0           1506              6.11  \n",
       "40             388         7100.4            443              4.51  \n",
       "69               1          400.0              2              4.50  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Filter for Golden Delicious Apples\n",
    "train=train[train['Commodities']=='APPLE GOLDEN DELICIOUS']\n",
    "test=test[test['Commodities']=='APPLE GOLDEN DELICIOUS']\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e01ef080",
   "metadata": {},
   "source": [
    "We may as well remove the \"Commodities\" column as it has no more useful information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9f39e242",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1952 entries, 1 to 64310\n",
      "Data columns (total 12 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   Province          1952 non-null   object \n",
      " 1   Container         1952 non-null   object \n",
      " 2   Size_Grade        1952 non-null   object \n",
      " 3   Weight_Kg         1952 non-null   float64\n",
      " 4   Date              1952 non-null   object \n",
      " 5   Low_Price         1952 non-null   float64\n",
      " 6   High_Price        1952 non-null   float64\n",
      " 7   Sales_Total       1952 non-null   float64\n",
      " 8   Total_Qty_Sold    1952 non-null   int64  \n",
      " 9   Total_Kg_Sold     1952 non-null   float64\n",
      " 10  Stock_On_Hand     1952 non-null   int64  \n",
      " 11  avg_price_per_kg  1952 non-null   float64\n",
      "dtypes: float64(6), int64(2), object(4)\n",
      "memory usage: 198.2+ KB\n"
     ]
    }
   ],
   "source": [
    "#Remove Commodities\n",
    "train=train.drop([\"Commodities\"],axis=1)\n",
    "test=test.drop([\"Commodities\"],axis=1)\n",
    "train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c29162a0",
   "metadata": {},
   "source": [
    "# Phase 3: Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce5d3386",
   "metadata": {},
   "source": [
    "## 3.1: Univariate Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7afcf1b",
   "metadata": {},
   "source": [
    "#### 3.1.1: Numeric data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c28bc653",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Weight_Kg</th>\n",
       "      <th>Low_Price</th>\n",
       "      <th>High_Price</th>\n",
       "      <th>Sales_Total</th>\n",
       "      <th>Total_Qty_Sold</th>\n",
       "      <th>Total_Kg_Sold</th>\n",
       "      <th>Stock_On_Hand</th>\n",
       "      <th>avg_price_per_kg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1952.000000</td>\n",
       "      <td>1952.000000</td>\n",
       "      <td>1952.000000</td>\n",
       "      <td>1952.000000</td>\n",
       "      <td>1952.000000</td>\n",
       "      <td>1952.000000</td>\n",
       "      <td>1952.000000</td>\n",
       "      <td>1952.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>40.460912</td>\n",
       "      <td>174.307377</td>\n",
       "      <td>215.648053</td>\n",
       "      <td>20053.533811</td>\n",
       "      <td>174.510758</td>\n",
       "      <td>2960.176332</td>\n",
       "      <td>408.393955</td>\n",
       "      <td>6.778893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>99.655169</td>\n",
       "      <td>373.553578</td>\n",
       "      <td>433.546159</td>\n",
       "      <td>39005.069445</td>\n",
       "      <td>308.810797</td>\n",
       "      <td>6097.416527</td>\n",
       "      <td>724.450582</td>\n",
       "      <td>2.248744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>1325.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>219.600000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>5.460000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>108.000000</td>\n",
       "      <td>5495.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>853.500000</td>\n",
       "      <td>126.500000</td>\n",
       "      <td>6.670000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>18.300000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>21082.500000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>3093.525000</td>\n",
       "      <td>468.000000</td>\n",
       "      <td>8.280000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>400.000000</td>\n",
       "      <td>2300.000000</td>\n",
       "      <td>3300.000000</td>\n",
       "      <td>369464.000000</td>\n",
       "      <td>4237.000000</td>\n",
       "      <td>74000.000000</td>\n",
       "      <td>6400.000000</td>\n",
       "      <td>21.240000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Weight_Kg    Low_Price   High_Price    Sales_Total  Total_Qty_Sold  \\\n",
       "count  1952.000000  1952.000000  1952.000000    1952.000000     1952.000000   \n",
       "mean     40.460912   174.307377   215.648053   20053.533811      174.510758   \n",
       "std      99.655169   373.553578   433.546159   39005.069445      308.810797   \n",
       "min       3.000000     2.000000     5.000000       5.000000        1.000000   \n",
       "25%       9.000000    50.000000    60.000000    1325.000000       12.000000   \n",
       "50%      12.000000    80.000000   108.000000    5495.000000       64.000000   \n",
       "75%      18.300000   127.250000   160.000000   21082.500000      200.000000   \n",
       "max     400.000000  2300.000000  3300.000000  369464.000000     4237.000000   \n",
       "\n",
       "       Total_Kg_Sold  Stock_On_Hand  avg_price_per_kg  \n",
       "count    1952.000000    1952.000000       1952.000000  \n",
       "mean     2960.176332     408.393955          6.778893  \n",
       "std      6097.416527     724.450582          2.248744  \n",
       "min         3.000000       0.000000          0.250000  \n",
       "25%       219.600000       9.000000          5.460000  \n",
       "50%       853.500000     126.500000          6.670000  \n",
       "75%      3093.525000     468.000000          8.280000  \n",
       "max     74000.000000    6400.000000         21.240000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b604f6b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Weight_Kg           3.328254\n",
       "Low_Price           4.222672\n",
       "High_Price          3.828819\n",
       "Sales_Total         4.250317\n",
       "Total_Qty_Sold      4.445169\n",
       "Total_Kg_Sold       5.529586\n",
       "Stock_On_Hand       3.391815\n",
       "avg_price_per_kg    0.082452\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.skew()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c881019",
   "metadata": {},
   "source": [
    "We can see that all of our numeric predictors are highly skewed. While our response variable is fairly symmetrical."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d4502fc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Weight_Kg            9.109698\n",
       "Low_Price           17.073369\n",
       "High_Price          13.630768\n",
       "Sales_Total         24.200524\n",
       "Total_Qty_Sold      32.338123\n",
       "Total_Kg_Sold       45.153801\n",
       "Stock_On_Hand       14.671224\n",
       "avg_price_per_kg     1.042921\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.kurtosis()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae5ec20e",
   "metadata": {},
   "source": [
    "As expected the kurtosis indicates strong outliers in the predictor variables. This is highlighted by the fact that the mean of our predictor values lean much closer toward their minimum values while the maximum values are many standard deviations away."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f443b588",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "features=[]\n",
    "for x in train.columns:\n",
    "    if np.issubdtype(train[x].dtype, np.number)==True:\n",
    "        features.append(x)\n",
    "train[features].plot(kind='density', subplots=True, sharex=False, figsize=(10, 10));"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3961f4a1",
   "metadata": {},
   "source": [
    "These variables appear to be normally distributed with either some skewness or some completely seperate bumps. This is caused by the outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8bc8dca9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Weight_Kg           3.328254\n",
       "Low_Price           4.222672\n",
       "High_Price          3.828819\n",
       "Sales_Total         4.250317\n",
       "Total_Qty_Sold      4.445169\n",
       "Total_Kg_Sold       5.529586\n",
       "Stock_On_Hand       3.391815\n",
       "avg_price_per_kg    0.082452\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "92f9a696",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Weight_Kg            9.109698\n",
       "Low_Price           17.073369\n",
       "High_Price          13.630768\n",
       "Sales_Total         24.200524\n",
       "Total_Qty_Sold      32.338123\n",
       "Total_Kg_Sold       45.153801\n",
       "Stock_On_Hand       14.671224\n",
       "avg_price_per_kg     1.042921\n",
       "dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.kurtosis()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93b36d05",
   "metadata": {},
   "source": [
    "Our predictor variables are highly skewed with strong outliers, but this is to be expected.\n",
    "\n",
    "For example, when looking at a column like Sales_Total, many places can have sales up to a certain point but after a certain amount only the biggest place can make that much in sales. This would skew our data toward the more 'acheivable' Sales_total values."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "105631b6",
   "metadata": {},
   "source": [
    "Convert date to datetime to avoid mismanagement when handling categorical data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1fb3a468",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Date (object) --> Date (datetime)\n",
    "train['Date'] = pd.to_datetime(train['Date'])\n",
    "test['Date'] = pd.to_datetime(test['Date'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "657619f1",
   "metadata": {},
   "source": [
    "We also require our numeric data to be standardized (for lasso and ridge regression) before the addition of dummy variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f675a785",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Weight_Kg',\n",
       " 'Low_Price',\n",
       " 'High_Price',\n",
       " 'Sales_Total',\n",
       " 'Total_Qty_Sold',\n",
       " 'Total_Kg_Sold',\n",
       " 'Stock_On_Hand']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#we dont want to scale the response variable\n",
    "features.remove('avg_price_per_kg')\n",
    "features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3018f84b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1952 entries, 1 to 64310\n",
      "Data columns (total 12 columns):\n",
      " #   Column            Non-Null Count  Dtype         \n",
      "---  ------            --------------  -----         \n",
      " 0   Province          1952 non-null   object        \n",
      " 1   Container         1952 non-null   object        \n",
      " 2   Size_Grade        1952 non-null   object        \n",
      " 3   Weight_Kg         1952 non-null   float64       \n",
      " 4   Date              1952 non-null   datetime64[ns]\n",
      " 5   Low_Price         1952 non-null   float64       \n",
      " 6   High_Price        1952 non-null   float64       \n",
      " 7   Sales_Total       1952 non-null   float64       \n",
      " 8   Total_Qty_Sold    1952 non-null   float64       \n",
      " 9   Total_Kg_Sold     1952 non-null   float64       \n",
      " 10  Stock_On_Hand     1952 non-null   float64       \n",
      " 11  avg_price_per_kg  1952 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(8), object(3)\n",
      "memory usage: 198.2+ KB\n"
     ]
    }
   ],
   "source": [
    "#scaling and adding scaled data back to train dataframe\n",
    "scalar=StandardScaler()\n",
    "train[features] = scalar.fit_transform(train[features])\n",
    "train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67b493bb",
   "metadata": {},
   "source": [
    "#### 3.1.3: Categorical Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "edd14326",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x648 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot countplots of categorical data\n",
    "fig, ax =plt.subplots(1,3, figsize=(20, 9))\n",
    "sns.countplot(x=train['Size_Grade'], ax=ax[0])\n",
    "sns.countplot(x=train['Container'], ax=ax[1])\n",
    "sns.countplot(x=\"Province\", data=train, ax=ax[2])\n",
    "ax[2].tick_params(labelrotation=45)\n",
    "fig.tight_layout(pad=4.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f7c0f80",
   "metadata": {},
   "source": [
    "Most of the Golden Delicious Apples are found in the cape, have a size grade of 1S and are in containers 'M4183', 'JE090 and 'EC120'."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd1e1f77",
   "metadata": {},
   "source": [
    "Before continuing the analysis we should note that numerical data is required for regression analysis. As such we will now transform our categorical data into numerical values using dummy variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "313d70ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dummies = pd.get_dummies(train,drop_first=True)\n",
    "test_dummies = pd.get_dummies(test,drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6182ac16",
   "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>Weight_Kg</th>\n",
       "      <th>Date</th>\n",
       "      <th>Low_Price</th>\n",
       "      <th>High_Price</th>\n",
       "      <th>Sales_Total</th>\n",
       "      <th>Total_Qty_Sold</th>\n",
       "      <th>Total_Kg_Sold</th>\n",
       "      <th>Stock_On_Hand</th>\n",
       "      <th>avg_price_per_kg</th>\n",
       "      <th>Province_EASTERN CAPE</th>\n",
       "      <th>...</th>\n",
       "      <th>Container_M9125</th>\n",
       "      <th>Size_Grade_1M</th>\n",
       "      <th>Size_Grade_1S</th>\n",
       "      <th>Size_Grade_1U</th>\n",
       "      <th>Size_Grade_1X</th>\n",
       "      <th>Size_Grade_2L</th>\n",
       "      <th>Size_Grade_2M</th>\n",
       "      <th>Size_Grade_2S</th>\n",
       "      <th>Size_Grade_2U</th>\n",
       "      <th>Size_Grade_2X</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.222433</td>\n",
       "      <td>2020-09-09</td>\n",
       "      <td>-0.065087</td>\n",
       "      <td>-0.105317</td>\n",
       "      <td>0.811807</td>\n",
       "      <td>0.510117</td>\n",
       "      <td>0.511073</td>\n",
       "      <td>0.571070</td>\n",
       "      <td>8.51</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.295704</td>\n",
       "      <td>2020-04-14</td>\n",
       "      <td>-0.332855</td>\n",
       "      <td>-0.382175</td>\n",
       "      <td>-0.103950</td>\n",
       "      <td>0.471248</td>\n",
       "      <td>0.091837</td>\n",
