JM2_Regression_Project_v0.12.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Table of Contents\n",
    "---\n",
    "1. [Introduction](#intro)\n",
    " * Project Description\n",
    " * Problem Statement\n",
    " * Datasets and Variables\n",
    "---\n",
    "2. [Load Dependencies](#imports)\n",
    "---\n",
    "3. [Data Cleaning](#data)\n",
    " * Load Data\n",
    " * Handle Missing Data\n",
    " * Handle Target Outliers\n",
    "---\n",
    "4. [Exploratory Data Analysis](#EDA)\n",
    " * Data Summary Statistics\n",
    " * Univariate Analysis and Visualisation\n",
    " * Bivariate Analysis and Visualisation\n",
    " * Encoding Categorical Data\n",
    "---\n",
    "5. [Feature Selection](#feature) \n",
    " * Split Training Set Into Training and Validation Sets\n",
    " * Feature Scaling\n",
    " * Feature Selection\n",
    "---\n",
    "6. [Base Models](#simplemodelling)\n",
    " * Linear Regression\n",
    " * Lasso Regression\n",
    " * Ridge Regression\n",
    " * KNN Regression\n",
    " * Support Vector Regression\n",
    " * Random Forest Regression\n",
    " * GradientBoost Regression\n",
    "---\n",
    "7. [Model Analysis](#modelling)\n",
    " * Random Forest Regression\n",
    " * AdaBoost Regression\n",
    " * GradientBoost Regression\n",
    " * Stochastic GradientBoost Regression\n",
    "---\n",
    "9. [Conclusion](#conclusion)\n",
    "---\n",
    "10. [Save Model and Output](#save)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"intro\"></a>\n",
    "# Introduction"
   ]
  },
  {
   "attachments": {
    "image-2.png": {
     "image/png": "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"
    },
    "image.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This Notebook is created for the Explore EDSA Apple Prices Challenge hosted on the Kaggle website. This competition is a project to predict the average price per kilogram of Golden Delicious Apples by training a regression model from explanatory features from a given dataset. The dataset is supplied by the kaggle competition.\n",
    " ![image.png](attachment:image.png)\n",
    "In this project, multiple regression models are constructed, used and compared to evaluate the regression model with the highest level of accuracy in predicting how much a kilogram of Golden Delicious Apples will cost.\n",
    "Regression models are created to predict a continuous value. In the case of this project this is an extremely useful method to finically forecast either the buying or selling of Golden Delicious Apples and conduct a Cost–benefit analysis to investigate the potential strengths and weakness of the product. The model itself is an artifact that is generated by the process of training data. This training data is used by machine learning algorithms, which learn from the data and produce a trained model.\n",
    " ![image-2.png](attachment:image-2.png)\n",
    "\n",
    "We are supplied with a labelled data set which means this is a supervised type of machine learning, where our regression model can learn from these labelled data and aid in classification and prediction.\n",
    "After comparing and analysing each regression model and machine learning technique employed throughout this notebook, we will have a reasonably accurate prediction of the price per kg of the Godlen Delicious Apples along with a detailed explanation and understanding of why out chosen regression model and techniques used are more suitable and accurate than the others we tested.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data size and structure"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The train dataset given consists of object, integer and float variables. Most of the object variables are nominal factors with date being the only ordinal one, most of them require cleaning and/or feature engineering to be usable. As for the integer and float variables, little to no cleaning needs to be done as there are little to no issues with them in this dataset . In total, there are 13 columns/variables but only 12 of them are used as “Commodities\" has no relevant information and is dropped. The last column/variable is the response variable (avg_price_per_kg). \n",
    "\n",
    "Below, is a display of the variables. All of them are discussed in more detail throughout this notebook\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"imports\"></a>\n",
    "# Load Dependencies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loading required python packages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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",
    "from scipy.stats import pearsonr\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",
   "metadata": {},
   "source": [
    "# Data Collection"
   ]
  },
  {
   "cell_type": "markdown",
   "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": 4,
   "metadata": {},
   "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",
   "metadata": {},
   "source": [
    "<a id=\"data\"></a>\n",
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kaggle tries to provide us with clean data but it's always a good idea to check for ourselves. We check for null and empty values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The data contains 0 empty values over all columns\n"
     ]
    }
   ],
   "source": [
    "i=0\n",
    "for x in train.columns:\n",
    "    if '' in sorted(train[x].unique()):\n",
    "        i+=1\n",
    "print('The data contains '+str(i)+' empty values over all columns')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "none of our columns contain any null or empty values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The table contains information about 'Commodoties that we are not interested in, we filter only for 'APPLE GOLDEN DELICIOUS'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>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": 8,
     "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",
   "metadata": {},
   "source": [
    "We may as well remove the \"Commodities\" column as it has no more useful information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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",
   "metadata": {},
   "source": [
    "We note that the DataFrame consists of 12 columns.(11 predictors and 1 response)\n",
    "\n",
    "The predictor variables are broken down into the following types:\n",
    "\n",
    "    -object: Province, Container, Size_Grade and Date\n",
    "\n",
    "    -float: Weight_Kg, Low_Price, High_Price, Sales_Total and Total_Kg_Sold\n",
    "\n",
    "    -integer: Total_Qty_Sold and Stock_On_Hand\n",
    "    \n",
    "with the response variable(avg_price_per_kg) being a float."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"EDA\"></a>\n",
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Univariate Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Numeric data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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",
       "    </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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the maximium values of our numeric data are many standard deviations(>3) away from the mean. This leads us to expect that they are outliers and will cause the distribution of the data to appear skewed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.skew()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that all of our numeric predictors are highly skewed(skew values greater than 1). While our response variable appears symmetrical(skew value close to 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.kurtosis()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The kurtosis of our predictor values are much greater than 3. This indicates that there are strong outliers in the predictor variables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These outliers are to be expected in data of this nature: For example:\n",
    "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 places can make that much in sales. This would skew our data toward the more 'acheivable' Sales_total values and cause the larger transactions to appear as outliers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now convert the date column from object to datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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",
   "metadata": {},
   "source": [
    "#### Categorical Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABVoAAAJGCAYAAACqdD6JAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAByq0lEQVR4nOzdd5hkVbm28fthiCIK6IBIEFQQc0IMmFEBUTGAoKKIKAbUY0CFg3pUxA9zADmCESNiOqDHhCiKiaRIFMFwYAQBE2IABd7vj7Vay6Fnpmdqqqum5/5dV19dtWtX1Vu1q3p3PbX2u1JVSJIkSZIkSZKW3SrjLkCSJEmSJEmSVnQGrZIkSZIkSZI0JINWSZIkSZIkSRqSQaskSZIkSZIkDcmgVZIkSZIkSZKGZNAqSZIkSZIkSUNaddwFDOOWt7xlbb755uMuQ5K0nJ1xxhm/rar5465jrnL/KUlzk/vP0XL/KUlzz/Led67QQevmm2/O6aefPu4yJEnLWZL/G3cNc5n7T0mam9x/jpb7T0mae5b3vtPWAZIkSZIkSZI0JINWSZIkSZIkSRqSQaskSZIkSZIkDcmgVZIkSZIkSZKGZNAqSZIkSZIkSUMyaJUkSZIkSZKkIRm0SpIkSZIkSdKQDFolSZowSdZN8tkkP01yfpL7J1k/yQlJLuy/1xtY/8AkFyW5IMkO46xdkiRJklZWBq2SJE2edwNfraqtgbsD5wMHACdW1ZbAif08Se4E7AHcGdgROCLJvLFULUmSJEkrMYNWSZImSJKbAQ8GPghQVX+vqj8CuwBH99WOBh7fT+8CHFNV11bVL4GLgG1ns2ZJkiRJkkGrJEmT5rbAlcCHk/w4yQeSrA1sWFWXAfTfG/T1NwYuGbj+gr5MkiRJkjSLDFolSZosqwL3Av67qu4J/IXeJmARMs2yutFKyb5JTk9y+pVXXrl8KpUkSZIk/ZNBqyRJk2UBsKCqTunnP0sLXi9PshFA/33FwPqbDlx/E+DShW+0qo6qqm2qapv58+ePrHhJkiRJWlkZtEqSNEGq6jfAJUnu0BdtD5wHHA/s1ZftBRzXTx8P7JFkjSRbAFsCp85iyZIkSZIk2uGJkiRpsrwI+ESS1YFfAHvTvhw9Nsk+wMXAbgBVdW6SY2lh7HXAflV1/XjKliRJkqSVl0GrJEkTpqrOBLaZ5qLtF7H+IcAho6xJkiRJkrR4tg6QJEmSJEmSpCEZtEqSJEmSJEnSkAxaJUmSJEmSJGlI9mjVRPn2gx8ylvt9yHe+PZb7laRxu/K/Pz6r9zf/+XvO6v1JkjRK937FR8ddwoyd8dZnjLsESZrzHNEqSZIkSZIkSUMyaJUkSZIkSZKkIRm0SpIkSZIkSdKQDFolSZIkSZIkaUgGrZIkSZIkSZI0JINWSZIkSZIkSRqSQaskSZIkSZIkDcmgVZIkSZIkSZKGZNAqSZIkSZIkSUMyaJUkSZIkSZKkIRm0SpIkSZIkSdKQDFolSZIkSZIkaUgGrZIkSZIkSZI0JINWSZIkSZIkSRqSQaskSZIkSZIkDcmgVZIkSZIkSZKGtOq4C5AkSZokX/rQTrN2X4951ldm7b4kSU2SVFWNuw5J0txj0CpJkiRJmrOS3AvYFDgTuLKq/ppklaq6YbyVSZLmGoNWSZIkSdKclORxwNtoIesTgKuTvKmqLptJ2JpkX2BfgM0222zU5UqSVnD2aJUkSZIkzVU7Aa+oqicD7wH+ALwrya1mMqK1qo6qqm2qapv58+ePulZJ0grOoFWSJEmSNOckWQWYB9wRoKp+BLwf+CnwiiRrjbE8SdIcZNAqSZIkSZozkqyeZI0+YvUIYPckT+4XLwC+AmwA3HxcNUqS5qaRBq1JfpXk7CRnJjm9L1s/yQlJLuy/1xtY/8AkFyW5IMkOo6xNkiRJkjS3JHkS8Engi70/6wXAQcAzkuxezQ+BdYG7jq9SSdJcNBuTYT2sqn47cP4A4MSqOjTJAf38q5LcCdgDuDNwa+AbSbaqqutnoUZJkiRJ0gosyVbAG4FnAZsDzwZuA3wbeC/w5iRbAn8Gbk9rISBJ0nIzjtYBuwBH99NHA48fWH5MVV1bVb8ELgK2nf3yJEmSJEkroPWAy6vqB1X1KeBQ2qjVBwNfB54GrANsAuxRVZeMrVJJ0pw06hGtBXw9SQFHVtVRwIZVdRlAVV2WZIO+7sbADweuu6AvkyRJkiRpsarqlCQX936sn6+q7ycBeA3wi6r6MvCqsRYpSZrTRh20bldVl/Yw9YQkizs0I9MsqxutlOwL7Auw2WabLZ8qJUmSJEkrnCT3BdYE/lZVpwInAQ8ArkjyvR62HgM8M8nXq+q6MZYrSZrjRto6oKou7b+vAL5AawVweZKNAPrvK/rqC4BNB66+CXDpNLd5VFVtU1XbzJ8/f5TlS5IkSZImVJKdgI/TWgIclOQ9VfUh4De0FnV79FULuIZpBvJIkrQ8jSxoTbJ2knWmTgOPAs4Bjgf26qvtBRzXTx8P7JFkjSRbAFsCp46qPkmSJEnSiinJPNrnyTdU1b7A04H7JPlQVR0KnA7skOQk4BXAO51oWZI0aqNsHbAh8IXeE2dV4JNV9dUkpwHHJtkHuBjYDaCqzk1yLHAecB2wnztCSZIkSdLCqur6JD8eOP8n4P5Jvp/knVX1UuDjSe5KmyDrikXemCRJy8nIgtaq+gVw92mW/w7YfhHXOQQ4ZFQ1SZIkSZJWXEm2qqqf9bO/Bg5I8u2qurgvexxwZJK7VNU5VXX2eCqVJK2MRtqjVZIkSZKk5SHJY4Az++RWVNXHaXOBfC/JZn3Zb4F/AGuPrVBJ0kprlK0DJEmSJEkaWp/344XAS4AHJPlUVT2lql7T29V9MckRwC1pR1baKkCSNOsc0SpJkiRJmmhV9RfgWcAngf2B1ZJ8ql/2GuB1tM+3WwC7VdUvx1SqJGklZtAqSZIkSZp4VXVpVf25twd4LrD6VNgK/Az4clU9u6rOGV+VkqSVmUGrJEmSJGmF0idZfi5wTZILgOOA68dblSRpZWfQKkmSJEla4fSRrWcBNweeUFULxlySJGklZ9AqSZIkSVrhJFkPeDTwqKo6e9z1SJK06rgLkCRJkiRpaVXVH5I8tqquGXctkiSBI1olSZIkSSsoQ1ZJ0iQxaJUkSZIkSZKkIRm0SpIkSZIkSdKQDFolSZIkSZIkaUgGrZIkSZIkSZI0JINWSZIkSZIkSRqSQaskSZIkSZIkDcmgVZIkSZIkSZKGZNAqSZIkSZIkSUMyaJUkSZIkSZKkIRm0SpIkSZIkSdKQDFolSZIkSZIkaUgGrZIkSZIkSZI0JINWSZIkSZIkSRqSQaskSZIkSZIkDcmgVZIkSZIkSZKGZNAqSZIkSZIkSUMyaJUkSZIkSZKkIRm0SpIkSZIkSdKQDFolSZIkSZIkaUgGrZIkSZIkSZI0JINWSZIkSZIkSRqSQaskSRMmya+SnJ3kzCSn92XrJzkhyYX993oD6x+Y5KIkFyTZYXyVS5IkSdLKy6BVkqTJ9LCqukdVbdPPHwCcWFVbAif28yS5E7AHcGdgR+CIJPPGUbAkSZIkrcwMWiVJWjHsAhzdTx8NPH5g+TFVdW1V/RK4CNh29suTJEmSpJWbQaskSZOngK8nOSPJvn3ZhlV1GUD/vUFfvjFwycB1F/Rl/ybJvklOT3L6lVdeOcLSJUmSJGnltOq4C5AkSTeyXVVdmmQD4IQkP13MuplmWd1oQdVRwFEA22yzzY0ulyRJkiQNxxGtkiRNmKq6tP++AvgCrRXA5Uk2Aui/r+irLwA2Hbj6JsCls1etJEmSJAkMWiVJmihJ1k6yztRp4FHAOcDxwF59tb2A4/rp44E9kqyRZAtgS+DU2a1akiRJkmTrAEmSJsuGwBeSQNtPf7KqvprkNODYJPsAFwO7AVTVuUmOBc4DrgP2q6rrx1O6JEmSJK28DFolSZogVfUL4O7TLP8dsP0irnMIcMiIS5MkSZIkLYatAyRJkiRJkiRpSAatkiRJkiRJkjQkg1ZJkiRJkiRJGpJBqyRJkiRJkiQNyaBVkiRJkiRJkoZk0CpJkiRJkiRJQzJolSRJkiRJkqQhGbRKkiRJkiRJ0pAMWiVJkiRJkiRpSAatkiRJkiRJkjQkg1ZJkiRJkiRJGpJBqyRJkiRJkiQNyaBVkiRJkiRJkoZk0CpJkiRJkiRJQzJolSRJkiRJkqQhGbRKkiRJkiRJ0pAMWiVJkiRJkiRpSAatkiRJkiRJkjQkg1ZJkiRJkiRJGpJBqyRJkiRJkiQNyaBVkiRJkiRJkoZk0CpJkiRJkiRJQzJolSRJkiRJkqQhGbRKkiRJkiRJ0pAMWiVJkiRJkiRpSAatkiRJkiRJkjQkg1ZJkiRJkiRJGpJBqyRJkiRJkiQNyaBVkiRJkiRJkoZk0CpJkiRJkiRJQ1p13AVIkiRJkjQqSR4JbAWsUlWHJUlV1bjrkiTNPY5olSRJkiTNSUkeCHwSuAbYPclhwHZJZjToKMm+SU5PcvqVV145ylIlSXOAQaskSZIkaa7aFji8qj4IPAK4CtgVuM9MrlxVR1XVNlW1zfz580dYpiRpLhh564Ak84DTgV9X1WOSrA98Gtgc+BXw5Kr6Q1/3QGAf4HrgxVX1tVHXJ0mSpEXb6bjnzdp9fWWX983afUlaaZwNvDzJVlX1syQHA/8FPA34wXhLkyTNNbMxovU/gPMHzh8AnFhVWwIn9vMkuROwB3BnYEfgiB7SSpIkSZI0I0k2TbJGkpsCJwEXAA9KslFVXQu8Adg2yTPHWKYkaQ4aadCaZBNgZ+ADA4t3AY7up48GHj+w/JiquraqfglcRDvMQ5IkSZKkJUqyM/AV4DDa59BbAR8DHgTsnGTrqroG+CJww9gKlSTNSaMe0fou4JX8+w5sw6q6DKD/3qAv3xi4ZGC9BX2ZJEmSJEmLlGZT4FDghcBrgB8B3weuAA4H7ggcmeTdfZ3TxlSuJGmOGlmP1iSPAa6oqjOSPHQmV5lmWU1zu/sC+wJsttlmw5QoSZIkSZoDqqqS/Br4Nu3oyCuq6i1J/gF8F3hAVb08yYOAOwCHVdVFYyxZkjQHjXIyrO2AxyV5NLAmcLMkHwcu771xLkuyEe3bRWgjWDcduP4mwKUL32hVHQUcBbDNNtvcKIiVJEmSJK08ktwP2Ar4Ne2oyKdV1ZsBquqdSdYADk7ygqo6GTh5fNVKkuaykbUOqKoDq2qTqtqcNsnVN6tqT+B4YK++2l7Acf308cAevWn5FsCWwKmjqk+SJEmStGJL8jjaQJxHAk8GjgSen+SVA6sdA/wDuGb2K5QkrUxGOaJ1UQ4Fjk2yD3AxsBtAVZ2b5FjgPOA6YL+qun4M9UmSJEmSJlySWwD7AU+tqnOSfBT4E7AncFySa4CvAg8A7gWsC/xhTOVKklYCsxK0VtVJwEn99O+A7Rex3iHAIbNRkyRJkiRphXYdsBawdZKLgQcB84FzgK/181sB9wX2ripDVknSSI1jRKskSZIkSUOpqquSvAc4ENgf+FBVHZzkUcCOwKer6pQk6xmySpJmw8h6tEqSJEmSNEpV9VngEbQJrn7cl30d2ALYqK/2x7EUJ0la6TiiVZIkSZK0wqqqPyT5JvDkJH8H1gRuA5zZL68xlidJWokYtEqSJEmSVnQ/ALYEDgKuofVk/dVYK5IkrXQMWiVJkiRJK7Sq+iPwniQfBlJVfxpzSZKklZBBqyRJkiRpTqiqq8ddgyRp5eVkWJIkSZIkSZI0JINWSZIkSZIkSRqSQaskSZIkSZIkDcmgVZIkSZIkSZKGZNAqSZIkSZIkSUMyaJUkSZIkSZKkIRm0SpIkSZIkSdKQDFolSZIkSZIkaUgGrZIkSZIkSZI0JINWSZIkSZIkSRqSQaskSZIkSZIkDcmgVZIkSZIkSZKGZNAqSZIkSZIkSUMyaJUkSZIkSZKkIRm0SpIkSZIkSdKQDFolSZIkSZIkaUgGrZIkTZgk85L8OMmX+vn1k5yQ5ML+e72BdQ9MclGSC5LsML6qJUmSJGnlZtAqSdLk+Q/g/IHzBwAnVtWWwIn9PEnuBOwB3BnYETgiybxZrlWSJEmShEGrJEkTJckmwM7ABwYW7wIc3U8fDTx+YPkxVXVtVf0SuAjYdpZKlSRJkiQNMGiVJGmyvAt4JXDDwLINq+oygP57g758Y+CSgfUW9GWSJEmSpFlm0CpJ0oRI8hjgiqo6Y6ZXmWZZLeK2901yepLTr7zyymWuUZIkSZI0PYNWSZImx3bA45L8CjgGeHiSjwOXJ9kIoP++oq+/ANh04PqbAJdOd8NVdVRVbVNV28yfP39U9UuSJEnSSsugVZKkCVFVB1bVJlW1OW2Sq29W1Z7A8cBefbW9gOP66eOBPZKskWQLYEvg1FkuW5IkSZIErDruAiRJ0hIdChybZB/gYmA3gKo6N8mxwHnAdcB+VXX9+MqUJEmSpJWXQaskSROoqk4CTuqnfwdsv4j1DgEOmbXCJEmSJEnTsnWAJEmSJEmSJA3JoFWSJEmSJEmShmTQKkmSJEmSJElDMmiVJEmSJEmSpCEZtEqSJEmSJEnSkAxaJUmSJEmSJGlIBq2SJEmSJEmSNCSDVkmSJEmSJEkakkGrJEmSJEmSJA3JoFWSJEmSJEmShmTQKkmSJEmSJElDMmiVJEmSJEmSpCEZtEqSJEmSJEnSkAxaJUmSJEmSJGlIBq2SJEmSJEmSNCSDVkmSJEmSJEkakkGrJEmSJEmSJA3JoFWSJEmSJEmShmTQKkmSJEmSJElDMmiVJEmSJEmSpCEZtEqSJEmSJEnSkAxaJUmSJEmSJGlIBq2SJEmSJEmSNCSDVkmSJEmSJEkakkGrJEmSJEmSJA3JoFWSJEmSJEmShmTQKkmSJEmSJElDMmiVJEmSJEmSpCEZtEqSJEmSJEnSkAxaJUmSJEmSJGlIq467AGnSHf7yL47lfl/49seO5X4lSZIkSZK09BzRKkmSJEmSJElDMmiVJEmSJEmSpCEZtEqSJEmSJEnSkAxaJUmSJEmSJGlIBq2SJEmSJEmSNCSDVkmSJEmSJEkakkGrJEmSJEmSJA3JoFWSJEmSJEmShrTquAvQeGx32HZjud/vveh7Y7lfSZIkSSunJI8EtgJWqarDkqSqatx1SZLmHke0SpIkSZLmpCQPBD4JXAPsnuQwYLskDjqSJC13Bq2SJEmSpLlqW+Dwqvog8AjgKmBX4D4zuXKSfZOcnuT0K6+8coRlSpLmgpEFrUnWTHJqkp8kOTfJ6/vy9ZOckOTC/nu9gescmOSiJBck2WFUtUmSJEmSVgpnA/dPslVVXQMcDPwVeNpMrlxVR1XVNlW1zfz580dZpyRpDhjliNZrgYdX1d2BewA7JrkfcABwYlVtCZzYz5PkTsAewJ2BHYEjkswbYX2SJEmSpDkmyaZJ1khyU+Ak4ALgQUk2qqprgTcA2yZ55hjLlCTNQSMLWqv5cz+7Wv8pYBfg6L78aODx/fQuwDFVdW1V/RK4iHaYhyRJkiRJS5RkZ+ArwGHAB4BbAR8DHgTsnGTrPrL1i8ANYytUkjQnjbRHa5J5Sc4ErgBOqKpTgA2r6jKA/nuDvvrGwCUDV1/Qly18m/bIkSRJkiT9myQbA28GXgi8BvgR8H3a59HDgTsCRyZ5d1/ntDGVKkmao0YatFbV9VV1D2AT2qEZd1nM6pnuJqa5TXvkSJIkSZL+KclawG+Bk4GfAVdU1VuAdwDfBX5TVS8HXk3r27pdVZ0/rnolSXPTSIPWKVX1R1pvnB2By5NsBNB/X9FXWwBsOnC1TYBLZ6M+SZIkSdKKKckuwNuAWwPrA3tXVQFU1TuBI4CDk6xVVSdX1Qeq6qLxVSxJmqtGFrQmmZ9k3X56LeARwE+B44G9+mp7Acf108cDe/Sm5VsAWwKnjqo+SZIkSdKKLclDaO0Cju9zfbwC2DfJywZWOwb4B3DNGEqUJK1EVh3hbW8EHJ1kHi3QPbaqvpTkB8CxSfYBLgZ2A6iqc5McC5wHXAfsV1XXj7A+SZIkSdKK7d7AB6rqa0k2A25Kaw9wRJJrgBOB+wP3AtYF/jCuQiVJc9/IgtaqOgu45zTLfwdsv4jrHAIcMqqaJEmSJElzynXA6v30MbT2cz+n9WF9FHAH4AG0dgKGrJKkkZpR64AkJ85kmSRJatx3SpK0aMtxP/lN4DlJjgHeX1W7Ah8Evg18uKr+A3hUVZ09VMGSJM3AYke0JlkTuAlwyyTrAekX3YzWaFySJA1w3ylJ0qIt7/1kVZ2TZH/gPcDP+rKfJdkAuHlf7Y/D1i1J0kwsqXXAc4GX0HZ4Z/CvneCfgPeOrixJklZY7jslSVq0UewnvwL8F/C6JP/Xl90deBNAVdWyFitJ0tJYbNBaVe8G3p3kRVV12CzVJEnSCst9pyRJizaK/WRVXQd8NMk5wK7AGrSerD9fHrcvSdJMzWgyrKo6LMkDgM0Hr1NVHx1RXZIkrdDcd0qStGij2E9W1Y+AHw1fnSRJy2ZGQWuSjwG3A84Eru+LC/DDoiRJ03DfKUnSormflCTNRTMKWoFtgDvZ20aSpBlz3ylJ0qK5n5QkzTmrzHC9c4BbjbIQSZLmGPedkiQtmvtJSdKcM9MRrbcEzktyKnDt1MKqetxIqpIkacXnvlOSpEVzPylJmnNmGrS+bpRFSJI0B71u3AVIkjTBXjfuAiRJWt5mFLRW1bdHXYgkSXOJ+05JkhbN/aQkaS6aUdCa5GraDJAAqwOrAX+pqpuNqjBJklZk7jslSVo095OSpLlopiNa1xk8n+TxwLajKEiSpLnAfackSYvmflKSNBetsixXqqr/AR6+fEuRJGnuct8pSdKiuZ+UJM0FM20d8MSBs6sA2/CvwzwkSdJClnXfmWRN4DvAGrT99Ger6r+SrA98Gtgc+BXw5Kr6Q7/OgcA+wPXAi6vqa8vvkUiStPz5GVOSNBfNKGgFHjtw+jraB7xdlns1kiTNHcu677wWeHhV/TnJasB3k3wFeCJwYlUdmuQA4ADgVUnuBOwB3Bm4NfCNJFtV1fXL8bFIkrS8+RlTkjTnzLRH696jLkSSpLlkWfedVVXAn/vZ1fpP0T58PrQvPxo4CXhVX35MVV0L/DLJRbQedz9Y1tolSRo1P2NKkuaiGfVoTbJJki8kuSLJ5Uk+l2STURcnSdKKaph9Z5J5Sc4ErgBOqKpTgA2r6jKA/nuDvvrGwCUDV1/Ql0mSNLH8jClJmotmOhnWh4HjaYckbgx8sS+TJEnTW+Z9Z1VdX1X3ADYBtk1yl8Wsnulu4kYrJfsmOT3J6VdeeeVMypAkaZT8jClJmnNmGrTOr6oPV9V1/ecjwPwR1iVJ0opu6H1nVf2R1iJgR+DyJBsB9N9X9NUWAJsOXG0T4NJpbuuoqtqmqraZP99duCRp7PyMKUmac2YatP42yZ79UMZ5SfYEfjfKwiRJWsEt074zyfwk6/bTawGPAH5KG/WzV19tL+C4fvp4YI8kayTZAtgSOHX5PhRJkpY7P2NKkuacmQatzwKeDPwGuAzYFbB5uSRJi7as+86NgG8lOQs4jdaj9UvAocAjk1wIPLKfp6rOBY4FzgO+CuxXVdcv58ciSdLy5mdMSdKcs+oM1zsY2Kuq/gCQZH3gbbSdoyRJurFl2ndW1VnAPadZ/jtg+0Vc5xDgkGELliRpFvkZU5I058x0ROvdpnaAAFX1e6b5EChJkv7JfackSYvmflKSNOfMNGhdJcl6U2f6t40zHQ0rSdLKyH2nJEmL5n5SkjTnzHRH9nbg+0k+CxStl46HKEqStGjuOyVJWjT3k5KkOWdGQWtVfTTJ6cDDgQBPrKrzRlqZJEkrMPedkiQtmvtJSdJcNONDM/pOzx2fJEkz5L5TkqRFcz8pSZprZtqjVZIkSZIkSZK0CAatkiRJkiRJkjQkg1ZJkiRJkiRJGpJBqyRJkiRJkiQNyaBVkiRJkiRJkoZk0CpJkiRJkiRJQzJolSRJkiRJkqQhGbRKkiRJkiRJ0pAMWiVJkiRJkiRpSAatkiRJkiRJkjQkg1ZJkiRJkiRJGpJBqyRJkiRJkiQNyaBVkiRJkiRJkoZk0CpJkiRJkiRJQzJolSRJkiRJkqQhGbRKkiRJkiRJ0pAMWiVJkiRJkiRpSAatkiRJkiRJkjQkg1ZJkiRJkiRJGpJBqyRJkiRJkiQNyaBVkiRJkiRJkoZk0CpJkiRJkiRJQ1p13AWsDC5+w13Hcr+bvfbssdyvJEmSJEmStLJxRKskSZIkSZIkDcmgVZIkSZIkSZKGZNAqSZIkSZIkSUMyaJUkSZIkSZKkIc2pybDu/YqPjuV+z3jrM8Zyv5IkSZIkSZImw5wKWiVJkiRJ0nAufsNdx13CjG322rPHXYIk/ZOtAyRJkiRJkiRpSAatkiRJkiRJkjQkg1ZJkiRJkiRJGpJBqyRJkiRJkiQNyaBVkiRJkiRJkoZk0CpJkiRJkiRJQzJolSRJkiRJkqQhGbRKkiRJkiRJ0pAMWiVJkiRJkiRpSAatkiRJkiRJkjQkg1ZJkiRJkiRJGpJBqyRJkiRJkiQNyaBVkiRJkiRJkoZk0CpJkiRJkiRJQ1p13AVIkiRJkjQqSR4JbAWsUlWHJUlV1bjrkiTNPSMb0Zpk0yTfSnJ+knOT/Edfvn6SE5Jc2H+vN3CdA5NclOSCJDuMqjZJkiRJ0tyX5IHAJ4FrgN2THAZsl8RBR5Kk5W6UrQOuA15eVXcE7gfsl+ROwAHAiVW1JXBiP0+/bA/gzsCOwBFJ5o2wPkmSJEnS3LYtcHhVfRB4BHAVsCtwn5lcOcm+SU5PcvqVV145wjIlSXPByILWqrqsqn7UT18NnA9sDOwCHN1XOxp4fD+9C3BMVV1bVb8ELqLtFCVJkiRJWhZnA/dPslVVXQMcDPwVeNpMrlxVR1XVNlW1zfz580dZpyRpDpiVybCSbA7cEzgF2LCqLoMWxgIb9NU2Bi4ZuNqCvkySJEmSpBnpbezWSHJT4CTgAuBBSTaqqmuBNwDbJnnmGMuUJM1BIw9a+87tc8BLqupPi1t1mmU3alDuoRuSJEmSpOkk2Rn4CnAY8AHgVsDHgAcBOyfZuo9s/SJww9gKlSTNSSMNWpOsRgtZP1FVn++LL0+yUb98I+CKvnwBsOnA1TcBLl34Nj10Q5IkSZI0KM2mwKHAC4HXAD8Cvk/7zHk4cEfgyCTv7uucNqZyJUlz1MiC1iQBPgicX1XvGLjoeGCvfnov4LiB5Xv0Qzy2ALYETh1VfZIkSZKkuaGqijZ45xTgZ8AVVfUW4B3Ad4HfVNXLgVfT+rZuV1Xnj6teSdLctOoIb3s74OnA2UnO7Mv+k/YN47FJ9gEuBnYDqKpzkxwLnAdcB+xXVdePsD5JkiRJ0gouyQOB21bVR5OsDuxTVQcDVNU7k6wBHJzkBVV1MnDyOOuVJM1dIwtaq+q7TN93FWD7RVznEOCQUdUkSZIkSZobkqwC3AQ4EpiX5G/AS4HTk1RVvbGvegxt0M8146lUkrSyGOWIVkmSJEmSRqKqbgD+nORo4HrgCbTBPrcHfpXkauDLtKMt7wWsC/xhPNVKklYGBq2SJEmSpBXZdcBmtDlCnkebZPn7wD2BuwN3BfauKkNWSdJIGbRKkiRJklZkxwG7VdWJSe5Bmxfk0Kp6DUCS9QxZJUmzYZVxFyBJkiRJ0hD+BtwhyXNoI1rfCNwzyfP75X8cV2GSpJWLI1olSZIkSSusqro0ySXAa4D9quqLSR4GXNQvr7EWKElaaRi0SpIkSZJWdO8HjquqM/r5b/fJsiRJmjUGrZIkSZKkFVpVXQJckiTVGLJKkmadPVolSZogSTZN8q0k5yc5N8l/9OXrJzkhyYX993oD1zkwyUVJLkiyw/iqlyRpvGwTIEkaJ4NWSZImy3XAy6vqjsD9gP2S3Ak4ADixqrYETuzn6ZftAdwZ2BE4Ism8sVQuSZIkSSsxg1ZJkiZIVV1WVT/qp68Gzgc2BnYBju6rHQ08vp/eBTimqq6tql/SJv7YdlaLliRJkiQZtEqSNKmSbA7cEzgF2LCqLoMWxgIb9NU2Bi4ZuNqCvmzh29o3yelJTr/yyitHWrckSZIkrYwMWiVJmkBJbgp8DnhJVf1pcatOs+xG/emq6qiq2qaqtpk/f/7yKlOSJEmS1Bm0SpI0YZKsRgtZP1FVn++LL0+yUb98I+CKvnwBsOnA1TcBLp2tWiVJkiRJjUGrJEkTJEmADwLnV9U7Bi46Htirn94LOG5g+R5J1kiyBbAlcOps1StJkiRJalYddwGSJOnfbAc8HTg7yZl92X8ChwLHJtkHuBjYDaCqzk1yLHAecB2wX1VdP+tVS5IkSdJKzqBVkqQJUlXfZfq+qwDbL+I6hwCHjKwoSZIkSdIS2TpAkiRJkiRJkoZk0CpJkiRJkiRJQ7J1gLSCOmTPXcdyvwd9/LNjuV9JkiRJkqRJ5ohWSZIkSZIkSRqSQaskSZIkSZIkDcmgVZIkSZIkSZKGZNAqSZIkSZIkSUMyaJUkSZIkSZKkIRm0SpIkSZIkSdKQDFolSZIkSZIkaUgGrZIkSZIkSZI0JINWSZIkSZIkSRqSQaskSZIkSZIkDcmgVZIkSZIkSZKGZNAqSZIkSZIkSUMyaJUkSZIkSZKkIRm0SpIkSZIkSdKQDFolSZIkSZIkaUgGrZIkSZIkSZI0JINWSZIkSZIkSRqSQaskSZIkSZIkDcmgVZIkSZIkSZKGZNAqSZIkSZIkSUMyaJUkSZIkSZKkIRm0SpIkSZIkSdKQDFolSZIkSZIkaUgGrZIkSZIkSZI0JINWSZIkSZIkSRqSQaskSZIkSZIkDcmgVZIkSZIkSZKGZNAqSZIkSZIkSUMyaJUkSZIkSZKkIRm0SpIkSZIkSdKQDFolSZIkSZIkaUgGrZIkSZIkSZI0JINWSZIkSZIkSRqSQaskSZIkSZIkDcmgVZIkSZIkSZKGZNAqSZIkSZIkSUNaddwFSJIk6cbe+ckdZu2+XvrUr83afUmSJElzlSNaJUmSJEmSJGlIBq2SJEmSJEmSNCSDVkmSJEmSJEkakkGrJEmSJEmSJA3JybAkLVfnH/LNsdzvHQ96+FjuV5IkSZIkCRzRKkmSJEmSJElDM2iVJEmSJEmSpCEZtEqSJEmSJEnSkAxaJUmSJEmSJGlIBq2SJEmSJEmSNCSDVkmSJEmSJEkakkGrJEmSJEmSJA3JoFWSJEmSJEmShmTQKkmSJEmSJElDMmiVJEmSJEmSpCEZtEqSJEmSJEnSkFYddwGSJEmSJEmjtN1h2427hBn73ou+N+4SJC2jkY1oTfKhJFckOWdg2fpJTkhyYf+93sBlBya5KMkFSXYYVV2SJEmSpJVHkkcm2S/Ji/r5jLsmSdLcNMrWAR8Bdlxo2QHAiVW1JXBiP0+SOwF7AHfu1zkiybwR1iZJkiRJmuOSPBD4JHANsHuSw4Dtknh0pyRpuRtZ0FpV3wF+v9DiXYCj++mjgccPLD+mqq6tql8CFwHbjqo2SZIkSdJKYVvg8Kr6IPAI4CpgV+A+M7lykn2TnJ7k9CuvvHKEZUqS5oLZngxrw6q6DKD/3qAv3xi4ZGC9BX3ZjbijkyRJkiTN0NnA/ZNsVVXXAAcDfwWeNpMrV9VRVbVNVW0zf/78UdYpSZoDZjtoXZTpeuTUdCu6o5MkSZIkLUqSTZOskeSmwEnABcCDkmxUVdcCbwC2TfLMMZYpSZqDZjtovTzJRgD99xV9+QJg04H1NgEuneXaJEmSJEkrsCQ7A18BDgM+ANwK+BjwIGDnJFv3ka1fBG4YW6GSpDlptoPW44G9+um9gOMGlu/Rv3XcAtgSOHWWa5MkSZIkrYDSbAocCrwQeA3wI+D7tAE+hwN3BI5M8u6+zmljKleSNEeNLGhN8ingB8AdkixIsg9tp/fIJBcCj+znqapzgWOB84CvAvtV1fWjqk2SpEmV5ENJrkhyzsCy9ZOckOTC/nu9gcsOTHJRkguS7DCeqiVJGq+qKtpRkT8ALgSuqKq3AO8Avgv8pqpeDrya1rd1u6o6f1z1SpLmppEFrVX1lKraqKpWq6pNquqDVfW7qtq+qrbsv38/sP4hVXW7qrpDVX1lVHVJkjThPgLsuNCyA4ATq2pL4MR+niR3AvYA7tyvc0SSebNXqiRJ45fk9knuA6wL3Bx4Wg9eqap3AkcABydZq6pOrqoPVNVF46tYkjRXTcpkWJIkCaiq7wC/X2jxLsDR/fTRwOMHlh9TVddW1S+Bi4BtZ6NOSZImQZLHAJ8H3ga8HvgE8IIkBw6sdgzwD+Ca2a9QkrQyMWiVJGnybVhVlwH03xv05RsDlwyst6AvkyRpzkvyAFrAuldVPQRYnfaF4wOA5yd5dZLbAw8F7kUb8SpJ0sgYtEqStOLKNMtq2hWTfZOcnuT0K6+8csRlSZI0aw6tqh/30wcB96yqS2nh6m2Bl9Emvtq7qv4wnhIlSSuLVcddgCRJWqLLk2xUVZcl2Yg2ezK0EaybDqy3CW0ikBupqqOAowC22WabacNYSZJWMKcA5wD0HuVrALfu+8xfJHk98Gtg7aq6aox1SpJWEo5olSRp8h0P7NVP7wUcN7B8jyRrJNkC2BI4dQz1SZI066rq+qr6Uz8b4I/A7/sXk3sC/wmsZsgqSZotjmiVJGmCJPkU7XDHWyZZAPwXcChwbJJ9gIuB3QCq6twkxwLnAdcB+1XV9WMpXJKkMaqq64A/J7kkyf8DHgU8s6r+NubSJEkrEYNWSZImSFU9ZREXbb+I9Q8BDhldRZIkTb4kAVYDHtR/b19VF463KknSysagVZIkSZK0QquqAv6e5GDgNENWSdI4GLRKkiRJkuaKo3voKknSrHMyLEmSJEnSnGDIKkkaJ4NWSZIkSZIkSRqSQaskSZIkSZIkDcmgVZIkSZIkSZKG5GRYkiRpIvz8sF1m7b5u96LjZu2+JEmSJK0cHNEqSZIkSZIkSUMyaJUkSZIkSZKkIRm0SpIkSZIkSdKQDFolSZIkSZIkaUgGrZIkSZIkSZI0JINWSZIkSZIkSRqSQaskSZIkSZIkDWnVcRcgSZIkaent9rlzZu2+PvOku8zafUmSJK2oHNEqSZIkSZIkSUNyRKskSZIkSZImxuEv/+K4S5ixF779seMuQRPEEa2SJEmSJEmSNCSDVkmSJEmSJEkakkGrJEmSJEmSJA3JoFWSJEmSJEmShmTQKkmSJEmSJElDMmiVJEmSJEmSpCGtOu4CJEmSJEmStPS+/eCHjLuEGXvId7497hKkkXNEqyRJkiRJkiQNyaBVkiRJkiRJkoZk0CpJkiRJkiRJQzJolSRJkiRJkqQhGbRKkiRJkiRJ0pAMWiVJkiRJkiRpSAatkiRJkiRJkjQkg1ZJkiRJkiRJGpJBqyRJkiRJkiQNyaBVkiRJkiRJkoZk0CpJkiRJkiRJQzJolSRJkiRJkqQhGbRKkiRJkiRJ0pAMWiVJkiRJkiRpSAatkiRJkiRJkjQkg1ZJkiRJkiRJGpJBqyRJkiRJkiQNyaBVkiRJkiRJkoZk0CpJkiRJkiRJQzJolSRJkiRJkqQhGbRKkiRJkiRJ0pAMWiVJkiRJkiRpSKuOuwBJkiRpSXb+3JGzen//+6Tnzur9SZIkacVn0CpJY3LsZ7Ydy/0+ebdTx3K/kiRJkiTNZbYOkCRJkiRJkqQhGbRKkiRJkiRJ0pAMWiVJkiRJkiRpSAatkiRJkiRJkjQkg1ZJkiRJkiRJGpJBqyRJkiRJkiQNyaBVkiRJkiRJkoa06rgLkKRRe93rXrdS3a8kSZIkSZp9jmiVJEmSJEmSpCEZtEqSJEmSJEnSkAxaJUmSJEmSJGlIBq2SJEmSJEmSNCSDVkmSJEmSJEka0qrjLkCSJElaUTzus8fN6v0dv+sus3p/kiRJWnYGrZIkSZIkSdIIHbLnruMuYcYO+vhnx13CCsvWAZIkSZIkSZI0JEe0SpIkSVpmx37ut7N2X09+0i1n7b4kSZKW1sQFrUl2BN4NzAM+UFWHjrkkSZImmvtOSZIkSeNw/iHfHHcJM3bHgx4+8vuYqKA1yTzgvcAjgQXAaUmOr6rzxluZJEmTyX2nJDU/PeLyWbuvrV+w4SIvu/xdp85aHRu+ZNtZu68VXZI7AOsDpwM3VNX1Yy5JkjQHTVTQCmwLXFRVvwBIcgywC+CHRUmaBXf/7NfGcr8/2XWHsdzvHOG+U5KkxUjyROBNwK/7z+lJPlJVfxpvZZKkuWbSJsPaGLhk4PyCvkySJE3PfackSYuQZDVgd2CfqtoeOA7YFHhlkpuNtThJ0pyTqhp3Df+UZDdgh6p6dj//dGDbqnrRwDr7Avv2s3cALlhOd39LYPY6+c/cJNY1iTXBZNY1iTWBdS2NSawJJrOu5VnTbapq/nK6rTltJvvOvnx57z8n5TVoHTc2KbVYx7+zjn83KXXA5NSyPOpw/7mQHrQeD3y6qj6SZBXgQcDOwC+AI2sxH4pH+PlzcSblNbm8+bhWHHPxMYGPa0UzW49rue47J611wALat4tTNgEuHVyhqo4Cjlred5zk9KraZnnf7rAmsa5JrAkms65JrAmsa2lMYk0wmXVNYk0riSXuO2H57z8nZXtbx41NSi3WYR0rQh0wObVMSh1zTVX9I8k7gBcl+XlVnZzku7SjPx4NHLmE64/k8+fizNXXgo9rxTEXHxP4uFY0K+rjmrTWAacBWybZIsnqwB60bx8lSdL03HdKkrR4JwNfB56e5MFVdX1VfRK4NXD38ZYmSZpLJmpEa1Vdl+SFwNeAecCHqurcMZclSdLEct8pSdLiVdU1ST4BFHBgkq2Ba4ENgcvGWpwkaU6ZqKAVoKq+DHx5DHc9q4eDLIVJrGsSa4LJrGsSawLrWhqTWBNMZl2TWNNKYUz7zknZ3tZxY5NSi3X8O+v4d5NSB0xOLZNSx5xUVX9I8n7gPOC5wDXAnlV1+Xgrm9ZcfS34uFYcc/ExgY9rRbNCPq6JmgxLkiRJkqRRSjIPqKq6Ydy1SJLmFoNWSZIkSZIkSRrSpE2GNSuSfCjJFUnOGVj2kSS7jqmeTZN8K8n5Sc5N8h8DNf01yToD6747SSW55Zhru1+SU5Kc2S973WzUM1DXdNtwbDUt5jU11u23mNqOT/L0gfPvT/KKWaxpca/5SXsfnpRkm4H1Nh98LsdY17i34XSvq7E+V1o6/W/RxwbOr5rkyiRfWmi9+yS5fvC9Od3278vvkeSH/e/w6Um27cu37cvOTPKTJE9YRE1/7r+3TPKlJD9PckZ/Dzy4X7Z1kh8kuTbJ/gtdf1F1rZ/khCQX9t/rzeD5+XN/Df9toPYzkzyjX37vJGcnuSjJe5KkL79NkhOTnNXfE5sM3OZevYYLk+w1kxoWOv/SJNckufk0627Wa95/YNmialwjyaf78lOSbL6YGq5f6PEf0JevluTQ/ljOSXJqkp36ZYckuWSa+l+W5Lz+3JyY5DZL89wkeUJ/3W49sGzbJN9JckGSnyb5QJKbpHlPf4xnJblXX3/NXutP0v6mvn7gthb7OskM3jNJHtqfp3OTfHtg+aJem2/tdZ+V5AtJ1u3LF37tvW8pts1J/fmYWr7rstTQLzuwP4cXJNlhMTWc25/TlyVZJckOA/f/54F6Prq4202yepKjkvys1/Skvvx5aa/lM5N8N8mdZrpdkqzXH9dZfdvfZVTbZTHbZNUkb+qvranLDlpSHQOX75+F/n9c0raRNHNJJq6lYgY+Q2rypP2vuvq469DoJNkkyWrLdOWqWul+gAcD9wLOGVj2EWDXMdWzEXCvfnod4GfAnXpNZ9F6B0ELxs8CFgC3HHNtFwB378vnAXeagG04tpoW85oa6/ZbTG2bA78E1gUe0OtabRZrWtxrftLehycB2yz03J0zAXWNextO97oa63Plz1Jvwz8DPwbW6ud3As4EvjSwzjzgm7T+r7sOLL/R9u/Lvw7s1E8/Gjipn74JsGo/vRFwxdT5aWpas7/OHzew/C7AM/vpDYD7AIcA+y/pddmXvwU4oJ8+AHjzDJ+fRb6GgVOB+wMBvjLwuD8D7NVPPxz4WD+9PvCL/nu9fnq9JdUwzX2ePPVcLHTZ5/p97z+DGl8AvK+f3gP49ExrGFh+KHA0sEY/vyHw5H76fn07L1z/w4Cb9NPPn7rfmT43wLH98b9u4D7/D7h/Px9g17780f0xp9dzysA6N+2nVwNOAe43k9cJS3jP0P4enwdsNvVancFr81H8673x5qn7XNxrbwbb5iQG/hYPUcOdgJ8AawBbAD8H5i2qBtp78xvA6xdXz+JuF3g98MZ+ehX6/0vAzQau/zjgq0uxXd4K/Fc/vTVw4qi2y2K2yaG0/3HW7OfXob+OF1dHv2xT2mSH/zfwfCxx2/gzt36AuwFbjbuOufgDPBR4ObDFuGsZqOlRwGuBtcddiz/Tbp+daNnDbcddiz8j28Y7A6cBt1qW66+UI1qr6jvA78ddx5SquqyqftRPXw2cD2zcL/4UsHs//VDge8B1E1DbBvQZOqvq+qo6b7Zq6vc53TYcW02LeU2NdfvB9LVV1a9ojaXfAhwBvLCq/jGLNS3uNT8Wk1hTr2XauiZgG07U31Ets6/Q/pEAeArtb9agF9ECvCsGFy5m+xdws3765sClff2/VtXU3741+3qL8jTgB1V1/MD9nVNVH+mnr6iq04Abvd4XU9cutFCQ/vvxi7n/JUqyES34+UG1/8Y+OnCbdwJO7Ke/1e8bYAfghKr6fVX9ATgB2HEp7vN2wE2BV9O21eBlj6eFk+fOsMbB5+OzwPZJG+06w1puAjwHeFFVXQtQVZdX1bH99A+r6kazeFfVt6rqr/3sD4Gp0b5LfG6S3BTYDtiHFg4D7AccXVU/6LdfVfXZahPb7AJ8tC/7IbBuko36+amRtqv1n6nX40xeJ4t7zzwV+HxVXdzr+ef7ZlGvzar6+sB7Y/A5We6WoYZdgGOq6tqq+iVwEbDtYm7/CmBf4IVLeD0t7nafBfy/fns3VNVv++k/DVx/bW78N2Rx2+Wf78mq+imweZIN+/mRb5eF3i/X9Nu/uqpeN3B/i9unvhN4Jf/+mJdq22jFlmRH2hdNEzfqckWXdiTGUcDlwKwdcbg4SR4JvAv4TlX9JcnEZTaTWNNs6UcQHEYbTPWLtH7PK6Qkt0ty53HXMWn639yDgFdX1W+SrLG0t7HSvkEmVdrhe/ekjbAAuBCYn3b42lOAY8ZU2sK1vRO4oB9O9dwka46rrgGTWNPEbL9pvI32Qfbc/g/+WEzzmh+7SawJpq1rIrahVmjHAHv0v5d3Y+A1n2Rj4AnAjQ5ZXoyXAG9Ncgnt9XngwO3dN8m5wNnA8wZCjIXdGfjR0jyIGdhwKvjrvzdYiuvebqFDgR9E+xJmwcA6C/jXFzM/AZ7UTz8BWCfJLfrllyziOjMxFR6dDNwhyQYASdYGXkUbCThocTX+s5a+Ha4CbrGI+11roce/O3B74OKFwq+ltQ8tHPu3eqapdcrjaaMYfwb8Pq0VwF2AMxZx+4u8zSTzkpxJ+wLhhKqaet3P5HWyyPcMsBWwXtqh+2ekt5lYCs/iX88JwBZJfpzk2/11t7Dpts2UTwwsX9S2XVINS/2arapf0D5fLO49Nu3t5l8tCw5O8qMkn5kKRAGS7Jfk57QvGF+80G0ubrv8BHhiv41tgduwdMHp0myXxb1frl6K+6TX+zjg11X1k4UuGvbviVYQSR4NvIb2hfp5SdZNsta465oLesD0bmCfqvp4/xJ33DU9hvaZ9qlVdVKSTYGnTcjn2qkvfamqG1bGsDXJo4D3044qvDm0AV5L82X1pOh/Rw4E9sxAO56VXZK7044QO7iqvtZf819PsvHSbOeV7s0xyfpojc8BL1now8vnaaM37kv7gDX22qrqDcA2tMNEnwp8dRx1DZrEmrqxb79FuBvtEMqtx7WjXMxrfmymqWm6kXeLG403Eot4rsa+DRcyEc+VZq6qzqIdCvsUWnuAQe8CXlVV1y/FTT4feGlVbQq8FPjgwH2dUlV3ph32f+BMPzT0L8/OSfL5pahjefp5Vd1j4Odk2vtuYVOv9f2BhyT5MfAQ4Ne0IxkWd52Z2IM2gu0G2n5lt7789cA7B0ZpTlnc/S1NLX9b6PF/eilqnlaSPWn767cuRT2DX1Yew0Kjeqe7m0XdZrWjXu5BC9u2zUDPziVZwntmVeDetJGVOwCvSbLVTG43rV/ndcAn+qLLaC0I7gm8DPhkkpstdLXFbZunDSz/3TLWsKyv2SV9EFnU7a5K2ybfq6p7AT+gfWHTVqh6b1XdjvbFwqv/7cqL3y6H0gLwM2mj9H/MDI8uWobtssT3S5K9ewh7SQ9RFnXfN6GNqHntdBdPs8z97RyT5LbAp2l/+7/Rv3D/Dq3NhIaQ1pP1lrQjaE4ed1A2cP8701rsnNn/9/8isM7UaPhxSusPf0iSQ2DlC1uTPAQ4HHhh/717kj2gHVEz7tfQ0qqqv9Fa46wL7JZ/730+1dN/2fqTrqD6ly9b0472mp/kDrQR71+qql9X1Yz3syvNG2PS9Rfx54BPVNXCHyaPAQ6mjbq4YVJqq6qfV9V/A9sDd1/