IF654_2020_Program_9_RandomForest.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prediksi Harga Rumah Menggunakan Random Forest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pertama kita load dataset yang akan digunakan dan kita ekplorasi data yang digunakan\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import library, khususnya RandomForest\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics,preprocessing\n",
    "from sklearn.ensemble import RandomForestClassifier,RandomForestRegressor\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Suburb</th>\n",
       "      <th>Address</th>\n",
       "      <th>Rooms</th>\n",
       "      <th>Type</th>\n",
       "      <th>Price</th>\n",
       "      <th>Method</th>\n",
       "      <th>SellerG</th>\n",
       "      <th>Date</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Postcode</th>\n",
       "      <th>...</th>\n",
       "      <th>Bathroom</th>\n",
       "      <th>Car</th>\n",
       "      <th>Landsize</th>\n",
       "      <th>BuildingArea</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>CouncilArea</th>\n",
       "      <th>Lattitude</th>\n",
       "      <th>Longtitude</th>\n",
       "      <th>Regionname</th>\n",
       "      <th>Propertycount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>85 Turner St</td>\n",
       "      <td>2</td>\n",
       "      <td>h</td>\n",
       "      <td>1480000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Biggin</td>\n",
       "      <td>3/12/2016</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yarra</td>\n",
       "      <td>-37.7996</td>\n",
       "      <td>144.9984</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>25 Bloomburg St</td>\n",
       "      <td>2</td>\n",
       "      <td>h</td>\n",
       "      <td>1035000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Biggin</td>\n",
       "      <td>4/02/2016</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>Yarra</td>\n",
       "      <td>-37.8079</td>\n",
       "      <td>144.9934</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>5 Charles St</td>\n",
       "      <td>3</td>\n",
       "      <td>h</td>\n",
       "      <td>1465000.0</td>\n",
       "      <td>SP</td>\n",
       "      <td>Biggin</td>\n",
       "      <td>4/03/2017</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>Yarra</td>\n",
       "      <td>-37.8093</td>\n",
       "      <td>144.9944</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>40 Federation La</td>\n",
       "      <td>3</td>\n",
       "      <td>h</td>\n",
       "      <td>850000.0</td>\n",
       "      <td>PI</td>\n",
       "      <td>Biggin</td>\n",
       "      <td>4/03/2017</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yarra</td>\n",
       "      <td>-37.7969</td>\n",
       "      <td>144.9969</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>55a Park St</td>\n",
       "      <td>4</td>\n",
       "      <td>h</td>\n",
       "      <td>1600000.0</td>\n",
       "      <td>VB</td>\n",
       "      <td>Nelson</td>\n",
       "      <td>4/06/2016</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>Yarra</td>\n",
       "      <td>-37.8072</td>\n",
       "      <td>144.9941</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Suburb           Address  Rooms Type      Price Method SellerG  \\\n",
       "1  Abbotsford      85 Turner St      2    h  1480000.0      S  Biggin   \n",
       "2  Abbotsford   25 Bloomburg St      2    h  1035000.0      S  Biggin   \n",
       "4  Abbotsford      5 Charles St      3    h  1465000.0     SP  Biggin   \n",
       "5  Abbotsford  40 Federation La      3    h   850000.0     PI  Biggin   \n",
       "6  Abbotsford       55a Park St      4    h  1600000.0     VB  Nelson   \n",
       "\n",
       "        Date  Distance  Postcode  ...  Bathroom  Car  Landsize  BuildingArea  \\\n",
       "1  3/12/2016       2.5    3067.0  ...       1.0  1.0     202.0           NaN   \n",
       "2  4/02/2016       2.5    3067.0  ...       1.0  0.0     156.0          79.0   \n",
       "4  4/03/2017       2.5    3067.0  ...       2.0  0.0     134.0         150.0   \n",
       "5  4/03/2017       2.5    3067.0  ...       2.0  1.0      94.0           NaN   \n",
       "6  4/06/2016       2.5    3067.0  ...       1.0  2.0     120.0         142.0   \n",
       "\n",
       "   YearBuilt  CouncilArea Lattitude  Longtitude             Regionname  \\\n",
       "1        NaN        Yarra  -37.7996    144.9984  Northern Metropolitan   \n",
       "2     1900.0        Yarra  -37.8079    144.9934  Northern Metropolitan   \n",
       "4     1900.0        Yarra  -37.8093    144.9944  Northern Metropolitan   \n",
       "5        NaN        Yarra  -37.7969    144.9969  Northern Metropolitan   \n",
       "6     2014.0        Yarra  -37.8072    144.9941  Northern Metropolitan   \n",
       "\n",
       "  Propertycount  \n",
       "1        4019.0  \n",
       "2        4019.0  \n",
       "4        4019.0  \n",
       "5        4019.0  \n",
       "6        4019.0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import dataset\n",
    "df = pd.read_csv('melb_data.csv', index_col=0)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "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>Rooms</th>\n",
       "      <th>Price</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Postcode</th>\n",
       "      <th>Bedroom2</th>\n",
       "      <th>Bathroom</th>\n",
       "      <th>Car</th>\n",
       "      <th>Landsize</th>\n",
       "      <th>BuildingArea</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>Lattitude</th>\n",
       "      <th>Longtitude</th>\n",
       "      <th>Propertycount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>18396.000000</td>\n",
       "      <td>1.839600e+04</td>\n",
       "      <td>18395.000000</td>\n",
       "      <td>18395.000000</td>\n",
       "      <td>14927.000000</td>\n",
       "      <td>14925.000000</td>\n",
       "      <td>14820.000000</td>\n",
       "      <td>13603.000000</td>\n",
       "      <td>7762.000000</td>\n",
       "      <td>8958.000000</td>\n",
       "      <td>15064.000000</td>\n",
       "      <td>15064.000000</td>\n",
       "      <td>18395.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.935040</td>\n",
       "      <td>1.056697e+06</td>\n",
       "      <td>10.389986</td>\n",
       "      <td>3107.140147</td>\n",
       "      <td>2.913043</td>\n",
       "      <td>1.538492</td>\n",
       "      <td>1.615520</td>\n",
       "      <td>558.116371</td>\n",
       "      <td>151.220219</td>\n",
       "      <td>1965.879996</td>\n",
       "      <td>-37.809849</td>\n",
       "      <td>144.996338</td>\n",
       "      <td>7517.975265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.958202</td>\n",
       "      <td>6.419217e+05</td>\n",
       "      <td>6.009050</td>\n",
       "      <td>95.000995</td>\n",
       "      <td>0.964641</td>\n",
       "      <td>0.689311</td>\n",
       "      <td>0.955916</td>\n",
       "      <td>3987.326586</td>\n",
       "      <td>519.188596</td>\n",
       "      <td>37.013261</td>\n",
       "      <td>0.081152</td>\n",
       "      <td>0.106375</td>\n",
       "      <td>4488.416599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.500000e+04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1196.000000</td>\n",
       "      <td>-38.182550</td>\n",
       "      <td>144.431810</td>\n",
       "      <td>249.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.330000e+05</td>\n",
       "      <td>6.300000</td>\n",
       "      <td>3046.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>176.500000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>1950.000000</td>\n",
       "      <td>-37.858100</td>\n",
       "      <td>144.931193</td>\n",
       "      <td>4294.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.800000e+05</td>\n",
       "      <td>9.700000</td>\n",
       "      <td>3085.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>1970.000000</td>\n",
       "      <td>-37.803625</td>\n",
       "      <td>145.000920</td>\n",
       "      <td>6567.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.302000e+06</td>\n",
       "      <td>13.300000</td>\n",
       "      <td>3149.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>651.000000</td>\n",
       "      <td>174.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>-37.756270</td>\n",
       "      <td>145.060000</td>\n",
       "      <td>10331.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>9.000000e+06</td>\n",
       "      <td>48.100000</td>\n",
       "      <td>3978.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>433014.000000</td>\n",
       "      <td>44515.000000</td>\n",
       "      <td>2018.000000</td>\n",
       "      <td>-37.408530</td>\n",
       "      <td>145.526350</td>\n",
       "      <td>21650.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Rooms         Price      Distance      Postcode      Bedroom2  \\\n",
       "count  18396.000000  1.839600e+04  18395.000000  18395.000000  14927.000000   \n",
       "mean       2.935040  1.056697e+06     10.389986   3107.140147      2.913043   \n",
       "std        0.958202  6.419217e+05      6.009050     95.000995      0.964641   \n",
       "min        1.000000  8.500000e+04      0.000000   3000.000000      0.000000   \n",
       "25%        2.000000  6.330000e+05      6.300000   3046.000000      2.000000   \n",
       "50%        3.000000  8.800000e+05      9.700000   3085.000000      3.000000   \n",
       "75%        3.000000  1.302000e+06     13.300000   3149.000000      3.000000   \n",
       "max       12.000000  9.000000e+06     48.100000   3978.000000     20.000000   \n",
       "\n",
       "           Bathroom           Car       Landsize  BuildingArea    YearBuilt  \\\n",
       "count  14925.000000  14820.000000   13603.000000   7762.000000  8958.000000   \n",
       "mean       1.538492      1.615520     558.116371    151.220219  1965.879996   \n",
       "std        0.689311      0.955916    3987.326586    519.188596    37.013261   \n",
       "min        0.000000      0.000000       0.000000      0.000000  1196.000000   \n",
       "25%        1.000000      1.000000     176.500000     93.000000  1950.000000   \n",
       "50%        1.000000      2.000000     440.000000    126.000000  1970.000000   \n",
       "75%        2.000000      2.000000     651.000000    174.000000  2000.000000   \n",
       "max        8.000000     10.000000  433014.000000  44515.000000  2018.000000   \n",
       "\n",
       "          Lattitude    Longtitude  Propertycount  \n",
       "count  15064.000000  15064.000000   18395.000000  \n",
       "mean     -37.809849    144.996338    7517.975265  \n",
       "std        0.081152      0.106375    4488.416599  \n",
       "min      -38.182550    144.431810     249.000000  \n",
       "25%      -37.858100    144.931193    4294.000000  \n",
       "50%      -37.803625    145.000920    6567.000000  \n",
