Project 8:Avocado Dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data processing\n",
    "import pandas as pd\n",
    "\n",
    "#Linear Algebra\n",
    "import numpy as np\n",
    "\n",
    "#Data Visualization\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import style\n",
    "\n",
    "#Algorithms\n",
    "from sklearn import linear_model\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.tree import DecisionTreeRegressor \n",
    "from sklearn.ensemble import RandomForestRegressor \n",
    "from sklearn.model_selection import train_test_split,cross_val_score\n",
    "\n",
    "from sklearn import metrics  \n",
    "from sklearn.metrics import confusion_matrix,classification_report\n",
    "from sklearn.preprocessing import StandardScaler,LabelEncoder\n",
    "\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "avocado=pd.read_csv('avocado.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Date</th>\n",
       "      <th>AveragePrice</th>\n",
       "      <th>Total Volume</th>\n",
       "      <th>4046</th>\n",
       "      <th>4225</th>\n",
       "      <th>4770</th>\n",
       "      <th>Total Bags</th>\n",
       "      <th>Small Bags</th>\n",
       "      <th>Large Bags</th>\n",
       "      <th>XLarge Bags</th>\n",
       "      <th>type</th>\n",
       "      <th>year</th>\n",
       "      <th>region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-12-27</td>\n",
       "      <td>1.33</td>\n",
       "      <td>64236.62</td>\n",
       "      <td>1036.74</td>\n",
       "      <td>54454.85</td>\n",
       "      <td>48.16</td>\n",
       "      <td>8696.87</td>\n",
       "      <td>8603.62</td>\n",
       "      <td>93.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>conventional</td>\n",
       "      <td>2015</td>\n",
       "      <td>Albany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-12-20</td>\n",
       "      <td>1.35</td>\n",
       "      <td>54876.98</td>\n",
       "      <td>674.28</td>\n",
       "      <td>44638.81</td>\n",
       "      <td>58.33</td>\n",
       "      <td>9505.56</td>\n",
       "      <td>9408.07</td>\n",
       "      <td>97.49</td>\n",
       "      <td>0.0</td>\n",
       "      <td>conventional</td>\n",
       "      <td>2015</td>\n",
       "      <td>Albany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2015-12-13</td>\n",
       "      <td>0.93</td>\n",
       "      <td>118220.22</td>\n",
       "      <td>794.70</td>\n",
       "      <td>109149.67</td>\n",
       "      <td>130.50</td>\n",
       "      <td>8145.35</td>\n",
       "      <td>8042.21</td>\n",
       "      <td>103.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>conventional</td>\n",
       "      <td>2015</td>\n",
       "      <td>Albany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2015-12-06</td>\n",
       "      <td>1.08</td>\n",
       "      <td>78992.15</td>\n",
       "      <td>1132.00</td>\n",
       "      <td>71976.41</td>\n",
       "      <td>72.58</td>\n",
       "      <td>5811.16</td>\n",
       "      <td>5677.40</td>\n",
       "      <td>133.76</td>\n",
       "      <td>0.0</td>\n",
       "      <td>conventional</td>\n",
       "      <td>2015</td>\n",
       "      <td>Albany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2015-11-29</td>\n",
       "      <td>1.28</td>\n",
       "      <td>51039.60</td>\n",
       "      <td>941.48</td>\n",
       "      <td>43838.39</td>\n",
       "      <td>75.78</td>\n",
       "      <td>6183.95</td>\n",
       "      <td>5986.26</td>\n",
       "      <td>197.69</td>\n",
       "      <td>0.0</td>\n",
       "      <td>conventional</td>\n",
       "      <td>2015</td>\n",
       "      <td>Albany</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0        Date  AveragePrice  Total Volume     4046       4225  \\\n",
       "0           0  2015-12-27          1.33      64236.62  1036.74   54454.85   \n",
       "1           1  2015-12-20          1.35      54876.98   674.28   44638.81   \n",
       "2           2  2015-12-13          0.93     118220.22   794.70  109149.67   \n",
       "3           3  2015-12-06          1.08      78992.15  1132.00   71976.41   \n",
       "4           4  2015-11-29          1.28      51039.60   941.48   43838.39   \n",
       "\n",
       "     4770  Total Bags  Small Bags  Large Bags  XLarge Bags          type  \\\n",
       "0   48.16     8696.87     8603.62       93.25          0.0  conventional   \n",
       "1   58.33     9505.56     9408.07       97.49          0.0  conventional   \n",
       "2  130.50     8145.35     8042.21      103.14          0.0  conventional   \n",
       "3   72.58     5811.16     5677.40      133.76          0.0  conventional   \n",
       "4   75.78     6183.95     5986.26      197.69          0.0  conventional   \n",
       "\n",
       "   year  region  \n",
       "0  2015  Albany  \n",
       "1  2015  Albany  \n",
       "2  2015  Albany  \n",
       "3  2015  Albany  \n",
       "4  2015  Albany  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avocado.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unnamed column here is not necessary it's better to drop this column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'Date', 'AveragePrice', 'Total Volume', '4046', '4225',\n",
       "       '4770', 'Total Bags', 'Small Bags', 'Large Bags', 'XLarge Bags', 'type',\n",
       "       'year', 'region'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avocado.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "avocado=avocado.iloc[:,1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>AveragePrice</th>\n",
       "      <th>Total Volume</th>\n",
       "      <th>4046</th>\n",
       "      <th>4225</th>\n",
       "      <th>4770</th>\n",
       "      <th>Total Bags</th>\n",
       "      <th>Small Bags</th>\n",
       "      <th>Large Bags</th>\n",
       "      <th>XLarge Bags</th>\n",
       "      <th>type</th>\n",
       "      <th>year</th>\n",
       "      <th>region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2015-12-27</td>\n",
       "      <td>1.33</td>\n",
       "      <td>64236.62</td>\n",
       "      <td>1036.74</td>\n",
       "      <td>54454.85</td>\n",
       "      <td>48.16</td>\n",
       "      <td>8696.87</td>\n",
       "      <td>8603.62</td>\n",
       "      <td>93.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>conventional</td>\n",
       "      <td>2015</td>\n",
       "      <td>Albany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2015-12-20</td>\n",
       "      <td>1.35</td>\n",
       "      <td>54876.98</td>\n",
       "      <td>674.28</td>\n",
       "      <td>44638.81</td>\n",
       "      <td>58.33</td>\n",
       "      <td>9505.56</td>\n",
       "      <td>9408.07</td>\n",
       "      <td>97.49</td>\n",
       "      <td>0.0</td>\n",
       "      <td>conventional</td>\n",
       "      <td>2015</td>\n",
       "      <td>Albany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2015-12-13</td>\n",
       "      <td>0.93</td>\n",
       "      <td>118220.22</td>\n",
       "      <td>794.70</td>\n",
       "      <td>109149.67</td>\n",
       "      <td>130.50</td>\n",
       "      <td>8145.35</td>\n",
       "      <td>8042.21</td>\n",
       "      <td>103.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>conventional</td>\n",
       "      <td>2015</td>\n",
       "      <td>Albany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2015-12-06</td>\n",
       "      <td>1.08</td>\n",
       "      <td>78992.15</td>\n",
       "      <td>1132.00</td>\n",
       "      <td>71976.41</td>\n",
       "      <td>72.58</td>\n",
       "      <td>5811.16</td>\n",
       "      <td>5677.40</td>\n",
       "      <td>133.76</td>\n",
       "      <td>0.0</td>\n",
       "      <td>conventional</td>\n",
       "      <td>2015</td>\n",
       "      <td>Albany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2015-11-29</td>\n",
       "      <td>1.28</td>\n",
       "      <td>51039.60</td>\n",
       "      <td>941.48</td>\n",
       "      <td>43838.39</td>\n",
       "      <td>75.78</td>\n",
       "      <td>6183.95</td>\n",
       "      <td>5986.26</td>\n",
       "      <td>197.69</td>\n",
       "      <td>0.0</td>\n",
       "      <td>conventional</td>\n",
       "      <td>2015</td>\n",
       "      <td>Albany</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  AveragePrice  Total Volume     4046       4225    4770  \\\n",
       "0  2015-12-27          1.33      64236.62  1036.74   54454.85   48.16   \n",
       "1  2015-12-20          1.35      54876.98   674.28   44638.81   58.33   \n",
       "2  2015-12-13          0.93     118220.22   794.70  109149.67  130.50   \n",
       "3  2015-12-06          1.08      78992.15  1132.00   71976.41   72.58   \n",
       "4  2015-11-29          1.28      51039.60   941.48   43838.39   75.78   \n",
       "\n",
       "   Total Bags  Small Bags  Large Bags  XLarge Bags          type  year  region  \n",
       "0     8696.87     8603.62       93.25          0.0  conventional  2015  Albany  \n",
       "1     9505.56     9408.07       97.49          0.0  conventional  2015  Albany  \n",
       "2     8145.35     8042.21      103.14          0.0  conventional  2015  Albany  \n",
       "3     5811.16     5677.40      133.76          0.0  conventional  2015  Albany  \n",
       "4     6183.95     5986.26      197.69          0.0  conventional  2015  Albany  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avocado.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d73ebc2c08>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Checking for null data\n",
    "sns.heatmap(avocado.isnull())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "it's clear that there is no null data present in this dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "avocado.columns=['Date', 'AveragePrice', 'Total Volume', '4046', '4225', '4770',\n",
    "       'Total Bags', 'Small Bags', 'Large Bags', 'XLarge Bags', 'Type', 'Year',\n",
    "       'Region']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 18249 entries, 0 to 18248\n",
      "Data columns (total 13 columns):\n",
      "Date            18249 non-null object\n",
      "AveragePrice    18249 non-null float64\n",
      "Total Volume    18249 non-null float64\n",
      "4046            18249 non-null float64\n",
      "4225            18249 non-null float64\n",
      "4770            18249 non-null float64\n",
      "Total Bags      18249 non-null float64\n",
      "Small Bags      18249 non-null float64\n",
      "Large Bags      18249 non-null float64\n",
      "XLarge Bags     18249 non-null float64\n",
      "Type            18249 non-null object\n",
      "Year            18249 non-null int64\n",
      "Region          18249 non-null object\n",
      "dtypes: float64(9), int64(1), object(3)\n",
      "memory usage: 1.8+ MB\n"
     ]
    }
   ],
   "source": [
    "avocado.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Earlier in info we have seen that Date is Object type not the date type. We have to change its type to date type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "avocado['Date']=pd.to_datetime(avocado['Date'])\n",
    "avocado['Month']=avocado['Date'].apply(lambda x:x.month)\n",