KERMjMYk1MebtN52+UzwCeDpt1O3zx1DD4l7zY7GImn5H6xk4ZX3gt+OuaxK24TTG/lxpmRxPCzMWbhuwDXBMkl/R+l4ekXaI+uLsRQsBoX0bfKNDWavqfOAvtNGI0zmXgQ+RVfUE4Jm019OyujztUPqpQ+qvWML6S7KAfx8Rtwn/apNwaVU9sQcxB/VlV/XrbDrddZYkyd2ALYET+vbYg38FjfcF3tKXvwT4zyQvXFyNg7X0D5s3Z+lagVwEbJZlmKgjySNoz8vjqrcdYAnPTd+XPxz4QH+cr6C15TmXFmxOZ4nPd1X9kdY/dKpNwUxfJ4t6zyygjbr9Sz/k/TvA3RdxG/+UNvnXY2jh6FQYfO1UQFpVZ9B6cM4otF0W09XAMrxmezB0PYt/jy3qdn8H/BX4Ql/+GaYPlI5h+rYO026XPkBg7x6uPwOYTxuNtFjLcbv82/ulqj7ca7mK1gd7UW5H67/6k/663wT4UZJbMcTfE61QLqO10rh3ksfSRtJ9qKq+N96yVmx9pPg7aV+i3GwwTJoKDpPsNDV6c5ZM/f/8ctr/Xj+k7Z/eUVVHDNQ3fxZrWthfgffS/p69Fv4Ztt7o+Ztr+pccvwOeXq211Q9pXwY+vB+5sMKErYOvoaq6EHgHbR+yW5K79uWV5Lm0/7sm/jEtD2ltRN5JOwrmE/xrkNyXq+qtA+HznTLNpLQLm5NvhBVN32gfBM6vqncsfHm1Xl8H0UKViagtyc4Db7otaf9U/3G26xs0iTXBeLffYjwXuLCqTqKNyHjlbO64l/SaH4fF1HQS7ZCKqdfWXrS+i+Oua6zbcBFOYozPlZbZh4A3VNXZgwuraouq2ryqNqd9s/uCqvqfJdzWpbRRnNCCsQsBkmzRAz3SZpq/A/CrRdzGJ4Ht+gehKTeZ8aOZ3vG01yP993HD3Fg/rPzqJPfrr/dnTN1mklsOfNA4kPb8QpvM5lFpM6CvR5vo4mszvMun0CbO2bz/3Jp2qPVtqupBA9vpXcCbqurwxdXIvz8fuwLfXJpv6av1Wf0g8J70GXeTbJQ2UnWRktwTOJIWsg4GcUt6bnal9Vu9TX+sm9KCsm8AeyW578B97NmDqOOBZ6S5H3BVVV2WZH7+NYP8WsAjgJ9O87ws7nUy7Xumr/+gtBnmb0L7J/38JTwnO9JGaD6u/tW/ll7nvH76trT/a36xuNtaVouqgfZ87JFkjSRb9BpOXcztzKe1Gjl8Ca+naW+3X+eLtJ720L40P6/f9pYD19+Z/rdlIdNul7RDradmhn42re/hYo+iWZ7bZeD9cnj6SP5+G4udrbqqzq6qDQbe3wtok2P+hqXcNlqxpLXa2ZfWX/gztG17NHB2Vb1rYL2HJtlmPFWumNIO/X4d8HHa3/7NgEdMhWQDg2LuDDxyNoLDHvAcm+STtNZD76bN6zG/qj46sN5TgVdkTK0jqs0FcSrw38AdB8LWf/T69qD97zen9O1zIrAWcHqSVar1gv8iLWzdPslu0ALK8VW6ZP3/oROTHJJkwyTr9LD1rbSwddf+v9jetC+13znpj2l5SOu7+xbgNdV6uX+bNsjpK8D1SdbsfyP2pv2vseR+2TUBM3rN9g/tm+7LaBNpLKD1CfsI7VuKBf3nB7NYzwNph/ucRZsl9UzabLkfYZoZ2GkfTmdl1vrF1HYMbVboM4HTgR0mYBuOrabFvKbGuv0WUdureg23GljnZcCHZ7Gmxb3mJ+19uDrt8JCzaN9wfZA+a/YY63rGBGzD6V7zY32u/FnqbXij2bFpAceXpln+b3/Pptv+ffkDaT0zf0LrkXjvvvzptNGHZ9L6rz5+mvtYFfhdP7017fDfX9D+if067YMQwNRorj/RvkxbQJ+RfDF13YL2T/KF/ff6S3huVu1/izYH/jbw3jsTeHFfZxvgHNqItsOB9OW79vv5GfABYI2B230WbXTbRcDeM91GtFBx64UuewettcPgstcB+w+cX1SNa9I+vF9E+9C0yFlzaV9aDj7+Q/vy1Wn/lF7U7+MU+n63L18A3NB/v64v/wZtwpGp2zp+Js8NfdTpQsteTPuwd3/aiIMLaKHmkbRgPrSRNz+n9QXepl/vbrRDx8/qdb924DYX+zphBu8Z2geT8/ptv2QG75mLaL02p56T9/XlT6K9Z35Ce888dim2zUlTj3eh9Zeqhn7ZQf05vADYaTE1TNW6P7DKNNtvm4WWTXu7tP6p3+nb50TaYfrQgoepvyHfAu480+3SXyMX0kKVzwPrjWq7LGabrEZrYXAR7fX3/f4crL64Oha67V8x8P/jkraNPyvmD63tyHm0D/PfBfamHeL+CtqXnvfq6z2D9rdts3HXvKL89Of2SuAjA8ueAfyB9j//1P8Se/b3+ZazUNNjaPvPB9NGs76f9gXgqrR92Df6ejv3vzt3muXn7BG00X33mvrbCazR/65+mr4P7c/Z74E7jns7L+fH/zDa/nz7RVx+K9oXs58CnjDuemfweO5La2n1c9r/ke+n/39JC1qPoIWLlwF3G3e9s/Sc7Eg7CudX9P+T+/J5/bJ30dpF7NP/Jt9lJrc79Q+3JEnS2KQ1n39/VY195uxJqkWStHJIm6ztW8BDq+q0PoLx1bSw66+0Njr3pYUkD6FNLHnumMpdoSTZGXgjcCywNnAN8Paq+ls/GuMFtC9w/0D7Qm73qjpnxDXdnBbifbWqntOXHQH8tqpe20fivxl4Gi0ce2q19kuzJsn+tBZ4b6e99l4F/LSq/tpHR74AuC0tcHxitX7ZK7yBo/PeClxSVe/uR8NsCjyI9mXIaVX1l34UzcOBb1WfUHMS9RHbUy0BoH3Rfl/a9j2cFrD+HDiA1qJkVl9r45DkPrQvtZ5Gm/vgkbTX8aX98nm0o2ueDWwHPGqmf3OXPORVkiRphJI8jzZC8SVjLmWiapEkrXROoo2iO62qPpnkycAtqurSfmj5TWnto3YyZJ2Z3rJod2C/qvp+D113AF6W5O1V9fEkJ9OOhliHFqyNPDCrqquSPA14c5IXVOvF+lf6ZH1V9fckBwB/Bz4+m8FXknvTjhr6IvA8Wih3MS1o/V2Sr1TVF5OsQTuq7oVzJWSFf7UASHIecL/eTmYv2tEyd6MdlbBNkndW1W+STE1WOpGmQtZ+9i/AXlV1ZJLLaUchrU47Ovgo4IBauolwV2Q30I6iOgs4K62X+ueTPLHanAvXJzmRdnTb/tVaQs6II1olSZIkSRqjtEmF7gm8iNbq4ia0CfWeVH3ywN7HepXqk7Jp8ZI8hjYS+CVVtaAvW502Sm0nWjubt9W/JmccR43b0dqjXAv8BnhyD3gGw7HZrGcn4PXAs6vqrP4F9FZV9bIkLwBeSwvrvg2cAPxPtRns55wk96e1bXgqraXVJ2htY55GO4T8ZWMsb4mS3LzahKz03rI39NMfoLXHui3tvXF82iSga1fVRWMreJb07Xrvqjp8msveBdyP1gpimb9wcTIsSZIkSZJmWW9VQ5JVq00q9BPaYbzbAc+sqsdU1bV95CBV9QdD1pnpE1/9F/CBqlqQZM0+qc3faQHhV2j9b1/dQ+7ZqGnHJG/pbSIAqKrv0Y6iuTVwUg9ZV6GNnpxVfVKg99DaUpzVD6E/G5if5Em0I352r6rb0V6r35pLIWuSnZK8OMldAKrqB1X1amC7qnoh8MMeyq8KbNJfU7O+nWYiyUOBbyR5U9oEnYNHs38A2Ah4UQ9Z16iqy1aSkHWqZ/p7knwiybOT3HXq8qp6CfBD4KTeFmKZGLRKkiRJkjSL+of47yX5H+DAJLevqmur6hTaqMHvJHkVwDhHXK6I+oi1/wX2raqvJrkd8GFaL1Gq6jpa2Ppt2mR1N5uFmgLcB9gX2C/JV5LcNsktquq7tEPTn5bkVVV1w2wfip7k0bRJJm9Fz4mq+R5tor/P0Cbh/Ha/ymFV9ZvZrHGUeri9My2cf3WSjySZn+SmVXVZHxFafYTv82kTfV4zjlHHM/RXYF3aJI5HAq8aCBTPoU14dQdYef6+JNm8f6G1P2108k9pgfPRSZ6V5M7wz7D188Bay3pfBq2SJEmSJM2uv9GCvr/RDhn/RpKnJblPVZ0OvA24b5JXj7PIFdRvaWHS/XvfxQ8BP66qX02t0MPW/wUOno1Rwj2Q+xbwC+A1tEPQXwi8K8k2VfUd4KXAI5KsN5sjJfsIztcDjwaeABzbWwhMOYgWVJ+dbpJ7ki6L/ni+DPyMNsK4aD1p35pkM2DtHkY/BXh6VZ03rloXJ8l9kty2qk6lbbOX0HqvXgqcnOQlwIa0Sa8elmSdSR2Vuzz13syfTrIpcAlt8rtvVtXBwP/Q2ne8JsnHAarqwKr65bLen0GrJEmSxirJrZIck+TnSc5L8uUkWy3D7bykHyK3pPU+kOROy1atJC27JPdOcl/gatohrHegjZ56HPB44KNJ/pM2UcubaYf5ailU1YW0WcT3oIXYH66qt/RRiyS5R5JbVdU/quovo6wlyaZJNuh1fRf4DvDSqjqINqJuF+AjSd4DzAce01tEzMpIyf6cXEmbIOmnVfUN4EDgvWmTQEGbGOtWwFP7KNdJHcU5lKr6MvBr4FlVtTewAHgG7f25P7Ae8LiqOmd8VS5a7z/8FNprH+Aq4I1VdTIt5J8HbEWb+OoFwNOq6uq5uj2n9JYYb6eNcL+kj8T+OS1YfRxtsrzn0L7oWK8H60MxaJWGlOSgJOcmOSvJmUnuu7w/wCV5WZKfJjk7yU+SvGOYXkJJNk8ykTsISdLKpY+k+AKtN93tqupOwH/SRlwsrZfQJpBZrKp69vIYjZJk1SWvJUlNH1X1IeCOwO1ps5d/g3b4agH3BQ4GHkCbcOfCuXR49mzq/SafReslunFfdkOSvWkzrY98RGYfFXoM8KjevgDgo8Dfk+xOGzG5M21irrOAH83mYdxJHkTrybrm4D6xqj5NC1uPSLJjD6NfAhw7W7XNhiRbJblD/z21P387sFYP4F4M7EBrFfA72v8pV42p3CXq/YfPo/0doareS2tP8ingRGCPqnoB7TV3SFX9aWzFzpIesr6fNtncP9/zVfUO2pcwHwf+s6qO6X1qd66qi4e+3zkeXksj1fv/vAN4aG9Uf0tg9aq6dDnex/No327vUVV/7N9UvQw4YuE/jknmVdX1M7jNzYEvVdVdlledkiQtiyQPp/U6e/BCy0P7MLwTLYB4Y1V9Om2Ch9fRDg29C3AGsCdtpu63ARcAv62qhyX5b1pPvLWAz1bVf/XbPgnYv6pOT/Jn2iFjj6EdwrtLVV2eZD7wPmBqZMNLqup7SV5Hm7Rk834/T13ez4mkuSfJQ4AP0kaRnTKw/KXAfrTRZv/RJ6dZG1itqv44lmJXYP2w9ho4vyUtaPkqrZ3Aq4Fnj3pUYg9Z3w28oI8SnVq+OvBp2kjWR01dNtPPccu5xiNpI/m+DxwP/KKqPjtw+VNofVt3r6qvzWZto9a/9HgzcArwQNqo1W/SRhx/GXgY8Iiq+mZff9XebmLiJfku8I2qel2Sx9PC42dV1beTrN4D2Tmv/7/4Dlrf3dVpX7y8vaq+2UdyPxvYqaqe0Ndfhd6aeNj79lt4aTgb0T5kXQtQVb+Ff32Ao30Qe0Nfdy1aCLtFknvT3vQ3pX1QfGZVXbaI+zgIePDUP1r9D+OhUxf2D4jvoH3b9vL+gfWx/f6+Dzy3N+6+N+0b9L8C3x24/rx+ew8F1gDeW1VHLvtTIknSUpkKSxf2ROAewN1pM0OfluQ7/bJ7Anem9Rz7Hm1G4PckeRnwsKn9MXBQVf2+7+tOTHK3qjproftZmzaT8EFJ3kL70PlG2gfkd1bVd/thZF+jjUIDuDfwwJpDsy1LGrl70yYQOqWPnru+H4X9zj5447Iesq466sPZ55Ik9wPuShu5esHCIw6r6sIkzwaOo302226U/TX7l4Rr0Ca3emVVfWMq/O3b9u9JXgSEdpj6VJ2zGrJ2r6Z9wXgx7TDzFyd5BC0IPrmqPpXkWtph1nNGb91xOPCMqjo5bZKoR9NaTfyONpL1XcC5U9eZ1JC11741cGJV/b4vfj3w+CRr0kbM/5b29+fbK1HIemtgfVq7gNP7gLj1gZf19+OJST5Em4hwv6p6by3HvsO2DpCG83Vg0yQ/S3JE/6b6n6rq+Kq6R1Xdg7bzf1s/5P8wYNeqmgo/D5nuxtOat9+0Ft+IeW3gnKq6b7W+P4dX1X36aNW1aCN0oDXDfnFV3X+h6+8DXFVV96GN+nlOki1m/hRIkjQSDwQ+VVXXV9XltElj7tMvO7WqFvR/is+kjS6dzpOT/Aj4MS2Yna6tz9+BL/XTZwzc1iOAw5OcSRvpc7O+XwY43pBV0kz04A1gC1oPTugha/8SCNph47fuoxonMtCZRL2H6AeAHWmH4T85ySoDzznwzzYCjwXuNcqQtd9XVdU1tABzKkid1y+b2ra3ogWb91+41lFLslkfMQ1t/xfgT1X1QdrgnX2AZ9K+3Lx9VX2+P38rvIHn+i7A53rIOq+qzqbNQv8L2pe8v6Rtn+1me/ssjSRr0UZGP5r2ZfKDk2wE/ID2JfWTqurPtKOA7jqw3ee0tEnL/pe2PX8E/xwQ9znaxFcvTfLI/n58PvDF5V2DI1qlIVTVn/tI0QfRDi/4dJIDFl4vySuBv1XVe9NmdbwLcEL/uz0PWNRo1tAOl5y6nR1ohzisS2tG/n3getofjSkP6/d3E9q3Nuf2EUDrVtW3+zofox2KCfAo4G5Jdu3nbw5sSdvBSJI0aucCu06zfHEfbgZ72F3PNP/T9i8N9wfuU1V/SPIRYM1pbusfA4eJDd7WKsD9Fw5U+77b0WaSZmTg78sXgP9Mcu+qOmPqMNV+2Xq0/83Xp01MpCXoA1w+A9y1qn6V5FnAw6vq/QPrrFKtL2uq6hezXOLatLY2p1XVdVOHbPftfhtaEHT68jhMeaaSbAi8HLgkyfuq6qoknwPenuRmtEOp96iqzyWZdiDQCm41Wrj8J9r2gbavv76qFiT5Ku019TZaIPfj2dw+SyPJY2j5w1uAK2gTOb2Q9r/Tp/rylyX5Oi1sPG1lGCmf5JG0x75/VZ05eFk/wuk4Wq/W1yb5c1V9dRR1OKJVGlIfaXNStb5vLwSeNHh5ku1pM/89b2oRcO7USNequmtVPWoRt/0n4C9TI0yr6mt9dOw5tD4jANdMHWrSDw84gjZa9q60fkRrslBgu5AALxqoZ4uq+voyPBWSJC2LbwJrJHnO1IIk9wH+AOyeZF5av9QHA6cu4bauBqZGnd6MFohe1T9c7rTIa03v67T9+lRN91jK60vSoFNo7bt272HrDT0EfBrt0N97VJUh68z9nRZKPwSgqj4E3CrJi5M8MMktpw4FHnVYNjjqceD0a4HbJHlBr2HqkO29aT15vzyG8PdK4DRaC4VnJblZVX2HNqLv1cArqupzvd6D5spIVvhnv86Xps1VcjGwS5K7VtU/kqzew/gzaO2I1qyqjy/hqNKx6SM2D6Ed3XN5H0X9DuCVtBHehwBPAe5P+yLi8vpXS6U5qx85/BhaT/+vJlk3yR2TPLlvf/rf2ONpOcmCUdXiiFZpCEnuANxQVRf2RfcA/o82YpUkt6EFnzsOjIi5AJif5P5V9YP+B2GrqjqX6f0/4L+TTE2GFaYfkcPA8t8muSlthNBn+/WuSvLA3l7gaQPX+Rrw/CTf7DuarYBfrwzfeEmSxq8fPvsE4F39qJBrgF/RZji+Ka31TtF63f0mydaLubmjgK8kuazaZFg/po2Y/QXtw9PSeDHw3iRn0f5n/g7/+tJUkpZKVf0lyftph2a/I8lptL93uwK7zaVQa5T6EX7rVpsc8Sm0v9Nr0MLDTWhtXx4MbJvktcBHl2fvxUW4OfDHqRG0fdn/0WY036sf0fhZWvuaF9C2959HXNM/pU0ItkpVXZDkE7TD4h8F7N1fk98Adq6q/+3rrzILz9ms6cHku2iH0M+v1if5XbT/F3bqrQNIsiftc/xEjmIFSLIxrU3Gc6rq1LTJ1Vajfcl8ZR/d/SDgvrRsYGRh4qTpWcZvgMcmOR94BW3k8u2Anye5e1W9u6p+m+Tjo3yNZ0JHQksrhN424DDaofzXARcB+9J2pPsDO9NmQZ76A3dpVT26j4p5D22nvCrwrsHDXBa6j9AO8XgO7VDJP9M+LL6xH+7x56q66cD6b6Q18v4VcAnwf9VmHBycDOtrtFGvd+mHr7yR1rcotG86H18LNZKXJEmSNJy0vor3pgWClwHfqqqfjbeqFUM/LPi9tAluTurLtqN9xlm1qm7Xl82jHcVw3ihHjfbPaRsBPwMeW1Xf6vd9Q/8ScQ1a+PtftHBzbdqs54saYDOKGm9B+3z3W9okSdfTvpR8Kq1v8B+q6vAkxwK/q6rnz1ZtsyHJnWhtO57de7JmaoRzklfQjlz5Du1Lj4fRPgefM7aClyCtz+pHaIfHX0DLHO5Laz/yXdqEexM5EneUprZrkrvRWmDsRpsA7+O0o4F3AW5fVa+ZlXoMWiVJkiRJ0qRKsjNtropnVptFfDPg1lX1wyT3owWw7+0tBGajnsHAbl/gTcATq+o7g2HrQtdZtcYw2VmSh9NGrf4HcFdaKPdnWvuFW9Ja5fyIdlTjpbNd3ygleTAtmN9zoK1DpkYzJrkrsDFtANSpkxpS9sexYVV9po/U3hrYHvgycBLtS5s9gaOr6sSxFTrLkqxWVf+YZvntB48SSLI/7ejjZ9InIxxlXbYOkCRJkiRJE6n36d4TOL+HrOvRJpN6H/DDHrbuB3yiBy9HzkJZWwC/6OHpUUmuBb6U5HFVdVIPW68fDGRpo0lnXVV9s7dceA9tNvoNgYfTjoK8L3B74CFV9cdx1DdiVwFr90Ps/9FHPaaHrjsCP6sRTYi0nK1Layf4B9rRqNvQeo1+bipoTPJUYLOxVTjL0iYEu1eSNy38BcZUyNq387NorROfOltfdDiiVZoQSd4LbLfQ4ndX1YfHUY8kSZIkjVPvb3o72kzxO9EmBN4OOGLhz0m9VdrvRz0qMckdgbNph2r/nDZL/U9p7SA+C+zQw9951SctngR9VPA7gftVm4F9PVp/z5tU1a/GWtyIpE1+9Tng4Kr6n4VGIr+c1jLgfUwzAnnS9H727wBeXlWfX+iyPYADaKOqZ3uStVnX24i8Hdi/FjGRd2+bsT2tV+ves9kSwqBVkiRJkiRNnCSvBx5YVdsneSDwUmCdqnrUwDpPpU388+HZCMuS3J4W3qxHG2l4Ia3v7tuBJwIPBZ5QVT8cdS1LK8lOwLuB+1fV78Zdz/LWJ3b+W1VdMrDsScAHgb2Bb/Z5TvYEDqT1ZL1w+lsbryTbA48BPgr8sk9wvRNwOG2C0M/1IPkxwPOBJ89m/99xSfIo4EjgSVX1oyRbAI+sqqOmWXdD2ryrV8xqjQatkiRJkiRpUiw08vDrwCeq6ujeb/TRwNVV9fokuwCH0EKm80Zc09pV9Zd+emvahFJXAz8Efg/sTgtf9wMup7UXuHbSRkr25+x1wL1HOfP6bEvyaNrEY0+ZGtU5MEnSE2kTTF9NaydwF2D3SZ34qk+adxTtkPcTgA1oAesvgQI+DDy9T/C1C3DuYE/Suaq35HgbcEfaCPd1aM/P56rqLeOsbZBBqyRJkiRJmkhJngNsWVWv7EHL9v3nXrTJnJ5aVeePuIadgb1owc5/VtWPe6uCJ/VVPjTQF/IRwIVV9X+jrGkYSW5aVX8edx3LS+9BeziwS1Wdl2SVqrphagKsHrbeGrgpbRteWlWXjbHkJUqyJfAq4Ce0Edu/B15I60/8VODWwPZV9b2xFTmLkmwHnAvcCngccGdgW+DQqjp6YL2bVdWfxlNls8o471ySJEmSJAkgybZJvpRkmyTr98XfAZ6QZPfe8/QbwLdoM60/fRZC1kcDB9OCvIuAtyZZr6rOAI4FAjyjB0FU1TcmOWQFmGMh607AfwM36z//VF0/fWlV/ayqzpjUkHXgNU9vafBe2hcK1wFfAh7cf38MOA2Y1UPix6W3C/gYcLuq+intObiSNnL86wPr7Q28qk9+NjaOaJUkSZIkSWPVJ776OXAocBNgU+C/quqUHqY9CXhNVV3WRyquWVV/G3FNG9MOTT6+qg7oy94HfK+qPtbP3x74D1ro87aqumaUNelfktwXOAx4CW3U57uBA6vqS+Osa1kkeQCt/mNpPWX/WlXX9JHTL6S9Nz42FeIPtrKYy/p7/xDgxVX13R5GXwtsCDwF2Ij2Rch9gNcDz6yqs8dVLxi0SpIkSZKkMeotAT4BzKuq3ZKsA7wI2AX4Hq0v5c2Ad466F+tATfOr6sokzwe2poWrxyb5DO3w8/WA9wA/BX5Nm7l+pRhhOAn6JEj3pfUnPbsv2w14LXBAVf1vX/bPfr+TLMkjgU/TWgT8Dy04PqR/sXAH2gRsvwE+WlU/W1Ee1zD64/4CcFhV/Xdv//AZ4OCq+mqSuwI701oJrE+bhG6kI9xnwtYBkiRJkiRpbHpLgGcD1yb5bFVdXVVvoo3k+z7wAGAf4E3QwrNR1tPbBXw9yU2B42h9Mh+e5MvAurQQ+EvA3YFjgL8Yss6evn3+B7gYOD9dVX0GeANwaF+HSQ4jB1/HVXUCbTKvt9DCxAXAaUn2B+bRRm2uB/yurz+xj2s5+iNtsrk1+sjWz9AmxvsqQA/Yv0BrJTIRISs4olWSJEmSJI1BkrsDqwFXV9UFfbb19wPrVNUuA+utTZsA6JtV9fMR17Qj7RDkV/fwi97z8enA82jtAT49WNvKcAj3pEiyPW0k8Uumts806zyJdhj+s6rq69OtMwmSzOtfMkydfx6wY1U9PslmwHnAEbRD5A8GPlJVfx9PtbMnyU1oo9uv7s/Dq4AHAl+rqlcOrPc44BTgyqq6YTzV3phBqyRJkiRJmlU90HwH8CvgKuDTVfU/SW5Gm3hqnap6wizXdD/gU8B+VfXlJJvTesa+kNa+YBfgnsCPq+pD/Tpz/hDuSZLkNcCvqupjvV/n7YG7AX+uqmMG1nsccE5V/WJMpS5WkkcAe9NGS/+kqr7Wlx8DrA3cEXhZVR3fD6G/oU+QNacleQzwXOBWwGur6itJNgL+k9YH+YtV9ZMkT6a9N3eYtOdl1XEXIEmSJEmSVh5JHgv8P+CRtBnV96D1QaWq/tT7or4vyYlVtf0s1bQp8DBa8PWzHm59EDi2qn7b1/lfYC3gHknW6S0ODFlnQd8+vwH+BLwgyXm08C20Q+rnJbl7VR0IUFXHj63YJUiyA60NxqeBWwJPTHJhD4X/mzaq+8U97F+jqi4YY7mzprcHOBh4MXBX4KgkT6iq05O8mba9H5PkCcCjgZ0nLWQFg1ZJkiRJkjS7tgNuX1W/BkjyLeCQJNvSZls/J8kLgDcm2aSqFoyymD6K7tnAc2gzmr8W2BZ4f1W9Z3BV4H3ATavq6lHWpH9JsjNtlOO+tBByM9rI4xOAo4EzgPvRDrGfaD1kPY42EvPbSbak9WZdq6/yI+AKWgALMOdbBQD0UauvA75SVScDJydZDdg6yXlVtSDJ64E3AvcGnj4pPVkXZusASZIkSZI0cknWrao/9tOHATsBW9EmMNoTuBRYnTbJ0XuBkwZ7WI6oph2AtwHPqKof92XPpc1m/v9obQKuSfJM2szv96+qP42yJv1LkkfRtsN/VtXX+gjPa5PcuqouHVjvxcDDgd2r6tpx1bs4/bV2MHADQFXdry//OvAP4CzgZFqbiv8Angj8ba6Pmk5yu6r6ed+GG9Hec8cm+Rxwa+AWwDtpbUa+Csyf5MnnVhl3AZIkSZIkaW7rIdO7e4hJVb2IFppcA2xbVZsDO9IOCT4f+OkshKw7Ah8Gbtbvk17bkbTA61nA3ZPsQxvxursh6+xJ8kDgeOCZPWS9PfDfgyFrkrWTPBvYixbGTnLIejjw7B6wXpLktCRvpX258AXgd8C7aKOp96yqv64EIesOwBeTbAB8CFgA3L+36ViHNgnem2ijmF8D3HKSQ1awdYAkSZIkSRqhfuj3G2mzh//zkPuqemGSq2k9Wukh5p+Ag2ahpgfSRko+BbgH8O0ku1fVr3otb03y8l73xsBuVXXuqOtS0ye6ugr4P2CrJBcBHwGOGwhZ1wIeQgvB96qq88ZU7mL1UbkfpYX31wNU1W5JPgy8HFizqv7e1z0SWGuqL/Bc1v8uHESbfO6KvuxI2iRhd6O17vgl8Mt+2WpV9Y9x1TtTtg6QJEmSJEkjkWRr4LPAs6rq1IHlTwOOr6qrk7wHeAZwm6q6ahZqujXwWOCUqjozyTzg1bRWBrtX1f8NrLsXcPKkzl4/FyW5HfBC4K20Q8nfD2xBmyDqY0lSVZXkLrRRoNdU1R/GV/GiJdmeNsHV64ENgQ2Ar1bVSf3yTwObAg9ZEULE5aX3ZD0d+EhVHZRkM1ro+lraly370J6XnwEfrqobxlbsUrJ1gCRJkiRJGpV1gLOr6tQkqwIkOZQ2idHRSW5WVS+mhWnzR11Mn/jqY8CPesi6Sm9R8Abgy8CxSW4ztX5VHW3IOuvmAXcGHlVVZ9Amwvo1fcKoHrLuDRwK/GNSQ9buT7TWB58A/pfWi3WHJA8FqKrdaSN3vzGuAsdkauT6Fkn2BT5O+ztxeVX9jdZG4LfA7YC1x1fm0nNEqyRJkiRJGokkD6b1ztyxn78NsAtwGO1Q8D/1fq2zUctOwJuBV1bVV6e5fB5wAG107SOr6uLZqEtNki2A31fVVUkeCRwD7FpV30pyH9oEae+jTZr2emCfqjpnfBXPXA/0b0iyJfB0YDXgK1X1nX75xlX167EWOcuS3Ax4DK2Fx7eq6pl9+WpV9Y/eGmLNCQ/Sb8SgVZIkSZIkLTdJbkrLG67uo1h/DPxvVR3QL1+zqq7p7QPuA7xq1JMYJVkNeDst3PpKkpsDtwK2BH5XVT/o660C7A8cO9WvVaOXZCvg08AFwEuq6jd9ErIHAa+rql8luTfweeAmwIOr6vxF3+Lk6mHrU4FbAJ+uqu9NtUMYc2kjNRA2//OxJlkbeDywK61FwPF9+bxRT4Y3KgatkiRJkiRpuUjyaNoEPzfQQs13JLkrrd/mOVW1f1/vqcCLaYdV/3SWajuCNsP7a2ij6DYA1qMFd2/ph3drDHog/xHa5FbfpIWua9MmRTq9qo7r690d+EtVXTSmUpeL3rv4CcAHqurKcdczar1lxxNpE5cx2HN1YGTr7sAnqurYsRS5nBi0SpIkSZKkoSXZkdY38wDa7OqfogWpX0pyx37+CuDPwG2Bp1fV2bNY3xbAO4B7A1/r9XwfeDJwx6o6cLZqUdMnQVqzqn6W5JbAi2jzCS2gjTZ+IPB/wF5Vdc34Kl3+pg6RH3cdo5ZkB+CNwEFV9fWB5YMjW9ehjWrdgRbG/mVFHeG76rgLkCRJkiRJK7YkGwIH0w65/2pf9hJgrd4q4Pwk9wK2oo0qvaKqfjPimh5MG7V6UlX9tqp+CTwhyd2q6qyB9TYBbrEyHL49Sfph468BVk/yhar6nyS/AK4Bvg7cGrgjsBvwR9qkWHPGShKyPhw4HHhWVZ3cezTvCRw62Bqgtxn5LPC5qvrzmMpdLhzRKkmSJEmSllmS21XVz5O8jDZS9UtV9dUknwe2pgVnX6K1Dpi1w4KTnAbcHvg5LdD75WCbgiSr0w5X/g/a6NoVsufniizJrYDtgbfQRj1eBDwPeFNVndED/H2BT1bVz8dXqZZWkjWBI4B5VbVXko2BY2ntAY4YWG9OfcFh0CpJkiRJkpZJbxfwKuApfQKjlwJ3BtbtPy8CNqKNSlwHOKCqFsxSbTsD2wIX0yZVWhU4h9Y+YG1aP9A3AnusKLPXz1V9tPObgROA7YCbA/v0AH+VwZ6emnxJblJVf01ye+CVwB+ARwP/vVDIunpV/X1cdY7CKuMuQJIkSZIkrXiSPIo2qdTBPWRdpareCZwK3BJ4d1WdX1XfpI0a3XvUIWuSLZKs1c9eAOwM/KyqngmcAryJNnryzcCZwMMMWcevqn4E7A38ntaf9cHA45KsAjhCcAXS2wW8PcnOfdKydwC3AH4GfGZgvecAH+nbeM6YUw9GkiRJkiSNXu9/+iFg36r6Zu+9eHSSLarqKOB4YKcku/bRbX8fdU/KJDsB7wbmA/SQ5+3AXkn2AF4APAX4LPCPvs6cn/F9RdFD+KNpk6l9APhyVd0wlw4rn+uSPJoWrH4H+AVAb9fxRlqf3ecluUWS3YFn0Xq1zqnRyrYOkCRJkiRJSyXJg4DP00aMXgh8GTimqt49sM4rgdsAr6yqv4y4nkfSRqq+sqpOSHITWpg6H3gbrU3AXlX1jVHWIa2sktyV9iXGc6rqOwPLHwucQRvs+VraBHVbArtV1XnjqHWUDFolSZIkSdKMJLkpcF1VXdN7oB4O3AAcUlUfGljvLlV1TpJ1q+qPI67p4bQRtPeuqguS3JY2KvJNVfWrJC8HnlZV9+rrz6nJd6Rxmno/JXkI8OSq2m9g2VHAfWm9d48C/g68FDiiqi4YY9kjY+sASZIkSZK0RH1k2keBrybZqqr+F3