diamondsPricesPred.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Linear Regression Model \n",
    "that Predicts the prices of Diamonds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pickle\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importing Data, and carrying out some EDA "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>carat</th>\n",
       "      <th>cut</th>\n",
       "      <th>cut_ord</th>\n",
       "      <th>color</th>\n",
       "      <th>clarity</th>\n",
       "      <th>clarity_ord</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.51</td>\n",
       "      <td>Premium</td>\n",
       "      <td>4</td>\n",
       "      <td>F</td>\n",
       "      <td>VS1</td>\n",
       "      <td>4</td>\n",
       "      <td>1749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2.25</td>\n",
       "      <td>Fair</td>\n",
       "      <td>1</td>\n",
       "      <td>G</td>\n",
       "      <td>I1</td>\n",
       "      <td>1</td>\n",
       "      <td>7069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.70</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>3</td>\n",
       "      <td>E</td>\n",
       "      <td>VS2</td>\n",
       "      <td>5</td>\n",
       "      <td>2757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.47</td>\n",
       "      <td>Good</td>\n",
       "      <td>2</td>\n",
       "      <td>F</td>\n",
       "      <td>VS1</td>\n",
       "      <td>4</td>\n",
       "      <td>1243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.30</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>5</td>\n",
       "      <td>G</td>\n",
       "      <td>VVS1</td>\n",
       "      <td>7</td>\n",
       "      <td>789</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  carat        cut  cut_ord color clarity  clarity_ord  price\n",
       "0           1   0.51    Premium        4     F     VS1            4   1749\n",
       "1           2   2.25       Fair        1     G      I1            1   7069\n",
       "2           3   0.70  Very Good        3     E     VS2            5   2757\n",
       "3           4   0.47       Good        2     F     VS1            4   1243\n",
       "4           5   0.30      Ideal        5     G    VVS1            7    789"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#importing datasets\n",
    "diamonds_data = pd.read_csv(\"/Users/mandeebot/Desktop/One/Diamond-App/DiamondsPrediction/data/diamonds.csv\")\n",
    "new_diamonds = pd.read_csv('/Users/mandeebot/Desktop/One/Diamond-App/DiamondsPrediction/data/new-diamonds.csv')\n",
    "diamonds_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>carat</th>\n",
       "      <th>cut</th>\n",
       "      <th>cut_ord</th>\n",
       "      <th>color</th>\n",
       "      <th>clarity</th>\n",
       "      <th>clarity_ord</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1.22</td>\n",
       "      <td>Premium</td>\n",
       "      <td>4</td>\n",
       "      <td>G</td>\n",
       "      <td>SI1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Good</td>\n",
       "      <td>2</td>\n",
       "      <td>G</td>\n",
       "      <td>VS2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.71</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>3</td>\n",
       "      <td>I</td>\n",
       "      <td>VS2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>5</td>\n",
       "      <td>D</td>\n",
       "      <td>SI2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.27</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>5</td>\n",
       "      <td>H</td>\n",
       "      <td>VVS2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  carat        cut  cut_ord color clarity  clarity_ord\n",
       "0           1   1.22    Premium        4     G     SI1            3\n",
       "1           2   1.01       Good        2     G     VS2            5\n",
       "2           3   0.71  Very Good        3     I     VS2            5\n",
       "3           4   1.01      Ideal        5     D     SI2            2\n",
       "4           5   0.27      Ideal        5     H    VVS2            6"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#prediction dataset\n",
    "new_diamonds.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((50000, 8), (3000, 7))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diamonds_data.shape, new_diamonds.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 50000 entries, 0 to 49999\n",
      "Data columns (total 8 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   Unnamed: 0   50000 non-null  int64  \n",
      " 1   carat        50000 non-null  float64\n",
      " 2   cut          50000 non-null  object \n",
      " 3   cut_ord      50000 non-null  int64  \n",
      " 4   color        50000 non-null  object \n",
      " 5   clarity      50000 non-null  object \n",
      " 6   clarity_ord  50000 non-null  int64  \n",
      " 7   price        50000 non-null  int64  \n",
      "dtypes: float64(1), int64(4), object(3)\n",
      "memory usage: 3.1+ MB\n"
     ]
    }
   ],
   "source": [
    "diamonds_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "diamonds_data.drop(columns=['Unnamed: 0','cut_ord','clarity_ord'], axis=1, inplace = True)\n",
    "new_diamonds.drop(columns=['Unnamed: 0','cut_ord','clarity_ord'], axis=1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['carat', 'cut', 'color', 'clarity', 'price'], dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diamonds_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "any duplicated value ; 12743\n",
      "any duplicated value ; 664\n"
     ]
    }
   ],
   "source": [
    "print(\"any duplicated value ;\", diamonds_data.duplicated().sum())\n",
    "print(\"any duplicated value ;\", new_diamonds.duplicated().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "any duplicated value ; 0\n",
      "any duplicated value ; 0\n"
     ]
    }
   ],
   "source": [
    "diamonds_data = diamonds_data.drop_duplicates()\n",
    "print(\"any duplicated value ;\", diamonds_data.duplicated().sum())\n",
    "\n",
    "new_diamonds = new_diamonds.drop_duplicates()\n",
    "print(\"any duplicated value ;\", new_diamonds.duplicated().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "carat      0\n",
       "cut        0\n",
       "color      0\n",
       "clarity    0\n",
       "price      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diamonds_data.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "at this stage we encode our categorical variables, thus transforming categorical variables to numerical variables, to train our model on"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#encoding of categorical features to numerical features\n",
    "from sklearn import preprocessing\n",
    "\n",
    "def cat_encodeer(data):\n",
    "    for x in data.columns:\n",
    "        if data[x].dtype == 'object':\n",
    "            lbl = preprocessing.LabelEncoder()\n",