    "avocado['Day']=avocado['Date'].apply(lambda x:x.day)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Date', 'AveragePrice', 'Total Volume', '4046', '4225', '4770',\n",
       "       'Total Bags', 'Small Bags', 'Large Bags', 'XLarge Bags', 'Type', 'Year',\n",
       "       'Region', 'Month', 'Day'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avocado.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Region and Date both are in Object format and Year is integer other than all these every attribute is in float"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### In which year Avocados has highly demand?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d73ed5cdc8>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "avocado['Year'].value_counts().plot.pie(autopct=\"%2.2f%%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mostly in 2017demand for avocados is very high compare to all other years.And in 2018 demand for avocados is too low compare to all other years.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Which type of avocados highly demand in Nature?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d73f19a788>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "avocado['Type'].value_counts().plot.pie(autopct=\"%2.2f%%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conventional and Organic Avocados types are same and only little bit difference Conventional avocados has more demand compare to Organic avocados."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### For which type of Avocados has more in demand while in terms of Large bags and Small bags?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d73f1e4ec8>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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t4Nrjx4/HbDZjNpuJj48nKyuLlJQTM+x/+OEHbr/9dkJCQgAC11cURTkTqsuusRWl1628lk6Vg7AiQNV0DMBDDz3Egw8+yNatW3nnnXdwOByB1063xURttr5QFEWpKxWQGltESt3Ka6k22zoMGDCAZcuWUVBQgMfjCezIClBUVERysj9r0ocfflinuseMGcOMGTMCW4urLjtFURqCCkiNbdSfwXjS3kdGq7/8DFS3rUNUVFTQMcnJyfzxj39k4MCBjB49mq5duxIREQH4Jy9cd911DBs2LLDrbG2NGzeOK6+8kn79+tGrVy9eeeWVM3oviqIo0MjbTzQHDbH9BFvm+MeMitL9LaNRfz6j8aO6KC0tJTQ0FI/Hw1VXXcUdd9zBVVdddVbqPlvU9hOKcu45X7afuPD0uP6sBaCTTZ06lR9++AGHw8GYMWOYOHFik9yHoijK6aiA1Myp7jRFUc4XagypnlRX57lB/TsoSvOhAlI9WCwW8vLy1IdhE5NSkpeXh8ViaepbURSlAaguu3pISUkhPT0dtZts07NYLEGLdhVFOX+pgFQPRqMxKLOBoiiKcuZUQFIURWkgUkpyS1043F5MBh1RIUZMBn1T39Z5QwUkRVGUBnIo186tM1dzNL+ccIuBNyb15qL2MViMKijVhprUoCiK0gDyy5z8fs4mjub7kykXOzzcP2sDReXuJr6z84cKSIqiKA3A65NsSS8MKit3eylzek5xhnIyFZAURVEagFGvY2DbmKCycIuBULMaGaktFZAURVEaQGSIiZev60GvlpEApERZ+eD2AUTZjKc5U6mgQreiKEoDSYkKYcbk/jg9XvQ6QazNjE6n9g6rLRWQFEVRGlC0zdTUt3DeUl12iqIoNSgud1PuUhMTzgbVQlIURalGcbmbjUcLeX/5AaJCTDw6uiMto0Mw6tX3+MaiApKiKEo1Nh0t5LYZawLPf9iZxc+Pj8Bi1KPTCUJM6uOzoTV6qBdC6IUQG4UQ87XnbYUQq4UQe4UQnwohTFq5WXu+T3u9TaVrPK2V7xZCjK1UPk4r2yeEeKpSeZ3rUBRFqVDicDN9xcGgsruGtWV7ZgkPfLKRx+ZsZm92CS6Pt4nusHk6G23PR4CdlZ7/A/inlLIjUADcqZXfCRRIKTsA/9SOQwjRFZgEdAPGAW9pQU4P/Bu4DOgK3KgdW+c6FEVRKjPo/HnoKrSPs9E9OYLbZqxh2Z4cFmw7zhXTVpBT4qp3HcXlbnZmFvOvH/exbHc2eaXOhrj181qjBiQhRAowHnhfey6AS4DPtUM+BCr21J6gPUd7fZR2/ARgtpTSKaU8COwDBmh/9kkpD0gpXcBsYEI961AURQmwmvQ8OroTNpM/B92IzvF8ueFY0DFOj48lu7LqdX2v13/uZW8s55Xvd3PbzLX88autFNjrH+Cag8ZuIb0OPAH4tOcxQKGUsmLKSjqQrD1OBo4CaK8XaccHyk8651Tl9alDUZQ6KHd5ySp2cLzIQXEzzdWWEmVlyZQRvHljb67tm0JMaNXp3DE2c72unW938dJ3u4PKFm3PuuDTDDVaQBJCXAFkSynXVy6u5lB5mtcaqvx09QcIIe4RQqwTQqxTm/ApSrBCu4uPVh5i+Ms/cdGLS3jyiy3NsrvJoNfRIsLCb3omkZoYzu8ubk+49cREhvZxNga0ja739R3uquNPXt+FvQt1Y04TGQJcKYS4HLAA4fhbTJFCCIPWQkkBMrTj04GWQLoQwgBEAPmVyitUPqe68tx61BFESvku8C5Av379LuzfEEU5SWaRgxcW7go8X7jtOL1bRXLnkLbom/GU6MRIK4t/P5y1h/IJNRvolhRBXFj9WkgRVhN3DWvHy4tOtJK6J0dc8HnvGu23R0r5tJQyRUrZBv+khB+llDcDPwHXaofdBnytPZ6nPUd7/UcppdTKJ2kz5NoCHYE1wFqgozajzqTVMU87p651KIpSS+sPF1QpW7YnB3s13/ibE71OkBBu4YoeSYzoHF/vYARgMui4aUAr3rmlL+PSWvDUZV2YMbk/MaH1v2Zz0BTh+ElgthDib8BGYLpWPh34WAixD3+rZRKAlHK7EGIOsAPwAA9IKb0AQogHgUWAHpghpdxenzoURam9Pq0iq5QN7RBHyDm2CZ3T48Wo052zueSibCbGdmvB8E5xmPTn7n2eTUI1EGrWr18/uW7duqa+DUU5ZxTaXfx31WHe/HEfTo+P0akJvHhNd2LPkW/3BWUu1h7K56uNx+iREsm1fVPOqDWj1I8QYr2Usl+dzlEBqWYqIClKVXanh2KnByklVqOeyJBzI6Goy+NjxoqDvPjdiTGutORwPrx9wAXfHXa21ScgXdgjaIqi1EuI2UDIOTgAX2h38d7yA0Fl244VU+L0qIB0Hmi+U2IURbngCAGWasayDGr9+3lBBSRFUZqN6BATT4zrHFR2add4bOdga06pSv0rKYrSbOj1OkZ2jue7R4axeGcWackR9EiOIOoC2TTP7vLgk5JQ8/m5bboKSIqiNCvhViPhViNdEsOb+lbOGofby5E8O6//sIdyt48HRranS4swQi3nV2BSAUlRFOU8l1Pi5Io3V+Dy+tOG/rQ7m28eHEL3lKprxs5lKiApiqKc57ZnFPHxXQNAgldK3ll2gBm/HOQf1/TEZDh/pgqcNiAJIdoD6VJKpxBiBNAD+EhKWdjYN6coiqLUzOXxYtDruGPmWspcXiKsRl6/oRdH8srQnT+xCKjdLLsvAK8QogP+1DttgU8a9a4URVGUWimwu5kyZzNlLn8uwaJyN//39TZGd03AcJ5FpNrcrU/Lmn0V8LqU8vdAYuPelqIoilIbLo+PopP2pEovKD8vc+PVZgzJLYS4EX+W7N9oZefX1A1FUc55uaVODuWWUeLw0C0pnNhQ83n5oXq2WYw6WkZbOZpfHijrnhyB6TzcCqQ2Ael24HfA81LKg9oWEP9t3NtSFOVCklvqZPLMNWw7VgxAZIiR+Q8NJSUqpInv7NwXG2rmg8kDePTTTWw9VkT/NlH88/pe52WqJJVc9TRUclVFqb1Shwe7y4MQgmibCX0tWzg/7c7m9plrg8puHtiKv/ymKybDiVRAhXYXGUUO1h/Kp2+baJIiLOdMYtemllfqxCslRp3unFgI3CjJVYUQW6m6zXcRsA74m5Qyry4VKorSPOWWOnn2m+18u/U4caFmXrymOwPaRAclYS1xuCl1eHB6fYSaDMRq20JkFTmqXC+zyEFxuZvYMH9AKnd5+WT1EV6qtMvqk+M6M3lwW6yms7MXU26pE6fbi1GvI9pmwnAOdYudjy2ik9Wmy24h4OXEzLpJgMAflD7gxLiSoigXKKfby7vLDjBvcyYAx4sd3PnhOpY/MTIQkArtLt75+QD/WbYfKaFNTAif3D2IpEgrQzvGYjbocHp8gWte2TOJ3FIXsWEWAIodbt5Ysjeo3td/2MvVfVLOSkA6mm/n7o/Wset4CbGhJt6Y1Jt+raMwn2MbE57PahPeh2jbkW/V/jwDDJdS/gNo07i3pyhKY8ordZJT4sDt9Z3+4BoUOzws2ZUdVOb1SfZllwae55a6eHupPxgBHMqz88r3u7E7PdhMej66YwAjOsfRr3UU/7imB3llTrZnFAfO90kZyERQweX14ZOSxh56KLS7ePzzzew6XhJ4L3d/tI7Ck2a3KWemNgEpVAgxsOKJEGIAEKo99TTKXSmK0qjKXR7WHcpn8sy1XPXWr7z38wEKylz1vl6ISU+P5Igq5a2iT0xKOJxXVuX1nZnFlLu9RFhNZJc46ZkSyeiuCXy+/ijPf7uTAW2jT9RhNHBpagJmg47kSCsmvY4xqQk4XF6e/nIr7y8/QHZJ1a6/huDy+lh/uCCozO7yUupUH4ENqTZddncBM4QQofi76oqBu4QQNuCFxrw5RVEaR77dzaR3V+Hx+VsWLy3aTZTNxKT+LRH12DvIZjbw+LjObM0oYl92KQadYMqYTkRXGlxPTQxHrxN4fSdaM2O6tiDcakSnEwzpEMv+nFKmLz9IRIiRD+8YEHR+RIiRv01Mo8DuYk9WCZ1bhGMz6bls2nKKy/2BYdbqI3x270WBsamGYtLr6NMqitUH8wNlISY9oafZ1qK43E2xw01OiZOkSCtRIcagSRpKsFrPshNCRGjHX1Apg9QsO6U5+nrTMR6ZvSmorH+bKN67td8ZzVrLLXFid3sw6fWEWQxB+xDZXR7WHy7gma+2kVXs4Oo+yTw2pnPQYLzL46Ow3IUOQUyoKSg4lrs9fL0xg6e+3Ar4N+N74erurNiby/wtmYHj5j4whF4tGz6p6NF8O3d9uI7dWbUbQyp1uPnv6iO8uNC/nbrVqOd/9wykV8uoBr+3c1GjbWEuhBgPdAMsFb8gUspn63yHiqKcE1rH2KqUtYsLPeMBen/LpPrWSYjJwNAOsXxx32AkklCzgRBT8EeQyaAjXpvEcLIiu5tn5+8IPJcSXliwizdv7B0UkBprLW3L6BA+uXsgDo8Po06cdpZdicPDS9/tCjwvd3t58vOtzLp7ILHNYEZcY6jNtO//ACHASOB94FpgTSPfl6IojahVtJXL01qwYNtxAFqEW3h4VEesNQSknBInx4vKMRn0xIaaAi2bEocbu8uLzaQ/7f47QgjiqulOyy9zse5QPkt35zA6NZ5eraKCuusA3F6JXcvXVqGo3B20ZXmnhFCSIqw1v/kzUJep1eVuL76TOqCO5NvxqbWfp1SbFtJgKWUPIcQWKeVfhRCvAl829o0pilK9/DIn+3PK2JlRzJAOscSHmwmr40Zs0TYzz1/VncfHdcHu8hAfZibuFC0TgKxiB9f+51eO5pcTGWLkwZHtubJXMh6v5K/fbGfD4UIGtI3mmfGpbEkv5Mdd2YzvnkiPlMjTLtIscbh56btdzF57FIBP1hzh9sFtmDK2c2CM5niRg8N5ZfRuGcnGoydGDYZ0iCE+zMw9w9rRLt7GqC4JVcaPisvdZBY5WLo7m7TkCLq0CDsra3bCLAYSws1kFTsDZZeltcBmUrv+nEpt/mYqEiTZhRBJQB7+jN+KopxlhXYXz83fyVcbjwXK/nVTby7r1gJ9HRdpRtlMtVrR7/H5+O+qwxzNLyc1MYy/X9Wdj1cdpnVMEa//sCcwNfvbrZkcL3ZwTZ9k5qxLZ866dB4Z1ZH7RrQPasWcrMzpYc66o0Fl/119mHtHtCfUbCCv1MmdH66loMzFv2/uw8erDrP5qD8A3jKoDdEhRv44PrX6e/f6WLIri99/ujlQdkWPRP42Ma3RMzzE2Mx8es9F/HneNvYcL2V0ajyPju4UNK6mBKvNb/B8IUQk8DKwATgE/K8xb0pRlOqVOj1BwQjg+W93km8//ZRth9tLdrGDvFLnaY+tzOOV7NXWEz19WSoPzNrAlxuOEW41BK0TAlh/uID28aGB5+8tP0Cxo55rdbSeLbvLy/aMYjKKHNw2cw1RISbuHNqW3w1vT1yYifBKgaXI7qa40tqgArsrMKmgwvwtmZQ5g7v+GoNOJ2gTa+PNG/sw76EhPDO+a4PP/mtuThuqpZTPaQ+/EELMByxSyqLGvS1FUapTOZNBhVKHp0pur5Pllzr5z88H+GJ9OgnhFp6bmEa3pPAaWy4VLEY9Nw5oydLd2QgBGVqaH49XEmY2UFJpLU60zUR5pXEeAVUTj53EZjZwfb+WgS47gN8ObE2oxf/xZDXp+fMVXdHpBIu2HWf6ioPE2Exc0iUh0M1Y6vCwPaOI17VMDn8Y3YmuSeFICLqfCt6TB3fOkMPtpcThQSKJsBoxV5raHWE1ojZIqJ0aW0hCiNZCiFjt8SDgIfyTG05LCGERQqwRQmwWQmwXQvxVK28rhFgthNgrhPhUCGHSys3a833a620qXetprXy3EGJspfJxWtk+IcRTlcrrXIeinOtKHG6OFznomhgeVH7LRa0Jr2EMqdThZtaaI7z78wHyylzsyCzmxndXkV/mwuWpXUuhZ0okf5uYFrTuZvaaI/xxfGoggapRL5h6ZVdmrT4SOOa+Ee0Jt9b8YRxmMfLEuC68e0tfbh7Qihm39eOhUR0JNRsoc3rYm1XC/C2ZfL7+KBN7J/PkuM78bWIaMaEnWkbpBRCyGBAAACAASURBVHYmvbeKlfvzWLk/j+vfXcmxwnIiLEZuHxI8wtA9OQKbpeHWAhXYXbz38wFGvPwTl7yyjJkrDlFQixarUtUpW0hCiP8DJgNSCDEbGA0sBcYLIUZIKR89zbWdwCVSylIhhBFYIYRYCPwB+KeUcrY2g+9O4G3tZ4GUsoMQYhLwD+AGIURX/PnzugFJwA9CiE5aHf8GLgXSgbVCiHlSyh3aubWuo/Z/XYrSdNxeyQe/HuL5q9L4dmsme7NKGd4pjpFd4k7Z0sktcbLpaAHfVpoWDf7MA2sP5bM/u5QbBrQiKcJS44LYyBATV/VOIbfUyYSeSXy9OYNvtmQSYTXy45ThFNrdJISbMegERr0/k8LYbi3o0iKsVq2waJuJMd1aMKZbi6Dy40UObnp/dSDd0JNfbOHTewbRPSUcY6Uxs/+tOULlyWtSwpy1R/nTFV2ZPLgNHeNDmb8lg7HdWjCyS3yDjh9tSy/i1cV7As9f/G4XvVtFMrBdTIPVcaGoqYV0I5AK9AMexh9cnsIfAEaf7sLSryKRVUWbVQKXAJ9r5R8CE7XHE7TnaK+PEv7/IROA2VJKp5TyILAPGKD92SelPCCldAGzgQnaOXWtQ1HOeVEhRsZ3T2TSu6vILnbSLSmcX/bnnHLWVrnby7Qle1l/uJC2sVXXHcWFmZn5yyEm/GsFObUYV9LrBAnhFv5yZTe+eXAo/7qpN/eNaE98uJmeLSNpEWElNszCZWmJTL2yGxe1jznjbRC+2ZLBybOk/7f2CEZ9cJCrbt+kirIom4nxPRKZemUaTq+P+VsyOV7kwHuG+fsq3+PJKqbTK3VTU0BySCldWmaG/VJKO4C2nXmt2qNCCL0QYhOQDSwG9gOF2jXA37JJ1h4nA0cr1VEExFQuP+mcU5XH1KOOk+/7HiHEOiHEupycnNq8VUVpdEIIRnSO47Xre5Jd4iCzqJznJqQhgU1HCzmYWxbUVVTicLNkVzafb0jn7mHtSAg/MaB+fb+W7M8uo8TpIbfUxYGcqnnmTiXaZqJ7SgRX9EgiOSoEq7HxZo21iakaaNrFhmI4afXrhF5JpESdWH/UMtrK2G4JvPr9Lt5eup+MIgdPf7mZp77Yyp/mbmPMP5eRVVK3yR2nMqialtDASjn4lNqr6TcpUghxNf5xyXDtMdrzqlkUqyGl9AK9tFl6X+FvcVU5rNJ1q3vtVOXVBdOajq+pjuACKd8F3gV/6qBqzlGUJhEZYmJ8jySGdojDbBBkFju5+q2fKbD7Z5Zd1zeFP16eSpTNhMWop3OLUH7clcOf523jlet6Emo2YNTr+HV/Hs9VynpwupxsTWVohzhSE8PYmenPsp0SZeWGavLtxYdb+Or+IezJKkEAbWJt3P7B2kB27nd/3s/Myf1ZvjcPj09S7PDw8arDPDG2c71y91U2vFMco7rEB7Kdj+2WoAJSPdX0W7iME3sd/Uzwvkc/16USKWWhEGIpMAh/oDNoLZQUoKK9mw60BNKFEAb8QS+/UnmFyudUV55bjzrOWy6Pj1Knh1CzAZPh3NksTGlcESFGShxu/r5gRyAYAXy2Pp27L25HlM1EuMXI1CvT2JW5km3Hipk8cy0vXt2dTglhvLxoF26v/7tWvzZRJEacelFsU4oNM/PxnQPJKCzH7fXRKjrklAt448LMxIWZcXt9/OXr7YFgBFBgd/Pr/jz6t4lm5QH/nqKlDg9S+jfdW3Mwn6xiB5d2TSAuzFyrca8KMaFmXrm+J3anFyH8SVfVLrb1c8qAJKW8/UwuLISIA9xaMLLiH3f6B/AT/vRDs4HbgK+1U+Zpz1dqr/8opZRCiHnAJ0KI1/BPauiIP3WRADoKIdoCx/BPfLhJO6dOdZzJ+2xK2SUO3v/5IKsP5jGsYyyTh7RVObIuIE6PjyN55VXKs4oddEoIA6BllJW5Dw6hzOnVkp3qEQh+emwEK/blkhJppUti+Dm922hsqLluv9fSv3eSUS/omhhOicPDgdwyf1eI1hgy6ASTB7chv8zFTe+tCqyzemHhTr5+YChdk8JPefnqRIWYqGYYS6mjxmynJwIfCiH0+LvX5kgp5wshdgCzhRB/AzYC07XjpwMfCyH24W+1TAKQUm4XQswBduDff+kBrSsQIcSDwCJAD8yQUm7XrvVkXeo4H+WXuXj4fxtZdcDfwNucXsSu4yW8el1PItS3swtCpNXIxN7J/KNSAk+LUUdHLRiBf9wpPsxCDk6W780ho9DB+O6JxIebmdS/VY3XLyhzcTi/jLUHCxjULpqW0SF1+uZf4vCn7Plmcwbt42wM7RB3VhaGGg06HhjRgUkDWrL+cAFRISYSwi20i7WRUVBOcoSFe4e3JznKwsYjhYFgBP6ZjK98v5tpk3qdNi+f0vAaLSBJKbcAvaspP4B/htzJ5Q7gulNc63ng+WrKFwALGqKO843D7Q0EowpLdmX7NztrontSzg6314dRr8Og13FD/5Y4PV4+X59OYoSFv17ZjeiQ4A/S3BInk95dxf4c/wfva4v38OX9g+mZcuotGsocbtYczOO5b3eSXuBvhT0xtjO3D2lb6+3CNx8t5JYZawKz5LolhfPhHQPOSive6fVy47urKXd7A3V/cHt/pk7ohpQEurdPTtYK/oW0DTQBT6kjNehwntLrBOaTxoxCTYYzHqBVzl15pU4+WX2YKXM28922TPLLXETbTNw/ogNf3j+Y927tR9ekiCobwO3PKQ0EI/BnKXhl0e5qU/q4vT6O5Nv5+8JdfLv1OH+/qju3DW4DwLQf99Y6DVB+qYuXF+0OmrK9PaOYrKLG2dG1sjKnh1e/3xMIRhV178suxajXBY21dk+OqJJV/L4R7YkIUa2jplCb7SdCgClAKynl3UKIjkBnKeX8Rr875ZTCLUYeG9uZ57/dGSh7Znwqkeo/UrNUYHfx2Geb+Wm3fxnCvM0Z/G54Ox4Z1QmrSX/KPYQAHNWkG3J5fPiqSZ+TW+rkstd/pkxrOXyzJYMPbx/Akp1ZZBSWV1kTdCoury8waaIydwOn7KmOxycpLK8aOKsriw01M/+hocxYcZDMIge3D2kTGH9Tzr7adNnNBNYDF2nP04HPABWQmpDVpOf6fi25pEs8OzOLSUvyf9Mzq+2RmyW70xMIRhVm/nKIO2rRhZaaGEZcmJmcSutu/nBpJ3RCkF3iIMR4Yh+jH3ZkB4IR+DMefLbuKKNTEyiyuwkx1+7361iBnVsuas3T2u6uAK1jQkiObLy9iipEWI3cPawtK/fnBcrCzAb6tKq6U6tOJ0iKtPLUZV3w+GSdZtcpDa82Aam9lPIGIcSNAFLKcpXd4NwQYTUSYTXSPi709Acr55UypweH20uE1YhBr6u2K9ZYy+0m4kLNfPPgUD5edYhjheXce3E7bGYjD83eyNb0Iga3j+H/ruhKQrgFWzUBx2Y2cGlqPF0Swwm3GMkpcbBsdw7ZpU6u6J5IfLgl6IM8v8zFc9/uZFxaC965pS/fbTtOUqSVSf1bVrs5X13llTrxSYgOMZ5yy42+raL58I4BzFhxkNhQEw+P6khsDVkjDHod6rtc06tNQHJp07b9syaFaI8/T52iKA1MSsmxgnJeWrSLfdll/KZnEtf3T8Fm0nN1n2S+3HBi64mHR3Wo1ViHEIIWERamXNoZr5QU2d1c/85KDuT6szPM3+Ifj3rr5j4M7RhLUoQlkNE7xKTn3uHtSI6wYjLqySlxcu1/VnI4zw7APxfvYd6DQ0mtlPBVJ/yJVl9cuIu2sTYGtYtmb1YJeaVOWkbXf250qcPD+iP5vLBgF+VuL3cPa8cVPRKrnfkXEWJkeKc4+rSKxKjXqZbPeaI2AekvwHdASyHELGAI/qSriqI0sNxSF9f+ZyXHi/0BYUdmMUXlLn4/uhPPXJ7KlT2TWHMwn9FdE2gXa6vSRevP3i2qXSSt0wl0CMrd3kAwqvDr/jwcbh8tIizMfXAIy/fkklfmZEDbaF79fg+jusQzKjWB7RlFgWAE/mnS/1y8h3/e0Cuw8VxkiIknxnbhhndXcjC3jIO5ZXRJCKNNrI0Sh3/L8dq27irLKnEweebawDjWn+ZuIynSwiVdEk55Tl130lWaVm32Q1oshNiAP8uCAB6RUuY2+p0pygUov8wVCEYVPl+fzh1D2xIfZmFE53hGdI6vcl65y8PRgnL+s2w/eiG4b0R7kqOs1Y4pmgw6rEZ90Cy0FuEWKmJErM2M1ajnp105vLJoDy4tIemSPwzHU818aKfHh++k2Q5dk8L54Q/D+WZLJm1jrAxsF8uCrcdZsDWT3q0iufWi1jVumV6dH3ZkVZlU8enadC5qH4tVtYCahdrMsuujPazIX99KCBEBHK6UwFRRlAZgq2aCQnyYBf1JY0hSSnJLXXh8Pkx6HXaXl8vfWI5Hm8X29aYMfpgynFZaF5nH66Oo3I3ZoCPcauDvV6Xx2Odb8PokZoOO167vSbTNP75T6vLw8erDgRQ7FX7dn8tveiQSYzORV3YiieuDIztUaYnYzAbaxYXyyKiOlLu8vLxoFzN+OQTAin25LN2dwwe39691hgi3x1vtWGnnFqGY9GpIu7moTZfdW0AfYAv+FlKa9jhGCPE7KeX3jXh/inJBCbUY+O3AVvxX2+TOpNfxyvU98EpJRmE5JoOOqBATu48Xc9+sDRzOs9M5IYxXr+9Juzgbe7L8641cXh+frTvKlDGdyS9z8cX6dL7cmE7LqBCevjyV0V0TWPHkSPJKXcSFmYmwGgMb7VmNenq3jAyapQb+7RzcPsn8h4by4cpDZBU7mTy4Da2rychdWYnTzSdrjgSVbT1WRJnLWzXV/ilkl7gwGgSD2kUHFoS3jbXx24Gt0evUcsrmojYB6RBwZ0VaHm3DvMeB54AvARWQFKWBRIaYmDK2M7cObkN6gZ205HCOFzm5+q1fSS8op1NCKG//ti/Pzt8RGMvZnVXCY59t5t7h7ZkyZ3PgWmEWA26vj/+tOcLLi3YDsDOzhDWH8vnukYsRiEA3XeWUjka9jlsuas0v+3LZnF6ETsCk/q3ILnHi8UkSI608OLI9mcVOCu1ulu3OYVD7GBLCT90FF2Y24nCfmAslBFW2kKjJFxvSmfHLQf56ZTceHd0Jl8dHZIixQWbtKeeO2gSkLpVyxCGl3CGE6C2lPKBmfytKw/Mn6jTRKSGM7BIHd36wLrCB3p6sUh7+30YmD24TlDpq1/ESWlXK7hljMzGhZzKFdhdz1h0Nun6h3c3B3DIenb2RrBInJr2Od27py9COsYHJBmaDjvtHdiDGZkInBMv35fLF+qNc2jWBgjInv+zL4yUt28M1fVKIDjWh14lAWqCKLkWvT2IxCP50RSqPzN4UuIebBrSqdor5qXROCOOOIW1ZsjObn3Zn4/VJbr2oNd2TVaKs5qQ2AWmPEOJt/Jmzwb/l9x4hhBmoXR4RRVHqxeHyVtnNdXtGMW1O2gG2XayNpEgrL1zdHb1OMLxTHHGhZoodbhLCLUEz4wD0OijS0gC5vD6mfLaZ7x4ZRrTNRH6ZC6fHS2piOH/5ehsbjxYyoE00b9zYm2ibiQM5pTw0e2NggsH0FQdJCLfQKd4/xuPx+tiZWcL9n6znaH45acnhvHNLX5b8YTgrD+TRLSmc1jE2IqymwPGF2vhWdbPickud7MkqYcW+XHq1jGTG5P5MmbOZWwa1UamympnaBKTbgPuBR/GPIa0AHsMfjEY23q0pyoUpv8zJ7uOlbDpawPgeSUSFGIP2POoYH0qMzRSYXJAYYWHG5P6EmPVcltYisC4nt9SJ3enh1et68uWGdKb9uA+vTzKxVxL7sktxuH2V6nTh8Ul2Z5Vw8/urKbS7aRkVwr9u6qUtmDUEgsXqg/lVZrst3Z3Nb3ok+q9ld3HbzDXkaxMfth0r5qFPNjL9tn78dlDrk96ri0/XHuWrjem0ig7hmfGptIq2Bcazisvd/N/cbSzUtgRfczCfnRnFzL5nEPGqu67ZqTEgaVtHvCel/C3wajWHlFZTpihKPRWVu3hh4S4+W5cOwKoD+bx5Y28e/XQTuaUuUqKs/PvmPrSKDmHhI8NwenyYDDp2ZRYz7bN9GHSC34/uRKsYK7fNWMve7FJ0An43vD1fPzCEMqeHmFATaw8WBNXbq2UkOgF/+HQzhVrwO1pg5/p3VrHs8ZFBLZfO1eR669wiLLAOye7yBoJRhQ1HCnGdlNvO5fHx31WHeG3xXsDfHbnucAHfP3ox8dp4lN3l5bvtx4POW77Pv+rEUI+1TMq5rcaAJKX0CiHihBAmKaWrpmMVRTlzZU5vIBgBLNuTg9WoY96DQ/H5JGajjhibGZ1OBD60t6QXctvMtYFzbnx/FV/eNzgwNdsn4a2l+7m4UxxT5mziWKGDWXcN5NaLWjFvcyb9Wkfx7IQ0jHodUTYj49JasO5QPrmlLpweH+Xu4NUdbWJDgrJGdIgP5f6R7XF6vGQXewk1GYiyGimolMw0NTEs0OqpUFTu5rP16UFlhXY3xwrLA+9NCAgx6oPy65kNOlRHXfNU21l2v2g7twaWd0spX2usm1KUC9XJC0wBlu7JYaqAxChrlTETh9vDrFWHg8qkhAVbMxnQNprvtp1oXRzIKcVi9P+Xf3/5QV69vgcPjOyI1aQn3GIkq9jB2G4tSC8o59Ybe/Pjrmy+3pgRaPlUiLaZ+fMVXZlyaWdcXh8hJj0r9+XzwsKd2F1ebh/Shi8fGMLEf/9CUbmb5Egr0yb1rrIPklEvaBFu4Wh+8K63lVMBRViNPDGuC3+ZF5hXxV3D2rHreAk2i4FwlYmhWalNQMrQ/ugAlZddURpRiMnAgLZRrKnUpXbzwFYs3HacMIuRUV3iiaqUJLS43ENsNRkPkqNC2KIlTh2dmkC5y0uvlEjSC/yTG6JtRkJMhkCOt9wSJ7dOX8PurBIAZvxykLdu7sOtg1oTY6s6VhMZYiJSm9S3J6uEh2dvDLz2+g97aR9n46fHhlPq9GI16ompJrFpZIiJv05I45q3fg1kjbiub0rQFioWo57x3VvQLSmcDUcKSU0MY09WCXd8uJblT4xUAamZqU3qoL+ejRtRFAWibSbeurkvX25IZ92hAoZ2jCUxwsIDszbi8vp46rIu3Dm0bWB69px16YzrlsDXm44FdnZtH2fj0tQEWkdbOVboYM66o4RZDAxqH839Izrw7s/7eeiSjkEJR48VlgeCkUEnuKRLPJkF5QxpH1ulq+1ky07aFgP8mSJGdUkIZH84lQ5xNpY+PoK9WSUkhFuIDTUTdVKyVAk889VWzEY9by+1ByZ4ZBU7SYmqf7JW5dxTm9RBccATQDcg8FVMSnlJI96XUktOt5eicjcGvTjtf37l/BAbaubOoe0Y183O459vYfXBE+uNPl55mKv7JAc25LuofTRmg463f9uH40VOQs164sIs/Lw3mxibmT/N3RY4d+3B1Sx4ZCiT+rckJjT4Q7+iqzA21MTbv+3Lz3ty+PVAHuFWI5ekJlTZVbWybknhVcp6tYzEbNCRW+qkzOnBqNdhMxuIsAa3aEwGPQnh+hoX1YaajQxoG8PHlbomLUYdKVGNv7eScnbVpstuFvApcAXwO/zTwKt+JVLOurwyJ+//fJAvNqSTGGHh2YlppLYIq7KFtXL+0esEXklQMAKIDDEG5bULNRu4bNoKvD5JYoQ/593Hdw1kdJcEHv9iS9C5Lq+PH3ZmY3d5uGtoO8KtJ2appUSF0DomhPtHdODlRbtZo9X7w85s7hjShsfHdsZqqv7jonNiGFf0SGT+Fn+6y7TkcG7o34rcMhe3TF/NnqxShIBbBrbm0Us71RjcqmM16XlkVEdKnW4WbD1OSlQIL1/bg0ir6q5rbmozbzJGSjkdcEspl0kp78Cf+VtpQm6vj1mrjvD2sv1klzjZnF7E9f9ZSX6ZWqvclPLLXBzNt3M0315l6nNdRViNjOvWIvBcrxP8aXxqICFpSbmLj1YexqslVM0scpBeWM4XG9KJCDFWuztrQriFb7dmUuYKnjkXF2bms3svolfLiEAwqjBr9RFKHKfOoxxjM/O3iWmseHIkyx4fwYe3DyDcauA/S/cHcutJCR+tOkxGYfkpr1OT2DAzz01MY/mTI/n03kH0aR2FWWX4bnZq00Kq+ITLFEKMxz/BIaXxbkmpjaJyN99szggqc3p87M0uoUVE3dL6Kw0jt9TJw//byK9aUtKhHWJ5fVKvKrPLaivaZuL5q9K4++K2HMwpY2jHWAw6QU6Jg8gQEz6oMgMO/IHMZNBz/4gOfLfteGD6d6+WkUSHGDmUa6922nR8uAWPTyIEQQtfLUY9p5pnXVzupszlQSCICjEF7qfQ7mJbRlGV4/dklZBWz3Q/oWYjoWbVKmrOahOQ/qZtNzEFeBMIx5+1QWlCFoOOtrE29mYHr01OjFD96k1l6e7sQDAC/zYLy/fmcFXv+n9/iwk1ExNqJi0pgr3ZpUydt52cUieT+rfk2r4pTOyVzGfrjgYG+uPCzIzv7s+YkBhh4duHh7IlvQidTiClZMpnm5nUvyWmUywqDTUbuKFfS2avPZH/7omxnYm0Vu1myyt18uw3O5i3JQODTnDH0Lb87uL2RNlMhFmMXN49kbWHTswWFAL6tY6qcp2cEgd2lxezQU+o2UCopTYfS0pzVJtZdvO1h0VoqYKEECogNbFQi5Fnxqey4UgBuaX+b8C/HdiqymC1cvZsOFxYTVnBGQWkCvl2F9e8/StOjz/dzz++243VqGdCryRm3TWIX/fnYtTrGJ0aT5z2O6DTCQrsblxeH1adnpUH8vnzFV0x6HW4fVXXOwGEa+t+ru6TzMYjhQzrFEdyhKXKDrRSSn7YmcXXWivd7ZW8s+wAo7sk0L9tNHqdYEKvJI7k25m95ihRIUamXtmN6JNai+kFdm5+fzWH8+wYdILHxnbmpgGtCFfjQxek+n4V+QPwekPeiFJ3LaP86WOKy93+b5cWQ9CiQuXsurJXUpV9f37TM7lBrr0jozgQjCp8seEYv+mZRFKkhav7JKNDEHnShIGoEBOPzt5EcpSVTglhLN2dTUK4hcHtT70TUbTNxIC2MQxoe+pjXF4fS6uZ7v3r/lz6t43WrmPmibFduG9EewSCaJspaAp5qcPN89/uDCR+9fgkLy7cxfjuiSogXaDqmwzqtJk7hBAthRA/CSF2CiG2CyEe0cqjhRCLhRB7tZ9RWrkQQkwTQuwTQmyptFMtQojbtOP3CiFuq1TeVwixVTtnmtCWsdenjvNRqdNDqdPDruMluH0Sncp83KS6tAjjL7/pSozNRGyoib9e2Y1OCVV3Oa2rwjJXtfv+tI4JwWzUExliItpmrhKMwL8n0kvX9iCr2MHiHVk4PT5euLr7GX9xMRv0jOwSV6V8SIfYoOdWk574MAtxYeYq65nK3T52ZBZXucaxek58UM5/9W0hVd/eD+YBpkgpNwghwoD1QojFwGRgiZTyRSHEU8BTwJPAZUBH7c9A4G1goBAiGvgL0E+rd70QYp6UskA75h5gFbAAGAcs1K5Z6zrq+XfQpBxuD3M3HePPX59IqfLchG6MS2tBmbY6PiLEGLT4UWkcLo+PwnJ/t+l1fVtyRY8kQBIZYgosYK2vAruLFxbuZGC7GMaltQikAoq2mXh8bGdCq5nUUJnNbOCy7i0Y0j4Wh8dLiMlAbAN1617cMY6JvZOZt+kYRr2OO4e2DWyZXhthFgMjO8fzwa+HAmVGvaBNjO3UJynN2il/m4UQJVQfeARw2pFzKWUmkKk9LhFC7ASSgQnACO2wD4Gl+IPFBOAj6d+6cpUQIlIIkagdu1hKma/d12JgnBBiKRAupVyplX8ETMQfkOpUh3av55Wicg8vLtwVVPbCwl10SgjjhndXYTXqee2GnozoFHfK9SPKmSsqd/HtlkwWbT/O3cPaE2E1EBNqpsThpszlJdJqPKPWSHG5mznr0vlmcyZ//k1XJg9ug93lITUxnIRqUgZVx2o0YI1o2N+BUoeLZXtyaBdr49N7L8LnkyzdnY3bW5vvqn4Wo54HRnYgv8zJgq3HaRFh4aVrehBhVb+vF6pT/stLKRssb50Qog3QG1gNJFQEACllphAiXjssGai8tWW6VlZTeXo15dSjjvMuIPmkxF4pAzL4U/XrtG6RcreX33+6iWWPj1QBqRFlFDqYseIg/76pD2UuL+VuHxsOF2B3+btShRA8fEkHIuoZlCrW/5S7vTz95VYsRh1tYmx8dOeAwL91UyhxePl07VE2HCmExSfKOyaEcbktEYupdi3zuDAzf7+qO3+6oisC4d+htgnfl9K0Gn1DESFEKPAF8KiUsmqHcaVDqymT9Siv8XZqc44Q4h4hxDohxLqcnHMzKYXVqGdYx+D++mEdY9l27MTaD4fbh9116gWNCtidHorL67+YeNnuHP52VXcW7chi0ruruP6dlfx76T46JoRxVe9kpq84SHENi0pPJz7MHNTF5nD7GNOtRbXTsM+mUIuh2nQ/kVYjpa66/X2GWoyBcaaGDkYer4+cEic5Jc7AAmLl3NWoAUkIYcQfjGZJKb/UirO0rji0n9laeTrQstLpKfgX4dZUnlJNeX3qCCKlfFdK2U9K2S8ururA7bkgMsTEa9f34t6L29E9OYJ7Lm7HsxPS+HTtiVleUSFGbKp1VC2318uBnFKe+GIL981az0+7simy1+2DNK/USYf4UKJtJl5bvAeX1z8LbmdmCR/+ehijXodBJyjSAl6p00NOiRP3SbPlahIbaubL+4YwvnsiqYlhPH15FyYPbl1lGvbZFmYxMmVMZ8IrrRka3D4Gs1GPUX9ujFsW2l38d9URfvPmCq5++xfmb8kI/Fso56ZG+7TSZrxNB3aetHfSPPz58F7Ufn5dqfxBIcRs/BMNirTutkXA3ytmygFjgKellPlCiBIhxCD8XYG34l+4W+c6Gvq9nwbCrQAAIABJREFUny1xYWbuHd6OoR1i+fVAHq99v5uXru3JH7/aisPt441JveqcN+xCkVfq5oo3VwS6PX/Zl8cHt/dnROf405zp5/R4eXvpfi5qH8PB3LIqr2/L8C9GDbUYSIqwkF5g58WFu9h1vIRx3VoweUibWmVw0OkErWJC+Me13XG6fURYjf51RB4fTo+X0CbcfiE+zMSX9w9mc3rR/7N33uFRlekbvs+c6TOZ9F5II3RCb0FAFAEVsYNdUFGwu7bfrmvb1d117V3sir2BFBVFeu8lBEghDVInbXo9vz9OGIgJdUFFz31dXJAvZ86ZmZDzzPe9z/e8RBm1+AJBUiMNhBs0tLh8WO1ypFX3JAtxYbpffUvC9n3NPDr3oOnnzk+3MP+O4YQbTiwpQuHUcyo/PucB1wDbBUHY0jr2V2SR+FwQhBuAcuCy1u8tAM4FigAnMAWgVXj+ARxoifn4AYMDMB14D9lk8V3rH473GqcrLm+A538q5IPVB1OQ15c28vWMYWjVqhOOrPkzsLbE2q4G9/aKvfTvFNmmXfcvcXn9NLt82Nx+1pU28On6CmbfmodKkDuzHmBk51gKa2x8NX0Yfkli8sw1ofYQL9cW0ej0cn1eOrM372NQRjRdE8KOmnh94MdZ3ezmzeUlFNXaubR/MsM7x7Zr2XAqsXt8BAIQbtQiCAJnZGtwev0YdWpiTDq8/iALtlfx4NfbQ4/5yzk5TM3L6DDq6FTgCwTbdN49wPc7qumRpAjS75VT9r9DkqQVHH6/0lkdHC8Btx7mXO8A73QwvgHo2cG49XivcTpic/uYvWVfm7HqFjmGJamDYE2Fg0R1YH2OMWtRqw6/FBYISmwoa2Tqe+u5IDeJgelRRBg0SJLEC5P68vi8nVgdHsZ2T2DayEwMGhGLQcPeekdIjA7wzeZ9nNM9nlcWF/PK4mImDUjhjrM6E2HSYFCrD1tLqbN5uPT1VaHzLd1Tx9/P78Z1w9KP+NxPBh5fgDKrk/8u3IXN7WfaiEwGdIoKtRs/QH2LmycXFLQZe2lREZf1T/3VBEmtEuidEs63v8h77KhVhsLvB6XAcBqjUgkkWgy0uGyhMUGQe8W4vAEMx+h0+jPSLdFCjyQL+ftln41Zp+bOs3KO+J41ODzc+8VWfAGJeduq+OimwVzSL4WJr6xkaFY0T17cC4teTVKkoc1sR69RtQssTQzXh0JPAb7YWMlVgzuxs6qFmhYP5/ZMaBezA1Bn97QTt/dWlXJBbhKxx2gDP5Rmlw+XN4AgQJhOjfEIglHv8DLh5RWhxIg1JQ18dOPgdpthvf4gdk9bI4c3EMQXOPba2f+KIAhM7JPMN5v3hX7Gw7NjGJAe9as9B4XjRxGk05gYs44nL+7JG0tLyIo1U1hrIyXSwNyt+2lw+Jg+KkupIR2GGLOO96cOYk+1jQanlwGdoo66YTQoQa3NA8jJ6u+uLKVbggWnN8CigloWFcjemWuGdOLRC7ojts5YTDo1t4zM4rUlxYC8+fP+cV15c1lJm3P7ghIWvYabPtjIjn3N/P387u1mFPoOzAxhOs0JpXRY7R4enpPPgh1VaEUVM0Zlcd2w9MPWepbtqWsXX/TOyr30SY1o8zzrbG7G9kjgu9ZNvACDWvPtOsLh8dPo9JK/v4WsWDMxZu1JqTfFhun4YOogml0+RJVAmF6j/D78zlEE6TQnLcrE0Kxolu6pY1hWNKO7xnPZ66todPromWxhYp+Tk6X2RyTGrCMm+9jrbHq1ipE5saEMtxaXD6mDnQYaUUA4ZLXaotdw84hMLuufQmWji+w4M8sL69hQdjAJ+5zu8RTX2VG33rS/2lTJ3WNy2glShFHL6C6x/Nz6HFQC/O2QHknHSjAoMXdrFfO3y54ejz/Icz8VMqpL3GHFIK6D+KL4MD0asa3QxIfruX5YOunRJtbubaB3SjjXDUsn2IHtOhCUWFNi5aYPNoRqcLedmc3NIzOPWMs7Vg6kpSucHiiCdBrT4vLx+Lx85m6VbypLdtexvrSR6aOyeXJBAd/tqGZsjwQlPugkEW7U8tQlvXl64W6WF9YTpldzcb8U3lmxN9T+wagVmZKX0a4GFGGUP/VnxsrZdmd1i+e5y1X8vKuWXikR9E4JRyUIPPqt7ArTq8UOC7BRJi3/vSyXolo7RXV2zsiOIaYDoTgaLl+ApXva77Fbt7eB3NSIDh+TmxJBt8QwCqrkJeJwg4YZo7LadSiOMGipbHChVQtc2j+ZpAgDW8ubyOvcPqy1weHlr99sb2MIeXVJEVcNSTspgqRweqEI0mmM0xtg/ra2rvWfCmq4eUQmAIMzog7b9+Zk0ODw4g8EMenUv1qx+rcmzqLn0Qk9sHv9GDQiJq2a7+4cwYLtVXj8QS7ok0Rc2NGXhWLMOibkJjKySyw+f5DqFg+PfpsfChu9b2yXw85UDnzqH5x5+DTuo2HQiIzMiWXx7to244MyDl9jiQnT8eHUwRTV2bG5ffROiejQyWkxaOiRbEEQ5CXOnfubuSA3OXRsg0Ne9owy6UCS2nXWDUoc114thT8Of467yB8UQQCNqGqzrq9RqQhIEsOzo5mQm3RKYlgCgSBFdXYe/Go7xfV2xnRL4MHxXTtMpP4jYvxF8T8hXM/U4RltjvEFgjQ6vFQ0uog2azvMtFOLIlEmeXahVYs8PKE7WyubGJwRTVIHPYhOJiqVwITcRNaVWvluRzVaUcX0UVmkRR85HDUmTHdMM7Jwg5aB6VE0u3zoNSImnRqb28f60gZeW1LMlLwMsmJNuLwBZs/I42+zd7ClQu4nlRNvPqK5QuGPiyBJSpzGkRgwYIC0YcOG3/ppdIjLG+CdlXv57w+7Q2N3jM7misFp6NTiKSvg1ra4Oef5ZTQdkmxwcd9k/nFhzz/NTOlo7K5u4ZLXVofcZlOGpXPn2Z1/d/2qQi3IBQHLUVx2x4LXF8Dh9eMPSkQZtYiHzNC3VzYz4eUVPHN5Lj/trAmZHmLMWmbdMJh/zt9JapSRO8/qTILS+fi0RxCEjZIkDTiexyh3j9MYg1bkqsFpjMiJZd3eBgamR5IaZTzlmySbXb42YgTw484aHhzfVREk5JYRf5+T38b6/O6qUqbkHd7B9lthMWhOSjO8eruHBdur2FDayIicGMw6DS0ur5y71/qaP9tQTpRJS7RJ28aBV2/38uqSIp6f1BeTTq1sV/gTo9w9TnMOFMt7Jf96u8/NenW7ZILMWBNqUUlpBrn+UdngbDfe4PSRduJln98tDQ4Pt3+8idUlcoDKt1v3M31kFk0uL9lxYfTrJAtSVqyZSKOWqmZ3u3OUWp2oVChi9Cfnt01oVDgtCdOp+eu53ThQngo3aPjPJb3lIrUC4UYNE/u2tdtbDHKm3R8RhycQEqMDfLimjPE9E9skiZzfOxGVIHfW1f2iPnZJ32TCf+MEc4XfHmWGpHDcmPUaJg9M5bzeibS4/EQalQ2Hh6JTi9w4PAMBmLttP2lRRh69oAfRf9D3qCPfjFoU8AcleqccnLnHhun55KYhWB0ePrlpCP+cv5M6u4crBqYxoU/yYTfOKvx5UATpT0idzcOqonrKG5yc1zuReIv+uGs/Zr0Gs15DopJT2SHRZh13nZ3DlLwMtGrhD/npPxiUqG+1cI/rkcD3+QfrQjePyGRbZRNXD+nU5jEBSWJRQS0ef4B/X9wbs15NbJjuf271rvDHQHHZHYXfs8uuI+psbioaXOi1IokWHZG/WEart3u4+q217KqWNzeqBPj85qFKxpfCcbOnxsa1b6/D5vbx8pX98PgDbKts5qxuccRb9Bg0YpuUhDqbhwtfWcm+poNZfC9O7sOE3CSEE4g+Uvh9o7js/uRUN7u56