gGEOA3A+vtCRyVZMNRh6zdb4G1gNv0fo+fAC6oql8BVNXbgYuT7GnIKi13a/Tf1wNbJ1mvh6w3A66mtQj4A/CY/p58+VwNWcGgVZIkSZIkLUGSHYA30HqengwcmWSNqjoZeC5weJIdkuxCm/jq2VV1+WzUVlVnAfcDPg1cCbyrh6tTtd8X+CHwdUNWaflJcnvgPUk2B84B/grcHaCq/lRVL6+qM/rqGydZtaquG0+1s8OgVZIkSZIkLVKSewJHAq+rqu/RRrUCvDnJTlV1ArAPcCytH+pes917sapOo81Uvwowb6D2ZwKvAD5aVVfMZk3SXJUk/eQ6wFXAy4HrgM8Bb03y8CS36us+A3g08L65HrICrDruAiRJkiRJ0kS7CrgEWKdPePNB2uRXfwCek+SWVfWxPur1t1V10TiKrKqzkzwK+HqS62mjW/cD9q6qS8dRkzRH3Qy4qqp+nOSvtC9aDqV9qVG0992mSc6hjTZ/clVdOLZqZ5GTYUmSJEmSpGklWaWqbkhyR+BdwG2Bo6rqrf3yvYHdgV2q6trxVfovSbYBTqUFrQ+tqvPHXJI0ZyR5GC1UPQY4pf/cFtgD2Ig2Ed0/gDv1q1y2Mn3R4YhWSZIkSZL0b6YC1h6yrlJV5yd5PvBe4G9J1q+q3wN/ofVlnLfYG5xFVXV6krsA18/lSXekMdkEuE8/vRktdP0Y8CfaxHivB94y0Jt1pWLQKkmSJEmS/inJg4A18//bu/ugS+u6juPvzz7J0wQhjEAiJMlgAlnMZIMrxYMRRIITDykzukEJ1RiDCUGK6DSF5IATCRNKo+mYGDukNUwbOEKCCsUG2yKBwIKjTMMQsDCxLMjupz+ui5l7Nsj0Xu6z933er7/O/bt+5zrfs3/sH5/zvb6/5Cvj4VFNsrTtuiTvBy4FNiUpwyPDp7XdMMmatzTXM2KlaTGOCdnEcDjeVcBXGcLX3wG+BxwBbEzyQWDztB1A5+gASZIkSZIEwDjj9Arg1La3jWtp2yRHAHsAdzLMaf0x4CRDTWlhS7I38FzbR2asnc3wQ8u7264e9+wOvAf4+LR2kxu0SpIkSZIkkhwHXAC8r+3Xx1PDd2z7QJKfBlYCH2q7Msk+DN1q351kzZJePkkCvA74D+Am4LPAF9o+N14/m6GT9V1tb51UndsSg1ZJkiRJkqZckl2BG4E72q4YQ9YbgY+0vXo8AGdD29uSLGn7/EQLljRnkvw18ASwHPg68CRw4djpfhrwMeDotrdPsMxtgkGrJEmSJElT7IWDr5IczXBy+MPAm4Gr2175IvszbXMXpWmUZDGwGTgPeAXwp8BewNVAGeY13wj8AnBP2/snVOo2w6BVkiRJkqQpleRI4FhgHUNgsgdwMfBI2+Nm7DuB4UDta9tunkCpkuZIkt2B9W2/P/69I0Mn6znAeuDzwHXA88BhwFteGCcw7RZNugBJkiRJkjT3kvwKcAlDyPp24FRgDXA+8EyS3x/3ncTQybbGkFVa2JL8KnANcH2SdybZre3TwIUMB12tBM5pezbwhwwjAwxZR0smXYAkSZIkSZpbSfYHrgXOaPu5JNcB/wj8c9vrkxQ4PclRwG7AiW3vm2DJkl5mY8j6YYZA9VCGH2D+Hfgvhh9kDgAua/vlJIuAtl0/mWq3TXa0SpIkSZI0fZ4C/hx4a5LXtn0IuJVhDiMMjwl/DtiFIYy9exJFSpobSXYBPgR8u+0dbS8H7gKOS7J927XABePfe7Td7Kzm/80ZrZIkSZIkTaEkewPvBt4AbGDoXD257bPj9SXAsrYbJlelpLmQZBlDF+tvA3e1vSjJZ4ADgZ0YRgYsArYD/rjtE5OqdVtm0CpJkiRJ0hRIcgTwBLCu7ZPj2t7AmQyPCi9ve2+SZc5clKZDklcDG4Glbf8zyVuA3wX2Ax4FTgZOYAhbz2T4McYxIi/BoFWSJEmSpAUuyU7AVxlGAfwDcF/bvxyv7Qy8D3gtcJFjAqTpkOR44DzgEWAv4MvAJ4CDGQ7AW9X2T2bsX9r2+5Oodb4waJUkSZIkaQok+QhwLPBe4JPAdcA9DLNYdwTOAfYFTjdMkRa2JIcDVwLvAB4AXgV8Fvgn4CLgEIbO1ofa/tH4njiX9f/mYViSJEmSJC1QSXYcZ60CfBRYD2zf9mDgx8e1G4AjgVXAWYas0lQ4FLis7WpgY9t7GcYE/BrD/wO3AFcBeyR5JYAh6w9m0CpJkiRJ0gKU5JcZHgM+PMmubZ9heDR43yQHAUczBCvXAcuB+z3gRlrYkmR8+WqGA/AAnk2yuO13gBXAMUl2Bb4B/F7bx+a+0vlpyQ/eIkmSJEmS5pMkxwEfA84Hbp8RoH6TIVjdHjil7c3AzUl2aLthMtVKmiszulJXAucnOaTt6iRNspThAKzHgOfabpxYofOUQaskSZIkSQtIkp8APgCc0fZrM9YXj4HKh4HD2q4a1zYZskpT51bgFuCUJIwjBDYnWc4wVmTpRKubpwxaJUmSJElaWDYyHG5zS5LFDE1sm9tuGq8/AByfZN+2D02qSEmT0/bpJJ8Cfgu4NMk3geeAE4F3OEbkR+OMVkmSJEmSFohx/uIyhhPDl4/dqpvHwJUkewJPA/djJiBNtbYPA38GfBB4HngceHvbNRMtbB6LB4ZJkiRJkjS/JXnlzANrkpwFvB64vO3aJGnbJGcDewPnzOhwlSRtBf56JUmSJEnSPJZkP+ADSX5yxvJNwBPAWUneCmyf5F3AacBVhqyStPU5o1WSJEmSpPltMXAg8IvAgwBt1yTZNK5dBvwrsA/wzrZ3T6pQSVrIHB0gSZIkSdI8NHawPt72ySRHAV8Efr3tTVvs251hLuuytuvnvFBJmhKODpAkSZIkaZ5Jsj9wLXBlkj3afgU4F1iRZJ8Z+3Zp+2jbDYaskvTyMmiVJEmSJGn+WQd8C3gzcHGSY4GngO8CbwRI8rPAp5IsnVSRkjRNHB0gSZIkSdI8keQ1wHZtv51kN+C9DE1U3wNeBywHHgJWtN2YZPe2j06sYEmaIh6GJUmSJEnSPJBkR+ACYFmSv2v7pSTrgI3A9cBewOuBk4EngTMMWSVp7hi0SpIkSZI0D7R9OskFwJHA5Un2BO4HzgTub7s6yWnAe4AvTLBUSZpKjg6QJEmSJGmeSfJzwMXADQxzWncGTm/7QJJFbTdPtEBJmkIehiVJkiRJ0jzT9t+A3wQeZ5jPehjwtiSLADuqJGkC7GiVJEmSJGmeSrIU2A64BLik7b0TLkmSppZBqyRJkiRJkiTNkqMDJEmSJEmSJGmWDFolSZIkSZIkaZYMWiVJkiRJkiRplgxaJUmSJEmSJGmWDFolSZIkSZIkaZYMWiVJkiRJkuapJJuS3JnkriTXJNlhK9zzbUnO2xr1SdMkbSddgyRJkiRJkn4ESf677U7j688Dq9teOuP64rabJlagNEXsaJUkSZIkSVoYbgZ+KskvJbkxyd8Aa5Nsl+TTSdYmuSPJ4QBJbkvyhhfenOSmJIckWZHkE+PaZ5JcluQbSdYlOXHG/nPHe65J8tFxbb8kq5KsTnJzkgPm9p9Ampwlky5AkiRJkiRJs5NkCXAMsGpc+nngwLYPJvkDgLYHjcHn9Un2B64GTgYuTLInsFfb1UkO2uL2ewLLgQOAvwdWJjkGOAF4U9sNSXYd934SOLPtfUneBFwBHPEyfW1pm2LQKkmSJEmSNH9tn+TO8fXNwF8BhwL/0vbBcX058BcAbe9J8h1gf+BvgRuACxkC12te4jO+1HYzcHeSV41rRwGfbrthvO/jSXYaP/uaJC+89xVb5VtK84BBqyRJkiRJ0vz1TNs3zlwYQ86nZy692BvbPpzksSQHA6cAZ7zEZzz7IvcKsOXBP4uA9VvWI00LZ7RKkiRJkiQtbF8DTgUYRwa8Brh3vHY1cC6wc9u1P8Q9rwdOS7LDeN9d2z4FPJjkpHEtSX5mK30HaZtn0CpJkiRJkrSwXQEsTrIW+CKwou0LXaorgd9gGCPw/9Z2FcO81tvH0QXvHy+dCpyeZA3wLeD42ZcvzQ9pt+zyliRJkiRJkiT9MOxolSRJkiRJkqRZMmiVJEmSJEmSpFkyaJUkSZIkSZKkWTJolSRJkiRJkqRZMmiVJEmSJEmSpFkyaJUkSZIkSZKkWTJolSRJkiRJkqRZMmiVJEmSJEmSpFn6HxUfxMh9t2m8AAAAAElFTkSuQmCC\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": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['CAPE', 53.53],\n",
       " ['EASTERN CAPE', 10.55],\n",
       " ['NATAL', 5.69],\n",
       " ['ORANGE FREE STATE', 0.46],\n",
       " ['TRANSVAAL', 0.77],\n",
       " ['W.CAPE-BERGRIVER ETC', 22.54],\n",
       " ['WEST COAST', 6.45]]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#count of Province as a percentage of all provinces\n",
    "dict_Prov=[]\n",
    "for x in sorted(train['Province'].unique()):\n",
    "    dict_Prov.append([x,round(len(train[train['Province']==x])*100/len(train['Province']),2)])\n",
    "dict_Prov"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Cape makes more than 50 percent of our province data, while the Orange Free State and Transvaal making up less than one percent of the Provinces."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['AC030', 1.59],\n",
       " ['DT063', 0.31],\n",
       " ['EC120', 22.95],\n",
       " ['EF120', 3.79],\n",
       " ['EG140', 0.15],\n",
       " ['IA400', 7.12],\n",
       " ['JE090', 28.94],\n",
       " ['JG110', 4.35],\n",
       " ['M4183', 30.12],\n",
       " ['M6125', 0.51],\n",
       " ['M9125', 0.15]]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#count of Container as a percentage of all containers\n",
    "dict_Cont=[]\n",
    "for x in sorted(train['Container'].unique()):\n",
    "    dict_Cont.append([x,round(len(train[train['Container']==x])*100/len(train['Container']),2)])\n",
    "dict_Cont"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The containers a little more spread out than the provinces woth 3 containers making up more than 20 percent of the data. There are still 4 containers that make up less than 2 percent of our data combined. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['1L', 11.53],\n",
       " ['1M', 16.34],\n",
       " ['1S', 27.25],\n",
       " ['1U', 0.1],\n",
       " ['1X', 8.76],\n",
       " ['2L', 6.15],\n",
       " ['2M', 11.89],\n",
       " ['2S', 15.78],\n",
       " ['2U', 0.87],\n",
       " ['2X', 1.33]]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#count of Size_Grade as a percentage of all Size_Grades\n",
    "dict_Sgr=[]\n",
    "for x in sorted(train['Size_Grade'].unique()):\n",
    "    dict_Sgr.append([x,round(len(train[train['Size_Grade']==x])*100/len(train['Size_Grade']),2)])\n",
    "dict_Sgr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Size grade with 'U' in their name make up less than one percent of the data, while size grades containing 'S' are the most commonly occuring."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multivariate Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "#T_f60cf00f_beea_11eb_8698_b27eab38d49frow0_col0,#T_f60cf00f_beea_11eb_8698_b27eab38d49frow1_col1,#T_f60cf00f_beea_11eb_8698_b27eab38d49frow2_col2,#T_f60cf00f_beea_11eb_8698_b27eab38d49frow3_col3,#T_f60cf00f_beea_11eb_8698_b27eab38d49frow4_col4,#T_f60cf00f_beea_11eb_8698_b27eab38d49frow5_col5,#T_f60cf00f_beea_11eb_8698_b27eab38d49frow6_col6,#T_f60cf00f_beea_11eb_8698_b27eab38d49frow7_col7{\n",
       "            background-color:  #b40426;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_f60cf00f_beea_11eb_8698_b27eab38d49frow0_col1{\n",
       "            background-color:  #dc5d4a;\n",
       "            color:  #000000;\n",
       "        }#T_f60cf00f_beea_11eb_8698_b27eab38d49frow0_col2{\n",
       "            background-color:  #d0473d;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_f60cf00f_beea_11eb_8698_b27eab38d49frow0_col3{\n",
       "            background-color:  #536edd;\n",
       "            color:  #000000;\n",
       "        }#T_f60cf00f_beea_11eb_8698_b27eab38d49frow0_col4,#T_f60cf00f_beea_11eb_8698_b27eab38d49frow0_col6,#T_f60cf00f_beea_11eb_8698_b27eab38d49frow0_col7,#T_f60cf00f_beea_11eb_8698_b27eab38d49frow7_col0,#T_f60cf00f_beea_11eb_8698_b27eab38d49frow7_col1,#T_f60cf00f_beea_11eb_8698_b27eab38d49frow7_col2,#T_f60cf00f_beea_11eb_8698_b27eab38d49frow7_col3,#T_f60cf00f_beea_11eb_8698_b27eab38d49frow7_col5{\n",
       "            background-color:  #3b4cc0;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_f60cf00f_beea_11eb_8698_b27eab38d49frow0_col5{\n",
       "            background-color:  #9dbdff;\n",
       "            color:  #000000;\n",
       "        }#T_f60cf00f_beea_11eb_8698_b27eab38d49frow1_col0{\n",
       "            background-color:  #d75445;\n",
       "            color:  #000000;\n",
       "        }#T_f60cf00f_beea_11eb_8698_b27eab38d49frow1_col2,#T_f60cf00f_beea_11eb_8698_b27eab38d49frow2_col1{\n",
       "            background-color:  #c73635;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_f60cf00f_beea_11eb_8698_b27eab38d49frow1_col3{\n",
       "            background-color:  #5470de;\n",
       "            color:  #000000;\n",
       "        }#T_f60cf00f_beea_11eb_8698_b27eab38d49frow1_col4{\n",
       "            background-color:  #3d50c3;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_f60cf00f_beea_11eb_8698_b27eab38d49frow1_col5{\n",
       "            background-color:  #94b6ff;\n",
       "            color:  #000000;\n",
       "        }#T_f60cf00f_beea_11eb_8698_b27eab38d49frow1_col6,#T_f60cf00f_beea_11eb_8698_b27eab38d49frow6_col1{\n",
       "            background-color:  #3e51c5;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_f60cf00f_beea_11eb_8698_b27eab38d49frow1_col7,#T_f60cf00f_beea_11eb_8698_b27eab38d49frow6_col0{\n",
       "            background-color:  #688aef;\n",
       "            color:  #000000;\n",
       "        }#T_f60cf00f_beea_11eb_8698_b27eab38d49frow2_col0{\n",
       "            background-color:  #cd423b;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_f60cf00f_beea_11eb_8698_b27eab38d49frow2_col3{\n",
       "            background-color:  #7396f5;\n",
       "            color:  #000000;\n",
       "        }#T_f60cf00f_beea_11eb_8698_b27eab38d49frow2_col4{\n",
       "            background-color:  #445acc;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_f60cf00f_beea_11eb_8698_b27eab38d49frow2_col5,#T_f60cf00f_beea_11eb_8698_b27eab38d49frow3_col6{\n",
       "            background-color:  #b7cff9;\n",
       "            color:  #000000;\n",
       "        }#T_f60cf00f_beea_11eb_8698_b27eab38d49frow2_col6{\n",
       "            background-color:  #485fd1;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_f60cf00f_beea_11eb_8698_b27eab38d49frow2_col7{\n",
       "            background-color:  #6384eb;\n",
       "            color:  #000000;\n",
       "        }#T_f60cf00f_beea_11eb_8698_b27eab38d49frow3_col0{\n",
       "            background-color:  #bcd2f7;\n",
       "            color:  #000000;\n",
       "        }#T_f60cf00f_beea_11eb_8698_b27eab38d49frow3_col1{\n",
       "            background-color:  #98b9ff;\n",
       "            color:  #000000;\n",
       "        }#T_f60cf00f_beea_11eb_8698_b27eab38d49frow3_col2{\n",
       "            background-color:  #b6cefa;\n",
       "            color:  #000000;\n",
       "        }#T_f60cf00f_beea_11eb_8698_b27eab38d49frow3_col4{\n",
       "            background-color:  #ee8468;\n",
       "            color:  #000000;\n",
       "        }#T_f60cf00f_beea_11eb_8698_b27eab38d49frow3_col5{\n",
       "            background-color:  #c12b30;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_f60cf00f_beea_11eb_8698_b27eab38d49frow3_col7{\n",
       "            background-color:  #aac7fd;\n",
       "            color:  #000000;\n",
       "        }#T_f60cf00f_beea_11eb_8698_b27eab38d49frow4_col0{\n",
       "            background-color:  #6b8df0;\n",
       "            color:  #000000;\n",
       "        }#T_f60cf00f_beea_11eb_8698_b27eab38d49frow4_col1{\n",
       "            background-color:  #3f53c6;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_f60cf00f_beea_11eb_8698_b27eab38d49frow4_col2{\n",
       "            background-color:  #4b64d5;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_f60cf00f_beea_11eb_8698_b27eab38d49frow4_col3{\n",
       "            background-color:  #f59c7d;\n",
       "            color:  #000000;\n",
       "        }#T_f60cf00f_beea_11eb_8698_b27eab38d49frow4_col5{\n",
       "            background-color:  #f6bda2;\n",
       "            color:  #000000;\n",
       "        }#T_f60cf00f_beea_11eb_8698_b27eab38d49frow4_col6{\n",
       "            background-color:  #d4dbe6;\n",
       "            color:  #000000;\n",
       "        }#T_f60cf00f_beea_11eb_8698_b27eab38d49frow4_col7{\n",
       "            background-color:  #a5c3fe;\n",
       "            color:  #000000;\n",
       "        }#T_f60cf00f_beea_11eb_8698_b27eab38d49frow5_col0{\n",
       "            background-color:  #d5dbe5;\n",
       "            color:  #000000;\n",
       "        }#T_f60cf00f_beea_11eb_8698_b27eab38d49frow5_col1{\n",
       "            background-color:  #b3cdfb;\n",
       "            color:  #000000;\n",
       "        }#T_f60cf00f_beea_11eb_8698_b27eab38d49frow5_col2,#T_f60cf00f_beea_11eb_8698_b27eab38d49frow6_col4{\n",
       "            background-color:  #d3dbe7;\n",
       "            color:  #000000;\n",
       "        }#T_f60cf00f_beea_11eb_8698_b27eab38d49frow5_col3{\n",
       "            background-color:  #c32e31;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_f60cf00f_beea_11eb_8698_b27eab38d49frow5_col4{\n",
       "            background-color:  #f7af91;\n",
       "            color:  #000000;\n",
       "        }#T_f60cf00f_beea_11eb_8698_b27eab38d49frow5_col6{\n",
       "            background-color:  #a7c5fe;\n",
       "            color:  #000000;\n",
       "        }#T_f60cf00f_beea_11eb_8698_b27eab38d49frow5_col7{\n",
       "            background-color:  #8db0fe;\n",
       "            color:  #000000;\n",
       "        }#T_f60cf00f_beea_11eb_8698_b27eab38d49frow6_col2{\n",
       "            background-color:  #4c66d6;\n",
       "            color:  #000000;\n",
       "        }#T_f60cf00f_beea_11eb_8698_b27eab38d49frow6_col3{\n",
       "            background-color:  #7a9df8;\n",
       "            color:  #000000;\n",
       "        }#T_f60cf00f_beea_11eb_8698_b27eab38d49frow6_col5{\n",
       "            background-color:  #85a8fc;\n",