       "75%      -37.756270    145.060000   10331.000000  \n",
       "max      -37.408530    145.526350   21650.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show rangkuman statistik\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Suburb               0\n",
       "Address              0\n",
       "Rooms                0\n",
       "Type                 0\n",
       "Price                0\n",
       "Method               0\n",
       "SellerG              0\n",
       "Date                 0\n",
       "Distance             1\n",
       "Postcode             1\n",
       "Bedroom2          3469\n",
       "Bathroom          3471\n",
       "Car               3576\n",
       "Landsize          4793\n",
       "BuildingArea     10634\n",
       "YearBuilt         9438\n",
       "CouncilArea       6163\n",
       "Lattitude         3332\n",
       "Longtitude        3332\n",
       "Regionname           1\n",
       "Propertycount        1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show missing value\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setelah di analisa ternyata dataset ini memiliki missing value yang terbilang banyak, untuk saat ini kita akan menghiraukan dan menghapus data yang memiliki missing value tersebut\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop semua data yang memiliki value NaN\n",
    "new_df = df.copy()\n",
    "new_df = new_df.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Masuk ke tahap filter\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Kita akan menghapus semua fitur yang bukan merupakan nilai numerik"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Delete df yang bukan numerik\n",
    "no_strings = new_df.drop(['Suburb','Address','Method','SellerG','Date','Regionname','CouncilArea','Type'],axis=1)\n",
    "\n",
    "# Delete df dengan batasan tahun, area, dan ukuran lahan\n",
    "no_strings = no_strings.drop(no_strings[no_strings.YearBuilt<1800].index)\n",
    "no_strings = no_strings.drop(no_strings[no_strings.BuildingArea>2000].index)\n",
    "no_strings = no_strings.drop(no_strings[no_strings.Landsize>24000].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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>Rooms</th>\n",
       "      <th>Price</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Postcode</th>\n",
       "      <th>Bedroom2</th>\n",
       "      <th>Bathroom</th>\n",
       "      <th>Car</th>\n",
       "      <th>Landsize</th>\n",
       "      <th>BuildingArea</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>Lattitude</th>\n",
       "      <th>Longtitude</th>\n",
       "      <th>Propertycount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1035000.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>-37.8079</td>\n",
       "      <td>144.9934</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>1465000.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>-37.8093</td>\n",
       "      <td>144.9944</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4</td>\n",
       "      <td>1600000.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>-37.8072</td>\n",
       "      <td>144.9941</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3</td>\n",
       "      <td>1876000.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>245.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>1910.0</td>\n",
       "      <td>-37.8024</td>\n",
       "      <td>144.9993</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2</td>\n",
       "      <td>1636000.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>256.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>1890.0</td>\n",
       "      <td>-37.8060</td>\n",
       "      <td>144.9954</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Rooms      Price  Distance  Postcode  Bedroom2  Bathroom  Car  Landsize  \\\n",
       "2       2  1035000.0       2.5    3067.0       2.0       1.0  0.0     156.0   \n",
       "4       3  1465000.0       2.5    3067.0       3.0       2.0  0.0     134.0   \n",
       "6       4  1600000.0       2.5    3067.0       3.0       1.0  2.0     120.0   \n",
       "11      3  1876000.0       2.5    3067.0       4.0       2.0  0.0     245.0   \n",
       "14      2  1636000.0       2.5    3067.0       2.0       1.0  2.0     256.0   \n",
       "\n",
       "    BuildingArea  YearBuilt  Lattitude  Longtitude  Propertycount  \n",
       "2           79.0     1900.0   -37.8079    144.9934         4019.0  \n",
       "4          150.0     1900.0   -37.8093    144.9944         4019.0  \n",
       "6          142.0     2014.0   -37.8072    144.9941         4019.0  \n",
       "11         210.0     1910.0   -37.8024    144.9993         4019.0  \n",
       "14         107.0     1890.0   -37.8060    144.9954         4019.0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show df yang telah di filter\n",
    "no_strings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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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>Rooms</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Postcode</th>\n",
       "      <th>Bedroom2</th>\n",
       "      <th>Bathroom</th>\n",
       "      <th>Car</th>\n",
       "      <th>Landsize</th>\n",
       "      <th>BuildingArea</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>Lattitude</th>\n",
       "      <th>Longtitude</th>\n",
       "      <th>Propertycount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>-37.8079</td>\n",
       "      <td>144.9934</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>-37.8093</td>\n",
       "      <td>144.9944</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>-37.8072</td>\n",
       "      <td>144.9941</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>245.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>1910.0</td>\n",
       "      <td>-37.8024</td>\n",
       "      <td>144.9993</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>256.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>1890.0</td>\n",
       "      <td>-37.8060</td>\n",
       "      <td>144.9954</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Rooms  Distance  Postcode  Bedroom2  Bathroom  Car  Landsize  \\\n",
       "2       2       2.5    3067.0       2.0       1.0  0.0     156.0   \n",
       "4       3       2.5    3067.0       3.0       2.0  0.0     134.0   \n",
       "6       4       2.5    3067.0       3.0       1.0  2.0     120.0   \n",
       "11      3       2.5    3067.0       4.0       2.0  0.0     245.0   \n",
       "14      2       2.5    3067.0       2.0       1.0  2.0     256.0   \n",
       "\n",
       "    BuildingArea  YearBuilt  Lattitude  Longtitude  Propertycount  \n",
       "2           79.0     1900.0   -37.8079    144.9934         4019.0  \n",
       "4          150.0     1900.0   -37.8093    144.9944         4019.0  \n",
       "6          142.0     2014.0   -37.8072    144.9941         4019.0  \n",
       "11         210.0     1910.0   -37.8024    144.9993         4019.0  \n",
       "14         107.0     1890.0   -37.8060    144.9954         4019.0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Menyimpan feature Price pada variabel Y dan sisanya di variabel X\n",
    "Y = no_strings.Price\n",
    "x = X = no_strings.drop(['Price'],axis=1)\n",
    "\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Karena ada cukup perbedaan antara varian nilai, kita akan menormalisasikannya. Dengan begitu, kita bisa fit data tersebut."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Melakukan fitting data\n",
    "min_max = MinMaxScaler()\n",
    "X = min_max.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pemisahan data: test & train\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setelah membagi menjadi train set dan test set, kita melakukan pembuatan model dengan menggunakan metode Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hasil:\n",
      "----------------\n",
      "RMSE score: 277109.10964345885\n",
      "Accuracy Score is: 0.816051132900901\n"
     ]
    }
   ],
   "source": [
    "# Pembangunan model RF\n",
    "rf = RandomForestRegressor(n_estimators=100, random_state=42)\n",
    "rf.fit(X_train, y_train)\n",
    "\n",
    "# Mempersiapkan data untuk menampilkan akurasi dan RMSE\n",
    "y_pred = rf.predict(X_test)\n",
    "acc_score = rf.score(X_test, y_test)\n",
    "\n",
    "# Menampilkan tingkat akurasi dan rata-rata kesalahan kuadrat\n",
    "print('Hasil:\\n----------------')\n",
    "print('RMSE score: {}'.format(np.sqrt(metrics.mean_squared_error(y_test, y_pred))))\n",
    "print('Accuracy Score is: {}'.format(acc_score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Melakukan analisis pada faktor(fitur) yang paling signifikan berdampak pada Harga"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Memerika fitur mana yang paling signifikan, kita akan memerika korelasi antara setiap fitur dan variabel yang kami inginkan yaitu Harga"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1796a2e6c88>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Memeriksa korelasi antar fitur\n",
    "no_strings_corr = no_strings._get_numeric_data()\n",
    "plt.figure(figsize=(15,8))\n",
    "sb.heatmap(no_strings_corr.corr(),annot=True,center=0,square=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setelah kita bisa melihat korelasi antar setiap fitur dan hasil tersebut kita akan plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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5zwUe817rE5HTvdXKT404Vz7XMBgqwqH+BF0Fxoj97xtdXHnfZl4/0Icl8OdnLOEfP/Gektnj1FUFWFBfRVWwcmXecqXcg92/Bn4oIkFgF/DnuOL5UxG5Angb+ANv34eBC4CdwIC3L6p6SERuAV7w9rs5PfEP/CXw70AV7iT/I972W/O5xkzEOL5ObdLhFdF4/r76tqN8/5nd3P+CO93bUB3g+gtWcsqRDVmOzI2Azy3+Uck6lfkixZaJmumsXr1aN23aVOlm5EVmHmVVwDeUR2nCK6YGtuO6V8QKSPzuisa55aFtvPJODwDvWVDHVy5cRXOJvMPmVAVorAmWpbhIvojIZlVdncu+U2/5wVA02fIoTW+tchTjq//i2653WPeAG1+27r2LuOLM0niHTcdeWCZGyGYgE+VRGn/+ylFo4rejyo+ef5t//983cdSNpr/m/GM54+jmkrSrvjpIQwEl2KYSRshmIGPlUXZF4wwkbK76j80I7hCivSdGwnbwiXDbo20l762Znt+79MdTdBQwqd8zkOQfH9nGb990vcOOmRvhxotWMW9OVdFtCvotmiPTtxeWiXGInYGMzKPs7IvRGU1QE/LhqJJyHA70xYmn3MBJR5XtHVHu+PV2btjwGh19sWG9tY1tHdkvOoJ0z68U55ru9AwkOdAby1vEXtvXw5X3bR4SsYtPnM8d604uWsTcEmxBFtRXzQgRAyNkM5KReZQDCZvW2iDNkTBBn4XtuJHBjiri/Qv4hLuf2T00tybi3gd8wl1P7cq7DZnzdMWeazrTFY1zsD+/4iCqyn++uJcv/mQLndE44YDF9Res5IsfXl60AWLQbzG/PkzDFJnQLxVmaDlDycyjPPO2J4fmzJojId465ObhqboJyg7K/Nowew/HWDzif+hCPcpmu9+ZqtLRF6c/z/CK/niKb/zqdZ7a7nqHHdlUzU0XreLIppqi2pMuhDunanrPhY2HEbJZQOacWV1VgJBPSNjuMMfvE5ojYfw+oSbohmqUwqNsNvudFeqr/0ZHlK8+tJW93YMAfHhlK3/zkWOoKnL4V8pCuFOVmfvODEOMnDNrjLg5c3PrQixtrsHvE5K28ukzl5bMo2y2+p0lUg77Dg/mLWKPvLKfz/74JfZ2DxLwCX/7keVct3ZFUSImIjTVhFhQXzWjRQxMj2xWsGZFKzfjzlvt7R5gSVOEy97byLO7DrG3e2BY1aQTFtYP7VdMNaWR15wNlZkKCa+IJW1uf2IHj712AIB5c8LceNEqjplbW1RbwgEfzTO8F5aJiezPwnSM7DdMPn2xJF3RRF4rk28fGuDmB7eyq6sfgDOObuKa81YQCRfevxBxi3+Us4bkZGEi+w2GSaQQX/2Nr3fwjce2M5i0sQSuPGsZf3DqwqIm4quCbi8sUIHiH5XGCJnBUCCqSlc0kZclddJ2+Nf/3sUvXnoHgOZIkBsuXMXxCwq33ZnMQrhTFSNkhiE2tnVw6yPb2O3ZJC9rruGa81fM6HmtQnEc5UBfjMFE7pP67b0xbn5wK23tfQCcurie//+jK2moDhbcjskuhDtVmd3v3jDExrYOrn5gCzs7+1FVVJUdHVG+9MCWWRmNPxEp22Ffz2BeIvbcroNcdd9m2tr7EODy9x/Jrb9/QsEiZonQUhviiDnhWS9iYHpkBo+7ntpFNJ7CJzLkBCqq9MVM9fFM8vXVtx3l//7Pbn70W9c7bE5VgOsvWMHqJY0Ft8H0wkZjhMwAuJH4tqP4MiabRdzex2yJxs/GQCJFR288Z0vqg9E4//Bf29iy1/UOO36+6x3WUluYd5jPclcka2fxXNh45CVkIlKjqv3laoyhcixqqKYrGkcdV8DATWHyW9asiMbPRm8sSVdf7jmTv9tzmFse2jrkHfaHqxfy6TOXFtyLqgn5aaoxvbDxyOlTEZEPiMhW3AK7iMiJIvLdsrbMMKlcddYyIiE/tiq242A7DinbwVZl+4FeLlv/3KydK0v76udC2jvs6p9toXsgSU3Qx80XH8dnzj6qIBHyWUJrXZi5dWYubCJy7ZF9GzgPt3gHqrpFRM4qW6sMRVGID9iaFa3886UnDq1aOo5iWUJTTZDmSGhWGjDm66vfM5jk1kfaeH63W1Li6FbXO2xBfWG2O5GQn6bI1CqEO1XJeWipqntGBOvlbzhuKAkTCVUxDrCZjhmXrX9uWNL3SLvsXNszXcnXV3/b/l6++uBWOrye24UnzONzv3d0QSlCPktojoSoKWF18JlOrp/yHhH5AKAiEhSRq/GGmYbJJZthYal8wPZ0D4xKWB7LhmcmGigmbTfxOxcRU1V+8dI7fOH+39HRFyfst7hu7Qr+9iPHFCRikZCfhQ3VRsTyJNdP+jPAZ4EFuEVuT/KeGyaZbEKVqwBlY1FDNYMjfshj2fDMNAPFWNJm3+HBnIqDDCRS3PLQNv7PkztJOcrixmru/OQpfGTV3Lyv67cs5taFaa0Lm6FkAeQk+6raBXyyzG0x5EA2w8JS+YBdddYybtjwGgOJ1LCSciNteGaSgWI0nqIzR1/9XZ1RbnrwXe+wc1a08ncfOaagYraRsJ+mGjMXVgy5rlreIyL1Gc8bROQH5WuWYTyy9ZRK5QM20i67tTY8Zl3MXHtuU52egSQdOfrqP/pqO5/90bveYV/88HKuv2BF3iLmtyyOmBOmtdb0wool14H4Cap6OP1EVbtF5OQytcnA+BPo2XpKpfQBy5z8H4/M9qRshwO9cZKOQ8ASNrZ1TItJ/65onN7B7Inf8aTNHU/u5JFX2wE4oi7MTRcX5h1WGw7QVBMcyqIwFEdOfmQisgVYo6rd3vNG4L9V9T1lbl/FqYQfWbZK4WmRK7VhYaGrjxvbOrjt0Ta2d0QJ+IS5tSH8PmvKVzd3HNdXfyCRPbxib/cANz24lV2dbjz4B45q4przj807yj7gc0uwFTIEnW2Uw4/sm8D/isgD3vM/AL5WSOMM2clWKTyXnlK+FBu2cddTu1jSVD1sbm68cI2pQMp2aO+NkUhln9R/ansn//TY6wwkXO+wT39wGX+0On/vsLqqAI3VphdWDnKd7L9XRDYB5+BWEvuEqm4ta8tmMeWaQJ+ox5VNPCvV5nKQa+J30na466ld/PxF1zusqSbIVy5cyQkL6yc8biSmF1Z+JhQyEalT1V5vKNkO/CjjtUZVPVTuBs5GylGBKFuPq1ghmi5VkwYTrq9+tsTvA70xbnloK1v3u95hJy+u5/oLVtJYk5/tjumFTQ7ZVi3TwrUZ2JRxSz83lIFyVCDKFu9V7OrjdKia1BdL0p6DiP129yGuum8zW/e73mF/evpi/un3T8hLxAI+i/n1VTRHQkbEJoEJe2SqeqG4EwFnq+rbk9SmWU85KhCle1x9sSSdfXEStruy2OOt1uUaNzbe8HSqV03q7k/QPTCxr77tKPc8+yb/8Zz7p14X9nP9R1fy3jy9w+ZUBWicYZW8pzq5rlpuVtVTJ6E9U47pWkVppOB098fpiSU5GE0i4tZfTH/zlsAlJ87jkpMWTihE2VZTpyKqSmc0TjQ28crkof4EX3t4Gy+97UYZrZpXyw0XrqK1LpzztQI+i5baEOEiC+oaXMqxavmciLxXVV8ool2GSWKs+bDewSTdAwlEhERq+H9ejsIvfrcfgB9fefq45y12QWCyydVXf8vew/zDQ9s46FVC+v1TFnDlWcvyqkZUXx2koTpgemEVIlch+z3gMyLyJtCPu3KpqnpCuRpmKJyxBAegeyBJ0GeRtMf+YW94uZ1vrxv/vNNpZTJpO7T3xCbMmXRU+ekLe7j7md04CtVBH18671jOPqYl5+sE/e6KpOmFVZZchWxtWVthKCnjCY7PEo6YE+aNzrFNfrNVyJ4uK5OxpE1H78ThFX2xJLc+8jrP7joIwFEtNdx00XEsaMjNO0xEmFMVML2wKUK28IswrvPF0cArwPdVNTeXOUPFWNRQzZsHo3T3J4l7AZ8BnzC3LkzSHi1WAihkzfcba0GgZ9Dt5Z1525NTwossF1/919v7+OqDW2nvjQFwwXuO4K9/72hCOfaqgn53LizkN72wqUK2SYB7gNW4IrYWN8I/L0TEJyIvichD3vOlIvK8iOwQkZ+ISNDbHvKe7/ReX5Jxjuu87a+LyHkZ28/3tu0UkWsztud9jUqysa2Dy9Y/x5m3PVkSS+n3L2vkQG+cmDehr0DCVnoGElx6ygKaqof31tI/+YtPOGLC82Ymkrf3xth3eJBD/Ql2d/XjE0Z5kZX6fWWjZzBJe8/44RWqyi9/t4/P3/8S7b0xQn6La84/lqvPPTYnERMRGqqDLKivMiI2xcgmZKtU9U9U9S7gUuCDBVzjCww3YbwN+LaqLge6gSu87VcA3ap6NK619m0AIrIKWAccB5wPfNcTRx9wJ67ArgIu8/bN+xqVpBzGhM/uOoRP3J6W4K5K+i1XzJ7ddYhv/uFJVPlH976WNkeynjuduF4d9KOA3xIU2N8Tx3Z0KDZtsg0XD0bjHIyO76s/mLD5+sNt3P7EDpK2srChijv/+GTOO25i8U4T9FvMrw/TYMIqpiTZhGzIEqCQIaWILAQ+CtztPRfcNKd0zuY9wMe8x5d4z/Fe/5C3/yXA/aoaV9XdwE7gNO+2U1V3qWoCuB+4pMBrVIxyGBPu6R4AEUJ+i3DAR8jv85K4HV58u5tP37uJmLdyKQJBnxDwCXdufIM7fr09ay8q3WbbUSwRLBFEoLMvPjT5P1mGi6quJXXPBO4Vu7v6+csfvsgT3nv5vWNb+Nc/OYVlLdmFW8QtwWZ6YVObbJP9J4pIr/dYgCrveXrVsi7L8d8B/h5I+5w0AYczRHEvruss3v0e3BOnRKTH238B8FzGOTOP2TNi+/sKvEZXZqNF5ErgSoDFixdneYvFUY6VwLFKu9mOYiv4VEllTOqrQtJW/JaQtB3u3PgGCxuqhnpRVz+whZZIiL54amgOLN3moM8iZSsi7nUStjM0+Z/ep3cwSVfUDcAN+ix6sgSl5oPtKO29MeITWFL/ausBvvP4dmIpB78l/OWao/jYSfNz6lWFAj5aIqGCLKsNk8uE35Cq+lS1zrvVqqo/4/GEIiYiFwIdqro5c/NYl8nyWqm2Z7v+uxtU16vqalVd3dKSfSm+mLmgchgTjlXaLWnrsOHmSFKOgkDKcagO+onGU+w9NEBXNEFbex/xZIqX9nRzxb2bOBRN0BWN0xwJ4aA4jnvziQxlA6TFdF/PICnbfS1hO/TF7ZIMLxMp11d/PBFLpBy++avt3PpIG7GUQ2ttiNvXncTHT16QVcQye2FGxKYH5fyWzgAu9mLP7scd7n0HqBeRdE9wIbDPe7wXWATgvT4HOJS5fcQx423vKuAaBVPsXFA5chTTpd2ObqlBRBAR/JawsKEKB3e+LJP0goCj4BehL5Zk3+EYCa/npkBnNOkJkit2ndEE8ZTN/DlhxAJbYWlzzVCU/1VnLRsqTitW+n8YobEmUPTwMpa02d8zvq/+O4cH+dyPXuK/XnGDfE9f1sj6Pz2VlfOyDSDcXtiC+irqq/NLDjdUlrKValHV64DrAERkDXC1qn5SRH6Gu3BwP3A58EvvkA3e82e9159UVRWRDcCPRORbwHxgOfBb3I7FchFZCryDuyDwx94xv8nnGsW8z2Kj3UuVo3jHr7dz9zO76U/Y1AR9fPrMpVy7duVQmtKhaIL9PTFSXviFBYwlA3FbeefwIJbIqL6qo4rfZ2E7SmttkP64TcBncfKihlFtXrOildqwn4F4iqSjBL30nUjIX9SwOZuv/jM7urjtsTb646532F+csZR1py1y388EiAiN1UHmVOdnlGiYGlSi5tQ1wP0i8g/AS8D3ve3fB+4TkZ24vaR1AKr6moj8FNgKpIDPqqoNICKfAx4DfMAPVPW1Qq5RDKWY4xrLKDEft9Y7fr2d25/cObQ6OZi0+c4TOwj63MmrpK1Dwa5pARtLBnzi9qySthKwRu+h6t6CPoummhB+K8nT15wz7vta3lo7KoB2IJEqeNh8eCDBof6x59hStsO/Pb2bn23eC0BjTZAvf3QlJy3K7h0WDvhoqQ3llZJkmFpMipCp6kZgo/d4F+6K48h9YrjOs2Md/zXGcLawb3IAACAASURBVKRV1YeBh8fYnvc1CqUS3mEjufuZ3VjiDt2StuMKDhBLKSGf4GRM7ju44RjgDiUtcW8Bn7sil7Jtkg4kHQj5hJS6OYvpM6hCS20op/eYq6NGNlSVrmiCvtjYK5OdfXFufmgrr+1z16VOWjSHL390VVbbHUuEhpogc6pML2y6Y6qAFkmpfqyZZBuujuytReMpfALJtGClQ/UZvsqRfuyzhKDPIp6yh1ZH4imb9GjNL6ACCxurSXnFapOOmx0wb45btiyX91iKYXM2X/0X3jzE1x9uGwq/+OT7FvNnH1iSNUuhKuijOWJ6YTMFI2RFUk7vsEzSw9WxemuK24MaImNUmHIcRNzeV5r0CqKI4NjKyH5OSsFSt6cT9FusXtLE+5c18sir7ezqcvM0lzbl1uMspr7ARL76tqPc99xb3PfsWyhQG/Zz3doVnL6sacJzWiI0RoLU5Vk0xDC1MUJWAkpdDGSi4epYvbX6Kj/dA2P3WLyoiiHSPTNBaIoEOJSRj5mJzye01IZI2jokYukqSbUhH28eHOCKe19geUuEa9euLHl+5US++t0DCb7+cBub3+oGYMURtdxw0SqOyOIdVhV048L8phc24zDf6BRkopCM7Qd6ae+J0dbey67OKL2Dyay9Cx3xOJFysMSdtE85SmskOCq2LGkrKVtJ2jZ3bnxjKJ8yZasbiuE4+ER489BAyVOPBhM2+w/HxhSxV/b2cOV9m4dE7OMnL+D2dSdNKGKWCM21IebNqTIiNkMxPbIpSk3Qx86OKClVRN2Vtb/+8YtEvbCCgBdVv69nEDJCEdKCNF5MieBG4Suwt3sQBTqiiaHjRN493Z7ugaGFg/Sx6ceOunmWmfmVpeiV9caSHIwmRoVXqCo/3bSXf3t615B32NXnHsOaYye+ZnXQT3MkaARshmOEbIqRngNLmx8K7kpjPGW7w0QvREIcxe8TSK8w+t34LjtjhXEsFFeo0ilLVsZ2GKaJjLQny3zqZIRilMpc8VB/gsNjpDBFYylue7SN/3nD9Q5b1lzDjRetYlHj+PN0PsuNzs+3gK5hemKEbIqRngM7GE1hWYJtvxtdr96DdMJ2yn7XDNFxHEAmFLFMHFUsxu+5wfDe2VikVDkiEmYw6QbhXrb+ubyrlIPnq98XJxofPc+3/YDrHba/x/UOO/+4I/j8h46e0JG1JuSnqcb0wmYTRsimGHu6B/AJDCTs4XNbXsyXo25vI7P3JQIpBybuiw0nM1JjPHwipCZQMr+A3+dWYhLc8I+RcW/AhIG9tuO6V8RG5EyqKg+9vJ9/+c1OkrYS9Ft84ZyjWfueeeO31xKaIm72gGF2Yb7xKUYk6GPnGFbUiht5rwrJlDNs2FdMktVYh1pecrmjOm6vzBI3TKO1NkzQZ5GwnVFxb7c+so2BpDNuYO94vvqDSZtvP76dX29zFxAWNlRx44WrOKp1fNudSMhPUySUNX7MMDMxfe8pRtqZwe+TUb2llANB39g5kqUkYAmWJV4S+dj7tNaGOG1JIz++8nT64m4wcCZVAR+7Dw6M60kWS9puoO0IEXvrYD9/9cMXh0TsrGOa+d4nTxlXxHyW0FoXprUubERsFmN6ZBVivFzKvniKBfVhuqKJMasdxW0IWJAsKtV9fNKLC001QTqjCRQI+Bjy+hfcfM6g3zcU2T9e3BswpsC9faif/T2xUSuTT2w7wDcf304s6XqHfebsZRPa7phemCGN6ZFVgImsfxY1VOP3WSxriQzlRKbtqt1KSCOi+EuMO4QVgn4fXzjnaObXV7G4sZojG6sI+y0sC45qiQwryjte3Nuy5ppRXmvReIqWSHiYiCVSDt/+9Xa+9nAbsaTrHfadPzqJT5yycEwR81sWc00vzJCB6ZFVgIlyKTNzNzPnwfyWa1U9QZnGkrG0uYZrzl/BmhWtnLCwfij96uTFo+16YPw0LWBYHmpfPEUi5bDuve/ayO07PMhXH9zKjo4oAKctaeC6tSvHtdOJhP001ZhemGE4RsgqwES5lGtWtHLp3sPc/czuodcs8VwtJqFtC+vDPPLFs4aep9Ov0kPhL//yVRY9NXr1cbw0rZuBf/3vN3j70ACttWHWvXcRpy1rBOB/dnZx66Pveof92QeW8MfvWzymd5jfsmiuDQ4bvhoMacxfRRnI5iU2US7lxrYOHnjxHVpqQ6AOvXEHuzjvx5zxWzJmAGm+tkKZfPCYFpYfUTvMkjplO3z/md38ZJPrHdZQHeDLH13JyYsbAPjtrkPc/8Ie9vcOMq+uij8/YwkXnTgfy/TCDONg5shKTC7W1xPlUqaHnb2DSXrjkzCOzMBnCds7oqPqDhRaEWksX/3Ovjh/97MtQyJ2wsI5rP/TU4eJ2O1P7uBgf5w54QA9sQTffHw7T23vLMM7NswUjJCVmFx/9NUBi73dg+zoiBL0WUOT53u6B0jZrif+ZOOoa8Q4Unz3dA+Mufo4UVrSYGK0r/6Lb3Vz1X2beeUd1wBx3XsX8c0/OJGmSGhon/tf2IPfEmpCfoJ+i0goUJYycoaZhRlalphs1teZw7TlrREGkzb9iXd7LIsaqnlpT/ektjlNylZqgn7ae2LEUjafv/8l7lh3ct4uuH2xJF0Zid+OKj987m3uefZNHHXDJq5deywfOKp51LHtvYM0VgfxZaQXlSqX0zBzMUJWYrL96Mdasezsi/H5+1+iripAbchPvJzxFROgwOHBJAGfYAG9sRRX3PsC82pDxL04smwuuN39CbozEr8PDyT4x0faeOFNV5yPnVvLDRetZN6cqlHH1lUFWNJUQ2c0TnWGkBVrHW6Y+ZihZYnJVt5t5DCtdzDJwf4E/YkU9VUBDg8mJmV1ciKStpL0ivv6ROgaSHol2GJsa++jsy/OpacsGDbRr6p09MWGidir77jeYWkRu+TE+dy+7qRRIhbwWcyvr6I5EuIzZx9V8vJ4hpmP6ZGVmGzW1yN7bF3ROABhv49oPMXB6NgFNiqBhStQsaQSww3KXdTgmhM+8OI7nLCwnjUrWnEc5UBfjEFviKyqPPDiO6x/ahe2o4QDFlefeyznjLHCOacqQGNNcCjwtRzW4YaZjxRZ1nHGs3r1at20aVPJzpc5R1YV8NHW3oft6Chf/alKwCfMn1OF3ye01oa574rT2J+R+B2Np/jGY6/z9I4uAJY0VXPjRas4sqlmxHncOpcT2fEYZjcisllVV+eyr+mRTTIjexwWbrHO6fL/SdJW2ntjLG+NsOdQP/syLKl3dkS56cHX2HfY9Q77yKq5fPHDy3llTw+3/3rnUFzYX5zpxoWNl0OZD/nU/zTMXIyQVYDMKPhVX3mUhDM6OXwqE085bNvfy5GN1aQcB1Xlzt+8wS9eemeovNzHT1rAZ885ihd2d3P7kzvwWzIUF/bPv9pOXThQtOAUE6hrmFmYyf4KMzKperpgK3RE4zyzvYurf/YyP/dELOATWmtDPLv7IC/s7h6KC4uUIS6s0EBdw8zD9MgqyB2/3l7xFcpiiCdtvv7INmJeOblIyMfcWteRYjBpc/8Le9y4sJogPqv0cWHZYvYMswcjZBViY1sHd258A4vyGyWWi6QDSW9+rDkSpKEqMDTvFQ5YdPTF3o0LC5YmLixzTqx3MIntODRH3i0FZ2LOZidmaFkh7vJCE4J+a0Lf/EqStrzOxvw5YaoCviERE3FrChzZVFPSuLCReazVQR8dfQm6ojETczbLMUJWIfZ0DxDyW8PqRk41gj6LxY1V+Cf4K2ms9hP2u4V+B5M2liUkbZuUw9AK4s0XH0drbZiewSStteFhpoz5MHJOrKU2TEskSH/cLvrchumNGVpWiEUN1aRsh4P9k58cnitJ2yHlKMuaI7T3DtIbe3dhwhKYWxsiEvbTF0vxpXOP5aeb97Lv8OCoINbxvMryZaw5seZIiJ7BJE9fc07R5zdMX4yQVYi0E2xTTZD23nilmzMmqvCOFxOWRnBXJlOOuu3ujRMJ+WiKhPjJVe8va3vyTV43zB7M0LJCpIdcS5vHL3FWaUYuQljiDoNTtrqVxnFvfp+M8lwrB9nyWA2zF9Mjm2Q2tnVw26Nt7Opya1cubaomErSIJqb22mUk5COetPF5xYDBFTZLhERKh+K3yjk/ZfIwDeNhhGwS2djWwZce2EL3QNKrGq60HYhWullZsQTiSbfwiWWBzwK/z0IQVJWE7Uxa/Fap5tsMMwsztJxE7npqF32xlFcBSCalIlIpUHVF1wFsx01uT3mNV3VXN81claGSlE3IRGSRiPxGRLaJyGsi8gVve6OIPC4iO7z7Bm+7iMgdIrJTRF4WkVMyznW5t/8OEbk8Y/upIvKKd8wd4gUyFXKNyWBP9wApx0EEN0dxMi9eBIqbkgTuZL94w8t4ysZBqQ37zVyVoaKUs0eWAv5OVVcCpwOfFZFVwLXAE6q6HHjCew6wFlju3a4EvgeuKAE3Au8DTgNuTAuTt8+VGced723P6xqTxaKGavyW5fVwJvPKxSNAyCcsbqom7PfhEzfwdU7Yz9LmiInfypGNbR1ctv45zrztyVFFXgyFU7Y5MlXdD+z3HveJyDZgAXAJsMbb7R5gI3CNt/1edQ3SnhORehGZ5+37uKoeAhCRx4HzRWQjUKeqz3rb7wU+BjyS7zW8tpadq85axhd+8hI9g6nJuFxJsHB7YJYI8+qrqA0HqA0HUNUpF7+Vr6XPZFsAGbeO8jEpc2QisgQ4GXgemJsWDu8+/Q0uAPZkHLbX2zbR9r1jbKeAa4xs75UisklENnV2lrYMmTMNu2Ihv4/m2iAAuzqjtLX3srMjSiQ0ddaKcinDV8z+pcC4dZSPsv8likgE+E/gi6raO4GZ3lgvaAHbJ2xOLseo6npgPbgOsVnOOcRYoRXXrl059L/tXU/tIukofssNKJ3qvGfBHAYSKQKWcLA/QWff4FD+ZcpROqNxNrZ15NybmKgHlE/vaKx9xyrqMpBIjRsSku/+pcC4dZSPsvbIRCSAK2I/VNWfe5sPeENGvPv0f4F7gUUZhy8E9mXZvnCM7YVco2jSoRU7OqKoKqrKzs5+rn5gCxvbOtjY1sGLb3cTSzrTQsTSfxhVAR/9CZummiB+n3ieYxYL6quYUxXIuTcxUQ8on97RePtuP9CbV+3NQmp1FsuihupR/nNmtbc0lHPVUoDvA9tU9VsZL20A0iuPlwO/zNj+KW9l8XSgxxsWPgacKyIN3iT/ucBj3mt9InK6d61PjThXPtcomszQCp9luTcRovEUtz6yjRs2vIbjTJN4CyDoZYqnf2jRhM3RLRFWHFHHspYIdVWBvH74Ew2r8hlyjbdv0ta8RKISomIyE8pHOXtkZwB/CpwjIr/zbhcAtwIfEZEdwEe85wAPA7uAncC/AX8F4E3y3wK84N1uTk/8A38J3O0d8wbuRD/5XqMUZIZWpBEB21F2HxwgkbLRKWvYM5p4yqErGhv6oRX7w5+oB5RP72i8fYN+Ky+RqISolNIJxDCccq5aPsP4dlYfGmN/BT47zrl+APxgjO2bgOPH2H4w32sUw8a2DnoHk6QcsB2HgM/CZwmqDN33xVL4REhNk+gxBXoGUnz3kycM/dC+9MAW3ukeJOU4+C2L2rCfr3x0VU7ny5bwnWsy+HjnWd5aOzRXlkv6UqXSnUxmQnmYOstO05SNbR1c/cAWYl5vRYGE7eBz3CBSsd0eQyxpE/BNr0SKhG0PL8IL4MWPIfn5qKXdPgYSqTGrlU/0Wq7nyVckjKjMHIyQFcmtj2zj8EASnwgBC5LeRL6tELBgfn0V8ZRDe6+NTpecJI+Uw9Cq5F1P7WJOVWBYlfDOvhifv/8l6qoCWVcas/WAcu0dmcRxw1iYAr1ZyFag99gvP4KqDiuuke6dHb9gztC2Nzr6GEhOLyEDWNxYzc0XH8eXf/kq9Rme/L2DSfb1DOKosvKIuqGeUbFzPsUEqZoalzOLfAr0Tq+xzjRhrP8aSlGMdrIJ+62h1cORk/1d0bi3j69kwZ3FBKlWIsDVMHUwQlYky5prcDx3CEVxvB5uwDdcuOKp6dcbqwv7h1YPR67yxVMOKLTUhob2LzYO67ZH2+jojfH2oQF2d/VjO5qzOJqo+dmNEbIiueb8FdSEfCRSDrGkQzzlYAkkbWVHRx+9gwkGEmnrnumDAD2DyaHVw5GhA9VBN22pNvxupHqxZd62d0RxVPF5mQ/7DsdI2U5O4phPCIdJ3J55GCErAQGfRShgDYlVOnA/nnTY0z1IwBIuOH5uBVuYPwrEbTcxPL16uGZFKz++8nSevuYc7lh3MgGfr2RxWOkeVfqfJYIIHOiN5ySOuca5mSHozMQIWZGkV/OWt9YSyOh1pf3sHYXOvjhP7zxYsTYWigAtkdD4q4clDO7c0z3A3NoQDorj6FCaV9JxchLHXANczRB0ZmLCL4okMxE4Ns482MGB5GQ2qWQ0RwJE4+NbDpUyDisd