    "            lbl.fit(list(data[x].values))\n",
    "            data[x] = lbl.transform(list(data[x].values))\n",
    "\n",
    "\n",
    "cat_encodeer(diamonds_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>carat</th>\n",
       "      <th>cut</th>\n",
       "      <th>color</th>\n",
       "      <th>clarity</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.51</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.25</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>7069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.70</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2757</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.47</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.30</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>789</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   carat  cut  color  clarity  price\n",
       "0   0.51    3      2        4   1749\n",
       "1   2.25    0      3        0   7069\n",
       "2   0.70    4      1        5   2757\n",
       "3   0.47    1      2        4   1243\n",
       "4   0.30    2      3        6    789"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diamonds_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>carat</th>\n",
       "      <th>cut</th>\n",
       "      <th>color</th>\n",
       "      <th>clarity</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>37257.000000</td>\n",
       "      <td>37257.000000</td>\n",
       "      <td>37257.000000</td>\n",
       "      <td>37257.000000</td>\n",
       "      <td>37257.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.901772</td>\n",
       "      <td>2.551574</td>\n",
       "      <td>2.723273</td>\n",
       "      <td>3.738143</td>\n",
       "      <td>4676.024398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.479521</td>\n",
       "      <td>1.080916</td>\n",
       "      <td>1.741578</td>\n",
       "      <td>1.703734</td>\n",
       "      <td>4200.609374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>326.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.510000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1399.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.840000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3350.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.140000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>6400.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.010000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>18823.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              carat           cut         color       clarity         price\n",
       "count  37257.000000  37257.000000  37257.000000  37257.000000  37257.000000\n",
       "mean       0.901772      2.551574      2.723273      3.738143   4676.024398\n",
       "std        0.479521      1.080916      1.741578      1.703734   4200.609374\n",
       "min        0.200000      0.000000      0.000000      0.000000    326.000000\n",
       "25%        0.510000      2.000000      1.000000      2.000000   1399.000000\n",
       "50%        0.840000      2.000000      3.000000      4.000000   3350.000000\n",
       "75%        1.140000      3.000000      4.000000      5.000000   6400.000000\n",
       "max        5.010000      4.000000      6.000000      7.000000  18823.000000"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diamonds_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At this point we quickly check for relationships among the variables, we achieve this by using a correlation matrix as a heatmap to find for possible correlations between our variables and the target variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = diamonds_data.corr()\n",
    "ax = sns.heatmap(\n",
    "    corr, \n",
    "    vmin=-1, vmax=1, center=0,\n",
    "    cmap=sns.diverging_palette(20, 220, n=200),\n",
    "    square=True\n",
    ")\n",
    "ax.set_xticklabels(\n",
    "    ax.get_xticklabels(),\n",
    "    rotation=45,\n",
    "    horizontalalignment='right'\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "from the above heatmap, we can deduce that the carat is strongly correlated with the diamond price more than the other variables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We move a step further by visualing the relationship between the diamond prices and \n",
    "the carat, we utilize the scatterplot for this. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.pyplot import suptitle\n",
    "from seaborn import despine\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "ax.scatter(x = diamonds_data['carat'], y = diamonds_data['price'])\n",
    "plt.ylabel(\"Price\")\n",
    "plt.xlabel(\"Carat\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "from the scatterplot above, we see that the relationship between the carat and the diamond price is a linear relationship, and as such, we shall adopt a linear model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = diamonds_data['price']\n",
    "x = diamonds_data[['carat','cut','color','clarity']]"
   ]
  },
  {
   "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>carat</th>\n",
       "      <th>cut</th>\n",
       "      <th>color</th>\n",
       "      <th>clarity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.51</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.25</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.70</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.47</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.30</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49991</th>\n",
       "      <td>0.43</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49992</th>\n",
       "      <td>0.70</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49995</th>\n",
       "      <td>0.71</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49997</th>\n",
       "      <td>1.14</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49999</th>\n",
       "      <td>1.77</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>37257 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       carat  cut  color  clarity\n",
       "0       0.51    3      2        4\n",
       "1       2.25    0      3        0\n",
       "2       0.70    4      1        5\n",
       "3       0.47    1      2        4\n",
       "4       0.30    2      3        6\n",
       "...      ...  ...    ...      ...\n",
       "49991   0.43    2      5        6\n",
       "49992   0.70    3      3        6\n",
       "49995   0.71    2      4        6\n",
       "49997   1.14    3      3        5\n",
       "49999   1.77    3      6        5\n",
       "\n",
       "[37257 rows x 4 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we now scale and preprocess our data before training our model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import  MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#scaling of features\n",