       "            color:  #000000;\n",
       "        }#T_f60cf00f_beea_11eb_8698_b27eab38d49frow6_col7{\n",
       "            background-color:  #a9c6fd;\n",
       "            color:  #000000;\n",
       "        }#T_f60cf00f_beea_11eb_8698_b27eab38d49frow7_col4{\n",
       "            background-color:  #799cf8;\n",
       "            color:  #000000;\n",
       "        }#T_f60cf00f_beea_11eb_8698_b27eab38d49frow7_col6{\n",
       "            background-color:  #81a4fb;\n",
       "            color:  #000000;\n",
       "        }</style><table id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49f\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Weight_Kg</th>        <th class=\"col_heading level0 col1\" >Low_Price</th>        <th class=\"col_heading level0 col2\" >High_Price</th>        <th class=\"col_heading level0 col3\" >Sales_Total</th>        <th class=\"col_heading level0 col4\" >Total_Qty_Sold</th>        <th class=\"col_heading level0 col5\" >Total_Kg_Sold</th>        <th class=\"col_heading level0 col6\" >Stock_On_Hand</th>        <th class=\"col_heading level0 col7\" >avg_price_per_kg</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49flevel0_row0\" class=\"row_heading level0 row0\" >Weight_Kg</th>\n",
       "                        <td id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49frow0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
       "                        <td id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49frow0_col1\" class=\"data row0 col1\" >0.863182</td>\n",
       "                        <td id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49frow0_col2\" class=\"data row0 col2\" >0.905852</td>\n",
       "                        <td id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49frow0_col3\" class=\"data row0 col3\" >0.180518</td>\n",
       "                        <td id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49frow0_col4\" class=\"data row0 col4\" >-0.133317</td>\n",
       "                        <td id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49frow0_col5\" class=\"data row0 col5\" >0.294117</td>\n",
       "                        <td id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49frow0_col6\" class=\"data row0 col6\" >-0.144105</td>\n",
       "                        <td id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49frow0_col7\" class=\"data row0 col7\" >-0.337886</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49flevel0_row1\" class=\"row_heading level0 row1\" >Low_Price</th>\n",
       "                        <td id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49frow1_col0\" class=\"data row1 col0\" >0.863182</td>\n",
       "                        <td id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49frow1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "                        <td id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49frow1_col2\" class=\"data row1 col2\" >0.938140</td>\n",
       "                        <td id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49frow1_col3\" class=\"data row1 col3\" >0.183230</td>\n",
       "                        <td id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49frow1_col4\" class=\"data row1 col4\" >-0.122823</td>\n",
       "                        <td id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49frow1_col5\" class=\"data row1 col5\" >0.269744</td>\n",
       "                        <td id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49frow1_col6\" class=\"data row1 col6\" >-0.127677</td>\n",
       "                        <td id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49frow1_col7\" class=\"data row1 col7\" >-0.141740</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49flevel0_row2\" class=\"row_heading level0 row2\" >High_Price</th>\n",
       "                        <td id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49frow2_col0\" class=\"data row2 col0\" >0.905852</td>\n",
       "                        <td id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49frow2_col1\" class=\"data row2 col1\" >0.938140</td>\n",
       "                        <td id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49frow2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
       "                        <td id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49frow2_col3\" class=\"data row2 col3\" >0.265672</td>\n",
       "                        <td id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49frow2_col4\" class=\"data row2 col4\" >-0.096456</td>\n",
       "                        <td id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49frow2_col5\" class=\"data row2 col5\" >0.372282</td>\n",
       "                        <td id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49frow2_col6\" class=\"data row2 col6\" >-0.094174</td>\n",
       "                        <td id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49frow2_col7\" class=\"data row2 col7\" >-0.164496</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49flevel0_row3\" class=\"row_heading level0 row3\" >Sales_Total</th>\n",
       "                        <td id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49frow3_col0\" class=\"data row3 col0\" >0.180518</td>\n",
       "                        <td id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49frow3_col1\" class=\"data row3 col1\" >0.183230</td>\n",
       "                        <td id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49frow3_col2\" class=\"data row3 col2\" >0.265672</td>\n",
       "                        <td id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49frow3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
       "                        <td id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49frow3_col4\" class=\"data row3 col4\" >0.771865</td>\n",
       "                        <td id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49frow3_col5\" class=\"data row3 col5\" >0.962338</td>\n",
       "                        <td id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49frow3_col6\" class=\"data row3 col6\" >0.284693</td>\n",
       "                        <td id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49frow3_col7\" class=\"data row3 col7\" >0.108473</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49flevel0_row4\" class=\"row_heading level0 row4\" >Total_Qty_Sold</th>\n",
       "                        <td id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49frow4_col0\" class=\"data row4 col0\" >-0.133317</td>\n",
       "                        <td id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49frow4_col1\" class=\"data row4 col1\" >-0.122823</td>\n",
       "                        <td id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49frow4_col2\" class=\"data row4 col2\" >-0.096456</td>\n",
       "                        <td id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49frow4_col3\" class=\"data row4 col3\" >0.771865</td>\n",
       "                        <td id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49frow4_col4\" class=\"data row4 col4\" >1.000000</td>\n",
       "                        <td id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49frow4_col5\" class=\"data row4 col5\" >0.651440</td>\n",
       "                        <td id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49frow4_col6\" class=\"data row4 col6\" >0.390168</td>\n",
       "                        <td id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49frow4_col7\" class=\"data row4 col7\" >0.087536</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49flevel0_row5\" class=\"row_heading level0 row5\" >Total_Kg_Sold</th>\n",
       "                        <td id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49frow5_col0\" class=\"data row5 col0\" >0.294117</td>\n",
       "                        <td id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49frow5_col1\" class=\"data row5 col1\" >0.269744</td>\n",
       "                        <td id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49frow5_col2\" class=\"data row5 col2\" >0.372282</td>\n",
       "                        <td id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49frow5_col3\" class=\"data row5 col3\" >0.962338</td>\n",
       "                        <td id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49frow5_col4\" class=\"data row5 col4\" >0.651440</td>\n",
       "                        <td id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49frow5_col5\" class=\"data row5 col5\" >1.000000</td>\n",
       "                        <td id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49frow5_col6\" class=\"data row5 col6\" >0.229062</td>\n",
       "                        <td id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49frow5_col7\" class=\"data row5 col7\" >-0.001007</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49flevel0_row6\" class=\"row_heading level0 row6\" >Stock_On_Hand</th>\n",
       "                        <td id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49frow6_col0\" class=\"data row6 col0\" >-0.144105</td>\n",
       "                        <td id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49frow6_col1\" class=\"data row6 col1\" >-0.127677</td>\n",
       "                        <td id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49frow6_col2\" class=\"data row6 col2\" >-0.094174</td>\n",
       "                        <td id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49frow6_col3\" class=\"data row6 col3\" >0.284693</td>\n",
       "                        <td id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49frow6_col4\" class=\"data row6 col4\" >0.390168</td>\n",
       "                        <td id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49frow6_col5\" class=\"data row6 col5\" >0.229062</td>\n",
       "                        <td id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49frow6_col6\" class=\"data row6 col6\" >1.000000</td>\n",
       "                        <td id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49frow6_col7\" class=\"data row6 col7\" >0.105028</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49flevel0_row7\" class=\"row_heading level0 row7\" >avg_price_per_kg</th>\n",
       "                        <td id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49frow7_col0\" class=\"data row7 col0\" >-0.337886</td>\n",
       "                        <td id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49frow7_col1\" class=\"data row7 col1\" >-0.141740</td>\n",
       "                        <td id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49frow7_col2\" class=\"data row7 col2\" >-0.164496</td>\n",
       "                        <td id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49frow7_col3\" class=\"data row7 col3\" >0.108473</td>\n",
       "                        <td id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49frow7_col4\" class=\"data row7 col4\" >0.087536</td>\n",
       "                        <td id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49frow7_col5\" class=\"data row7 col5\" >-0.001007</td>\n",
       "                        <td id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49frow7_col6\" class=\"data row7 col6\" >0.105028</td>\n",
       "                        <td id=\"T_f60cf00f_beea_11eb_8698_b27eab38d49frow7_col7\" class=\"data row7 col7\" >1.000000</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x17c147b3430>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr = train.corr()\n",
    "corr.style.background_gradient(cmap='coolwarm')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Date and avg_price_per_kg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2020-9', '2020-4', '2020-5', '2020-3', '2019-12', '2020-8',\n",
       "       '2020-7', '2020-2', '2020-6', '2020-1', '2020-10'], dtype=object)"
      ]
     },
     "execution_count": 19,
     "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']=[str(x.weekday()) for x in train['Date']]\n",
    "training_day['Month']=[str(x.month) for x in train['Date']]\n",
    "training_day['Year-Month']=[str(x.year)+'-'+str(x.month) for x in train['Date']]\n",
    "training_day['Year-Month'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": [
    "#Scatterplot of data_components and avg_price_per_kg\n",
    "fig, ax =plt.subplots(1,3, figsize=(20, 9))\n",
    "i=0\n",
    "for a in training_day.drop(['Date','avg_price_per_kg'],axis=1).columns:\n",
    "    sns.scatterplot(x=training_day[a],y=training_day['avg_price_per_kg'],ax=ax[i])\n",
    "    i+=1\n",
    "ax[2].tick_params(labelrotation=45)\n",
    "fig.tight_layout(pad=4.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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": [
    "fig, ax =plt.subplots(1,3, figsize=(20, 9))\n",
    "i=0\n",
    "for a in training_day.drop(['Date','avg_price_per_kg'],axis=1).columns:\n",
    "    avg_date_comp=training_day.groupby(a)['avg_price_per_kg'].mean()\n",
    "    sns.scatterplot(x=avg_date_comp.index,y=avg_date_comp,ax=ax[i])\n",
    "    i+=1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The relationship between the day of the week and the mean of the avg_price_per_kg is the most clear. Also,considering the data we have is not even for 1 complete year, the relationship between month and mean of avg_price_per_kg cannot be determined to any meaningful degree. Since each month only appears once and some of them are in different years.\n",
    "\n",
    "We will use the day of the week in our modelling since it's average is taken over many months."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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   Low_Price         1952 non-null   float64\n",
      " 5   High_Price        1952 non-null   float64\n",
      " 6   Sales_Total       1952 non-null   float64\n",
      " 7   Total_Qty_Sold    1952 non-null   int64  \n",
      " 8   Total_Kg_Sold     1952 non-null   float64\n",
      " 9   Stock_On_Hand     1952 non-null   int64  \n",
      " 10  avg_price_per_kg  1952 non-null   float64\n",
      " 11  Day               1952 non-null   object \n",
      "dtypes: float64(6), int64(2), object(4)\n",
      "memory usage: 278.2+ KB\n"