       "      <td>-0.563874</td>\n",
       "      <td>4.55</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>-0.315779</td>\n",
       "      <td>2020-04-16</td>\n",
       "      <td>-0.319467</td>\n",
       "      <td>-0.370639</td>\n",
       "      <td>-0.488870</td>\n",
       "      <td>-0.506948</td>\n",
       "      <td>-0.459029</td>\n",
       "      <td>1.515476</td>\n",
       "      <td>6.11</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>-0.222433</td>\n",
       "      <td>2020-05-04</td>\n",
       "      <td>-0.252525</td>\n",
       "      <td>-0.220674</td>\n",
       "      <td>0.306871</td>\n",
       "      <td>0.691504</td>\n",
       "      <td>0.679187</td>\n",
       "      <td>0.047781</td>\n",
       "      <td>4.51</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>3.608756</td>\n",
       "      <td>2020-09-28</td>\n",
       "      <td>4.353082</td>\n",
       "      <td>3.655338</td>\n",
       "      <td>-0.468098</td>\n",
       "      <td>-0.562012</td>\n",
       "      <td>-0.419986</td>\n",
       "      <td>-0.561112</td>\n",
       "      <td>4.50</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Weight_Kg       Date  Low_Price  High_Price  Sales_Total  Total_Qty_Sold  \\\n",
       "1   -0.222433 2020-09-09  -0.065087   -0.105317     0.811807        0.510117   \n",
       "7   -0.295704 2020-04-14  -0.332855   -0.382175    -0.103950        0.471248   \n",
       "24  -0.315779 2020-04-16  -0.319467   -0.370639    -0.488870       -0.506948   \n",
       "40  -0.222433 2020-05-04  -0.252525   -0.220674     0.306871        0.691504   \n",
       "69   3.608756 2020-09-28   4.353082    3.655338    -0.468098       -0.562012   \n",
       "\n",
       "    Total_Kg_Sold  Stock_On_Hand  avg_price_per_kg  Province_EASTERN CAPE  \\\n",
       "1        0.511073       0.571070              8.51                      0   \n",
       "7        0.091837      -0.563874              4.55                      0   \n",
       "24      -0.459029       1.515476              6.11                      0   \n",
       "40       0.679187       0.047781              4.51                      0   \n",
       "69      -0.419986      -0.561112              4.50                      1   \n",
       "\n",
       "    ...  Container_M9125  Size_Grade_1M  Size_Grade_1S  Size_Grade_1U  \\\n",
       "1   ...                0              0              0              0   \n",
       "7   ...                0              0              0              0   \n",
       "24  ...                0              0              0              0   \n",
       "40  ...                0              0              1              0   \n",
       "69  ...                0              0              1              0   \n",
       "\n",
       "    Size_Grade_1X  Size_Grade_2L  Size_Grade_2M  Size_Grade_2S  Size_Grade_2U  \\\n",
       "1               0              0              0              0              0   \n",
       "7               0              0              1              0              0   \n",
       "24              0              0              0              1              0   \n",
       "40              0              0              0              0              0   \n",
       "69              0              0              0              0              0   \n",
       "\n",
       "    Size_Grade_2X  \n",
       "1               0  \n",
       "7               0  \n",
       "24              0  \n",
       "40              0  \n",
       "69              0  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dummies.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74713669",
   "metadata": {},
   "source": [
    "## 3.2: Multivariate Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45265130",
   "metadata": {},
   "source": [
    "We start by evaluating the relationship between the date and our response variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8451c5d8",
   "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>avg_price_per_kg</th>\n",
       "      <th>Date</th>\n",
       "      <th>Day</th>\n",
       "      <th>Month</th>\n",
       "      <th>Month_year</th>\n",
       "      <th>Quarter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.51</td>\n",
       "      <td>2020-09-09</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>2020-09-09</td>\n",
       "      <td>2020Q3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4.55</td>\n",
       "      <td>2020-04-14</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>2020-04-14</td>\n",
       "      <td>2020Q2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>6.11</td>\n",
       "      <td>2020-04-16</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>2020-04-16</td>\n",
       "      <td>2020Q2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>4.51</td>\n",
       "      <td>2020-05-04</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2020-05-04</td>\n",
       "      <td>2020Q2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>4.50</td>\n",
       "      <td>2020-09-28</td>\n",
       "      <td>28</td>\n",
       "      <td>9</td>\n",
       "      <td>2020-09-28</td>\n",
       "      <td>2020Q3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    avg_price_per_kg       Date  Day  Month Month_year Quarter\n",
       "1               8.51 2020-09-09    9      9 2020-09-09  2020Q3\n",
       "7               4.55 2020-04-14   14      4 2020-04-14  2020Q2\n",
       "24              6.11 2020-04-16   16      4 2020-04-16  2020Q2\n",
       "40              4.51 2020-05-04    4      5 2020-05-04  2020Q2\n",
       "69              4.50 2020-09-28   28      9 2020-09-28  2020Q3"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#seperating date,month and year\n",
    "training_day=pd.DataFrame([])\n",
    "training_day['avg_price_per_kg']=train['avg_price_per_kg']\n",
    "training_day['Date']=train['Date']\n",
    "training_day['Day']=[x.day for x in train['Date']]\n",
    "training_day['Month']=[x.month for x in train['Date']]\n",
    "training_day['Month_year']=[pd.to_datetime(x, format='%m%Y') for x in train['Date']]\n",
    "training_day['Quarter'] = pd.PeriodIndex(train.Date, freq='Q')\n",
    "training_day.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4161e4be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date', ylabel='avg_price_per_kg'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting the mean avg_price_per_kg against the Date\n",
    "avg_date=training_day.groupby('Date')['avg_price_per_kg'].mean()\n",
    "sns.scatterplot(x=avg_date.index,y=avg_date)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d9a8da4",
   "metadata": {},
   "source": [
    "As we can see from this plot, there isn't much of a relatioship between these two variables, in the following cells we attempt to see if there is a relationship between the components of the date (Day,Month,Year) and our response variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f07fe36e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Day', ylabel='avg_price_per_kg'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "avg_day=training_day.groupby('Day')['avg_price_per_kg'].mean()\n",
    "sns.scatterplot(x=avg_day.index,y=avg_day)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9d02952",
   "metadata": {},
   "source": [
    "-There is no clear trend amongst this data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "54b9eaca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Month', ylabel='avg_price_per_kg'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "avg_month=training_day.groupby('Month')['avg_price_per_kg'].mean()\n",
    "sns.scatterplot(x=avg_month.index,y=avg_month)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e412781",
   "metadata": {},
   "source": [
    "The middle months seem to have lower means while the end and start months have higher means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4d037099",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Quarter', ylabel='avg_price_per_kg'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "avg_quarter=training_day.groupby('Quarter')['avg_price_per_kg'].mean()\n",
    "sns.scatterplot(x=avg_quarter.index.to_timestamp(),y=avg_quarter)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fd1bd59",
   "metadata": {},
   "source": [
    "As we saw by the trend in the months plot, the response variable appears to fall as we go toward the middle of the year and then rise as the year ends"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "380fb6ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dropping the non-numeric 'Date' column and adding a Month column\n",
    "train_dummies['Month']=training_day['Month']\n",
    "train_dummies=train_dummies.drop('Date',axis=1)\n",
    "testing_day=pd.DataFrame([])\n",
    "testing_day['Month']=[x.month for x in test['Date']]\n",
    "test_dummies['Month']=testing_day['Month']\n",
    "test_dummies=test_dummies.drop('Date',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f7eeef80",
   "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>Weight_Kg</th>\n",
       "      <th>Low_Price</th>\n",
       "      <th>High_Price</th>\n",
       "      <th>Sales_Total</th>\n",
       "      <th>Total_Qty_Sold</th>\n",
       "      <th>Total_Kg_Sold</th>\n",
       "      <th>Stock_On_Hand</th>\n",
       "      <th>avg_price_per_kg</th>\n",
       "      <th>Province_EASTERN CAPE</th>\n",
       "      <th>Province_NATAL</th>\n",
       "      <th>...</th>\n",
       "      <th>Size_Grade_1M</th>\n",
       "      <th>Size_Grade_1S</th>\n",
       "      <th>Size_Grade_1U</th>\n",
       "      <th>Size_Grade_1X</th>\n",
       "      <th>Size_Grade_2L</th>\n",
       "      <th>Size_Grade_2M</th>\n",
       "      <th>Size_Grade_2S</th>\n",
       "      <th>Size_Grade_2U</th>\n",
       "      <th>Size_Grade_2X</th>\n",
       "      <th>Month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.222433</td>\n",
       "      <td>-0.065087</td>\n",
       "      <td>-0.105317</td>\n",
       "      <td>0.811807</td>\n",
       "      <td>0.510117</td>\n",
       "      <td>0.511073</td>\n",
       "      <td>0.571070</td>\n",
       "      <td>8.51</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.295704</td>\n",
       "      <td>-0.332855</td>\n",
       "      <td>-0.382175</td>\n",
       "      <td>-0.103950</td>\n",
       "      <td>0.471248</td>\n",
       "      <td>0.091837</td>\n",
       "      <td>-0.563874</td>\n",
       "      <td>4.55</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>-0.315779</td>\n",
       "      <td>-0.319467</td>\n",
       "      <td>-0.370639</td>\n",
       "      <td>-0.488870</td>\n",
       "      <td>-0.506948</td>\n",
       "      <td>-0.459029</td>\n",
       "      <td>1.515476</td>\n",
       "      <td>6.11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>-0.222433</td>\n",
       "      <td>-0.252525</td>\n",
       "      <td>-0.220674</td>\n",
       "      <td>0.306871</td>\n",
       "      <td>0.691504</td>\n",
       "      <td>0.679187</td>\n",
       "      <td>0.047781</td>\n",
       "      <td>4.51</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>3.608756</td>\n",
       "      <td>4.353082</td>\n",
       "      <td>3.655338</td>\n",
       "      <td>-0.468098</td>\n",
       "      <td>-0.562012</td>\n",
       "      <td>-0.419986</td>\n",
       "      <td>-0.561112</td>\n",
       "      <td>4.50</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Weight_Kg  Low_Price  High_Price  Sales_Total  Total_Qty_Sold  \\\n",
       "1   -0.222433  -0.065087   -0.105317     0.811807        0.510117   \n",
       "7   -0.295704  -0.332855   -0.382175    -0.103950        0.471248   \n",
       "24  -0.315779  -0.319467   -0.370639    -0.488870       -0.506948   \n",
       "40  -0.222433  -0.252525   -0.220674     0.306871        0.691504   \n",
       "69   3.608756   4.353082    3.655338    -0.468098       -0.562012   \n",
       "\n",
       "    Total_Kg_Sold  Stock_On_Hand  avg_price_per_kg  Province_EASTERN CAPE  \\\n",
       "1        0.511073       0.571070              8.51                      0   \n",
       "7        0.091837      -0.563874              4.55                      0   \n",
       "24      -0.459029       1.515476              6.11                      0   \n",
       "40       0.679187       0.047781              4.51                      0   \n",
       "69      -0.419986      -0.561112              4.50                      1   \n",
       "\n",
       "    Province_NATAL  ...  Size_Grade_1M  Size_Grade_1S  Size_Grade_1U  \\\n",
       "1                0  ...              0              0              0   \n",
       "7                0  ...              0              0              0   \n",
       "24               0  ...              0              0              0   \n",
       "40               0  ...              0              1              0   \n",
       "69               0  ...              0              1              0   \n",
       "\n",
       "    Size_Grade_1X  Size_Grade_2L  Size_Grade_2M  Size_Grade_2S  Size_Grade_2U  \\\n",
       "1               0              0              0              0              0   \n",
       "7               0              0              1              0              0   \n",
       "24              0              0              0              1              0   \n",
       "40              0              0              0              0              0   \n",
       "69              0              0              0              0              0   \n",
       "\n",
       "    Size_Grade_2X  Month  \n",
       "1               0      9  \n",
       "7               0      4  \n",
       "24              0      4  \n",
       "40              0      5  \n",
       "69              0      9  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dummies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c3530159",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1952 entries, 1 to 64310\n",
      "Data columns (total 34 columns):\n",
      " #   Column                         Non-Null Count  Dtype  \n",
      "---  ------                         --------------  -----  \n",
      " 0   Weight_Kg                      1952 non-null   float64\n",
      " 1   Low_Price                      1952 non-null   float64\n",
      " 2   High_Price                     1952 non-null   float64\n",
      " 3   Sales_Total                    1952 non-null   float64\n",
      " 4   Total_Qty_Sold                 1952 non-null   float64\n",
      " 5   Total_Kg_Sold                  1952 non-null   float64\n",
      " 6   Stock_On_Hand                  1952 non-null   float64\n",
      " 7   avg_price_per_kg               1952 non-null   float64\n",
      " 8   Province_EASTERN CAPE          1952 non-null   uint8  \n",
      " 9   Province_NATAL                 1952 non-null   uint8  \n",