NWVxIXpeOi87mxudhFh1BJj0mLQikQatZQ3OENiBLIx4b8/7OaNa/r/7hxgvyUeX4Amlw9JksNRlfemLVa7h7s+3UJ1i2xQmPLeevqmRvDulIGHfa8qG51txAjg9aUl5GXHKPE+CoAiSH8YfP4gb60oweH189gF/bjxgw3U2+Ud8H1TI7h7TA7xFj2aDj6Jev1BlO7OB2lx+5i7ZT9PLijA4Q0wumssT12aq/SXOoSAJIVSJQ6wuaKJRqfvsILU0UZfvUZ1QsGwCn9MFEH6ndLg8OLw+AkEJcL06qN+gvQFg+ytdzBjZBZfbqoMiRHIN4p6u4fv86u5/cxsZs8YRkCCLeWN2Dx+JvZJJhCUCASCbTYy/lmx2j38bfaO0Nc/76rjnRV7uevsnN+8dfjvBa2oYnBGFGv3HnTXRZm0mI5g24636MlNDWdrRTMgLxc/MK4rkX9Qs4fC8aPUkI7Cb1FDsto93P/lNhbtknPGuidaeH/qoA6jeax2D/V2L0FJwqARkSSJx+btDCVSH+DBcV05o3MML/5cyA/5NQgCXNgnmauHpDHpjTVEmrT8Y2IPhneOxfwn3tza5PTS4PBSXGfn/VVlrCiqByA3JZz3pgxSbp6HsK/JxR2fbGZjWSOZMSZeuKIv3RLCUB/hQ0293cOG0kZK6x2M7ZlAXJhO2Uz9B0WpIf1B2L6vOSRGADurWvhiQwU3j8xqY42tt3u44f31oU+cOfFm3psyiLvO7syqIive1oZoGlEgLzuGbZXNrC6xMqF3Ir6gxPc7qhmWFU3neDMFVTamf7SJZfedeVoJki8QxOHxY9apj3gjPBZqWtzc9ekWVpdYibfoePj8HiRYdCzaVcuQzGjlxvkLkiMMvHXtALyBICpBOKYswxizjnE9E36FZ6dwOqL8hv0OOdDh8gCRRg390iIpqGrB4fWTEW0iwqhm6e66kBgB7Kmx88WGCiKNWr6ZMYz/+2Y7YXq5OZzT68ft8/POdQOZt60KnVrFJ9OGsLGsgVE5sTw2oQexYToCkkR1s+u0yBKrt3uYtaaMjWUN3Dwii4xYE6IgIKoE1KLquCKU7G4fj83dyeoSKwA1LR7u+XwLC+8ewYQ+SeTEhynLdR2gzBgVTiaKIP0OObtbfJvA1Kcvy+XZn3azbq/c0C3WrGPBncPJ39/c7rHlDU72N7vpnmThr+O7EqbXUGfz4Ar6GZodwwUvrQzNnD5ZX87c24Zjd/uos3u54s21VLe4yYo1MfOaAWTFmY/7ufsCARoccltso1Ykwqhp1y/nePEHg7S45HYPB6JlmpxeHvs2H0GAv4zpylsrSliwvQqNqOKG4Rl0T7KQGWOmc7wJjSjiDwSPOINqcftZ0ypGB/D4gxTXObjt401EGDR8PWPYaSHUCgqnK8pHvt8hieF6Xr6yLymRBvqlRhCUpJAYAdTZPby5vIRxPRPbPXZM9wTW7W2gosFJICjx1aZKIowaSuocfLC6NCRGAC0uP4sKavAH4Z7Pt4YsvMV1Dm7/ZDMF+1va9ao5EoGgxLbKZsY8t5RRTy/h7GeXsqm8CX/gxHvbWO0e3ly2l+vfXcejc/OparUNu7wBrh7SiYHpUWwoa2DetiqCkiwiry4pxqARufvzzTQ4fMzdup+/fLGVbzZXYm3dyPlLVALt8gDVKoEoowaXL8D+ZjdvryjFq/TpUVA4ZSiC9DvEYtAwvmci38zI4+3rB2C1txeF3dV27B4fz0/qQ5f4MLJizTw+sQeVjU7KrA7Sok3YPH5WFlm5+u11jMiJRa1q/+MWBIFgB03Sdla14PD5eWbhbhyHpFYfCavdw60fbabFJR/f4vZz60ebjkvUDsXlDfDioiL+8/0utlU289n6Cq56ay0Ndi+1Ng9XvLmGikZXaJntUPbW27nrrBxe/rmQ2z/ZzJwt+7n7s638Y+5OWly+dsdrRRX3jMkhK9YEyJ1fH5nQg6827eOA76ek3o7vfxBXBQWFI6Ms2f1OUQlyAz5fUCIvOwaNKOALHHREXtwvmZcWFWHUirxxTT/2N7n5YHUpBdU2np/Uh282VVLV4mZ01zheW1rM4t113Dwik8/WV4Qa+oUbNJzTPQGb20ekURNqw60VVUwdno5KAK8/gN3jP6aCvj8ohWZZB7A6vG1mZceDzePji40VbcZK6h24fH5e/LmQoASFNXb6dYpgUYFsAokxa3nq0lw0KoEGp5eL+6VQ3uAKteues3U/D4zvisWgwe72YfcEEAQw69QYtSL3j+tKlFFLnEXHi4sK+WrTwXDQyQNTFWODgsIpRPnt+p0QCErY3D4MWrnesbGsiX/O30mLy8/jE7vz+c1D+fd3u7C5/Uwdns7QrGhqWzxsKGtArxExakUmD0qjxeXj0/UVrCq2cnG/5FBPHpUAn22o5Kvpw5i/rQpBgEv7p+ANBPhxZw3PT+7LfV9sxeMP8t6UgSzeVcs/5xUwKCOaY90ZoFWr6BIfxu6ag0kQGTEm9CdYQ1IhEGPWUX5IKwdBoI3TcOmeWqaNyOSc7vH8WFDDU5fm8uKiwlD3UaNW5L0pg9hTY6Oq2Y1KEBAQaHB4eGbhHj5bX4FKEJiSl860EZmEGzXY3X70apHLB6RSXOfA7vFz4xkZDFTilRQUTinKPqSj8GvsQ2pweJmzZR/zt1XRLy2SyYNSOfvZpYgqgQfHdyU3JQKtqCLOopPrGiYdKpVAIBCk3u7lijfX8MRFvXh4zg4Ka+2A/In//amDmPbBBiTg29vycHkDBIISkUYNEuD0Bhj/wnI8/iDDsqK5YXgGXeLDeGxePj/uPGg7H5YVzd1jcog2aY8axFpmdXDXp1vYXNFEr+RwXryiLxkxphN6X4JBiRVF9Ux5bz2B1iiJqXnp3DMmh5J6Bxe+spKgBBa9mnvHdmFM93hK6x1c8ebaNucZ1zOB9Ggjry8t4ZohnXhwfFeW7K7j1o83tTnuoxsHk5cd84ufjYegBJFGrZJGraBwHCj7kE5DXN4AX2+qRCUIXNwvhTKrg4U7awhK8Mh53dhVbeMf8woQVQJPXNST7glhLN1TR7cEC0mRBhburKak3sF9X27lqUt7U9XkJihJDM2KZlWRlWuGdmJcjwREQc4bq2yUTQEPndcNXyAYWr5bVWxlVbGV+XcM56eC2jbPcVWxlXvGwFnPLmXWDe1v2ofSKdrE29cPwB+QUKtURJlP3BasUgkM6BTJsvvOZFtlE5mxZuItOsx6DdlxZhbePZKP15YRb9Fzdrd4BGiTUHGARoeXSQNTycuOoUeSBb1GxU8FNe2OW7K7liEZUW3SKpQeTwoKvx6nTJAEQXgHOB+olSSpZ+tYFPAZkA6UApdLktQoyFG/LwDnAk7gekmSNrU+5jrgodbT/lOSpPdbx/sD7wEGYAFwpyRJ0olc47fE5vbRKdrIa0uK5aWh4RmkRZmYeU1/MmNMnNE5lql5GQgC1No8GHRyjJDbH2RjWSMRRrn9dGWjiyvfXEtWrJmL+iahAlYV19Po9OH0+qlp9oTECOD7HdVc2j+lw+dk0qpDS30Heh0FJAlJgv98v4v3pgw84o36ZN7EjTo1Rp2a5Mhf2K0luendzSOzEAV4bN5O5m+r4uvpwzDr1G3ah0/JS+fMLnGhr91eP3nZ0XyzeV+bU/ZOieCbzfs4s2ucEvapoPAbcCpddu8B434x9iCwSJKkzsCi1q8BxgOdW/9MA16DkIA9AgwGBgGPCIIQ2fqY11qPPfC4cSdyjd8apzfALbM2sam8iT01dv42ewdp0UZeWVxEi9vPvV9sY8xzy9CoVOjVIrPWlLNzfwsScqDqwPQoDs2mLK6zM65nAk5fgHvOyeGsbnGkRZooqrO3ua5WrSIvO4aUQ2704QaNXNgf24UwnZqXrujLc5P6cO/YLkQYtORlR+PyBn7zIFaXz8+PBTVc8cYanphfwH1fbuOy/qn0Sg7nmR/38O6UgZzfK5EhmVG8fnU/hmS27Rve5PIRY9ZxQW4Sqtaa1OSBqQjAvV9u4+E5+R068RQUFE4tp2yGJEnSMkEQ0n8xPBEY1frv94ElwAOt4x9IckFrjSAIEYIgJLYe+6MkSQ0AgiD8CIwTBGEJYJEkaXXr+AfAhcB3x3sNSZKqTubrPl6WF9aF6iMAo7vG8+m6cnISwpizdT+byhvplRxORZOLa95eGzIYZMSYeOKinviDEq9f3Z83l5UQlODB8V0oszp55Nt8qprdjO+ZwIxRWTS6POTvb8GgEXn96n4U1tp5Z8Ve3p86iD3VNpzeAEOzohGAnsnhLLjzDP42ezvL9shZbmE6NW9fP5Bam5vw39hp1uz00+Dw8uC5ci0oNzUck1Zk2sgsbv1oE3tqbHwzYxgmrYbw1hnkodTbvUyftYlbz8xm8b2jqLV5WLanjns+3wrAgh1V/P38blgM7R97gDqbhzqbG42oItqsVZb2FBROAr/2nSX+gABIklQlCMKBdZRk4FB/b2Xr2JHGKzsYP5FrtBMkQRCmIc+iSEtLO86XeHx0ipYL/imRBuLCdKRG6imqs9MrOTxkLDi/dyLP/7injdttb72DZpcPjz9IerSRKcMzCDeoMWrVXPXW2pBFfN62KiKMWmaMyqLZ6SfGrGXu1ipUKrggNxmnx0+0WYvo9FHR6GTJrjom9kmi3u4JiRGAzePnrRUlzBiZxXOLCpmal0HMMWSXnQokJIKSxE0fbAyN9e8UydOX9pa/L4FGVHUoRgAxYToCQYmnF+5mYEYk1769LlRLA9CrxSM2jKttcXP5G6sptcruv8EZUbxyVT9izDrsHh9aUWwXM1Rv9xCUJExatWIdV1A4DL+X34yOfvulExg/kWu0H5SkmcBMkF12Rznv/0SPJAtzb89DpxaRJDDpRAqqWvh2y34GZ0SxqbwRQRA6fKJaUYXb52d5oRWtqGLnfg9d4sPa7FcCeRY2vkc81w6V9xaVNTjZWNbIEwt2khFjYvrILOLidWypaCIvO4ZbP97MLSMz25xDEMDl8bOpopFXlxSzY38zL07ue9Ib17W4fDS75OghfzDIJ+vKSYowcHG/FOItegCCksQ7K0rbPG5jWSNuf5CeSRaevbxPmxmL1S7Xz5pdPromhGHRqXn9mv7c+8VWFubXcMPwDF5dUhw6/u4xOYedHQWCQWatLQuJEcDavQ1sKW/CrFfz+tJi0iKN3DIqi8RwPV5/kB37m7n/y+1UNjo5r1cifz23228m5goKv2d+bUGqObBM1rokd8DOVQmkHnJcCrC/dXzUL8aXtI6ndHD8iVzjN0UCKhpcPDInnzq7h8EZUTw3KZfSegd90yIpb3Dy3Y4qbjojg41lB+OD0qKMdI4z85fPt/LYxB4s2F7NyJxYxA7SGLomWNBpRB74ehuPTujOooIa3llZCkBBlY3N5U08c3ku2XFhvLqkiL31drLjzBi1Ik5vgAtyk5iSl47d4yeydbb16pJiXL4AESfxvbC5fby7qpQF26q4d2wO0z7cGJoVfrC6jG9vyyM2TI9GpaKj7Qo6tYr3pw5qY0iot3uYMWsj60obEVUCYXo1c28bzhmdY/j+zjPwByXUKoHzeieyqayRAelRJIbL7bc7wheQ2FNjbze+u8bG2hIrywrlWeWCHVXMv+MMAkGJolo7eo0Kjz/I15v3YTFoeHB8V/SHuYaCwp+VX1uQvgWuA/7d+vecQ8ZvEwThU2QDQ3OroPwAPHmIkeEc4P8kSWoQBMEmCMIQYC1wLfDSiVzjFL7WY8LtC3DXp1tCaQZr9zbwxLwCbj8rm6pmDw+d3w1JkmcOn988hDlb9pMSaWB01zimfbiRXdU2Hpq9gysGpVJmddAvLZK7z87hpZ8L8Qcl0qON3HV2Z9QqgdXFVpzeALO3tNXhqubWdIXWe3xQghd+KuTd6+UNsv06RTLpjTV4A0EEAe47pws35KWf9E6fdref53/aw11n5/DB6rI2S5RVzW7y97cwqosejShw04hMHpu7k+HZMQzPjkGvVhFh1LSr5RTX2umVEsFfz+2Gyyc//3lb93N9XgZxFnkG0+TyEmvWcUm/FIyHLKcd+J4oyOnhXn+QKKOGSQNT+X5Hdeg4QZCX7WYuKwmN1du9bKts4t8LdqHXijwwritbypvo2ykCk1ZNo8NLYoQS1KqgcCin0vb9CfLsJkYQhEpkt9y/gc8FQbgBKAcuaz18AbIduwjZkj0FoFV4/gGsbz3u8QMGB2A6B23f37X+4Xiv8Vvi8vqpafG0i9ZZW9rAgzoNnaJFNCoVQUnixg82EAhKfDptCI98m88zC/fgbzVDbCpv4sUr+uLxBdhS0cSFfZO4tH8yNo8fnVrFZ+sruSA3CYA9NTbiwnTt8uUMGhGtRsWUvAx+yK9hyZ46iuvtvHplP6Z/tCn0HCUJnv+pkPl3Dj9i0f9EcHgDSBJIktSh2B0YW7K7DlEQ+PnekawqtPL15n0kR+o5s1sc4Ya2G1hFQZDF5vXVBIIS4QYNM6/pjz8YpNkVZPbmSp5ZuAePP8iVg9O4fXRnokxaGhxe3ltVymfry4kx67hvbBeW76kjLdrEub0SePKiXry5vASDRuSBcV3Ysa+Z5l8480SViganl8Z6Hz/urGZoVgy3fbyZZpePHkkW3rx2AEmKKCkohDiVLrsrDvOtszo4VgJuPcx53gHe6WB8A9Czg3Hr8V7jt8Bq9/DSoiKmDE9HVAltnHa5KRGUWu1oRZH8/S1kx5m4ZWQWD83eQYPDy8bSxpAYAfRMtuALBNGoVdS0eJi5rITJg1KJ0Gs569mleANBkiMMnNUtjk/XVXD/2C7cPGtjqNZ09ZA0tGqBBIueBIueH+8ewafrKogya4k269jf5Grz3L2BIGqVihaXjw9WlVJv9zJ5UCqJFn2bGcbxYjGoiQ3TMW9bFX89txvLC+tCFvOUSAPdEi0AhBs1ZMWZKbc66ZFsYXWJlblbq1i2p54f7x5BXGutyWr3kBChJ65RR+c4M7uqbTS7fPxj/k7eunYA+5pcPPLtztD1311ZSq/kcCbmJvHN5kpeXFQIyL2RbvpgA5/fPJTL31hNlEnLpAEpjOkeh0oQiDZvNEWFAAAgAElEQVTriDQ1tckb7JcWgdcfpNHpQxDg4n4pXP7G6tD38/e38PCcHTw7qQ8W/ckVdgWF05Xfi6nhT0UwKDFvWxXvrS4lyqzl6ct68/fZ+dg9frrEh/G387rh8Pi56YONobDS5yb1YdaNg2lx+Xh2Uh8e+GobDQ4vnaKNPHx+D5Dgiplr2dcqHh+vK+ed6wbSOd5M/v4Wnvp+Fy9e0ZdGpxd/MMiiv4xk3d5GEsP17Gt04fIGUQlg1muwGDT89bxugLxUeHa3eBbuPJhskB5tRK9WMeGlFdTa5HYO76zcy9zb8uie1LaFw/EQY9Lxxc1DeWRuPhtKG/hmRh7zt1eRHGFgfM8EYsN0ODw+mpw+7vl8K01OHzq1ipev7EdVs4tN5U0U1doxaEVaXD5ueH8Du6ptdIkP4/ELe/D43J3k72+huNZBk9PHz7vq2j2H73dUMyonltmb2y5r+gISBVU20qJMfLa+gpE5scSG6UPfz4kPY/G9o1heWE9ShFyDumWWvO/aoBFpdPramU02lTfh9gYUQVJQaEURpFOE1x+gyekjEJTQaUSiDumsafP4QzWIZ3/cwzVD0ph96zDUKhWiKPDO8hI0arFNcvbdn23h/akDGJoZw+LdtTw/qQ86tbwk9PrSIq4ekh4SI5CX1mYuK+HcXonk72/B5vEz5b31PHZBdwZnRjHl3fV4/EEa7F5sHj/RJi3f3jYcm0cOFj3QCdRi0PDERT2JNmtZuruOHsnhPHx+d4pq7SExAjkc9oVFhTw3qQ9G7Yn9t1KpBNJjTLw0uS9uXwCLQUNu6kHbhNXuYVtlM3vrHbx2VX+W7qnl9aUl/Of7XUwfmcWm8iZElcBfvtjC2O6JXDEojUe+zWd3jY37v9zGA+O6MuOjTYzuFse60gZ6JVvaPYdBGVHoNSKZsSa272vbADEl0oDV4SEvOxqdpq15RK8RSYk0csUgeZtAca09ZOt0+wKkRRnRiqo2y7MD0yNDDQcVFBQUQTolOD1+lhfW88DX22hy+ujfKZJXruxHQrj8idqgEclNDWd1iZXUKAOX9EvhwzXl7Kmx8X+tXV7LGw6KS/dEC/+8sCcIcvuFXikR/HP+ThZsr0YQYGpeBh3lfgqC7Dw7QIRRw1nd4qlu9rRzilkdXvY1Obn8jTUMy4rmhcl9QjOA2DA9D5/fHdsYuWtrmF7T7mYNsifiZGT1WgyadvWpBoeXe7/YyuLd8qzmpZ+LeOayXIZnx7CxrJEYs5YRnWNweQMszK9lYX4t/7q4F5f0TyExXI/bFyA5Qs9Tl/RidNc47B4/giDw9nUDeHRuPhUNLvKyormwbzIOr5/rh6WzpsRKTYssuhfkJlHd4kavFpk2IhPdURLM02NMfHfXGbS4/Jh0IgaNiteu7se9X2yl0emjT2oEj07oQZgyO1JQCKEI0img2eVj0a4aXr+6PypBYF+Ti282V3DdsAyMWjVatYqpeRksL6znttHZPPDV9lDLhge+2sYLk/qyv9nFruoWJg9M45zucZTUO9CoVWypsaFRCdw3tgsPndedWpubVUVW9BqRtChjqFWDSoAbz8ggMdzA38/vhkGjpkeShdI6B25/sF2biLgwHU5vAJBrWHU2D1sqmukSbybSpCVMr8FwyMxnQKdIYsN01LXOkkSVwB2jO5+yTZ82ty8kRgd4ZXERt47OJt6iI9qs5fphGei1Ip/fPITH5u4kGJSY0DuR91aVYtKqMWjVSJJErc3Df77bxbrSRvqkRvDBlEGoRRU1LW7sbh9uX5C/z9nBM5f1QZIk4sP1iAI0OH18PWPYMRkRRJVAXJieuLCDYyNyYvjhrhH4gxJ6jUpJd1BQ+AVK+4mjcCLtJ/bU2NhV1UKd3UOEQUO3pHDK6h0EJYmB6VHEhukQBAGr3UOzy8clr63ivrFd6JJgQUDOozujcwwVDS6aXF68/iAPz8nH6vAyOCOKxy7owVsrSji7WwJ9UyN4ckEBF/ZNIic+jP1NLlYU1nNu7yTqbW4qGl0My45GCkos2FGNWiVwbq9Ealo83P/VNopq7aRGGXjqkt4891MhyREGeqeE89jcg8X+Jy7sySX9U9rsmzlwY/960z7qbG6uHNyJpPD/zdRwJIpq7Zz97NI2YwkWPS9d2Re1SuCez7aw1+pEIwp8PSMPjy+AhMRlr68JHa9Tq/jhrhHc+8UWNpQ1hcaz48zMvKY/o59ZikqAD28YHOpSa9CI+AJBhneO4b+X9CbWokdBQeHoKO0nfieYtCI6jciighqevKgXn62v5MeCGopq7cSYtcy7fTgJ4QaizTo8/iCvXd2fV5cUsbywnk7RRsL1aty+ILNWl3L/+K6MenpJqCC+dm8DrywuYmpeBi8tLmLJ7lruGpPDm8uK+XhdBdNHZjGxTzLFdXYGZ0aRGmVg9DNLkSS5PmLQiBRU2XhgfBfeuLo/EqARBe7+bAubypuYdcNgps/a2Ob1PLGggLO7x7cRJEEQiLfomT4q61d5TyOMcsuJotqDS403DM+gptnNfV9uw+WTZ3e+gERhjY1vNu3jphFt0yY8/iDL9tS12zxcVGsPJWEEJXj6h93cPCKTWz/ejMsXQKdWcd/YLooYKSicYhRBOgXY3H7+MW8nb1zdnwXbq6lqdnPXWZ2xOrw88m0+H68t5+4xOQiCnBzQSefgzYmJNLolZm13kBZtosHh4cYRmaFNq4nheqx2uR342r0NjOkez8+75BCKH/KrefPaAVz6+mpWFNbz2c1D6Jpo4fv8GtQqgSX3juKNpcXsrrFzbq8EYsw63N4gD83ZwepiK7eNzmZ8r0R2V9vQqlXYDmndAHIieeAwEd91rcGkNTY35/VKJC5M12Zp72QRY9bx8Y2D+XidnHZ+Sf8U+qdF8ujc/JAYHSA5wsCGskYeNLWPNQo3atD8ImfOYlC3eX0ef5B+aZHMvjWPqiYXfVIj2phSFBQUTg2KIJ0C5m+v4pEJ3fnLF1vZVS3XaWZv2cc/Jvbgy1uGUt3ixun1Y3f7ifFXETb7CqjbRUJYAjec+ybzGzQ4fQFWF1u55+wcPr95KKVWJ2lRRn7cWU1JnYPiOkfoeo1OH6VWB+nRRkqtTjaVNzEwPZIHvtqGJMn1oW9mDMPtCzDtw000OuV9Q09d0psPV5exobSBKcPSW6NugpyRHcPyooPBqoNbnWe/pM7m4bLXV4Vy3Z77cQ9zbh1O96T27rWj0ej0UlRrZ2F+NYMyounfKaJdjSXOouf20dl4/VLInfbg+K5sqWiistGFIMCUYels29dMp2gjRq2IRa+mxS0LbFasic5xZq4anMbG0sbQ7OfpS3N585CUhVtGZZEQricxwkCf1JMZjqSgoHAkFEE6BWTEmDBq1SExOsBbK/Zy7zk5/FRQS/+0SPz2esTvboS6XfIBtmoi51zD5TevZvTMXdw+OpstlU3c8clm4sL0mHQiVw3uxKQBqVw+c02bc5t1mtBMIcakJdKo5Zvpw/h5Vy3xFj3FdXYijVqm5KXz0OwdvLq4mPgwHWN7xjNpYCpbKho569mlRBo1zLxmAGnRRjaWNTI4I4pbz8zucIZQUNXSJmTUF5B49sfdPD+5D2bdsbvHbC4fVrsXlzfAub0S+XBNGfO27efxC3q2S+wWVSoMhzyVlEgjX08fRpPLh9sXYPGuWj5bV8Ezl+fy2pJi3p86iK2VzZi0IoMyorhl1kbSo018eMMgAkGJ1Cgjokpgx/5mzu+dyFWD0+ieFH7EtG8FBYVTgyJIp4BhWdFUNLrajatVKty+IHO27Mfh8fPaxBTY17Zeg7sJtd/O0PQIMmPNPPDVNt68dgC+gESzy0dOvBmDVkR7SJvtnskWtK0pDX1SI0iLNrKisI7iOgfXDU3n6YW7WLzbz91jchjdNZYIgxqtRiQ50kiCRY9aJXBG51j0ahX1di9Xv72WyQNTeeu6AcSYdYcNAXX/YqkM5OWu42ng5wsEWF/WwPRZm/D4g+jUKv51cS++31GNw+s/bAuJQ4mz6Ik2y3FIlw5I5arBnQgEg6wstvLlpkq6J1k4q2s8cRYHBVU2CqpsLNxZw/tTBpIYrkcQBO46Kwd/MIj2KHZuBQWFU4fisjsKJ+Kyq2524/YFuPeLrWw4JKH7lSv7srGsgXdWlqESYMcDAzB+MRn2HdJJXR8OV3yKx+uhwtybypYAz/9UyJYK2RVmMaj5ZnoeOo2KH3fWkBZlpFuihQqrk4AkkRJlYEVhPQ/PyccflMjLjub+sV2Z+MpKokxavrxlKL5AELNOzUOzd7BkTx3JEQaevjSX9Bgj32zej6iS993EmHWoxcM3Fa5tcTP+heVYD8nF+2zaEAb/okPrkahpPceh2XoRRg0vTu5LVqyJ5EjjMZ/rl1Q3yy0ntGoVOrWIRlRRZ3NTUu+Q60JG7SlzBSoo/NlRXHa/E2pa3AgC3DMmh+I6B6VWByNzYvH4A0zNy2R1cQMuXwCHGI524huo174KcV3B54aEnrDqRXR7l9NpxnoK64WQGAG0uPy8vLiIKXnprCq20inKyN/n7OD20dkkhOk576XlWB0HQz5XFlkJv1CeZTQ4vOytd5C/r5mSekdoX09lo4vr3l3HsvvPDLnmvP4gDQ4vokrAFwiGXH5qlYDFoMGka82du2M4H6wqo7rFzZS8dDJiTMf1XgWCUrug1yanjwij5n/e05QQbiAhvO2eodgw3f8Ub6SgoHDqUATpFBBt1lJca+dvs3fw9KW5DM6IIojE6mIrQUniofO7EW7QEBmworbtg/gesPlDCEuCTnmyMHntCF47Ll/7GUKdzYO/1d68stiKKAg8+u1OnpuU2ybpGuQmfv7gwbgai0FDdryZD9eUtznO4w9S3ewm3qKnzubmzeV7WVNiZXjnGK4a1IkHv97GyiIrGlHglpFZTB2eQaRRS2K4gfvGdiEgSWiOMJs6HDq1ip7JFnbsawmN9UiyEGvWnfTmfwoKCr9vjv8OonB0ghJp0UY+unEwj83L55znlzH+heVUNDpJijCQEWNma3kjgZJlUL0dFtwLVVthz3cw6yI486+gDUPSmlsdbm1/TBf2ScKgVVFqddK/UyR7amxsqWhCqxa5ZVR2m2Onj8riu9bcvIv7JbNjXzN7653tnHAqQZ49NDq93PnpFmYuK2FbZTPlVicfry1jZZEVkI0LL/1c1CYBXKUSTkiMAKLNOmZeM4Azu8Ri0asZ1SWWmdf0V3oFKSj8CVFmSKcArUbEoBJ4+eci9tTY6ZsagTcQpLzeTobeiRh0YhAD6KLSYPta6DYBSleAqxE8NmiuxH/V1zQLYTQ7fcyekcczC3fT4PRx5eA0+qVF8MCX27jxjAwcHj+lVie5KeFIwSDjeyQwNDOaLRVNdEu0EGnUUFLn4NNpQyiutfP4vJ1Em7S8O2UgpVYHZVYnOrWKhyd0p7LRSVyYnlXF1tBryYo1s760od1r3FbRTI9Dlr4aHB62VzazML+GvM4xDM6IatO59UgkRRh4oTVQVa8RT3qfJQUFhdMDRZBOAQ1OL1FGDdFmLT/dM5K1e60MShDp1LAScdY0CPg4b9x/kaL6IERlgs4MQ2bAlo9g8yykqEyaDBmMeWE1jU4fKZEGHj6/O1mxZrSiAAL8+5LefL1pHw9+vZ2sWDNPXZrLnZ9uYWN5EyM6x/L0pb3525ztrN/bwKCMaP52Xjc+XltOz+RwxvVMoM7m4eMbB9Pg8BJh1LKyqJ7L31jDF7cMRa+R3YAAu6pbGJQR1UakAPqnR4b+7fL6eWv5Xl5dUgzAR+vKubBPEo9N7EG44diW3ToKVFVQUPhzoQjSKaDR4SUhTMd5vZO44KUVGHUi8y8LR/zmJjBGQ8oAdJZ4eGccNLXWcta8Cld+AQgIOjPRNBFp0pARY+a2QeFEqZoRA2rKW4KkRZkxagTO7hbHWd3iiDHrmD5rIzv2y3WYZYV1PDIvn0cn9GBfo4sWjx+nN0BsmI5uiWGsKKznyQUFPHNZLn1aG8l9vbGSD6YOknPhxuTw5AJ5b9RPBbXcN7YLRbV2FmyvwqhVc985OcRbDs5+Wtx+3l6xt817MGfrfh4Y15VwZeVNQUHhGFEE6RQQbdLgD8I3myq58+zOTOidSJR1PdyyUp4Nla4CWw04D1kKCwZg3Ztw5v/B/i1QX8in196AyVuPacMrsGsBgd6TkXrcwhUzd3DjiAxSIo3c9ekWnp/chwijlreuG0CYTk15g5OP15azu8ZGpFHLzIXFPHVpbjvRcHkD3PbRZsINal64si/Xvr2Owlo7957ThS9uHkpVs4tuiRZ0ahX3j+3C3WfnYNCKRJu06A6zN0lBQUHhRFEE6RQQYdQRITVyWzcHar0FQWgAUzT43VBVApmjwNMM05bIAlWzE5Y8CUEfqA2w8nmEia8TK7QguGug6/nQ9xrEL64jUR9F/7RRPPrtTt6bMpDbRmeTFmng6iGd+OvX26m1eeiZbOE/l/Rm7d4GFu+qIz3GzMayRj64YRDXv7OOoCTnvXVJCGNnVQv9O0WyeFctha3BpU8v3I1Zp+buszvz864iZm+Ru6d2TQjjoxsHtxMji17DDcMzQkt2ABNzkzDqFNFSUFA4dhRBOsl4/UEiaEHtbgJjOAT98PUtULoMRC2MfghMcaAxwPpXQBcGg6bBhBchEACVCBe9AbvmI5SvgcyRkHkmiBq4+hv0xYt4alwS5U1e5m+r4t6xOfgDEn/5fAuO1n5GO/a18MT8As7tlcDuGhtndI5hd40Nk07k5Sv7UtPiITclgge+2gZApFHDvl8kS9g9fvL3t5AVZwagb2oEL0zu06FRwaAVufGMTAZnRrNwR3XI1HCs9SMFBQUFUJIajsrxJjXYXT5MrkoETwtojBDwQksVGKPg539A8c9w+yZoLIWVz8PeZaCzwPQVEPBBfSGsfQNKFh886YVvQHJfWPUCuJph8M34TfEsqdGDqCc2TMfEV1a2eR5aUcVb1w1gc0UTO/Y1MzUvg3/O28kjF3THrFVz/ssrQhE/4QYNn04bwnkvLm8T+/PZtCGkRcn7oHRKQzkFBYXj4ESSGpR9SCcZvVaUrdumWPDawVYNeossTGP/BWlDoXEv/Pw4DLsd0oeDpwV2fSfPloxRbcXovGcgpT+8eSZs/gh2zYP3J6Bu3MuIVB1GnUhAkjD8YhktNzWcYFBiWFY0E3OT+HlXLf6gRFyYHrNBzd/P706MWUu0ScvtZ2Vj0Kj45KYhDM2Mpn+nSJ6b1IfVJVZWFlvRqhUxUlBQOPUoS3YnGZXXgWCKh4Abvr0DelwIKQPB64DIdHlpTtSCMV7+/oTn5T1IBOXucKIWVGp5qS9tKFiSYfcC+fGHsmUWwvAkyq0RrCyq59+X9OKRb/NpcvpkG/glvfH6g2wob+TrTfvYVN7IW9cOQCMKNLl8uDwBnryolxwN5A/iD0r4AkHO6ByDWlTx4eoyNpU3MjA9kj6p4bS4fJj0aqJNunZpEAoKCgonA0WQTjJC0A2CAJs+lGc3y5+FxU9ATI4sRpEZ8nJdZBoU/QBaM4QlQMog8DtBFw6Dp8Pql6DzGNg9HxL7tr+QNoyAzsKTCwpocftx+4N8ctOQUHBqca2d5CgjqZFGzuuVwL8u7oVZL1Ld7Gbxrjpe+rmozem+v/MMRJXAUz/sbjMeadJitXuZvWUfK4uszLy2P10Tjr/fkYKCgsLRUATpZBMMykt26cNg1ctyHBBAbQF8dClMXw2WJLkWFJkhC9Kkj6BwIfS7Vo4S6jQMuk8EdxPs3wxx3SAqExpam8hpTUhDZ7DfqcYflFCrBMINaiKMGt5evheNqGJi3yRmrS5jTUkDz0/uQ6RBQ1WLG69fYmyPBATA5vFzbs9EIk0aDBqRcKOGvqkRbG4NczVqRe4ZkwOSxLVD07lheCaPz83n2Ul9EAWBersHq8NLRoyJaJP2iMngCgoKCkdDMTUcheM1NUhN5QjowBwJ7hbQmuD1M6GhQD5g+mowRELzfjDHwI6v5PpS36vhyylQsQ4yRsC4/8DOObLLbv69cN7TULcLyeeCbhNYXenluyI3kwamEmnUohKgqtlJekwYM5cW897qUty+IJf2T+EvY3JQiwJ/+XwLywqtCAL87dxupEQaSI824vIFeWv5XmLMWqaNyKSgykZ1i5uB6VFo1QKXv76aBqePC/skcdOITMINGv61YBffbpXt4OEGDQvuGI5BK6JTiyec0u3w+HF4/Igq4ZhjhxQUFH6fnIip4U8nSIIgjANeAETgLUmS/n2k449bkHw+BFc97PlenvWkDoLek6FkGXw7A+7cCnt+gM5joWKtPBtSG8Brk5v1bXwXIjrJUUJOK9iqILYrVG9Diu6MzdyJfW65qZ5OI1JYY8PtC/DFxkqW7K7j7rM7c1a3eDSiCpUAG8saEQWBQa2J4+c8twxfQEIjCq0tyyUuf301No+f167ux9M/7MbjD2LRa9hdY2N01zhyUyJ4eqG8lPfC5D70TongzKeXAKARBV67uj+7qm18v6OKzBgT943tSkqk4bi6rtbbPDy5oIDv86tJjzbxn0t70zXBjEZU9jIpKJyOKP2QjoIgCCLwCjAGqATWC4LwrSRJO0/aRdxWWPYUbHhH/nr3AihZChNfgXP+IScyGKOhJh9SBsibYYNq2ciQ3E/uhySoQaWSl++Kf5b/TswFUyx/+66SuduqAPjpnpHc/+U2nr4slyWtvY3ysmO46NWVof5FIM9gPrlpMOEGLUMyo1leWI8vIC/1bS5vxObxAxBt0lFcd8A8Ie9LWlRQw43DM0LnWranjrTogy0xJvZJZkNpA68vlZcTd+xrYXVxA/PvGE6cRX9Mb5nT4+e/C3fz9eZ9AOysauGKmWv4+S8jibMogqSg8GfhTyVIwCCgSJKkEgBBED4FJgInT5ACPtg8q+1YyeJW4QnIsx6NUR6L7ISkC0NwNiAZIqC+CKF0OTjqAEFuQxHbFZrLYd8GHAmDqbV5Qqd1ev1Em7XU2z1tLneoGAHY3D5ElYDEwZ5FKZEGPP4AcWEHl8ZUgrx/yRs42D8p3qKn0Xmw4d/QrBgMahGzTo3d42d4dgxPfb+rzfXq7B5qbZ5jFiS7x8+igpp2Y8dzDgUFhdOfP1sVOhmoOOTrytaxNgiCME0QhA2CIGyoq6s7zksIoP7FTVRQyX8yz4TwVHlZLnMkkiDwU4WKem0SC8v82BMGExhwI4FRf8U18iGCiEgVawkGfFTmXE+5x8zmctlwEGXSolYJFNXayUkIw6SVZxIl9Q76pUW2ufz4ngno1CqCQYniOhujcmJ5f+og3lm+l6xYM/3SIgCYu62KO8/qHHqcRhR47IIefLKuDFElMGlAKuEGDU8sKOCTm4YwsnMMgaDUoWiY9cf+WUerVpHdmggRessE2eGnoKDw5+FPVUMSBOEyYKwkSTe2fn0NMEiSpNsP95jjriHZ62HrRwg/PnxwsN91MOr/5Lus2giLHkMaNA1Jb2Fzo55dVc307xRFhEGNKKpwePwU1TkorrUztkc8q4utDMmKZunuOmYuKyEj1sSjE3rg9QdpcfvIjDXT4PDy3+934w8GefSCHny8tpyNZY2MyIll8sBURBVsKmuie5IFX0DCGwgAAjpRhUErUmp1UGfzMKBTJJ6AxP4mF2lRRkSVQCAoUWfzsGRPLW8sLSE92sTLV/YlwqhBLaooszq4/PU1oZnVxf2S+fv53Yk8jo6vxbV2Js1cTb3di0qAB8Z35cpBaYTplZYUCgqnI4qp4SgIgjAUeFSSpLGtX/8fgCRJ/zrcY45XkACk5n2yGaFoEaQOhpjOgAo0JvA2gUq+UdcSTjAICBAMBBBFEbVKQJIk7N4ABo2IWqVCVEno1Cps7iDeQACdWsQflNhbbycz2oRarQJJ7tzq9QcJBAOoBBUaUYVRp8YXCNLi8tHk8mHSqXH7AqworKdHkoX0aBMef4AIowZBEPAFgvgDEJQkRAEEQaC2xU2MRU9xrR2NKJAWZcSkVxPRmlXn8QdocHjJ39dCcqSBeIueqOOc3QSDEvUODza3H5NWXhI0K2KkoHDaogjSURAEQQ3s+f/27j1WjrIO4/j3IVgIFgpSQMKtldAiQVrbilwqSIqmNKYVqdoKKKZqBCERL5FIAkQSYrwEI9oUxAY1CoRi8FgrNUoRlJ7aCqVQlKZFLhWwVIGCWOG0P/943y3L6Z6zc3b37M56nk+y6e7O7MzTzcy+M+/M+b3ADODvwGrgYxGxfqDPNNIgmZmNdL7Lro6I6JN0MbCcdNv34sEaIzMza58R1SABRMQyYFmnc5iZ2RuNtLvszMyspNwgmZlZKbhBMjOzUnCDZGZmpTCibvtuhKTngCca/PhYYGsL47RLN+buxszQnbm7MTN0Z+5uzAwp95sj4qChfMgN0jCStGao9+GXQTfm7sbM0J25uzEzdGfubswMjed2l52ZmZWCGyQzMysFN0jD64ZOB2hQN+buxszQnbm7MTN0Z+5uzAwN5vY1JDMzKwWfIZmZWSm4QWoBSTMlPSppo6TLakzfS9KtefoqSePan3K3TPUyf0HSI5LWSfqdpKM6kbO/ermr5psrKSR1/A6lIpklfSR/3+sl/azdGWspsI0cKWmFpAfydjKrEzn7ZVosaYukhweYLknfzf+ndZKmtDtjjUz1Mp+bs66TdJ+kSe3OWEu93FXzvUvSDklz6y40Ivxo4kGqGr4JeBswCngQOK7fPBcBi/LzecCtXZD5DGCf/PzCTmcumjvPty9wD9ALTCt7ZuAY4AHggPz64G74rknXCS7Mz48DHi9B7tOAKcDDA0yfBfwaEHASsKoLMp9StW2cVYbMRXJXbUd3kQpaz623TJ8hNe9EYGNEPBYRrwK3AHP6zTMH+FF+vgSYIUltzNhf3cwRsSIiXskve4HD25yxliLfNcDVwDeA7fpnwb4AAAYySURBVO0MN4AimT8NfD8ingeIiC1tzlhLkdwB7JefjwGebmO+miLiHuBfg8wyB/hxJL3A/pIObU+62upljoj7KtsG5dkXi3zXAJcAtwOFtmk3SM07DHiq6vXm/F7NeSKiD3gROLAt6WorkrnaAtJRZafVzS3pncAREbG0ncEGUeS7ngBMkPRHSb2SZrYt3cCK5L4KOE/SZtIR8CXtidaUoW77ZVOWfbEuSYcBZwOLin5mxI2HNAxqnen0v3WxyDztVDiPpPOAacDpw5qomEFzS9oDuBa4oF2BCijyXe9J6rZ7L+no915Jx0fEC8OcbTBFcs8HboqIb0s6GfhJzr1z+OM1rGz7YmGSziA1SNM7naWg7wBfiYgdRTuE3CA1bzNwRNXrw9m966Iyz+Y8jPoY6p/qDqcimZF0JnA5cHpE/LdN2QZTL/e+wPHA3XkHeCvQI2l2RHRqHPqi20dvRLwG/E3So6QGanV7ItZUJPcCYCZARKyUtDephlkZuhwHUmjbLxtJJwA3AmdFxD87naegacAteV8cC8yS1BcRdwz0AXfZNW81cIyk8ZJGkW5a6Ok3Tw/wifx8LnBX5Ct+HVI3c+76uh6YXZJrGlAnd0S8GBFjI2JcRIwj9bd3sjGCYtvHHaSbSJA0ltSF91hbU+6uSO4ngRkAkt4O7A0819aUQ9cDfDzfbXcS8GJEPNPpUIORdCTwc+D8iNjQ6TxFRcT4qn1xCXDRYI0R+AypaRHRJ+liYDnpjpLFEbFe0teANRHRA/yQ1J2xkXRmNK9ziQtn/iYwGrgtH+E8GRGzOxaawrlLpWDm5cD7JT0C7AC+3Omj4IK5vwj8QNKlpG6vCzp8oIWkm0ldn2Pzta0rgTcBRMQi0rWuWcBG4BXgk51J+roCma8gXXNemPfFvihBwdUCuYe+zA5vP2ZmZoC77MzMrCTcIJmZWSm4QTIzs1Jwg2RmZqXgBsnMzHYpWjQ1z3utpLX5sUFSU3/M7QbJrMUkXZ6rdq/LO+q7W7Tcl/O/42r9WOT3/5PX+WCuDD2xFeu2EeUm8h881xMRl0bE5IiYDFxH+nuphrlBMmuhXELnA8CUiDgBOJM31k4bbpvyD8QkUkHfr7Zx3fZ/oFbRVElHS7pT0p8l3Svp2BofnQ/c3My63SCZtdahwNZKqaWI2BoRTwNIelzSNZJWSlojaYqk5ZI2Sfpsnme00vhT90t6SFKtauZF7Qc8n5c7Lv+Q3J8fp+T395C0MJ/RLZW0rDJujaSv6/Uxsb7VRA7rfjcAl0TEVOBLwMLqiUrjpY0nDTXRMFdqMGut3wBXSNoA/JY0jtTvq6Y/FREnS7qW1DVyKqnkznpSVeTtwNkRsS2XEeqV1DOECghHS1pLquu3D1DpLtwCvC8itks6hnQkOw34EDAOeAdwMPAXYLGkt5AqNR8bESFp/0a+DOt+kkaTxmSqVG0B2KvfbPOAJRGxo5l1uUEya6GIeFnSVOA9pPp0t0q6LCJuyrNUyhs9BIyOiJeAlyRtzz/6/waukXQasJM0NMIhwLMFI2zK/flI+ijpyHYmqaTL9yRNJpUnmpDnnw7clit0PytpRX5/G6lxvFHSr4CyDOdh7bcH8EJluxrAPOBzrViRmbVQROyIiLsj4krgYuCcqsmVquk7q55XXu8JnAscBEzNPwD/IJ1BNaKHNKonwKV5WZNIZ0aj8vs1xwXI43adSBpc7YPAnQ1msC4XEdtIVeg/DLuGgd81jHq+ceYAYGWz63KDZNZCkibmLrGKycATQ1jEGGBLRLymNP7NUU3EmU4ahryy3GfymdD5pIKpAH8AzsnXkg4hFcusdNOMiYhlwOfz/8NGgFw0dSUwUdJmSQtIB0oLJD1I6l6uvrY5H7ilFYV13WVn1lqjgety91sfqar0Z4bw+Z8Cv5S0BlgL/HWI669cQxLwKvCp/P5C4PZ8lLuC1DUI6QxoBvAwsAFYRRrReF/gF0pjHIl0hmUjQETMH2BSzVvBI+KqVq3b1b7NRjhJo/O1rwOBPwGnRkTRa1ZmLeMzJDNbms/oRgFXuzGyTvEZkpmZlYJvajAzs1Jwg2RmZqXgBsnMzErBDZKZmZWCGyQzMysFN0hmZlYK/wOMdV/JZq3Z7QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x='Small Bags',y='Large Bags',hue='Type',data =avocado)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Small_Bags and Large_Bags are highly cor-related and if small bags increases Large bags going to increase.And Organic type avocados demand is too low comapare to Conventional in terms of Large Bags and Small Bags."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How Average price is distributed over the months for Conventional and Organic Types?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d73f268c48>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x=\"Month\", y=\"AveragePrice\", hue='Type', data=avocado)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From here it's clear that for Organic avocados average price is too good and average price for organic type is in the month of 8-12 in these months price is distributed very highly.And for conventional avocados average price is too low compare to organic and price is distributed in 10-12 months in between thse two months."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d73f2cc088>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#to check Cor-relation\n",
    "corr_hmap=avocado.corr()\n",
    "plt.figure(figsize=(8,7))\n",
    "sns.heatmap(corr_hmap,annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Average_price is highly Negatively cor-relate with all other attributes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Average_price with Type attribute is Highly Positively cor-relate each other."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's better to drop Average_price"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Engineering For Model Building"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I will drop the Date Feature as well because I already have 3 other columns for the Year, Month and Day.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "avocado.drop('Date',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#cleaning data from string to integer format\n",
    "le=LabelEncoder()\n",
    "list1=['Type','Region']\n",
    "for val in list1:\n",
    "    avocado[val]=le.fit_transform(avocado[val].astype(str))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>AveragePrice</th>\n",
       "      <th>Total Volume</th>\n",
       "      <th>4046</th>\n",
       "      <th>4225</th>\n",
       "      <th>4770</th>\n",
       "      <th>Total Bags</th>\n",
       "      <th>Small Bags</th>\n",
       "      <th>Large Bags</th>\n",