6Dp/ThVd0fhQ78on74rMRNfKNSzDJG7PTIyQFclYkeYzhe6BFEe3hLLvWALSga4Bn9AcCQ6VoZtfH87ZtysXYTUl5WYmZmhZJFedtYyewSQ7Ovoq3ZSSkh4k5xM2UswkeuZQtb03jt8SFjZUUVcVLOnwzyRuz0xmXjeiAiRth/g0DHadCL9PsIDtHVEuW/9cTm6rme6nbx6MctV/bCYS8nHM3LqcglPTPaozb3tyWPAtTLwCmW8QbE3QN8wz7isfXWUCZ6c5RsiK5LZH2+gbTLldmBmQJJFerxAEByXst3Ia2mVOovfFkhyMJlGUWNLJ29I51+FfvuKZuf/y1giDSbti2RYmC6G0mKFlkezo6MPBLYs2E3C8wijqqXJzJDTh0C49nPztm4fYf3iQvliSzr44Iu5EfcJ28h4aFrICGY2nOBhN4uhw8cwc3k6VFUsTAlJ6jJAVyTTLA8+ZpK3D/jjGGtpl/iDDfoukF8Tq5l06xFIOtqP0xZJ5rQzmGtqRGQSbi3hWwhV2LKaKoM4kzNDSMC4JW3nr0ICbbqWKZVnD5ssyf5DNkRD7egax1RlW6s4S2Hc4Rk3IwnbgzNuezGkole8KZMJ2XO83xy0YDKNFaqqsWJoQkNJjemSGcUnrUdJWkg7UhX3DhkGZPZy6qgDz51QNDbEF8Av4LYuU49A9kKIm5Bs2l3XqLb/KaXVzvNXQzCFowBJsR3FQmiNuyMhIkZoqK5bGu7/0GCErkhkyNZYVvyUMJIYP2dI/yL5Ykl2dUc/WB0J+i8WN1YQCPmxP2QToHUyxrb2P9p44KccZdy4rk4nmkzKHoNUhP5YITTVBasP+MUVqqlhNTxVBnUmYoaVhXNILsZa4QpbwJgTTw6BbLjmeLz2whe6B5FCZOIBkyq1dsKwlAsBr+3pQ3BJyqu4ygu1Af8Jmd1c/9V41pkLKtmUOQdMrgRNF9k8FV1hjDll6jJAZxkdAMuoOpOeeMh0xmmqC9MVT2I4S9FnMqfLRPZCkvSdGJORnMGmjgM9yq5WPrISnCocGkry2r2fMJuQznzQVRCpXplNbJ5v0f0iBliXvyfUYI2SGcbEEmmqCHBpIYqMcEQmNGgaly8RlBq9WB32098Zp7xkkYSuqrvX3oGOPvojX7etPjPEaU2eC3jA5ZMb6oc74ib4jMHNkhmEIEPIJ9VV+aoJ+QgEfy1sjHN3iGkiOnFcaa+La77NY2lRNdShAddA3YRG89OKAM07RYjOfNLsYOZWQK6ZHZhhGus6AqutF9vQ150y4/3hVjYI+1yb7YNQ1lbQ9oRopV+lMgkho7D9FM580uxhrKiEXjJAZhvAL7OqMDsVkLWkcnRI0VlrNWEKTLlaSsB38lmuUmHKcYRkQIb8MZRJ8+syl47ZrrAn9L//y1SmR2mNSjUpLoW4yRshmOT5L8Ikb/Kq4CfACpGzlYH9iKMxhZF7jyPzJkT/eRU+5f5BBn0XKdu2rRSz8lhDyW/TEUqQcN4H702cu5fMfPiZrW7O1YbKZau2ZCWT28PPBCNksRoD5c8IsbKhmb/cAnX1xbHWHhS21IXyWDIU5ZAuDGEn6D7I27Odgf8KdA1OoqwkQ8Pn41h+elPePfaw25FNbs9Tk+5kYspPZw0esnPXJCNksYaQ5hyXwxQ8tH+oJnXnbkxzdOnz1UVWHwhzyTavJ/INM2b0kbCXot6ivCqKq7tDwqfyEZ2QbegeTrkiqsrixetJ7RCbVqDyke/hy1Zuv5HqMWbWc4VjAyiNq+ZsPL6cu7MdnCXVh/zARg+xpM4Wk1aSLlWz6yrm8fNN5/POlJ9KfsEk6WpDrw2TU1swHk2o0dTBCViRTvchbUyTINeev4PMfPoaXbzqPN75+AS/fdN6oOalsYQ6lCIMo1vVhMmpr5oMJDZk6GCErlimmZAIELHcSf+URtXzj0hNzGmZly0MsRZ5isTY65a6tmS9TJXfTYObIimeKZI3Xhd2vMugTludoLT2SbGkzxabVlCJKf2QoxlgxbJPZIzKpRlMDI2RFIlJ5d9gVcyM8+jdnV7YROTBe8GyhwmOCZQ1pjJAVSSTkpz+RQtUVtMnQNMEV0KqAj+ZIcNw8xalGOYTH9IgMYISsaD595lJuf3InPssNVxinRm/JOKqlZtjQbCCRorU2XN6LlhAjPIZyYISsSNKrf3c/s5veWH7RyPliiRs7BVRsTshgmIqYVcsSkA5tWNhQVfS5LMb/UnyerY5ZJTMYhjPremQicj5wO+AD7lbVW0t17kUN1fTHk3QPZO+Z1YZ8HL+gfmiOKNPdtKMvTjzlYHkLCW51IHf+rT9h8+jfnF6qJhsMM4JZJWQi4gPuBD4C7AVeEJENqrq1FOdPr8oB44rZx0+ax7fXnTJqe+bc0WXrn+OlPd2oA5bnc+OoImCixg2GMZhVQgacBuxU1V0AInI/cAlQEiEbuSoXCflRVfoTdl4rdFedtYyrH9jC4YEk6vl4OQoN1QEzH2YwjMFsE7IFwJ6M53uB943cSUSuBK4EWLx4cV4XKMWq3JoVrfzzpSdy6yPb2H3QjXpf3lLDNeevMPNhBsMYzDYhGyuhaFTol6quB9YDrF69uiLhriZMwWDIndm2arkXWJTxfCGwr0JtMRgMJWK2CdkLwHIRWSoiQWAdsKHCbTIYDEUyq4aWqpoSkc8Bj+GGX/xAVV+rcLMMBkORzCohA1DVh4GHK90Og8FQOmbb0NJgMMxARCvtQTPFEZFO4K0cd28GusrYnFwx7RiOacdwpks7jlTVllxOZISshIjIJlVdbdph2mHaMbntMENLg8Ew7TFCZjAYpj1GyErL+ko3wMO0YzimHcOZce0wc2QGg2HaY3pkBoNh2mOEzGAwTHuMkJUAETlfRF4XkZ0icm2Zr7VIRH4jIttE5DUR+YK3/SYReUdEfufdLsg45jqvba+LyHklbMubIvKKd71N3rZGEXlcRHZ49w3edhGRO7x2vCwio90lC2vDsRnv+Xci0isiX5yMz0NEfiAiHSLyasa2vN+/iFzu7b9DRC4vUTu+ISJt3rV+ISL13vYlIjKY8bn8a8Yxp3rf506vrXmVnx6nHXl/DwX9nlTV3Iq44eZsvgEsA4LAFmBVGa83DzjFe1wLbAdWATcBV4+x/yqvTSFgqddWX4na8ibQPGLbPwHXeo+vBW7zHl8APIJrpXQ68HyZvot24MjJ+DyAs4BTgFcLff9AI7DLu2/wHjeUoB3nAn7v8W0Z7ViSud+I8/wWeL/XxkeAtSVoR17fQ6G/J9MjK54h11lVTQBp19myoKr7VfVF73EfsA3XMHI8LgHuV9W4qu4GdnptLheXAPd4j+8BPpax/V51eQ6oF5F5Jb72h4A3VHWiTIySfR6q+hRwaIzz5/P+zwMeV9VDqtoNPA6cX2w7VPVXqpr2W38O17JqXLy21Knqs+oqzb0ZbS+4HRMw3vdQ0O/JCFnxjOU6O5GwlAwRWQKcDDzvbfqcN5T4QXpIU+b2KfArEdnsueoCzFXV/eCKLpB2h5yMz2kd8OOM55P9eUD+738yPpe/wO1hpVkqIi+JyH+LyAcz2re3TO3I53so6PMwQlY8ObnOlvyiIhHgP4Evqmov8D3gKOAkYD/wzUlo3xmqegqwFvisiJw1UZPL2A7E9Ze7GPiZt6kSn8dEjHfdcn8u1wMp4Ifepv3AYlU9Gfhb4EciUlfGduT7PRTUDiNkxTPprrMiEsAVsR+q6s8BVPWAqtqq6gD/xrvDpbK1T1X3efcdwC+8ax5IDxm9+45yt8NjLfCiqh7w2jTpn4dHvu+/bO3xFg4uBD7pDRfxhnIHvcebceejjvHakTn8LEk7CvgeCvo8jJAVz6S6znorSd8HtqnqtzK2Z843fRxIrxxtANaJSEhElgLLcSd1i21HjYjUph/jTi6/6l0vvfJ2OfDLjHZ8ylu9Ox3oSQ/BSsRlZAwrJ/vzyCDf9/8YcK6INHjDrnO9bUUhbv3Wa4CLVXUgY3uLuGUREZFluO9/l9eWPhE53fsb+1RG24tpR77fQ2G/p0JWa8xt1ArMBbirh28A15f5WmfidrVfBn7n3S4A7gNe8bZvAOZlHHO917bXyXMlaoJ2LMNdUdoCvJZ+30AT8ASww7tv9LYLbk3RN7x2ri7hZ1INHATmZGwr++eBK5z7gSRuT+KKQt4/7hzWTu/25yVqx07cuab038i/evv+vvd9bQFeBC7KOM9qXKF5A/gXvMyfItuR9/dQyO/JpCgZDIZpjxlaGgyGaY8RMoPBMO0xQmYwGKY9RsgMBsO0xwiZwWCY9hghM5QdEbE954MtIvKiiHwgh2PuFpFV3uM3RaR5jH1uEpGrvcc3i8iHi2znx0VERWRFMecxTD6zrkCvoSIMqupJAJ5dyz8CZ090gKp+Op8LqOoNhTdviMuAZ3CDMG8a+aKI+FTVLsF1DCXG9MgMk00d0A0gImtE5KH0CyLyLyLyZ97jjSIyKirtRgAAAnpJREFUqlSYiFzveVX9Gjg2Y/u/i8il3uM3ReSrXu/vlXQPy4tqf9zbfpeIvJXu6Xm5q2fgBnGuyzjvGnH9336EG9iJiPyJiPzW62XelREp/z0R2SSuT9xXS/qpGSbECJlhMqjyfvRtwN3ALYWcREROxRWZk4FPAO+dYPcudRPavwdc7W27EXjS2/4LYHHG/h8DHlXV7cAhGW78eBpuhPkqEVkJ/BFuwvxJgA180tvvenXrNJ4AnC0iJxTyPg35Y4TMMBkMqupJqroC12vrXi+fL18+CPxCVQfUdfyYKAfv5979ZlwzQXDTu+4HUNVH8XqGHpelX/PuL8t47bfqemaB63l2KvCCiPzOe77Me+0PReRF4CXgOFzzQMMkYObIDJOKqj7rDedacO1lMv8zDedyihwvFffubd79Ox9TPEWkCTgHOF5EFNelVEXk771d+jN3B+5R1etGnGMpbs/vvaraLSL/Tm7vx1ACTI/MMKl481U+3CTvt4BVngPCHNzezUQ8BXxcRKo8542L8rz8M8Afeu04F9daGuBSXPfWI1V1iaouAnbj9uBG8gRwqYi0eudpFJEjcef++oEeEZmLaytkmCRMj8wwGVR5wzBwezSXe6t/e0Tkp7jOCDtwh2TjoqovishPcN0c3gKezrMdXwV+LCJ/BPw3rlNDH+4w8tYR+/4n8MfAT0a0YauIfBnXGdfCdXr4rKo+JyIv4TpL7AL+J8+2GYrAuF8YZg0iEgJsVU2JyPuB76XDQgzTG9MjM8wmFgM/9XpSCeD/q3B7DCXC9MgMBsO0x0z2GwyGaY8RMoPBMO0xQmYwGKY9RsgMBsO0xwiZwWCY9vw/aav6RDMtFoUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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vE/YW4Jf9LvcBvwv8GXCT/xy8mMj/7Pe/CXhQVXPACRE5DrzR73dcVUf8Yz0I3CQizzV6DK1nadUAwPPnZpjJFrHwhFfRUc7P5Sk6M3XvYzlThWb6g4XZ3G458FhD4TGtCKdppxCdem+IKPtlVPuxsTTzuSKZggsCccvCEk+QBT9E9ZzrUhrXa0Rkxv9LA68OnotIPVfqHwP/F+D6rzcAU2V2slG8rKr4jyfBE5LAtN+/1F61TVT7co5RgYjcKiJHROTI+Ph4Hae5dig43iVdvioHkHfqu9SXq1W02gu80dXSpfovZ8q32iE6rdb47nn4ec7P5ZkvuCiefbHguBRdJWFbDZ1rTcGlqraq9vt/faoaK3veX2tbEfnXwJiqPlHeHHaYJd5rVvtSx19oUD2gqntUdc/Q0FDIJmuXRMwCBVcVRXFVQf32OljulK/VITyNCsZa/VspnFtpA2tleNaho2N87tALi25ABYqu0p2wmZjNMZ0plM7NSvZGyphWVrL+aeBGEXkReBBv+vbHwICIBFPUbcBp//kosB3Af38dcKG8vWqbqPaJZRzDUCe7hvvY2JcgZnnqfcwSNvYl2DXcV9f2y9UqatlfmnEzN8swfdt1O1smnFutEbVS49t/eATH16zChM6FuTznZnL0JO3Sudn9Gy+P2l/d5ckaRVU/hpfDCxHZB3xEVf+tiPwtnqH/QeC9wJf9TR7yXz/qv/+IqqqIPAT8jYj8EXAZsAt4HE972iUiO4BTeAb8X/a3+VYjx2jVZ3ApErhDOK6iqjiuUlxC8ym3ac1kCjiuy8beVOn9eqd8YXaTWiXKgLptaY3Gz9Xq//EvP1O3Pa7a3nfz67by6MiF0DG02ga2HB+9ejk5OU8yZlF01LtzFx4QILA0zGSKJGyb/q441Lg3Wya4anAH8KCIfAr4PvB5v/3zwF/6xvcLeIIIVX1WRL4E/AgoAh9QVQdARD4IfB2wgS+o6rPLOYahMRpxh6gWLEXHZSydB2BDT3LFgb9RN/PdXzvKXN5pqOZio46yUf3rFQDVcZMTszmOjaUXxU0Gwu3xFy+QtIXh/hR9KU8wNtMG1sqg7O2D3RQdl/NzedyyC0YA2xKKfmPRUU5PZ/x31V20o2A7o3DUxsQqVtJorOItBx7jxfOzzGSK5B2XhG2RiAmOC+u64iuukn3t3Y8w0BWnfMVbVTk2Nsu2wa5VCe6uN+avnrjJ8n2dnc6Sd1wE4bIBT3g1+5yalZ45bL+feOhZ8kWHsXQO19e4YrZgiZAvequMqZiN6yoxWzj2Z7+ezY+/2BW2v9XQuAwdTKNuCcfG0kzPF7AsKf2y5rMu67rj/PMdb2no2GFuFFHaTTCuesfZTOqddpbHTQKe8HK1Im6yXKPc2Jvk9HQGRRmbyWJb0jSNqPqz/eRNr2qqC0b5Z1J0XNI5h/U9ccbTni+XiBfBkCk4iEDBgYpfoyqM4DI0xPbBbk5MzJLOLmhQfakYOzb2hvYPfkktWbg5XVGvvQGibFlBJoLq6c2ODd01w06ijtGsPF3Nis8s/6Ho9x8nZnNkiy7DfammaES17ITNFl7lFZju/tpRzk7nKPrGBtsXXsEk0M2kJ6L2ZQSXoSHetHM9j794wdMUBPKOy/hsnl9+Y3gm77gtZArguguxgQAJuzFn5v2HR8gXHc7PVgrMR0cucNeNVy/SboCG7DUX6+Ytp564yWqNsr8rTsyWpk4Pl2P0D4T88+dmKDhKImaxa7gvVJCG/SAAzOUdNvUnOTfjaV2BgT4IwxpNdEW6QxjBZWiIR0cuMNyXqLBZ9Xd5AuT2kP4v39QfoqHF2bGxtyENp5bHfpR208gq4Wp4rd9x/W4+evAp0tkiRccLWA/iJgMuRhbTRqf/5faqmazn553JO7x4fnaRsI/6QeiOW8RtYV1XirF0jnJTuwIX5gtILBGpHhvBZWiIk5PzJOxKT5yEbUVe5MGNt3ldrOLGe9PO9Q1pOOUe++BPOV0l7yj3PPw8937nBHN5LzPB+6/dwe1vfXmkQAsTmM0MKapXIO/bPcy7917Bvd85QSHvaS3v3ntFRd9mpDleikbdIAIhf37W+yGxLKHgOJybyWGJcPuD3+eed11Tik0sOJWacn9XjBMzWXYNh5sXSm4SYtmhHTCCy9AgfckYx8ZmscuM7aemoi/CqBuvUQ0nEbPI5B3csmkV6nldf/aR41gCMcu74T77yHEAXr1tIHSKEiYw+5Kxhm1iYTQy5Tx0dIyDT55iqC/J5b5QP/jkKV69bWCR8GplrGKjWl0g5POO62u/LoHJUlWZyRa57a+e4AP7Xha6ODPhu8NUf94BC8pXe/lxGSLohCIOqorreo6ngfOgUNNXMPTGa8RJEzyP/Wq3iv6eOGPpvC+0PC3QEii6Ln/+7RE29iUjpyjVAlNVS17rK5mSNSKQ2yWoet/uYW4eneLe75xgNldEROiKL3j6V/uUzWQKnJ3O4qriojhV0zxLvJCwzx16AduS0uKM4ypF18VVr89MxsvhkIpZzBdCF2uM4Gp3VsM4vBzGZ3OIVekaKJa30tUIfckYx8dmcdQLAxnqS2JbUor1qxbgUVNO11VisUpDvyUwX3BKQiGdLTCezpEtOrgKlw9WugZ1xW2mMwV+8fXbFk05G/3sG5lytroCUr2FLN60cz0HnzxFT9Imk3dAIFdUTkxU2qyCazTop7qQPaEcS3w7pKs4rouIlIKpSyjkCg5nco6XKSIUiQxJbGWsoqEBVqv+YKMUHMWPqwYoRfnXmx0CvBtqfDZH0VUEL0PA6GSGmUyhZPuqjscDQmMV+1KxCk9s8FbmRDwhkM4WOD2VpejHVarCqaks6WwpY1Mpa2cwbXvF5j6G+pIcfPJUw3GAjQRrbx/s5vxcjpHxWY6enWFkfJbzc7nI6WkjMZlRcY33PPz8ovbPHXrBM7RniliWELMsLIR0tlhxDQbX6MbeFFsHu4jysnLVu0aSMQvbstjQk/CC8fF+VGLiTf3nCy4uSlc8QgzVWHg2Gleb0Ckl44uuhgqKYnVjGdW/8JNzOdZ1xelJxJiYzZF3XGK2sKEnwaMjF0LdHvYfHuGBW/cu0oDef+0O/vibxyg4C8LCEti6LkWm4DCeziH+VMV1IRnzpixnp7P0+natguNpfVHHhfpjHhuxF4W5loyl89zyhsWuJeUreelskbPTWZ58aZIP7HsZt7/15Yv6R01D7/3OCYb6khXtRdctrfraZYsfecetuAbLr9G+VNyzc6pSrTC5Ci5KXyrOQFec+YKLCCRtARVcvDhXS8BxlUox75sflrA/GI2rTWjXqsPVZAvVl1nt9kNHx/jowaf4/kuTnJ3O8P2XJnl+bJai49LfFWfnUC+7N/dz1VAvc3mH58/N+G4OWuH2cOxcePq3V28boDdh499vWAK9CZtf2rOdgqNkiw7gTSldlM39KbYOpFCo0NzG0tnQ4z5zarKhjAyNZBF9dOQCQ70JEraFq97q7FCvJ7yrKfmx+WOMWVKyI4WNJSrTw1zeWdSetC1yRU9YB6JC/fGUX4PV16gtQsH1hEy5W54IbOhJkIjZ3HnDK7jrxqvpScRwXC/E57J1XRRcz6ZYdDx7aWlbIBW3iXsr18bG1e60a9XhaqIUq6j2u792lMn5QimkI+svP52aytLflQA8I+25dBYNNDdVYn5+r3K3hzD2Hx5hqD/FFVUxiYFj6u0Pfp/5vEMy5k1x+rvijKezJGNWxV0R5W4xX1CGGzSg17sKeHJyno29SYb6FjJlqGqkPSydXXA/AE9YFBw3dCyNFLJY1x3nwlyB/q4YE+k8rnifRV8qXnENVl+jwfRPFRwW3BhsgR0beys003vedU3Jhlt03NL1ErcEhdK+ABzfgK/qhv8aYjSutqHd6g9GEWV2iGofmZgDvF9W9W1P4Amo8XSWmUyeU1MZio6yuT8J6q1SFRy3rkSFtXJI7ds9zD3vuobLBrrYvC5FXyrGeDrL+Gy+Iu9TYEMLS5Coqi2LeaxXyw5W8ubyDnnHLWkoqp62FDaWRgpZxG2bD+x7GVdu6GVdd5yuuE0qJsznnZKQvufh59l/eIS5nLfQcXYmWwqULv/ubQsGuuKLpvX7dg9z8+u2Mp7O8eMLGQRP+FiWELMF2/KDrv3MuruGe3Gmx1+M+uyMxtVGdEL9wW2DXZyczIS2R+EENpAq6XYu7a1Exi3hsoEu+lJxUrN5f/VPcVxKbg9Xbgj3E1sqdrLaj2w+7zDclyjlAws0KICN1REBPXHmck5T/LvCqEfLLl/JS2e9qL6844K3+MdAdyx0LLUcVwP/tur226uOGWS3ePH8LI+/6E1re5MxzqVzzOUd1PepS8YWBHvRdUO140NHx/jLx35cilH13fDI+a+74xbd3TGOfPznStvIh2cj08MbwWVoiD1XDIQKrj1XDIT237Ghm6PnZr0XVddzsMpXboLd2JtkdGre8/WpI1FhI7GTwY2SjAkj47MlAbWxN0HClpK9pTxB4vuv3REaxN2MKXw9XvF3f+0oYzNZHNXSVCzAsmAm6/CmneFxoo0WsgioNuzPZIpYApPzBVxVP+wK8v4PUsFxiAX2sQjt+O6vHeXCXJ6oxecg1rFejOAyNOT4+s2j4cVDotrvvOEVvP/+7+G4lTddTDzjbt63Z7x0YZ7L13f7tiWvT9EFV11yEYZ/8Azc/Smb6UyRgu/YuK4sdrLaP+7cdIbxWZeY5ZVVKzqe5//mvgRZPztneYLEV28bKGkox8bS5ItuhYvASjXkWkLk0NExnh+bxZayZHtlmUNTMbtmnOhyOHR0jCdfmqTouH7YjXi+cpbn8pKwLSzfLiWOYlme7TJIyxxox9XX1PPn0pFCC2AqU4wUwGEYG9cap9E85kFQbb3t+3YPc/tbdpGIWZ79An8FyndKDFYDXYVTkxlOTs5XCDgFZnMOd3/taOj+nz83w2zOIW5ZpGIWcctiNueUViGr/eNs38Pe0UoDzYVMMbJe5L7dw9x23U66EzGG+pJsWdd1UWoeBmMP/pV/MD0Jm51DvWzoSTbVYfUTDz3raZy+Juz69rSCG8QPen0DPy11FzRmx48+CPPFq8fN77OPHKu7MKzRuDqEVoUDXYywkxMTsxTKVpJchYRnmfXydKmDi3djOFU+QYGz67GxdOi+awVfw2L/uNKUSyHrOyCtS9qk844nSEOyTxw6Ola2OmmxsTdJf1e8KZ9Tre/15OQ8m/qSjE5lcf2SXvif01BfknTWC71RvEyzK70mFgTlwnGqccs+v3VdMSbm8n6ZMU9oFYp5vnTkJEVXK3zi6sFx4ei52VKcYy2MxtUBtLK6S6tr+X34wSf5+x+cqXCXUCDneAYR11XEErYPdrN7c7SNIyrv4FLl0qpX7lR10Q05nXNK46uuFzmXd/jEQ88yly9iWws50WcyhRV/Tkt9r9sHu8kV3VA/zPlckdHJDEXXW41txjURXAsuELcoacMCDPXGiVkLgn/LuiRTmULpcwuUV0dhdCrrRUaU+cQ1QuCftlrlyQxNopXhQK12fH3o6bOApwkFfwHlDon9XfFF46gm7KZcqlxatVtAuUZXvZRfLgCLrkvBUTIFl7GZLHHLAt+HykKYmM2t+HNa6nu97bqdTM4