      " 10  Province_ORANGE FREE STATE     1952 non-null   uint8  \n",
      " 11  Province_TRANSVAAL             1952 non-null   uint8  \n",
      " 12  Province_W.CAPE-BERGRIVER ETC  1952 non-null   uint8  \n",
      " 13  Province_WEST COAST            1952 non-null   uint8  \n",
      " 14  Container_DT063                1952 non-null   uint8  \n",
      " 15  Container_EC120                1952 non-null   uint8  \n",
      " 16  Container_EF120                1952 non-null   uint8  \n",
      " 17  Container_EG140                1952 non-null   uint8  \n",
      " 18  Container_IA400                1952 non-null   uint8  \n",
      " 19  Container_JE090                1952 non-null   uint8  \n",
      " 20  Container_JG110                1952 non-null   uint8  \n",
      " 21  Container_M4183                1952 non-null   uint8  \n",
      " 22  Container_M6125                1952 non-null   uint8  \n",
      " 23  Container_M9125                1952 non-null   uint8  \n",
      " 24  Size_Grade_1M                  1952 non-null   uint8  \n",
      " 25  Size_Grade_1S                  1952 non-null   uint8  \n",
      " 26  Size_Grade_1U                  1952 non-null   uint8  \n",
      " 27  Size_Grade_1X                  1952 non-null   uint8  \n",
      " 28  Size_Grade_2L                  1952 non-null   uint8  \n",
      " 29  Size_Grade_2M                  1952 non-null   uint8  \n",
      " 30  Size_Grade_2S                  1952 non-null   uint8  \n",
      " 31  Size_Grade_2U                  1952 non-null   uint8  \n",
      " 32  Size_Grade_2X                  1952 non-null   uint8  \n",
      " 33  Month                          1952 non-null   int64  \n",
      "dtypes: float64(8), int64(1), uint8(25)\n",
      "memory usage: 264.7 KB\n"
     ]
    }
   ],
   "source": [
    "train_dummies.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21dc084b",
   "metadata": {},
   "source": [
    "All of our variables are now numeric and we can begin building regression models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4f5a4e9",
   "metadata": {},
   "source": [
    "# Phase 4: Model Building"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf2ef0bb",
   "metadata": {},
   "source": [
    "## 4.1: Feature Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1108701d",
   "metadata": {},
   "source": [
    "## 4.2: Modelling Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8da917b2",
   "metadata": {},
   "source": [
    "## 4.2.1: Random Forest Model"
   ]
  },
  {
   "attachments": {
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H0CwS8BOBefPmybPPPquNGcbQhPfee08OHjzoJwQR+8qkPHVUGOr00UcfSfPmzbVfWB5++GGZNm2aHD16NPWKPEoCJEACDhNgUu5wB1A9CfiVwMKFC6VTp05SsGBBueeee2TMmDHy119/+RVHxH4zKY8YlZw+fVo++eQTefTRRyVTpkzaOwmTJ0/mF77IEbIkCZCAjQSYlNsIm6pIwO8Eli5dKj169NBWcsSCMW+//bbs2rXL71ii8p9JeVS4kgufP39evvjiC2nVqpVky5ZNGjRooP0is3///uQy/IMESIAEnCTApNxJ+tRNAj4g8Ntvv0m/fv3kX//6l1SuXFleffVV2bx5sw88t8ZFJuXGuV66dEnw7kLbtm0lR44ccv/998v48ePlzz//NN44WyABEiCBGAkwKY8RHKuRAAmEJ7BhwwYZPHiwVKpUScqXLy8DBgyQdevWha/AIxETYFIeMaqIC86ePVueeeYZyZMnj9x7773aglT/+c9/Iq7PgiRAAiRgBgEm5WZQZBskQAKybds2ef3116Vq1apSunRp6d27t8THx5OMyQSYlJsMNKg5vHTcvn17KVCggLZS7IgRI2Tnzp1BpbhJAiRAAuYTYFJuPlO2SAK+IYCniVgOvVatWlK0aFHp0qWL/Prrr77x3wlHmZTbR/3777+X559/XooUKSI1atSQN998U7Zu3WqfAdREAiTgKwJMyn3V3XSWBIwT+Pvvv+Xdd9/Vlj3Pmzev9lQRyQvFHgJMyu3hHKwFswXhS2eJEiXkjjvukGHDhsmmTZuCi3GbBEiABGImwKQ8ZnSsSAL+IYA5nqdMmaKtnojlzZ988kn59ttv/QNAIU+ZlDvfGYsWLdJmESpVqpRUqVJFhgwZIomJic4bRgtIgARcTYBJuau7j8aTgHUEsFz5jBkzpFmzZtoqiS1bttSmlMPMFRTnCDApd459KM2Y5hPvT5QtW1ZuvvlmGTRokKxduzZUUe4jARIggVQJMClPFQ8PkoC/CFy5ckW+/PJLefzxx+W6666Thx56SD7++GNtERZ/kVDXWybl6vYNXmzG9J+Yceimm26S/v37y6pVq9Q1mJaRAAkoRYBJuVLdQWNIwBkCc+bMkaefflqbsxmLqmDVw8OHDztjDLWmSoBJeap4lDm4evVqbSpQTAtarlw56dOnjyxfvlwZ+2gICZCAegSYlKvXJ7SIBGwh8OOPP0rHjh0lf/78UqdOHRk3bpzs27fPFt1UEjsBJuWxs3OqZkJCgjZvPxbPKlOmjPTs2VMWL17slDnUSwIkoCgBJuWKdgzNIgErCCAR6NatmxQrVkyb4g1zMCclJVmhim1aRIBJuUVgbWp248aN8tprr8ntt98uJUuW1K7Hn3/+2SbtVEMCJKAyASblKvcObSMBEwisXLlS+vbtKzfccIPcdttt2lRuW7ZsMaFlNuEEASblTlC3Rufvv/8ub7zxhlSvXl2b579z586CX7AoJEAC/iTApNyf/U6vPU4A07NhFgi8bFaxYkXtb07Z5o1OZ1LujX4M9mL79u0yfPhwbSGuQoUKSYcOHWT+/PnBxbhNAiTgYQJMyj3cuXTNXwTw9Hvo0KHavMl4Ko6n45z5wXvnAJNy7/VpsEe7d+/WVsqtXbu25MuXT5599lnBy9gUEiABbxNgUu7t/qV3HieA4I1x4VgCvHjx4tK9e3e+QObxPmdS7vEODnLvjz/+kLFjx0rdunUlV65c8tRTT8nXX38tmL6UQgIk4C0CTMq91Z/0xgcE9u7dq82Ucu+990qBAgW0GVQ4DtUHHf8/F5mU+6evgz3F7EgTJkyQevXqCVbWfeKJJ2TmzJly4cKF4KLcJgEScCEBJuUu7DSa7D8CmDN80qRJUr9+fW0uccwpPnfuXP+BoMfCpJwnAQj8888/2j2hYcOGkjlzZnnsscfks88+E6zESyEBEnAnASbl7uw3Wu0DAqdOndJW08SqmpkyZZJWrVppq23yZ2sfdH4qLjIpTwWOTw/hS/vUqVOladOmkjFjRnnkkUdk+vTpcuLECZ8Sodsk4E4CTMrd2W+02qMELl68KF988YW0bNlSMmTIIM2aNZMZM2bI2bNnPeox3YqWAJPyaIn5q/zx48flo48+kubNm0v69OkFX+qnTZsmR48e9RcIeksCLiTApNyFnUaTvUdg9uzZ0rZtW22cKH6O/uCDD+TYsWPec5QeGSbApNwwQt80cPr0afnkk0+0L/n4ta1x48YyefJkOXjwoG8Y0FEScBMBJuVu6i3a6ikCCxYskPbt20vevHm1mRXeffdd+fvvvz3lI50xnwCTcvOZ+qHF8+fPa7/CYRhctmzZpEGDBjJx4kTZv3+/H9ynjyTgCgJMyl3RTTTSKwR+/fVX6dKlixQpUkRbJGTkyJGyZ88er7hHP2wgwKTcBsgeV3Hp0iWZNWuW9utczpw55f7775fx48fLn3/+6XHP6R4JqE2ASbna/UPrPEBgxYoV0qtXLyldurRUrVpVW1Ybq/dRSCAWAkzKY6HGOqkR+Pbbb+WZZ56RPHnyCKZaHT16tCQlJaVWhcdIgAQsIMCk3AKobJIE1q5dKwMGDJDy5cvLzTffLIMHD5YNGzYQDAkYJsCk3DBCNpAKgXnz5mnD6rAGwl133aUtTrZz585UavAQCZCAWQSYlJtFku34nsDmzZvl1VdflVtvvVXKlSsn/fr1k9WrV/ueCwGYS4BJubk82Vp4At9//7106tRJG26HVYPffPNN2bp1a/gKPEICJGCIAJNyQ/hY2e8E8ATprbfekurVq0vJkiXlhRdekGXLlvkdC/23kACTcgvhsumwBBYuXKi9D1OiRAm54447ZOjQobJp06aw5XmABEggegJMyqNnxho+J4CXocaMGSP33HOPFCpUSDp37iwIWBQSsIMAk3I7KFNHagQWLVokPXr00N6TqVKligwZMkQSExNTq8JjJEACERBgUh4BJBYhAczr+95778kDDzwguXPnlmeffVa+++47giEB2wkwKbcdORWmQgC/DPbu3VvKli2rvT8zaNAgWbNmTSo1eIgESCAcASbl4chwv+8JYIlqrISHBTeyZMkiTzzxhHz99de+50IAzhJgUu4sf2oPTyA+Pl57lwYvuFeoUEH69+8vq1atCl+BR0iABK4hwKT8Ghzc8DsBLLDx2WefSYsWLQTJDz4//fRTwX4KCahAgEm5Cr1AG9IigJfcBw4cKJUqVdJefO/Tp48sX748rWo8TgK+JsCk3NfdT+d1AngCjifheCKOJ+N4Qo4n5RQSUI0Ak3LVeoT2pEUgISFBmxa2cuXKUqZMGe2F+MWLF6dVjcdJwHcEmJT7rsvpsE4AY8IxNhxjxDFWHGPGDxw4oB/mJwkoSYBJuZLdQqMiJLBx40Z57bXXtIXUMGNVt27d5Oeff46wNouRgLcJMCn3dv/SuyACuPljthTMmoLZU7ByHZeWDoLETaUJMClXuntoXBQEtmzZoq1wjCllixYtqt2bf/zxxyhaYFES8BYBJuXe6k96E4IAZgfA/OF4KlOtWjVtXnGuUBcCFHe5ggCTcld0E42MksD27dtl+PDhUqtWLSlYsKB06NBB5s+fH2UrLE4C7ibApNzd/UfrwxDAS0ZYURMra2KFTay0yYUuwsDiblcRYFLuqu6isTEQ2L17t4waNUpq164t+fLl04YZzpkzJ4aWWIUE3EWASbm7+ovWpkIAYxVfeeUVba5cTMn10ksvydq1a1OpwUMk4D4CTMrd12e0OHYCf/zxh4wdO1bq1q0ruXLlkieffFKbmvbKlSuxN8qaJKAoASblinYMzYqMAH7yfOONN7SXhkqXLi29evWSFStWRFaZpUjAhQSYlLuw02iyKQT27dsnEyZMkPr160tcXJw2Y9bMmTPlwoULprTPRkjAaQJMyp3uAeqPmsCePXtk5MiRctddd0mRIkWkS5cu8ssvv0TdDiuQgBsJMCl3Y6/RZrMJ/PPPPzJp0iRp2LChZM6cWR577DFtjYkzZ86YrYrtkYBtBJiU24aaiowQwA0YT0jwE2bevHmlffv2smDBAiNNsi4JuJIAk3JXdhuNtpDA4cOHZerUqdK0aVPJkCGDPPLIIzJ9+nSuNWEhczZtDQEm5dZwZasmEDh27Jh88MEH2pMQ/FTZtm1bmT17tgktswkScC8BJuXu7Ttabj2B48ePy0cffSTNmzeX9OnTy0MPPaQtBnf06FHrlVMDCRgkwKTcIEBWN5fA2bNnZcaMGdoNFU88WrZsKV988YVcvHjRXEVsjQRcRACLregSmJQPHTpU381PEiCBIAKnT5+WTz75RIsjmTJlkkaNGsnkyZPl4MGDQSW5SQJqEGBSrkY/+NoKvEX/1VdfSatWrQQ3TjzZwJOOU6dO+ZoLnScBEBg0aJA2ZhZTe+KXIiTl+MQ2xtLiOIUESCB1AufPn9ce8LRu3VqyZcsmDRo0kIkTJ8r+/ftTr8ijJGAjASblNsKmqmsJzJ07V55++mnJkSOH9jY9XtrB2EAKCZDAtQRuuukmLRnPmTOn9sUVn0jOsZ9CAiQQHYFLly7JrFmztCGRuJbuv/9+GT9+PFd3jg4jS1tAgEm5BVDZZHgCWEL5+eeflwIFCsi9994r48aNk71794avwCMkQAKCad9y586tJeJIxvEP29hPIQESMEbg22+/lWeeeUabRABxafTo0ZKUlGSsUdYmgRgIMCmPARqrREdgyZIl0r17dylevLjUqFFDW0oZK7ZRSIAEIiegPy3Xk3I+JY+cHUuSQKQE5s2bp83uhQdHmHZ3xIgRsnPnzkirsxwJGCLApNwQPlYOR2DVqlXSt29fueGGG6RKlSqCF9K2bNkSrjj3kwAJpEEg8Gk5n5KnAYuHScAEAt9//7106tRJWw/jzjvvlDfffFO2bt1qQstsggRCE2BSHpoL98ZAIDExUV5++WWpWLGiNtYVL6AlJCTE0BKrkAAJhCKgPy3nU/JQdLiPBKwjsHDhQm2huhIlSsgdd9yhPWjatGmTdQrZsi8JMCn3Zbeb5zSeGgwbNkxuu+027al4nz59ZOXKleYpYEskQALJBPC0PEuWLBxLnkyEf5CA/QQWLVokPXr0kNKlS2u/BA8ZMkTwUIpCAkYJMCk3StBj9Tt37ixYxj41wQswGGeH8eHFihWTbt26yeLFi1OrwmMkQAIGCBy+dFiWn1ous47OkpHrRmqf2MZ+CgmQgHMEli1bJr1795Z//etfcvPNN2tTlK5ZsyZNg86dO8fpTNOk5L8CTMo90OcnLp+Q7ee2y4azG7RPbMciderUkYwZMwpWRAuWffv2aTOloEz+/PmlY8eO8sMPPwQX4zYJkIBJBDae3Sh9/uojpTaWkszrMkuexDySOzG35EzMqX1iG/txHOVQnkICJOAcgfj4eOnXr5+UL19eKlSoIP379xe8XxVKtm/frq0z0K5du1CH09xnVtxPUxEL2EqASbmtuM1RdvnqZZlxZIY02dlE8q/PL5nWZUoO1AjY2MZ+HEc5lE9L6tatq81/jDnDdTly5Ii2+hkWWcD+p556SubMmaMf5icJkIAFBBLPJEr9HfUlLiFOMq7NKOnWpkvzH8qhPOqhPoUESMBZAqtXr5aBAwdKpUqVtKfoGNq5fPnya4xKnz695MqVS5uO8ZoDITasiPsh1HCXwwSYlDvcAdGoP3flnAzaN0gLvnhiFkmwRjkEa9RD/VDywAMPaN/YMdUavuF//PHH2qqaWF3z8ccfly+//FIuX047sQ/VNveRAAlETmDY/mHal+pIru1wZfClHO1QSIAE1CCACQ8GDx4slStXljJlysgLL7ygDfnENMH6mgN46BVKrIr7oXRxn/MEmJQ73wcRWTDz6EzJm5hXsidkjygZDw7YqIf6aCdQ8BQcS3Xrcx9j+EqzZs1k+vTpcubMmcCi/JsESMBCAo12NtKGpQRfu7Fs48s42qOQAAmoRU8CY2UAACAASURBVGDjxo3y2muvSdWqVbXhonrsxTSnbdu2vcZYq+L+NUq4oRQBJuVKdUdoY/rv7a8NT4klOAfXwfAWtAe57777rknIcXPIkyePNGzYMLQh3EsCJGAJgdrbamu/aAVfr0a28QsZ2qWQAAmoRwBjyTEsVE/K9fj7yCOPaMZaFffVI0GLAgkwKQ+koeDfHfd01F7wMhKcg+viBTG0mzVrVrnuuuskZ86c2sub+s0B+2rVqsUn5QqeDzTJewRaJ7WWHAk5YvoFLPjaDt5Gu2ifQgIkoA4BDFXJnj17ckKOh2GIw5juNC4uTovPiNPB17ORbT3uq0OBloQiwKQ8FBVF9r26/1XTE3L9osYFivYxRAULIMydO1fGjBkj3bt3l5o1a2rf4K9cuaIICZpBAt4kMOXQFMuu8cBrHXooJEACzhP4+++/tUkVypUrJxg+igX3MFwUs7RgcgU74r7zFGhBOAJMysORcXj//OPzYx4/rgfjtD4xzhx6KCRAAvYTOHn5pGVPyIOvfTwxhz4KCZCA8wTCTZzAuO983zhtAZNyp3sgjP6SG0ua+tNVcJDWt6GHQgIkYD+BwfsGmz6OXL+ugz8xvhz6KCRAAuoSYNxXt2/ssoxJuV2ko9Az7sA402ZhCA7OwduYpQH6KCRAAvYSyJWYy5Yv3vo1D30UEiABNQkw7qvZL3ZbxaTcbuIR6Cu+obitwRr6KCRAAvYRmHd8nuVjyfVkXP/EeyTQSyEBElCPAOO+en3ihEVMyp2gnorOJaeW2PaUXA/WeFoOvRQSIAF7CHT6o5OtX7z1ax16KSRAAmoRYNxXqz+ctIZJuZP0Q+jG3KTp16a3NWBDnz53eQiTuIsESMBkApV+r2TrNa4n5dBLIQESUIsA475a/eGkNUzKnaQfQnfVLVUdCdbQSyEBErCHABbx0hNlOz+hl0ICJKAWAcZ9tfrDSWuYlDtJP4TuvIl5HQnW0EshARKwh0CGtRkcuc6hl0ICJKAWAcZ9tfrDSWuYlDtJP4TujOsyOhKsoZdCAiRgDwG7h6jpT+Ohl0ICJKAWAcZ9tfrDSWuYlDtJP4RuPXg68RnCHO4iARKwgEC2hGyOfPmGXgoJkIBaBJyI97pOtUjQGiblip0DmddldiRYQy+FBEjAHgKlN5Z25DqHXgoJkIBaBBj31eoPJ61hUu4k/RC6i20o5kiw5lzlITqDu0jAIgItdrVw5DqHXgoJkIBaBBj31eoPJ61hUu4k/RC6G+xo4Eiwhl4KCZCAPQSmHZ4mTqzoCb0UEiABtQgw7qvVH05aw6TcSfohdGOp3ewJ2W1NzKEPeikkQAL2EDhy6Ygj6xFAL4UESEAtAoz7avWHk9YwKXeSfgjdSeeTJGtCVluTcuiDXgoJkIB9BFrubmnrdQ59FBIgAfUIMO6r1ydOWcSk3Cnyqeitva22rcEa+igkQAL2Elh/dr1kWZfFlmsdeqCPQgIkoCYBxn01+8Vuq5iU2008An0LTywUu1b8gx7oo5AACdhPoMefPSRHQg5LE3O0Dz0UEiABdQkw7qvbN3ZaxqTcTtpR6Hp418OSca21CwmhfeihkAAJOEegwuYKliblaJ9CAiSgPgHGffX7yGoLmZRbTTjG9g9cOiBWL72L9qGHQgIk4ByBHed3SNanrXmPJOuTWQXtU0iABNQnwLivfh9ZbSGTcqsJG2j/55M/S/oX01vyFC19v/QctmKgb1iVBMwiMHHiRLnx5hul4IqCpl7rBX8rKPc8eI88/fTTZpnKdkiABCwmwLhvMWDFm2dSrnAHzZs3T3LkySHpvkxnarBGeyXKl5BXX31VYe9pGgl4n8CCBQskV65csnHjRtl3cZ/U2lZL8iTmMXS9oz7aQXsQJOVNmjSRs2fPeh8oPSQBlxNg3Hd5Bxo0n0m5QYBWVd+0aZPkyZNHvvvuO1lyaokU2VDE8FSJmPqw8IbCWnt///233H333dK9e3erXGC7JEACqRDYsmWL5M+fX+bMmXNNKcxZjJczcybkjCo5R3nUC7XmQK9evaRmzZry119/XaOLGyRAAuoQYNxXpy+csoRJuVPkU9F7+vRpqVKlirz77rvJpc5cOSPt9rSLOTFHQo76p6+cTm7z0qVL0qxZM2nTpk3yPv5BAiRgPYFz585J1apVZezYsSGVnb96XkYfGC3lNpWTuIS4sKt/YlVQHEc5lEe9cPL666/LjTfeKOvXc2rEcIy4nwScIsC47xR5tfQyKVerPzRrkCj37t07pGWJZxKlTVIbybQuU5o/c+NnbJRDedQLJ88995w8+OCDcvLkyXBFuJ8ESMBEAi1atJAXXnghohaxsMhnRz6TgXsHSuuk1pK9VHbtE9vYH83CX++//77ky5dPfv7554h0sxAJkIA9BBj37eGsuhYm5Yr1UM+ePQUBOy25ePWizD0+V/ru7St1tteR8pvLS9z9cdontrEfx1EuEnnxxRfljjvukKQkruwZCS+WIYFYCfTp00f7hSrW+unSGbttz5o1S9KnTy8zZ86M1QTWIwESMJEA476JMF3elLG7u8udV818/JSNn7RjfSHLaLAePny4lClTRtasWaMaGtpDAp4ggCFpGJp26tSpmP0xep1D8aJFi6RAgQKCmV8oJEACzhFg3HeOvYqamZQr0itz587VflbGy1+xihnBeurUqdpsED/88EOsZrAeCZBACALz58+X3LlzC17mMiJmXOfQjxlfypcvL0OHDjViDuuSAAnESIBxP0ZwHq7GpFyBzkVwxLRomB7NiJgVrL/99lvJlCmTfPrpp0bMYV0SIIH/Efj9998lb968gunOjIpZ1zns2Ldvn9x1110Rj283ajvrkwAJ/JcA4z7PhFAEmJSHomLjPrxcWblyZZkwYYJhrWYG62XLlkmRIkVk3Lhxhu1iAyTgZwJnzpyR22+/3bRryczrHP1y4cIFeeihh+TJJ5/0czfRdxKwjQDjvm2oXaeISbnDXfbwww9L3759TbHC7GC9detWufnmm+WVV14xxT42QgJ+JPDII48I5gk3S8y+znW7nn32WWnYsKFgajYKCZCAdQQY961j6/aWmZQ72IOYEu3RRx81zQIrgvXBgwfl3nvvlS5duphmJxsiAb8QwNSmzZs3N9VdK65z3UDMDFO9enXZs2ePvoufJEACJhJg3DcRpgebYlLuUKeOGTNGm4Lw/Pnwi31Ea5pVwfrKlSuCp32tWrWK1iSWJwHfEhg/frzcdtttguErZopV17lu41tvvSVly5aVdevW6bv4SQIkYAIBxn0TIHq8CSblDnQwXqTE8toYHmKmWB2sO3bsKPXq1ZNjx46ZaTbbIgHPEcALnXny5JHNmzeb7pvV1zkMnjJlimb/Tz/9ZLr9bJAE/EiAcd+PvR69z0zKo2dmqMaGDRskZ86c8v333xtqJ1RlO4L1Sy+9pL20tmvXrlAmcB8J+J4AEnEk5N99950lLOy4zmH4N998IxkzZpTPP//cEj/YKAn4hQDjvl962rifTMqNM4y4hRMnTsgtt9xi2YIddgXrd955R0qVKiW//fZbxL6zIAn4gQBeksTiQBi6YpXYdZ3D/iVLlkjhwoUFix5RSIAEoifAuB89Mz/XYFJuY+9j2jEsZ2+V2BmsP/zwQ8mePbvhudWtYsF2ScAJAnipEy93Wil2XufwA3OsV6xYUYYMGWKlW2ybBDxJgHHfk91qmVNMyi1De23D3bt3l8cee+zanSZv2R2sMW42a9asMn36dJM9YXMk4D4CPXv2lBYtWlhuuN3XORz6559/5J577pFu3bpZ7h8VkIBXCDDue6Un7fODSbkNrEePHi3VqlXTFumwUp0TwTo+Pl6KFy8u8JFCAn4lgEW2sEDQ2bNnLUfgxHUOpy5duiTNmjWTJ554wnIfqYAE3E6Acd/tPeiM/UzKLeY+e/ZsKViwoGzfvt1iTSJOBWv4duutt8qgQYMs95EKSEA1AnPnzpV8+fLJli1bbDHNqetcd659+/bSoEEDwVhZCgmQQEoCjPspmXBPZASYlEfGKaZSiYmJkiNHDvnhhx9iqh9tJSeD9eHDh6Vu3brSuXPnaM1meRJwLYGNGzdK7ty5Zf78+bb54OR1rjvZr18/bZ2FpKQkfRc/SYAERIRxn6eBEQJMyo3QS6Xu8ePHtSXq33///VRKmXtIhWDdsmVLy8fOm0uNrZFAbAROnTollStXlgkTJsTWQIy1VLjOYfrw4cOldOnSsmbNmhg9YTUS8BYBxn1v9acT3jApt4h6kyZNBE+T7BRVgnWnTp20p+ZHjhyx033qIgFbCWB8NZalt1tUuc7h99SpU7V1F+z6NdBu1tRHAtEQYNyvK4z70ZwxKcsyKU/JxPAezFDw+OOPG24n2gZUCtYDBw7UniLu2LEjWjdYngSUJ/DCCy/Io48+6oidKl3nADBnzhzJnDmzfPLJJ47woFISUIEA474I477xM5FJuXGG17QwcuRIufPOO+XixYvX7LdjQ7VgPWrUKClRooSsXLnSDvepgwRsITBmzBipWrWqnDt3zhZ9wUpUu85h3/Lly6Vo0aIyduzYYHO5TQKeJ8C4//9dzLj//yxi+YtJeSzUwtTBstSFChUSp54OqxisMYd5tmzZBHOaU0jA7QTwVDh//vyydetWx1xR8ToHDDC5+eabZfDgwY6xoWISsJsA435K4oz7KZlEuodJeaSk0iiXkJAgcXFx8tNPP6VR0rrDqgZrzEwBNh999JF1zrNlErCYwIYNG7Tx0wsWLLBYU+rNq3qdw+qDBw9KnTp1pEuXLqk7waMk4AECjPvhO5FxPzyb1I4wKU+NToTHjh07JpUqVZJJkyZFWMOaYioH61WrVsn1118v+JmPQgJuI4A5uTEX/8SJEx03XeXrHHCuXLmirWzqxHs1jncODfANAcb9tLuacT9tRsElmJQHE4lhu3HjxvLSSy/FUNPcKqoH6507d8ptt90mAwYMMNdxtkYCFhN46KGH5MUXX7RYS2TNq36d61507NhRHnjgAUHyQiEBrxFg3I+sRxn3I+Okl2JSrpOI8bNr167SqlWrGGubW80Nwfro0aNaoEbAppCAGwj06NFDMP++KuKG61xnhYcV+CK+a9cufRc/ScD1BBj3o+tCxv3IeTEpj5xVipLvvPOO1KhRQy5fvpzimBM73BSs8dN2ixYt5OrVq06gok4SiIjA6NGjpVq1anLhwoWIyttRyE3XOXjgPomha7/99psdeKiDBCwlwLgfO17G/bTZMSlPm1HIErNmzZLChQsLfppRRdwWrP/9739rL4UdOnRIFYS0gwSSCcyePVsKFCgg27ZtS96nwh9uu87BDC95Z8+eXfDyF4UE3EqAcd94zzHup86QSXnqfEIeXbdunTbN38KFC0Med2qnG4M1pk+75ZZbHJ1izqn+ol51Caxfv15y5MghKq5U6cbrHD2NaVGzZs0qmC6NQgJuI8C4b16PMe6HZ8mkPDybkEewhOxNN90kkydPDnncyZ1uDdZYcKRYsWLaAiRO8qNuEgCB48ePa18U33vvPSWBuPU6B8z4+HhtQTEMC6KQgFsIMO6b31OM+6GZMikPzSXs3kaNGik7e4ibgzWW6MZS3VichUICThJo2rSp9OvXz0kTUtXt5uscjm3fvl0qV64sgwYNStVPHiQBVQgw7lvTE4z7KbkyKU/JJOwejIV64oknwh53+oDbg/X333+vLc4ybdo0p1FSv08JdOvWTR577DGlvXf7dQ64hw8flrp160qnTp2UZk3jSIBx39pzgHH/Wr5Myq/lEXZrxIgRUrNmTW1hjLCFHD7ghWC9evVqKV26tIA3hQTsJDBq1CipXr26XLx40U61UevywnWuO42pJlWablK3i58kAAKM+/acB4z7/8+ZSfn/swj711dffSVFixaV3bt3hy2jwgGvBGtwrlq1qtJDCFTob9pgHoFvvvlGChUqJDt27DCvUYta8sp1ruPB03I8Nce4XQoJqEKAcd/enmDc/y9vJuVpnHdr167VZgz4+eef0yjp/GEvBWssa16/fn1p376982BpgacJJCQkaNP1/fjjj67w00vXuQ4c48sxzhzjzSkk4DQBxn1neoBxX4RJeSrnHsY9VqhQQT744INUSqlzyIvBGmP4mzdvrswCTer0Ni0xgwBWmqtUqZJMmjTJjOZsacOL1znAYUaW4sWLazO02AKSSkggBAHG/RBQbN7l57jPpDyVk61hw4YycODAVEqodcirwRov39WuXVv++ecftYDTGtcTaNKkifTv399Vfnj1OkcnYA5zzGWOOc0pJOAEAcZ9J6in1OnXuM+kPOW5oO3p3LmztGnTJsxRNXd7OVgPGTJEKlasKL///rua8GmV6wh07dpVWrVq5Tq7vXydozOw6idW/8QqoBQSsJMA476dtNPW5ce4z6Q8xHnx9ttvS61ateTq1ashjqq7y+vBevz48VK4cGFZunSpup1Ay1xB4J133pE777xTLl265Ap7A430+nUOX1etWiXXX3+9jBw5MtB1/k0ClhFg3LcMraGG/Rb3mZQHnS5ffvmltrpkUlJS0BH1N/0QrD/77DO57rrrZPbs2ep3CC20jMDhS4dl+anlMuvoLJlxZIb2iW3sT0tmzZqlfbnbuXNnWkWVOX763EXZve+4JO44KLjO8Ylt7PeiwK9fVyRKhYq3yDMdu3veXy/2oZt8YtxXu7f8FPc9mZSfu3BZDhw9K/sOndI+sR2JYK5MrCr5yy+/RFJcmTK6vwjW8DtSf5VxIEpDZn8/W3LmzimvTHxFtp/bLicun4iyBRZ3I4GNZzdKn7/6SKmNpSTzusySJzGP5E7MLTkTc2qf2MZ+HEc5lA+WdevWSVxcnPz000/Bh5Tb3nfotHy9aJf0H79Sur6zRNoPXiGtusdL/dZfap/Yxn4cRzmUd7OE8veR53+Ue5qO96S/bu4rFW3X46Bf4j7iHuIf4r4f4uB387+XXLlyyzuj3/V0nuOJpPzK1auyess/Mu6LTdJrzHLpOnKJdH5juTw/dLn2iW3sx3GUQ/lgOXTokJQvX16mTp0afEi57XD+1m/9esT+KudUKgZdvnpZexraZGcTyb8+v2Ral0niPouTbEOyaQkZtrEfx/HUFOUp3iGQeCZR6u+oL3EJcZJxbUZJtzZdmv9QDuVRD/UhmAcb7yVMnjxZaTh/HTwloz7dIF2GL5W7H4qXwqV+k3Tp1ob9h+Moh/Koh/puEr/566a+UdnWcHHQq3E/VBzEA4m4kXGejIOh+vfRLlOlTvMBnsxz9GvN1Un5xUtXZN6KJOk+aqk80SteKt65UnIXWB0yeGE/jqMcyqMe6uvSoEEDefnll/VNJT/N9FdJB4OMOnflnAzaN0hLrvBENJJkDOWQjKEe6lPcTWDY/mHal7BI+j5cGXxpQzuNGjWSAQMGKA3kh1V7pOvIxVKlTnzI+1hqyTmOoR7qox03iN/8dUOfqG6jmXHQDXHfb3HQzP5V/VwOZZ9rk/J12w9IzzHLpcmz8ZK3UOhEPFwAQ3nUQ320gxXl2rZtG4qPMvvM9FcZp1IxZObRmZI3Ma9kT8geUTIenJChHuqjHYo7CTTa2UgblhLct7Fs5/g0hxRrWkxpEGM/3yhP9o2XnPmiu58F3+dQH+2gPZXFb/6q3Bdusc3MOOiGuO+3OGhm/7rlnA6205VJ+bdLd8u/31wmJcuviulpkh7EUL9+6yFy0y13BHNRattMf8EN7aks/ff2136OiyX5Cq6Dn/fQHsVdBGpvq6394hHcn0a249bGCdpVUd7+OFEaPxPb03H9fhb8ifbQroriN39V7AO32WRmHHRD3PdbHDSzf92Q54S7/lyXlM/4frs83T9essatMZSQ6wEM7bTo/LWgXRXFCn/BT1V/O+7pqL3AZyT5Cq6LFwDRLsUdBFontZYcCTli+oUkuO+Dt9Eu2ldJpszeIs06mJuQ6/c3tIv2VRK/+asSe7faYkUcVDnu+y0OWtG/Kuc5qV2HrkrKF6z8jzwzYIVkyBD+pSc9GEXzifbQLtpXSfzm76v7XzU9IdeTMiTmaJ+iNoEph6ZYdg4EngvQo4Ks2LRfOryywpQHDOHueWgfelQQv/mrAnO328A4mPaL7fq9La1PFeOg3/o3revRNUn55qTD0uXtZZI9lzlPyIMDGNpF+9CjgvjN3/nH58c8fjytG5F+HOPMoYeiJoGTl09a9oRcPwf0Tzwxhz4nBVO49Ri1VIqUTn12leB7VbTbaB96nJ4q1W/+OnlueUU346B5Cbl+71MpDvqtfyO5Ll2TlPcbv1LK3mpsDHlawQztQ48K4jd/S24saclwBf1GpH9CD0VNAoP3DTZ9HLne78GfmKEH+pyU71b8Rxo/bc2wleB7HfRAn5PiN3+dZO0V3YyD5ifluBeqEgf91r+RXJeuSMoXJ+6VVj3sCV7QA31Oit/8HXdgnGmzbAQnX8HbmDIR+ijqEciVmMuWL2b6OQF9TsoLo5dJviLGZloJTr7DbUMP9DkpfvI3af8J+WXtn2n+Q7lIRPX2IvEh2jKMg9Yk5Lj/qRAH/da/kZ7/rkjKXxy3UoqVtfYnXj2YQQ/0OSl+87f4huK2JmPQR1GLwLzj8ywfS64n4/onxldCrxOyafdheXaAPQ8a9Hsb9EGvE+I3f+ev/I90HbUozX8oF4mo3l4kPkRbhnHQuqQc90Cn46Df+jfS81/5pHznX8ek42vWvgilBy39E/qg1wnxm79LTi2x7Sm5nozhKQH0UtQh0OmPTrZ+MdPPBeh1Qj79YYfcXtfepBz6oNcJ8Zu/qifRZttn9jnFOGhtQo77n5Nx0G/9G831oXxSjrkrazayN3hBn1NzefvNX8zFmn5telsTMujj3OXR3CasL1vp90q2ngN6Ug69Tsjg99dIcZt+/dMfNkAf9DohfvPX7KRX9fbMPqcYB61Pyp2Mg37r32iuD+WT8qEfrJPrK1j7gqcetPRP6INeJ8Rv/lbdUtWRZAx6KeoQwCJPeqJs5yf0OiEYXx1n0UxS+n0s+BP6nBpX7jd/VU+izbbP7GuIcdD6pBz3WafioN/6N5rrQ/mkvOeY5ZIzrz0vQ+lBDPqg1wnxm795E/M6koxBL0UdAhnWZnDkPIBeJ6TrqMWmr7eg37/CfWI9Buh1Qvzmr9lJr+rtmX1OMQ7ak5Q7FQf91r/RXB/KJ+XdRi2WjBnNXSwoXNDS90Mf9DohfvM347qMjiRj0EtRh4DdQ5j0p/HQ64TgJcD06e29r0Ef9DohfvNX9STabPvMPqcYB+1Jyp2Kg37r32iuD+WTctzM9WTZzk8ng5edfuq6nPJXT46c+IzmQmFZawlkS8jmyJcz6HVCuo9aKlmyWbMQmn5NB39CH/Q6IX7z1+ykV/X2zD6n/Bb3nYh/uk6z+y6S9vzWv5Ew0cson5R3G7VEMmWxN3hBH/Q6IX7zN/O6zI4kY9BLUYdA6Y2lHTkPoNcJ6f/uKilYwp5pXvXkHPqg1wnxm7+qJ9Fm22f2OcU4aM+TcqfioN/6N5rrQ/mkvO+4eNsW2NCDFxbagF4nxG/+FttQzJFkzOk5Wp04t1TW2WJXC0fOA+h1QibO2iyVaq609VdA6INeJ8Rv/pqd9KrentnnFOOgPUm5U3HQb/0bzfWhfFI++tMNUr6qvcEL+qDXCfGbvw12NHAkGYNeijoEph2eJk6s6Am9TsjKzX/LY13tneoV+qDXCfGbv1v3HJFvluxK8x/KRSKqtxeJD9GUYRy0Jyl3Kg76rX+jOfeVT8qxFGvTdvYGL+iDXifEb/5iyfvsCdltTcyhD3op6hA4cumII/PVQ68TcubcRe2lS7vGlUMPxnFCrxPiN3+dYOwlnYyD1iflTsZBv/VvNNem8kn54ePnpOs7S239mRf6oNcJ8Zu/SeeTJGtCVluTcuiDXopaBFrubmnreQB9Tsr7X/8uVe+354ED9ECfk+I3f51k7XbdjIPWJ+VOxkG/9W8016PySTmcefvjRKlUw54hLNADfU6K3/ytva22rckY9FHUI7D+7HrJsi6LLecC9ECfk7L34Cnp+s4SyZrd2hfZ0T70QJ+T4jd/nWTtBd2Mg9Ym5k7HQb/1b6TXpCuS8m1/HJXObyy35Wk59ECfk+I3fxeeWCh2regIPdBHUZNAjz97SI6EHJYm5mgfelSQL3/ZKc06WPu0HO1DjwriN39VYO5WGxgHrUvKVYiDfuvfSK9DVyTlcObdLzfJXU2tDV5oH3pUEL/5+/CuhyXjWmsXEkL70ENRm0CFzRUsTcrRvkoy6L3VUuVea+5taBftqyR+81cl9m6zhXHQ/MRcpTjot/6N5PpzTVJ+8swF6Tl6uZStvMqSJ+Zlb12ltQ89Kojf/D1w6YBgyV99QQMrPtE+9FDUJrDj/A7J1jubpFttckBanU5rF+2rJAeOnhUsO232FIloD+2ifZUE9tzfsrfcdOevpt7L4W+tRh2U81cl9m6zhXHQ5Hvg2nRanFUlDvqtfyO5/lyTlMMZ/NzRtOPbUvJGcxPzkjf+Jk07Dnd82Epwh8FfzJhghb9dRy1Wzt+fT/4sGSZnsCQxT/92eg5bCT7BFN0+dOiQZMqcSfIvyG/quYD20C7aV03+OnhKeo9dIdXqm/PEHO2gPbSrohQoUFCeHfCZqf52GjZPsufIIQkJCSq6TJtiJOC3uK/FwbXWxMEMazMoFwfRv490GWNJnvPg068ql+ekdRm4KimHMwULFZHHer4vN95uzoufaAftoV0VZf3OQ1pibqa/SPTRropSvFxxSfehyU8HPk8nWeKyyP79+1V0mTYFERg0aJB07txZ9l3cJ7W21ZI8iXkMJeeoj3bQHtpF+yrK8dPn5c0PE+SpF+OlUMnYVvtEPdRHO2hPRfn000/lwQcf1Owz298xY8ZI48aNVXSbNhkg4Le4ULQDHQAAIABJREFU/82xbwzd88L90ox2VZTSN/xLHu3+rql5Xas+H0i2bHGui/uuSspxw23ZsqXs3Htc+oyNl3qt4yVTlthmLkA91Ec7aA/ton0VxQp/VfRzwYIFcuedd8qSU0ukyIYihqdKxJRPhTcU1trr37+/PPfccyq6TZsCCJw6dUpy5swp27ZtS96LOeXxcmbOhJxRBSqUR73AOenRLtqHHlUFc/j2GLVMWnSOF/yKp680nNonyqE86qG+ytKgQQP57LPPkk00298aNWrIjBkzktvnH+4m4Ne4b0UcVPFM0OO+FXmOG+O+q5LyMmXKyPLly7Xz6sLFy/Lxd9uk64ilcscD8RKXK7LkHOVQHvVQH+1A0C7aV1XM9ldFP1u1aiUTJkzQTDtz5Yy029Mu5sQcCTnqn75yWmvv0qVLUrp0afnpp59UdJ02/Y/AsGHDpF27dil4nL96XkYfGC3lNpWTuIS4sKt/YlVQHEc5lEe9YEH70KOyXLp8RRYl/CUDJ/4mXYcvlce7xUv1BvHa1LBYcRhTt2Ib+3Ec5VAe9VSWLVu2SOHChVOYaKa/Cxcu1K51XPMU9xPwc9w3Ow6qeDYExn2z8xw3xn3XJOXvvfeePPTQQynOKcx9+8G3W6TbqMXydP94bYxi2VtWSeFSv0n+Yqu1T2xjjCWOoxzKhxprifahR2Ux01+V/Pz7778lc+bMKZ5gJp5JlDZJbSTTukxpDmPAMAWUQ3nUCxY8PcOTeIqaBC5evCgFChSQDRs2pGogFn767MhnMnDvQGmd1FqbUQef2Mb+tBaGQvvQA31uECy0sWbrAZm7PEmmzN6izRCFT2xjP467RfDkqm/fvqmaa4a/+FUMuijuJsC4/9/+MzMOqnRGhIv7ZuY5bov7rknKK1SoIL/88kvY8+nylauyafdh+Wbxbhkxfb0Meu836f/uKu0T29iP4ygXTtA+9LhBzPBXJT/ffvttad++fViTLl69KHOPz5W+e/tKne11pPzm8lJqYyntE9vYj+Mol5o0adJERo8enVoRHnOIwIgRI6RNmza2aIce6KPYS6BYsWKyaZP1087i/ZHs2bPL+vXOLhBlL13vaWPcv7ZPzYqD17bq3FZacd+sPMdNcd8VSfmHH34oGIdoh0AP9FHsJVCpUiVZunSp5UoTExMlR44cgm/oFLUIlChRQlavtmdObeiBPop9BGbOnCkPPPCAbQpHjRolTZs2tU0fFZlLgHHfXJ4qtsa4n7JXXJGUV6lSRfAygB0CPdBHsY8AxnlXrVrVNoX9+vVL9am8bYZQUTKBcePGSYsWLZK37fgD+qCXYg8BzIoyffp0e5T9T0v16tUFs71Q3EeAcd99fRaNxYz7oWkpn5TjLf177703tPUW7YW+wNkBLFLDZv9HoG3btjJ27FjbeGAscalSpeTnn3+2TScVpU6gbNmysmzZstQLmXwU+qCXYj2BHTt2SP78+a1XFKThxx9/1F7gv3z5vy/0Bx3mpqIEGPcV7RgTzWLcDw1T+aQcL+bNnj07tPUW7YU+vhBoEdygZrGQS4YMGeTYsWNBR6zdxBO7mjVrWquErUdEYNKkSYIxf04I9EI/xVoCmBu+Z8+e1ioJ0zpm23nppZfCHOVuFQkw7qvYK+bZxLgfnqXSSfnXX38tmHPWCYFe6KdYS2DkyJHyzDPPWKskTOuNGjWy9Ql9GDN8v7tixYqCaeycEOiFfoq1BK6//nrB+xxOyL59+yQuLi7NWX2csI06UxJg3E/JxGt7GPfD96jSSXnt2rXl888/D2+9hUegF/op1hKoXLmy/Prrr9YqCdP6unXrJFeuXHLgwIEwJbjbagIfffSR1KtXz2o1qbYP/bCDYg0BJFl16tSxpvEIW0USEGpK3Qirs5iNBBj3bYTtkCrG/fDglU3Kv/vuO7ntttvCW27DEeiHHRRrCCAZx8XppGDO5I4dOzppgq91q3CNqXCv8fJJ8PDDD8u0adMcd7FatWp8V8jxXkjdABWuRRXuSalTcvdRxv3U+0/ZpFyFp1cqPMVLvfvcfRTDVvAEy0k5f/68lCxZ0rGn9U767rTuL774Qplfo/B0DvZQzCWQlJQkuXPnFhVetPzhhx+0F3uvXg2/VoW53rO1aAkw7kdLzH3lGfdT7zMlk3LMiqHKOE/YwVk6Uj+JYjmKFzvxgide+HBa8OXrrrvuctoM3+nHi7aqvLcBO/jir/mn4JAhQ6Rr167mNxxji0gIBg4cGGNtVrOSAOO+lXTVaJtxP+1+UDIpx4IPqsyIADu4AEXaJ1K0JTAFIqZEUkUefPBBzlltY2eoOMOREzM+2IjcEVU33HCDrFmzxhHdoZT+9ddfkjVrVltWFQ2ln/vCE2DcD8/GK0cY99PuSeWScswdjBu5SgJ77J5DWSX/rbAFiwVh8QBVBIkDfmY/ePCgKiZ52g68+KfaWgCwx+kXEr3U6XPmzJG7775bOZdGjBghzZo1U84uPxvEuO+P3mfcT7uflUvKH330UeWeWGLVP9hFMYfA0qVLBcvrqiZ9+vSR559/XjWzPGcPVs11+gXfcFBhl12rB4ezwSv7sWKqKr94BjNFcsB3CIKpOLfNuO8ce7s0M+5HRlqppHz16tVSvHjxyCy3uRTsgn0U4wTat28vb7/9tvGGTG7h3LlzUqJECVm0aJHJLbO5QAINGjRQYjaOQJv0vzFLCOyjGCPw559/Svbs2QUvUqso+OJVrlw5FU3znU2M+/7ocsb9yPpZqaS8TZs2Mnz48Mgst7kU7IJ9FGMETp06JZkzZ5a///7bWEMW1f7www+V/MndIndtb/aXX36RChUq2K43GoWwD3ZSYifw+uuvS6dOnWJvwIaaTz31lLz88ss2aKKK1Agw7qdGxxvHGPcj70dlkvINGzZIgQIF5MKFC5Fbb2NJ2AX7YCcldgITJkyQVq1axd6ADTXxpPTdd9+1QZP/VGABl/fee09px2EfF5ox1kU33nijxMfHG2vE4tp//PGH9oBg8+bNFmti8+EIMO6HI+Ot/Yz7kfenMkl5u3btZOjQoZFb7kBJ2Ac7KbETwAwXqo/Zxc+pefPmlcOHD8fuKGumILBixQopU6ZMiv0q7oCdsJcSPQFc3zVq1Ii+ogM18Ato8+bNHdBMlSDAuO+P84BxP/J+ViIp3759u+TMmVNOnjwZueUOlIR9sBP2UqIngCdn5cuXj76iAzV69eolnTt3dkCzd1W2bNlSxowZ4woHYSfspURP4PHHHxc8GXOL3H777TJz5ky3mOsZOxn3PdOVqTrCuJ8qnhQHlUjKkfwMGjQohXEq7oCdTNZi6xmMMR02bFhslW2udebMGSlWrJgsWbLEZs3eVLd27VqNp5u8Q//DbkrkBPCuSJYsWQRjSN0i8+fPFwy3odhLgHHfXt5OaWPcj46840n5nj17JFOmTEqs7BgJOqxACXthNyVyApjZJC4uTrB4h1tk6tSpyiwD7xZm4ex88sknlZxxJ5y92I8ZgmA3JXICYIZZFtwm6OfBgwe7zWzX2su479qui8pwxv2ocGmFHU/Ke/bsKX379o3ecgdrwF7YTYmcwOTJkwXzFrtN6tWrJxMnTnSb2UrZu2nTJsmXL5+y0+OFg4Xp/GA37KdERqBixYqC+YjdJnqSuGXLFreZ7kp7Gfdd2W1RG824HzUycTQpx0+dGTJkkL1790ZvuYM1YC/sVnVaPwfRhFV91113CVb4c5usWrVK8ufPL0ePHnWb6crYiyenr732mjL2RGMI7Hbjk99ofDSrLFboveOOO8xqzvZ23nrrLVc+OLAdlEGFjPsGAbqoOuN+9J3laFLev39/6datW/RWK1ADdsN+StoEsIR92bJl0y6oaIkXXnhBunTpoqh1apu1Y8cObRGZEydOqG1oGOtgNxbBgR+U1AlgvumxY8emXkjxo1WqVJGvvvpKcSvdbR7jvrv7L1LrGfcjJXVtOceScjx5zJYtm+zatetai1yyBbthP5+gpt1h+AIzZMiQtAsqWgIvrRUpUsSVP8s7jRRfZgYMGOC0GYb0w35+KUsdId61yZgxoxw7diz1goofnTdvnvKLWymOMFXzGPdTxeOpg4z7sXWnY0n5K6+8Ih07dozNakVqwX74QQlP4PLly5I7d25JSkoKX8gFRz744AO59957XWCpOiZicZbrrrtODhw4oI5RMVgC++EH/KGEJjBy5Eh55plnQh902V488XfzQwSVcTPuq9w75tnGuB87S0eSckw3lydPHvn9999jt1yBmrAffsAfSmgC06ZNk4cffjj0QZftfeCBB5RfjVIlpJjrvXfv3iqZFLMt8AP+UEITuPXWW+XXX38NfdBle/EAAU/9t23b5jLL1TaXcV/t/jHTOsb92Gk6kpS/8cYb8vTTT8dutUI14Qf8oYQmUKdOHfn6669DH3TZ3pUrV0qBAgVc/xO9HdjxdBmJzZ9//mmHOst1wA/44/an/laAQjKOsdheEtzTuXiUuT3KuG8uT5VbY9yPvXdsT8qvXLkihQsXloSEhNitVqgm/IA/8ItyLYHExES5/vrrr93p8q0ePXpI165dXe6F9eZjHLbXOMEft4+Pt6LnMWwFw1e8Jnj6P2vWLK+55Yg/jPuOYHdEKeO+Mey2J+WjRo2SVq1aGbNasdrwB35RriWAuWjdslLrtZaH3zp58qT2JWz58uXhC3ngyOFLh2X5qeUy6+gsmXFkhvaJbexPS44fP64tFLVz5860irrqOPzBAljwLy05fe6i7N53XBJ3HJTVW/7RPrGN/W6StPzAi53p06d3zeJv0bDHFK6Ydz2UpMUlVB0/72Pc90/vM+4b6+uYkvJzFy7LgaNnZd+hU9ontiOVUqVKCYYBeEngD/yKVIzwi1SHHeXS8gPze3txKjksiHDfffeFRXzi8gnZfm67bDi7QfvEthtk49mN0uevPlJqYynJvC6z5EnMI7kTc0vOxJzaJ7axH8dRDuVDyauvviodOnQIdcj1++AX/Asl+w6dlq8X7ZL+41dK13eWSPvBK6RV93h5pFO89olt7MdxlEN5FSUaP9p1fVlatGytohum2PTEE08k93c0XFTu31jBpHW/T61dxn0RI/xSY2v3sbT88GvcT4tLpP0UUVJ+5epV7WnPuC82Sa8xy6XryCXS+Y3l8vzQ5dontrEfx/FUCOVDyYQJE6RZs2ahDrl+H/yCf6HELH6h2rZzXzR+DHlrvDRu3NhO82zVVbduXZk0aZKm8/LVy9rT5CY7m0j+9fkl07pMyYksElpsYz+O46kzyqskiWcSpf6O+hKXECcZ12aUdGvTpfkP5VAe9VBfFyyrjFUwN2/erO/y1Cf8gn/wU5e/Dp6SUZ9ukC7Dl8rdD8VL4VK/Sbp0a8P+w3GUQ3nUQ30VxCt+mMly9+7dkj59Bnlp5FzX92+0XKK53zPup6RrFr+ULdu7Jxo//BT3o+GS2vUR3JupJuUXL12ReSuSpPuopfJEr3ipeOdKyV1gdchgg/04jnIoj3qoHyg33nijLFq0KHCXZ/6GX/AvUMzmF9i2nX/H6kfnt34KeR7YabtVulasWCEFCxWUvtv6askpnihHksyiHJLZQfsGybkr/5/YWWVnWu0O2z9M+9IQie3hyuBLB9qBYFXEp556Ki21rj4O/+An5IdVe6TryMVSpU58yPtiask5jqEe6qMdJ8UrfpjNEFzuatrZ9f0bDZdY7/eM+/+lbDa/aPrOzLKx+uH9uF9IZv600bS8OLjPwibl67YfkJ5jlkuTZ+Mlb6HQiXi4gIPyqIf6aAeCeZ4bNmwYrN9T2/APfkLM5ucUKK/4YTa/mUdnSpbWWSRuYVxEyXhwQps9IbvkTcwraMcpabSzkTYsJdi2WLbxZaPRjkZStGhRWbdunVMu2aIX/sHPsZ9vkCf7xkvOfNHdH4Pvm6iPdsZ+Hno4kNVOQa8X/DCbkx+5mH2/Z9wP/4tZ8H0gVN5k9jkdaXtmnweR6lW9HLhUqf2o1Gv1nSl5cSh/Qybl3y7dLf9+c5mULL8qpqc/+smG+mgH7d1yyy3yww8/hLLBM/vgH/y0gp8TkLzih9ns+u/trw1PiSV5Da6D4S1oz26pva229sQ+2B4j21nezyIFGxa02xVH9N1avb7UaDDe0P1Rv0/qn42fiZe3P/7/oUB2OAZ90KvbYManE36YzcqPXKy43zPuR56U69deYN5k9nkdSXtWnAeR6FW9jF1cUiTlM77fLk/3j5escWtMuVGjnXuaviIVK9dQnbkp9sFP+GsmP/QH+sVOseI8cMIPs5l13NNRewHSSPIaXBcvUKJdu6R1UmvJkZAjpif8wbYHb6NdtO9lmTJ7izTrYG4iqwdktIv27RCv+GE2Kz9yseJ+z7gffUKu3weQPzgRL604D5zww+x7gp1crknKF6z8jzwzYIVkyBD7yaSfVIGfaA/ton0vi1f4ecUPs8+1V/e/anpCrie1SMzRvtUy5dAUy3wI9AV6vCgrNu2XDq+sMOWBReA9MvBvtA89VopX/DCbkR+58H5v7CzyCj+v+GGsN1PWtptLclK+OemwdHl7mWTPZc4T8sAgg7/RLtqHHi+KV/h5xQ+zz7H5x+cLxoHriacVn2gfeqySk5dPWvaEPJgHnphDn5cEU171GLVUipROfXaV4HtftNtoH3qgzwrxih9ms/EjF97vjZ1FXuHnFT+M9WbK2k5wSU7K+41fKWVvNTaGPK3gg/ahx4viFX5e8cPsc6zkxpKWJuR6Ugs9VsngfYNNH0eu2x38iRlmoM9L8t2K/0jjp60ZthJ874Qe6LNCvOKH2Wz8yIX3e2NnkVf4ecUPY72ZsrYTXLSkfHHiXmnVw55gAz3Q5yXxCj+v+GH2uTXuwDjTZikJTl6DtzGLCfRZIbkSc9nyxUL3Cfq8JC+MXib5ihibaSU4+Q63DT3QZ4V4xQ+z2fiNC+/3xs4gr/Dzih/GejNlbae4aEn5i+NWSrGy1v4kqwcf6IE+L4lX+HnFD7PPreIbituazEKf2TLv+DzLx5Lrybj+iXHy0OsF2bT7sDw7wJ4HF/q9Evqg10zxih9mMkFbfuTC+72xs8gr/Lzih7HeTFnbKS7pdv51TDq+Zu2LS3qQ0T+hD3q9IF7h5xU/zD6nlpxaYttTcj2ZxdNy6DVTOv3RydYvFrov0OsF+fSHHXJ7XXuTcuiDXjPFK36YyQRt+Y0L7/fGziCv8POKH8Z6M2VtJ7mkw9yLNRvZG2ygD3q9IF7h5xU/zD6nMId4+rXpbU1ooc/sucsr/V7JVh/0pBx6vSCD318jxW36NVF/eAF90GumeMUPM5mgLb9x4f3e2BnkFX5e8cNYb6as7SSXdEM/WCfXV7D2BU89yOif0Ae9XhCv8POKH2afU1W3VHUkmYVeMwWLFOmJsp2f0OsFwXjjOItmptLvi8Gf0Gf2uHKv+GH2OeU3LrzfGzuDvMLPK34Y682UtZ3kkq7nmOWSM689Ly/pQQf6oNcL4hV+XvHD7HMqb2JeR5JZ6DVTMqzN4Igf0OsF6TpqsenrN+j3w3CfWN8Bes0Ur/hhJhO05TcuvN8bO4O8ws8rfhjrzZS1neSSrtuoxZIxo7mLBYULMvp+6INeL4hX+HnFD7PPqYzrMjqSzEKvmWL3EBz9aTz0ekG6jlok6dPbe5+EPug1U7zih5lM0JbfuPB+b+wM8go/r/hhrDdT1naSSzrcjPRk2c5Ps4NNSqz27PEKP6/4YXav68mlE59m+pItIZsjXy6g1wvSfdRSyZLNmoXVwt13oQ96zRSv+GEmE7TlNy683xs7g7zCzyt+GOvNlLWd5JKu26glkimLvcEG+qDXC+IVfl7xw+xzKvO6zI4ks9BrppTeWNoRP6DXC9L/3VVSsIQ908bqSTr0Qa+Z4hU/zGSCtvzGhfd7Y2eQV/h5xQ9jvZmytpNc0vUdF2/bghh6sMHCGNDrBfEKP6/4YfY5VWxDMUeSWbPnKm+xq4UjfkCvF2TirM1SqeZKW39VhD7oNVO84oeZTNCW37jwfm/sDPIKP6/4Yaw3U9Z2kku60Z9ukPJV7Q020Ae9XhCv8POKH2afUw12NHAkmYVeM2Xa4WnixIqe0OsFWbn5b3msq71Tx0If9JopXvHDTCZoy29ceL83dgZ5hZ9X/DDWmylrO8klHZYSbdrO3mADfdDrBfEKP6/4YfY5hSXvsydktzUxhz7oNVOOXDriyHzr0OsFOXPuovYyoF3jyqEH4xqh10zxih9mMkFbfuPC+72xM8gr/Lzih7HeTFnbSS7pDh8/J13fWWrrz7LQB71eEK/w84ofZp9TSeeTJGtCVluTcuiDXrOl5e6WtvoBfV6S97/+Xareb88DDOiBPivEK36YzcZPXHi/N3b2eIWfV/ww1pspazvJJR3MefvjRKlUw54hLNADfV4Sr/Dzih9mn1u1t9W2NZmFPitk/dn1kmVdFlt8gR7o85LsPXhKur6zRLJmt/bFeLQPPdBnhXjFD7PZ+I0L7/fGziCv8POKH8Z6M2Vtp7hoSfm2P45K5zeW2/K0HHqgz0viFX5e8cPsc2vhiYVi14qY0AN9VkmPP3tIjoQclibmaB96vChf/rJTmnWw9mk52oceK8UrfpjNyE9ceL83dvZ4hZ9X/DDWmylrO8VFS8phzrtfbpK7mlobbNA+9HhRvMLPK36YfY49vOthybjW2oWE0D70WC0VNlewNClH+16WQe+tlir3WnOvRLto3w7xih9ms/ITF97vjZ09XuHnFT+M9WbK2k5wSU7KT565ID1HL5eylVdZ8sS87K2rtPahx4viFX5e8cPsc+zApQOSNzGvpcks2oceq2XH+R2W+QIf0L6X5cDRs4JlmM2eIhHtoV20b4d4xQ+zWfmJC+/3xs4er/Dzih/GejNlbSe4JCflMAeP6/HGf8kbzU3MS974m3Qdtdhzw1aCu9Ar/LziR3D/GN3++eTPkmFtBksSc7Rr5bCVYN8TzyRKofWFTPUF7aFdP8hfB09J77ErpFp9c56Yox20h3btFK/4YTYzP3Hh/d7Y2eMVfl7xw1hvpqxtN5drknKYs37nIS0xv/F2c178RDtI9NGuH8Qr/Lzih9nn3DfHvjE1kU23Np3WHtq1W/Zd3Ce1ttWSPIl5DPmE+mgH7flJjp8+L29+mCBPvRgvhUrGtton6qE+2kF7TohX/DCbnZ+48H5v7OzxCj+v+GGsN1PWtpNLiqQc5uzce1z6jI2Xeq3jJVOW2GYaQD3URztoz0/iFX5e8cPsc2/JqSVSZEMRw1MlYurDwhsKC9pzUjAnOl7OzJmQM6rkHOVRz+w51Z1kEYtuzGnbY9QyadE5XvCroL5ycWqfKIfyqIf6KohX/DCbpV+48H5v7MzxCj+v+GGsN1PWtotLyKQc5ly4eFk+/m6bdB2xVO54IF7ickWWnKMcyqMe6qMdP4pX+HnFD7PPwTNXzki7Pe1iTsyRkKP+6SunzTYtpvbOXz0vow+MlnKbyklcQlzY1T+xKiiOoxzKox5F5NLlK7Io4S8ZOPE36Tp8qTzeLV6qN4jXpprFCsaYChbb2I/jKIfyqKeSeMUPs5n6hQvv98bOHK/w84ofxnozZW07uIRNynVzMHfrB99ukW6jFsvT/eO1MZRlb1klhUv9JvmLrdY+sY0xkTiOcihv99hI3V7VPr3Czyt+mH1+YAx1m6Q2kmldpjSHgWCYB8qhvMpjr7Fw0WdHPpOBewdK66TW2oww+MQ29luxsJHZ/eJke1h4Ys3WAzJ3eZJMmb1Fm3EKn9jGfhx3g3jFD7NZ+4EL7/fGzhqv8POKH8Z6M2VtK7mkmZTr5ly+clU27T4s3yzeLSOmr5dB7/0m/d9dpX1iG/txHOUoKQl4hZ9X/EjZQ8b2XLx6UeYenyt99/aVOtvrSPnN5aXUxlLaJ7axH8dRjkICJEACbiDA+72xXvIKP6/4Yaw3U9a2gkvESXlKc7iHBEiABEiABEiABEiABEjADAJMys2gyDZIgARIgARIgARIgARIwAABJuUG4LEqCZAACZAACZAACZAACZhBgEm5GRTZBgmQAAmQAAmQAAmQAAkYIMCk3AA8ViUBEiABEiABEiABEiABMwgwKTeDItsgARIgARIgARIgARIgAQMEmJQbgMeqJEACJEACJEACJEACJGAGASblZlBkGyRAAiRAAiRAAiRAAiRggACTcgPwWJUESIAESIAESIAESIAEzCDApNwMimyDBEiABEiABEiABEiABAwQYFJuAB6rkgAJkAAJkAAJkAAJkIAZBJiUm0GRbZAACZAACZAACZAACZCAAQJMyg3AY1USIAESIAESIAESIAESMIMAk3IzKLINEiABEiABEiABEiABEjBAgEm5AXisSgIkQAIkQAIkQAIkQAJmEGBSbgZFtkECJEACJEACJEACJEACBggwKTcAj1VJgARIgARIgARIgARIwAwCTMrNoMg2SIAESIAESIAESIAESMAAASblBuCxKgmQAAmQAAmQAAmQAAmYQYBJuRkU2QYJkAAJkAAJkAAJkAAJGCDApNwAPFYlARIgARIgARIgARIgATMIMCk3gyLbIAESIAESIAESIAESIAEDBJiUG4DHqiRAAiRAAiRAAiRAAiRgBgEm5WZQZBskQAIkQAIkQAIkQAIkYICAK5LyoUOHSrp06a75d91110mhQoWkbt26Mnr0aDlx4oQBDJFXHT9+fLIdiYmJkVf8X8kWLVpo9XPnzh11XTMqnDt3Ltn+YKahtrdu3WqGWlPb+P3336VGjRrJfsybN8/U9tmY2gR4PzCvf9x+P9i9e7c89dRTcsMNN0jWrFmlTJky8vjjj8vmzZvNg8SWlCWQMWPG5Digx6+cOXNKhQoVpF27dhIfH2+L7Zs2bUq2A/lItGK0frT6QpXv169fsg86y3CfuMZUkWXLliXbnSHvpvFoAAAgAElEQVRDBlm/fn0K0/SY4VTelcKgVHa4NikPPlmKFy8uK1euTMVVcw59/PHHUqpUKe0fksNopXPnzlrdW265JdqqppR3cxC+ePGivPbaa5I5c+bkixDnAZNyU04N1zSi32CD7wGB27wfRNadbr4f7NmzR/Lly3fNvUA/B7JkySKrVq2KDAJLuZZAqKRcPwf0z44dO8rly5ct9XH79u3JecHUqVOj1mW0ftQKQ1TwQlKOPq9Tp04K7/SYwaQ8BZrYduhAAfzrr7+WQ4cOyf79+wXfkNq3b598U86bN6/gRk1JnQD4Bf67+eabNYYIcIH78Xeom9nVq1dD7k9dq/GjeAKBc6Bo0aLy3HPPJfc7k3LjbN3UAu8H5vZW8DXvlvvBk08+qd0D8Ksp4sLRo0flq6++Ejwtw33ioYceMhcUW1OOgJ6U33nnncmxa8eOHYLEGL+a4DzAv549eypnu2oGnTlzJpkh7gnTp09P5vfWW29dc+zkyZMhzb906VLI/VbuDHxSrvc37geBoscMJuWBVAz8rQMF8IULF6ZoadSoUcknD36yChT8LPTYY49pQ10yZcok119/vfTq1SvFcBckmpMmTZI77rhDsmfPLnny5NGGSHzyySeBzUm44Sv4MtClSxf517/+JdmyZdOe4NSsWVM+/PDDa+qHG75y4cIFGT58uNx2222afvwUW758eenTp48cPnz4mjb0J8X9+/fXfqrBEB7YjKeDsOH06dPXlE9ro3Llyhq//Pnzpyj6+uuvJ7PFF6EHH3xQ8BRqxowZyWXnzp2rfTvNlSuXdgxBHZzANFDgR48ePbSbJXyAvpYtW8rOnTsDi4X9G/0M/zBUafbs2cl2MSkPi8yTB3g/4P0AJzaS8gYNGgie8AUKhi4gVlSsWDFwN//2IAE9Kb/rrrtSeHfkyBEpXbq0di7gi1tSUlJyGTxsGjlypCD2IV7nyJFDateuHTK/2LVrlzZEqlixYtqvtEj28TDwjz/+SG4v3PATxOI333xTbr31VkFCiDiN8/KVV16Rs2fPplkfBRISErQcpkiRIgI/8PCsfv36Mn/+/OT6+OONN95IjolIsF999VUpWbKkxMXFSbVq1eTXX3+9pnxaG4ExFvE8WPQ85KWXXtLyAfBB/qNLNPE+0hxCbzvwMzApRx/i2sdwtvPnzycX02MGk/JkJMb+0IGGS8pxgeHkw3GcsFeuXNEU4udLnJB6J2EcFMahYxtjkgO/1QU+ccdFp9/YUfbFF19MdiBUUn7w4EEpWLCg1i4unHr16kn16tWTn9gMGTIkuX6opBwnj34yQR9uJJUqVZL06dMn237gwIHkNvCFAeWaNm2q+YsbC/RiH/516NAhuWwkf6SWlI8ZMya5XSTQaB83Qj0pHzduXPJxPK145JFHkoeX4AuFLniKhQsW9WE/2sLNCdsFChSI6BcOfHHRJfCGwaRcp+KPT94PbhDeD0Kf60jEkPjgvoKEneJtAqkl5fB88uTJyfEpcKy3Hofxq0qTJk20h0o4Z7A9Z86cZGh4N0GPt0jeq1atKhizjrKIuXinARIuKcdQCpTFOYm/77vvvuScJHCYRbj6sEVPfpHL3H777YIRAWgT/0aMGJFsa2CsxhdWPPSCvTojtBP4xSS5Ypg/AmNsqKRc54JfpPCgDnr0pDyaeB9pDhHGTG3EhM4DuQBswTae7uuixwwm5ToRg586UIAO9aQczSPh1jsGT3Qh+HaIfUhy9afHf/75Z/JNe9q0aVq5JUuWJNdFcq5L3759k/frT3NDJeX4qQx6cEEHvnA6ZcoULTnHBaI/NdZvBoEnB75J67bjybQugRcFhmvogotNL6/7AP9uuukmbT++TQd+4dDrhftMLSkP9BdfVnCTwpceJMj4JowbFWzBrxG6fPPNN9o+8MBTBojOEvs2btyo7cMYcfwygfp4WSsaCWTDpDwacu4vy/tBOm34lt6TvB/8lwTuJ40bN9buJ7infPfddzoifnqUgJ5whnpSDpcxUYEeKzG2HIInzPq+CRMmJJPp3r27th/5gh6vkUSjLJLqbdu2aWX37t2bnBi3bdtW2xcqqUbCrusJPBc3bNggVapUkYYNGwryEUio+ojp+rWN2Au9kFOnTgn8Rdv/1955wE1R3H3cSpEqotiwREBje+3GLrYoNixBxAKxG+trTSzRJJpYsKIvJmqUN6JEo9gxMdhLUAmKMa+9K1hRY43ovJ/fJP/N3LJ3z92xd7e3+53P53nubnd25j/f2Z357ex/ZtXXv/LKK3572FdrztpHH33kt0sjmB3hAKHfWeFf2McmiXKzTYOHp556qtP8FP0pVNvf16IhypkajpQ/8cQTUd66eZo5c6Y/zPqMUHeVS6/V29tuomc5UX7wwQdHJ558ymbMmBH9PuKII0o4685YJ6lGmhW0307acLUR3e1J8OnvjTfe8HHDE99WX5kwYUJ0vOzQY6JQnIeZJ4lyXUDKXye5OpYwyJ1F+3R3bA2FXQwrrLBCGNWdfPLJkR213BFXK8rDBkwZX3fddVF+oQ+X7LTRBD0iVJArjsoRn+A6evRov12uLzrurbfeco8//njJ36xZs0rKqR9hg4EonwNPrjdYA6vzifbARR13kdsDCZAtt9wyao/k5kbIP4GORHmoA0aMGOGBhFpBT1YsSNCpTdHf1KlT/aCTPa2Orzaip/DqdyZPnuwPTxLVytuOl4up+iwbMLQ87TPpePWpZo9cN8OgEXTbZ08AQm0if3ALGkDTYJji1zL4FfaxlUS5nhjE555V29/XoiHiusAW2ghFueJIe5lHhA1mWp+BKLezYi4/DahOqnKd8C677BKdpJqEoJVY7KQt9zlw4EBvmfykLU7oIpFkdnjimyjX3aFcNywNfeoi0KMj+Ynb3aPSSxLl8h/XMUojHsInANaAmCgfMmRISfTwMZAu8mpDtaL8gQceKEkyHOEPyx5+N1cae6QU7ot/lyCXr118uxqHeAgbDER5nE6+f9Me/Es40B786zzXSKG5wqntkNucDWDk+0qgdB2Jco1KW3+iuWQKcmuybeU+x48f71fvsf3ym64UkkS14msOlaVhnxr1Pvroo/0AlKWZdLzcL+yYSZMmWVT/GT4BsEHHUJs89thjJfFtlSLpj2pD2MdWEuW64YiHavv7WjSEsbBP6RaFuCjXNs0PVDzpMPnk29w4RHm8pur83VEnLCFtPt2ayKmgO1mrPIldXRzxPz1yUQgv0lBA+52xf+GJb6JcUeQuorvT3Xff3a8OYnnrUyM45ueeJMrNBUR+6PFgftxKRyP3CibKd95555LooW2NEOVheZVx2GioXHG++q0lJBXsxkOTUZPiaZv8ZBHlJVXKjwQCtAf/EuW0B85pKTmtxqT2Ue1ofGJ+wunDphwR6EiUh4tAmKtnOAgnl5Wk/ujhhx8uGdiLTyaOI0wS1RZHo+kanBo0aFA0cq7zVfPgzCUl6fizzz470jB33nmnJec/NUqsNPSnMiiE/X+8rzbN0AhRHtchsqXa/r4WDWHltc9Kolx6S5NrFVfz9WzAElHuT5W5/9dRJ6y1q62iTjrpJJ+hfIlsm/mSlbPkkEMOieKGL53QBE7dyelvypQp/vBKJ36YvmZmh24xdnySKLeRat3Nxt1XbHky3XRYsAssfjGEtjVDlIduO9dee62Zl/hpE2fVMKURwrt4RsrTINo+adAezOMHIazGitoeaNk2DcKonVf7aG2sceEz/wQqiXIN8oQ3bOZjHfb35dxJRC7UEBpsC4PmTUkXmHtmkqgO49t33UhfccUV0eRLCW+FpOPDPs7ysXS09KfpG3MrDfv/Vovyavv7WjSElT3+mTRSrji6GTJGclXWd0R5nF6dv8NO+Oabb3byMdbJrUdThx9+eARed56h/7FGnlUR6rTsjlTLEA0dOtRPCLLlCuUSY5U3cuTIyMrQR9sa/KQTX3d7clWRz1r42PShhx6K0rXjk0S5XFwsf7tIZYRGmW27HndZyEonrMfnNsqvmeS2BJFGrzbZZBPP2cqtFWysLHfddZcVxa9so+WdwpVaop0VvoQNFqK8Aqgc7qI9mMc//raqLWp7YG2p2pXrr7/ezyOSH2/4F7bHxovP/BAwUa7V1NT3609CW+eDrfal8yMUtRp1tr5IywpbkH+zJnbKZdR0hE2oVD9nPsxys7Qn8+ZCmiSqNaF044039ssch3PMNIHTVi6x/j7peC1raPnI5UXntYK0j/SGyiC7bHuSNrGyWRvRrJHyavv7WjSElSX+WU6UK55WhrG61ieiPE6vzt9hJxwCDr/rpI2/XlWv2LXHKJpIKKFod8462W1lEJllL6JQmpp9bXd6+i3hbyHpxJeo1yxoxVVDoAtVF7ctzaX1yiu5r8j9xpZOUhryddeIsr7rTzO1w8X67QJr9Ui5mISPB7VO6WabbRaVW25DNvKvi091pPKIlfaFq8Vo5KGjoPNA7i/6MwZKT99t+7333ttRMuxvcwK0B7QHGiW39rHSp4mrNj/lMb8MARPllc4B+ZKHN2f6roE5O0bzESTqbTKk+Z4rS40426IF0hISw/ZbS/naiixJoloj7f369fP5SG9o8EmusrZ8sfotW0Ai6Xjlr1VbbElEraUe5i97bWBRcZO0iWGz/rJZoryW/r5aDWFliX9WEuVaNc/4qb4R5XF6df4u1wnrRNNouFbwsEdT8Sx0UWntbF1AenmQRtM1I9eWEbL4Es2XXHKJv6vV3acEtS7Uq6++uuSCLnfia1lFTTaVONQkB7miaE1RjYLbcozKy0Z34ieHhLniypVF+asB0FrlWsIoPF5p2AWWBVEueySodcOjRkO2a+b1T3/6U6c7/TDocaJWRdAjZ9WFGiotYSZ21YRjjz02akitQY1/xifEVJMucdqLAO1B6cvBitgeaHQwfu0n/UaUt9e1Xau1SaJcIkwDQFquUIItKWgOmEap5R6q/lqDdtIS5nceHiPhrafgEtPqtzT4JA1RzcuDtAqaVnvRykjSFPpT/6gn39UcLzs0UVFzy7SiiAa01G+q79eT+DCU0yaKY21Es0S58qylv69WQ4Tlte+VRLni6CbL2oa47rI0svTZFksiZgkYtkAAAhCAAAQgAAEIQCBtAojytImSHgQgAAEIQAACEIAABGokgCivERjRIQABCEAAAhCAAAQgkDYBRHnaREkPAhCAQBMJaFmxWl6f3UTTyAoCEGgyAdqDJgNPOTtEecpA00iODjYNiqQBgWIQUCccfw14MUpOKSEAgTgB2oM4kfb6jSjPYH1ppjABAhCAQDUE6ISroUQcCBSDAO1Be9cz6i+D9Ycoz2ClYBIEMkqATjijFYNZEGgBAdqDFkBPMUtEeYow00oKUZ4WSdKBQP4J0Annv44pIQSqJUB7UC2pbMZDlGewXhDlGawUTIJARgnQCWe0YjALAi0gQHvQAugpZokoTxFmWkkhytMiSToQyD8BOuH81zElhEC1BGgPqiWVzXiI8gzWC6I8g5WCSRDIKAE64YxWDGZBoAUEaA9aAD3FLBHlKcJMKylEeVokSQcC+SdAJ5z/OqaEEKiWAO1BtaSyGQ9RnsF6QZRnsFIwCQIZJUAnnNGKwSwItIAA7UELoKeYJaI8RZhpJYUoT4sk6UAg/wTohPNfx5QQAtUSoD2ollQ24yHKM1gviPIMVgomQSCjBOiEM1oxmAWBFhCgPWgB9BSzRJSnCDOtpBDlaZEkHQjknwCdcP7rmBJCoFoCtAfVkspmPER5BusFUZ7BSsEkCGSUAJ1wRisGsyDQAgK0By2AnmKWiPIUYaaVFKI8LZKkA4H8E6ATzn8dU0IIVEuA9qBaUtmMhyjPYL0gyjNYKZgEgYwSoBPOaMVgFgRaQID2oAXQU8wSUZ4izLSSQpSnRZJ0IJB/AnTC+a9jSgiBagnQHlRLKpvxEOUZrBdEeQYrBZMgkFECdMIZrRjMgkALCNAetAB6ilkiylOEmVZSiPK0SJIOBPJPgE44/3VMCSFQLQHag2pJZTMeojyD9YIoz2ClYBIEMkqATjijFYNZEGgBAdqDFkBPMUtEeYow00oKUZ4WSdKBQP4J0Annv44pIQSqJUB7UC2pbMZDlGewXhDlGawUTIJARgnQCWe0YjALAi0gQHvQAugpZokoTxFmWkkhytMiSToQyD8BOuH81zElhEC1BGgPqiWVzXiI8gzWC6I8g5WCSRDIKAE64YxWDGZBoAUEaA9aAD3FLBHlKcJMKylEeVokSQcC+SdAJ5z/OqaEEKiWAO1BtaSyGQ9RnsF6QZRnsFIwCQIZJUAnnNGKwSwItIAA7UELoKeYJaI8RZhpJYUoT4sk6UAg/wTohPNfx5QQAtUSoD2ollQ24yHKM1gviPIMVgomQSCjBOiEM1oxmAWBFhCgPWgB9BSzRJSnCDOtpBDlaZEkHQjknwCdcP7rmBJCoFoCtAfVkspmPER5BusFUZ7BSsEkCGSUAJ1wRisGsyDQAgK0By2AnmKWiPIUYaaVFKI8LZKkA4H8E4h3wi+88EL+C00JIQCBRAK0B4lY2mYjojyDVYUoz2ClYBIEMkRg8ODBbsCAAW7SpEnOOmGJ8VGjRrm+ffu6qVOnZshaTIEABBpJgPagkXSbmzaivLm8E3MbPXq069Kli7vwwgv9fhPl+q3t2k+AAAQgYASuueYat9BCC7lu3bq5/v37u8UXX9wtuOCCboEFFnBDhgyxaHxCAAIFIEB7kJ9KRpRnoC4/+eQT36F27drVLbLIIk6ivE+fPk6/1dFqPwECEIBASGDQoEG+rVB7YX9qMxglDynxHQLFIEB7kI96RpRnpB5//OMfu06dOkWdqzpZ/dZ2AgQgAIE4AY2O9e7du6TNGDp0aDwavyEAgQIQoD3IRyUjyjNSjxoN79y5c0kHq9+MkmekgjADAhkkEI6OMUqewQrCJAg0kQDtQRNhNygrRHmDwNaTbDhazih5PQQ5BgLFIhCOjjFKXqy6p7QQiBOgPYgTab/fiPIM1Vk4Ws4oeYYqBlMgkGECAwcO9K5u+JJnuJIwDQJNIkB70CTQDcoGUd4gsPUmq9FyTe7El7xeghwHgWIRuOSSS9xuu+1WrEJTWghAIJEA7UEilrbZiCjPWFVptFwdLL7kGasYzIEABCAAAQhAAAINJIAobyBckoYABCAAAQhAAAIQgEA1BBDl1VAiDgQgAAEIQAACEIAABBpIAFHeQLgkDQEIQAACEIAABCAAgWoIIMqroUQcCEAAAhCAAAQgAAEINJAAorxKuFtuuWXJi33stdb6XGihhdzqq6/uTjvttMxN0Hz66acjuy+44IKS0ur3pZdeWrKtox8ffPCBGzlyZJTmscce29Eh7IdA7gjQHjj38ccfu1NOOcWtvPLKvg1cYokl3BZbbOHuvPPO3NU3BYLAgw8+GPV7Yf+v7/PPP79bfPHF3c477+zuvffezMHS4hGys1evXiW2TZs2zeuWZ555pmR7uR8qp5V9zJgxc0QLGSltQu0EEOVVMqvUCdtJqs/VVlvNffrpp1Wm2vho5UT5q6++6i+uVVZZpWojbrjhBtevX7/oolR5EeVV4yNijggUvT345z//6TbYYIOStiBsB+MDADmqeopSUAKh4AzP9fj3eeed140bNy5TlMqJ8lGjRvlrWH17NSEU5X369HEapAtDyAhRHpKp/juivEpWYSc8c+ZM99577/k/fX/kkUfcJptsEnVQ559/fpWpNj5aOVF+3nnneXurFeW6wNT4dOnSxR1//PFRWRHlja9DcsgegaK3B1deeWXUBvzkJz9x77//vtPLi5Zaaim/feGFF3bffPNN9ioOiyBQJ4FQcB5zzDGRBpAWeOmll5zWBzfRKsH69ddf15lT+ocliXLZJzvVr9cjynXcEUccUWJsyAhRXoKm6h+I8ipRhZ1w0sUWit8999yzJNVbb73Vbb755q5nz55Ob+pcddVVnR79fPvttyXxJO533XVX179/fx9Pj4N33HFH98ADD0Tx1ADYnfmJJ54YbdeXww47LNr3xRdf+H2hXRq9ku12fPip8lUKjz/+uNtqq63c888/72bNmhWlgSivRI19eSVQ9Pbg3HPPdd///vd9+xS2h4ccckjUNrz77rt5rX7KVUACoeA8+eSTEwmY+FXf+txzz0VxdNN61FFHueWXX96/fXeRRRZxP/jBD9wLL7wQxdEXPWX/1a9+5d1h5WrSrVs37x4m19jPP/88ipvU12tnOX1gdpn7iuwP+3/7rjJWCnbTsemmm/rjF1hgAff3v/89OiRkhCiPsNT0BVFeJa5aOmF1TBYuvvji6ORff/31veju1KmT33bcccdZNDd58uToLnullVZy22yzjVtxxRV9PL3h8+677/Zxy1102pl0ocZFuUavVJbevXv7tHXR67dGvysFPa62gCg3EnwWlUDR24Ny9W4uLWpXQrFeLj7bIdAuBELBWY0o11N0hQ8//NANGDDA97fqdyXGNQ9DQrhv375OrqQWNHin7bp+9H3w4MF+voa26beFpL5e+8rpg7gov+qqq9xaa63l81LacrtVmya9UCmYKL/oooucdIqO3XbbbaNDQkaI8ghLTV8Q5VXiKtcJS+Rq9FijyDpB9aeRcQXdHXft2tVvGzZsWJTTTTfd5LfNN9987sUXX/Tb99lnH79tnXXWieLpy8EHH+w23nhjd8455/jt5S467Uy6UOOi3BLfbLPNfH7Vuq/YcfpElIc0+F5EArQHc9b6mWee6dsUtYEd3eTPeTRbIJBtAqHgjItyvYH7mmuucRpA0/kvwWvB3D3V30+fPt1v1iCX+nrF3Xffff02ucDot/5uv/12O9w99dRTbo011nDbbbede/311/32pL5eO8rpg7goV1xNSLX8anVfGT16tLvjjjui4/VdIWSEKPdIav6HKK8SWdgJ24mc9Ln//vtHKV533XXRSXvjjTdG2+W20qNHD79Pvt0K9thXIv6ss85yTzzxhPvqq6+iY+xLuYtO+5Mu1FpEuRoWuamEf9YIWP76RJSHNPheRAK0B/+pdbVnJ5xwQtTWrb322s7c5/4Ti28QaG8CoeBM6vttm0a///rXv0aFtSfeGo0Og4StjpFbq66hGTNmOE0S1TatYjRx4kT39ttvh4dE35P6eu0spw9qEeXPPvtsiQaQHpg9e7bP20bK5b6moFFy2atRcz0ZCxkhyj2imv8hyqtE1lEnrGURJ02aVJKafMPsQi33eeCBB/pjdCEstthiJfE1qXLrrbd2EyZMiNItd9EpQtKFWosoD++czV75wcUDojxOhN9FI0B78K8al5+rdfhqMyQmtFQiAQJ5IxAKTusf45/Dhw935rZi5dc8sni8+O+33nrLR1d/G98nP/Sjjz7aWRxFTOrrtb2cPrBr1HzKFTfs78ORcnuKHtqhPl8hLsq1lKL8yhX3wgsvdA8//HBkP6LcI6v5H6K8SmRhJxyuvhKOEF122WUlqWnE205sXRS64OJ/4dJJWl5Id6ByhbGRdDv+Zz/7mU87vOiUdxgk8C2+jVQhykNCfIdAOgRoD/41Kc0mfKnd0YoU+JGnc36RSvYIhKI8XH1FkzVtjtYKK6zgPvvssxLjNbim60MrE8X7f/v9zjvvRMdofpn68kGDBkUj5zpeC0C8+eabPl4oysMJoBLupgHChSAaJcpljNmiFZdCoY8oj6q0pi+I8ipxhZ1w2PHogtCdrC4EPYZ64403ohQ1wm0XyLXXXhttr+aLfNV1Umuih9LQSLyCLnhL84c//GFJUmEHWY8oL0mswg9GyivAYVchCBS9PdDj9h122MG3RRo9u/zyywtR7xSyuARCUR73KR87dmzUL0uwh8EmREpk1xo0SfSKK66IRqjPPvtsn0Q4GPjaa69Fyd5zzz2RHfWK8iixhC/xkXJF0dw5uynRanGmTxDlCQCr2IQorwKSopTrhLUvnPCgjsqCRr5toqdmTn/55Zd+l5ZK0rrmQ4cOdVOmTPH+ZCNGjPAzsrVaSxjOOOMMf5JLlKsjVNCbw3Ti60U+tnj/n//855K76o5EuZVH65SaXWG+lb4jyivRYV8RCNj1o+swvElX2YvQHmhJV+t89Whd/rDxv1rblSKcN5SxfQlUEuUaRNPqaromNKFT/bqFUEDfddddttnPw9Aqa7YKm9oNLeqw5pprlriAaZlEE70myvVU3q4/TbBW0By0sF3qSJSH5bGFJCLjynxJEuWKqnezmD32iSgvA7GDzYjyDgDZ7vBkj3fCiqP1xe1kHD9+vB1WcrIuueSSTv5aWu5IcXUR21KDWodU2zTRQ7Oyt99+e79fF7i2hxdYuMao/NCVjtLUCi9mgz1CK+e+cvjhh0dxNdKvG4RKQaJfj9/0p3JYPt27d4+2//KXv6yUBPsgkBsCRW8PtGqTtQHlPjVRjQCBvBAIRWx8pFxllAg10arrwxZq0MCZPU2X/7X66+9+97v++tFvrcamILdYe2P2oosu6pdF1rsAbBBOa5vbk3iNTss/XNeeNMO6667rll56aSefdvPxDldASnJfUX5mr5ZpXm+99fyAQqX6svg20dPiSsfoSUDYFiDKjU5tn4jyKnl11AnrYpFA1Ump2dfhizN00WlkXPs1cq7Z2D/96U/n8D377W9/6+SCogtSSyvp5UFyX9EqLmHQCJRE+rLLLuvT00UuX65LL700uijke65QTpRrVrcmZcnfTRd3uGRjmJd9v+2226K0wwsv/B7eONhxfEIgjwSK3h7YihLh9R//jijP45lf3DJ1JMpFRk+N7DrQQJsF+YzL93qZZZbxfbv6eA283X///RbFf7788st+GWT5pmugTX+61pRu6KaiyBqN32ijjbyukJiXf7qekCtt2aD8LCSJcu2T2410hkT5csst5+677z47JPGznChXZC0FbWXXJ6I8ESfUPi8AACAASURBVGGHGxHlHSIiAgQgAAEIQAACEIAABBpLAFHeWL6kDgEIQAACEIAABCAAgQ4JIMo7REQECEAAAhCAAAQgAAEINJYAoryxfEkdAhCAAAQgAAEINJ3Ak08+6aZPn970fMmwfgKI8vrZcSQEIAABCEAAAhDIJAEtx2jLKGbSQIyagwCifA4kbIAABCAAAQhAAALtTUBroMeXL2zvEuXfekR5/uuYEkIAAhCAAAQgUDACervoeeedV7BSt3dxEeUZrj+tTWpv7MywmZgGAQhAAAIQgEDGCEhDXHDBBRmzCnMqEUCUV6LT4n19+vRxH374YYutIHsIQAACEIAABNqNwJFHHukuuuiidjO70PYiyjNc/QsvvDCiPMP1g2kQyAKBCRMmuNNPPz0LpmADBCCQIQKHH364GzNmTIYswpSOCCDKOyLUwv2I8hbCJ2sItAkBifI99tijTazFTAhAoFkEfvSjH7lLL720WdmRTwoEEOUpQGxUEhLls2bNalTypAsBCOSAAKI8B5VIESDQAAKHHHKIGzt2bANSJslGEUCUN4psCun27t0bUZ4CR5KAQJ4JIMrzXLuUDQL1EzjooIPcr3/96/oT4MimE0CUNx159RkiyqtnRUwIFJUAoryoNU+5IVCZwAEHHOAuv/zyypHYmykCiPJMVUepMRLlH330UelGfkEAAhAICCDKAxh8hQAEIgL77befu/LKK6PffMk+AUR5huuoV69eiPIM1w+mQSALBBDlWagFbIBA9giMGjXKXXXVVdkzDIvKEkCUl0XT+h2I8tbXARZAIOsEEOVZryHsg0BrCOy7775u3LhxrcmcXOsigCivC1tzDpIo//jjj5uTGblAAAJtSQBR3pbVhtEQaDiBvffe2/3ud79reD5kkB4BRHl6LFNPqWfPnojy1KmSIATyRQBRnq/6pDQQSIvAiBEj3Pjx49NKjnSaQABR3gTI9WaBKK+XHMdBoDgEEOXFqWtKCoFaCAwfPtxdd911tRxC3BYTQJS3uAIqZS9R/sknn1SKwj4IQKDgBBDlBT8BKD4EyhAYNmyY+/3vf19mL5uzSABRnsVa+bdNPXr0QJRnuH4wDQJZIIAoz0ItYAMEskdg9913dzfccEP2DMOisgQQ5WXRtH4Horz1dYAFEMg6AUR51msI+yDQGgK77rqru/HGG1uTObnWRQBRXhe25hwkUf6Pf/yjOZmRCwQg0JYEEOVtWW0YDYGGExg6dKibOHFiw/Mhg/QIIMrTY5l6St27d0eUp06VBCGQLwKI8nzVJ6WBQFoEdtppJ3fLLbeklRzpNIEAorwJkOvNAlFeLzmOg0BxCCDKi1PXlBQCtRDYYYcd3G233VbLIcRtMQFEeYsroFL2EuWffvpppSjsgwAECk4AUV7wE4DiQ6AMgSFDhrg77rijzF42Z5EAojyLtfJvm7p164Yoz3D9YBoEskAAUZ6FWsAGCGSPwLbbbusmTZqUPcOwqCwBRHlZNK3fgShvfR1gAQSyTgBRnvUawj4ItIbANtts4/74xz+2JnNyrYsAorwubM05SKL8s88+a05m5AIBCLQlAUR5W1YbRkOg4QS22mord/fddzc8HzJIjwCiPD2Wqae00EILIcpTp0qCEMgXAUR5vuqT0kAgLQJbbLGFmzx5clrJkU4TCCDKmwC53iwQ5fWS4zgIFIcAorw4dU1JIVALgc0339zde++9tRxC3BYTQJS3uAIqZS9R/vnnn1eKwj4IQKDgBBDlBT8BKD4EyhDYdNNN3f33319mL5uzSABRnsVa+bdNXbt2RZRnuH4wDQJZIIAoz0ItYAMEskdg4403dg8++GD2DMOisgQQ5WXRtH4Horz1dYAFEMg6AUR51msI+yDQGgIbbrihe/jhh1uTObnWRQBRXhe25hwkUf7FF180JzNygQAE2pIAorwtqw2jIdBwAt/73vfco48+2vB8yCA9Aojy9FimnlKXLl0Q5alTJUEI5IsAojxf9UlpIJAWgfXWW89NmTIlreRIpwkEEOVNgFxvFojyeslxHASKQwBRXpy6pqQQqIXAOuus4x5//PFaDiFuiwkgyltcAZWylyj/8ssvK0VhHwQgUHACiPKCnwAUHwJlCKy11lpu6tSpZfayOYsEEOVZrJV/29S5c2dEeYbrB9MgkAUCcVH+wgsvZMEsbIAABFpMYI011nDTpk1rsRVkXwsBRHkttJocF1HeZOBkB4E2ITB48GA3YMAAN2nSJGeiXGJ81KhRrm/fvoyOtUk9YiYEGklg9dVXd0899VQjsyDtlAkgylMGmmZyEuVfffVVmkmSFgQgkAMC11xzjdPLxbp16+b69+/vFl98cbfgggu6BRZYwA0ZMiQHJaQIEIDA3BJYddVV3dNPPz23yXB8EwkgypsIu9asOnXqhCivFRrxIVAQAoMGDXLzzDNPyZ+WUcWHtCAnAMWEQAcEVl55ZffMM890EIvdWSKAKM9SbcRsQZTHgPATAhCICGi0vHfv3iWifOjQodF+vkAAAsUmsNJKK7n/+7//KzaENis9ojzDFSZR/s9//jPDFmIaBCDQSgLhaDmj5K2sCfKGQPYIqH147rnnsmcYFpUlgCgvi6b1O+QjiihvfT1gAQSySiAcLWeUPKu1hF0QaA0BTQZnNabWsK83V0R5veSacByivAmQyQICbU5g4MCBTk/V8CVv84rEfAikTOA73/mOe+mll1JOleQaSQBR3ki6c5m2RPnXX389l6lwOAQgkGcCl1xyidttt93yXETKBgEI1EFgueWWc6+88kodR3JIqwggyltFvop8tbwZorwKUBmNEl8Zg9+lK4XAAx6cA+15DmS0ycWsGIFlllnGvfbaa7Gt/MwyAUR5hmsHUZ7hyqnCNAkOAgQgAIE8EaBda5/aXHrppd0bb7zRPgZjqUM1ZPgkkCifPXt2hi3EtEoE6Lwq0WEfBCDQjgRo19qn1pZcckn31ltvtY/BWIooz/I5MP/88yPKs1xBHdhG59UBIHZDAAJtR4B2rX2qTG/6nTFjRvsYjKWI8iyfA4jyLNdOx7bReXXMiBgQgEB7EaBda5/6Wmyxxdw777zTPgZjKaI8y+fA6aef7r755pssm4htFQjQeVWAwy4IQKAtCdCutU+1LbTQQu69995rH4OxFFGe5XNAb+hj9ZUs11Bl2+i8KvNhLwQg0H4EaNfap866devmPvzww/YxGEsR5Vk+B7ScETOns1xDlW3Le+d14oknOpVx7NixlUHUuXfSpEk+/T322KPOFNrrsKyWV28V7tGjh/v+97/fXkAzYG1W63Ru0OS9XZsbNlk69rPPPnPdu3fPkknYUgUBVl+pAlKromy11VburrvualX25DuXBJrVefXq1cuL1759+7qvvvqqotXvv/++f/ujbNtoo40qxu1o51lnneWWXXZZp1e9NyLcd999Pv3DDz+8JPkHHnjAXXTRRSXb5vbHE088UTVD5aXrUgz1xrxvv/225uyTylCuvDUnnvIB999/vy/reeedV1PKn376qevZs6c/dvPNN6/p2LxEzmqdzg3fZrVrc2Mjxzr36KOPunXXXRcUbUYAUZ7hCjv11FOdRiMJ7UmgWZ2XRPm8887rxc8NN9xQEZbErOzS39yK8ooZNXDn8OHD3QorrJB6DurAxOW6667rMO3dd9/dxz3nnHM6jJsUoVFlSMprbreddNJJvqxPP/10TUn9+te/9seprsT12Wefrel4ImeTgOqSkH0CZ5xxhjv66KOzbygWlhDg6irBka0fjz/+uNNrcgntSaBZnZdE+YABA5z8B7fddtuKsNZYYw23+uqre5GUVVGuEdZKoX///g0R5VdddZXnMnjw4ErZ+9UMFlxwQde5c+e6J1E1qgwVDa9z59prr+203nGtQeeaHp/fcccdnmtWBIKebHz++ee1Fof4/ybQrHYN4HNHYJVVVnF6UkNoLwKI8ozX18477+xqfWyc8SLlxrzRo0e7Tz75pGx5mtV5SZQPHDjQafR2vvnmc6+//nqiTX/961+9ODrmmGPKivJXXnnFHXbYYW7QoEFOM/f1t9Zaa7lLL710jpWAyvmUa7b/scce69Po0qWLF2Zrrrmmk7vLF198UWLbySef7G259dZb3SWXXOK0rq6OUYj7444ZM8bHFdfw7yc/+Yn/fdxxx5WkbT/kW6lyaOL0xx9/bJvn+JRQ69Onj3/q8MILL8yx3zZodFz577PPPrbJye/6wgsvdOuss44vrwS7RojlehO+vKNcGTShO15eJX7vvff6vP77v//brze87777un79+jndFOiGXU/TkiaDa5R6tdVW8ywV/8ADD3QffPCBt0e2K69qgupST2FGjhxZTfQoziOPPOLt3nvvvf15I1G/8MILz1H/p512mo+nczIpaJKayipxr3q0IC7bb7+9k8uW9i+11FLexni9iY3Kq3NY9SA3GtXNr371K0vK1XLO66BXX33V7bnnnj5vnVOq8xtvvNHJNUx5rb/++lHaWazTyLg6v6iM5YLaQ7WLhNYSUFu6zTbbtNYIcq+LQPmrq67kOChtAs8884wXFLfffnvaSZPeXBKQeJQg+PGPf5wozit1XnOZdcnhEuXLL7+8k+uK8vzZz35Wst9+HHHEEX6//JkVLz5SLrGxxBJLeBGmEfcTTjjBHXTQQV6oKr6EYRiSRPnbb7/txaLiSwDJllNOOSUand9ss81KROQvfvELb4vy0ki/3Dr2228/n01c0Dz55JNOj2SVtsTYBRdc4P+ef/55v03iM0mg/v73v/f7lXZHwW5YZE+5oBsW2SDhqaBlS7fbbju/Tft0Pqhctk2C9LXXXvNxy5VBo7fx8uoAE7e77bab918/4IAD3Pjx491vfvMb993vftfnKWEbhrPPPttvlz/3j370I6cbn//6r/9yq666qtOkWdl+zz33hIeU/X7ttdf6+MqzlqAbFuXz5z//2R8mnvo9bty4kmSs7jQ3ISlceeWV/rjwpkBl142CbrR++MMfOpV/11139dt0LUybNq0kKb3vYcUVV3RDhw71HMTw5ptv9nFqPed1Y6NXl6ssun6Ut2xQW/Dzn//cb990002j/LNYp5FxdX5R2eNBYlznvdpDu6mOx+F3cwhovoveBq5BGEL7EZjz6mq/MuTeYj3+1YgMI+bZqmqNjKoD6tSpkx99i4vzpM6rESWQEJGo+fLLL72A1ghqfPKhJoAussgifsRQo58mKkJ75F6g7fER5xdffNGXUQ19uLxWkigfMWKET0Mj5WGYPXu2k1uI0v+f//mfaNcvf/lLv01l0ITCMCQJGrM97lO+ySab+HRuu+22MAn/XYJN+d55551z7ItvkEiU4NNLNzT6HQ96HKy0JHItSCRq24YbbujrwLbrUyPZ2jds2LBoc7kyJJVXk7V0vP7OP//8KA19kY+3toeCVk8CJFZVV7oBsCD+O+ywgxdNOkYjzdUEiWHxqOUFJBox1nURnofyJ1e+G2ywwRzZyj1G++SuFw9a8UX7TNzr5kYj3bqB+9vf/lYS3W5K45PbdH1qpF1piUMYaj3n7cnOD37wgzAZ99BDD/k2WrbqxtNCFuvUbKv3U2W0YGJcdSLO6qfULhJaQ0ADFaqHW265pTUGkOtcE/jP1TXXSZFAIwloxHynnXbyI6J6XP+nP/3JP47l5UKNpN5x2nJ3UCelv7g4DzuvjlOqP4aJcqVgo+E6P8Jw/fXXexv1WNNEYXykfOrUqW7ChAnuzTffDA/13/VIXuWR+LAQF+XyBRcD/X300UcWLfr84x//6NMI85UbgdINH/nbAUmCxmyPi3IbUZUAD4NEgwSiRtHjgiyMF37feuutvU1iFg977bWX3yf3EAsqj8oge+NB+YuHRhDNj7lcGZLKa6JcN1RJNwlaqlB526o7N910k/+tUfp4eOmll/w+xa9WlOvJiVyPagnm3hN/YqObFuX91FNPlSSnwQZt101tGCTudXMh1xRr5+zJyvHHHx9Gjb7LlURphYJdglHbkuqn1nPe5mNMmTIlytO+yLVI+VQryltVp2ZvvZ8qY1yMa5v+VCZCcwjompBLltp6aQKtBLXjjju66dOnN8cAcmkIAUR5Q7A2LtHHHnvMuwNsueWW/jGqOhy9+dMaRT5L/Y1bwUMCTO4GyrsZIRTlGh1VvvG1veWOonNFI90mCkNxHLdTAnbWrFk+ruLreKV79913R1HjotwEpCb4JQU9+lcaGuW0YKL8qKOOsk3RZ5JINdvjovwf//iHT1cCWGLOgpZrVJ5x1xvbn/Q5ceJEf4zEeRjETgJfbiHKT0FPJLRNeSTdiCiOfLu130aCy5UhqbzGVII2Kcg1JszbRKtGdJOCubxUI8olnpW26rnaIB6qG81tMJcdO/aKK67w6cmlJgwSFoqveRFhsCcQoQDXaL9s0s1HUlA9a78m7VowUa7zuVLo6JyXCNL5JVvtJihMz9ykqhXlrajT0N56v4uv2je1c/rOX2sY6LyWK9UWW2zhNUHSjWK9dcxxrSPQHNXQuvKRMwQaSkAjQ9YpqcNWQ2luLNrejBCKcuUndwDZIRGsoJFv+dWaUDdRGBflcn8588wz3corr+zjW7nCz0qiXJM1FbfSS2Zkh+LYhE8T5co3HpJEqtkeF+U6Vq4WSvviiy+OktLIkbbF/YyjCAlfJM7U2cltQ6PLFmw5SU2EtaARQ6Uv3uWCOk3FsZHacmVIKq+Jck1qTAoaRVbaJjhNlOoxdlIwP/dqRPm5557r0548eXJSUonbrAxJk8zsxkk3NfEVdsy1KRxF13saVLZw23rrree3aXulP7lFWVDd6NpMCrWc83INUp663pKCRJH2VyvKW1GnSXbXuk1lLDdSrieHhMYTSLopbHyu5NAMAs1RDc0oCXlAoMkE5DspH8q4GDcz1Hk1I8RFuXy2lbf5dprfttxHFEwUxkW5rb2tyYpyKZBfokS4/sz1oBpRniTIjINGGWWbxJCCiXIJwHgwgWc3E9pvtieJcglNpa2VNhQ0cq260Uh1rcEm7emxsAVNlFT6oWuEifJyok/HmuC0F4GVK0NSeWsV5eYjbXVvttvnkCFDfBmqEeUSxfJPt7qyNCp9ysVOjDr60yh4GC6//HJ/jHzwFd59911/Yyh3kTCYG9WoUaP8JEtNtEz6CyeySpSHT2fC9Go553U+qVy9e/cOk4i+6ymm9qctytOs08jYufiiMlqIi3N8yo0MnxCoj8B/rq76jucoCBSWgNwW9AjXRsbjIMLOK74vzd9xUS7xoM7RfIElspdZZpnIL9dEYSjKn3vuOS8oFl100UQ3DLlLqTyVRLmNFMaFlJXV8pUftIU0RblcJ+RXKTtVHlt3PEnwW/7lPmfMmOHrVks0auT8L3/5i083XFlDxypPCVflGU6CDdPVesHaL/9lBeMQv7FIQ5RLoCovfSYFc6XpSJTL/11iNsk3PSldbdNSnHoSItGqpxZJfyaC9TQnDBrp142NntIoXHbZZb4c8brTErEqn1xFqg3lRHmt57zOA91UqoxJq/xoWUTZlrYoT6tOq+XVUTyVMR5MnKs9VLtIgAAE6iMw59VVXzocBYHCEcjSOuXhChyqCK0Prc7TXEpsBFL7TBSGolwrkyh+0iN1iU2NNGp/JVEuIacOOb5Ki50YZovcOSykKcqVpiYXyk6NdMsnXCIqXCfc8q3mUytsKC2t/HHkkUf670lv+5QIU7yklV/ETjx0k2Qjzsa/EaLcfOjjE15VXvl4yyVHtnYkyu18KOcGk8TPVibpyH9/pZVW8jaYj72lZYJbq/3oJjCp7uypj9Z/TwrirRulMJQT5VbGWs55vaRL/JIm02kpT+1LW5SnVachk7n5rjKWCxLnrFNejg7bIdAxgfJXV8fHEgMCEKhAoFLnVeGwmnfFR8qVgLlyaJRcQuzll1+O0jVRGIpyiQzZq8l2GhG0IKGtyXUaMdZ+LTtnIT7RU9vlVqB48YmbEqTmeiCRYaFWUS6/ZKUv39W4+FKaWnda5dVNikY0K7nSmA3lPo2h1rXWKiRaJjHJl/Pqq6/2Nql8JrwtTXGQvUrDQrkypDFSrnXiVW7dBOilOBZUpxK9EqiypyNRbi4TWvWpmqCVYewc6egY81Xff//9S5K2iZKarKobGbnPxIMtiajy/f3vfy/ZbWvka83+8EVD5UR5Pef8oYce6vkdcsghJXlrPXlbCSdtUZ5WnZYYPBc/dP4QIACBxhDg6moMV1KFgO+8m4EhSZRLsGokVh1o/LXxSaJcK0tofWfF1yillrXTC18kbrX2uN7GqX1yibGRsCRRrvWszYVE6Uhgyb1HNwc6Xqs2hGK6VlEunnLFUVoa4ZQ40shqGGxipeKENwBhnGq/a7USCUSlddJJJyUepvLYhFKNAutlSRqxtxF0lT1cEUaJJJUhDVGutLW6iezVTYTWnJf7g1bE0VrueoGO9nUkylVuTSKtNpig3njjjTs8RP7iclXR05dwxRrdAErYmriNv2jIEh47dqy/8dLa43pTqZ6K6FNvDNUNmd4+G4Zyoryec17nmtkn1x69zEo3XLLFloJMW5SrLGnUachkbr7r/CFAAAKNIcDV1RiupAoBL36agSFJlCtfrWiiDvR3v/tdiRlJolwR5OYhAa5RYY1Eyhdao5oSLzpGgksCR8JTIUmUa7sEqF4epEf9El8SLBpFlphSWmGoR5RrwqqEv/xXddOgF9OEQeVVuZVvOGIaxqn2u1ZyUVpypdAofLkgH2OtzqJJpvIxN06aKGoro4THJpUhLVGuUWstk6oRY/HXDYBusCR6balOvQSpXHjjjTd8mfWmymqD3t4qTuWEdDwdcw0aM2ZMyS5b61sM9UShXNBETt2UafUjnQc6ZzXJ1F4yFB5XTpQrTq3nvI7R3AA9gdH5pT+VXTc59jIn/baQlTo1e9L4VD0TIACBxhDg6moMV1KFgBcpecZQTpS3usz2avpaRGWrbW5W/pqoKlEV9+duVv55zkc3CmKb5KPeyHI3u04R5Y2sTdIuOgFEedHPAMrfMAJ577z0UheVMXy7ZcNg1pCwllCUXVoxpWhBTyK0NN+11147R9E1mq83m4pN3J1mjshsSCQwc+ZMd8cdd5SsnW4RNeovtnqrbpoha3WqMhIgAIHGEODqagxXUoWA76DzjGH48OG+jFoKLitBb3qUX3G4wktWbGuWHea/f/vtt5dkaS8/KvcmyZLI/EgkMH78eH/Oi6HchCzoRV22Motee552yFKdIsrTrl3Sg8B/CCDK/8OCbxBIlUBeO6+bb77Zr0GtCZDy59X61K0MGgHWqP2wYcO877d87LUGdVGDxLjqRv7ke+21l5Nfu1wqdD7KV1sj6YT6CGj1HXMX0ZwLnXeahKk3wIqvfOUbEbJUp3lt1xpRb6QJgVoJIMprJUZ8CFRJIK+dlyYRatm9/v37O72JsdVByxBqRQytka6VL8LXsrfatlbl/9BDD/kVYZZccsnoJUhy69FkRMLcEdAEVK0qJFGuiZ6aFK1Vic4///yS5UTnLpc5j85Knea1XZuTOFsg0HwCiPLmMyfHghCg8ypIRVNMCBSIAO1agSqbojadAKK86cjJsCgE6LyKUtOUEwLFIUC7Vpy6pqTNJ4Aobz5zciwIAXVe/MGAc4BzIG/nQEGacIoJgaYTQJQ3HTkZQgACEEiPgHzq5U9PgAAEIACB9iaAKG/v+sN6CECg4AT0qnqtOEOAAAQgAIH2JoAob+/6w3oIQKDgBN555x232GKLFZwCxYcABCDQ/gQQ5e1fh5QAAhAoMAGtE7/MMssUmABFhwAEIJAPAojyfNQjpYAABApK4Pnnn3cDBw4saOkpNgQgAIH8EECU56cuKQkEIFBAAnoh0KqrrlrAklNkCEAAAvkigCjPV31SGghAoGAEnnjiCbf22msXrNQUFwIQgED+CCDK81enlAgCECgQAb1+faONNipQiSkqBCAAgXwSQJTns14pFQQgUBACkydPdltssUVBSksxIQABCOSXAKI8v3VLySAAgQIQuPPOO912221XgJJSRAhAAAL5JoAoz3f9UjoIQCDnBG666Sa3yy675LyUFA8CEIBA/gkgyvNfx5QQAhDIMYHrrrvODR8+PMclpGgQgAAEikEAUV6MeqaUEIBATglcffXVbuTIkTktHcWCAAQgUBwCiPLi1DUlhQAEckjgsssucwcddFAOS0aRIAABCBSLAKK8WPVNaSEAgZwRuOiii9yRRx6Zs1JRHAhAAALFI4AoL16dU2IIQCBHBM455xx3/PHH56hEFAUCEIBAMQkgyotZ75QaAhDICYGf//zn7pRTTslJaSgGBCAAgeISQJQXt+4pOQQgkAMCJ598sjvjjDNyUBKKAAEIQKDYBBDlxa5/Sg8BCLQ5geOOO86de+65bV4KzIcABCAAAUQ55wAEIACBNiZwxBFHuIsvvriNS4DpEIAABCAgAohyzgMIQAACbUzgwAMPdL/5zW/auASYDgEIQAACIoAo5zyAAAQg0MYE9t13Xzdu3Lg2LgGmQwACEICACCDKOQ8gAAEItDGBPfbYw02YMKGNS4DpEIAABCAgAohyzgMIQAACbUxg6NChbuLEiW1cAkyHAAQgAAERQJRzHkAAAhBoYwLbbrutmzRpUhuXANMhAAEIQEAEEOWcBxCAAATajMD06dMjiwcPHuzuueee6DdfIAABCECgPQkgytuz3rAaAhAoKIH77rvPderUye23337utddecxtuuKF7+OGH/Wj5yiuv7I488siCkqHYEIAABNqbAKK8vesP6yEAgQISWGONNdyCCy7o5plnHterVy+3/PLLu+7du7suXbrgylLA84EiQwAC+SCAKM9HPVIKCECgQARuvvlm17NnTy/KJcztb+utty4QBYoKAQhAIF8EEOX5qk9KAwEIFITAPRgC3QAACPFJREFUiiuuGIlxifKuXbu6Rx99tCClp5gQgAAE8kcAUZ6/OqVEEIBAAQhcffXVrkePHl6Yy21lt912K0CpKSIEIACB/BJAlOe3bikZBCCQcwL9+vXzonyBBRZwzz77bM5LS/EgAAEI5JsAojzf9UvpIACBHBO48MILXefOnd2hhx6a41JSNAhAAALFIIAoL0Y9U0oIQCCHBGbPnu323HNPN3PmzByWjiJBAAIQKBYBRHmx6pvSQgACEIAABCAAAQhkkACiPIOVgkkQgAAEIAABCEAAAsUigCgvVn1TWghAAAIQgAAEIACBDBJAlGewUjAJAhCAAAQgAAEIQKBYBBDlxapvSgsBCEAAAhCAAAQgkEECiPIMVgomQQACEIAABCAAAQgUiwCivFj1TWkhAAEIQAACEIAABDJIAFGewUrBJAhAAAIQgAAEIACBYhFAlBerviktBCAAAQhAAAIQgEAGCSDKM1gpmAQBCEAAAhCAAAQgUCwCiPJi1TelhQAEIAABCEAAAhDIIAFEeQYrBZMgAAEIQAACEIAABIpFAFFerPqmtBCAAAQgAAEIQAACGSSAKM9gpWASBCAAAQhAAAIQgECxCCDKi1XflBYCEHDOzT///G6eeeap+LfHHnt4VmPGjIniTZs2reH8FllkEZ/fzjvvXDGvX/ziF5Fd8bJ06tTJfec733EHHHCAe+mllyqm04qd5co4ceJEd9ppp7nPPvusFWaRJwQgAIGWEkCUtxQ/mUMAAq0gUIsoHzdunFt22WX93zPPPNNwc8sJ1njGlUR5KNJ79erlpk+fHj+8pb/LlXH55Zf3NxrvvfdeS+0jcwhAAAKtIIAobwV18oQABFpKwET5+uuv7yQAk/4++eSTlthYTrDGjQlF+Y033lhShr/97W/u4IMPjkbSd9ppp/jhLf2dVMapU6dG9iLKW1o9ZA4BCLSIAKK8ReDJFgIQaB0BE+UbbbRRh0Ykua888sgjkYC866673PXXX+9WX31117VrVzdo0CB38cUXz5Huk08+6eQS079/f9e5c2c/8r7PPvu4V155pSRukmAtifDvH6Eov/vuu+eI8u2337pFF13U27nEEkuU7H/66afdsGHD3GKLLeYWXHBBt8wyy7hjjjnGffzxxyXxXn31VXfYYYe5AQMG+LL16dPHbbDBBu6qq64qiac4Njr/xRdfRPskrm37iSeeGG2Pl1H1YPHsU3VEgAAEIFAkAojyItU2ZYUABDyBuRXlEtgmHvfee28vWNdZZx3XvXv3aPv48eMj2hLBPXr08PuWW245t+uuu7q+ffv63/369XPvv/9+FDcuWKMdsS/ViHLLY6WVVoqO/stf/uIWWmghn7f8znWjIHGu8nzve99zX3/9tY/77rvvRqJ+8cUXd1tvvbVbb7313Hzzzefjnn766VGacyvKjz76aGeuK7Jj0003ddtss02UPl8gAAEIFIEAorwItUwZIQCBEgJzK8olsk2US+Dqt8Jzzz3nFlhgAb9v8803j/KUgF1hhRX8iPNbb73ltz/44INRGmPHjo3ipiHK33nnHXfsscdG6WsU3MK6667rt+vm4NNPP/WbX3/9ddetWze//be//a3fduWVV/rfEuHhCPrll1/uxblG+TUarzC3olxpaIKnMcV9xWPlHwQgUDACiPKCVTjFhQAEqlt95dxzz/WoktxXQlG+3377lSA1Vwy5hCSFDz74wL3xxhteyJsIPemkk6Ko9YhySyfpUyP4H374oU9/xowZkfA94ogjojz1ZYcddvD7dtxxR799woQJUVz5p99zzz0l4jw8GFEe0uA7BCAAgfoIIMrr48ZREIBAGxOwkfIkEWvbqhXl55xzTgkJuaYoDYlrCxodHzlyZDQabXnYZyV/a0sj/hm6r1g68U/ZFvp4P/roo5HQjse13wMHDvRZ6ThNhLXt+tSo+dprr+3i6SLK47XDbwhAAAK1E0CU186MIyAAgTYnYKK80uorn3/+uS9lRyPlF1xwQQmN3XbbbQ5Rvuaaa/ptcnUZPXq0u+WWW1w4Ej23ojxcfUVpm5AePnx4iW3yJ7d9KvtRRx01x9+pp54aHSP/8v/93/91u+++u9NkUTtWn1tuuaX75ptvfNxQlBs37dDNiB3TURlxX4mw8wUCECgoAUR5QSueYkOgyARMlNe7+krovtKRKH/55ZcjYRq6umjt8GoFa1JdhSPl8dVXNHnT0r711lujw2fOnBltP+igg6Lt1X557bXXnNxeLO0pU6b4Q0844YRom+JYkMuLxUWUGxU+IQABCCQTQJQnc2ErBCCQYwLNFOXyHzdhuueee3qqs2fPdkOGDIm2H3rooRHtenzK46L87bffdj179vTpL7XUUu6jjz6K0tcKKrJH+bz55pt+u0a3hw4d6rbffvtoucOzzjrLu6qMGDEimtCpyA899FBkt4nyyy67LNp25pln+jS/+uorP5puZe9IlIc3GY899lhkL18gAAEIFIUAorwoNU05IQCBiEAzRbkyXW211bxonXfeeb0YX2WVVZyWKdRLfSRatVyifM4V0hDlSkdrpZsgPvDAA33a+qc11rt06eL3SbhvsskmkWuK1jV/8cUXfVwJfVtJRuuU6yZi8ODBkV+81is39xUt6ag3hyo/lVErvCy99NJO7jOWxvHHHx/ZkFTGP/zhD5G9Wg9dE1RZhSVCxhcIQKAABBDlBahkiggBCJQSaLYol9CVqJVwleDca6+9nJYtnDZtmtPESgnXDTfc0BuZJFhLrf/Xr3BkOT5SrhgSzJqUaUJZriQWlK+tla6XB+mFRvvvv/8cLzK6//773S677OI02q4XHsn2tdZay0/0tOUULU2NmssdSGu1a+11+atrsqi9wEh+5xaSyih7DzjgAM9IL2HSjcysWbPsED4hAAEI5J4Aojz3VUwBIQABCEAAAhCAAASyTgBRnvUawj4IQAACEIAABCAAgdwTQJTnvoopIAQgAAEIQAACEIBA1gkgyrNeQ9gHAQhAAAIQgAAEIJB7Aojy3FcxBYQABCAAAQhAAAIQyDqB/wdbG96ehSZfmQAAAABJRU5ErkJggg=="
    }
   },
   "cell_type": "markdown",
   "id": "ed281a34",
   "metadata": {},
   "source": [
    "The Random Forest Model utilise multiple randomly created decision trees by combining their output to generate a prediction\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ce73bf9",
   "metadata": {},
   "source": [
    "The  combination of learning models will increase the accuracy of the result. The random forest algorithm allows easy  measurement of the relative importance of the features used on the prediction. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f9414d0",
   "metadata": {},
   "source": [
    "Utilising the Random Forest Model:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b57dc77d",
   "metadata": {},
   "source": [
    "### 1. Preprocessing:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff0a602f",
   "metadata": {},
   "source": [
    "Setting up the basics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "1a7a3408",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=train_dummies.drop('avg_price_per_kg',axis=1)\n",
    "y=train_dummies['avg_price_per_kg']\n",
    "\n",
    "# standardize features\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "\n",
    "# Train test split\n",
    "x_train, x_test, y_train,y_test = train_test_split(X_scaled,y,test_size=0.2,random_state=6)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdebb2b2",
   "metadata": {},
   "source": [
    "### 2.Training "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c45d7ba",
   "metadata": {},
   "source": [
    "Fitting the model to our data and applying hyperparamers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "18e45468",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(max_depth=5)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Our forest consists of 100 trees with a max depth of 5\n",
    "RF = RandomForestRegressor(n_estimators=100, max_depth=5)\n",
    "RF.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f9bd0c6",
   "metadata": {},
   "source": [
    "### 3.Testing "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c0ec4c4",
   "metadata": {},
   "source": [
    "To understand the performance of this model, we will get the mean square error and R^2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d1aa64ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 0.7401667648650816\n",
      "R^2 0.883056039583353\n"
     ]
    }
   ],
   "source": [
    "# Get predictions\n",
    "y_pred = RF.predict(x_test)\n",
    "\n",
    "# Compute RMSE\n",
    "print(\"RMSE:\",np.sqrt(metrics.mean_squared_error(y_test,y_pred)))\n",
    "print(\"R^2\",metrics.r2_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1d23429",
   "metadata": {},
   "source": [