       "      <th>XLarge Bags</th>\n",
       "      <th>Type</th>\n",
       "      <th>Year</th>\n",
       "      <th>Region</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.33</td>\n",
       "      <td>64236.62</td>\n",
       "      <td>1036.74</td>\n",
       "      <td>54454.85</td>\n",
       "      <td>48.16</td>\n",
       "      <td>8696.87</td>\n",
       "      <td>8603.62</td>\n",
       "      <td>93.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.35</td>\n",
       "      <td>54876.98</td>\n",
       "      <td>674.28</td>\n",
       "      <td>44638.81</td>\n",
       "      <td>58.33</td>\n",
       "      <td>9505.56</td>\n",
       "      <td>9408.07</td>\n",
       "      <td>97.49</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.93</td>\n",
       "      <td>118220.22</td>\n",
       "      <td>794.70</td>\n",
       "      <td>109149.67</td>\n",
       "      <td>130.50</td>\n",
       "      <td>8145.35</td>\n",
       "      <td>8042.21</td>\n",
       "      <td>103.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.08</td>\n",
       "      <td>78992.15</td>\n",
       "      <td>1132.00</td>\n",
       "      <td>71976.41</td>\n",
       "      <td>72.58</td>\n",
       "      <td>5811.16</td>\n",
       "      <td>5677.40</td>\n",
       "      <td>133.76</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.28</td>\n",
       "      <td>51039.60</td>\n",
       "      <td>941.48</td>\n",
       "      <td>43838.39</td>\n",
       "      <td>75.78</td>\n",
       "      <td>6183.95</td>\n",
       "      <td>5986.26</td>\n",
       "      <td>197.69</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   AveragePrice  Total Volume     4046       4225    4770  Total Bags  \\\n",
       "0          1.33      64236.62  1036.74   54454.85   48.16     8696.87   \n",
       "1          1.35      54876.98   674.28   44638.81   58.33     9505.56   \n",
       "2          0.93     118220.22   794.70  109149.67  130.50     8145.35   \n",
       "3          1.08      78992.15  1132.00   71976.41   72.58     5811.16   \n",
       "4          1.28      51039.60   941.48   43838.39   75.78     6183.95   \n",
       "\n",
       "   Small Bags  Large Bags  XLarge Bags  Type  Year  Region  Month  Day  \n",
       "0     8603.62       93.25          0.0     0  2015       0     12   27  \n",
       "1     9408.07       97.49          0.0     0  2015       0     12   20  \n",
       "2     8042.21      103.14          0.0     0  2015       0     12   13  \n",
       "3     5677.40      133.76          0.0     0  2015       0     12    6  \n",
       "4     5986.26      197.69          0.0     0  2015       0     11   29  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avocado.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now data is looking good ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Identify and Removing Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d7401b1688>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "avocado['Large Bags'].plot.box()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d73ff76f08>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "avocado['Small Bags'].plot.box()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d73ffdde88>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYkAAAD4CAYAAAAZ1BptAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAaoklEQVR4nO3df3DV9b3n8ec7JyGBpBDAyDAkindvdvfY3F5vm6vZkbUXWwXdncI4asXumltimXFtFrfe5drN7jhthxnqunWFaZ0ykhFmuvGi9xaxK3IZSXc3O201tlawKTWIkgy0xiFBiQTy471/nE/Yk5hvcmL95psDr8dM5pzv+/v5ns8HB/Ly8/18z/dr7o6IiMhECpIegIiIzF4KCRERiaSQEBGRSAoJERGJpJAQEZFIhUkP4JN22WWX+fLly5MehohIXnn11Vffc/eK8fWLLiSWL19Oe3t70sMQEckrZvbORHWdbhIRkUgKCRERiaSQEBGRSAoJERGJpJAQEZFICgmRmLW0tFBTU0MqlaKmpoaWlpakhySSs4vuEliR2aSlpYWmpiZ27NjBihUraGtro6GhAYB169YlPDqRqdnFdqvw2tpa1/ckZLaoqalh27ZtrFy58kKttbWVxsZGDh8+nODIRMYys1fdvfYjdYWESHxSqRQDAwMUFRVdqA0ODlJSUsLw8HCCIxMZKyoktCYhEqN0Ok1bW9uYWltbG+l0OqERiUyPQkIkRk1NTTQ0NNDa2srg4CCtra00NDTQ1NSU9NBEcqKFa5EYjS5ONzY20tHRQTqdZvPmzVq0lryhNQkREdGahIiITJ9CQkREIikkREQkkkJCREQiKSRERCSSQkJERCIpJEREJJJCQkREIikkREQkkkJCREQiKSRERCRSTiFhZm+b2SEze83M2kNtkZkdMLM3w+vCUDcz22pmnWb2upl9Nutz6kP7N82sPqv+ufD5neFYm6wPERGZGdOZSax092uybgD1EPCSu1cDL4VtgFuA6vCzAXgCMr/wgYeB64BrgYezfuk/EdqOHrd6ij5ERGQG/DGnm9YAO8P7ncDarPouz/g5UG5mS4FVwAF3P+XuvcABYHXYN9/df+aZW9LuGvdZE/UhIiIzINeQcOAfzexVM9sQakvc/SRAeL081JcBXVnHdofaZPXuCeqT9TGGmW0ws3Yza+/p6cnxjyQiIlPJ9aFD17v7CTO7HDhgZr+dpK1NUPOPUc+Zu28HtkPmeRLTOVZERKLlNJNw9xPh9V3gx2TWFP4QThURXt8NzbuBqqzDK4ETU9QrJ6gzSR8iIjIDpgwJMys1s0+NvgduBg4De4HRK5TqgefC+73APeEqpzrgdDhVtB+42cwWhgXrm4H9Yd8HZlYXrmq6Z9xnTdSHiIjMgFxONy0BfhyuSi0E/oe7v2hmrwC7zawBOA7cEdq/ANwKdAIfAl8FcPdTZvYd4JXQ7tvufiq8vw94CpgL7As/AFsi+hARkRmgZ1yLiIiecS0iItOnkBARkUgKCRERiaSQEBGRSAoJERGJpJAQEZFICgkREYmkkBCJWUtLCzU1NaRSKWpqamhpaUl6SCI5y/UGfyLyMbS0tNDU1MSOHTtYsWIFbW1tNDQ0ALBu3bqERycyNX3jWiRGNTU1bNu2jZUrV16otba20tjYyOHDhxMcmchYUd+4VkiIxCiVSjEwMEBRUdGF2uDgICUlJQwPDyc4MpGxdFsOkQSk02na2trG1Nra2kin0wmNSGR6FBIiMWpqaqKhoYHW1lYGBwdpbW2loaGBpqampIcmkhMtXIvEaHRxurGxkY6ODtLpNJs3b9aiteQNrUmIiIjWJEREZPoUEiIiEkkhISIikRQSIiISSSEhIiKRFBIiIhJJISEiIpEUEiIiEkkhISIikRQSIiISSSEhIiKRcg4JM0uZ2a/M7Cdh+yoz+4WZvWlmf2dmc0K9OGx3hv3Lsz7jm6F+xMxWZdVXh1qnmT2UVZ+wDxERmRnTmUlsBDqytr8LPObu1UAv0BDqDUCvu/8p8Fhoh5ldDdwFfBpYDfwgBE8K+D5wC3A1sC60nawPERGZATmFhJlVAv8KeDJsG3Aj8GxoshNYG96vCduE/V8I7dcAT7v7OXc/BnQC14afTnd/y93PA08Da6boQ0REZkCuM4n/DmwCRsL2YqDP3YfCdjewLLxfBnQBhP2nQ/sL9XHHRNUn60NERGbAlCFhZv8aeNfdX80uT9DUp9j3SdUnGuMGM2s3s/aenp6JmogkpqWlhZqaGlKpFDU1NbS0tCQ9JJGc5TKTuB74kpm9TeZU0I1kZhblZjb6ZLtK4ER43w1UAYT9C4BT2fVxx0TV35ukjzHcfbu717p7bUVFRQ5/JJGZ0dLSwsaNG+nv78fd6e/vZ+PGjQoKyRtThoS7f9PdK919OZmF54Pu/hWgFbg9NKsHngvv94Ztwv6Dnnn83V7grnD101VANfAy8ApQHa5kmhP62BuOiepDJC9s2rSJVCpFc3Mz586do7m5mVQqxaZNm5IemkhO/pjvSfwt8A0z6ySzfrAj1HcAi0P9G8BDAO7+BrAb+A3wInC/uw+HNYevA/vJXD21O7SdrA+RvNDd3c2uXbtYuXIlRUVFrFy5kl27dtHd3Z300ERyUjh1k//P3X8K/DS8f4vMlUnj2wwAd0QcvxnYPEH9BeCFCeoT9iEiIjNjWiEhItNTWVnJnXfeSXl5OcePH+eKK66gr6+PysrKpIcmkhPdlkMkRmvXruX06dN0dXUxMjJCV1cXp0+fZu1afeVH8oNCQiRGe/bsYf78+VRVVWFmVFVVMX/+fPbs2ZP00ERyopAQiVF3dzfPPPMMx44dY2RkhGPHjvHMM89o4VryhkJCREQiKSREYlRZWUl9fT2tra0MDg7S2tpKfX29Fq4lbygkRGL0yCOPMDQ0xPr16ykpKWH9+vUMDQ3xyCOPJD00kZwoJERitG7dOh5//HFKS0sBKC0t5fHHH2fdunUJj0wkN5a5+8XFo7a21tvb25MehohIXjGzV929dnxdMwkREYmkkBCJmW4VLvlMt+UQiVFLSwtNTU3s2LGDFStW0NbWRkND5im8WpeQfKA1CZEY1dTUsG3bNlauXHmh1traSmNjI4cPH05wZCJjRa1JKCREYpRKpRgYGKCoqOhCbXBwkJKSEoaHhxMcmchYWrgWSUA6naatrW1Mra2tjXQ6ndCIRKZHISESo6amJhoaGsZ847qhoYGmpqakhyaSEy1ci8RodHG6sbGRjo4O0uk0mzdv1qK15A3NJEREJJJCQiRGLS0tbNy4kf7+fgD6+/vZuHGjvisheUMhIRKjTZs2UVhYSHNzMwMDAzQ3N1NYWMimTZuSHppIThQSIjHq7u6mvr6exsZGSkpKaGxspL6+Xg8dkryh70mIxMjMmDt3LkNDQwwODlJUVERhYSFnz57lYvu3J/lN35MQSYCZcfbsWe699176+vq49957OXv2LGaW9NBEcqKZhEiMzIzi4mJGRkYuzCQKCgo4d+6cZhIyq2gmIZKQgoKCSbdFZjP9bRWJ2cDAAIsWLcLMWLRoEQMDA0kPSSRnCgmRmLk7PT09Y15F8sWUIWFmJWb2spn92szeMLNvhfpVZvYLM3vTzP7OzOaEenHY7gz7l2d91jdD/YiZrcqqrw61TjN7KKs+YR8i+aS4uJhUKgVk7gpbXFyc8IhEcpfLTOIccKO7/zlwDbDazOqA7wKPuXs10As0hPYNQK+7/ynwWGiHmV0N3AV8GlgN/MDMUmaWAr4P3AJcDawLbZmkD5G8MW/ePPbv38/58+fZv38/8+bNS3pIIjmbMiQ840zYLAo/DtwIPBvqO4G14f2asE3Y/wXLXO+3Bnja3c+5+zGgE7g2/HS6+1vufh54GlgTjonqQyRvDAwMsGrVKubMmcOqVau0JiF5Jac1ifB//K8B7wIHgKNAn7sPhSbdwLLwfhnQBRD2nwYWZ9fHHRNVXzxJH+PHt8HM2s2svaenJ5c/ksiMGF2oXrx4MQUFBSxevPjCQrZIPsgpJNx92N2vASrJ/J//RE9MGV2Nm+hbQv4J1ica33Z3r3X32oqKiomaiCRi3rx5LFiwgJKSEtydkpISFixYoFNOkjemdXWTu/cBPwXqgHIzG30eRSVwIrzvBqoAwv4FwKns+rhjourvTdKHSF44ceIEW7dupbS0FDOjtLSUrVu3cuKE/ipLfsjl6qYKMysP7+cCXwQ6gFbg9tCsHnguvN8btgn7D3rmmr+9wF3h6qergGrgZeAVoDpcyTSHzOL23nBMVB8ieSGdTlNZWcnhw4cZHh7m8OHDVFZW6vGlkjdymUksBVrN7HUyv9APuPtPgL8FvmFmnWTWD3aE9juAxaH+DeAhAHd/A9gN/AZ4Ebg/nMYaAr4O7CcTPrtDWybpQyQv6PGlku907yaRmK1atYoDBw7g7pgZN910E/v37096WCJj6N5NIglobGzk4MGDPProo/T39/Poo49y8OBBGhsbkx6aSE40kxCJUUlJCbfffjuvvfYaHR0dpNNprrnmGp599ll9X0JmlaiZROFEjUXkk3Hu3Dn27t3LwMAAIyMj/O53v+P48eOcO3cu6aGJ5ESnm0RidubMmTF3gT1z5szUB4nMEgoJkZi5O7fddhu9vb3cdtttugus5BWFhEjM6urqaG5upry8nObmZurq6pIekkjOFBIiMTty5Aj79u3j/Pnz7Nu3jyNHjiQ9JJGcaeFaJEaLFi2it7eXm266ieHhYVKpFCMjI7rBn+QNzSREYnT33Xfj7gwPDwMwPDyMu3P33XcnPDKR3CgkRGK0Z88eysvLWb58OQUFBSxfvpzy8nL27NmT9NBEcqKQEIlRd3c3u3fv5tixYwwPD3Ps2DF2795Nd3d30kMTyYlCQkREImnhWiRGlZWV3HHHHSxcuJB33nmHK6+8kt7eXiorK5MemkhONJMQidHatWt5//336erqwt3p6uri/fffZ+1aPa5d8oNCQiRGe/bsYcGCBVRVVVFQUEBVVRULFizQwrXkDYWESIy6u7u54YYbOHnyJCMjI5w8eZIbbrhBC9eSNxQSIjF7/vnnKS8vx8woLy/n+eefT3pIIjlTSIjEbPSJdKM/usGf5BNd3SQyA37/+9+PeRXJF5pJiMyAVCo15lUkXygkRGbAhg0b6OvrY8OGDUkPRWRa9IxrkRiZGalU6sIN/oAL2xfbvz3Jb1HPuNZMQiRmo7cIBz4SGCKznUJCJEZmBsD8+fMpKChg/vz5Y+ois51CQiRG7s6cOXPo7e1lZGSE3t5e5syZo1NNkjcUEiIxKywspKioCICioiIKC3XlueQPhYRIjAoKChgYGGDLli309/ezZcsWBgYGKCjQPz3JD1P+TTWzKjNrNbMOM3vDzDaG+iIzO2Bmb4bXhaFuZrbVzDrN7HUz+2zWZ9WH9m+aWX1W/XNmdigcs9XCCduoPkTyxcjICKlUigcffJDS0lIefPDBC8+5FskHufzvzBDwoLungTrgfjO7GngIeMndq4GXwjbALUB1+NkAPAGZX/jAw8B1wLXAw1m/9J8IbUePWx3qUX2I5I3BwcFJt0VmsylDwt1Puvsvw/sPgA5gGbAG2Bma7QRGb5C/BtjlGT8Hys1sKbAKOODup9y9FzgArA775rv7zzyzmrdr3GdN1IdIXrnvvvvo6+vjvvvuS3ooItMyrROjZrYc+AvgF8ASdz8JmSABLg/NlgFdWYd1h9pk9e4J6kzSx/hxbTCzdjNr7+npmc4fSSR2xcXFPPnkk5SXl/Pkk09SXFyc9JBEcpZzSJhZGfD3wAPu/v5kTSeo+ceo58zdt7t7rbvXVlRUTOdQkdgVFhaybNkyCgoKWLZsma5ukrySU0iYWRGZgPiRu/9DKP8hnCoivL4b6t1AVdbhlcCJKeqVE9Qn60Mkb/T399PV1cXIyAhdXV309/cnPSSRnOVydZMBO4AOd/9e1q69wOgVSvXAc1n1e8JVTnXA6XCqaD9ws5ktDAvWNwP7w74PzKwu9HXPuM+aqA+RvDJ6NZOuapJ8k8u893rg3wKHzOy1UPtPwBZgt5k1AMeBO8K+F4BbgU7gQ+CrAO5+ysy+A7wS2n3b3U+F9/cBTwFzgX3hh0n6EMkLZsa8efMuzB7cndLSUj788MOERyaSG90FViRGo/do+tKXvsSOHTtoaGhg7969ALo1h8wqugusSEKqq6s5evQoS5Ys4ejRo1RXVyc9JJGc6TILkZgdPXqUiooK3J333nsPXaYt+UQzCZGYpVIpTp06hbtz6tQpPcJU8opCQiRGpaWlDA4OUlZWhplRVlbG4OAgpaWlSQ9NJCc63SQSow8//JCioiJ6e3sB6O3tpaioSFc3Sd7QTEIkRqlUirKyMg4ePMj58+c5ePAgZWVlOuUkeUMzCZEYDQ0NcebMGW688cYLtaKiIoaGhhIclUjuNJMQiZluFS75TCEhMgN0q3DJV/rGtUiMzOzC860HBwfHvL/Y/u1Jfov6xrXWJERiNjIywvDwMJAJBy1aSz7R6SaRmA0PD1NSUgJASUnJhcAQyQcKCZEZMDAwMOZVJF8oJERiVldXd+GRpcXFxdTV1SU8IpHcKSREYnbo0CGWLl2KmbF06VIOHTqU9JBEcqaFa5EYlZaW0t/ff+GhQ2+//faFukg+0ExCJEZR36zWN64lXygkRGJ07tw5AJYsWYKZsWTJkjF1kdlOISESs3Q6TV9fH+5OX18f6XQ66SGJ5EwhIRKzjo4O1q9fT19fH+vXr6ejoyPpIYnkTLflEImRmUXuu9j+7Ul+i7oth2YSIiISSSEhErO5c+dOui0ymykkRGJ29uxZysrKACgrK+Ps2bMJj0gkdwoJkRlw5syZMa8i+UIhISIikRQSIiISacqQMLNmM3vXzA5n1RaZ2QEzezO8Lgx1M7OtZtZpZq+b2WezjqkP7d80s/qs+ufM7FA4ZquFawaj+hDJR9nPkxDJJ7nMJJ4CVo+rPQS85O7VwEthG+AWoDr8bACegMwvfOBh4DrgWuDhrF/6T4S2o8etnqIPkbwzeq8m3bNJ8s2UIeHu/xs4Na68BtgZ3u8E1mbVd3nGz4FyM1sKrAIOuPspd+8FDgCrw7757v4zz3yzaNe4z5qoD5G8o5CQfPVx1ySWuPtJgPB6eagvA7qy2nWH2mT17gnqk/XxEWa2wczazay9p6fnY/6RRERkvE964XqiexD4x6hPi7tvd/dad6+tqKiY7uEiIhLh44bEH8KpIsLru6HeDVRltasETkxRr5ygPlkfIiIyQz5uSOwFRq9Qqgeey6rfE65yqgNOh1NF+4GbzWxhWLC+Gdgf9n1gZnXhqqZ7xn3WRH2IiMgMmfLxpWbWAvwVcJmZdZO5SmkLsNvMGoDjwB2h+QvArUAn8CHwVQB3P2Vm3wFeCe2+7e6ji+H3kbmCai6wL/wwSR8iIjJDdKtwkRjpVuGSL3SrcBERmTaFhIiIRFJIiIhIJIWEiIhEUkiIiEgkhYSIiERSSIiISCSFhIiIRFJIiIhIJIWEiIhEUkiIiEgkhYSIiERSSIiISCSFhIiIRFJIiIhIJIWEiIhEUkiIiEgkhYSIiERSSIiISCSFhIiIRFJIiIhIJIWEiIhEUkiIiEgkhYSIiERSSIiISCSFhIiIRFJIiIhIpFkfEma22syOmFmnmT2U9HhERC4lszokzCwFfB+4BbgaWGdmVyc7KhGRS8esDgngWqDT3d9y9/PA08CahMckInLJKEx6AFNYBnRlbXcD141vZGYbgA0AV1xxxcyMTC4qf7bzz2L53Jqnama8z0P1h2L5XLk0zfaQsAlq/pGC+3ZgO0Btbe1H9otMJa5frGYT/RXOcNdfVZn9Zvvppm6gKmu7EjiR0FhERC45sz0kXgGqzewqM5sD3AXsTXhMIjmLmi1oFiH5YlafbnL3ITP7OrAfSAHN7v5GwsMSmRYFguSzWR0SAO7+AvBC0uMQEbkUzfbTTSIikiCFhIiIRFJIiIhIJIWEiIhEsovtygsz6wHeSXocIhO4DHgv6UGIRLjS3SvGFy+6kBCZrcys3d1rkx6HyHTodJOIiERSSIiISCSFhMjM2Z70AESmS2sSIiISSTMJERGJpJAQEZFICgm5pJhZlZkdM7NFYXth2L7SzJab2eEEx/ZTMztiZq+ZWUd44qJIohQScklx9y7gCWBLKG0Btrv7H/UFTDP7pO6o/BV3vwa4HvhueI6KSGIUEnIpegyoM7MHgBXAf5ussZl9zcxeMbNfm9nfm9m8UH/KzL5nZq1kfqFXmNkBM/ulmf3QzN4xs8tC239jZi+HWcIPzSw1xRjLgH5gOBz/hJm1m9kbZvatrLHdama/NbM2M9tqZj8J9c+Hvl4zs1+Z2ac+5n8rucQpJOSS4+6DwH8kExYPuPv5KQ75B3f/S3f/c6ADaMja90+BL7r7g8DDwEF3/yzwY+AKADNLA18Grg+zhGHgKxF9/cjMXgeOAN9x9+FQbwrf1v4M8Hkz+4yZlQA/BG5x9xVA9i0V/ga4P/T3L4GzU/wZRSakkJBL1S3ASaAmh7Y1ZvZ/zOwQmV/un87a90zWL/IVwNMA7v4i0BvqXwA+B7xiZq+F7T+J6Osr7v4ZMgHzN2Z2ZajfaWa/BH4V+r8a+OfAW+5+LLRpyfqc/wt8z8z+PVDu7kM5/DlFPmLWP5lO5JNmZtcANwF1QJuZPe3uJyc55Clgrbv/2sz+GvirrH392R8d1SWw092/mesY3b0nhMJ1ZlZAZmbwl+7ea2ZPASWT9Ie7bzGz/wncCvzczL7o7r/NtX+RUZpJyCXFzIzMwvUD7n4c+K/Ao1Mc9ingpJkVEX2aCKANuDP0czOwMNRfAm43s8vDvkVZM4Socc4D/gI4CswnE0anzWwJmVkQwG+BPzGz5WH7y1nH/xN3P+Tu3wXaycw6RKZNMwm51HwNOO7uB8L2D4C/NrPPk7nF/D8zs+6s9v8B+C/AL8L+Q2RCYyLfAlrM7MvA/yJzOusDd3/PzP4z8I9hVjAI3M/Et7T/kZmdBYqBp9z9VQAz+xXwBvAWmVNJuPtZM/t3wItm9h7wctbnPGBmK8msf/wG2Jfbfx6RsXRbDpFPiJkVA8PuPmRm/wJ4Iiwcx9lnmbufCTOk7wNvuvtjcfYplxbNJEQ+OVcAu8Ns4TyZWUvcvmZm9cAcMovaP5yBPuUSopmEiIhE0sK1iIhEUkiIiEgkhYSIiERSSIiISCSFhIiIRPp/VCR5FjNurWUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "avocado['XLarge Bags'].plot.box()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AveragePrice       AxesSubplot(0.125,0.536818;0.0824468x0.343182)\n",