     ]
    }
   ],
   "source": [
    "train['Day']=training_day['Day']\n",
    "train=train.drop('Date',axis=1)\n",
    "\n",
    "test['Day']=[str(x.weekday()) for x in test['Date']]\n",
    "test=test.drop('Date',axis=1)\n",
    "\n",
    "train_dummies=train.copy()\n",
    "test_dummies=test.copy()\n",
    "train_dummies.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Categorical data  and avg_price_per_kg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "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_container=train_dummies.groupby('Container')['avg_price_per_kg'].mean()\n",
    "sns.barplot(x=avg_container.index,y=avg_container)\n",
    "plt.tick_params(labelrotation=45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "for x in avg_container.index:\n",
    "    if x in ['AC030','DT063','IA400']:\n",
    "        train_dummies=train_dummies.replace(x,'Lowest_mean_cont')\n",
    "        test_dummies=test_dummies.replace(x,'Lowest_mean_cont')\n",
    "    if x in ['JE090','JG110']:\n",
    "        train_dummies=train_dummies.replace(x,'medium_mean_cont')\n",
    "        test_dummies=test_dummies.replace(x,'medium_mean_cont')\n",
    "    if x in ['M6125','M9125']:\n",
    "        train_dummies=train_dummies.replace(x,'high_mean_cont')\n",
    "        test_dummies=test_dummies.replace(x,'high_mean_cont')\n",
    "    if x in ['EC120','EF120','EG140','M4183']:\n",
    "        train_dummies=train_dummies.replace(x,'Highest_mean_cont')\n",
    "        test_dummies=test_dummies.replace(x,'Highest_mean_cont')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "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_prov=train_dummies.groupby('Province')['avg_price_per_kg'].mean()\n",
    "sns.barplot(x=avg_prov.index,y=avg_prov)\n",
    "plt.tick_params(labelrotation=45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "for x in avg_prov.index:\n",
    "    if x in ['Natal']:\n",
    "        train_dummies=train_dummies.replace(x,'Lowest_Mean_Prov')\n",
    "        test_dummies=test_dummies.replace(x,'Lowest_Mean_Prov')\n",
    "    if x in ['Eastern Cape','Orange free State','West Coast']:\n",
    "        train_dummies=train_dummies.replace(x,'Medium_Mean_Prov')\n",
    "        test_dummies=test_dummies.replace(x,'Medium_Mean_Prov')\n",
    "    if x in ['Cape','W.CAPE-BERGRIVER ETC']:\n",
    "        train_dummies=train_dummies.replace(x,'High_Mean_Prov')\n",
    "        test_dummies=test_dummies.replace(x,'High_Mean_Prov')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "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_sgr=train_dummies.groupby('Size_Grade')['avg_price_per_kg'].mean()\n",
    "sns.barplot(x=avg_sgr.index,y=avg_sgr)\n",
    "plt.tick_params(labelrotation=45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Grouping by letter grade\n",
    "for x in avg_prov.index:\n",
    "    if 'L' in x:\n",
    "        train_dummies=train_dummies.replace(x,'L')\n",
    "        test_dummies=test_dummies.replace(x,'L')\n",
    "    if 'M' in x:\n",
    "        train_dummies=train_dummies.replace(x,'M')\n",
    "        test_dummies=test_dummies.replace(x,'M')\n",
    "    if 'S' in x:\n",
    "        train_dummies=train_dummies.replace(x,'S')\n",
    "        test_dummies=test_dummies.replace(x,'S')\n",
    "    if 'U' in x:\n",
    "        train_dummies=train_dummies.replace(x,'U')\n",
    "        test_dummies=test_dummies.replace(x,'U')\n",
    "    if 'X' in x:\n",
    "        train_dummies=train_dummies.replace(x,'X')\n",
    "        test_dummies=test_dummies.replace(x,'X')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, the mean of the avg_price_per_kg has some variance depending on the goup it belongs to for each of the categoical columns, the affect is not extremely drastic in most cases. Here we have group each value in the categorical columns with values that have similar affect on the mean. Now we will convert those column to numerical values using dummy variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dummies = pd.get_dummies(train_dummies,drop_first=True)\n",
    "train_dummies.columns = [col.replace(\" \",\"_\") for col in train_dummies.columns]\n",
    "train_dummies.columns = [col.replace(\".\",\"_\") for col in train_dummies.columns]\n",
    "train_dummies.columns = [col.replace(\"-\",\"_\") for col in train_dummies.columns]\n",
    "test_dummies = pd.get_dummies(test_dummies,drop_first=True)\n",
    "test_dummies.columns = [col.replace(\" \",\"_\") for col in test_dummies.columns]\n",
    "test_dummies.columns = [col.replace(\".\",\"_\") for col in test_dummies.columns]\n",
    "test_dummies.columns = [col.replace(\"-\",\"_\") for col in test_dummies.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1952 entries, 1 to 64310\n",
      "Data columns (total 28 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   int64  \n",
      " 5   Total_Kg_Sold               1952 non-null   float64\n",
      " 6   Stock_On_Hand               1952 non-null   int64  \n",
      " 7   avg_price_per_kg            1952 non-null   float64\n",
      " 8   Province_High_Mean_Prov     1952 non-null   uint8  \n",
      " 9   Province_L                  1952 non-null   uint8  \n",
      " 10  Province_S                  1952 non-null   uint8  \n",
      " 11  Container_Lowest_mean_cont  1952 non-null   uint8  \n",
      " 12  Container_high_mean_cont    1952 non-null   uint8  \n",
      " 13  Container_medium_mean_cont  1952 non-null   uint8  \n",
      " 14  Size_Grade_1M               1952 non-null   uint8  \n",
      " 15  Size_Grade_1S               1952 non-null   uint8  \n",
      " 16  Size_Grade_1U               1952 non-null   uint8  \n",
      " 17  Size_Grade_1X               1952 non-null   uint8  \n",
      " 18  Size_Grade_2L               1952 non-null   uint8  \n",
      " 19  Size_Grade_2M               1952 non-null   uint8  \n",
      " 20  Size_Grade_2S               1952 non-null   uint8  \n",
      " 21  Size_Grade_2U               1952 non-null   uint8  \n",
      " 22  Size_Grade_2X               1952 non-null   uint8  \n",
      " 23  Day_1                       1952 non-null   uint8  \n",
      " 24  Day_2                       1952 non-null   uint8  \n",
      " 25  Day_3                       1952 non-null   uint8  \n",
      " 26  Day_4                       1952 non-null   uint8  \n",
      " 27  Day_5                       1952 non-null   uint8  \n",
      "dtypes: float64(6), int64(2), uint8(20)\n",
      "memory usage: 255.4 KB\n"
     ]
    }
   ],
   "source": [
    "train_dummies.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All of our data are now numeric."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Numerical values and avg_price_per_kg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Weight_Kg</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.863182</td>\n",
       "      <td>0.905852</td>\n",
       "      <td>0.180518</td>\n",
       "      <td>-0.133317</td>\n",
       "      <td>0.294117</td>\n",
       "      <td>-0.144105</td>\n",
       "      <td>-0.337886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Low_Price</th>\n",
       "      <td>0.863182</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.938140</td>\n",
       "      <td>0.183230</td>\n",
       "      <td>-0.122823</td>\n",
       "      <td>0.269744</td>\n",
       "      <td>-0.127677</td>\n",
       "      <td>-0.141740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>High_Price</th>\n",
       "      <td>0.905852</td>\n",
       "      <td>0.938140</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.265672</td>\n",
       "      <td>-0.096456</td>\n",
       "      <td>0.372282</td>\n",
       "      <td>-0.094174</td>\n",
       "      <td>-0.164496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sales_Total</th>\n",
       "      <td>0.180518</td>\n",
       "      <td>0.183230</td>\n",
       "      <td>0.265672</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.771865</td>\n",
       "      <td>0.962338</td>\n",
       "      <td>0.284693</td>\n",
       "      <td>0.108473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total_Qty_Sold</th>\n",
       "      <td>-0.133317</td>\n",
       "      <td>-0.122823</td>\n",
       "      <td>-0.096456</td>\n",
       "      <td>0.771865</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.651440</td>\n",
       "      <td>0.390168</td>\n",
       "      <td>0.087536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total_Kg_Sold</th>\n",
       "      <td>0.294117</td>\n",
       "      <td>0.269744</td>\n",
       "      <td>0.372282</td>\n",
       "      <td>0.962338</td>\n",
       "      <td>0.651440</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.229062</td>\n",
       "      <td>-0.001007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stock_On_Hand</th>\n",
       "      <td>-0.144105</td>\n",
       "      <td>-0.127677</td>\n",
       "      <td>-0.094174</td>\n",
       "      <td>0.284693</td>\n",
       "      <td>0.390168</td>\n",
       "      <td>0.229062</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.105028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>avg_price_per_kg</th>\n",
       "      <td>-0.337886</td>\n",
       "      <td>-0.141740</td>\n",
       "      <td>-0.164496</td>\n",
       "      <td>0.108473</td>\n",
       "      <td>0.087536</td>\n",
       "      <td>-0.001007</td>\n",
       "      <td>0.105028</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Weight_Kg  Low_Price  High_Price  Sales_Total  \\\n",
       "Weight_Kg          1.000000   0.863182    0.905852     0.180518   \n",
       "Low_Price          0.863182   1.000000    0.938140     0.183230   \n",
       "High_Price         0.905852   0.938140    1.000000     0.265672   \n",
       "Sales_Total        0.180518   0.183230    0.265672     1.000000   \n",
       "Total_Qty_Sold    -0.133317  -0.122823   -0.096456     0.771865   \n",
       "Total_Kg_Sold      0.294117   0.269744    0.372282     0.962338   \n",
       "Stock_On_Hand     -0.144105  -0.127677   -0.094174     0.284693   \n",
       "avg_price_per_kg  -0.337886  -0.141740   -0.164496     0.108473   \n",
       "\n",
       "                  Total_Qty_Sold  Total_Kg_Sold  Stock_On_Hand  \\\n",
       "Weight_Kg              -0.133317       0.294117      -0.144105   \n",
       "Low_Price              -0.122823       0.269744      -0.127677   \n",
       "High_Price             -0.096456       0.372282      -0.094174   \n",
       "Sales_Total             0.771865       0.962338       0.284693   \n",
       "Total_Qty_Sold          1.000000       0.651440       0.390168   \n",
       "Total_Kg_Sold           0.651440       1.000000       0.229062   \n",
       "Stock_On_Hand           0.390168       0.229062       1.000000   \n",
       "avg_price_per_kg        0.087536      -0.001007       0.105028   \n",
       "\n",
       "                  avg_price_per_kg  \n",
       "Weight_Kg                -0.337886  \n",
       "Low_Price                -0.141740  \n",
       "High_Price               -0.164496  \n",
       "Sales_Total               0.108473  \n",
       "Total_Qty_Sold            0.087536  \n",
       "Total_Kg_Sold            -0.001007  \n",
       "Stock_On_Hand             0.105028  \n",
       "avg_price_per_kg          1.000000  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To perfrom linear regression we make the assumption that there is an underlying linear formula to the data we are modelling. So to optimize our model we select features so that they have a correlation with our data.\n",
    "\n",
    "In the following section we will take alot look at the importance of each variable one the response by looking at it's correlation with the response variable as well as it's p-value.\n",
    "\n",
    "We will also make sure we try and avoid multicollinearity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>Correlation_Coefficient</th>\n",
       "      <th>P_Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Province_High_Mean_Prov</th>\n",
       "      <td>0.262051</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Province_L</th>\n",
       "      <td>-0.365193</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Size_Grade_2S</th>\n",
       "      <td>-0.352996</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weight_Kg</th>\n",
       "      <td>-0.337886</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Province_S</th>\n",
       "      <td>-0.165182</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>High_Price</th>\n",
       "      <td>-0.164496</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Size_Grade_2M</th>\n",
       "      <td>-0.153372</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Low_Price</th>\n",
       "      <td>-0.141740</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Container_medium_mean_cont</th>\n",
       "      <td>-0.370769</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Container_Lowest_mean_cont</th>\n",
       "      <td>-0.393817</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Size_Grade_1X</th>\n",
       "      <td>0.251451</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Size_Grade_1M</th>\n",
       "      <td>0.175779</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sales_Total</th>\n",
       "      <td>0.108473</td>\n",
       "      <td>0.000002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stock_On_Hand</th>\n",
       "      <td>0.105028</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Size_Grade_2L</th>\n",
       "      <td>-0.089733</td>\n",
       "      <td>0.000072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total_Qty_Sold</th>\n",
       "      <td>0.087536</td>\n",
       "      <td>0.000108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Day_1</th>\n",
       "      <td>-0.084622</td>\n",
       "      <td>0.000182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Size_Grade_2U</th>\n",
       "      <td>-0.069457</td>\n",
       "      <td>0.002137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Day_5</th>\n",
       "      <td>0.065042</td>\n",
       "      <td>0.004042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Size_Grade_2X</th>\n",
       "      <td>-0.051624</td>\n",
       "      <td>0.022556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Day_4</th>\n",
       "      <td>0.050394</td>\n",
       "      <td>0.025983</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Correlation_Coefficient   P_Value\n",
       "Province_High_Mean_Prov                    0.262051  0.000000\n",
       "Province_L                                -0.365193  0.000000\n",
       "Size_Grade_2S                             -0.352996  0.000000\n",
       "Weight_Kg                                 -0.337886  0.000000\n",
       "Province_S                                -0.165182  0.000000\n",
       "High_Price                                -0.164496  0.000000\n",
       "Size_Grade_2M                             -0.153372  0.000000\n",
       "Low_Price                                 -0.141740  0.000000\n",
       "Container_medium_mean_cont                -0.370769  0.000000\n",
       "Container_Lowest_mean_cont                -0.393817  0.000000\n",
       "Size_Grade_1X                              0.251451  0.000000\n",
       "Size_Grade_1M                              0.175779  0.000000\n",
       "Sales_Total                                0.108473  0.000002\n",
       "Stock_On_Hand                              0.105028  0.000003\n",
       "Size_Grade_2L                             -0.089733  0.000072\n",
       "Total_Qty_Sold                             0.087536  0.000108\n",
       "Day_1                                     -0.084622  0.000182\n",
       "Size_Grade_2U                             -0.069457  0.002137\n",
       "Day_5                                      0.065042  0.004042\n",
       "Size_Grade_2X                             -0.051624  0.022556\n",
       "Day_4                                      0.050394  0.025983"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Corellation and P-Values against avg_price_per_kg\n",
    "corrs = train_dummies.corr()['avg_price_per_kg'].sort_values(ascending=False)\n",
    "# Build a dictionary of correlation coefficients and p-values\n",
    "dict_cp = {}\n",
    "\n",
    "column_titles = [col for col in corrs.index if col!= 'avg_price_per_kg']\n",
    "for col in column_titles:\n",
    "    p_val = round(pearsonr(train_dummies[col], train_dummies['avg_price_per_kg'])[1],6)\n",
    "    dict_cp[col] = {'Correlation_Coefficient':corrs[col],\n",
    "                    'P_Value':p_val}\n",
    "#choosing columns with p values<0.1    \n",
    "train_cp = pd.DataFrame(dict_cp).T\n",
    "train_cp_sorted = train_cp.sort_values('P_Value')\n",
    "train_cp_sorted[train_cp_sorted['P_Value']<0.1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_data = train_dummies['avg_price_per_kg']  \n",
    " \n",
    "#Choosing features based on P-Values\n",
    "X_names = list(train_cp[train_cp['P_Value'] < 0.1].index)\n",
    "X_data = train_dummies[X_names]\n",
    "\n",
    "X_data_test=test_dummies[X_names]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To deal with collinearity we plot a matrix of the columns that are highly correlated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>High_Price</th>\n",
       "      <th>Low_Price</th>\n",
       "      <th>Weight_Kg</th>\n",
       "      <th>High_Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Low_Price</th>\n",
       "      <td>0.938140</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.863182</td>\n",
       "      <td>0.938140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>High_Price</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.938140</td>\n",
       "      <td>0.905852</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>High_Price</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.938140</td>\n",
       "      <td>0.905852</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weight_Kg</th>\n",
       "      <td>0.905852</td>\n",
       "      <td>0.863182</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.905852</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            High_Price  Low_Price  Weight_Kg  High_Price\n",
       "Low_Price     0.938140   1.000000   0.863182    0.938140\n",
       "High_Price    1.000000   0.938140   0.905852    1.000000\n",
       "High_Price    1.000000   0.938140   0.905852    1.000000\n",
       "Weight_Kg     0.905852   0.863182   1.000000    0.905852"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# correlation matrix and find rows and columns where correlation coefficients > 0.9 or <-0.9\n",
    "corr = X_data.corr()\n",
    "r, c = np.where(np.abs(corr) >0.9)\n",
    "\n",
    "# We are only interested in the off diagonal entries:\n",
    "off_diagonal = np.where(r != c)\n",
    "\n",
    "# Show the correlation matrix rows and columns where we have highly correlated off diagonal entries:\n",
    "corr.iloc[r[off_diagonal], c[off_diagonal]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we drop High_Price since it has a high correlation with the Weight_Kg and the Low_Price columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#correlation matrics\n",
    "fig = plt.figure(figsize=(15,15));\n",
    "ax = fig.add_subplot(111);\n",
    "X_data=train_dummies.drop('High_Price',axis=1)\n",
    "X_data_test=test_dummies.drop('High_Price',axis=1)\n",
    "plot_corr(X_data.corr(), xnames = X_data.corr().columns, ax = ax);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems like most of the colinearity has been taken care of."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"feature\"></a>\n",
    "# Feature Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We seperate our model building (test and training) sets for linear and non-linear modelling "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1952 entries, 1 to 64310\n",
      "Data columns (total 28 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   int64  \n",
      " 5   Total_Kg_Sold               1952 non-null   float64\n",
      " 6   Stock_On_Hand               1952 non-null   int64  \n",
      " 7   avg_price_per_kg            1952 non-null   float64\n",
      " 8   Province_High_Mean_Prov     1952 non-null   uint8  \n",
      " 9   Province_L                  1952 non-null   uint8  \n",
      " 10  Province_S                  1952 non-null   uint8  \n",
      " 11  Container_Lowest_mean_cont  1952 non-null   uint8  \n",
      " 12  Container_high_mean_cont    1952 non-null   uint8  \n",
      " 13  Container_medium_mean_cont  1952 non-null   uint8  \n",
      " 14  Size_Grade_1M               1952 non-null   uint8  \n",
      " 15  Size_Grade_1S               1952 non-null   uint8  \n",
      " 16  Size_Grade_1U               1952 non-null   uint8  \n",
      " 17  Size_Grade_1X               1952 non-null   uint8  \n",
      " 18  Size_Grade_2L               1952 non-null   uint8  \n",
      " 19  Size_Grade_2M               1952 non-null   uint8  \n",
      " 20  Size_Grade_2S               1952 non-null   uint8  \n",
      " 21  Size_Grade_2U               1952 non-null   uint8  \n",
      " 22  Size_Grade_2X               1952 non-null   uint8  \n",
      " 23  Day_1                       1952 non-null   uint8  \n",
      " 24  Day_2                       1952 non-null   uint8  \n",
      " 25  Day_3                       1952 non-null   uint8  \n",
      " 26  Day_4                       1952 non-null   uint8  \n",
      " 27  Day_5                       1952 non-null   uint8  \n",
      "dtypes: float64(6), int64(2), uint8(20)\n",
      "memory usage: 255.4 KB\n"
     ]
    }
   ],
   "source": [
    "train_dummies.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Setting the training and testing data for linear models\n",
    "\n",
    "X_lin=train_dummies[X_data.drop('avg_price_per_kg',axis=1).columns]\n",
    "y_lin=train_dummies['avg_price_per_kg']\n",
    "\n",
    "test_lin=test_dummies[X_data_test.columns]\n",
    "X_lin.head()\n",
    "# standardize features\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X_lin)\n",
    "test_scaled = scaler.fit_transform(test_lin)\n",
    "# Train test split\n",
    "x_train_ln, x_test_ln, y_train_ln,y_test_ln = train_test_split(X_scaled,y_lin,test_size=0.2,random_state=6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On the other hand we achieve our best model scores on the non-linear models. The non-linear models do not assume an underlying linear relationship and are better suited to the data we are working with"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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   Low_Price         1952 non-null   float64\n",
      " 5   High_Price        1952 non-null   float64\n",
      " 6   Sales_Total       1952 non-null   float64\n",
      " 7   Total_Qty_Sold    1952 non-null   int64  \n",
      " 8   Total_Kg_Sold     1952 non-null   float64\n",
      " 9   Stock_On_Hand     1952 non-null   int64  \n",
      " 10  avg_price_per_kg  1952 non-null   float64\n",
      " 11  Day               1952 non-null   object \n",
      "dtypes: float64(6), int64(2), object(4)\n",
      "memory usage: 278.2+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Setting the training and testing data for non-linear models\n",
    "\n",
    "X=train_dummies.drop('avg_price_per_kg',axis=1)\n",
    "y=train_dummies['avg_price_per_kg']\n",
    "\n",
    "test_x=test_dummies\n",
    "# standardize features\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "test_scaled = scaler.fit_transform(test_x)\n",
    "\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",
   "metadata": {},
   "source": [
    "<a id=\"simplemodelling\"></a>\n",
    "# Base models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Linear models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lasso(alpha=0.01)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Linear Regression\n",
    "linreg = LinearRegression()\n",
    "linreg.fit(x_train, y_train)\n",
    "\n",
    "#Ridge Regression\n",
    "ridge = Ridge()\n",
    "ridge.fit(x_train, y_train)\n",
    "\n",
    "#Lasso Reg\n",
    "lasso = Lasso(alpha=0.01)\n",
    "lasso.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Non-linear models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Support Vector model\n",
    "svr = SVR()\n",
    "svr.fit(x_train,y_train)\n",
    "\n",
    "#Gradient Boosting model\n",
    "gbr=GradientBoostingRegressor()\n",
    "gbr.fit(x_train,y_train)\n",
    "\n",
    "#KNN model\n",
    "knn= KNeighborsRegressor(n_neighbors = 3)\n",
    "knn.fit(x_train,y_train)\n",
    "\n",
    "# Decision Tree\n",
    "decision_tree = DecisionTreeRegressor()\n",
    "decision_tree.fit(x_train, y_train)\n",
    "\n",
    "# Random Forest\n",
    "random_forest = RandomForestRegressor(n_estimators=100)\n",
    "random_forest.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>1.464579e+00</td>\n",
       "      <td>1.289196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ridge</th>\n",
       "      <td>1.464664e+00</td>\n",
       "      <td>1.287903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LASSO</th>\n",
       "      <td>1.470660e+00</td>\n",
       "      <td>1.281837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVR</th>\n",
       "      <td>1.339353e+00</td>\n",
       "      <td>1.188144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GBR</th>\n",
       "      <td>4.571737e-01</td>\n",
       "      <td>0.520595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KNN</th>\n",
       "      <td>1.082548e+00</td>\n",
       "      <td>1.414603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>9.246959e-16</td>\n",
       "      <td>0.554206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random Forest</th>\n",
       "      <td>2.427893e-01</td>\n",
       "      <td>0.465884</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Training MSE  Test MSE\n",
       "Least Squares  1.464579e+00  1.289196\n",
       "Ridge          1.464664e+00  1.287903\n",
       "LASSO          1.470660e+00  1.281837\n",
       "SVR            1.339353e+00  1.188144\n",
       "GBR            4.571737e-01  0.520595\n",
       "KNN            1.082548e+00  1.414603\n",
       "Decision Tree  9.246959e-16  0.554206\n",
       "Random Forest  2.427893e-01  0.465884"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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)\n",
    "\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the results of the simple models we can see that the decision tree performs the best out of the base regression models. Considering the ensemble methods, the GradientBoost and Random forest methods seem to improve on the decision tree further, as such we will further eplore these 2 methods."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"modelling\"></a>\n",
    "## Modelling Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest Model"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "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",
   "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. \n",
    "\n",
    "Random Forest utilises an ensemble approach. It makes use of the predictions from several decision trees.\n",
    "1.\tSelect K random points\n",
    "2.\tIdentify n where n is the number of created regressors forthe decision trees. Steps 1 and 2 need to be repeated multiple times as many regression trees need to be created.\n",
    "3.\tEach branch’s average is assigned to the leaf node in every decision tree.\n",
    "4.\tTo predict an output for a variable, the average of every single predictions from all the decision trees are taken into consideration.\n",
    "\n",
    "Decision trees commonly suffer from overfitting. Random Forest prevents this by creating random subsets of the features and builds smaller trees using these subsets\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Utilising the Random Forest Model:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.Training "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fitting the model to our data and applying hyperparamers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(max_depth=5, n_estimators=200)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Our forest consists of 100 trees with a max depth of 5\n",
    "RF = RandomForestRegressor(n_estimators=200, max_depth=5)\n",
    "RF.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.Testing "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To understand the performance of this model, we will get the mean square error and R^2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 0.7403760581446333\n",
      "R^2 0.882989894907366\n"
     ]
    }