XsC0hGbe8EBrxkjCOz+aJ2cLWgS76uxJNuSaCa8GzY1kkYzZJ26I7YdPfleANV27gQ/60f3QqW9JqJfiv6rvNOS7ZokuuWo1egqKjFB0Xq2dgc1QfI7g6gFZqRa0utFruc1S9gjjQHcdRT4M5NpZmOlOI2ItHmEZx23U7Q1cDb7tuZ2kaVu57FDhIlrQJ8bQLgN6UTcFxyRY8oRXci66qHzDsa3Si5Iruij+npb7XfbuH6UvFiJcJ5e3ru3n5pj4sS7hqqLeUzrl62+UQXAv9XTHcIJMDStwWRiczPHt6ms8deoHuhM0rNvfVCiVcEZ5DKkgsnorqYwRXB9BKrahRx9f+VLhZNKrdiri6Sz/QvpNnsFpWiyiNQv0dlq8GPj06xW8+8CTfPXGe83MFZnNFcgWH7oRdSpEc3CCOCwlLmMk62FaljcdzjPRySZVWIv3Kyyt1EN4+2M3EbGWQ9cRsZZD1ruE+tgx0sXunr6QAACAASURBVHtzPzuHeulLxSs84APS2QLHx2YZS+eWXRQ3uBbWpeIgnubjuspsrsj6nrjv8e7Z/tLZYin9dhBo3/yCYdHZIYxxvgNodTjQl38wyuMvXsBxldPTWTb3J0LTn9x23c5ID/bq9mDbqFAgwcufvnndguPqfL7I+Gw+cpxhGsX+wyOkM3ly/j1ccBR18/zJI8cqgn9dhXQufCrqAqmETU/SZiZTrAjYdlxwxS0tEqRiFuu7E/xBVe3DsHOvTmBY3ba5P8GjIwvpgAqO5+z6y29cKOAc9d2X+5cVHZdTU1kAtg6kVpwSab7gsrE3wfR8wcuTpZ4QK7iKbQnqemmM+rviNb+vlbCUDDSCqwNoZfrewHge4LjK3//gDGen/wenpnOL8oNNRFyo58vay32nonAJLx9WizAt88iL5xdlJ4iQTzVJZ4tcvr4bx1Hm8mXGfBY0icCWU+umCsur9tGDT6F4dSSDto8cfIrJucWfpSp89ZmzpYwP1d99bzJG3FK+9MQofcmYH9uYI2YLm/pSpanjclc8vco7GfL+VDlgfDbvpyDysjoEn3m1U2wzEVOerPNpVThQufE8QBUePTHJy4Z6FrlJRFF+8Va7WEQRFkpTizAtMzIHXYMonhYRpUEIcMWG7lIR1iihsP/wCOlsnulMsZTpU4B4zGKLr112J2KcmsqE+jYpcHxstqIt+O6rhWKgffUmbbas66ooQ7gce1fg8Oq4GjptD4YbxCgGAj4QXkFK7aYJMzFTRUMETtRcjsY1ooCw3GJhTGcK3g3sT0F6k7Uvx1YHnU/OF0rpqKs/FcuSusreP3NqqmJKGny8TsGtCDOqVVcyKrdZlM+dt/q58njKYP9OmY+D6NJCqORfppWvW4kRXGsc21+xCjOslt8MgbNjFOW/0GEpVaqJW9XGeSllDAi7pWNW61NY96Vi5GfzpbzpMcvys1R4Yzx6dqZUpSYsrOW263ZG5U4HqPBrqvF7EXnjn5ycxxYWx1nGrMh8+fWmbw4qHW3qS/LShYw3hpCBWH74EQoFt8wdomzs/tstMNYvYATXGueNVwzw6InJRe27N/UwX9BFxt8oyq/RCoNyDDIhM8yuRKwh47wqoWXImsmu4T7i9iznZwu+C4RbMT2Kl2Up3bsjEVojoJYGW+7rZAt1hcGUU6vC0h3X744silttb0vGLE5NZX2/Os8n7SMHn2KoN0ne8QzzE74AL9c+E7ZVypBa9ANKLanMxWb508XN65KMp/M1M+OuBCO41jhTmeKiqZEXSmhx143ezfDkS5Ml4++PLyxtNyk3KL8wPhfaZ8Y3hpdTrOGo6CiRZciaRSBwe5MuF+YX+5QVHC83V18qxjePji9KgVzLBgheNoxAU9rc38WPLyzOsuH1C9++VoWlMBvoLQceq6hpaPvxoYEriPr/CTA1XyBlC5Zt098Vpzthcy6d81dpFbEWjlW+WBHYuwJc9TSyDT1JXHchdVGzMYKrDbkYZcqCYzx31osBDC4+8Z0zRybmSjfDtXc/wkBXnBqLPJHjfnTkfGT/6qnoUlpdWBmyZSwgRhII3Nv+6onQ9131DNK5ghdbWS14a9kABdg5tJBTbD5fLPmEVbMuwieuVoWlsM++uqZhrizeEYK87p6DbcwSxucK7P93ry5pbtdsHyzVwAyrRj6dKZAvLgjCIIA+YQvn53JMzhcip/51UUNZM4KrzbgYZcrC3BXKDaxelZyFq6Y8WV8twsZdi9HJDOt74mzoSda0nwVUO7NGObeulCC5XdTqWNFvPD+XKyUkBE8Q96di5ArFCpeMmAW2ZS2yQUWNP1MMv2O9bYREWVWcousyn3dDP/v5vOPVPayqzBRQnl/ecZWYLZGr12Gl4boTLr1Jm/NzhVJ22/U9cVThwlwBR926hJbgaWleXYCwES7GCK42I2rl6NNffa70ixr478zmnYY1siDTwVy+SCpWY5Ww7JopT9ZXi7Bx12LQz3VecLypz2B3jPHZ6LCffFEprwnTbLkVfDb1cmGuQHei8ma+ekvfIpth0YWrhro4PZ2rsM/98TePhe43qvBIVDXvguOGfvbZvFO3tuMobOlNhL4X5Uf46a8+x/PnZkvHcBXGZwvELC+nWr3HtohYSTV+XJ1DmCtB0XF58XyGK13FFjjm+/k06iUdaFrzeSdymhJQbm56dOQCw35a43Kv8mqqp0q1bFYAQ30pepIxhvtSTM7lOB5hD4NwY3azzb7BZ1Pv/vuSNsN9qYqb+ba/PBLa9+i5OV421MPlvpA7+OSp0nvVPnRR9+tQb5LJuTyFMo2sN2mXbG/ldMVttAGHKgtK7h5hhGliHzn4VKhw0iifkgiWM903gqvNCHMlOJfOEbcsuhMxRsZnS8vRE7N5dg711u0lHWhzyZhF0dFS6pYwyq+5k5PzbOhJsrE3xQ9PTUdu06jNaiZTYGI2x4vn572kdDXWzxtdgVsOwWdD0a1rNWzXpn4euHVvRVuuxkAXaaNlU7WKfvHF38uho2P8+Pzcos9hNuewsSce6sdVjw8W+Fk78GxojXA+YgXY0dZN4wNMkHWbEZWtYVN/EvCW44P0MHlfo6nXSzrIRrCxN4nr58Kqh7Ag7zDKx12Pzer0dIa845K0paZD5sUi+GxEKGVrrcVKYkWDvFdhhCXe2394JNJHbNKfbldfM7VCroIxeBV9bCyrvu+gvJp2raunK26zxOFXRMs0LhHZDtwPbMab7h5Q1c+KyHrgi8CVwIvAL6nqpB+X9FngHcA88Cuq+qS/r/cCH/d3/SlVvc9vfz3wF0AX8BXgQ6qqyzlGuxBmT0jYVklIJWyrlHs8uMDr9ZIOtLkgnm1iNhcZ1xcr+8ks98uqxV03Xl0at+JNZaOW/AMEYbg/xXjaq2jtXAzVKoLRyXlmssWF5Hh1THduOfDYomDqeqj1Q3AhUwzJ2R5Z8AaHys8+mLa+//7vRW4jeJliS7YyvNXA4Ljl+fVfvqmf267bydOjU3zu0AsUXZfkEllNe5Mxb5GjRV6oUks9X9GORbYAW1T1SRHpA54AfgH4FeCCqn5aRO4EBlX1DhF5B/CbeELlJ4HPqupP+kLoCLAH7zJ6Ani9L4geBz4EPIYnuO5R1a+KyO83coxa57Fnzx49ciTcbnGxKF8FrM4EELM9r+l6wmGqy05lCg4nxudCf/m3rUvynY+9tWLb/YdHaro3vPjpny89v+XAY4yls5F+XOCVpBruT9GXijOTKXB6OlNKxdwJXL6+u+KzLDjKSzX83F421FN33+p9j05mSqudYZR/9gG7/sNXSt7t1Qx0xSg4WlHSbaArznzBpeA4TKTzpdWPDT0JHFeZ8vOl2ZaQL9ReMYxZQsKWmpEES3Hmvt8id+ZYqN7Wsqmiqp4JtBlVTQPPAVuBm4D7/G734Qkz/Pb71eMxYMAXfm8HvqGqF1R1EvgGcL3/Xr+qPqqe9L2/al+NHKOtKc+Z5SrsGu7lqqEeXKWhwrFhubfW90QYZKsMxPt2Dy+y59QimPLWYotfSxG8FDcbesJXtQJa7Q7R6P7DspfW4oXxOZ45PcML43Ohdqxa+478nnzKp3BBPq6oNbm4LcRsyysYgpeOeS5X5KXJDCcvzHNmOkchcHR1lXS2yHSmUMorny8u7ebg1wEiFfMytzabi2KcF5ErgWuA7wKbVPUMeMJNRII7bitwsmyzUb+tVvtoSDvLOMaZsjZE5FbgVoDLL7+cdqBZ2SGq9/MTH/9qaL+w0Jt7Hn6+oePcBfzKX0RPV6rj6xK13DMo8xQve95MSl7gJcfM2v2XG4QO3ipjLapXZJcqOPGRg08xmyviuMrEbI7ffODJ0B8OAVIxqYgTdV2YK7qLztdVz8ZTzDuL2pfCsizmCw621Ne/UVpunBeRXuC/Ar+lqtET9fDrUJfRXnM49WyjqgdUdY+q7hkaGlpil51NIWL6Ud1+6OgYnzv0QkP7XkrQ3nXj1SRsi2Njs4xOZuiO174cAw9tWMhe2myCUJh6dh2WlbZZnJrKks56U7N6VmgnZvPk/KR/6npJExVvyhak1gkcPecLyoX5PLYlbBvsJl4Wg9gsckW3VDyjFZP/lgouEYnjCa2/VtW/85vPBdMz/zHIMTsKbC/bfBtweon2bSHtyzlGJJPzef752DgvjKWZmsszlyuSLTi1g2k7iajrtap9/+GRUmBts3h6dIqRiTmKrostMJ2tnXN+ked3U0fTOOUreROzWUZDKnwvl6KrnJ7K1L1CW75d+ediiRC3LZJxyw+TUlDFAubzDi+en/fyarXA1t3K76eVq4oCfB54TlX/qOyth4D3Ap/2H79c1v5BEXkQz3A+7U/zvg783yIy6Pd7G/AxVb0gImkR2Ys3BX0P8CfLOUat8xidzPDuzz8OeJ7LG3sTbOxNMtSbZKgvyab+FJvXpdji/23qT5GM2diWeDXqlojva2fKV7bG0zlslucsGMWffOt4aTqTcV3y6dppgGN+1eQgvMS7EZs4oAbpjluICGdnsqSzRQa745ydaU5QsS1e5eigwEc9K7TBFLf8B+aygYUVW1e9lUNLhKyvEQVT72YlZLxYtNLG9dPAu4EfisgP/Lb/gCdMviQi7wNeAn7Rf+8reKt9x/FcFX4VwBdQnwQCY8ldqnrBf/4bLLhDfNX/o9Fj1KJc7OSLLqenspyuobZbAht6kgz1eQJuuD/Jpv4uNvcnfeHWxZaBLroTtld8oc5ima2iNxljNldclJokFbMr4t8m0jkiQuhKVLsGLDVVDISWlG641dahGmPKd6B1/CrPkyEZJZZLoABNzHpTuqUqIHkbeQ+u+pWMLGFsJluxsmfLQi6wzvq0K2mZO8SlwjWve71+6auHODOd5dxMlrPTWcbSOcbSWSbSOcbSOcZnc6WqyPUy0BVnY1+SYf9vU3+KTetSbOlPcdlAl18vL95yra0653zAxp4467oTFZ7wQQHSKKqX+++68eqaxvlqGogSaQtWlPmgAWxrcYXvWgiwsTfBhp74kosAAf1Ji5lce6ldtdwhTMjPEtiWsGtTH7s2VVZZdvzlYsdVCo7DdKbAmWlPsJ2dznIunePcTJaJWU+4TaRzzJRlV5jKFJjKFBblFy+nJ2GXCbcUm/qTbO73tbbBFFsHutjYkyC+xGpcLQ4/PxHaPjFXKCX5S2e9uoRL/chVh7Q0Wpy0k4QWtF5oeXnRIGHb5HFqCq9g9U7xiuy+e+8V/NHD4UHc1cR8g30nYQTXMgmyUAJ04VX63b6+Z1E/r7CmJ+BmcwXOTec4cPg4//D02ZKfTbCK1R23yRYXysHP5R3mzs/z4/PRjoqB3W2ot1Jr27zOE2yXDXSxZV0qckp6vsb0JuMvQpz2s2XaltTt2d6sgrVrmWCFM1f03Apq2RctS0jZFht7k8Rs4dGRCzV6LxCsNC4VEN9uGMHVYixLSAQCLmEz1Jfi0PMTxGwpJcUDr6aebcHTv/M2zs7kODOV4cx0hnPpHGems4zNeFPU8XSOidlcyT5UaXcLD4C2BDb0LkxLN69LsaW/i80DkYWC/TEpYzNZXHVxnMY0oiAM6WQTV9rWGoFPmSospRBdtq6Lidkcp6czJGyLsZnan3vcEhzVRRlMOwUjuFaBubyzKD2v51/j0puKc1UqzlXDvaHbqioFx2U8neP0dJbTUxnOzWR94eZNT8dnc4zN5Ep+Ra7CuC/0aqf2q+Q129bxTz+cL2mAMaGmgT6sWEOtECFDbbT039KUp9SulXoowBLB8itzAzhavzbdDhjBtQoE5dPLff5c9dqXQkRIxGy2DnaztUZgtaoyNZ/n1JQn3M7OZDkz5S8wlGlvtVar/uHpSqP9UquKPYkYE7M5Ll/fza+/+WUtr8pjWD45x/Od29zfxehUBqvD3HaM4FoF3n/tDj77yHGKrluqkuIqTa1aIyIM9iQZ7Enyqq3rIvtdeec/Rb4Xt6WhoOen/VxdE3N5Pv7lZxh+JFn/gA0XFQvPqz3wDUslGvuuVxsjuFaBoLx6damtoL1duHx9N2ens6UUwPVe1vmiy+hkpqme5IbmUm2Kn8sb47yhDm5/68vbTlBVEyTWOz2dIeaXNc7VmC8++ds/x2l/UeH0lGd3+/NvNxbfaDDUgxFchkgyBacy6eASRq71PQnW9yQqpqZGcBlagRFchkhmMgVOTWYoui4xy2KwOx5ZaXo5WIDKQuyh6MXxRDd0PibnvCESBRC/6oxEl81aNmVpg0uVYQyGOjCCyxBJzBJsf5lcXWUuKkH9MtnUn6QnYRO3hZ6EXSoIYjAshZkqGiI5P5fHwhNeuaLb9FjCoORZgKpyZro5aWEMlzZGcBlqEtRebIWHz/m5HDOZYqlgQ3+XuRwN9WGulA6hulxVPfmuVoxSKvdeD7cceKyhcQXalQCuOmTTxjRvqA9j4+oAgrJiY+ksA11xxtJZPvHQsxw6Orb0xitgY1+CmCV1p6ludFyWn8VUAdeF/tTy0/MY1hZGcHUA+w+PhJbCajTfVaPEbZvN61L8xKY+Nvcnl1z0a3RcCdsiGfOqKSdillfswWCoAyO4luDo2XSpTl0zCauDF8XJyfnQUlitzndVXoNxx8ZePvzWXUtu08i4io5LruiQLTgUHJeMEVyGOjE2riWIWVKaAt3F0iW36qG8onT51C9q/9sHuxlLZ0sZRmEh31UrKa/BGNjYlqKRcZU74psM4oZGMBpXHTR7atbo1C+oCh2UwprPF0v5rhqlWtOrd5vAxlaLRsdVffH1Jo2Ny1AfRuOqk2ZOzU5OzjPQVVlSvdb+g6rQ+w+PMDo5z7YGVhXLVyN7Ezbn5/L0d8VLml49lAvaWgz3pRpaVQwmhpafFtrYuAz1YgRXnTRzaracqV/5tK1eqqekx8dnKTpKT3JB06uHMEEbxgO37m1ofPGq9NXNLjhruHQxU8U6WMnULIxmTv1qUT0ldVzFEi+NcyNsH+xuann5clxXUVXcDqupaFhdjOBaAsdVhvtS3HXj1U1z+Ny3e7hixa7Z+w+oXo1M+JV+8mUVXaLq0W5btxA3eNt1O5nOFDg2lm7q+Db0JIjZXtGGmC1s6EnU7J+qStRf/dqwdjBTxSX4ic19DU+B6mE5U79GqZ6SbuxNcmoqQ8wSVJVMwWF9d4J0tkDe8Sq+WOLlvv/UO19dsS+BpmtFiZjN5nWxigIbtbhquLeiOK6q8szpmaaNJ0ijXS8JCyzLwlGl6Ci2BUUz270omJ+sS5jqKWnMFga64+zY2FPS9P7g5tfwf+67it5kDNsSepMx/o+fqTSw7z88QsyWhm7qegjTOuMRV6RAabqazhYYGZ/lubPNE1oA2we76EnYpXqZS9GbipN3XAqOlrz/DRcHo3FdwoStRv72z7+yQigdOjrGwSdPMdSX5HJf8zn45ClevW2g1O/k5DwXZvMUa0iu5DKmbWFa51XDfTx3dvGUdOtAioKjTMxmmUjnQbwSW06N8O/13XGmMoWSwLWXCF+K2RY7h7yycM+fS5NbQn26UFVM18iti4cRXJc4S01J9x8eIV90OD+7kKWhLxVj/+GR0nbbB7sZncwghGeJsAT6muSDlc4VuWJ9FxOz+dJ4NvYmcNXT0G5/8PsokPSrNpfXE6ymNxVnsCdB0XE5NbW060egnXbF7VK211qCLlDMggyuQUVyQ+sxU8U1zvPnZjg/l/dsNCIUHeX8XJ5j5xamYcFqZ9RNOdyXZNem/tD3omZdUe3bB7tLms/uzf3sHOolZltsG+xm3+5h+rvi7N7cx86hXvq74pHxk8mYVZqKugpXDfWwK6LIbkB1iNOH3nJVzf5xW0jGbFJxm2TMJhEzKVwvFkbjWuMEBvEg75aIZ4TPlxnK9+0eZtu6JKMhSf4StlegNsqVoydhkw7JnBpV/Pa263byiYeeDa2KDYsXHKKEqeO6odrmjjv/KXQbIVw7/aOHj0UcgdJiRvlrw8XBaFxrnETMKuXdUhTXn+8kqmxWv/SGy0O1pOG+ZE1XjldtHWCwO1ba1hIY7I7xqq0Dof337R7m5tdtZTyd47mzacbTOW5+3dbS/qsXHALE/7MEbAHbCr+0f+G1Wxpqr6VDueo5zbrq+o81OhuaihFca5xdw30VebdilrCxL8Gu4b6Kfo+OXFiUI37LuiTb1/fUtKHddt1O+lIJdmzs4VWX9bNjYw99qUSkhla+WPCKzX0M9SU5+OSpUvaMah+4rriFLUGKHIu4ZSEi7NgQHoVwdibPQFelIB3oinF2Jrx60fru8EnJhu4YH3rLVXTFbYquF7K11NTSTCSbh5kqrnGCqVm1P1W1YDk5OR+aI36p+M1G4yyr4yK7EzHm88WKxYLqrBUfOfgUs7kijqvYljCQjHPnDa8I3f9JfwzV/mBR59GVjNPvuMzkFtYM+5MW3cl4aFHfWlPLvlSMmWwx8n1D/RjBtcapV7CsJLVOI862JyfnsQVGxmcrVhVrBaC/Z+8V3PudE8w5Dl22xXv2XhF5vEbPY/tgN2MxiyvK+s/niwz3pUL71+Lp3307txx4jBfPzzI2k2MJf9u2YbA7RtyyGJ/No0BX3KI7bnF+vsVCWKOTHZmpooF9u4d54Na9/PMdb+GBW/eG3vQXK76yLxnj1FSWoq89FV3l1FSW3mT4b+xSU8uVnkej/V+xua9m+23X7SRu2wyvcik221c4Y5a3wBKzhI29Cd752i30pzxn5P5UjHe+dgu7N68jGbfZu3MDf/Erb+C5T97AE594e0vHZwk4c1Ono943GpehLlaSWqcRSj+ywW+tVrVXUc/UciXn0Wj/O67fzUcPPkU6WyxVAO9Lxbjj+t2L9nd2OhfuF0drnVn7kjZbB7qYyzuM+QH3V23s5s4bXtHQ9/mmHYM8emJyUXvCFjb0JpieL5BzXATve5nJFkt2vlrK5is293HH9bv5V58+fzaqj9TQxi5JROR64LOADdyrqp+u1X/Pnj165MiRizI2A1x79yPYQqgD6j/f8ZbQ/gNd8UU2q+lMIbT/xSDIgbaUoDt0dIz33/+9ivjGmAX3vucNPD06xb3fOcFsruilIIp7+cryIRItYXmrwLNlbwqwe3MfN7xqszeNzjv0JGzef+2ORXa5lXDL/v9RIbzetGOQ2958Vej53/Pw89z7nROks0UUT6tK2Baq4KJs6EmwY2NvKTZYRJ5Q1T1hx11TgktEbOB54OeAUeB7wC2q+qOobYzgurjccuCxRTaowKYUFuzeaP92o14hF3Dt//NwhT/dtnVJPvXOV5fyrpUvsLQi40izuOfh5/ncoRdwXCUZ87TSRMyuGHMtwbXWpopvBI6r6giAiDwI3ARECi7DxWUpB9SV9m83Gs0S8p2PvTW0/WJM45vJ7W99Oa/eNrDsMa81jetm4HpVfb//+t3AT6rqB6v63QrcCnD55Ze//sc//vFFH+taplEtpNH+hs7AaFwLhPkALpLcqnoAOADeVLHVgzJU0qgWcjFymxnai7XmDjEKbC97vQ2IXHI1GAztyVoTXN8DdonIDhFJAO8CHlrlMRkMhgZZU1NFVS2KyAeBr+O5Q3xBVZ9d5WEZDIYGWVOCC0BVvwJ8ZbXHYTAYls9amyoaDIZLgDXlDrEcRGQcaKU/xEZgooX7byfW0rnC2jrfVpzrFao6FPaGEVyrjIgcifJVudRYS+cKa+t8L/a5mqmiwWDoOIzgMhgMHYcRXKvPgdUewEVkLZ0rrK3zvajnamxcBoOh4zAal8Fg6DiM4DIYDB2HEVxNRkS+ICJjIvJMWdtrReQxEfmBiBwRkTf67ftEZNpv/4GIfKJsm+tF5F9E5LiI3Lka51IPEef7GhF5VER+KCL/ICL9Ze99zD+nfxGRt5e1t/35NnKuInKliGTKvts/L9vm9X7/4yJyj5Snb20TRGS7iHxLRJ4TkWdF5EN++3oR+YaIHPMfB/128c/luIg8LSKvK9vXe/3+x0TkvU0ZoKqavyb+AdcBrwOeKWv7/4Eb/OfvAA75z/cB/xiyDxt4AdgJJICngFeu9rk1cL7fA97sP/814JP+81f655IEdvjnaHfK+TZ4rleW96vaz+PAm/DSLH01uDba6Q/YArzOf96Hlzn4lcDvA3f67XcCd/vP3+GfiwB7ge/67euBEf9x0H8+uNLxGY2ryajqYeBCdTMQaB3rWDqVTilTq6rmgSBTa9sRcb4/ARz2n38D+N/95zcBD6pqTlVPAMfxzrUjzrfBcw1FRLYA/ar6qHp39v3ALzR7rCtFVc+o6pP+8zTwHLAV73u5z+92Hwtjvwm4Xz0eAwb8c3078A1VvaCqk3if0fUrHZ8RXBeH3wL+QEROAn8IfKzsvTeJyFMi8lURudpv2wqcLOsz6rd1Cs8AN/rPf5GFHGhR59XJ5xt1rgA7ROT7IvJtEfkZv20r3vkFtP25isiVwDXAd4FNqnoGPOEGBBkcL+p3awTXxeE3gA+r6nbgw8Dn/fYn8eKxXgP8CfDf/Pa6MrW2Mb8GfEBEnsCbZgT17aPOq5PPN+pczwCXq+o1wL8H/sa3f3XUuYpIL/Bfgd9S1ZlaXUPaWvbdGsF1cXgv8Hf+87/FmxqhqjOqOus//woQF5GNdHimVlU