       "Total Volume    AxesSubplot(0.223936,0.536818;0.0824468x0.343182)\n",
       "4046            AxesSubplot(0.322872,0.536818;0.0824468x0.343182)\n",
       "4225            AxesSubplot(0.421809,0.536818;0.0824468x0.343182)\n",
       "4770            AxesSubplot(0.520745,0.536818;0.0824468x0.343182)\n",
       "Total Bags      AxesSubplot(0.619681,0.536818;0.0824468x0.343182)\n",
       "Small Bags      AxesSubplot(0.718617,0.536818;0.0824468x0.343182)\n",
       "Large Bags      AxesSubplot(0.817553,0.536818;0.0824468x0.343182)\n",
       "XLarge Bags           AxesSubplot(0.125,0.125;0.0824468x0.343182)\n",
       "Type               AxesSubplot(0.223936,0.125;0.0824468x0.343182)\n",
       "Year               AxesSubplot(0.322872,0.125;0.0824468x0.343182)\n",
       "Region             AxesSubplot(0.421809,0.125;0.0824468x0.343182)\n",
       "Month              AxesSubplot(0.520745,0.125;0.0824468x0.343182)\n",
       "Day                AxesSubplot(0.619681,0.125;0.0824468x0.343182)\n",
       "dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "avocado.plot(kind='box',subplots=True,layout=(2,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see all above cases there alot of outliers present in this data set and the next step is to removing outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.18868864, 0.22771641, 0.23081597, ..., 1.7002522 , 1.64763162,\n",
       "        1.29185687],\n",
       "       [0.13901962, 0.23042664, 0.23110251, ..., 1.7002522 , 1.64763162,\n",
       "        0.49423066],\n",
       "       [1.18206895, 0.21208462, 0.23100731, ..., 1.7002522 , 1.64763162,\n",
       "        0.30339555],\n",
       "       ...,\n",
       "       [1.15237477, 0.24233073, 0.2306933 , ..., 1.70081131, 1.46495942,\n",
       "        0.60817726],\n",
       "       [1.30138182, 0.24162464, 0.2304279 , ..., 1.70081131, 1.46495942,\n",
       "        0.18944895],\n",
       "       [0.53151208, 0.24125273, 0.22934712, ..., 1.70081131, 1.46495942,\n",
       "        0.98707516]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Removing Outliers\n",
    "from scipy.stats import zscore\n",
    "z=np.abs(zscore(avocado))\n",
    "z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  346,   359,   780, ..., 17304, 17402, 17428], dtype=int64),\n",
       " array([2, 2, 8, ..., 0, 0, 0], dtype=int64))"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "threshold=3\n",
    "np.where((z>3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.234544829955079"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z[346][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5012483657070911"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z[359][6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "avocado_new=avocado[(z<3).all(axis=1)]#removing outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17651, 14)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avocado_new.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18249, 14)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avocado.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "467 rows had been removed as an outliers"
   ]
  },
  {
   "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>Total Volume</th>\n",
       "      <th>4046</th>\n",
       "      <th>4225</th>\n",
       "      <th>4770</th>\n",
       "      <th>Total Bags</th>\n",
       "      <th>Small Bags</th>\n",
       "      <th>Large Bags</th>\n",
       "      <th>XLarge Bags</th>\n",
       "      <th>Type</th>\n",
       "      <th>Year</th>\n",
       "      <th>Region</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>64236.62</td>\n",
       "      <td>1036.74</td>\n",
       "      <td>54454.85</td>\n",
       "      <td>48.16</td>\n",
       "      <td>8696.87</td>\n",
       "      <td>8603.62</td>\n",
       "      <td>93.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>54876.98</td>\n",
       "      <td>674.28</td>\n",
       "      <td>44638.81</td>\n",
       "      <td>58.33</td>\n",
       "      <td>9505.56</td>\n",
       "      <td>9408.07</td>\n",
       "      <td>97.49</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>118220.22</td>\n",
       "      <td>794.70</td>\n",
       "      <td>109149.67</td>\n",
       "      <td>130.50</td>\n",
       "      <td>8145.35</td>\n",
       "      <td>8042.21</td>\n",
       "      <td>103.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>78992.15</td>\n",
       "      <td>1132.00</td>\n",
       "      <td>71976.41</td>\n",
       "      <td>72.58</td>\n",
       "      <td>5811.16</td>\n",
       "      <td>5677.40</td>\n",
       "      <td>133.76</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>51039.60</td>\n",
       "      <td>941.48</td>\n",
       "      <td>43838.39</td>\n",
       "      <td>75.78</td>\n",
       "      <td>6183.95</td>\n",
       "      <td>5986.26</td>\n",
       "      <td>197.69</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total Volume     4046       4225    4770  Total Bags  Small Bags  \\\n",
       "0      64236.62  1036.74   54454.85   48.16     8696.87     8603.62   \n",
       "1      54876.98   674.28   44638.81   58.33     9505.56     9408.07   \n",
       "2     118220.22   794.70  109149.67  130.50     8145.35     8042.21   \n",
       "3      78992.15  1132.00   71976.41   72.58     5811.16     5677.40   \n",
       "4      51039.60   941.48   43838.39   75.78     6183.95     5986.26   \n",
       "\n",
       "   Large Bags  XLarge Bags  Type  Year  Region  Month  Day  \n",
       "0       93.25          0.0     0  2015       0     12   27  \n",
       "1       97.49          0.0     0  2015       0     12   20  \n",
       "2      103.14          0.0     0  2015       0     12   13  \n",
       "3      133.76          0.0     0  2015       0     12    6  \n",
       "4      197.69          0.0     0  2015       0     11   29  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=avocado_new.drop('AveragePrice',axis=1)\n",
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=avocado_new['AveragePrice']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.33\n",
       "1    1.35\n",
       "2    0.93\n",
       "3    1.08\n",
       "4    1.28\n",
       "Name: AveragePrice, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Checking Distribution Of Skewness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d7413f78c8>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(avocado_new['Small Bags'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "collist=x.columns\n",
    "ncol=12\n",
    "nrows=10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 13 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,20))\n",
    "for i in range(0,len(collist)):\n",
    "    plt.subplot(nrows,ncol,i+1)\n",
    "    sns.distplot(x[collist[i]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Total Volume    3.767138\n",
       "4046            4.909848\n",
       "4225            4.455745\n",
       "4770            5.117170\n",
       "Total Bags      4.066771\n",
       "Small Bags      4.222706\n",
       "Large Bags      5.053434\n",
       "XLarge Bags     6.135607\n",
       "Type           -0.037741\n",
       "Year            0.229976\n",
       "Region          0.012798\n",
       "Month           0.107408\n",
       "Day             0.011778\n",
       "dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.skew()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "skewness of each and every attribute is morethan .55 so have to remove those skewness by applying techniques"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in x.columns:\n",
    "    if x.skew().loc[col]>0.55:\n",
    "        x[col]=np.log1p(x[col])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Total Volume   -0.049950\n",
       "4046           -0.420273\n",
       "4225           -0.604619\n",
       "4770            0.068532\n",
       "Total Bags     -0.384493\n",
       "Small Bags     -0.788988\n",
       "Large Bags     -0.610874\n",
       "XLarge Bags     1.216238\n",
       "Type           -0.037741\n",
       "Year            0.229976\n",
       "Region          0.012798\n",
       "Month           0.107408\n",
       "Day             0.011778\n",
       "dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.skew()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Still Xlerge Bags has skewness it's better to remove this column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "x.drop('XLarge Bags',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>Total Volume</th>\n",
       "      <th>4046</th>\n",
       "      <th>4225</th>\n",
       "      <th>4770</th>\n",
       "      <th>Total Bags</th>\n",
       "      <th>Small Bags</th>\n",
       "      <th>Large Bags</th>\n",
       "      <th>Type</th>\n",
       "      <th>Year</th>\n",
       "      <th>Region</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>11.070344</td>\n",
       "      <td>6.944801</td>\n",
       "      <td>10.905146</td>\n",
       "      <td>3.895080</td>\n",
       "      <td>9.070833</td>\n",
       "      <td>9.060055</td>\n",
       "      <td>4.545951</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>10.912867</td>\n",
       "      <td>6.515127</td>\n",
       "      <td>10.706381</td>\n",
       "      <td>4.083115</td>\n",
       "      <td>9.159737</td>\n",
       "      <td>9.149429</td>\n",
       "      <td>4.589955</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>11.680313</td>\n",
       "      <td>6.679222</td>\n",
       "      <td>11.600485</td>\n",
       "      <td>4.879007</td>\n",
       "      <td>9.005325</td>\n",
       "      <td>8.992584</td>\n",
       "      <td>4.645736</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>11.277116</td>\n",
       "      <td>7.032624</td>\n",
       "      <td>11.184108</td>\n",
       "      <td>4.298373</td>\n",
       "      <td>8.667708</td>\n",