    "Then we will evaluate it against the expected output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b922e642",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/c1ZS7e5KkpcDO7XMY/2WylqJq6S0jLumLmbdlkr2Xvct748eCMDEw37F0H0HwLBpFEZJdKHJsG1+Hpu376CyynlgMpNGJom1T3a2JUfPRGQgMBCgc+fOPkdjTOLuKFnEmNkra52rrIZ1WyqBmsQ195u1jP9kFZVVyv4/ruSdp/8AwL+7ncVffn1t8LOREl14CWp9RWWdOJKVNDJJKvfJTndv9fcisieA++cP0S5U1dGq2l1Vu3fo0CFtARqTbOPmrIp7TUVlFePmOInx4B/+F0yMTxzTt1ZiDL1+5Js1vdWRSlCRJCNpZJJU7pOd7uT4CjDAPR4AvJzm5xuTFiWlZXT761sUDZtGlccdPqtUOWLNl7z+7A0APHx8P+495Yqo14cmOq9JLxlJI5Okcp/slFWrRWQcTufLriKyGhgOjAAmiMiVwErgglQ93xi/lJSWMXTSgmB7n1fHlC1hwpihAPz9lwN4rEfsX4/QRNexIJ+yOAkyWUkjk6Ryn2zbt9qYBMTqCe45YnrcRBXu5NWLeG7srQDcddrVPNu9T8zr8/NyOe/oQmZ8Uc636ysoaJXHz1t3UFld8/uclyPs1LJZnU4fUyPWvtUZ2yFjTKYJJMSy9RUIEEhD4R0k9WnXE6Dv9wt5YOxtAJTePoKS5t1gS90OlYDCgnxOOagDL80rC7YzrttSSV6uUJCfx4YKS4bJYMnRGA/Ce4PD61uhPcFeqrjgJLmZB22Ec53EyPPP0+3SSyn1EE/PEdPrdMBUVimtWzTj0+G/9nAHE4/NrTYmjpLSMoZMWBC3NzhQYhzaqwt5ubGXD8vLFUaxFM491zkxfjxceqnnmFI5hMU4LDkaE0OgxOilxznQQVLcrZCR53elXau6m1sBtGuVx/jmS/nFrX9wH1ICF15Yr7hSOYTFOKxabUwMXscPhvcEx9y75fHH4drBzvHrr8MZ4UsQxDe0V5da1fxIMZiGseRoTAyxqqmBTplo0/kieughGOwmxhkz4OSTE4orlUNYjMOSozExROtcyRXhgQu71i8Z3Xsv3OZ2vsycCccf36DYbGfB1LI2R2NiiDYDo96J8c9/rkmMn3zS4MRoUs9KjsbEkJTq65Ah8OCDzvGCBXDEEREvC12RB6AgP487ex/a8OebhNgMGWNS6dprnQ4YgM8/h4MPjnhZtCmHOUBurtQ6n5+Xy719D7cEmQSxZshYtdqYVBkwoCYxfvVV1MQITskw0lzsaqhzPnxFHpMaVq02JhXOPx9eesk5/vpr2HvvmJfXd/C2DfZOPSs5GpNsZ5xRkxjLyuImRqj/4G0b7J16lhyNSaaTToI333SOv/8eOnb09LFoUw5zoM55G+ydHlatNiZZjjoKSt1lI378EXbZJfjWsfe8zfebtte6XKBO73NobzVA21Z5nHXEnsGlyay3On0sORqTDAceCMuWOcfr10PbtsG3IiVGcGbXRNoPJnRa4Lotlbw0r8x6p31g1WpjGmrPPWsS46ZNtRIjEDExhgrtfY61m55JLys5GtMQrVvDli3O8ZYtkJ9YR0mg99mWIsscVnI0JhGqIFKTGLduTTgxQk3vsy1Fljms5GhMfalCTk254uU5Kxg+4v3gXtHtWuUx/JxDg22EO7fIZeO26MuehfY+21JkmcOSo8lY0fZsgdoJ6JInZzFz+dpany3Iz+PsrnsybeGaWr2/XoQ/K2DnFrns3KIZH91+evDcfkNfpmry4lrXrdtSyaDxn/LojGV8Vb6ZWDN0c0VqtSnaUmSZw+ZWm4wUvmdLJHm5QtEurVj2w+a0xJRTXcX/RtbsCrjPLa+gkryWKZsznX42t9pkHS8rcFdWadoSY25YYiy6ZWpSEyNYr3SmseRoMlIm9c7mVVWyPCwxIrE30EpUJn3vps6So8lImdI7m799K8vud3YIrMzJTWlihMz53iZOchSR9rH+S1eQpumJtAJ3uLxcoVlO6hJVm22bWfLQ+QBsaNGaA4a+nNLEaL3SmSVeyXEeMNf9sxz4EljmHs9LbWimKSvuVsi9fQ+n0C1Jhaekdq3yGHl+V6qqo3co9u/ROer2qLEI0G7LBhaNuih4ruug8fW6R7tWefTcrz25bjINj79VXg79e3SmsCAfwdmkyzpjMoun3moReRx4RVVfc1//BviVqg5J6KEiNwFX4YyYWARcrqpbo11vvdUmmp4jpkfcAKuwIJ+Zw05N7KZr1tReTUc1OKwodHgN2JCbbBert9rrOMdfqOo1gReq+rqI/F+CwRQCNwCHqGqFiEwALgaeS+R+pmlL+qDplStrrb/Y8953GVpaFnWnP0uGjZfX5PijiNwBjMEp7fUHfmrgc/NFpBJoBXzbgHuZJiypg6aXL4f99w++LPrTqxBh1RzTNHhNjv2A4cAUnOT4gXuu3lS1TETuB1YCFcBbqvpWIvcyBpK0f/Pnn8OhhwZfFv3p1eBxYPyhJcemxdNQHlVdq6o3Aieq6lGqOkhV18b9YAQi0g7oA+wDdARai0j/CNcNFJG5IjK3vLw8kUcZ401paTAxVubk1kqMAWXrKygpLUt3ZMZHnkqOInI88BSwE9BZRLoCv1fVPyTwzF8BK1S13L33ZOB4nCp7kKqOBkaD0yGTwHOMiW/2bDjuOAC2FbTnkGteINpk6PDqdWgnTdv8PERg/ZZKOhbkc8pBHVK6enekDiIr2SaX10HgDwG9cNsZVXUBcFKCz1wJ9BCRViIiwGnAkgTvZUzi3nsvmBh/7lTEkX8cS1WM0Ruh0/sCc7/L1legwPqKStZtqQyu7j1m9srge4HVvpNV8gx/drLvbxyeZ8io6qqwU7Envka/zxxgEjAfZxhPDm4J0Zi0ef11OOUU5/jooznxqifizuUGgsOGvMz9DpXMedO2Wnh6eO2QWeVWrVVEmuMMxUm4tKeqw3E6eIxJqYjVz68/hr59nQtOPpmSB8ewbvynnu4n7j0TmQOdrHnTtlp4engtOV4DXAcUAquBI4FE2huNSZuS0jKGTlxQq/r54fBRNYmxTx+YMYO7pi6OdZtaFKfklsgc6GTNm7bVwtPDa3LsoqqXqOruqrqbqvYHDk5lYMY01J2vLKYyZHrhxZ++wQNT73de9O9PyfBH6fbXt+q9GO636ys8zf0OlZcrSZs3HenZNi87+bxWq/8JHOXhnDEZI7BtAcDlc19m+LtPAvBCtzNpM3gEQyctoLKq/gMhOhbkR91nOqokjrew1cLTI2ZyFJHjcIbZdBCRwSFv7Qx4/2fTGB9d99/xDP3wBQAeP6YvI065gnZTF3tKjPl5uTGnJv68bYenGCqrNakDyZMy8N3EFK/k2BxnbGMzoE3I+Y3A+akKyhjwNpYvdJ+ZXBGqVCl0xxkKcPP7z3Pd7IkAPNTztzx8wm8BPFelj+rcltn/W0eVKrkinHd0Ya2SW31KnpE6TGy8YuaKmRxV9X3gfRF5TlW/SVNMxtTZQ6Yswhzn8GsCYxQD4wyHv/MEl8+bCsDdp1zBU8f0rXcc/12+NlgjrlLlpXlldN+7PcXdCuvdOxzeYeLlOxr/eO2QeUpECgIvRKSdiLyZmpCMiT6W77bJC+k5Yjr7DJvGkAkLoo41vO+1UcHEeMev/5BQYoS6TYWh4wnr0zscqcPExitmNq/JcVdVXR94oarrgN1SEpExRB+zt6WyOjg0J9psln9N+RsXLnoHgMFn3cSYbmemJLZ4vcPtWuXFXMg21njFktKy4D8CPUdMt9kvPvDaW10tIp1VdSWAiOxNUvvfjKmtY0F+xEVs4/n3+D9z0telAFzbZxivH3RCskOjwF1dvLhbIRPnrqyzZ3ZAq+bNKP3Lr6PeJ9p3LGiVZ9XtDOC15Hg78JGIvCAiL+AsWXZr6sIyjZmXUlEiY/am/HtIMDFefv7wBifGvCi/HaEF1rFXHxf18/HaJKONV1TFqtsZwOuSZW/gjGkcD0wAjlZVa3M09eZ10YTiboUU5Hvf/+Xtp66l2xonefz2oruZsd8vGhRnjkBldeT3NlTU7ukuTHDGSug+OaHV7/D7B9j0wPSKN87xIFX9QkQCg70DK3Z3dqvZ81MbnskmXoalxOqEKO5WWOseBR43x5r96KXs8bNTtT3vkvuYt9chDf4u1erMo47UdhSe9BqyVUOk8YqBoUnxnmtSK16b4xDgauCBCO8pkOAORiYTJHOMXaRhKUMnLeDOVxazoaIyeP94nRCh9/AyFnHqczcGE+PZA0bx2R77x/mEd0rdBBkp6SV7xkrS98UxCfG0+6DfbPfB5AtPROD8AnrdHjQ8sW7etqPWdL1I8vNyadEsJ+51Xj0x+W56LZsNwK+veIQvOxQl5b7hCgvy0z5I2waHp0fCuw+KSMzBYao6uSGBGf/Eq97GEqmU6EVFZRUt83LqTMlLRGiv9C+u+zflO7Vv0P2iadAWrw1g0wP9F69afY775244c6ynu69PAd4DLDlmqYasCVjfhV5Drd9SySU9OjNuzqqYq27HMnHMLfyi7HMAjrp+LGtbta33PXLEaVeMxaqyTVu86YOXA4jIqzj7TK9xX+8JPJr68EyqRBtj56XRv0G9pgJjZq9M+OOvP/NHDi7/GoCuN4xjQ36b2B+IIDdHqIqTGQutKtvkeR0EXhRIjK7vgQNTEI9Jk/o0+oe3fxW0yovYWdIuyvlQDWni/uDxK+m84XsADhs0gZ9btKr3PXIET4nRj6q0ySxeB4G/JyJvishlIjIAmAbMSGFcJsWijbGLtOpN+LjEn7fuIC9Xal2Xn5fL8HMOjTrmr6Hm/vOSYGI8+KZJCSVGiF+VBhtPaByeSo6q+kcROZeaHQdHq+qU1IVl0sFLo3+k9sXKaqUgP4/WLZpF7E0NL5E21BcP9KXlju0AdBn8EtvyWiR8Ly9tjW3rMfjcNF5eq9Xg7Ba4SVXfcbdVbaOqm1IVmMkM0UpRGyoq+XR43XnDgQQ5yOOGVQF5ucJOLZrVrpar8vV95wRfHnDzFCpzG5i41HlWrHUYN2/fQUlpmbU3NnGeqtUicjXOdqpPuKcKgZIUxWQySCKbORV3K6x39bqySlGlZq5xWGLc/+aSuImxsCCfnvu1J1ck6jXVQOvmzYLNCTkRLq2sUpvHbDyXHK8DjgHmAKjqMhGxJcuymNdBxl47bsLvd8pBHXhpXlm9qtcbKip56KIjuf/1JXx0++nB8/sNfZmqnJoFGgRYMeKsuPfbZ9i0iNP/Qku9+wybFvGz1u5ovCbHbaq6Xdx/kUWkGbZkWdaqzwrUXqbGRbrfS/PKOO/oQmZ8UU7Z+oqo85RDdSzIp/iIPSg+aq+ak1VVLM/x2m9Y937xhis1ZEiTady8/q17X0RuA/JF5HRgIjA1dWGZVKrvCtTF3QqZOexUVow4i5nDTvW8mMSML8qZOexU2rXKi5sY8/Ny2bcgD5rV/HtddMtUet73XsILvXrZwtS2OTXReC05/gm4ClgE/B54DXgqVUGZ1GrI7Jj6fK5sfYXnfaF3bN3KC9f+Jvi66JapIELZ+goGjf802MEj4oyVDB+kfUfJouCsm1wR+h3bibuLD2fuN2trnQ/dIAtsm1MTXdzkKCI5wEJVPQx4MvUhmVRLdlUy2v0EbyvrtKjcxtIHzwNgW24zutxcEvXawCDy0KaAud+srTXrpkqVMbNXsqL8Z+av3BCcphi+QVaAzWM2kcStVqtqNbBARDqnIR6TBsmuSka6H3hrlM7fvjWYGH/K3zlmYgwXaAoYN2dVxPdnLl9rK2qbhHmtVu8JLBaRj4HNgZOq2juRh7o7GT4FHIbzO3SFqs5K5F6m/pJdlQx87q6piz3vBw2w07YtfDbqQgBWtd2dE695ut7P/taduVPfzxgTj9fkeFeSn/sw8Iaqni8izYHE5oKZhCW7KlncrZCRby71nBx33vozCx++GIAlHYr4zRWPJPTcjgX5fLdha71W+LGeaONFvPUcWwLXAPvjdMY8rao7GvJAEdkZZxriZQCquh3Y3pB7mszgtUTWfssG5v/zEgDmdTyI8353f0LPCzQFhLc5BvTcrz3zV26wFbVNQuK1OT4PdMdJjL8h8nYJ9bUvUA48KyKlIvKUiLQOv0hEBorIXBGZW15enoTHmlTzUiLr8PPaYGL8cO8joybGURcdSbsYe8iELpRxd/Hh9O/ROTgzJleE/j06M/bq4zwtrmFMJDG3SRCRRap6uHvcDPhYVY+K+gEvDxTpDswGeqrqHBF5GNioqn+O9hnbJiEzBWbFlK2vIFckbtV2z43lzHrscgDeOqAHA/veEfG6/j06c3fx4Q3eysGYeBLeJgEINiCp6g6JMWe1HlYDq1V1jvt6EjAsGTc26ROeuOIlxk7rv+PDJ65yPnvILxl0ztCI1wUSI9gYROOveMmxq4hsdI8FZ4bMRvdYVXXn+j5QVb8TkVUi0kVVlwKnAZ/X9z7ZLts2UAofZN2imVARbWPnMPv+tJrpT10DwH+69uLOs25AqtTT97YxiMYv8bZJqDt4LTmuB8a6PdX/Ay5P0XMyUn3mNmeCO0oW1RlkvaXSW+/wgeVf89YzfwTg6e59+L/TrgZ3ubCy9RUMnvApkJnf2zRtic3obyBV/VRVu6vqEaparKrr/IjDL/Wd2+y3sXMS2/Pl0O++CibGR3tc4CTGMNUKt01e2KD4jEmF+ix2a5Ik2XObUy2RfV+6lX3BlDE3A/DACZfwz579ol67xWP13Jh08qXk2NQlsoBsNjlm1WfBxHj3KVfETIzGZCpLjj7ItmWyWuV5/2tywopSJvzHGXzw59Ov4alj+sb9THIGQRiTXJYcfeB1579M8be+R0TcTiDcqV99zJgJznDVob+5gReOOtvT/S851tY0MZnH2hx9kk1DVMLHG7bNz2PTth219n8+Y+lMHi+5F4AbzrmZVw45udY9Ausvhq+vGFh30ZhME3OGTKawGTKZpeeI6bXWb+yzeAYPv+rMLL2m+Fbe6NKz1vVfe9jvxRg/NGSGjGlCIg1Mh7ozVEJ71S9c8Bb3vfEPAC4/fzgz9vuFL7Ebk2yWHA0QeWD60EkLQKGyumbQ9q2TF1HQKo91Wyq5dN5U/vqOs1vvJRfdzcyiI+vcN9biEcZkMkuOBog8MD3SxvcVlVVsrazi2k8m86fpzwBw4W9HUFp0ODnVSkgzJHm5wvBzDk1p3MakiiXHRs7LHO6S0rKIe8BEc/3McQz+aCwAxb97gPJDjmRkSBU8sEpPZZUGZ/3E63zKtrnmpvGz5NiIeZnDHbjGq1vef44/zJ4EwFmXPcz/9jqQe8MSWX3njWfbXHPTNNg4x0bMyxzuSNdEM/ydJ4KJ8fQrHmXx7vt5ul+keeMlpWV0++tbFA2bxqDxn2bVXHPTNFjJsRGLtZ90zxHT67U51YjX/8HFC98C4JSrn2BF+5oSXehzvMwbLyktY+ikBRHbNL3Eb0w6WMmxEYs2V1twEqTXxPjwKyODifGE3z9VKzGGP8fLvPGRby6Nmxhj3cuYdLDkmGQlpWX0HDGdfYZNo+eI6ZSUlvkWS6Q53IK3/aQDnnzp/+iz5H0Aelz7HKsL9qj1fviccC/zxr2UCDN5rrlpGqxanUSZ1rEQaZuB+vRKj33xNnp+46y1+IvrXqB8p3Z1rgmfE+5la4N4cRRab7XJADZ9MInCp9UFFBbkM3PYqT5EVFe0GMNN+fcQuq1xOkS6XT+Wda3a1rlGgBUJTA2M1uaYlyOMvKCrJUWTNrGmD1q1OomyYRHbob26kJcbe4mdN5/+QzAxHnHjixETI0BOjiTUbFDcrZCR53etNXumID/PEqPJKFatTqJo1cVkdCzEGiQd7b1oc6VDV9MJ99FjV7DXxh8AOGzQBH5u0SrqtVXVziDvRBJaNq1KZJomS45JNLRXl4j7LDe0YyFWWyZEHnQ995u1vDSvrM75lnk5RMuNpQ/3o93WTQAcfNMkKpq3jBtbJpWKjUkmS45JlKp9luMNrI70XmDNxPDz0QZ8fzmymObVOwDoMmQy25o19xRboqXiktIy7pq6mHVbnK3RC/LzuLP3oVaaNBnDkmOSpaK6mEhbZnhijEqVr+87J/jygJunUJnrbSWdREvFkTpk1ldUMnTiAsCmDJrMYB0yWSBa6axtfl7D2jPDEuN+Q1+ukxgP2K11cDuHgvw82rXKq7O1Q33HdkYbBF5ZrTZl0GQMKzlmgaG9ujB04oLguooBm7fv4Oyue9ZqW/RKtJoV9/UOvt536MtU59QevN2/R+e4WxgkMrYzVonX2jBNprDkmGEueXIWM5evDb7uuV97xl59XK32uYDKKmXawjW0zMsJJicvM2Byqqv438g+wdf73PIKKrUrEQJ037t93HhjtYdGS46xBoHblEGTKaxanUFOf/C9WokRYObytVzy5CzWhyXGgHVbKmslzXiJsVnVjlqJseiWqXUSY+A+QycuiFtFTqQ9NNpYy7wcsSmDJmNYcswQJaVlLPthc8T3Zi5fm5QSVfMdlXx1f3HwddEtU2NuGl1ZrQyZsCBmW6KXhSbC2SBwkw18q1aLSC4wFyhTVW8bHDdi8ToiIo2hrI8WldtY+uB5AFQ0a8HBQ17y9LlAr3e0tsREx3baIHCT6fwsOd4ILPHx+RklXkdEcbdC7u17eLDnuLAgn4L8yENu2rXKIzekRNhqe0UwMZa3LvCcGMNFWoA2Ulzhi1EYk418KTmKyF7AWcA9wGA/Ysg0sTopeu4XuWPk7K57Mv6TVbWGxQQ2tRo0/lMA2mzbzKJRFwGwot2enDLwyQbFGSmJWynQNEZ+lRxHAbcA1T49P+NEWgcRnHGGY68+LjhkJrBIbdn6CsZ/vKruPGmFud+sRYC2FZuCifHz3fZpcGIE6002TUfaS44icjbwg6rOE5GTY1w3EBgI0Llz5/QE56N4Uw8jbp0aYZJ0ZbUybs4q2m9ez7xH+gPwSeEhXND/Ps+xjLroSICUzBM3Jlv4Ua3uCfQWkTOBlsDOIjJGVfuHXqSqo4HR4KznmP4w0y9W9bQ+g6Pbb/qJTx69FID39zmKARf+NfheYUE+366vcMdG1i249+/RuVYMtl2qaarSnhxV9VbgVgC35HhzeGI0dXldxbvjxh/472NXAPD6gcdz7bm3Bd8LX3T3jpJFwQUqckXod2ynWjNirC3RNGU2QyZLRBoyk5cjIAQ7ZDqvW8MHo68GYPKhpzD47CHBayNVie8uPjzu9EBjmipfk6Oqvge852cM2SJam2TgXMvlX/LuU9cC8OJRZzLs9D8EPyvAeUdbKdCY+rCSYxaJVs0tbrYWjjgNgHEnnM+tPS+r9b4CM74oT0OExjQelhyz3bx50N3dH+j227ltx3ERL7PVboypH5tbnUZJ39N61qyaxHjPPXD33QnNdTbG1GXJMU0iDeK+dfKixBPke+/B8cc7xw88ALc5vdKRBpPb+ERj6s+q1SkSvvPf5m076r3uYVRvvQW9ejnH//oXXHtt8K1U7WNjTFNjyTEFSkrLgnObgZjjE+vdFvjKK9DHXY/x2WfhssvqXGLjE41pOEuOKTB04qeer61XW+DEiXDhhc7xuHFw8cX1C8wY45m1OaZAhFl5EdWrLfCFF2oS45QplhiNSTErOSYovE3Ra7teYG5zvdoCn3wSBg50jqdNgzPPbGD0xph4LDkmIJEd9wJC5zZ78o9/wI03Osfvvgun1vPzxpiEWLU6AbF23IPoi9NGOx/V3/9ekxg//NASozFpZMkxAfF23Bt79XF1EmFgi1XP7rwThg1zjj/+GE44IZFQjTEJsmp1AqItHxba81yvRBjulltg5EjnuLQUjjwy8XsZYxJiJccEpHQWyvXX1yTGxYstMRrjEys5JiBls1CuuMIZ2A3w5ZdwwAENjNQYkyhLjglK+iyUiy6CCROc4xUroKgoefc2xtSbJcdMcPbZzvhFgNWrodCm/hnjN0uOfjv1VJgxwzn+7jvYfXd/4zHGAJYc/XXMMfDJJ85xeTnsuqu/8Rhjgiw5+uWQQ2DJEud43TooKPA1HGNMbZYc/dCpk9O2CLBxI7Rp4288xpg6LDmmW9u2TkIE2LyZkqXrGPnmJ7YwrTEZxpJjkt1Rsohxc1ZRpUquCP2O7eTsDa0KzZpBtbueWUUFJUt+SngBC2NMatkMmSS6o2QRY2avpEoVgCpVxsxeyR1TFkJOTk1i3LYNWraMu4CFMcY/VnJMonFzVtU9qcrdfbvWvK6sdEqQxF/AwhjjHys5JlGgxBggWs3X950TckFVMDFC9C0SbBtVY/xnyTGJckVqjqurWHFf75o3q6qcqnUI20bVmMyV9uQoIp1EZIaILBGRxSJyY7pjSJV+x3YCoFnVDpaP7BM8f8fkBXUSIzidLvf2PZzCgnwEZwuFe/sebp0xxmQAP9ocdwBDVHW+iLQB5onI26r6uQ+xJE2gl7r5jkq+fOBcAKoR/jL5U+4+94ion7NtVI3JTGkvOarqGlWd7x5vApYAWZ0dAr3UedsrgolxU/N8/jJlQczEaIzJXL62OYpIEdANmONnHA01bs4qWm/bwhcPng/A9zu15/CbJkbuvTbGZAXfkqOI7AS8BAxS1Y0R3h8oInNFZG55eXn6A6yHVlt/ZvEoZ0/p5e0LOfa6fwN1e6+NMdnDl3GOIpKHkxjHqurkSNeo6mhgNED37t0zJsuE71d927G7sWjURQAs3GN/eg8YFbw2tPfaGJNd0p4cRUSAp4Elqvpgup8fT3jyC53rHL5f9dayNZx16mkAzOl0GBf9dkStewV6r40x2cePanVP4HfAqSLyqfvfmT7EUUcg+ZWtr0CpmetcUloG1N6vevdNPzLvkf4AzDrwGKb+4z/BkmKuCP17dHbmVBtjslLaS46q+hGQkfXNWHOdi7sVBqf1FW74gZmPXwHAtC49+WPxrawoPtySoTGNiM2QCRFvrnPb/Dz2XvdtMDFOOuw0riu+lbb5eWmL0RiTHpYcQ8Sb67zvjyt5f/RAAP7d7SxuPusmAKzfxZjGx5JjiJhznRcsYPIjVwPwxDF9+cuvrw1es35LZVrjNMakni1ZFiLQK12nt3rHt85mWMDDx1/MQyf2r/U5W0XHmMbHkmOYOnOdZ86EE04AYPEfh/F4wS8hpNPGVtExpnGyanUsM2YEEyOjRnHoP++1VXSMaSKs5BgmsLrOCcvn8vzE4c7JJ56AgU5HjK2iY0zTYCXHEIHVdU5b+t9gYhx81k3csdtxPkdmjEk3S44hxs1ZxdlLPmD0lHsA+GPvW5h82Gm2uo4xTZBVq0P0WfQOD057CICr+97B2wf0AGx1HWOaIkuOAU88EUyMAy64i/f3PTr4lq2uY0zT0+iSY6xVdaIaNQpucma79Lv4b8zau/bq3ba6jjFNT6NKjuFLigVW1QGiJ8h774XbbnOOZ85kvx/a8PGcVVSpkitCv2M72YISxjRBjSo5xltVJyBQurxo6pPc8N8XnZOffALdu3M3WDI0xjSu3up4q+pATeny0imPBhNjn6v/RUnunmmJ0RiTHRpVcoy3qg44pcvbpv2T33/s7M5w2pWPsaB9Z0a+uTQtMRpjskOjSo4xV9VxDR57D78rfQ2AkwY+yfJdnc6WaKVOY0zT1KjaHKOuqhNob7zgAs77bDoAPa95hrK2uwU/ayvrGGNCNarkCDHmPp95Jrz+OgAn3TiGspYFwbdsZR1jTLhGVa2O6qSTgomR779n8ICTbWUdY0xMja7kWMfRR8P8+c7xjz/CLrtQvFuMcY/GGENjT44HHgjLljnH69ZBQYGv4RhjskfjTY4dO8KaNc7xpk2w007+xmOMySqNMzm2bQsbNzrHW7ZAvvVEG2Pqp/Elx82baxLj1q3QooW/8RhjslLj661u3Rq++gqqqy0xGmMS1vhKjgD77ed3BMaYLNf4So7GGJMEviRHETlDRJaKyFciMsyPGIwxJpa0J0cRyQUeBX4DHAL0E5FD0h2HMcbE4kfJ8RjgK1X9n6puB14E+vgQhzHGROVHciwEQvc6Xe2eq0VEBorIXBGZW15enrbgjDEG/EmOkbbyq7P3qaqOVtXuqtq9Q4cOaQjLGGNq+JEcVwOh2/ntBXzrQxzGGBOVH8nxE+AAEdlHRJoDFwOv+BCHMcZElfZB4Kq6Q0T+CLwJ5ALPqOridMdhjDGx+DJDRlVfA17z49nGGOOFqNbpC8k4IlIOfBPhrV2BH9McTjwWkzeZGBNkZlwWkzeJxLS3qkbs8c2K5BiNiMxV1e5+xxHKYvImE2OCzIzLYvIm2THZ3GpjjInAkqMxxkSQ7clxtN8BRGAxeZOJMUFmxmUxeZPUmLK6zdEYY1Il20uOxhiTElmZHDNtPUgR6SQiM0RkiYgsFpEb/Y4pQERyRaRURF71O5YAESkQkUki8oX7MzsuA2K6yf1/95mIjBORlj7F8YyI/CAin4Wcay8ib4vIMvfPdhkQ00j3/99CEZkiIgV+xxTy3s0ioiKya0OekXXJMUPXg9wBDFHVg4EewHUZEFPAjcASv4MI8zDwhqoeBHTF5/hEpBC4AeiuqofhzNy62KdwngPOCDs3DHhXVQ8A3nVf+x3T28BhqnoE8CVwawbEhIh0Ak4HVjb0AVmXHMnA9SBVdY2qznePN+H8stdZhi3dRGQv4CzgKb9jCRCRnYGTgKcBVHW7qq73NShHMyBfRJoBrfBpMRRV/QBYG3a6D/C8e/w8UOx3TKr6lqrucF/OxllAxteYXA8BtxBhpa/6ysbk6Gk9SL+ISBHQDZjjcygAo3D+olT7HEeofYFy4Fm3uv+UiLT2MyBVLQPuxyltrAE2qOpbfsYUZndVXQPOP8TAbj7HE+4K4HW/gxCR3kCZqi5Ixv2yMTl6Wg/SDyKyE/ASMEhVN/ocy9nAD6o6z884ImgGHAU8pqrdgM2kv5pYi9uG1wfYB+gItBaR/n7GlC1E5HacZqWxPsfRCrgd+Euy7pmNyTEj14MUkTycxDhWVSf7HQ/QE+gtIl/jND2cKiJj/A0JcP7/rVbVQMl6Ek6y9NOvgBWqWq6qlcBk4HifYwr1vYjsCeD++YPP8QAgIgOAs4FL1P8xgfvh/OO2wP07vxcwX0T2SPSG2ZgcM249SBERnDa0Jar6oJ+xBKjqraq6l6oW4fyMpquq76UhVf0OWCUiXdxTpwGf+xgSONXpHiLSyv1/eRqZ1Yn1CjDAPR4AvOxjLIAzYgT4E9BbVbf4HY+qLlLV3VS1yP07vxo4yv37lpCsS45uI3BgPcglwIQMWA+yJ/A7nNLZp+5/Z/ocUya7HhgrIguBI4G/+RmMW4qdBMwHFuH8XvgyA0RExgGzgC4islpErgRGAKeLyDKcntgRGRDTI0Ab4G337/vjGRBTcp/hf2nYGGMyT9aVHI0xJh0sORpjTASWHI0xJgJLjsYYE4ElR2OMicCSo8lYInKuu7rKQXGuG+TOkEj0OZeJyCOJft40TpYcTSbrB3xE/BVyBuEsFmFM0lhyNBnJnafeE7gSNzm6a1PeLyKL3HUErxeRG3DmQ88QkRnudT+H3Od8EXnOPT5HROa4C168IyK7p/t7mezRzO8AjImiGGfNxy9FZK2IHAUcizN/tpuq7hCR9qq6VkQGA6eoarw9iz8CeqiqishVOCsWDUnllzDZy5KjyVT9cJZcA2fhjH44y509HlhHUFUjrecXy17AeHfxhubAiuSEahojS44m44jILsCpwGEiojgrcyswD2/L04VeE7rdwT+BB1X1FRE5GbgzGfGaxsnaHE0mOh/4t6ru7a6y0gmnlDcfuMZdrRsRae9evwlnEYSA70XkYBHJAc4NOd8WKHOPB2BMDJYcTSbqB0wJO/cSTsfLSmChiCwAfuu+Nxp4PdAhg7N47qvAdJyVvQPuBCaKyIdAvPZJ08TZqjzGGBOBlRyNMSYCS47GGBOBJUdjjInAkqMxxkRgydEYYyKw5GiMMRFYcjTGmAgsORpjTAT/D6wuGvaPXqV/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create figure and axes\n",
    "f, ax = plt.subplots(figsize=(5,5))\n",
    "\n",
    "# Plot on axes\n",
    "ax.set_title('Actual vs Predicted')\n",
    "ax.set_xlabel('Actual')\n",
    "ax.set_ylabel('Predicted')\n",
    "ax.scatter(y_test,y_pred)\n",
    "ax.plot(y_test,y_test,'r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff91d28b",
   "metadata": {},
   "source": [
    "### 4. Adjusting Hyperparameters "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95e3c308",
   "metadata": {},
   "source": [
    "The standard hyperpameter values will not give the best model performance. For that reason, adjustments to the model hyperparameters must take place to get the best performing model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2393e9b",
   "metadata": {},
   "source": [
    "Creating different hyperparameters of the \"n_estimators\" to compare the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "cd5c7e5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2 trees in forest\n",
    "forest_1 = RandomForestRegressor(n_estimators=2, max_depth=5, random_state=23)\n",
    "\n",
    "# 20 trees in forest\n",
    "forest_2 = RandomForestRegressor(n_estimators=20, max_depth=5, random_state=23)\n",
    "\n",
    "# 100 trees in forest\n",
    "forest_3 = RandomForestRegressor(n_estimators=100, max_depth=5, random_state=23)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "286f0c5e",
   "metadata": {},
   "source": [
    "Training the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "6925aa5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(max_depth=5, n_estimators=2, random_state=23)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_1.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c147b241",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(max_depth=5, n_estimators=20, random_state=23)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_2.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "29d3d498",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(max_depth=5, random_state=23)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_3.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ff6f4cc",
   "metadata": {},
   "source": [
    "Evaluating the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "991d2914",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create figure and axes\n",
    "f, ax = plt.subplots(figsize=(15,5), nrows=1, ncols=3,sharey=True)\n",
    "\n",
    "# Create list of titles and predictions to use in for loop\n",
    "pred = [forest_1.predict(x_test), forest_2.predict(x_test), forest_3.predict(x_test)]\n",
    "title = ['trees = 2', 'trees = 20', 'trees = 100']\n",
    "\n",
    "# Loop through all axes to plot each model's results \n",
    "for i in range(3):\n",
    "    rmse = round(np.sqrt(metrics.mean_squared_error(pred[i],y_test)))\n",
    "    ax[i].set_title(title[i]+\"  (RMSE: \"+str(rmse)+ \")\")\n",
    "    ax[i].set_xlabel('Actual')\n",