9qqpvU9XXAw/g2a8g+rw69nyjztWfDp/3nz/ht78c71y3le2ibc9VROJ4QuuvVTW4fs/5U8Bg2htU3r2o360RXBeH08Cb/edvAY4BiMjmYEXJX2m0gPN0eKZWERn2Hy3g40CwovYQ8C4RSYrIDmAXnqG6Y8836lxFZMgvh4eI7MQ71xF/epUWkb3+d/8e4MurMvga+GP7PPCcqv5R2VsP4f0Q4z9+uaz9Pf7q4l5g2j/XrwNvE5FBfwXybX7byljt1YtL7Q/vV/cMUMD7tXkfcC3wBN5q2XeB1/t9Pwg867c/BvxU2X7egbeS8wLwH1f7vBo83w/5Y38e+DR+34pwUwAAAstJREFUhIbf/z/65/QvlK2mdcL5NnKueEb64Lt9Evhfy/azB8829gLwn8s/n3b5869ZBZ4GfuD/vQPYAHwT78f3m8B6v78An/PP6YfAnrJ9/RreQsxx4FebMT4T8mMwGDoOM1U0GAwdhxFcBoOh4zCCy2AwdBxGcBkMho7DCC6DwdBxGMFlWHV835/viMgNZW2/JCJfa8K+/0pETvgZGo6KyMfr2OadIvJR//mnROS3/Oe/JiKbVzomw8pZcwVhDe2HqqqI/DrwtyLyLbxsEb/HCoNxRSS4vj+sqv9NRLqAoyJyn6qejNpOVf8+4q1fw/PJOruScRlWjtG4DG2Bqj4D/ANwB/A7eJkGXvBzOT3ua0x/6nuoIyIHxMtt9qxU5jEbFZHfFpH/Dryz6jBdeE6V82V9B/zne0XkYf/5+0Xkj8s3FJF/A7wW+KI/lkQrPgdDfRjBZWgn/hPwy8ANwO+LyKvwhM9Pqepr8WYI7/L73qleHb/XAD8nIq8s28+cqv60qv6t//ozIvIDvCwF96sfQ9gIqvpFPO/xf6Oqr1Uv/Y5hlTBTRUPboKpzIvJFYFZVcyLyVuANwBE/pLOLhRQpt4jI+/Cu4cvwktz9yH/vi1W7DqaKfcC3ROQfVfXxVp+PoXUYwWVoN1z/D7z4ty+o6m+XdxCRXXgxgm9U1SkR+SsgVdZlLmzHqpoWkW/jxeE9DhRZmHWkwrYxtCdmqmhoZx4GfslP9YOIbBCRy/GyyaaBmbIsm0vip2l5Iwtpdl4EXu8/r5m51CeNl3PLsMoYjcvQtqjqD0XkPwEP+0b5AvDrwBG8aeEzeDnM//sSu/qMiPwuXq77r7OQMud3gf9PRM7iaWBL8V+Ae0Ukg6ftGTvXKmGyQxgMho7DTBUNBkPHYQSXwWDoOIzgMhgMHYcRXAaDoeMwgstgMHQcRnAZDIaOwwgug8HQcfxPqUUeg3esM04AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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9Arf+xSHe8ZlH+fCDT3LklQH6hrP85NQFdn39CHsePbYwHTeqwlTCda2q/rqq7gXuAN45i2f8LmODDt4PfF5VtwGDwIfD8g8Dg6p6NUGo6PsBRORa4E7gOuAW4K9CMYwAXyQQ1GuBu8J7Z/wMo/qU27VaG+Ksb28g6gjnhnOXbaYu8nj3AJtXN9HZHC+NsEqIMDBS4I7r13Hfe69h101baE5EOdab5tRAhlzB5XhvmteGcqCQ95Scp0RE8FX54sGXLQ59DTOVjatQPFBVVyb6+ZsAEVkP/CLBvsbfD33C3gP8m/CWB4BPAV8Cbg+PAfYDfxnefzvwsKrmgJMicgJ4a3jfCVXtDp/1MHC7iByd6TNUx09EjPmgfIrWl8pxRWuidK0lGaM5EWVotMBD99xQsX7RFtYYjzKS93B9RQBPle1XtJLJuzzePcAbi6O54Syokiso2YJfmmYGjqoBrq/EIw6ur5ftX7QpZe0wlXC9SUSGw2MBGsJzIXDxap2i/l8A/xFoCc9XARfL7GQ9BFFVCf+ehpJIDoX3rwOeKHvP8jqnx5W/bZbP6C9vtIjcA9wDsHHjxim6aFRifPz4/nSO1y5mERFakpXtWuMpGug9X8nkPZTgi5eIXtri0zOYKY3m8p5fMtrD2K0eEFRWDV6JqMPx88Ol+PUtiSh96RwrGmJjXDQswcbSZNKpoqpGVLU1fLWoarTseFLREpFfAnpV9Uh5caXHTHFtvsqnev6lAtV9qrpDVXd0dHRUqGJMxXiXhzUtSQDODWWnlWoMAgN9fzrHqQuZ0oekQMHzSWULJeEbH/K50sSg3MDvo8QiQirnlXzJTvaPcDFTwPXUsmDXANXMZP1zwG0icgp4mGD69hdAm4gUR3rrgTPhcQ+wASC8vgIYKC8fV2ei8v5ZPMOYZ8aLSWtDjHVtSRQqBgmciJGcW/HX5txQtiR8xcinRVPGZBN/AVY1xRkt+KxsipWE1VPFEehP50r3mj/Y0qVqwqWqf6iq61V1E4Fx/TFV/TXghwSGfoC7gW+HxwfCc8Lrj4W2pwPAneGK4GZgG/AT4ElgW7iCGA+fcSCsM9NnGPPMhvZGLozk6O5Lc/TsMC+eGeLVgVFcPxhtTYf7v9uF6wejJUcgIoHw+BqU3XH9OvYe6uZ4b4qewVEclMgEZthisQpsXt1MSzLKqqZLNrdihIm8d8knzPzBli7VHHFNxMcJDPUnCOxLXwnLvwKsCst/H/gEgKq+CHwTeAn4LnCvqnqhDesjwPcIVi2/Gd4742cY88+NW1bSm8qTdQOjelFsPF8Zzrqc7E9fts2nnINdvRzrTQNFg2ogWFEnyJe4eVUj+596LXSvSLKyKQYieBP8DJWmmhpMQbd1toyJX9/RksBXiDgy7amssXiIDTgmZ8eOHXr48OHFbkbNcde+JzjZn6Y3lRtjMHcEYo5DNCJcsSJJZ0uy4qriXfue4OnTg7ie4vpjv6OxiHB1RzN5z78s0ukrFzIkog4jE2wVAuhojvMbN1zF/qdeIxaRkuPq8GiBVU1xRvKeRZVYAojIEVXdUemaxY03qsLpwQyrmxNcGMkTd4RcwS8ZyEWCKVm5DanoinDs/DAFT0nnXGIRB9+//Ic1GXXoGczga/A+8UgQFbUlGSXiBII4PFqgL52/rK4DpLKBG8Xu265j76HuUvib//SL15pQ1QgmXEZVKLoyFH2minsNi3/jEadkQyq6TuRdj+Fww7Sv4Pp+KTJE0UYVjwhNiSjnh3NEIwIahrwZCAQwEXUYGi2woiF2mXAJEI85uJ5Pz2BmysQcxtLFhMuYM5UcN4t7DVsbovSn8oGLQpk7QksyVrIhFV0nLqRdnDADte97lNnJSzYqz1cGMwWcUADHTyNzro+byaP+2I3XJQdUT4k6jhnda5zFMM4bdcREsbUAdt92HZtWNbOiMUZTPEpLIkJzIsqKZJTNq5tL7hBF14m85yMSiFOFGSIArgbi5Gtlfy0BPB8GRl3aG6OlVUYNX66vtCSj3LhlJXfte4J33P8Yd+17wrb/1Bg24jLmRLmjKVDKtrP3UDcP3XPDtKZiY6aVnuL6/uVeweMIHFEvv6t89XAw49LWEGM075IP741GhHdcvYovHnwZ1/dJRBw83zcv+RrDRlzGnBjvaAozd9wsZqBuSUbxmXi0NRsujhZoa4xz1apGElEHVfi7Z8/i+j6xiIOncCFdoOB55iVfQ9iIy5gTG9obOdmfJpV1Syt8LeFUcDL2PHqML//oJCN5j6Z4hJu3d3BuOI/rDdM/Upi07kw5nwq84R25FIAw2HitwcolylCmQI9jXvK1go24jDlx45aV9KVDR1NPGcl7nBvOcUVrfMI6ex49xhceO8FowSPqBB7qB547x41bVnL4P72PVU2xCevOhfEjOddXvHDFM+f5ZrCvIUy4jDnxePcAK5JR/HFbc77zwvkxBu+DXb0lY/gXHjuBarC654gT/oUv/+gkANesmSroyPxR8Hw8P2iLecnXDjZVNObE6cEM2YJHPOrgiOD5SsHzybk+/+4bT7GxvYG+dI50zmNlU4xVTQl6BkcBcD2faLhH0JHAMfSufU9wvDe1YO0PxFa4d+dWM8zXEDbiMubEhvZGcmVuDAXPL8UWyuQ9TvSNkM66+KpcSBdI59xSNFOvbLtZcYWwN5WlKR5ZsC+mAPfu3Mp9771mgZ5ozAcmXMac2HXTFqKOg+crec8fEzerSM5TCp5S8H3ODWXpaA6iMvgKvvqBh7xCe2MQZqY/nScWre5XM+oI8YjDFSsSPN5tkY1qDRMuY07s3N7JvTu3TnjdHReSNOv6DGQubcXJuUrUEVqTUda2NQDB/sNqb/5PRB3WtiXDqautJtYaJlzGnLnvvddwdcfE7g+lWFjh3+K0MOoEI5+mRJR1bQ2lMDMRkZLDaDVIRp0gC3Yqx4WRnK0m1iAmXMa80JvKEpng2xQI1NiyeMQhFokQESGdc1ENppOZvIs7bp/hfJNzfdDAdePsUI7TF9K25afGsFVFY14oeIojQiQiwZYdvTTCKkYxHU/JmO/ByQsZ7t25lUdeOIdbXd0Ktgv5wV5HB+jPFPjkgRe5o+cij3cPWJafGsBGXMa8EI86EG58jkedwD0ivFZpM3Te80vGfAnv2f/Ua6gqyZhD9LJEivNPVCTY9uMredfjiwdfvmyzuI3EliY24jLmhY7mBMOjBfLhcCkRDaKc+hVCz4ynGNL5zMVRPFVWN8XoT8/vtp9KuL6igK/K+eFcqR3FLD/96Sz3Pfw0rQ0xG4EtMWzEZcyZg1299KWDEM2xiBCLCK4f2Ky8ae6YLniKoKjCwEhhyugQ80ExzI0jl6ayZy5mSWULpLIF+lN5MnnPRmBLEBMuY87sPdTNioYY69oaiEUclCB8DFS2bVVCCGJtxSITJ7yoFsWAhVFHEIG+VI6+VA4kGDlansWlh00VjTlzejBDW0MMiQutDcEGaVXlxTPDU9S8hIb/iYigjlbdQH/ZswlGX66v5Aj3XDrC6uZLKcwsz+LSwYTLmDPFQIDFYILDowXOp7Iznu45ErgqLIW8UwrEHKE/neN0mVg1J6Ic7Oo1W9ciY1NFY87cuGUlPYOjHD07zNEzQ7wykCFbmPmQydfpTy2riRCIaKbgM1rwwq1Jl/I6mq1r8THhMubEwa5e9j/1GiubYjgo7lJQnjkSrC6OLXMksL/lXN9sXUsAmyoac2LvoW7yrhdEQF1Au9RCUMygnQwN9Ipelg/SWBxMuIw5cez8MMPZIK1YvVEM9TxRPkhj8TDhMuZEccO04whMnPW+JlEN9lj6aJBRW6G16VI+SGPxMOEy5kQ86jCa9/BVKcv5Whf4QHsyhgjkPSUeddi0qtk86JcAJlzGnNjW2cKpC2mGR10K4XRqunQ0x+kfyc+ozkIScSCd99j7628xoVpi2KqiMWsOdvVyMZOnN5XHU2VDewNtDdP/LVzTmmTL6iYWYD/1rFA/2LtoK4hLDxtxGbNiz6PH+OLBl8MMOULB9XllYPSyuFuT8cqFNG9Y187ZoSyZ/NIzkPlATLAVxCWICZfBwa5e9h7q5vRghpZEFFUlnfcmjIhwsKuXLx58GV8D0XJ9xdPiNhln2oEAUzmfF167uCRFq0jOU/rTOfOWX2LYVHGZc7Crl08eeDGIYCpwvDfNib4RIsKEERH2HurG9X0ijiBhSjIIXAdyM9xkmMotXdEqMlrw+fcPPWXe8ksIE65lzt5D3cQiUsquE3GEiAj96fyEERFOD2ZIRBxUAxvQErWtzyupnMdnHzm62M0wQky4ljmnBzM0xCJAEJVUhHD5Pxg5VfIS39DeyIrGGKpBJujlwk97LTb9UqFqwiUiG0TkhyJyVEReFJHfDctXisj3ReR4+Lc9LBcR2SMiJ0TkORG5vuy97g7vPy4id5eVv0VEng/r7BEJggTP5hnLlQ3tjaXsOvFwFOWGaem7zg1zoi9NUzwyps6um7YQi0RY1RxbFqOtIqrw4QcPc+tfHDIBW2SqOeJygf+gqq8HbgDuFZFrgU8AP1DVbcAPwnOAW4Ft4ese4EsQiBDwp8DbgLcCf1oUovCee8rq3RKWz+gZy5ldN20pZddZ3Ryn4Pq4oaFdANdTLozkx/xD3bm9k923XcemVc3EHKnDzT4T4/nKy31pixCxyFRNuFT1rKo+FR6ngKPAOuB24IHwtgeAD4THtwMPasATQJuIXAm8H/i+qg6o6iDwfeCW8Fqrqj6uQfbQB8e910yesWwpilBnSxJfIRmPEI8ERvdYxGF9ewOtDbHL7Fw7t3fy0D03sLWjiYgjC5LcYrGR8JX3lILnmX/XIrIg7hAisgl4M/BjYI2qnoVA3ESkuMa8DjhdVq0nLJusvKdCObN4xtlx7b2HYETGxo0bZ9bZGmTn9s7SUv877n8siGZalppHVSf0ZUrnPda1JelP51HXn3aM+Zok3NMkAkOZAj2O+XctFlUXLhFpBv478FFVHZZKuarCWyuU6SzKJ23OdOqo6j5gH8COHTtq+l9iuY/WdDLVFKOZuqH/Ut4L3B42rawcDaE5HuHUQAbP1yBDtOsxixiCtUH4TYg5Qs7zLULEIlJV4RKRGIFo/Y2q/o+w+LyIXBmOhK4EioaCHmBDWfX1wJmwfOe48oNh+foK98/mGXVJ0UcrFpExmWp2wxjxGu+A2jucZbTgV7Rz7dzeWbq/GNLGD0dZGc+ra2N9MZyzAlHHsQgRi0g1VxUF+ApwVFU/V3bpAFBcGbwb+HZZ+QfDlb8bgKFwuvc94H0i0h4a5d8HfC+8lhKRG8JnfXDce83kGXVJuY/WRJlqyh1Q2xpi5D2frOuXUnaNt3PtefQYu75+hCdPDTCYKZSmht4SCbtcbXwNMnbfu3OredIvItUccf0c8BvA8yLyTFj2R8BngW+KyIeBV4FfDa99B/gF4ASQAT4EoKoDIvJp4Mnwvt2qOhAe/w7wNaABeCR8MdNnzDdHXhlgJOexvr2B9sY4sahDPOIQC43eC0Ux+0454/2yysUNoDEeRcM0YVs7W0r3qSrHzw/z1KuDpa0+2XqI0zxDfGDzqkbeuL5tsZuyrKmacKnqj6hsUwK4ucL9Ctw7wXt9FfhqhfLDwBsqlF+Y6TPmk91//xLP9gwBsKo5zob2RjasbGDjyiY2r25ka0czG1c2koxFiEWCdPXVYHz2HeCy6J2VxC0Rdci6Y7fijBY88p7i+j4OkK9nI/wkqMJPz6f5tw88ydrWBC0NcVI51zJdLzC2yboKDGTypeML6TwX0nmeOX1xzD3xqMP6tgY2rGxk48pGNq9uZEtHM9s6m2lvihOLOMQicxO0XTdt4ZMHXiSTd2mIRRgteJdF72xJRDnRm8ZTJR5x6GhJ0JKM4mb0snrxqEM2T93Flp8pSiBgPUM5nKEcG1Y2TGg/NKqD6FKN4rZE2LFjhx4+fHjG9U72p+k6l+Jk/wivXsjw6kCG0wMZzgxlp3QZWNUUZ8PKRq5a2cim1U1s6Wji6o5mrloVjNKiMxC0oiG9ZzDD+nGjgoNdvfzB/me5mCmUYmL5Cu2NMX7jhqt4vHtgTL29h7o58soA+YVONb3ESUSEa65oJZN36WxJ8tA9N9BRF1UAABSMSURBVCx2k+oCETmiqjsqXbMRV5XYvLqZzaubAfB9peD7FDxlNO9y6kKG7r403X0jvHIhw+nBQNSGsy4AF0byXBiZfJS2aXUTW0NBu2ZNC+1NcSIVnEDLfbTGs/dQNysaYjTFoyXXh2hEWNUU5773XsN9Fep8+MGBCqXLm1wo5Jb9Z+Ew4VoAHEdIOBES0SATckdLkp/dtBIIjN6F0HbUm8pxojcQtJP96UDUykZpedenu3+E7v4R/vlY35hnrGqOs3FlI5tWBSO0rR3NXLOmmatWNhGbwIZWtG9JXGgN7VyqytBogT2PHuPLPzrJSN6jKR7ht96xmfveew3XdDZz9Fyquv/DapBUtkDEEfPtWiBMuBYZESEeFeI4bFoVZdOqJnj9pesFz2c073Gyf4QTvSlO9mfoLhO10igttKU9/WrlUdpVqxvZsrqZrR1NbFvTwuvWtIwx3qeyBfpSObKuR9Rx+Pyjx4lGhKgTGOa/8NgJAD5+y3Z2ff3IjONu1TvnhrJ0tibNt2uBMBvXFMzWxrUQuJ5P73CO430pXu5N090/wqn+DK8MjHDm4tS2tNZklEzew/eVogyVZ+qJOVKyp7l+kAj1uU+9nz2PHuNzjx6vWr9qkUTUqZhUY6Y7F4xLmI2rTolGHNa2N7C2vYF3XTP2H8No3uVk/wjHe9O8HE49T/UHNrXiKK34t5xyqSv4iq8eTpjFOZ0L7r/vvdfwyAvnONk/Qs71l4Xj6WSsbIzxuitaK4rWdHYuGDPHhKtOaYhHuXbtCq5du+Kya32pHMfPp/ijbz3PwEieVChglQTIU/DKRuU7/uz7bFrVxOrmBGeHsjTEHQYzlwvgcmIgU6BnMHNZXPpKzr2ZvMveQ90mXHPEhKtOmWyK0tGSoKMlgesr7Y0x0jmX6fqT9qfz9KfzU9+4zDg9OMqHHniSD7zpSj5/5/Vh2SXn3uHRQmnltmdw1JJvzBEL3VyHjN9/OFHSi5ZElNcuZnGmsQ1JgIhcXmZcQhW+9cxZfu/hp4BL0WWHRwucGRrF9bQU08sCEc4NE646ZDqbqyFwfVBV3GkMtxwJpo1FJCwzLufvnjnLwa7eUnTZ86lsIPLhf65YkRzzeRzs6uWufU/wjvsf4659T5igTQMTrjqkPAFGkUrOkf0j+WlPEcc7y2uFMiNAgc8+crQUXVaV0sb0tW1JWpKx0ucx3dGxMRYTrjqkPAFGkfGbqwHy4YqgDZzmnxN9aSBYPbx+YztXrWpiS0czLcnA5lX8PKY7OjbGYsJVh5QnwFAN/o7fXA1B6BpYHnG0FhrXpzRqmuzzmM7o2KaSl2OrinXIzu2d7IZJN1fvPdRNOueNcTg15pff/FoQQk4IfL06W5MMjRbGfB4bDk0eesh8wSpjnvNTMBvP+YXylp5oP+FUbSv+Q3DDpfnZ2qocwDb+TJ/WRIQ9d11/Wdjs4udRHkLojuvX8Xj3AIdfGcD1FMcRktEg7FDEkWURhcI85xeQ+fqFPNjVy2cfOcrJC8GUYcvqJj5+y/bSe+x59BhfeOwEjnDZfsLJxGu8U+QGEU4PZphNQmoTrZkxnPO47+Gn2XPnm0ufY6XR8Y1bVrL/qdcoeIGIQZDPcSTvMXIhQ2dznPwy3ytqwjXPzIe3dKU4Wcd703xs/7OlOFlPnLyAKogjFHxFw5jvf32omzeubyuN+JrjEUSkFKXz2PlhrlzRUHpWSzJGIuKQ8f2yJfvAJ8mYf1JZt+IP2cVMnt5Ujt5UjmdOD9LWGGNgAkff3nSeWES4a98Ty3bvo00Vp2CmU8ViXsJ0zqUvFXhKxxyhMRHl8J/8/IT1yqeXw6MFsgUPQXAcwfOVgheuAAqsaUlwbjg3pr7IJbHpaI7T2hDD9Xxeu5gFAhvLcNYl6/rEHFjX3lha4Xrp7BCezyXhWiaJLxYLR6C9Ico1V6zgeG+KwZF8abo+E5tj1BEiTpC4YyoTwWIwV5OJTRUXkJZElKPnhktTLyEIJOipO+E2j+L0Mu96pLIuI/miK4PCWK8GVINAg+VCNZ5U1uWKFQ1096WJiOCp0pfOE486RJ1gxatncJR1bUo04hB1HFA/+MdjilV1fIULGZfHuy9cdm0m//ujTvDZfvHgy7xxfduijLwmEqdqLyqYO8Q8crCrl55x9qKio2bB9bn/u10V6+091E3e9bgwki9tC5kIR8BBiIwrLxcx1w8akHV9cp6P6ysK5Fwf1w9GZ9GIcG44R2dLknt3bmVlU/yyLT3G0ibrBlF1c67PZx85uuDPn8x5ttr+aTbimkf2HuqmMIErugLHetMVR12nBzOksi6qUNDJw8T4CrkJLOmxiFDwFF/h3NDohPG4fA2mm77CQ/fcwMGuXlY1neXiaAHP3OFrkq7zabb/yXco+MFWruZElJu3d3BuOF+11e3J7LnTSY03F2zENY+cHsxMuNoTJFet/IvTkogykvdwfZ32FpxyHIKRWDGjtK/QN0UEh7NDWda3N3Kwq5eP7X+WUxcyJlo1TtZVvPA7lMq6fOuZszx5aoDzw1mePj3IH+x/dsbOq5M5v07mPDvd3RuzxYRrHtnQ3jjpapz6yk9ODYz5AhSnl5Mx1RTOJxArX6e/8Tkfem7f/90uBjOFYJRnU8W6ofg1dP0gp0G24DM4kp/RlHKqfZSTidN0d2/MFpsqziM3bllZ0eBaxCdIZVVuqPzsI0cZLUzukzPVQKioN/GoQ2EGDlk7t3ey6+tHAruZyKxGe0bt4GkwpfzMd46ytaOZbWuaubrz0v7J8VSaCvans9z38NNBchXf53w6j69KIuKwojFGLBIpTUkn270xV0y45pHHuweIR6Ri3sEgDpPQnIhybihLzvW57+GnyeQ95rqUV6w92UrjeGLjhma5cb+cRv0y3lyxqjnOplVNbF7dxJbVTYGgdTTz6sAI7Y3x0n2pbIH+VB4lyL35WiqP7yuxqEPeUwZGCty7c+MY59pqrXSacM0jpwczXLkiSc/FLH64klck5kBbQ5TB0UKwKujASN6dlcf6RORdf9ridXVnkPNx86pGus6n568RxpJHgMZ4pOR2U8wQdeSVwbH3CfSmciSjEeJRh1S2gK9KPCKcC1PmKUEmqg3tjUQjwuPdAxXzcc43JlzzyIb2Rk72pyl36i06FK5sTpDKujihU6mvEBXw0XlxnVrZGGNwtIBM481aExE+fst2AD5x6+v50NeeNPetZYQCq5oTfObnt9G5ooHj51O83DdCd1+aUxcyvHYxWJFWhYKnFDwXyvydR92x3xZfgx/tNS1xXg1tWjKNqLpzwYRrHtl10xZ2ff0IkdCjWTUY/TQnIgxmCni+EnGCD9r1fERkXgRDgLVtDTiOsLopzk/Ppyl+b4p2KyFwWNza0cQnbn39mOH8dEZpQpCCK+/5ZgurA2IR4aEne3jonhu4YcuqMdfyrkd3X5Ah6gcvnedfTvQznC2U9k1Wwlc4O5xHgJv/8z+zcWVjKZfnm9avYPuVrSRj470PZ48J1zyyc3snLckomZxLwVfikWA3f3MiyrnhLHnXZyTvkow6CA6q4M6DdCWiDqMFj22dLQBs6dAxYVIyeXfSaAJB/cpzVkdgZVOcK1qTpV/RVLbA2YujpdTzFhqn9pjMpyoejbD9yla2X9nKL79pban8h0fP88d/9wLnhoJtZJW+MQqlbOscu1T+jd96G2+/evW8td/cIeaZbZ0tXNnWwPYrWksRL4uisufON7OurZErViTxVFG0NBKKRy59FMVBdsQJrk1GRKC9KTajwHTlHOzqJR6t/DWIOPC6NS1s62y5bNnbC10vImKitRg4QDIqs/JgEWbnU/Xu16/h//qVf0VzMoo4kIw6JMNtZHEHNq1s5HP/+k185N1X84v/6kresLaV1mTwA7o1tKnOFzbimmd23bSFTx54kUzeHRNf6fIl4lEE6GyJM5gpIALxSGD/6mxJ0hSPMJgJch76BW/MlK/oId8Qi9CYiLBpVfO0A9ONZ++hblY3J0BhqCxBbMSBdW2NJVtYeZ+Kv7irm+IMZAoWSmIBiUWErasvTfeL22sOnxoobe2aChFm7VO1c3sne+58c8UYYp+67boxq4i+r+Q9n0zOpb0pPsm7zhyLDjEFcwkkOJn/yviAfueHcxR8n20dzXzi1tcDjPly9KdzDGYKtCSjbOtsmdAnZqLAdLvHfamKFKNZiMiY3H+OyJiU8uV96k3luKI1QWtDnOPnU5bNuspEHOG2N15RytdYieLnXvA8eodzk/r+tSajY2KCzYbpfMfnymTRIUy4pmA2wjVdpvrwZ/vlmEm9u/Y9cdkIbSqbWHmdrnPDwUKEH2S8nk6qM2NqVjRE+cL/MTNxKX7ux3tTDIzL4BRzBEJH4/IfpKWMCdccqKZwLQVmOkIbX+fsxdHSxvK1KxoQgXNDWRS4fmN7aTqy91D3pLsKliON8Qi/fdMW3ri+bdJot7NhfKiknOsv6dhdlTDhmgP1Llwwu5Fd+a97KuvS3hhjdXNiUuHb8+gxPvfo8Xlt+0KvaArQFHdI52fuOVwUqoUSjoWYzlUTE64yROQW4AtABPiyqn52svuXg3DNlZn8Aykm+EhlL3cESUQdrmhN0pKMMjRa4F8+/h4OdvXyJ996jteGc6gG//jff20n54bzY2K0P/LCOY73pvA0DN44mygbMnm9De0NfPr2NwQG6rAfw2ULGmPeC4hFHTavahzjN2dMHxOuEBGJEHiX/DzQAzwJ3KWqL01Ux4SruszGxjYd9jx6jC/988uMFoJY+h3NcVxfg1XQEEeCjekxR1jf3kg65zI0WqAxHqGjJTnt9swm25IxNSZcISJyI/ApVX1/eP6HAKr6mYnqmHBVl9nY2Gb6/uNHgzB5zslqtseYPhZz/hLrgNNl5z3A28bfJCL3APcAbNy4cWFatkypdviTiSIUTPT+1W6PMT8sN+Gq5Gh82ZBTVfcB+yAYcVW7UcudaoY/mQ1LrT3G5Sy3LT89wIay8/XAmUVqi2EYs2S5CdeTwDYR2SwiceBO4MAit8kwjBmyrKaKquqKyEeA7xG4Q3xVVV9c5GYZhjFDlpVwAajqd4DvLHY7DMOYPcttqmgYRh2wrPy4ZoOI9AGvjCteDfQvQnMWAutbbVKPfbtKVTsqXTDhmgUicngix7hax/pWm9Rz3yphU0XDMGoOEy7DMGoOE67ZsW+xG1BFrG+1ST337TLMxmUYRs1hIy7DMGoOEy7DMGoOE64KiMinReQ5EXlGRP5JRNaG5StE5O9F5FkReVFEPlShbqOI/KOIdIX3TBphdTGYS//C+94iIs+LyAkR2SPVzrc+Aybp28fCsmdE5AUR8URkZYX6N4vIU+F9PxKRqxe+F5WZh76JiPy5iBwTkaMict/C92KeUFV7jXsBrWXH9wF/HR7/EXB/eNwBDADxcXUbgXeHx3HgX4BbF7tP89W/8NpPgBsJwgQ9spT6N1Hfxt3zy8BjE9Q/Brw+PP53wNcWu0/z2LcPAQ8CTnjeudh9mu3LRlwVUNXhstMmLsXsUqAlHGE0E/zDdsfVzajqD8PjPPAUQficJcNc+iciVxL8A3pcg2//g8AHqt/q6TFJ38q5C3hoorcAWsPjFSyhsEfz0LffAXarqh++X+/8tnABWWzlXKov4M8JoqW+AHSEZS3AD4GzQBr4xSneow3oBrYsdn/mq3/ADuDRsvN3Av+w2P2Zqm9l1xoJBHnlBHXfCVwgiN32EmWjnKXwmmPfLgB/DBwmGClvW+z+zPa1bEdcIvJoaA8Y/7odQFX/WFU3AH8DfCSs9n7gGWAt8DPAX4pI6wTvHyX45dujqt1V79Dlz69W/6YVRbaazLJvRX4Z+J+qOjDB2/8e8Auquh74L8DnqtWPSlS5bwkgq8HWoP8P+Gq1+lF1Fls5l/oLuAp4ITz+R+CdZdceA946Qb2vEojWovdhPvsHXAl0lZ3fBexd7H5M1beysm8B/2aC+zuAl8vONwIvLXY/5qNv4fUuYFN4LMDQYvdjtq9lO+KaDBHZVnZ6G8EHDvAqcHN4zxrgdQRTwfH1/4zAPvLR6rZ0dsylf6p6FkiJyA2hLeyDwLer3uhpMknfEJEVwLuYuL2DwAoRKeYW+3ngaDXaORvm2DeAvwPeEx6/i2AhojZZbOVcii/gvxPYEJ4D/h5YF5avBf4JeD68/utldZ4J/64nmDodJZh2PQP81mL3ab76Fx7vCK+/DPwl4Q6MpfCaqG/htd8EHq5Q5zvA2vD4V8L+PwscZAnZJ+ehb20Eo+rngceBNy12n2b7si0/hmHUHDZVNAyj5jDhMgyj5jDhMgyj5jDhMgyj5jDhMgyj5jDhMpYEIpKewb07ReTtZecfEJFry853i8h7w+OPikhjNdtjLDwmXEYtshN4e9n5B4CScKnqJ1X10fD0owR7+Iw6woTLWLKIyC+LyI9F5OlwD98aEdkE/Dbwe2H8qXcReJH/3+H5VhH5mojcEcabWgv8UER+GL5nuuz97xCRr4XHm0XkcRF5UkQ+Pa4dHwvLnxOR/3NBOm9MigmXsZT5EXCDqr4ZeBj4j6p6Cvhr4POq+jOq+s/AAeBj4fnLxcqquocgLM27VfXdUzzrC8CXVPVngXPFQhF5H7ANeCvBxvO3iMhN89ZDY1ZEF7sBhjEJ64G/DWOAxYGTVXzWzwH/e3j8X4H7w+P3ha+nw/NmAiE7VMW2GFNgwmUsZf5f4HOqekBEdgKfmof3LN/jlpzkWhEBPqOqe+fh2cY8YVNFYymzAngtPL67rDxFEPRwonMmuXZeRF4vIg7Bhuoi/xO4Mzz+tbLy7wH/VkSaAURknYh0zqgXxrxjwmUsFRpFpKfs9fsEI6z/JiL/AvSX3fv3wK+Exvh3Eti/PhYa8beOe999wCNF4zzwCeAfCGKNnS2773eBe0XkSQLBBEBV/wn4BvC4iDwP7GdikTQWCIsOYRhGzWEjLsMwag4TLsMwag4TLsMwag4TLsMwag4TLsMwag4TLsMwag4TLsMwao7/BbO9pkfeMWSIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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C3ABcCmwHvigibiiIX+CsoO4M6876HkZ1U+i71dYYY83yRpwwoJYTxtcqhusITTGXtsZo0XhehQELXYEDvQkO9o3iChzqT/C5Rw9yeCBh+RmrkJmEK5t/M5cpooisBX4d+HJ4LATbhvJ7Hr8OvCt8f314THj+bWH964F7VTWtqoeAg8Abw9dBVe1W1QzBCOv6Od7DqCCdXb3zSlQxOeVYW2MU1xEiDqxf2TQeFFBgXNCAIOqprxw/M0bO88/Z4lMoiP2JDK4juCKcHBqjdySN5yunhtOMjOUsP2OVMdNU8TIRyTvHCNAYHguBi1fbDO3/GvgTINw1xirgTIEI9hBEVSX8eZTgwjkRGQrrrwH2FlyzsM3RSeW/NMd79Bd2WkR2AbsA1q9fP8OvaExHsU3Qt933ArdDydOudSua6B0ZG/eWh2A05ftw/MwYvp61bemk944IPoEAbVzdzM7deznQO0Im54eC5LK6JU7G83EdwfN9smd3C4V+YCkAWhsitr+xSph2xKWqrqq2ha9WVY0UvJ9WtETknUCvqj5ZWFzsNjOcK1f5TPc/W6C6W1W3qurW9nYz6s6HcoRhLpZyrCUeCcQJnWCQh3ON8+mcz1jO5+enRug6OcRQMjtutE9lPY4PpVA/CHdTKFr5EZyD0J9I2/7GKqKSmazfDFwnIocJpnHXEIzAlotI/tG5Fjgevu8B1gGE55cBpwvLJ7WZqrx/DvcwKkQ5MksXSzn22R2X0dYQIeY6OI4gQGSGSb+vMJjMoSgRx8F1BFXw/CCRhk56hCnByA4JRc32N1YNFRMuVf24qq5V1Q0ExvVHVfV3gO8TGPoBbgS+G76/LzwmPP+oqmpYfoOIxMPVws3AT4AngM0islFEYuE97gvbzPYeRoWYbRjmqexh27Z08K1dV/DJ61/NmWSGm+55goHRLKMZj6gjtLfEcN3SPs75BcmIIzgFRrGGiDNuIxMg6gox1yHnK00x95wcjsbiUckR11TcCvyxiBwksC99JSz/CrAqLP9j4GMAqvoC8G3gZ8CDwAdU1QttWB8EHiJYtfx2WHfW9zAqRynZp/PMlJass6uXj+55hp+fHKEgag1jOZ+B0Qw5zz/HQF+MvHDlfEVVx21k5y9rYP3KJmKuMx6j/vxlDaxZ3sRdN7zORKuKEBtwTM/WrVt13759i92NmibvKzVTZIadu/eeY4RPZnJ0tDbwrV1XsHP3Xp4+Mkja8wMjvJw7vYOzhsxicZjyuE6wn9GRvBEfXMfhFcuDgIQnh8ZQ4PL1K8x/a5EQkSdVdWuxcxY33qg4pYZhnikt2f5TwxO2+BSuIOYpJlrFjkFwUBqjLk0xlzOpLIrSOzzGBcsb6WhrsKlhFWPCZVQNxdwe8vawzq5eEmmvBEEKmCxok/F8JR5xaIq5JDMeXjh/9Hylo7XBRllVzmLYuAyjKNPZw+5+rJuVzVEiMxiwREIfrhLul8759CUyjGa88aln1HVMtGoAEy6jaijm9pCfru0/NcxwKodyrvFdODtFnGILY0l4fhBFwrzjqx+bKhpVRTF7WH6a6KviALkCcYq5wiuWN3L8zNiELD6zRQme4mNZz7zjawAbcRlVT36aqArZSUOqvG3qFcsbim6LmA0+kMr65h1fA9iIy6h6jg4mibkOfuj7UChdnsLhgWCE5EzhHjEbFDi/rbQIq8biYSMuo+ppjUc4VrCZeqqR1XzsW4X8609PcNVfPmwhbKoYEy6j6sk7SY9HfViAe/YMpfnInmdMvKoUEy6j6klkPNYsbyAWWdiPayKdsxXGKsVsXEZV09nVy3AqSzLjEY84xOMuw+mFydjj+WorjFWKjbiMqiW/6bo57iJAxvMZSXszbqIuF64jtsJYpdiIy6ha8kEIlzU2EI+49I2kyXrevFcOS6UlHik5/lZh0g1LrFF5bMRlVCWdXb08dWSQlwdG6e5LALC6JT5vX61SWbsszmd3XFaS+MwUjscoPzbiMqqOvBCIBDHj0zl/3FdrPsy08RqCcDev7GjlgQ9fXfJ1C8NTAzTFIiQzgWHfRl2VwYTLqDidXb38+Xefp2cwhQKNUYf3v+UibvmVVxate8u9T5PMeLiOkPXOTfo6E/nopRKKXmH5TNfyfDh2JjWr+80UjscoPyZcRkXp7OrlQ//4NEOps9ntUlmfOx8+ADBBvPIjrdF0Dl+ZMunrTLiOkPHO9fgqdSfjaGZ2q5bTheMxKoPZuIyKcvdj3Qyncud4vCvwt5N8pO5+rJvEWBZP5+dkOlfBy+PNsv1swlMb5cGEy6goRweTE9KFFZLMeBMM2Ad6RzidzFJrTBeOx6gMNlU0Ksq6FU0cC21bk3GECQbsTG7uYWnKyVxWLksNT22UBxMuo6LcfPUmfnZiaIKNK8+q5ui4Abuzq5fkLG1LlaIhOr+JiPl0VR7L8jMDluVn/nR29XLzN/aR9iZ+1hwJnDxvumoje546Ru9wEAywXFEe5ko84tDeGp9WdKYSp/wCQ9QVGqMuqaxH1lObOs6B6bL8mHDNgAlXebjr4f187tGD43kM86xsijKa8VjZHCUecekZTM3buF4O4q6QU0WAC5Y1ojAuUMCU4nT3Y93TplgzSsfSkxmLzuPdp+lojXFqOA3k8xgGflaerwwls1zU0UDEGasK4Up7Ou73dXQwRTzikPP8YO9kzJ3S4dR8uhYGW1U0FoSjg0lWNcdxHaEh4hCPuKG/lU884pAO48XPJ258uSmUz0yYLTuT8+juH6Ux6k6omxendSuaSGUn2urMp6v8mHAZFaOzq5edu/dy1R2PMpBIs//UCDlPSed8cmE26pjrEIsIng/PHRtadPvWdDgII2PBIsNU4mQ+XQuDTRWNilBopHYlGLF4GjwpfcKkF76SBkYzQZtStuQsFiLBK53zubi9mWTWJ5nJTbBx5Q30txO4efQMJsfFzAzz5cWEy6gIhRuPu/sSRBwHB8XzFZnCM17KkOyiUgjgqeI6wseufRUwtTiZT1flMeEyKkKhkXos5+MXzAGn0qZqniZ6ChERPrDtogkCZSwOJlxGRchvPM55iu8HER6qeSo4Ha5AxHW4+3dfb2JVJZhx3qgIeSP1iaHUlHsVawERiLoOl69fMe5gml9w2Ll7rwULXCRMuIyKsG1LBzsuXxOGl6ldVAMXjZuv3mSRTqsImyoaFeP+504sdhfKgq/w6QdeREQs0mmVYMJlVIxDA0kiDlRJ0Id5sb83iHu/bkXjhHLzil8cbKpoVITOrl4yOb8uRAuCUZcqnBgam1BuXvGLQ8WES0TWicj3ReRFEXlBRD4Ulq8Uke+JyIHw54qwXETkLhE5KCLPisjlBde6Max/QERuLCh/vYg8F7a5S0RkrvcwykfeFrRQ+Q8XCkcgY17xVUElR1w54H+o6quAK4APiMglwMeAR1R1M/BIeAxwLbA5fO0CvgSBCAGfAH4JeCPwibwQhXV2FbTbHpbP6h5Geck7n9a4Xf4cfIWIg0U6rQIqZuNS1RPAifD9iIi8CKwBrge2hdW+DnQCt4bl92gQZ2eviCwXkQvCut9T1dMAIvI9YLuIdAJtqvp4WH4P8C7ggdneI+yrUSaKRUioBxTY3NFq4WmqgAUxzovIBuB1wI+B8/JCoaonRCT/uFoDHC1o1hOWTVfeU6ScOdxjgnCJyC6CERnr16+f3S9rsG5FE10nhxa7GxXh2lefDwTxxb78w0OMZjyaYy43XbWxaLo1ozJUXLhEpAX4Z+DDqjocmqGKVi1SpnMon7Y7pbRR1d3AbggCCc5wTaOAzq5eBkfTDCbPDdVcD9z58AG+/qPDDKayuI4QcQID/ecePQhg4lUEz1ccgWm++7OmosIlIlEC0fqmqv5LWHwqPz0Lp4J5770eYF1B87XA8bB826TyzrB8bZH6c7mHUQIzxVIvjAhRrygwEGYiiiA4IjgCOd/nyz88tKSFS1XJeD6ZXPjyfE4nMhzqH2VkLMc7XnMBy5rKY0KomHCFK3xfAV5U1b8qOHUfcCPw6fDndwvKPygi9xIY4odC4XkI+N8FBvm3Ax9X1dMiMiIiVxBMQX8P+Pxc7lHu370eKRSlQq/x2zm72bgwIoQj1b1puhxkPB/CsFwijMfqmi21mFwjGwpU1vNJZz2OD43xUl+CIwNJXj6d5OWBJEdPJxnIxywCLlzVxJsuXl2W+1dyxPVm4N3AcyLy07DsTwnE5Nsi8j7gCPDb4bn7gXcAB4Ek8F6AUKA+CTwR1rs9b6gH3g98DWgkMMo/EJbP6h7GzBSKEhT3Gi80yq9ujtGbyEx5vXpDNbBDdHb1zkp0SnkgLCa+H4yi0jmfVMbjyOlRDvYmODyQ5MhAkiOnR3n5dJLR9PQZmjpa44yky2c+qOSq4g+ZOkXd24rUV+ADU1zrq8BXi5TvA15dpHxgtvcwpqeUWOqFqegbYxFg6QgXBNPIP//u8/xgyzUltynlgbAQ5Kd5WU9JjOU42JvgYG+CQ/0JjpwORlE9g6lpc18KcP6yBi5c1cT6lU1cuLKJDaubuaijhYvbW4i45fO+si0/RkkUilKeyV7jN1+9idvue4G+kTH6EunF6Oaic3QwNatR12Ik18h5of1pNMP+UyMc7E3Q3TfKywNJjpxOcmIoNe00P+oKa1ecFacLVzdxUXsLm1Y309oQJeIKUdch6jq4FfJCNuEySiIvSsXCFefZtqWDHT1nuPORA1UbyXQhKHW01NnVy3Aqy4mhFA0Rl/bWOK0N0bJtI/J9JZ3zODk8xs9PBgL1Ut8oLw8E07uBGabyTTE3EKdVTVy4splN7c1c3NHC+lVNNEbdiovTdJhwGTOSNx6PprNkPSUWcdjc0Vp0VfGevS8vadGKOZQ0WsrbtppiwUMg4/kcG0yxutUj6rqz3kaUznocHhgNBKovwUu9oxzqH+XoYHLGRYMVTdFweheI00XtLVzc0cIrljcQj7hEHCnrNK8cmHAZ09LZ1ctH9jxDIp3D84OY67GIU3Tl6+7HukmU0QBbawiwqjVe0mgpb9ta1hiIQ38iTTrnM5r2uOuG1045YktlPA70jrD/1AgHTiV4qS8wlB89nSRdgv1p/comNq4OR0/tLWw+r5XVLXGibvWJ03SYcBnT8ukHXuRMMosrgiuC+nAmmeXTD7w4HhE0v5TfN5KuimSui4VCyaOlQttWW2OUtsYoqspQKsu2LR2MjGXpOhEKVG+C7r4Eh/pHOXZmevtTxBHWrGhkw6pmLmpvZlM4enplRyutjRGiNSRO02HCZUzLoYEkjoAT2jFEQH3l0EDynKX8/pE03hIWLoAdl68pyb61bkUT3X0jjIzlSOd8HBEi4ajnjX/xML0j0y9uNEZdLlwVrtq1N3NxeyuvPL+FjauaaYy5ZfVSr0ZMuIxZ4fkahmNW3vu1J8b3XsUjDm0NEcaWkO9WMT7/6EEe7z49YSrt+8qxMyn2nxzh5+Eq3qH+BKdGzv6tPNUg12TWn2CTWt4UZcOqZjatDgzjF3W0sOW8VtatbMRx6mP0NBdMuIxp2bS6mQO9CUQ19PU5N82YEqQgy4VbYZYyWV95vHuAx7sHEIIYXkppuwgiDqxsjvMH2y5i83mtvOr8Vla2xCvd5Zpk6Uq2URK3bt/CiqYoAhNEa/JERGBJ27eKoQT5GPN/Fkdgw6om3voLHTREgj2OhV9Az4dUJsd73ryRN1+82kRrGky4jBlZ3RIfH13FXRkfRRRikjU9q5ujvGHDSjo/+lb+7r1vIBZxA3shZx8CCoykPV715w9w18P7F7G31Y9NFeuc+WzgLTS+b+5o4WBvgpyvOECdhJJfMFJZn+NnUkDwd01lPLwp/oiprM9fP3IAsDA5U2HCVcfMdwPv5H105y9roGcwVXchmReC0YyHr2l++Y5HOT40hs7gpesrfOH7B3nt2uU1FzliIbCpYh1TKDwiwc+oK9z9WHdJ7Y8OJmmMuuPHrQ1R1ixvsGnhHEnnfI4OplBfSzLWpz3lo3uesQS0RTDhqmMmCw/MbgPvuhVNDIym6e5L0HVymO6+BBnPr7vsPQtFfpA1m2n2yFhuzg+eesaEq45Zt6KJVHZinKTZbOC9ctNKekcy42KV8Xx6RzJLei/ifJjLny3nT4vSLOcAAA+DSURBVJQ5S0AbYMJVx9x89Say88gD+Hj3adpbYsRcB1+DD4tgK4gLiefDyFjgHzecynKwL0HvSJqdu/eeM2W86+H9vPZ/PcRFf3o/r/1fD9X1yqTMZCRc6mzdulX37du32N2YM/lVxZ7BJM3hVpCRdK4kQ+9VdzzK8sYoIsJwKsuxMynz1Vokoq6Q8xQl8AeLuQ6tDRE+s+Mytm3p4I/ufYp//WkQhVyAwKle+NA1F9fsyqSIPKmqW4uds1XFGmA+Lg3btnSMb4YuZYWx8F7DqSw5z6ch6nJ0MFn3MeSrmWzBUm6wM8hnMJnljge7APjOM6FohUNi3wfH0bpN4GHCVeWUIjilCFspIYI7u3r56J5nGEplx73kh+eYAMKoLJ4Pjijd/aPc/Vj3RLtjgXiNZqaPBV+rmHAtIuUQnFJHUgd6RxgpEKSIA47jcHggyc7de7n56k3c8WAXp0cz5qdVI+RHwEcHk+dkVcq/9X0d///Wk/+XCdciUargzBSTfDphy58/0DvCQCIzvr1EgaxP8EgGfnJogB8fGrCpYA3iigT/x/B/N9lk7Trwk8MDPHVkkA9su6hupo0mXItEqVO34VSWk0NjxCMOq1vitDVG6U+kSWY8rrrjUfpG0pzfFmc4laU/kSbj+cRch2ODo/z3b5yeYBuB4iuCNsKqXRwHeoaKx+4SwHUcRIJwRF/ofAkIVosLR/nAOSP/fNn+U8PThuueiUrljLRVxRmYzaribP5JhSt2efIRMH9w6zXjI7Ks59E/khnfidsccxlJe3S0xljVHOdgb4Ks5wfRN8MPaSbn215CAwhWHyFMXjteJlywrIGI6zCcyqLAssboeBKUoVQ2FD05m9BVYXVrjKjrcvt1l5acDCQ/qyhMsFJq++lWFc2Pq0zk/0mlbs+YyTk0PyJb3dLAmhWNuCJkPeVMKocrEI8EHvHN8ch46JS05zNmomUUkAlTkU0sU46dGSPnKSNjORLpid75iXSOkbHg5SBEHAfHEYZTuVl57s93y9l02FSxTJQ69bvjwS66+0fx/GCU5IrgqxJxAr+cP//1S4DAtuUKdPclSGU9fD1rn8p4yuEB85425oYQTB37E2lyvn9OmGfPD4JGehrkGYDAzSLj+bPy3K9kzkgTrjIx0z/prof38/nvHxy3OeVFyAutTp7vM5bIcNt9z7N5bwsnzpwbhcEm9UZZkMCIn/F8Io5zTlRI1xFQwXUCp1cJ68dcZ1ZbxkpJIjxXTLjKRP6flPOCJ1l+GijAJbc9SHKSP81UgfiOnE5x5HSq4v01ag9XABH8cPg9k3nalbMLL3mblecr6Nnj1ngEhQmJflvikQk2Lj9s09YcnbBlbCabbilJhOeK2bjKxM1Xb2Io3BaTzARTO1+DD85k0TKMudAcj3BBayzIbek4NESm//qKCK1xl7a4i+uEoaLzUVcFNqxs4jM7LuOzOy6jo7WBoVSWjtYGPrvjMj6z4zI2rm5hWUOExqjLsqYoG1a1jBvWS7HpbtvSwe3XXTrh2qUa5mfCRlxlYtuWDlY1xzg9mrEpnTElc92k7grEAq9h1ixvoD8RRO1oiDhkcj6OI6xZ3sCJoTEynhJ1hIvam/nYta8CgvyYhwaSRFzhlaubuXX7lgkCUkxMphOYUmy6+WtUwvHVhKuMJNK5eTtxTvaANuqH1rhLzldS2anXfYuFxY44Mu7msrmjld6RMTa1t4yf70+MMZoORvmvv3BlUTeccotHJQ3vpWDCVUbWr2zm+JmxeY24LEZffRJzhQtXNZPKehzuHy3q9Bt3hdbGKP1hbkohEC1HBB8l5kpRu1HUdbnrhtcu6JaeShreS8FsXGXk5qs3BcP5edAcj9Acc+YkYKW2WdYQKVpXJJiSBNuQIks+0qkIfO09b6ApjCI73Z/DkUB4ip4DLljWOO7L1N4aJ+oKDRGHiAMNEYf2lhh3v3sr+/7sV7ly0yrOb4vTFHNBIOIKq5pjbD6vraJ2o9kw31hv88U852dgtvG47np4P3c+cmBOUUIbow5f+p3Xc9t9L5DJeePp2UUg6giprD/laG51c5SRtEcmN3UdgPNa43S0NQDBSlJHawPf2nXF+Pmdu/eOP0mHU1lODY8xlluaLq2OQPdf/voED/Cc54/bkSIObO5o5dpXn8+ep46Nnz9+JkXWD/5nCqxZ3kBbY2z8uqrKyaEUm89ro2cwydpJK3Lz9ThfKApjvU3+HcqBxeNaQPKbWL/Q+VK49KzkZhCx/HP6/W+5KHiiwjkfCDi7YfpMMoMjwZfCdYSWeITP7riMZ3vO8IXOl8h5/gQ7WUvcZc3yRgZGM7Q0RFDVKZemC20XbY1R2hqjDKcy9JxJTZlOC+DClY28XEduHA7QEg++HpP/J8XsSPlsPD2DSbZuWDV+Pv8gKCSV9dh8XtuEB0YhU30Gqkm0oHKG91KwEdcMzDUCauHTaHA0TSJT/FvvSPAFuemqjSXv3J/uSTfXc3kKR1x5kpkcUUfoOpWYsk/rVgTCWIuuH4UrfULwP0HKEz20VkZP1ch0Iy4TrhkoV+jmux7ez9/+ZzfJrIcIrFnWwKfe9Zqq+/BO90W7+e+fJOf5EwzLrkDEdfj5p66ls6uXW771FMPp2hGvmCu0NUb55YtX8UhXH6MZj+aYO6sHyUxUekpVr5hwFSAi24HPAS7wZVX99HT1az3m/FyY6ou2/c7/5GDfKK7I+DYQT5WL25t58I/eMt72z77zHD1nxma4S3Gm83OKu0I8Igynp7e5RUIx9VTPCesDQRDFmOvSFHfnFKrFWBhMuEJExAX2A78K9ABPADtV9WdTtVmKwjUVnV29fGTPMyTSOTxfJ9jXJn/xO7t6x50eCxcMBFjRFMVxhPaWOIl0btbT1nQmx3DaO2d0NFX9yQsQRm1gxvmzvBE4qKrdACJyL3A9MKVwGWfZtqWDz+64rKRpz2TD7VymS1PtdfvUbxT3Wark3jijulhqI64dwHZVvSk8fjfwS6r6wUn1dgG7ANavX//6l19+ecH7agTMVvDMnlQ/2IjrLMU8BM9RblXdDeyGYKpY6U4ZUzPbJffFXKI3Fo6l5jnfA6wrOF4LHF+kvhiGMUeWmnA9AWwWkY0iEgNuAO5b5D4ZhjFLltRUUVVzIvJB4CECd4ivquoLi9wtwzBmyZISLgBVvR+4f7H7YRjG3FlqU0XDMOqAJeUOMRdEpA+otD/EaqC/wveYD9a/+WH9mxsXqmp7sRMmXFWAiOybyl+lGrD+zQ/rX/mxqaJhGDWHCZdhGDWHCVd1sHuxOzAD1r/5Yf0rM2bjMgyj5rARl2EYNYcJl2EYNYcJV5kRka+KSK+IPF/k3EdEREVk9aTyN4iIF4bdKXbNmIjsFpH9ItIlIr9VZf3bKSLPicizIvLg5PaV6p+IbBORIRH5afi6bYprbhSRH4vIARH5x3CfajX175si8nMReT68frRYvcXqX0H7z4vI1IkHFhATrvLzNWD75EIRWUcQefXIpHIXuINg/+RU/E+gV1VfCVwC/Ge19E9EIgShsN+qqq8FngU+WKxuJfoH/EBVfzF83T7FNe8A7lTVzcAg8L4q6983gS3Aa4BG4KYq6x8ishVYPo9+lRUTrjKjqo8Bp4ucuhP4E86N//WHwD8DvdNc9r8Bfxle31fVOXs5V6B/Er6aRUSANuYRKmgO/ZuWsE/XAHvCoq8D76qW/oXXvF9DgJ8QhFuqmv6FD6/PhO2rAhOuBUBErgOOqeozk8rXAL8B/O00bfNPuU+KyFMi8k8icl619E9Vs8D7gecIBOsS4CsL0b+QK0XkGRF5QEQuLXJ+FXBGVXPhcQ+wpor6V3idKPBu4MEq698HgftU9UQ5+zUvVNVeZX4BG4Dnw/dNwI+BZeHxYWB1+P6fgCvC918DdhS51mqCp+Rvhcd/DHyjivoXBR4BLiIYef0N8GcL1L82oCV8/w7gQJFrtRPkGcgfrwOeq5b+Tbru/wX+egE/f6X8/V4B/BCIhMeJcn9f5vQ7LnYH6vE16YPzGoJp1uHwlSOwM5wPHCooT4T13jXpWgKMAk54vA54oYr69wbgkYLjq4H7F6J/RdqNfykn/f36C754VwIPVUv/Cs59AvhO/v9cLf0Dfh04WdDep+BBsFivJRePa6FR1eeA8SDoInIY2KqBnWpjQfnXgH9X1e9Maq8i8m/ANuBR4G2UMSvRfPsHHAMuEZF2Ve0jMAC/uBD9E5HzgVPh3+iNBKaPgUntVUS+D+wA7gVuBL5bLf0L29wE/BrwNlWdPmnkAvdPVf+D4CGWb59Q1YvL2cc5sdjKWW8v4FvACSBLYE9536Tzhyny1GXSVAz4acH7C4HHCFbsHgHWV1n/fp9ArJ4F/g1YtRD9I7C9vAA8A+wF3lRQ737gFeH7TQRG74ME0994lfUvB7wE/DR83VZN/ZvUviqmirblxzCMmsNWFQ3DqDlMuAzDqDlMuAzDqDlMuAzDqDlMuAzDqDlMuIxFodJRBkTkwyLSVHB8v4gsD19/MIfrbRORfy9vL425YsJl1CsfJtjuAoCqvkNVzxBEOJi1cBnVhQmXUTWIyIUi8kgY1+sREVkfln9NRO4SkR+JSHc+LpiIOCLyRRF5QUT+PRxV7RCRWwj22H0/9JpHRA6Hcag+DVwUxp/6zOSRlIj8jYi8J3y/XYL4Zz8EfrOgTnMY9+oJEXlaRK5fqL+REWDCZVQTfwPco0Fcr28CdxWcuwC4CngngfhAICYbCPbj3USwDxFVvYsgUsVbVfWtk+7xMeAlDeJPfXSqjohIA8Gm5/8C/DIF214I4qM9qqpvAN4KfEZEmmf92xpzxoTLqCauBP4hfP8NAqHK8x0NYpH9DMiH9bkK+Kew/CTw/TL2ZQtwSFUPaLC95O8Lzr0d+JiI/BToBBqA9WW8tzEDtsnaqGYK96OlC97LpJ/zIcfEB3jDFPcvRAjCDP28DPc35oCNuIxq4kfADeH73yGIAzUdPwR+K7R1nUcQQSPPCNBapM3k8pcJolvERWQZQfQNgC5go4hcFB7vLGjzEPCHYXRVROR1M/TTKDM24jIWiyYR6Sk4/ivgFuCrIvJRoA947wzX+GcCoXke2E8QMG8oPLcbeEBEThTauVR1QET+X5hM4gFV/aiIfJsgssUB4Omw3piI7AL+Q0T6CUTy1eFlPgn8NfBsKF6HCWxvxgJh0SGMmkZEWlQ1ISKrCELXvDm0dxl1jI24jFrn38O4/DHgkyZaSwMbcRmGUXOYcd4wjJrDhMswjJrDhMswjJrDhMswjJrDhMswjJrj/wMzBkvut+nyXQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting data\n",
    "for i in no_strings:\n",
    "    plt.figure(figsize = (4,4))\n",
    "    sb.regplot(data = no_strings, x = i, y = 'Price')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sebelumnya kita telah melakukan pembuatan model dengan menghapus semua missing value, disini kita menggunakan cara yang berbeda yaitu dengan mengisi missing value dengan nilai 0 atau mean dari kolom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Suburb               0\n",
       "Address              0\n",
       "Rooms                0\n",
       "Type                 0\n",
       "Price                0\n",
       "Method               0\n",
       "SellerG              0\n",
       "Date                 0\n",
       "Distance             1\n",
       "Postcode             1\n",
       "Bedroom2          3469\n",
       "Bathroom          3471\n",
       "Car               3576\n",
       "Landsize          4793\n",
       "BuildingArea     10634\n",
       "YearBuilt         9438\n",
       "CouncilArea       6163\n",
       "Lattitude         3332\n",
       "Longtitude        3332\n",
       "Regionname           1\n",
       "Propertycount        1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "even_newer_df = df.copy()\n",
    "even_newer_df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Disini kita akan mengisi nilai pada missing value dengan mean setiap kolom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Suburb              0\n",
       "Address             0\n",
       "Rooms               0\n",
       "Type                0\n",
       "Price               0\n",
       "Method              0\n",
       "SellerG             0\n",
       "Date                0\n",
       "Distance            0\n",
       "Postcode            0\n",
       "Bedroom2            0\n",
       "Bathroom            0\n",
       "Car                 0\n",
       "Landsize            0\n",
       "BuildingArea        0\n",
       "YearBuilt           0\n",
       "CouncilArea      6163\n",
       "Lattitude           0\n",
       "Longtitude          0\n",
       "Regionname          1\n",
       "Propertycount       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_df = even_newer_df.fillna(even_newer_df.mean())\n",
    "mean_df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Kita dapat lihat bahwa masih ada beberapa kolom yang memiliki missing value, itu terjadi karena kolom-kolom tersebut adalah tipe string dan tidak memiliki nilai rata-rata. Saat ini kita akan menghapus kolom tersebut."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_df = mean_df.drop(['CouncilArea','Regionname'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>Rooms</th>\n",
       "      <th>Price</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Postcode</th>\n",
       "      <th>Bedroom2</th>\n",
       "      <th>Bathroom</th>\n",
       "      <th>Car</th>\n",
       "      <th>Landsize</th>\n",
       "      <th>BuildingArea</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>Lattitude</th>\n",
       "      <th>Longtitude</th>\n",
       "      <th>Propertycount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1480000.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>151.220219</td>\n",
       "      <td>1965.879996</td>\n",
       "      <td>-37.7996</td>\n",
       "      <td>144.9984</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1035000.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>1900.000000</td>\n",
       "      <td>-37.8079</td>\n",
       "      <td>144.9934</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>1465000.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>1900.000000</td>\n",
       "      <td>-37.8093</td>\n",
       "      <td>144.9944</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>850000.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>151.220219</td>\n",
       "      <td>1965.879996</td>\n",
       "      <td>-37.7969</td>\n",
       "      <td>144.9969</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4</td>\n",
       "      <td>1600000.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>142.000000</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>-37.8072</td>\n",
       "      <td>144.9941</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rooms      Price  Distance  Postcode  Bedroom2  Bathroom  Car  Landsize  \\\n",
       "1      2  1480000.0       2.5    3067.0       2.0       1.0  1.0     202.0   \n",
       "2      2  1035000.0       2.5    3067.0       2.0       1.0  0.0     156.0   \n",
       "4      3  1465000.0       2.5    3067.0       3.0       2.0  0.0     134.0   \n",
       "5      3   850000.0       2.5    3067.0       3.0       2.0  1.0      94.0   \n",
       "6      4  1600000.0       2.5    3067.0       3.0       1.0  2.0     120.0   \n",
       "\n",
       "   BuildingArea    YearBuilt  Lattitude  Longtitude  Propertycount  \n",
       "1    151.220219  1965.879996   -37.7996    144.9984         4019.0  \n",
       "2     79.000000  1900.000000   -37.8079    144.9934         4019.0  \n",
       "4    150.000000  1900.000000   -37.8093    144.9944         4019.0  \n",
       "5    151.220219  1965.879996   -37.7969    144.9969         4019.0  \n",
       "6    142.000000  2014.000000   -37.8072    144.9941         4019.0  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_nostrings = mean_df.drop(['Suburb','Address','Method','SellerG','Date','Type'],axis=1)\n",
    "mean_nostrings = mean_nostrings.drop(mean_nostrings[mean_nostrings.YearBuilt<1800].index)\n",
    "mean_nostrings = mean_nostrings.drop(mean_nostrings[mean_nostrings.BuildingArea>2000].index)\n",
    "mean_nostrings = mean_nostrings.drop(mean_nostrings[mean_nostrings.Landsize>24000].index)\n",
    "mean_nostrings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "meanY = mean_nostrings.Price\n",
    "meanX = mean_nostrings.drop(['Price'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_minmax = MinMaxScaler()\n",
    "meanX = mean_minmax.fit_transform(meanX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_mean, X_test_mean, y_train_mean, y_test_mean = train_test_split(meanX, meanY, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hasil:\n",
      "-------------\n",
      "RMSE score: 317596.4597261129\n",
      "Accuracy Score is: 0.7583695154376695\n"
     ]
    }
   ],
   "source": [
    "rf2 = RandomForestRegressor(n_estimators=100,random_state=42)\n",
    "rf2.fit(X_train_mean, y_train_mean)\n",
    "y_pred_mean = rf2.predict(X_test_mean)\n",
    "acc_score2 = rf2.score(X_test_mean, y_test_mean)\n",
    "print('Hasil:\\n-------------')\n",
    "print('RMSE score: {}'.format(np.sqrt(metrics.mean_squared_error(y_test_mean, y_pred_mean))))\n",
    "print('Accuracy Score is: {}'.format(acc_score2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dapat dilihat hasil ketika kita mengisi nilai pada missing value dengan mean setiap kolom, akurasi yang didapat lebih rendah"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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