    "scaler = MinMaxScaler()\n",
    "x = scaler.fit_transform(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "split data into training and test groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_val, y_train, y_val = train_test_split(x, y, test_size = 0.2, random_state = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we then fit and train our data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing the ridge regression model\n",
    "from sklearn.linear_model import Ridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Ridge(alpha=0.01)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Ridge</label><div class=\"sk-toggleable__content\"><pre>Ridge(alpha=0.01)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "Ridge(alpha=0.01)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creating a pipeline to scale and train\n",
    "model = Ridge(alpha=0.01)\n",
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Diamonds data set\n",
      "ridge regression linear model intercept: -2085.4090949289675\n",
      "ridge regression linear model coeff:\n",
      " [41252.54133347   314.77195591 -1879.28977329  2620.67410812]\n",
      "r-squared score (trainng): 0.868\n",
      "r-squared score(test): 0.867\n",
      "Number of non-zero features: 4\n"
     ]
    }
   ],
   "source": [
    "#lets print out our results\n",
    "print('Diamonds data set')\n",
    "print('ridge regression linear model intercept: {}'\n",
    "      .format(model.intercept_))\n",
    "print('ridge regression linear model coeff:\\n {}'\n",
    "      .format(model.coef_))\n",
    "print('r-squared score (trainng): {:.3f}'\n",
    "      .format(model.score(X_train, y_train)))\n",
    "print('r-squared score(test): {:.3f}'\n",
    "      .format(model.score(X_val, y_val)))\n",
    "print('Number of non-zero features: {}'\n",
    "      .format(np.sum(model.coef_ !=0)))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have our model trained, and as we can see the model has an r-squared value of 0.877\n",
    "on the training set and a score of 0.878 on the test data.\n",
    "This goes to show that our model did perform pretty well, but the presence of negative predictions also implies that model made some wrong predictions too."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we fit our new diamond data set to our already trained model to make a prediction "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>carat</th>\n",
       "      <th>cut</th>\n",
       "      <th>color</th>\n",
       "      <th>clarity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.22</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.01</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.71</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.01</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.27</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   carat  cut  color  clarity\n",
       "0   1.22    3      3        2\n",
       "1   1.01    1      3        5\n",
       "2   0.71    4      5        5\n",
       "3   1.01    2      0        3\n",
       "4   0.27    2      4        7"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#encoding the categorical features on the new data set\n",
    "cat_encodeer(new_diamonds)\n",
    "new_diamonds.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Ridge(alpha=0.01)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Ridge</label><div class=\"sk-toggleable__content\"><pre>Ridge(alpha=0.01)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "Ridge(alpha=0.01)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#fitting our pipeline on the entire dataset\n",
    "model.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New Diamonds Prediction Data set\n",
      "ridge regression linear model coeff:\n",
      " [41200.93219177   330.04929367 -1866.33539711  2600.95438584]\n",
      "New Diamonds predictions:\n",
      "[48770.64477807 47261.21358798 32158.41101723 ... 51366.39490489\n",
      " 47638.92836969 42096.53559913]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mandeebot/miniconda3/lib/python3.10/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but Ridge was fitted without feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "print('New Diamonds Prediction Data set')\n",
    "print('ridge regression linear model coeff:\\n {}'\n",
    "      .format(model.coef_))\n",
    "print(\"New Diamonds predictions:\\n{}\".format(model.predict(new_diamonds)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mandeebot/miniconda3/lib/python3.10/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but Ridge was fitted without feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([48770.64477807, 47261.21358798, 32158.41101723, ...,\n",
       "       51366.39490489, 47638.92836969, 42096.53559913])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_df =model.predict(new_diamonds)\n",
    "t_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "now, we append the new predicted diamond prices to the new diamond dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>carat</th>\n",
       "      <th>cut</th>\n",
       "      <th>color</th>\n",
       "      <th>clarity</th>\n",
       "      <th>predicted_prices</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.22</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>48770.644778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.01</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>47261.213588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.71</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>32158.411017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.01</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>47988.360301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.27</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>20438.146434</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   carat  cut  color  clarity  predicted_prices\n",
       "0   1.22    3      3        2      48770.644778\n",
       "1   1.01    1      3        5      47261.213588\n",
       "2   0.71    4      5        5      32158.411017\n",
       "3   1.01    2      0        3      47988.360301\n",
       "4   0.27    2      4        7      20438.146434"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = new_diamonds.assign(predicted_prices = t_df)\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we visualize the new prediction vs the carat, and we see that\n",
    "that the our predicted prices on the new diamonds data set are in a much narrower range than actual prices from the main diamond dataset.we see here that the data points are less \n",