    "    ax[i].set_ylabel('Predicted')\n",
    "    ax[i].plot(y_test,y_test,'r')\n",
    "    ax[i].scatter(y_test,pred[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72ea603e",
   "metadata": {},
   "source": [
    "## 4.2.2: Boosting models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3f23b6f",
   "metadata": {},
   "source": [
    "Boosting is an ensemble method in which many predictors are trained sequentially and each succesive predictor learns from the errors of it's predecessor. \n",
    "In simple terms 'Many weak learners are combined to form a strong learner.\n",
    "\n",
    "The following Boosting methods will make use of CARTS( Classification and Regression Trees) similar to the Random forrest.\n",
    "\n",
    "We will use the following models:\n",
    "        \n",
    "        -AdaBoost (Adaptive Boosting):\n",
    "            After training, each predictor is assigned a coefficient that depends on the training error.\n",
    "            This coefficient adds more weight to the instances wrongly predicted, so that the next predictor \n",
    "            pays more attention to those values.\n",
    "        \n",
    "        -GradientBoost(GB):\n",
    "            Gradient boosting also does sequential correction of predecessor errors. But, unlike AdaBoost, the weight of\n",
    "            training instances are not tweaked. \n",
    "            Instead each predictor is trained using the residual errors of the previous \n",
    "            labels. \n",
    "            GB's exhastive search procedure means that each cart is trained to find the best split points \n",
    "            and features.\n",
    "            \n",
    "        \n",
    "        -Stochastic GradientBoost(SGB):\n",
    "            This method imporves the generalizability GB method by adding further variance to the ensemble diversity. Each \n",
    "            predictor is trained on a random subset of the training data that's been sampled without replacement. \n",
    "            Features are sampled without replacement when choosing split points.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22179875",
   "metadata": {},
   "source": [
    "### 1. Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "c3ba92bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(max_depth=5, max_features=0.75, n_estimators=200,\n",
       "                          random_state=1, subsample=0.9)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Decision tree/ Base estimator\n",
    "dt = DecisionTreeRegressor(max_depth=5, random_state=1)\n",
    "\n",
    "# Instantiate Ada\n",
    "ada = AdaBoostRegressor(base_estimator=dt, n_estimators=200, random_state=1)\n",
    "ada.fit(x_train,y_train)\n",
    "# Instantiate GB\n",
    "gb = GradientBoostingRegressor(max_depth=5, \n",
    "            n_estimators=200,\n",
    "            random_state=1)\n",
    "gb.fit(x_train,y_train)\n",
    "\n",
    "# Instantiate SGBR\n",
    "sgb = GradientBoostingRegressor(max_depth=5, \n",
    "            subsample=0.9,\n",
    "            max_features=0.75,\n",
    "            n_estimators=200,                                \n",
    "            random_state=1)\n",
    "sgb.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1eb52d77",
   "metadata": {},
   "source": [
    "### 2. Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "4ac98e1a",
   "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>Training MSE</th>\n",
       "      <th>Test MSE</th>\n",
       "      <th>Train R^2</th>\n",
       "      <th>Test R^2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ada</th>\n",
       "      <td>0.567527</td>\n",
       "      <td>0.657085</td>\n",
       "      <td>0.937395</td>\n",
       "      <td>0.907836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GBR</th>\n",
       "      <td>0.127468</td>\n",
       "      <td>0.422210</td>\n",
       "      <td>0.996842</td>\n",
       "      <td>0.961948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SGB</th>\n",
       "      <td>0.122324</td>\n",
       "      <td>0.433410</td>\n",
       "      <td>0.997092</td>\n",
       "      <td>0.959903</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Training MSE  Test MSE  Train R^2  Test R^2\n",
       "Ada      0.567527  0.657085   0.937395  0.907836\n",
       "GBR      0.127468  0.422210   0.996842  0.961948\n",
       "SGB      0.122324  0.433410   0.997092  0.959903"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dictionary of results\n",
    "train_ada = ada.predict(x_train)\n",
    "train_gb = gb.predict(x_train)\n",
    "train_sgb = sgb.predict(x_train)\n",
    "\n",
    "test_ada = ada.predict(x_test)\n",
    "test_gb = gb.predict(x_test)\n",
    "test_sgb = sgb.predict(x_test)\n",
    "\n",
    "results_dict = {'Training MSE':{\n",
    "                        \"Ada\": metrics.mean_squared_error(y_train, train_ada,squared=False),\n",
    "                        \"GBR\": metrics.mean_squared_error(y_train, train_gb,squared=False),\n",
    "                        \"SGB\": metrics.mean_squared_error(y_train, train_sgb,squared=False),\n",
    "                },\n",
    "                'Test MSE':{\n",
    "                        \"Ada\": metrics.mean_squared_error(y_test, test_ada,squared=False),\n",
    "                        \"GBR\": metrics.mean_squared_error(y_test, test_gb,squared=False),\n",
    "                        \"SGB\": metrics.mean_squared_error(y_test, test_sgb,squared=False)\n",
    "                },\n",
    "                'Train R^2':{\n",
    "                        \"Ada\":metrics.r2_score(y_train, train_ada),\n",
    "                        \"GBR\":metrics.r2_score(y_train, train_gb),\n",
    "                        \"SGB\":metrics.r2_score(y_train, train_sgb)\n",
    "                    \n",
    "                },\n",
    "                'Test R^2':{\n",
    "                        \"Ada\":metrics.r2_score(y_test, test_ada),\n",
    "                        \"GBR\":metrics.r2_score(y_test, test_gb),\n",
    "                        \"SGB\":metrics.r2_score(y_test, test_sgb)\n",
    "                }\n",
    "                }\n",
    "pd.DataFrame(results_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "7c1763c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create figure and axes\n",
    "f, ax = plt.subplots(figsize=(15,5), nrows=1, ncols=3,sharey=True)\n",
    "\n",
    "# Create list of titles and predictions to use in for loop\n",
    "pred = [test_ada, test_gb, test_sgb]\n",
    "title = ['Ada', 'GB', 'SGB']\n",
    "\n",
    "# Loop through all axes to plot each model's results \n",
    "for i in range(3):\n",
    "    rmse = round(np.sqrt(metrics.mean_squared_error(y_test,pred[i])),2)\n",
    "    ax[i].set_title(title[i]+\"  (RMSE aprox: \"+str(rmse)+ \")\")\n",
    "    ax[i].set_xlabel('Actual')\n",
    "    ax[i].set_ylabel('Predicted')\n",
    "    ax[i].plot(y_test,y_test,'r')\n",
    "    ax[i].scatter(y_test,pred[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c722a044",
   "metadata": {},
   "source": [
    "## 4.3: Simple modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "dbabeeb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=train_dummies.drop('avg_price_per_kg',axis=1)\n",
    "y=train_dummies['avg_price_per_kg']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "2b63fae8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split train dataset into train and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, \n",
    "                                                    y, \n",
    "                                                    test_size=0.20,\n",
    "                                                    random_state=1,\n",
    "                                                    shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "b44890e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Linear Regression\n",
    "linreg = LinearRegression()\n",
    "linreg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "7606d212",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ridge()"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Ridge Regression\n",
    "ridge = Ridge()\n",
    "ridge.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "aea6e705",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lasso(alpha=0.01)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Lasso Reg\n",
    "lasso = Lasso(alpha=0.01)\n",
    "lasso.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "eec200fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVR()"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Support Vector model\n",
    "svr = SVR()\n",
    "svr.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "7db74dc2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor()"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Gradient Boosting model\n",
    "params = {}\n",
    "gbr=GradientBoostingRegressor()\n",
    "gbr.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "74d72301",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsRegressor(n_neighbors=3)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#KNN model\n",
    "knn= KNeighborsRegressor(n_neighbors = 3)\n",
    "knn.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "c5803322",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor()"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Decision Tree\n",
    "decision_tree = DecisionTreeRegressor()\n",
    "decision_tree.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "34423efc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Random Forest\n",
    "random_forest = RandomForestRegressor(n_estimators=100)\n",
    "random_forest.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "42eb2dd3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Compare models\n",
    "\n",
    "# Make training set predictions for each model\n",
    "train_lin = linreg.predict(X_train)\n",
    "train_ridge = ridge.predict(X_train)\n",
    "train_lasso = lasso.predict(X_train)\n",
    "train_Knn = knn.predict(X_train)\n",
    "train_svr = svr.predict(X_train)\n",
    "train_gbr = gbr.predict(X_train)\n",
    "train_DcT = decision_tree.predict(X_train)\n",
    "train_RnF = random_forest.predict(X_train)\n",
    "\n",
    "# Make test set predictions for each model\n",
    "test_lin = linreg.predict(X_test)\n",
    "test_ridge = ridge.predict(X_test)\n",
    "test_lasso = lasso.predict(X_test)\n",
    "test_Knn = knn.predict(X_test)\n",
    "test_svr = svr.predict(X_test)\n",
    "test_gbr = gbr.predict(X_test)\n",
    "test_DcT = decision_tree.predict(X_test)\n",
    "test_RnF = random_forest.predict(X_test)\n",
    "\n",
    "# Dictionary of results\n",
    "results_dict = {'Training MSE':{\n",
    "                        \"Least Squares\":metrics.mean_squared_error(y_train, train_lin,squared=False),\n",
    "                        \"Ridge\": metrics.mean_squared_error(y_train, train_ridge,squared=False),\n",
    "                        \"LASSO\": metrics.mean_squared_error(y_train, train_lasso,squared=False),\n",
    "                        \"SVR\": metrics.mean_squared_error(y_train, train_svr,squared=False),\n",
    "                        \"GBR\": metrics.mean_squared_error(y_train, train_gbr,squared=False),\n",
    "                        \"KNN\": metrics.mean_squared_error(y_train, train_Knn,squared=False),\n",
    "                        \"Decision Tree\": metrics.mean_squared_error(y_train, train_DcT,squared=False),\n",
    "                        \"Random Forest\": metrics.mean_squared_error(y_train, train_RnF,squared=False)\n",
    "                },\n",
    "                'Test MSE':{\n",
    "                        \"Least Squares\": metrics.mean_squared_error(y_test, test_lin,squared=False),\n",
    "                        \"Ridge\": metrics.mean_squared_error(y_test, test_ridge,squared=False),\n",
    "                        \"LASSO\": metrics.mean_squared_error(y_test, test_lasso,squared=False),\n",
    "                        \"SVR\": metrics.mean_squared_error(y_test, test_svr,squared=False),\n",
    "                        \"GBR\": metrics.mean_squared_error(y_test, test_gbr,squared=False),\n",
    "                        \"KNN\": metrics.mean_squared_error(y_test, test_Knn,squared=False),\n",
    "                        \"Decision Tree\": metrics.mean_squared_error(y_test, test_DcT,squared=False),\n",
    "                        \"Random Forest\": metrics.mean_squared_error(y_test, test_RnF,squared=False)\n",
    "                }\n",
    "                }\n",
    "results=pd.DataFrame(results_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "0051057a",
   "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>Training MSE</th>\n",
       "      <th>Test MSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Least Squares</th>\n",
       "      <td>1.382874e+00</td>\n",
       "      <td>1.452307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ridge</th>\n",
       "      <td>1.384628e+00</td>\n",
       "      <td>1.450756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LASSO</th>\n",
       "      <td>1.423054e+00</td>\n",
       "      <td>1.462211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVR</th>\n",
       "      <td>1.321668e+00</td>\n",
       "      <td>1.426086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GBR</th>\n",
       "      <td>4.521227e-01</td>\n",
       "      <td>0.599106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KNN</th>\n",
       "      <td>7.943513e-01</td>\n",
       "      <td>1.286517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>1.017583e-15</td>\n",
       "      <td>0.635761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random Forest</th>\n",
       "      <td>2.431358e-01</td>\n",
       "      <td>0.504498</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Training MSE  Test MSE\n",
       "Least Squares  1.382874e+00  1.452307\n",
       "Ridge          1.384628e+00  1.450756\n",
       "LASSO          1.423054e+00  1.462211\n",
       "SVR            1.321668e+00  1.426086\n",
       "GBR            4.521227e-01  0.599106\n",
       "KNN            7.943513e-01  1.286517\n",
       "Decision Tree  1.017583e-15  0.635761\n",
       "Random Forest  2.431358e-01  0.504498"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "d595b416",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create submission dataframe\n",
    "# Create Dataframe of Order_No and Time from Pickup to Arrival\n",
    "submission = pd.DataFrame(\n",
    "    {'Index': test_id,\n",
    "     'avg_price_per_kg': random_forest.predict(test_dummies)\n",
    "    })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ffe6cd49",
   "metadata": {},
   "outputs": [],
   "source": [
    "# save DataFrame to csv file for submission\n",
    "submission.to_csv(\"AnswerDcT.csv\", index=False)"
   ]
  }
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