       "      <td>8.644425</td>\n",
       "      <td>4.903495</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>10.840377</td>\n",
       "      <td>6.848515</td>\n",
       "      <td>10.688288</td>\n",
       "      <td>4.340944</td>\n",
       "      <td>8.729874</td>\n",
       "      <td>8.697389</td>\n",
       "      <td>5.291746</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total Volume      4046       4225      4770  Total Bags  Small Bags  \\\n",
       "0     11.070344  6.944801  10.905146  3.895080    9.070833    9.060055   \n",
       "1     10.912867  6.515127  10.706381  4.083115    9.159737    9.149429   \n",
       "2     11.680313  6.679222  11.600485  4.879007    9.005325    8.992584   \n",
       "3     11.277116  7.032624  11.184108  4.298373    8.667708    8.644425   \n",
       "4     10.840377  6.848515  10.688288  4.340944    8.729874    8.697389   \n",
       "\n",
       "   Large Bags  Type  Year  Region  Month  Day  \n",
       "0    4.545951     0  2015       0     12   27  \n",
       "1    4.589955     0  2015       0     12   20  \n",
       "2    4.645736     0  2015       0     12   13  \n",
       "3    4.903495     0  2015       0     12    6  \n",
       "4    5.291746     0  2015       0     11   29  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Total Volume   -0.049950\n",
       "4046           -0.420273\n",
       "4225           -0.604619\n",
       "4770            0.068532\n",
       "Total Bags     -0.384493\n",
       "Small Bags     -0.788988\n",
       "Large Bags     -0.610874\n",
       "Type           -0.037741\n",
       "Year            0.229976\n",
       "Region          0.012798\n",
       "Month           0.107408\n",
       "Day             0.011778\n",
       "dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.skew()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now data is looking good ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Building a MachineLearning Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe 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>Total Volume</th>\n",
       "      <th>4046</th>\n",
       "      <th>4225</th>\n",
       "      <th>4770</th>\n",
       "      <th>Total Bags</th>\n",
       "      <th>Small Bags</th>\n",
       "      <th>Large Bags</th>\n",
       "      <th>Type</th>\n",
       "      <th>Year</th>\n",
       "      <th>Region</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>11.070344</td>\n",
       "      <td>6.944801</td>\n",
       "      <td>10.905146</td>\n",
       "      <td>3.895080</td>\n",
       "      <td>9.070833</td>\n",
       "      <td>9.060055</td>\n",
       "      <td>4.545951</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>10.912867</td>\n",
       "      <td>6.515127</td>\n",
       "      <td>10.706381</td>\n",
       "      <td>4.083115</td>\n",
       "      <td>9.159737</td>\n",
       "      <td>9.149429</td>\n",
       "      <td>4.589955</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>11.680313</td>\n",
       "      <td>6.679222</td>\n",
       "      <td>11.600485</td>\n",
       "      <td>4.879007</td>\n",
       "      <td>9.005325</td>\n",
       "      <td>8.992584</td>\n",
       "      <td>4.645736</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>11.277116</td>\n",
       "      <td>7.032624</td>\n",
       "      <td>11.184108</td>\n",
       "      <td>4.298373</td>\n",
       "      <td>8.667708</td>\n",
       "      <td>8.644425</td>\n",
       "      <td>4.903495</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>10.840377</td>\n",
       "      <td>6.848515</td>\n",
       "      <td>10.688288</td>\n",
       "      <td>4.340944</td>\n",
       "      <td>8.729874</td>\n",
       "      <td>8.697389</td>\n",
       "      <td>5.291746</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total Volume      4046       4225      4770  Total Bags  Small Bags  \\\n",
       "0     11.070344  6.944801  10.905146  3.895080    9.070833    9.060055   \n",
       "1     10.912867  6.515127  10.706381  4.083115    9.159737    9.149429   \n",
       "2     11.680313  6.679222  11.600485  4.879007    9.005325    8.992584   \n",
       "3     11.277116  7.032624  11.184108  4.298373    8.667708    8.644425   \n",
       "4     10.840377  6.848515  10.688288  4.340944    8.729874    8.697389   \n",
       "\n",
       "   Large Bags  Type  Year  Region  Month  Day  \n",
       "0    4.545951     0  2015       0     12   27  \n",
       "1    4.589955     0  2015       0     12   20  \n",
       "2    4.645736     0  2015       0     12   13  \n",
       "3    4.903495     0  2015       0     12    6  \n",
       "4    5.291746     0  2015       0     11   29  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.33\n",
       "1    1.35\n",
       "2    0.93\n",
       "3    1.08\n",
       "4    1.28\n",
       "Name: AveragePrice, dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=43)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14120, 12)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3531, 12)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14120,)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3531,)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr=LinearRegression()\n",
    "dtr=DecisionTreeRegressor()\n",
    "rfr=RandomForestRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*********************** LinearRegression ************\n",
      "\n",
      "\n",
      "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)\n",
      "\n",
      "\n",
      "score 0.5501492535611359\n",
      "\n",
      "\n",
      "Performance Measures:\n",
      "MAE 0.20397516061474708\n",
      "MSE 0.06629867736780033\n",
      "RMSE 0.25748529543995385\n",
      "*********************** DecisionTreeRegressor ************\n",
      "\n",
      "\n",
      "DecisionTreeRegressor(criterion='mse', max_depth=None, max_features=None,\n",
      "                      max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
      "                      min_impurity_split=None, min_samples_leaf=1,\n",
      "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "                      presort=False, random_state=None, splitter='best')\n",
      "\n",
      "\n",
      "score 1.0\n",
      "\n",
      "\n",
      "Performance Measures:\n",
      "MAE 0.12464174454828658\n",
      "MSE 0.03488232795242141\n",
      "RMSE 0.18676811278272695\n",
      "*********************** RandomForestRegressor ************\n",
      "\n",
      "\n",
      "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
      "                      max_features='auto', max_leaf_nodes=None,\n",
      "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "                      min_samples_leaf=1, min_samples_split=2,\n",
      "                      min_weight_fraction_leaf=0.0, n_estimators=10,\n",
      "                      n_jobs=None, oob_score=False, random_state=None,\n",
      "                      verbose=0, warm_start=False)\n",
      "\n",
      "\n",
      "score 0.9746915675336115\n",
      "\n",
      "\n",
      "Performance Measures:\n",
      "MAE 0.0975916171056358\n",
      "MSE 0.018955915604644574\n",
      "RMSE 0.13768048374640676\n"
     ]
    }
   ],
   "source": [
    "#prepare models\n",
    "models=[]\n",
    "models.append(('LinearRegression',lr))\n",
    "models.append(('DecisionTreeRegressor',dtr))\n",
    "models.append(('RandomForestRegressor',rfr))\n",
    "#evaluate each model\n",
    "Model=[]\n",
    "Score=[]\n",
    "MAE=[]\n",
    "MSE=[]\n",
    "RMSE=[]\n",
    "for name,model in models:\n",
    "    print('***********************',name,'************')\n",
    "    Model.append(name)\n",
    "    print('\\n')\n",
    "    model.fit(x_train,y_train)\n",
    "    print(model)\n",
    "    pred=model.predict(x_test)\n",
    "    print('\\n')\n",
    "    sr=model.score(x_train,y_train)\n",
    "    print('score',sr)\n",
    "    Score.append(sr*100)\n",
    "    print('\\n')\n",
    "    print('Performance Measures:')\n",
    "    mae=metrics.mean_absolute_error(y_test, pred)\n",
    "    print('MAE',mae)\n",
    "    MAE.append(mae)\n",
    "    mse=metrics.mean_squared_error(y_test, pred)\n",
    "    print('MSE',mse)\n",
    "    MSE.append(mse)\n",
    "    rmse=np.sqrt(metrics.mean_squared_error(y_test, pred))\n",
    "    print('RMSE',rmse)\n",
    "    RMSE.append(rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>models</th>\n",
       "      <th>score</th>\n",
       "      <th>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>LinearRegression</td>\n",
       "      <td>55.014925</td>\n",
       "      <td>0.203975</td>\n",
       "      <td>0.066299</td>\n",
       "      <td>0.257485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>DecisionTreeRegressor</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.124642</td>\n",
       "      <td>0.034882</td>\n",
       "      <td>0.186768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>RandomForestRegressor</td>\n",
       "      <td>97.469157</td>\n",
       "      <td>0.097592</td>\n",
       "      <td>0.018956</td>\n",
       "      <td>0.137680</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  models       score       MAE       MSE      RMSE\n",
       "0       LinearRegression   55.014925  0.203975  0.066299  0.257485\n",
       "1  DecisionTreeRegressor  100.000000  0.124642  0.034882  0.186768\n",
       "2  RandomForestRegressor   97.469157  0.097592  0.018956  0.137680"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result=pd.DataFrame({'models':Model,'score':Score,'MAE':MAE,'MSE':MSE,'RMSE':RMSE},columns=['models','score','MAE','MSE','RMSE'])\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Well as we can see the RMSE for RandomForestModel is lower than the two previous models, so the RandomForest Regressor is the best model in this case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted wins [1.337 1.403 1.097 ... 1.356 1.58  1.351]\n",
      "actual wins 8959     1.14\n",
      "9794     1.40\n",
      "3452     1.14\n",
      "4203     0.88\n",
      "13673    1.82\n",
      "         ... \n",
      "8470     0.80\n",
      "2825     1.18\n",
      "5032     1.40\n",
      "11250    1.51\n",
      "16221    1.31\n",
      "Name: AveragePrice, Length: 3531, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "pred_rfr=rfr.predict(x_test)\n",
    "print(\"predicted wins\",pred_rfr)\n",
    "print(\"actual wins\",y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Saving Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "avocado_new=pd.DataFrame(pred_rfr)\n",
    "avocado_new.to_csv('rNDOMFORESTRERESSOR_avocado.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.337, 1.403, 1.097, ..., 1.356, 1.58 , 1.351])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.externals import joblib\n",
    "#save the model as a pickle in a file\n",
    "joblib.dump(rfr,'avocado.obj')\n",
    "#load the model from a file\n",
    "dct_from_joblib=joblib.load('avocado.obj')\n",
    "#use the loaded model to make prediction\n",
    "dct_from_joblib.predict(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally model had been saved."
   ]
  }
 ],
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