    "spread out.  This sortof reinforces the idea that there are likely other factors omitted from the modelthat would help improve our accuracy.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BajbgShsRERFRF9WxAXVnDyJFFdWyAhvQ3j9NBWDJloN4+uMfoK9tNt2nVUdgaU46tzWSV/j0TNu+ffuQk5ODlJQUqFQqfPjhh2b3C4KAp59+GikpKYiMjMSNN96IH374wWxMc3MzHnzwQSQmJiI6OhpTp07FL7/8YjampqYGubm5UKvVUKvVyM3Nxfnz583GnDx5Ejk5OYiOjkZiYiLmz5+PlpYWszEHDx7E2LFjERkZiZ49e+Jvf/sbuFBJREREgSivTIcxK3dj2puFeGhTKaa9WYgxK3cjr0zn66l5hKul/wUANQ2tZoENAPSGJszZWNJp3y/yLz4NbfX19bj66quxZs0am/e/8MILWLVqFdasWYMDBw5Ao9EgKysLdXV1pjELFizA1q1bsWnTJuTn5+PChQuYMmUKjEajacz06dNRWlqKvLw85OXlobS0FLm5uab7jUYjJk+ejPr6euTn52PTpk3YvHkzFi1aZBpTW1uLrKwspKSk4MCBA3j11Vfx0ksvYdWqVR54Z4iIiIg8J69MhzkbS6yaTnfmIKJ0DzTxn+2XbSuHsY3/iE+e5Tdn2lQqFbZu3YrbbrsNQPsqW0pKChYsWIDFixcDaF9VS05OxsqVK3H//ffDYDCgR48e2LBhA+68804AwJkzZ5CamopPP/0UkyZNwqFDh5Ceno7CwkKMHDkSAFBYWIjMzEz8+OOPGDBgAD777DNMmTIFp06dQkpKCgBg06ZNmDlzJiorKxEbG4t169ZhyZIlOHv2LMLD2ysLPf/883j11Vfxyy+/QKWStpWAZ9qIiIjIl4xtAsas3G0V2ERiz7D8xeM71VZJ8XXb65Xmjg9mjbLbo43IEanZwG9L/ldUVECv12PixImm28LDwzF27Fjs378fAFBcXIzW1lazMSkpKcjIyDCNKSgogFqtNgU2ABg1ahTUarXZmIyMDFNgA4BJkyahubkZxcXFpjFjx441BTZxzJkzZ3D8+HG7r6O5uRm1tbVmX0RERES+UlRRbTewAe0rSDpDE4oqqr03KS+QUvrfVa5uvSSSym9Dm16vBwAkJyeb3Z6cnGy6T6/XIywsDHFxcQ7HJCUlWV0/KSnJbIzl88TFxSEsLMzhGPF7cYwtK1asMJ2lU6vVSE1NdfzCiYiIiDxIasDwVRAxtgkoOFqFj0pPo+BolaJbD+31ShNL/bsa5pTeeklkye+rR1puOxQEwelWRMsxtsYrMUbcWepoPkuWLMHDDz9s+r62tpbBjYiIiHxGasCQOk7JCpR5ZTos21ZuthKodJVGe73SdpbrbT53Y6sRhoZWm1sqxa2kI9LiFZkbkT1+G9o0Gg2A9lUsrfbSD2llZaVphUuj0aClpQU1NTVmq22VlZW47rrrTGPOnj1rdf1ff/3V7Dpff/212f01NTVobW01G2O5olZZWQnAejWwo/DwcLMtlURERES+NCItHlp1hN2zXXKCiJyQ5SzcicVRLOckFkdZN2OYYsEtOEhldQbNUZibs7EEKsBsbuLMl+akd6qzf+Sf/HZ7ZFpaGjQaDXbu3Gm6raWlBXv37jUFsuHDhyM0NNRsjE6nQ1lZmWlMZmYmDAYDioqKTGO+/vprGAwGszFlZWXQ6S5VStqxYwfCw8MxfPhw05h9+/aZtQHYsWMHUlJS0Lt3b+XfACIiIiIPcHS2S04QkVOB0ll7AWObgGXbym2GSG9WaRTD3K1DeiKzbwKCg1R2t1Rq1BGKBkkiR3xaPfLChQs4cuQIAGDo0KFYtWoVxo0bh/j4eFx++eVYuXIlVqxYgXfeeQf9+/fH8uXL8cUXX+Cnn35CTEwMAGDOnDn45JNP8O677yI+Ph6PPPIIqqqqUFxcjODgYADAzTffjDNnzmD9+vUAgPvuuw+9evXCtm3bALSX/B8yZAiSk5Px4osvorq6GjNnzsRtt92GV199FQBgMBgwYMAAjB8/Hk888QQOHz6MmTNn4qmnnjJrDeAMq0cSERGRP3BnK6KcCpTiSpXlL5xiJFw3YxjUkWGY9mah0zn7skpjV2pETt4jNRv4dHvkN998g3Hjxpm+F89+3X333Xj33Xfx2GOPobGxEXPnzkVNTQ1GjhyJHTt2mAIbAKxevRohISG444470NjYiJtuugnvvvuuKbABwHvvvYf58+ebqkxOnTrVrDdccHAwtm/fjrlz52L06NGIjIzE9OnT8dJLL5nGqNVq7Ny5E/PmzcM111yDuLg4PPzww2bn1YiIiIgChb3tgFKCiNQKlIXHqhyuoKnQvoL2WPaVkubsyyqNtrZUEnmL3/Rp6yq40kZERESB7qPS03hoU6nTcQ+M64c1e444HXf7kBRsLT3jdJyclTaujFEgCIiVNiIiIiIKPNJL3EtbG3AW2ORWafRGFUoib/LbQiRERERE5J/ECpT21q1UaA9JmX0S3X4uuVUa5RRIIQoUDG1EREREJIvUCpSGxla4uyNRTpVGf6lCSaQ0bo8kIiIiItnEUviW2xA1v21DBIB571tXjZTqgXF9MbpfD1ln0aQWSCmqqGZREQooDG1EREREZEVKIY/sDC3GX5mMDQXHcaK6Ab3io5Cb2RvBQSqMWbnb5cAGAP2TY2QHK6nVJX1ZhZLIFQxtRERERGRGaiEPW+P+kV+Bu6693OGKlxTSi53If4wr1ybyJZ5pIyIiIvIhY5uAgqNV+Kj0NAqOVvn8vJXUQh6Oxq3e9bPLzy8WMZFaKbIjqQVSXLk2kS9xpY2IiIjIR/ytNL2zQh5iM+zxVyY7LfjhCrmVIi2JBVLmbCyBymIu7l6byJe40kZERETkA/5Yml5qIY8NBcfd3v5oi5xKkfaIBVI0avMtkEpcm8hXuNJGRERE5GVSV7Sy0jVeXRWSWqDjRHWDR57/yckDFQlV2RlaZKVrnBZSIQoUDG1EREREXuavpemlFujoFR+l+HOrADyz/RAmZWgVCVfBQSqW9adOg9sjiYiIiLzM06XpXS1uIrWQR25mb4fjXNExqBKROa60EREREXmYZc+zxOhwSY9zpTS9O8VNgoNUeHLyQMx9/1ur+zoW8ggLCbJb8MNdn/12lo/bGYkuUQmC4Nu6sl1MbW0t1Go1DAYDYmNjfT0dIiIi8jBbIUoTG4Gmi0YYGlptBh4V2gtn5C8eLyu4iMVNLK8pXsFZIQ5bcxVJ7dOmFF9W0STyFqnZgKHNyxjaiIiIug5HIUqw8Wfxe8B5wLJkbBMwZuVuuwHKWRC0N1fR2unDcMtg6/mIq4iflenwz4ITkufrjKvvA1EgkZoNeKaNiIiIyAOkVIiMiwpFcqz5VklXS9PLKW4iZ66AWCSk3ObZOLHgx80y5itl7VB8pmXbbD8vUVfCM21EREREHiAlRNU0tOK9e0ciSKVyuzS9vlbaFsWd5XqrqopKVLMUi5joDU0Ot3w+OTkdz2yXtqXSV1U0ifwNV9qIiIiIPEBq5cdzF5qR2TcBtw7picy+CS4FtrwyHZ755AdJY9/+6rhV424lqlkGB6mwNCcdgPVKWsciJrcM1iJ/8Xh8MGsU/pTZy+3nJeoKGNqIiIiIPEBq5UdXKkR2JJ5Fq65vlTRebNzdccuhUnPNztBi3Yxh0KjNx1lu+ZS7pdLd94go0HF7JBEREZEHSN0uOCIt3uXncHYWzRZbWw5dmatlGwNxW2d2hhZZ6Rqb91nyxntE1BkwtBEREVGXZi98uEvcLmirl1nH7YLuPJezs2iOdNxyKHeuznrBiStpznjjPSLqDLg9koiIiLqsvDIdxqzcjWlvFuKhTaWY9mYhxqzcbXXmy1VStwu6yp2zXpZbDrMztLjvhjSoLPKRSgXcd0Oaaa7idkzLsKg3NGHOxhLZ752n3yOizoB92ryMfdqIiIj8g7uNqOXw1GpewdEqTHuzUNZj7PVrc9SnTYX29yMrXeNWLzhHPPUeEfkzqdmA2yOJiIioy5HSQ+3/bS1DY4sRGnWk2wFC6nZBuZydCbNkueVQDEr62iY888kPDq+xbFs5YsJD3W4NYI+n3iOizoChjYiIiLocKX3JqupbsPDf3wEwP6/lTxydCbNF0+F12DqXZo8YxgqOnZM0L5boJ1IWQxsRERF1OXJDhXheyx/PWIlnwmwVBnly8kDERYebbTkEgFd2HcbqXT+78GzSVhtZop9IWQxtRERE1OXIDRXiCtYTWw+isbUNmlj7Z67cOZvl6mOllNk3tglYs/sw3s6vgKHpoqT5WMrsm4DNJb+wRD+RlzG0ERERUZczvFcc4qPDUF3fIutx1fWtWPivUgC2t0w6K4XfkWVAq6lvwTPbba2WpSMuOsxpkHN0JiyvTIfHtxzE+QZpDbgtiWFsVJ8Elugn8gFWj/QyVo8kIiLyLTlnuRyxrDIppxqlO3PoGAKlrMw5qgophdT5++u5PyJ/JjUbMLR5GUMbERGR77gbYGzRqiOw99FxGPviHkml8HeW6xUJUffdkIaPv9M5DE7GNsFhiX4ppK4UskQ/kXws+U9ERETUgaMy/+7QGZqwoeC4pFL4hceq3J6D+Nj1+yqs7rMsmOKsSqYtKgDx0WH46+SBDtsdsEQ/kfcwtBEREVGX4EqAkep4VYOkcQVHqzw2B+BSj7ll28qRla5xufT+c7dncJsjkR9haCMiIqIuwbO9w6SunXn+VErHBtdyq2TyXBqRfwry9QSIiIiIvMGV3mETBiZJGjfksu7QqiPsdjFToT0QZfZJlD0HV1XWNWFEWrzDeXW0cEJ/5C8ez8BG5IcY2oiIiKhLGJEWj+5RoZLGiiHr7szeksanxEVhaU666bGW1wLaS+GP6psgOUS5KykmAsFBKrvzEnWPCsXrM4bhoQlXsJAIkZ9iaCMiIiKyYerVWjz6v985HRekAmrqW5CdocW6GcOgUZuv6GnUEWbl8u+6NtWjmyTFwCk2uLY3r+5RoVg44QoU/zWLq2tEfo5n2oiIiKhLKKqoltRcOj46FL8fdpnN6oy2tAnAvPdLsC6oPZhlpWtslsJXqj+cJjYctw5JwRu/zU9Kg2tH8yIi/8fQRkRERF2C1EIkdU0X8eaX0gKbSADw/7aWYfyVyQgLCbIqha9Uf7iFE67AA+P7IThIhaGXx1mFQI2DQiIs0U8UuBjaiIiIqEuQWoik1ehatKqqb8HI5buw4neDzEKTEv3hNLHheHrqVWbX5eoZUdfB0EZERERdglhJUW9o8tiZspqGVszeWILXZwwzBaqvjvyqQG828yBmbBPMwtqUwSkMa0SdmEoQBM83DCGT2tpaqNVqGAwGxMbG+no6REREXYpS2xSdiQ4LRkxECPS1zYpcT4X2LZgLJ/RHbWMrtpaeRnX9pfN57K9GFJikZgOGNi9jaCMiIlKe5cqTo22CKz4txxv7KrzQ5tp7xFfasUolEfk/qdmA2yOJiIgooNmqymhv5SmvTNfpAhvQvgqnArBsWzmy0jXcKknUybBPGxEREQUscbuj5ZkxvaEJczaWIK9MZ7pNiYIg/kwAoDM0oaii2tdTISKFMbQRERFRQHIUwsTblm0rh7Gt/buiimoFCoL4P6mtDYgocDC0ERERUUByFsIsV566SpiR2tqAiAIHz7QRERFRQJIawsRxnT3MqNDeXHtEWryvp0JECuNKGxEREQUkqSFMHCf2afMVb5QGWZqTziIkRJ0QQxsREREFJDGE2YsoKrRXkRRXnoKDVJh6tffK4cdHh5p9r1FHYOGE/rKu0T0yFFMGa6GC49CnVUew3D9RJ8btkURERBSQgoNUWJqTjjkbS0zNp0ViwOm48mRsE/DxdzrLyyhO3Ka499FxKD5RY9Y7DgA2HTgFvaHJaRXLhRP644Hx/REcpMKUwdZtDRKiw3DrkBRkpWsc9qUjosDH5tpexubaREREypLap63gaBWmvVno0blIaXIttikAYDO4dY8KxfO/G2T1eDkNxIkoMLC5NhEREXUJ2RlaZKVrnAYa3flGj89FY6epd0fZGVqsmzHMKmh2jwrFn69LwwPj+9kMY8FBKmT2TfDIvInIvzG0ERERUcCTEmi+PVXjsecf0y8R4wb0QG5mb4SFOC8ZIDVoEhEBDG1ERETURXjyPEj+kXPIP3IO/8ivcLrSJuLKGRFJxeqRRERE1CWkJUR7/Dn0hibM2ViCvDLPFzwhoq6DoY2IiIi6hNzM3vD07kNxNW/ZtnIY21jrjYiUwdBGREREXUJYSBBmXZ/m8ecRAOgMTSiqqPb4cxFR18AzbURERCRZoJedX3JLOo6dq8fO8kqPP1dlXZPzQUREEjC0ERERkSRS+6H5s7wynVcCGwAkxUR45XmIqPPj9kgiIiJyyNgm4JVdP2P2xhKzwAYEVuENY5uAh//9nVeeS6tuX4UkIlKCX4e2ixcv4q9//SvS0tIQGRmJPn364G9/+xva2tpMYwRBwNNPP42UlBRERkbixhtvxA8//GB2nebmZjz44INITExEdHQ0pk6dil9++cVsTE1NDXJzc6FWq6FWq5Gbm4vz58+bjTl58iRycnIQHR2NxMREzJ8/Hy0tLR57/URERL6WV6bD6Od3Y/WuwzbvD6TCG/sPn0NDi9ErzzX1am1AbRslIv/m16Ft5cqVeP3117FmzRocOnQIL7zwAl588UW8+uqrpjEvvPACVq1ahTVr1uDAgQPQaDTIyspCXV2dacyCBQuwdetWbNq0Cfn5+bhw4QKmTJkCo/HS/3FPnz4dpaWlyMvLQ15eHkpLS5Gbm2u632g0YvLkyaivr0d+fj42bdqEzZs3Y9GiRd55M4iIiLwsr0yHORtLoK91fDbLk4U3Wi624a0vj+Gpj8rw1pfH0HKxzfmD7PjfklMKzsyxj7/T+X2IJaLAoRIEwW//H2XKlClITk7GW2+9Zbrt97//PaKiorBhwwYIgoCUlBQsWLAAixcvBtC+qpacnIyVK1fi/vvvh8FgQI8ePbBhwwbceeedAIAzZ84gNTUVn376KSZNmoRDhw4hPT0dhYWFGDlyJACgsLAQmZmZ+PHHHzFgwAB89tlnmDJlCk6dOoWUlBQAwKZNmzBz5kxUVlYiNjZW0muqra2FWq2GwWCQ/BgiIiJvEQuN6A2NeGb7IVTXS99R8spdQ3DrkJ6KzWXFp+V488sKdMw+QSpg1vVpWHJLuuzr/WHdV/jmxHnF5ufMB7NGsXk2ETkkNRv49UrbmDFj8Pnnn+Pnn38GAHz33XfIz8/HLbfcAgCoqKiAXq/HxIkTTY8JDw/H2LFjsX//fgBAcXExWltbzcakpKQgIyPDNKagoABqtdoU2ABg1KhRUKvVZmMyMjJMgQ0AJk2ahObmZhQXF9t9Dc3NzaitrTX7IiIi8kd5ZTqMWbkb094sxMJ/fycrsAHKFt5Y8Wk51u8zD2wA0CYA6/dVYMWn5bKveVlclEKzk4bVI4lIKX4d2hYvXoxp06bhyiuvRGhoKIYOHYoFCxZg2rRpAAC9Xg8ASE5ONntccnKy6T69Xo+wsDDExcU5HJOUlGT1/ElJSWZjLJ8nLi4OYWFhpjG2rFixwnROTq1WIzU1Vc5bQEREXYyxTUDB0Sp8VHoaBUervLbFTtwKaVloRAoVlC280XKxDW9+WeFwzJtfVsjeKvn7YZe5My3ZWD2SiJTi1yX///Wvf2Hjxo14//33cdVVV6G0tBQLFixASkoK7r77btM4lcr8oK8gCFa3WbIcY2u8K2MsLVmyBA8//LDp+9raWgY3IiKyyVcl9Y1tApZtK4c78XBpTrpihTc2FBy3WmGz1Ca0j7vn+j6Sr3tdv0REhQW7XYxEBUAdFQpDQ6vN90wFQMPqkUSkIL9eaXv00Ufx+OOP46677sKgQYOQm5uLhQsXYsWKFQAAjUYDAFYrXZWVlaZVMY1Gg5aWFtTU1Dgcc/bsWavn//XXX83GWD5PTU0NWltbrVbgOgoPD0dsbKzZFxERkSV7K11ySuq7ukpXVFHt0gobAGhiw7FuxjBFQ+WJ6gZFx4mCg1RYdcfVrkzJRKuOwGvTh+HP1/W2G9gAZUMsEZFfr7Q1NDQgKMg8VwYHB5tK/qelpUGj0WDnzp0YOnQoAKClpQV79+7FypUrAQDDhw9HaGgodu7ciTvuuAMAoNPpUFZWhhdeeAEAkJmZCYPBgKKiIowYMQIA8PXXX8NgMOC6664zjXnuueeg0+mg1bb/h2nHjh0IDw/H8OHDPfxOEBFRZ+ZopUtAexBYtq0cWekau0Egr0yHpz/+AfraZtNtmthwPD31KqeBytWzVwsnXIEHxvdTPJz0ipd29kzquI6yM7R4fcYwzP+gBFIW3IaldsfkwVrER4dBo45ETX0zntlebjfkagKs2TgRBQa/Dm05OTl47rnncPnll+Oqq67Ct99+i1WrVuEvf/kLgPbtigsWLMDy5cvRv39/9O/fH8uXL0dUVBSmT58OAFCr1bjnnnuwaNEiJCQkID4+Ho888ggGDRqECRMmAAAGDhyI7OxszJo1C+vXrwcA3HfffZgyZQoGDBgAAJg4cSLS09ORm5uLF198EdXV1XjkkUcwa9Ysrp4REZFbnK10dSypb6saYV6ZDrM3lljdrq9txuyNJXjdyUqY3LNXnt6ymZvZG899esjhFskgVfs4V4WoVGiRsCF0YnqyaQtmXpkO897/1u6jPBViiYj8OrS9+uqrePLJJzF37lxUVlYiJSUF999/P5566inTmMceewyNjY2YO3cuampqMHLkSOzYsQMxMTGmMatXr0ZISAjuuOMONDY24qabbsK7776L4OBg05j33nsP8+fPN1WZnDp1KtasWWO6Pzg4GNu3b8fcuXMxevRoREZGYvr06XjppZe88E4QEVFnJnWly9Y4Y5uAx7ccdPi4x7ccdLhKNyItHlp1BPSGJruBJD46FE9OuQqa2PazWp4MJmEhQZh1fRrW77NfjGTW9WkIC5F/ykPchir1/F5tcysA5+f+VAA2HTiJB8b3kz0nIiJn/LpPW2fEPm1ERP5F7EtWWdeEpBjPBxJbCo5WYdqbhU7H2er79dXhc/jjW187fex794zE6P6Jdu8XwwwAs2AivhNKn1uTQuk+bcY2AWNW7pZ1fm/ujX3xWPaVbn1GRET2SM0Gfr3SRkRE5EmertYoNRA6W+kSqxEO7xWHgqNVZtcrOHZO0lwKjp1zGNqyM7RYN2OY1fuhUUfgycnpUEeG4aPS014NtktuSceiiVdiQ8FxnKhuQK/4KORm9nZphQ1wreBKXFQYAPdWQ4mI3MXQRkREXZK9bXJitUZ3V5bkBMLgIBWW5qRjzsYSqGB7pWvq1VqMfXGP1fWGpHaXOCPnISs7Q4usdI1Z0LRVeMMbbQhEYSFBssr6O+JKoEqMCQcg/dwfe7MRkSf4dcl/IiIiT3BWrRFor9boamNrV8r3iytdGrX5L/0adQTuuyENb+yrsHm9z8rM29HYI3XLXnCQCpl9E3DrkJ4wNLZg3vvfutWGoCNfNQ4XuRKokn4LbeJqqL3oq3SDcSKijrjSRkREXY7Uao2FR6scbim0xZ3y/bZWuob3isPYF/c4vJ6z6BMeEoRRfeSds1KiDUFHvmoc3pGUgitWfhsoZTWUvdmIyFO40kZERF2O1G1y8953vJpka+VITvl+WzqudGX2TUDxiRqn13Om5WKbzVUtRytf7r6OjpRoHK4EMXjJca7+Ut87R6uhvijUQkRdh+yVtv/5n/9BYmIiJk+eDKC95P4bb7yB9PR0fPDBB+jVq5fikyQiIlKS1G1y5xtbTefbbJ/1OmS1cnRLhkbStb1Z2EIAsKHguNnZMGcrX0rNT+kVO3dlZ2jbt5t+WQEp9bMt/67YWg31RcVRIupaZIe25cuXY926dQCAgoICrFmzBi+//DI++eQTLFy4EFu2bFF8kkREREqSu01uyZaDePrjH6CvbXY4Tm9owltfHZc0B28XtjhR3WD6s5QiLFKf9/DZCyg4WmU3uLjbOFxpeWU6vLGvwunnLlbstHVGTVwNJSLyFtnbI0+dOoV+/dobR3744Yf4wx/+gPvuuw8rVqzAl19+qfgEiYiIlCZuk5MS2AQANQ2tTgObOBZo7yWmVMEKZwUwpOoVHwVAehGW4b3iJD3vmj1HMO3NQoxZudvmNkd/KpXvrEG2JZ5RIyJ/ITu0devWDVVVVQCAHTt2YMKECQCAiIgINDY2Kjs7IiKiANQmXNr615ErBSuCg1R4crLtgKnq8OVIkArIzewNQPrKV/GJGtP5LykztXc+zZ9K5Uvt06ZSAffdkMYzakTkN2SHtqysLNx7772499578fPPP5vOtv3www/o3bu30vMjIiJSnLji4kl/Gd1bkYIVeWU6PLPd9lzF6913Q5rDa8y6Pg3BQSoUHK3CZxKLflTWNdktvGGLvVYJ/lQqX+pqniAAb+yr8FqBFCIiZ2SfaXvttdfw17/+FadOncLmzZuRkNC+p7u4uBjTpk1TfIJERERKk7ri4o6sdA3+3+R02QUrxAqU+tomfHX4V/xvyWm7Y5+cPBDZGVpTCHzzywp0LBKpAjB5sBZXX9YdY1bulvWaxZWv7Awtxl+ZjA0Fx/F1RRV2lFfafYypVcKxKozu194qwZ9K5ctdzfNmgRQiIkdUgiCldhIppba2Fmq1GgaDAbGxsb6eDhFRl/RR6Wk8tKnUI9cWC1jkLx4v+5d9WxUdpT5PXpkOSz8qw9m6Ftcm7uC6Uuck6h4Ziud/P8hsRdEf+rQZ2wSMWblbVp+2D2aNYtERIvIYqdnApebaX375JdavX49jx47hP//5D3r27IkNGzYgLS0NY8aMcXnSRERE3pDYLdwj13Vn5cheRUd7OlZdNDS2yHqsPZbzlzsnUcdWCWIg84dS+R1X/aTyRoEUIiJnZJ9p27x5MyZNmoTIyEiUlJSgubm9mlZdXR2WL1+u+ASJiIgU56E9JnLPrInNrbeW/IIntpa5NC19bZOsioiOdJy/3EqLtlieb7NsHO6LbYfiOb346FBJ471RIIWIyBnZK23PPvssXn/9dfzpT3/Cpk2bTLdfd911+Nvf/qbo5IiIiDxBydWThOgw/HXyQGjUkbJWjvLKdJJ6vzlTfaHZ7fN5f8rshZsztGbzd/fcn7f7r8khntMbteJzVNfb3k7qqE8bEZG3yV5p++mnn3DDDTdY3R4bG4vz588rMSciIiKPOnfBvXNfwKVS+8/dnoHbh10meeXI2CbglV0/Y/bGErcDm1YdgfjoMLeuAQCxEdarTkoFW3/dXhgWEoTlt2fYbJng7QIpRETOyF5p02q1OHLkiFV5//z8fPTp00epeREREXnM+Qb3Q5tGZhENY5uANbsP4+2vjsPQ2Or28wNAY6sRJ6vd75G6Zs8RrNlzBPHRYbhtSAqy0jWKnfvz5+2F4lZJywIpcj9bIiJPkx3a7r//fjz00EN4++23oVKpcObMGRQUFOCRRx7BU0895Yk5EhERKUrl4uJJRGgQnrs1AylxUbK3Qj6+5SDONygT1kSGhlas3vUzVKr23mLuqq5vwdtfHcfbXx2HJjYc3aNCYWhotd/YWwWzFgOW9wfC9kJ/KJBCROSM7ND22GOPwWAwYNy4cWhqasINN9yA8PBwPPLII3jggQc8MUciIiJFZfZJxJo9R2U/rqm1Dc999iOW354hK7ApUdnRFvGanmje03Hrpr3+arOuT8Mb+yrM5tLx/kDZXigWSCEi8lcu92lraGhAeXk52trakJ6ejm7duik9t06JfdqIiHzP2CZg+LM7XV75UgGSqkSKfcE83chbjhG941B0vEbSWBUAdVQoIkKCoa+13V/NH/qvEREFKqnZQHZoMxgMMBqNiI833+5QXV2NkJAQBhEnGNqIiPxDXpkOs2X06+rIVgNtY5tgtcWuqKIa094sdOk5osOCUd9idOmx9nSPCnUpqL53z0gEBansbh+09doDYYWNiMjXPNZc+6677kJOTg7mzp1rdvu///1vfPzxx/j000/lz5aIiMjLsjO0eH3GMCzZchA1MoOMWM5+9c6fMLpfD9TUN+OZ7YesVptuydC4PD+lA5s7ztU349YhPe3ez+2FRESeJXulLT4+Hl999RUGDhxodvuPP/6I0aNHo6qqStEJdjZcaSMico/SqzotF9sc9utyleU5sED2waxRDGVERB7gsZW25uZmXLx40er21tZWNDa6X3aYiIjIHk+cnwoLCcJ/De+J9b8V1FCKGNiCHFRYtNQtPBgXmv1nhS1QKkC6gls6iSiQyG6ufe211+KNN96wuv3111/H8OHDFZkUERGRJbEKo2VRD72hCXM2liCvTOfSdVsutmHj1yeVmKJNUgMbAPwps7fH5iFXoFWAlCOvTIcxK3dj2puFeGhTKaa9WYgxK3e7/HeIiMjTZK+0Pffcc5gwYQK+++473HTTTQCAzz//HAcOHMCOHTsUnyAREZGxTcCybeU2txsKaA8Yy7aVIytdIytg5JXp8MTWMtR7eHVrbP9E7Dt8zuF2ydiIEIzsHY+1kN+KwBM6a4Npey0YxPAvpSooEZG3uVTyv7S0FC+++CJKS0sRGRmJwYMHY8mSJejfv78n5tip8EwbEZF8BUerJFVhlHP2ypP901wVpAIiQoPR2GK029Ba/VvDa0D5M3MLJ/RH78ToTrtd0FkLBltVQYmIPMljZ9oAYMiQIXjvvfdcnhwREZEclXXS+pxJHedo5c6X2gSg4beqkfYaWj//u0EAYHW2zx0qAPfdkIaHJlyhyPX8VVFFtcP3TKwKWlRRzcIrRORXJIW22tpaU/Krra11OJarR0REpLSkmAhFxzn75d3XVCogqVs4ztY1m26z3K6Yla5BUUU19LVNeOaTH1Bd71qjcKA9rLyxrwJDL4/r1FsDlQ7/RETeIim0xcXFQafTISkpCd27d4dKZb1lQBAEqFQqGI3+U/WKiIi8w9OV+EakxUOrjoDe0GR326CcKof+/ku5IAD3Xt8HGT3VqKxrQmK3cEBo75dWcLTK9P5m9k1AwdEqtwJbR66cCwwkSod/IiJvkRTadu/ejfj49v8Q7tmzx6MTIiKiwOKJMvyWgoNUWJqTjjkbS+xuG5RT5TAQfik/Wd2AWTf0QV6ZDo/85zu7769SAbQrbA1UOvwTEXmLpNA2duxYAMDFixfxxRdf4C9/+QtSU1M9OjEiIvI8d1fIvFmJLztDi3UzhlkFRFeqHA7vFSerf5pvCJLeX6UDqL+vQrpD6fBPROQtsgqRhISE4KWXXsLdd9/tqfkQEZGXuLtC5qky/I5kZ2hNZ7nc2YpZfKLGzwMbMLinWtL7u/fRcdCqIxQ7oxcIq5DuUDL8ExF5i+zqkTfddBO++OILzJw50wPTISIib1BihcyTlfgcrQCKZ7ncEQirSbVNFyW9v8UnarA0Jx2zN5a49XxdaWugUuGfiMhbZIe2m2++GUuWLEFZWRmGDx+O6Ohos/unTp2q2OSIiEh5Sq2QuVKJT8p2TG+ckYuPDFPkOp6iVUcgvlu4pLGVdU24dUhP/Pm63nhn/3G3nrcrbQ1UIvwTEXmL7NA2Z84cAMCqVaus7mP1SCIi/6fUCpncSnxSwpi3zsiV6x23r/G1xlYjTlbVSxorvr8Tr9K4HNriokKw4neDuTWQiMhPBcl9QFtbm90vBjYiIv+nVK8qsRKfvXUZFdpD2Yi0eFMYswyLYhjLK9M5XQEE2lcAjQocRis+UeP2Ndy1cMIV6B4VavM+Q0MrVu86jO5RoZLeX6D987B3PWeu6ZUAdWSYIu8tEREpT3ZoIyKiwKZUryqxEh8Aq2DRsRIfAElhrPBYleQVQHdFhQW7fQ13xEQE454xaYgIsf2fYXGbqsjR+6vEdsadh85i2puFGLNyN/LKdG5fj4iIlOVSaPv8888xZcoU9O3bF/369cOUKVOwa9cupedGREQeIGeFzBmxEp9GbR7wNOoI01