   ],
   "source": [
    "# Get predictions\n",
    "y_pred = RF.predict(x_test)\n",
    "\n",
    "# Compute RMSE\n",
    "print(\"RMSE:\",metrics.mean_squared_error(y_test,y_pred,squared=False))\n",
    "print(\"R^2\",metrics.r2_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we will evaluate it against the expected output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\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",
   "metadata": {},
   "source": [
    "### 3. Adjusting Hyperparameters "
   ]
  },
  {
   "cell_type": "markdown",
   "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",
   "metadata": {},
   "source": [
    "Creating different hyperparameters of the \"n_estimators\" to compare the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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)\n",
    "\n",
    "# 1000 trees in forest\n",
    "forest_4 = RandomForestRegressor(n_estimators=1000, max_depth=5, random_state=23)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Training the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(max_depth=5, n_estimators=2, random_state=23)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_1.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(max_depth=5, n_estimators=20, random_state=23)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_2.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(max_depth=5, random_state=23)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_3.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(max_depth=5, n_estimators=1000, random_state=23)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_4.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluating the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 4 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=4,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), forest_4.predict(x_test)]\n",
    "title = ['trees = 2', 'trees = 20', 'trees = 100', 'trees = 1000']\n",
    "\n",
    "# Loop through all axes to plot each model's results \n",
    "for i in range(4):\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",
   "metadata": {},
   "source": [
    "## Boosting models"
   ]
  },
  {
   "cell_type": "markdown",
   "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",
   "metadata": {},
   "source": [
    "### 1. Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(max_depth=5, max_features=0.75, n_estimators=200,\n",
       "                          random_state=6, subsample=0.9)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Decision tree/ Base estimator\n",
    "dt = DecisionTreeRegressor(max_depth=5, random_state=6)\n",
    "\n",
    "# Instantiate Ada\n",
    "ada = AdaBoostRegressor(base_estimator=dt, n_estimators=200, random_state=6)\n",
    "ada.fit(x_train,y_train)\n",
    "# Instantiate GB\n",
    "gb = GradientBoostingRegressor(max_depth=5, \n",
    "            n_estimators=200,\n",
    "            random_state=6)\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=6)\n",
    "sgb.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>0.546870</td>\n",
       "      <td>0.637974</td>\n",
       "      <td>0.941870</td>\n",
       "      <td>0.913119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GB</th>\n",
       "      <td>0.126466</td>\n",
       "      <td>0.428094</td>\n",
       "      <td>0.996891</td>\n",
       "      <td>0.960880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SGB</th>\n",
       "      <td>0.121824</td>\n",
       "      <td>0.423570</td>\n",
       "      <td>0.997115</td>\n",
       "      <td>0.961703</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Training MSE  Test MSE  Train R^2  Test R^2\n",
       "Ada      0.546870  0.637974   0.941870  0.913119\n",
       "GB       0.126466  0.428094   0.996891  0.960880\n",
       "SGB      0.121824  0.423570   0.997115  0.961703"
      ]
     },
     "execution_count": 53,
     "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",
    "                        \"GB\": 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",
    "                        \"GB\": 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",
    "                        \"GB\":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",
    "                        \"GB\":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": "markdown",
   "metadata": {},
   "source": [
    "### 3. Adjusting Hyperparameters "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The GB and SGB models perfrom the best so we will focus on choosing their hyperparameters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### SGB Hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 200 trees in GB\n",
    "SGB_1 = GradientBoostingRegressor(n_estimators=200, max_depth=5, random_state=6)\n",
    "\n",
    "# 500 trees in GB\n",
    "SGB_2 = GradientBoostingRegressor(n_estimators=500, max_depth=5, random_state=6)\n",
    "\n",
    "# 1000 trees in GB\n",
    "SGB_3 = GradientBoostingRegressor(n_estimators=1000, max_depth=5, random_state=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(max_depth=5, n_estimators=200, random_state=6)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SGB_1.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(max_depth=5, n_estimators=500, random_state=6)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SGB_2.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(max_depth=5, n_estimators=1000, random_state=6)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SGB_3.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>SGB_1</th>\n",
       "      <td>0.126466</td>\n",
       "      <td>0.428094</td>\n",
       "      <td>0.996891</td>\n",
       "      <td>0.960880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SGB_2</th>\n",
       "      <td>0.039792</td>\n",
       "      <td>0.422546</td>\n",
       "      <td>0.999692</td>\n",
       "      <td>0.961888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SGB_3</th>\n",
       "      <td>0.039792</td>\n",
       "      <td>0.420675</td>\n",
       "      <td>0.999692</td>\n",
       "      <td>0.962224</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Training MSE  Test MSE  Train R^2  Test R^2\n",
       "SGB_1      0.126466  0.428094   0.996891  0.960880\n",
       "SGB_2      0.039792  0.422546   0.999692  0.961888\n",
       "SGB_3      0.039792  0.420675   0.999692  0.962224"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dictionary of results\n",
    "train_1 = SGB_1.predict(x_train)\n",
    "train_2 = SGB_2.predict(x_train)\n",
    "train_3 = SGB_2.predict(x_train)\n",
    "\n",
    "test_1 = SGB_1.predict(x_test)\n",
    "test_2 = SGB_2.predict(x_test)\n",
    "test_3 = SGB_3.predict(x_test)\n",
    "\n",
    "results_dict = {'Training MSE':{\n",
    "                        \"SGB_1\": metrics.mean_squared_error(y_train, train_1,squared=False),\n",
    "                        \"SGB_2\": metrics.mean_squared_error(y_train, train_2,squared=False),\n",
    "                        \"SGB_3\": metrics.mean_squared_error(y_train, train_3,squared=False)\n",
    "                },\n",
    "                'Test MSE':{\n",
    "                        \"SGB_1\": metrics.mean_squared_error(y_test, test_1,squared=False),\n",
    "                        \"SGB_2\": metrics.mean_squared_error(y_test, test_2,squared=False),\n",
    "                        \"SGB_3\": metrics.mean_squared_error(y_test, test_3,squared=False)\n",
    "                },\n",
    "                'Train R^2':{\n",
    "                        \"SGB_1\":metrics.r2_score(y_train, train_1),\n",
    "                        \"SGB_2\":metrics.r2_score(y_train, train_2),\n",
    "                        \"SGB_3\":metrics.r2_score(y_train, train_3)\n",
    "                    \n",
    "                },\n",
    "                'Test R^2':{\n",
    "                        \"SGB_1\":metrics.r2_score(y_test, test_1),\n",
    "                        \"SGB_2\":metrics.r2_score(y_test, test_2),\n",
    "                        \"SGB_3\":metrics.r2_score(y_test, test_3)\n",
    "                }\n",
    "                }\n",
    "pd.DataFrame(results_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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_1, test_2, test_3]\n",
    "title = ['SGB_1', 'SGB_2', 'SGB_3']\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])),3)\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",
   "metadata": {},
   "source": [
    "#### GB Hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 200 trees in GB\n",
    "GB_1 = GradientBoostingRegressor(n_estimators=200, max_depth=5, random_state=6)\n",
    "\n",
    "# 500 trees in GB\n",
    "GB_2 = GradientBoostingRegressor(n_estimators=500, max_depth=5, random_state=6)\n",
    "\n",
    "# 1000 trees in GB\n",
    "GB_3 = GradientBoostingRegressor(n_estimators=1000, max_depth=5, random_state=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(max_depth=5, n_estimators=200, random_state=6)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GB_1.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(max_depth=5, n_estimators=500, random_state=6)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GB_2.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(max_depth=5, n_estimators=1000, random_state=6)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GB_3.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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>GB_1</th>\n",
       "      <td>0.126466</td>\n",
       "      <td>0.428094</td>\n",
       "      <td>0.996891</td>\n",
       "      <td>0.960880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GB_2</th>\n",
       "      <td>0.039792</td>\n",
       "      <td>0.422546</td>\n",
       "      <td>0.999692</td>\n",
       "      <td>0.961888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GB_3</th>\n",
       "      <td>0.008914</td>\n",
       "      <td>0.420675</td>\n",
       "      <td>0.999985</td>\n",
       "      <td>0.962224</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Training MSE  Test MSE  Train R^2  Test R^2\n",
       "GB_1      0.126466  0.428094   0.996891  0.960880\n",
       "GB_2      0.039792  0.422546   0.999692  0.961888\n",
       "GB_3      0.008914  0.420675   0.999985  0.962224"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dictionary of results\n",
    "train_1 = GB_1.predict(x_train)\n",
    "train_2 = GB_2.predict(x_train)\n",
    "train_3 = GB_3.predict(x_train)\n",
    "\n",
    "test_1 = GB_1.predict(x_test)\n",
    "test_2 = GB_2.predict(x_test)\n",
    "test_3 = GB_3.predict(x_test)\n",
    "\n",
    "results_dict = {'Training MSE':{\n",
    "                        \"GB_1\": metrics.mean_squared_error(y_train, train_1,squared=False),\n",
    "                        \"GB_2\": metrics.mean_squared_error(y_train, train_2,squared=False),\n",
    "                        \"GB_3\": metrics.mean_squared_error(y_train, train_3,squared=False)\n",
    "                },\n",
    "                'Test MSE':{\n",
    "                        \"GB_1\": metrics.mean_squared_error(y_test, test_1,squared=False),\n",
    "                        \"GB_2\": metrics.mean_squared_error(y_test, test_2,squared=False),\n",
    "                        \"GB_3\": metrics.mean_squared_error(y_test, test_3,squared=False)\n",
    "                },\n",
    "                'Train R^2':{\n",
    "                        \"GB_1\":metrics.r2_score(y_train, train_1),\n",
    "                        \"GB_2\":metrics.r2_score(y_train, train_2),\n",
    "                        \"GB_3\":metrics.r2_score(y_train, train_3)\n",
    "                    \n",
    "                },\n",
    "                'Test R^2':{\n",
    "                        \"GB_1\":metrics.r2_score(y_test, test_1),\n",
    "                        \"GB_2\":metrics.r2_score(y_test, test_2),\n",
    "                        \"GB_3\":metrics.r2_score(y_test, test_3)\n",
    "                }\n",
    "                }\n",
    "pd.DataFrame(results_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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_1, test_2, test_3]\n",
    "title = ['GB_1', 'G_2', 'GB_3']\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])),3)\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",
   "metadata": {},
   "source": [
    "The GB and SGB models perform very similarly and the difference in accuracy from using 500 base estimators to using 1000 is not very big."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Performance Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compare Models on Performance Metrics\n",
    "We can compare the performance of each of our models using two statistical measures that determine the average squared difference between the estimated values and the actual value (MSE) and the proportion of variance explained by the model (R2):\n",
    "\n",
    "Mean Squared Error (MSE):\n",
    "𝑀𝑆𝐸=1𝑛∑𝑖=1𝑛(𝑦𝑖−𝑦̂ 𝑖)2\n",
    "R-squared (R2):\n",
    "𝑅2=1−∑(𝑦𝑖−𝑦̂ 𝑖)2∑(𝑦𝑖−𝑦¯)2\n",
    "For this project, we will be looking at the root mean squared error (RMSE). The lower the RMSE of the model, the better the regression line fits the data.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compare RMSE values between models\n",
    "fig,axis = plt.subplots(figsize=(12, 6))\n",
    "rmse_x = ['Least Squares', 'Ridge', 'LASSO', 'SVR', 'GBR', 'KNN', 'Decision Tree','Random Forest']\n",
    "rmse_y = list(results['Test MSE'])\n",
    "ax = sns.barplot(x=rmse_x, y=rmse_y,palette='plasma')\n",
    "plt.title('Regression Model RMSE Values',fontsize=14)\n",
    "plt.ylabel('RMSE')\n",
    "plt.xticks(rotation=90)\n",
    "for p in ax.patches:\n",
    "    ax.text(p.get_x() + p.get_width()/2., p.get_height(), '%d' % int(p.get_height()), fontsize=12, ha='center', va='bottom')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"conclusion\"></a>\n",
    "# Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"save\"></a>\n",
    "# Save model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create submission dataframe\n",
    "submission = pd.DataFrame(\n",
    "    {'Index': test_id,\n",
    "     'avg_price_per_kg': gb.predict(test_scaled)\n",
    "    })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save DataFrame to csv file for submission\n",
    "submission.to_csv(\"Answergb.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}