ZGqdsxC45WSZq/EkVE0rWxbl/DHXVNRgxa9n/Q1zbbHSMAON/QigUTrnD4/oqKKqpxvsG9JtsdVz6JiMh/yD7TtmbNGixcuBB/+MMf8NBDDwEACgsLccstt2DVqlV44IEHFJ8kEREpR+leVc4q8UkPWdK25ilRkj5BYpEPTxIk7kTsnRiF/MXjrd5fACg4WmW6TV/rfpj1VKsGIiJyj+zQtmLFCqxevdosnM2fPx+jR4/Gc889x9BGRBQAlO5V5agSn9SQldknEZtLTkNvaLIZ35QoSS9Wr9z7U6XL1/C2pJgIq/fXVlGX+GjXzrNZcqdVAxEReYbs0FZbW4vs7Gyr2ydOnIjFixcrMikiIlKeZbn9rHSNV3pVidsxnYWxUX0TFF0BtGQr6PgzeyHVXoXN6nr3tkZaCoRedkREXYXs0DZ16lRs3boVjz76qNntH330EXJychSbGBERKccbvc/skbMdU+kVQJG9oOOv7IVURxU2labENlQiIlKGShCk7qpv9+yzz+Kll17C6NGjkZmZCaD9TNtXX32FRYsWITb20uHu+fPnKzvbTqC2thZqtRoGg8HsvSIiUlLHVbXj5xrw8q6frX7RF6OAUr3PnJETHKU04ZbK2CZgzMrdfr3CFhMRjLqmS21z7L0vBUerMO3NQqfX6xYejAvNrrXhEVf48heP55k2IiIPk5oNZIe2tLQ0SeNUKhWOHTsm59JdAkMbEXmanG2A3v4FXckwJpXUoOMrQSrgh2XZKD113un78lHpaTy0qdTpNefe2Adrv5D/32BvB3kioq5OajaQvT2yoqLCrYkREZFnGNsErNl9BKt3/Sz5Md4uOuGoYImn+PvZrFnXpyEyLFjS+yJ1y+Lovj2w9dszds8R2uPuNlQiIvIM2aGNiIj8T16ZDk9//IPDvl+OdAw2nlwN88VKmz+fzcpKT8KSW9KdjhPfN31tE+KjQ+0WHZFS1MWRJycPZGAjIvJDDG1ERAFOiSIbx8/Vm67lqYIlviqG4qx6pS/tKq9EXpnO4euXut1ValEXR49/ZvshTMrQ8iwbEZGfkX2mjdzDM21EpCQli2zcf0Ma3thX4ZGCJfaCpbfOUInPD0hfdfIGZ2cK5QRyZ0VdvjpyDmv2HHF6nQ9mjWJ/NiIiL5GaDYK8OCciIlJYUUW1IoFNBeDNL60DG3Ap5CzbVg5jm/zI46hMfcdrt1xsQ8HRKnxUehoFR6tcei57xFUnjdq/tkp2PFNoyVl5fxWAhOgwrL7janwwaxTyF4+3GXzFc4T9k7tJmpO/nwEkIuqKuD2SiMhHlDjfpdQv2AIAR/su3ClY4ixYitcetWKX2VktpbdOZmdoTc3E1+w5jK+OVClyXSXsLNdbva9S3req+hZo1JGKFjHx5zOARERdlaTQ9v3330u+4ODBg12eDBFRV6HU+S5v/4LtSkiU+hjL4hp6QxPmbCxRdOukuOr06cEzfhXa3v7qOEakxZu9Tqnvm9Rxzs72iVs1R6TFS7oeERF5j6TQNmTIEKhUKgiCAJXK8b8CG42uNfMkIuoq7J1TciWkeLvIhish0dVgKaA9SCzbVo6sdI2ixTEG9VQrdi2lPLH1IBpb26CJbQ9OSq+MBQep7FaUtCxiQkRE/kXSmbaKigocO3YMFRUV2Lx5M9LS0rB27Vp8++23+Pbbb7F27Vr07dsXmzdv9vR8iYgCmtTzXVLPc4m/iHs6sKnQvhLoyiqMGCxdiQKOzny54+Bpg6LXU0J1fSsW/qsU094sxJiVu1FT3+zwfXPlM7F3tk+jjmBDbSIiPyZppa1Xr16mP//Xf/0X/v73v+OWW24x3TZ48GCkpqbiySefxG233ab4JImIOgup57u81exaDldXYRyt8EildHEMBWuceITe0IR573+L+36r6KnkyljHs33e7JdHRESuk1098uDBg0hLS7O6PS0tDeXl5YpMioios1L6nJK4cudJ3SND3VqFMbYJUEeG4c+jeyMuOszsvgSL7+1R+uyev+cTMaB9/J0Or01XfmVMPNt365CeyOybwMBGROTnZFePHDhwIJ599lm89dZbiIho/49Ic3Mznn32WQwcOFDxCRIRdSZKn1NSquS/I6/9cRhG90t06bG2Cq7ER4fi9iE9MSFdg+G94jD2xT1eL44x2Atn2sTVse5RoTjf0OpsuBVx1TUuOgz5i8dzZYyIqAuTHdpef/115OTkIDU1FVdffTUA4LvvvoNKpcInn3yi+ASJiDoTpSv4ebKnljiXUX1c26Zpr+BKTX0r3v7qOK5Ni0dYSJBPimPUNl1U9Hq2aH6rBgoAs39r7O2Kyrom08oYERF1TbJD24gRI1BRUYGNGzfixx9/hCAIuPPOOzF9+nRER0d7Yo5ERAFBSt81pSv4ebrkv6uByVnBlY5VIcXiGJYrchqF+7SJ8yqqqMaxc/WKXbOjIBXw4u8HIyUuyuzz//N1vfDO/hMuXZN904iIyKXm2lFRUbjvvvuUngsRUcCwDGg19S14Zru0vmtKhhRPlvyff1N/lwOT1IIrhceqMLpfoleKY9jaqqm0NgFIiYuyWhW7LC5K9rXYN42IiESyC5EAwIYNGzBmzBikpKTgxIn2fzlcvXo1PvroI0UnBwCnT5/GjBkzkJCQgKioKAwZMgTFxcWm+wVBwNNPP42UlBRERkbixhtvxA8//GB2jebmZjz44INITExEdHQ0pk6dil9++cVsTE1NDXJzc6FWq6FWq5Gbm4vz58+bjTl58iRycnIQHR2NxMREzJ8/Hy0tLYq/ZiLyb3llOoxZuRvT3izEQ5vaS7TPfb/EKgyIfdfyynRW18jO0CJ/8Xh8MGsUXrlrCD6YNQr5i8fLDkniyh0Al0rqO2Jsa3Nyv4CCo1X4qPQ0vjpyDl8dPoePSk+j4GgV9IZGSc8x771L748ni2OIWzU9ff4PsL1lNb5buKxriK/8rmsvxyffn0HB0SrJbSCIiKjzkb3Stm7dOjz11FNYsGABnn32WVMz7bi4OLz88su49dZbFZtcTU0NRo8ejXHjxuGzzz5DUlISjh49iu7du5vGvPDCC1i1ahXeffddXHHFFXj22WeRlZWFn376CTExMQCABQsWYNu2bdi0aRMSEhKwaNEiTJkyBcXFxQgODgYATJ8+Hb/88gvy8vIAAPfddx9yc3Oxbds2AO1NwydPnowePXogPz8fVVVVuPvuuyEIAl599VXFXjMR+Td757RscdYc2p1zSpYrfa9NH4a/flSG6nrn/5B0Ta/u+ObEeQnPcmm+1iuLzXhm+yG7IahbuLT/vJxvbDU1FPfUSpujrZqekNgtHAVHq8xehyZW3hbH7lGhEACs3vWz6TZ7K7dERNT5qQRBkPXfsfT0dCxfvhy33XYbYmJi8N1336FPnz4oKyvDjTfeiHPnzik2uccffxxfffUVvvzyS5v3C4KAlJQULFiwAIsXLwbQvqqWnJyMlStX4v7774fBYECPHj2wYcMG3HnnnQCAM2fOIDU1FZ9++ikmTZqEQ4cOIT09HYWFhRg5ciQAoLCwEJmZmfjxxx8xYMAAfPbZZ5gyZQpOnTqFlJQUAMCmTZswc+ZMVFZWIjY2VtJrqq2thVqthsFgkPwYIvIPLRfbMGrFLlTXy68E+MGsUQ4Dmq3zcABshhhb2/y6R4WiudWIxlbHq2MAMG9cX7y256jTce/dOxKj+yV6fFuhCoA6KhQRIcHQ1zrfXipXwdEqTHuz0M1ZSqNSAeqIEJxvvFToRKuOwJOTBzoMuUB7Vc0np1yFk1X1WL3rsPW1f/tfNsEmIuo8pGYD2SttFRUVGDp0qNXt4eHhqK9X9mD3xx9/jEmTJuG//uu/sHfvXvTs2RNz587FrFmzTHPR6/WYOHGi2TzGjh2L/fv34/7770dxcTFaW1vNxqSkpCAjIwP79+/HpEmTUFBQALVabQpsADBq1Cio1Wrs378fAwYMQEFBATIyMkyBDQAmTZqE5uZmFBcXY9y4cTZfQ3NzM5qbm03f19bWKvb+EJH35JXp8MTWMpcCG+C4yqO9EAbArFR898hQjOmfiO3f66xWjeSUlB/VOwHvRZ10+Ji4qFCM6pMga2XRVQLE+ZvPR9xe6m5I8WSFTUuCALPABlxqlH3v9Wl488sKq8eIYWz57YOQla7BmJW7bV8bjlduiYio85J9pi0tLQ2lpaVWt3/22WdIT09XYk4mx44dw7p169C/f3/83//9H2bPno358+fjn//8JwBAr9cDAJKTk80el5ycbLpPr9cjLCwMcXFxDsckJSVZPX9SUpLZGMvniYuLQ1hYmGmMLStWrDCdk1Or1UhNTZXzFhCRHxCDi5Sth/Yct1Ot0N5Zq/MNrVah6nxjKz6xEdjkCgpW4fnfDXI4ZsVv93tzW6El8XmXbSt36zyXr6svCr99vZVvHdgA80bZUgu4FFVUe2SuRETkn2SHtkcffRTz5s3Dv/71LwiCgKKiIjz33HN44okn8Oijjyo6uba2NgwbNgzLly/H0KFDcf/992PWrFlYt26d2TiVyvxfGwVBsLrNkuUYW+NdGWNpyZIlMBgMpq9Tp045nBcR+QexyMbWkl/wxNYyt4PL6l2HrQqSePuslej/ftBDHRmGtdOHWp210qoj8PpvAaLwWJVXCnc4okRIESts+npdyl7ufHLyQNNKotRVQW+uHhIRke/J3h755z//GRcvXsRjjz2GhoYGTJ8+HT179sQrr7yCu+66S9HJabVaq9W7gQMHYvPmzQAAjUYDoH0VTKu9tHWmsrLStCqm0WjQ0tKCmpoas9W2yspKXHfddaYxZ8+etXr+X3/91ew6X3/9tdn9NTU1aG1ttVqB6yg8PBzh4fKqhhGRb3niDJetbW3OVlU85Z8FJ/DPghPQqiPw1JR0xEWH2Tw39/jmg16fmz3uhBRHvfFckdEzFmWnldnqrgLwzPZDmJShRXCQSvKqoK9XD4mIyLtcKvk/a9YsnDhxApWVldDr9Th16hTuuecepeeG0aNH46effjK77eeff0avXr0AtG/V1Gg02Llzp+n+lpYW7N271xTIhg8fjtDQULMxOp0OZWVlpjGZmZkwGAwoKioyjfn6669hMBjMxpSVlUGnu/Qv5Tt27EB4eDiGDx+u8CsnIl/xVGl4WytGvl4taT9rVQJDY4tZmX3xPTjf6Nr5PU9wN6SIvfE0avfDjlpiZUwpLP9eOFsVVKF9NZS924iIuhbZoW38+PGm/mWJiYmms2C1tbUYP368opNbuHAhCgsLsXz5chw5cgTvv/8+3njjDcybNw9A+3bFBQsWYPny5di6dSvKysowc+ZMREVFYfr06QAAtVqNe+65B4sWLcLnn3+Ob7/9FjNmzMCgQYMwYcIEAO2rd9nZ2Zg1axYKCwtRWFiIWbNmYcqUKRgwYAAAYOLEiUhPT0dubi6+/fZbfP7553jkkUcwa9YsVoEk6iS8sV2xY1BLlNm7S2m2zox5e8tmckwYukeFeiWkdOyNN/fGPi5fJyEmXPHtluLfC0d998Tvl+akswgJEVEXIzu0ffHFFzYbSjc1Ndktze+qa6+9Flu3bsUHH3yAjIwMPPPMM3j55Zfxxz/+0TTmsccew4IFCzB37lxcc801OH36NHbs2GHq0Qa0N/6+7bbbcMcdd2D06NGIiorCtm3bTD3aAOC9997DoEGDMHHiREycOBGDBw/Ghg0bTPcHBwdj+/btiIiIwOjRo3HHHXfgtttuw0svvaToayYi3/HGdkVxxSivTIdF/y716HNJYbnSo9R7EBcVaqqAKYoKC0Z0eLDZbSpVEO685rL2P1tcwxMhReyNN0Dj+j+2pcZFKd7QvONKor1VwY4FSzrq2OScTbiJiDonyX3avv/+ewDAkCFDsHv3bsTHX/pXT6PRiLy8PKxfvx7Hjx/3yEQ7C/ZpI/JfH5WexkObSj12fa06AvmLx2Nnud7jZfTleuWuIbh1SE9F3oOE6DAULLkJwUEqU5+54+ca8PKun61esxh67rshDR9/pzMLjJ5sJu1O7zZH/evifmuK3bHyZ5DKfhESFdrDWP7i8VbB1FbvPssxtubAJtxERIFD8T5tQ4YMgUqlgkqlsrkNMjIyEq+++qprsyUi8gOeLu4w9er2X6KV2n74h2E9kdknAc999qNb7QiA9q2aBUercPhsndvzqqpvQfGJGmT2TUBm3wQY2wSMWbnb5msWe499/J0Oex8dh+ITNVYNxguOVqGyrgmJ0eGACjh3odluiJGqpr7FYZiyR+xfB7SviGWla5w2Ra+pb8G890tMr1fkbCVRXBW0x14PPaX62xERkf+QHNoqKiogCAL69OmDoqIi9OjRw3RfWFgYkpKSzLYbEhEFmpr6FkWqC9rz8Xc63NA/Sfb2w6jQYDS0Gq1u/9+S09j9069uBzaVCpj7XjEMFk2h3aE3NJr+7Kx1gLhFUwx6ImdVPF1dUcor02He+66tdK743SCzgGUvWFneti5omNVr0bixIubo7CGbcBMRdT6SQ5tYsbGtrc1jkyEi8pW8Mh3m/rYa4ik6QxPe3W+7wbIj63OH46PS0/jfktNW97kb2ABAEKBoYAMuzUtO64CORVrsrSJ15MqKUsvFNjyx9aDswBakAtZMc33lyt6qnKuBSk4TbkerdUREFBhk1y1esWIFkpOT8Ze//MXs9rfffhu//vorFi9erNjkiIi8wdgm4PEtzoPFzMxe+Pj7M6iud70U/s5DlbLGR4cF45H/fIezdc0uP6cvHK+qxyu7fsbqXYclP0bcniq1gqXcFaW8Mh2e2Frm0ud3z5jeuGWw48Dm7Ayas+2OcrAJNxFR1yI7tK1fvx7vv/++1e1XXXUV7rrrLoY2Igo4hUerzApH2PNuwQkvzMZcfYsR9S3WWyP93YbCk5LHisU4Op4Hk7qFVOqKkpSVO0c++V6Px2+2X8XS2wVB2ISbiKhrkV3yX6/XQ6u1/g9Qjx49zBpPExEFioJj53w9hS6vYzEOV1aHHD1Gid5zlo3RO7LXkF3cvplXpvx/G9mEm4ioa5Ed2lJTU/HVV19Z3f7VV18hJSVFkUkREXmTP5Xe72q6R4VanUlzZXXIUaNypXrP2QqGzgqCAObNy5XCJtxERF2L7NB27733YsGCBXjnnXdw4sQJnDhxAm+//TYWLlyIWbNmeWKOREQe1T0yzNdT6LJes1Hcw9kqkk0OMpFS57pshUk5BUGUJrcJNxERBS7ZZ9oee+wxVFdXY+7cuWhpaa8OFhERgcWLF2PJkiWKT5CIyNMSuzG0eZt4jm2UjXNo4irSnI3Sq3meq7dfqEWJc132thr6uiCI0lUpiYjIP8kObSqVCitXrsSTTz6JQ4cOITIyEv3790d4uP2tKURE/kyjjvT1FLocAZe279mquiiuIj2x9aCkao8dg5nl9Yb3ioNWHQG9ocnlrbBTr9baDEL+UBBEyaqURETkn2SHNlG3bt1w7bXXKjkXIiKfELfjKXHuiaR56KZ+UEeG4ZltP2Br6WmzYNax6uL4K5MxasXndvvRWVaetFfFcerVWryxr8Ll5ukff6fDY9kDrYKb+HfHXiC0nB8REZErVIIgOP3v1+9+9zu8++67iI2Nxe9+9zuHY7ds2aLY5Dqj2tpaqNVqGAwGxMbG+no6RPQbd0vCkzzqyFAYGm2voImxSDyXJX42gHngsjfO8jMUx913Qxo+/k7ncjj/YNYomytaUudHRERkSWo2kFSIRK1WQ6VSmf7s6IuIKBBlZ2hx3w1p8opfeNHwXt19PQVF2QtsgHXVRSkFN6RUcfz4Ox32PjoOH8wahdV3XI34aHlnGe2dS2NBECIi8jRJ2yPfeecdm38mIuos8sp0WL+vwtfTsBKkAmZdn4Ylt6TjlV0/Y/Wuwz6bS3hIEJovtnnluSybZjsruCG1imPxiRpk9k1AwdEqu1su7XF0Lo0FQYiIyJNcPtNGRNRZiKs0/mTaiMvQr0cMcjN7IyykfVNE78Ron86pxUuBraOOq1uOCm7IreIot5pjkAoY3ivO4RgWBCEiIk+RFNqGDh1q2h7pTEmJ9BLNRET+QKnmy5a6R4Xiv4Zfhje/lL+Ct/XbM5gztq9ZdcXDZ+sUn6Mcvjjvp3R1RnGc3GqObQJMq3RERETeJim03XbbbaY/NzU1Ye3atUhPT0dmZiYAoLCwED/88APmzp3rkUkSEXmS3tDokevmjuqF/y3+xaXHNrW2YfWuw3hj3zGEBgfhvIMzYJ2R3KqLcqs4Ohtvi6d6rRERETkjKbQtXbrU9Od7770X8+fPxzPPPGM15tSpU8rOjojIC+SebZLq1d1H3L5GfYsRgNH9yQQgsY+bFB0bcluW9Rev0PF64vjZMhp4e7LXGhERkSOSqkd29J///Ad/+tOfrG6fMWMGNm/erMikiIi8KTYy1NdT6HIcRTGti1UXPVXFUfXbnNhrjYiIfEV2IZLIyEjk5+ejf//+Zrfn5+cjIoL/CklEgef7X877egpdhgrtZ/3CQ4Kgr2023Z4QHYZbh6QgK13jVtVFqVUc5RafkbPqR0REpDTZoW3BggWYM2cOiouLMWrUKADtZ9refvttPPXUU4pPkIjI8/jLuLcIAGoaWvHevSMRpFJ5pDy+lCqOUovPxEeHYvntg9hrjYiIfEp2aHv88cfRp08fvPLKK3j//fcBAAMHDsS7776LO+64Q/EJEhF52uXxUb6eQpdz7kIzbh3S02fPL7WoyJNTrmJgIyIin3OpT9sdd9zBgEZEncYVSd18PYUux9dFPaQ+vyaW2/6JiMj3ZBciAYDz58/jH//4B5544glUV1cDaO/Pdvr0aUUnR0TkDQdO1Ph6Cl2G3KIexjYBBUer8FHpaRQcrYKxTZlucWLJf0cbMhOiw5w21CYiIvIG2Stt33//PSZMmAC1Wo3jx4/j3nvvRXx8PLZu3YoTJ07gn//8pyfmSUQkm9iUWl/bhOoLzYiPDoNGHWnj/JQv2kYrS6uOQPZVyXhn/wlfT8UuW6X3OxI/L/GcW019C57ZXm529kyrjsDSnHS3tyw6ahEgqqpvwdgX9yjyfERERO5QCYIg67eVCRMmYNiwYXjhhRcQExOD7777Dn369MH+/fsxffp0HD9+3ENT7Rxqa2uhVqthMBgQGxvr6+kQdVp5ZTos21Zus9iE5S/+X/78K3LfLvL2FBU1ZZAWIcEqfFh6xtdTsctR4HL0eXUkRj13Svh3tOLTcrz5ZQXsLeAp/XxEREQdSc0GslfaDhw4gPXr11vd3rNnT+j1ermXIyJSXF6ZDnM2lthdP9MZmjBnY8mlX8QDf6ENnxzU+XoKNqkAzLyuFyZepbVbIdLZ59WR8Ns1l20rR1a6xq2Kk3llOryxr8Lh8yr5fERERK6SfaYtIiICtbW1Vrf/9NNP6NGjhyKTIiKyJPVsk9h/y1kAEND+i7ixTcDXx6sUny+1e236MCydmoHMvgl2t0RK+bw6EtAevIsqql2el5znVeL5iIiI3CF7pe3WW2/F3/72N/z73/8GAKhUKpw8eRKPP/44fv/73ys+QSIiW1vn7G21k9p/C7j0i3gnWGjzO92jQvH875z3N5PzeVmSWrZfqed15/mIiIjcIXul7aWXXsKvv/6KpKQkNDY2YuzYsejXrx9iYmLw3HPPeWKORNSFiVvnLH/B1v+2xTGvzHxboNxfrCvrmqCOCHV7nmTutWnSzoC5E4TcaRvgyvP6uk0BERF1XbJX2mJjY5Gfn4/du3ejpKQEbW1tGDZsGCZMmOCJ+RFRF+ZoC5u9s0Zyf7FOionAT/o6t+dKl2jVERjVN0HSWFeCkAqARkbbAHefV4nnIyIicoes0Hbx4kVERESgtLQU48ePx/jx4z01LyIKIJal2u0VnJDL2Ra2jmeNMn8LCWL/Lb2hyem2R7Ff2JeHK92eK10y9Wqt5M9fzucFOG8bIJXc53X3+YiIiNwha3tkSEgIevXqBaPR6Kn5EFGAySvTYczK3Zj2ZiEe2lSKaW8WYszK3VbbFl0hdQtbx3Fi/y1nVLj0i3hcVLirUyQb/v3NLw4LxXQsKAPA9HlJiUQadYQi5fc7/j1x9LxahZ6PiIjIHbK3R/71r3/FkiVLsHHjRsTHc6sIUVdmr1S7eN5swYQr0DsxyuXVt8RoaWHKclx2hhbrZgyT3KctPjpM1rzIsZqGVhQeq8LofolmtzsqKGPr89KqI/Dk5IGIiw5XfBUXsP/3JCE6DLcOSUFWukbR5yMiInKV7ND297//HUeOHEFKSgp69eqF6Ohos/tLSkoUmxwR+S9n580AYPWun023OWqsbJfU35VtjMvO0CIrXYOiimroa5tQfaEZ8dFh0KgjrX4Rr65vkT4nkqTgqHlocxbw180YhvzF4z2yzdaRjn9PvPm8REREcrhU8l+l4n/MiLo6uSXT9ZYNrSU4d6FZ0rjKuma75+oyJRTEON/A0Ka0NqHN9Gc5BWWkfF5Kk/r3hIiIyFdkh7ann37aA9MgokAjt2S6vWqPjkit8PfkhwexbNsPON/QarpNzsoe/x1KeZsO/ILBl3VHdobWpYIyREREdInkQiQNDQ2YN28eevbsiaSkJEyfPh3nzp3z5NyIyI+5Uqq94y/nUgzvFQcpu9QuNBvNAhtgv4+bLSN7MyhIdXdmL0SHBzsdV1PfYnr/XSkoQ0RERJdIDm1Lly7Fu+++i8mTJ+Ouu+7Czp07MWfOHE/OjYj8mFgy3ZVFKqm/nBefqIGdIoROiQ9btq3cbiVDE660SaKODMFTOVfhv//raqdjO77/id2kFZRh82oiIiLbJIe2LVu24K233sIbb7yBv//979i+fTs+/PBDlv8n6qKklky3Reov5+6uvEhd2fta4spfV2dovIjVO3+GOjIMa6cPRVyk4x324vsPAQ4DvgqXeuYRERGRNcmh7dSpU7j++utN348YMQIhISE4c+aMRyZGRP5PLJmuUUsLYXJ/OVdq5WX93iMoOFpld8WtY9EMcmzNniOY9mYhnviwDM1Gacug5+qb7QZ8pZplExERdWaSC5EYjUaEhZn3MgoJCcHFixcVnxQReVbHSouJ3cIBof0Xa1fKnVuWTD9+rh6rdx22OVaAvF/OR6TFIyosGA0t7q3of/HzOXzx8zloYiPw9FTr4iTdI9mnTS7LM4SOJMVEILNvgs2eaBpXWkEQERF1MZJDmyAImDlzJsLDL51NaGpqwuzZs816tW3ZskXZGRKRomw1OO7IlX5qHUumSyn8IZWxTUBjq3JbsPW1TZi9sQSvW7QdkBNASDoV2kOZuLLKnmhERESukRza7r77bqvbZsyYoehkiEh5HVfVjp9rwMu7frbZL0vkSj+1js+1bFu53fvllvzfUHAcgouFSBxZsuWg2RxO1zQo/yQEwHpllT3RiIiI5JMc2t555x1PzoOIPMDZqpotrvRTEyndj+tEtWfCVE1DKwqPVWF0v0QAQKXEJt4kXfeoUDz/u0Hc9khERKQAyYVIiCiw5JXpMGdjiazAJpLbT02kdD+u1LhIWc8vx//sP24qTnKujqFNaa9Nk79SS0RERLZJXmkjosAhblN0d2eh3JL7Uqs9Sh13pSZW1vPLsaP8LHaUn4VWHQFBYOsSJWnVERjFLZBERESK4UobUSfkbJuiVHJL7jtruC235H91Q4us53eF3tAEfS0LkShp6tVaFhchIiJSEEMbUSfkblNqMVwN7xWHgqNV+Kj0tMM+Z6LgIBWenJxuc4XPlX5cid3CnQ9ykwfqnHR5H3+nc/p3hYiIiKTj9kiiTsjdptQCgCmDtRj74h6zFTvLdgAdK1MmxUSgpr4Zz2w/ZPOarvTjuniRTa/9ze1DUrC19IzDMXKKzRAREZFzDG1EnZC4TVFvaHJ5JenNLyusbuvYDgCArMqUT04eKLswxdbS07LGdxXdo0IBeLe/nNhz7YYrejgNbYD7q71ERER0CUMbUScUHKTC0px0zNlYouh1xQC4ZMtB1MgIDCoAz2w/hEkZ8s46nfJQyf9ANKpPPK5IjkGv+CjkZvZuv23FLlTXey+4Lc1JhzoyTNJYd1d7iYiI6BKGNiI/YLnNcERavNuFHLIztFg3Y5jsPm1SyAlsgO3+bFJec1iwUjMOfIXHqlF4rL0Fwz/yK3DXtZd7LbB13BZrbBMcruKKK3JSi80QERGRcwxtRD5mqwG25dkxqSyDUFa6BlnpGhRVVOOfBRX4rOys0tOXRdwyl1emw9Mfl0Nfe+k1a2Ij8PRUi9es8nytJBUCrxiJ3tCE1bt+9spzLZzQHw+M728K1B1XcS3fO1eKzRAREZFzDG1EPiQ2wLYMDR3PjkkNbvbC35OTByIuOhw9/GC7WlJMBPLKdJhtY9umvrYJszeW4PUOr7nVC4VINOoIZGi7YeeP5zz+XErxRsh09A8H9lZxXSk2Q0RERM4xtBH5iKMG2ALaVy2WbStHVrrG6aqFvfCnMzRh7vvfKjRj14lb5oakdsc1z+10OHbJloOm15wSFwWcPO+R+cy8rhcmXqXFiLR4PPXhQcWfI1A9MK4vRvfr4XSLbnaG1rSKq+S2XiIiIrLGPm1EPuKsAXbHc2COOAp//kJAe8Pl0St3o77Z6HBsTUMrCo9VAQAGart5bD7v7j8BQ2MLgoNUqLzQ7JHnCSRib76FWQOQ2TdBUvgKDlIhs28Cbh3SU/JjiIiISD6GNiIfkVoS3dk4Z+HPX6zfV4Hq+hZJYwuOtoe22qaLnpwSlm0rh7FNQGRo4G46WDihv6kFgKt4Fo2IiMi/MbQR+YjUkujOxnXOfljt64anaxo9+gziSmZ4SOAFFXFl7IHx/VH81ywsnHAFIkNd+790jTpC1vlJIiIi8q7A/edlogDnrAG2s9LpYqXIw2cveHSevpDZJxEAUFXn+W2LlXVNKPXAuTlPsrUy9tCE/rimVxz++NbXTh//0E39MKpPIs+iERERBYiAWmlbsWIFVCoVFixYYLpNEAQ8/fTTSElJQWRkJG688Ub88MMPZo9rbm7Ggw8+iMTERERHR2Pq1Kn45ZdfzMbU1NQgNzcXarUaarUaubm5OH/+vNmYkydPIicnB9HR0UhMTMT8+fPR0iJtuxeRJbF0OnDpl3CRs+1qeWU6jFm5G9PeLMSaPUc8O1Ev6x4VilG/9XJraPXs9kigfSXznMRtm/6ie1SozZWxUX0ToFU7XpnVqiMw/6YreBaNiIgogARMaDtw4ADeeOMNDB482Oz2F154AatWrcKaNWtw4MABaDQaZGVloa6uzjRmwYIF2Lp1KzZt2oT8/HxcuHABU6ZMgdF4qSDC9OnTUVpairy8POTl5aG0tBS5ubmm+41GIyZPnoz6+nrk5+dj06ZN2Lx5MxYtWuT5F0+dllg6XWPxi7aj7WpipchAOMfmiud/N8gUIlo8XPJf+9tKZoKbZ8K8LTwkCFnpGqvbxX8IsBfBVOC5NSIiokCkEgTBn4vOAQAuXLiAYcOGYe3atXj22WcxZMgQvPzyyxAEASkpKViwYAEWL14MoH1VLTk5GStXrsT9998Pg8GAHj16YMOGDbjzzjsBAGfOnEFqaio+/fRTTJo0CYcOHUJ6ejoKCwsxcuRIAEBhYSEyMzPx448/YsCAAfjss88wZcoUnDp1CikpKQCATZs2YebMmaisrERsbKyk11JbWwu1Wg2DwSD5MdT5WTbFtrddzdgmYMzK3Z0ysHWPDMHzvx9sFlRvfy0f354yeOw5xZ5wd79VgL2HHVfp9DcfzBqFzN9WJC0p2bCdiIiIPEdqNgiIlbZ58+Zh8uTJmDBhgtntFRUV0Ov1mDhxoum28PBwjB07Fvv37wcAFBcXo7W11WxMSkoKMjIyTGMKCgqgVqtNgQ0ARo0aBbVabTYmIyPDFNgAYNKkSWhubkZxcbHduTc3N6O2ttbsi7omY5uAgqNV+Kj0NAqOVsHYJphu++T7MwCAKYNTHG5Xk1op8orkaEXn7g1Lp2ZYBYpmD6603TO6t+n56jxcpVKq+OgwzBh1uaSxjgrQZGdokb94PD6YNQqv3DUEH8wahfzF4xnYiIiIApTfFyLZtGkTSkpKcODAAav79Ho9ACA5Odns9uTkZJw4ccI0JiwsDHFxcVZjxMfr9XokJSVZXT8pKclsjOXzxMXFISwszDTGlhUrVmDZsmXOXiZ1MpYrZzX1zXhm+yGzwCWWaT/f0HrptshQ/Hl0bzwwvr9ZcBOv91mZTtLzh4cEK/RKvEcTa30Wq8WDZ9omdNheeKHZs9swpaqub0FTi+M+diJnVUXFHmpEREQU+Pw6tJ06dQoPPfQQduzYgYgI+7+gqFTmqxKCIFjdZslyjK3xroyxtGTJEjz88MOm72tra5GamupwbuS/pGxjtLU1zZaOYc10W2MrVu86jHf2H8fzvxuE7Ayt5Ot1NKinGgdPB86qbkJ0GPSGRhQcrTJ7T539HLvCVlXOhGj/OdP2vyWn0T0qFIaGVpeqihIREVHn49ehrbi4GJWVlRg+fLjpNqPRiH379mHNmjX46aefALSvgmm1l7b9VFZWmlbFNBoNWlpaUFNTY7baVllZieuuu8405uzZs1bP/+uvv5pd5+uvzUtp19TUoLW11WoFrqPw8HCEh4fLfenkh6ScExKLhLh7UPR8QyvmbCzBfTek4Y19FbKuF6QCsgYm4/2iU27Ownuq6luw8N/fATB/T5UumGGvKqe/HuxVwXxubIJNRETUNfn1mbabbroJBw8eRGlpqenrmmuuwR//+EeUlpaiT58+0Gg02Llzp+kxLS0t2Lt3rymQDR8+HKGhoWZjdDodysrKTGMyMzNhMBhQVFRkGvP111/DYDCYjSkrK4NOd2l72o4dOxAeHm4WKqnzMbYJeGXXYcy2UbFRb2jCnI0lyCvTwdgmYNm2csUCgADgzS/lBTYAaBOAb07UKDQL7+v4ntY2Wq9GusNeVc6qC57vByfH+YZWLJhwhayqokRERNR5+fVKW0xMDDIyMsxui46ORkJCgun2BQsWYPny5ejfvz/69++P5cuXIyoqCtOnTwcAqNVq3HPPPVi0aBESEhIQHx+PRx55BIMGDTIVNhk4cCCys7Mxa9YsrF+/HgBw3333YcqUKRgwYAAAYOLEiUhPT0dubi5efPFFVFdX45FHHsGsWbNYBbKTMrYJWLP7MN7Or4DBTqEKAe2rH8u2lSM6LETxqo5tLibAU9X1is5DabERwTAKQH2z9fmtju/pBRv3u+JPmb1wc4Y2oJpI906MQv7i8ZKqihIREVHn5tehTYrHHnsMjY2NmDt3LmpqajBy5Ejs2LEDMTExpjGrV69GSEgI7rjjDjQ2NuKmm27Cu+++i+DgS8Ua3nvvPcyfP99UZXLq1KlYs2aN6f7g4GBs374dc+fOxejRoxEZGYnp06fjpZde8t6LJa/JK9Ph8S0HbZ47syQA0BmacO//WBfL8ZVf6/xr5cjSxTagwUHBDfE9jYtU5v+ibs7QOizKkRgTjsO/NijyXPaEh6jQfFF6Ck+KiWAxESIiIgIQIH3aOhP2afN/Sp1L8wWxSEVMeAh+rrzg6+m47cqkKPxY6XqYEt+P/MXjHa5QPfh+CbZ9L60yp6s2/GUEQoKDoK9twt+2laGmwfbqrdQ5ExERUeDrVH3aiLxF6XNp3tSxSAU6yb/FdO/mfhEfKUU7gjxQpbKjuKhQXNcvEZl9E3D70J5Y8bvBUOHSZyZioREiIiKyhaGNqAOpzauV0D0yBFFhyvVT61ikIj7Gf0rYu0KF9iqS3cJcfx1aPyraseJ3g8xCWHaGFutmDGOhESIiIpIk4M+0ESmpss47gW3hhCvwwPh+AIA1u4/gna8qcN7FSom2imz0iHbceNmfdVxt2vdzpezHp2tj8PthlyE3szfCQqT9u5S2u2facsRHh2H57Rk2Q1h2hhZZ6RoWGiEiIiKnGNqIOkiK8XzYGXtFoqkxcnCQCg9N6I85N/bFqBWfo7q+Rfb1bBXZ8ERTak+JiwoxO9+VHBuOp6dehewMLUpPnpd9vXJdHcq3H8I/8ivw5OSBiIsOdxqKEjwQcuOjQ1G45CaHwZGFRoiIiEgKhjaiDkakxUOrjoDe0OSxc217fz6HvT+fM2siXXyiRnZgEwtWiAGwoyoXwp+3qQCoo0IRHhIM4KLFPe30ta6vfOoMTZj7/rdmt2liI/D01HSrla/EGOVX2pbfPkjySh8RERGRI/yNgqiD4CAVluake6UQSccm0nK3ZdorWGFsE1BwtArnG/0/tAlobyJtGczO1l56X9ra2hR9Tn1tE2b/du2ONLHKrbR1jwrF6zyXRkRERAriShuRj3RsIv3Sf10t67GaDqt0orwyHZZtK/daIRV3RYQGoanVOpR1fF96dvfMdtUlWw4iK11jCrzDe8UhSOV6M3OgPaz9+bo0PDC+H8+lERERkaIY2og6EEv+e4vYRBoCHG7LVAGIiQjB1CEpSEuIRm5mbwQHqVBwtAqVdU04fq4BL+/6OaBaFdgKbCLxffFUAK1paEXhsSqM7pcIACg+UeNyYHtgXD+M7pfIIiJERETkMQxtRB14s+R/R+fqm7E0Jx1zNpZABViFLwFAbdNFbCw8CQB4dc8RAO3bC8k1BUcvhTZXq4Z2jwrFwqwrGNaIiIjIo3imjToN8TzXR6WnUXC0CkYXlk68VfLfUmJ0ONSRYfjz6N6Iiw5zOv58QysDm9su/f1wtWoooxoRERF5A1faKCAZ2wSz/lY19S14Zrv5eS6tjXNfznij5L8ldWQIFv3nO7cqJZJ8mX0STX8ekRaP7lGhsoNwTUMriiqqWbafiIiIPIqhjQJOXpkOT3/8A/S1zQ7HidUZ18mo5OeNkv+WDI0XYWi86HxgF2Fre6jSukeFYpRF0Gq56Fqlyp3leoY2IiIi8ihuj6SAklemw+yNJU4DG3DpF/9l28olb5X0Zsl/sk2jjsDa6cOgVctf9YwOD4Y6MtTpuOd/N8jsHFrh0So0tBhlPx8AvP3VcasWAkRERERK4kobBYyWi2145D/fyXqMWIXQ1hY2yy2WYvW/7Awt/jK6N97+6rhykyeH4qND8eSUq6CJvfQ5BAUBszeWyLpOfbMRgP3wpYkNx9NTr7JaeS04ds6VaQO41J6gYwsBIiIiIiUxtJHfM7YJWLP7CN7YdwT1La5tYbMsMGKrp1nHM3BZ6RqGNi9afvsgqyCVnaHFiN5xKDpe4/TxESFBiAgLdngmLSE6DPseG4+wEOsNBu6srDr6hwEiIiIiJTC0kV8SV8F2leux6cAp1Lu4dU10/FyD6c95ZTrM2Vhi9Yt6xzNwbe50WfaxhRP645Pvz+BwZb2vpyLJwglX2D1zGBsh7f+i0jUxKPnF4HBMVX0Lik/U2AxW3SOdV+x0xleVR4mIiKjzY2gjv2NrFcxdL+/6GQM03ZCVrsGybeU2V1YEtG91W7LlIGoCsJy+JjYcT01JR1x0ODYUHPf1dCTr0yPa7n1So3OzUdoKrL1gldjN/dDmi8qjRERE1DUwtJFfsbcKpoRl28oREx7qMAwKQEAGtoUTrkD/pG5WbQ8CwYMffIvQYJXN1TaVStoZsYjQYEnj7AUrjTpS0uNtUaG9eMqItHiXr0FERETkCKtHkt9oudiGJ7aWeSSwieeO3Ck44a/+fF1vDNB0w7z3SwIusInsVfjsFi7t35VS46OgVUfYbXatQvuZRXvBanivOLhSQ0R8yNKcdBYhISIiIo9haCO/kFemw6gVu1Bd3+LR5wngo2p2TUhPtrvlM1CIhTws3T6kp6TH3z6kJ5bmpAOAVXCTEqyKT9RI+rsRHW6+oqdRR8jqA0hERETkCm6PJJ/z5JZIS29/VeGFZ/EerToCEBCwK2wd2TpvFmKj0qMtISFByL4yCetmDLM6DxkXHYrbh/SEOjIMxjbBZnCTWkSkvaUA0D0yFH8enYYHxvfjChsRERF5HEMb+ZSxTfDqKlFTq2stA/xNx9Wjc/XOG40HAlvnzc5dkPbaxHFiu4aiimrsLNfjw9IzqK5vwVtfHcdbXx03a+vg7LkdMTS2morbcJWNiIiIPI3bI8mrjG0CCo5W4aPS0yg4WoXCo1V+tUp0zeVqX09BEpUKuO+GNGRnaAO+aqGj82ZSX1vHccFBKhgaW/DOV8etttuKbR3yynRmt49Ii3d4Js6S+I8M9s7iERERESmJK23kNbZK+XePDPXhjKz1T47FNycd9/vyB20CsH5fBaLCQnB5QjTiokIDsuol0B6A7J03E8OU3tBkczXWVuVGR6u3YluHZdvKkZWuMT1ncJAKS3PSMWdjCVSQ1mqATbWJiIjIW7jSRh5nbBPwyq7DmL3Rurrh+Ub/Chq/BliD5NW7DmPhv0oDNrABwMzretndYiiGKUcsA19RRbXTtg62Cp9kZ2ixbsYwaNTyVi7ZVJuIiIg8jStt5DHGNgFrdh/B2/nHYGi66OvpSNLYavT1FLqc1Lgoh/dnZ2hx3w1pePPLCrMKj0EqYNb1aVaBT2qIsjWu45m4r478ijV7jjq9TqBvTyUiIiL/x5U28oi8Mh2GP7sTq3f9HDCBLUgFJHYL9/U0upx4J+95XpkO6/dVWJXkF7eIWp5PkxqiDp+9gIKjVVZn0oKDVMjsm4CFWQPc6v1GREREpBSGNnKZZVER8ZffvDIdZm8swfkA27I3/sokXOZk1YeUp4m1H7KMbQIe33LQ4eOXbDloFrykFhVZs+cIpr1ZiDErd1sFP8B8a6Yrvd+IiIiIlMLQRi7JK9NhzMrdmPZmIR7aVGr65ffT79uLjQSi3T9W8gdCYd2jHBeaSYgOg97QaHPFCwAKj1U5Df81Da0oPFZl+t5R2LLFXkVJwP45NzbVJiIiIm9SCYLAetVeVFtbC7VaDYPBgNjYWF9PxyX2mmFLrbpHXYMKgDoqFIbfQpezvxu2eqi99H8/Yc2eI06f64Fx/fDIpAFmt9mqVuporhp1BPIXj7e5cmZsE1BUUY3KuiYkxbRvieQKGxEREblLajbgwgLJ4qycOpFIAHC+oRXZGRrERYc5HW97xUvq3yrrcdkZWuQvHo8PZo3CA+P6On20rYqSIvGc261DeiKzbwIDGxEREXkVQxvJ4qycOpGlz8r0qK5vQXx0KP58XW/ER9veMmmrYXVmn0RJz2FvnBi2+ifHSLoOy/cTERGRP2LJf3LIcluYvpa/1JJraupb8c7+4w7HWDasvjYt3um2WxWAa51UcJRaUZLl+4mIiMgfMbSRXbbOBNlbJSHv0qoj8OTkdMRFh6Gyrqm9VYEAFByrknQGzBfkbJ8VV7yKT9Q4fZzw27jMvgl2x4gVJfWGJpvXE8+0sXw/ERER+SOGNrLJXrGR6vrAKuPfmcRHh+L2IT0xIV1jtxDGufpmH8xMeeKKlzuNsjsSK0rO2VhitXLH8v1ERETk7xjayIqjYiPkfVMHazFtZC9JFQsDfXuf5YqXktsaxfL9lqvHGhtVK4mIiIj8CUMbWWGxEf8S9FsxDSnEbYCB+PnZWvFSeltjdoYWWekalu8nIiKigMLqkWSFFfT8y9FfL9htPm2pY2PpQJMcG27VsNpRo2xXtzWyfD8REREFGoY2shLoW+w6m4OnazHtzUKMfn63RQ8z27IztLg5I9kLM1PWf98xxOYWRXFbo0Zt/vdSo46wCnlEREREnRG3R5KVQN5i15npa5swe2MJXpcQVGaM7I3Pys56aWbKOHfBfhEVbmskIiKiroyhjWwa3isOn3zvfFWHvG/JloPIStcAgN0QI6W/mb9J7Bbu8P5gGWf7iIiIiDoThjYyMbYJWLP7CN7+qgKGxq5T2j8+OgzP3pqBuOgwbCo6gY++8++wWtPQilc/P4x/fXPKbDVU26EKopT+Zv5m0b9L8fTUq7jdkYiIiMgCz7QRgPa+bMOf3YnVu37uUoGtW3gwCpfchEkZ7StXVfUtPp6RNC9/fthq+6re0IQ5G0uQV6aD3tDo1fmM6B3n9jXO1jab5k9ERERElzC0EfLKdJi9sQTnG7pOWBO99F9XY/ePZzFm5W5Me7MQ+UeqfD0ll4kra8u2lXu1AqgmNhwb7x0Frdq9AjYd5y+lUiYRERFRV8HQ1sWJjbS7mvjoMPz5ul7YVX4WszeWdJqiKwIAnaEJP5yp89pzPjUlHWEhQYq0GhDnX1RR7f7EiIiIiDoJhrYurqs20q6ub8E7+0/gf0tO+3oqHnHmfIPLj9WqI/D6jGG4/4Y0SCnO+Mz2Q8gr0yE7Q4u104dKeowz7BVIREREdAlDWxfHX447p/CQYJcelxAdhr2PjkN2hhZDL49Dj25hTh/T8SzdLYNTsGbaMJvj5GS5xGjHlSSJiIiIuhKGti6OjbQDhzoyBGunD4VWHWE3AKnQvlI26DK1S89RVd+C4hM1yCvTYc7GEpytc16YxfIs2i2DtXh9xjCrM24adQQW3NRf2kTYfo2IiIjIhCX/u7jhveIQpAJY98H/RYWFYFKGFkFBKszeWGJzjABgaU46YiJC8freYy49j762CS/k/SirZUDHs2iZfRPsNsP+5Pszkq7nqNE2ERERUVfD0NbFFZ+oYWALEHIKdIzqk4CosGA0tBhlP0/1hWaXzzl23G5rqxm21JVdrgATERERXcLtkV0cz7QFFr2h0WG1TxUubVN0ZYehVh2B+Gjn59jscRa2RqTFS9reOSIt3uU5EBEREXU2DG1dHFc0Akt1fYvDVTBxm+L/7D+OehdW2aZerYVGHSn7cVLDVnCQytQawDK4id8vzUlHsBIlKImIiIg6CYa2Lm5EWjy6R4X6ehrkhArtlR2PV9VLGn/guGt9zj7+TofhveIcrobZmhsgPWxlZ2ixbsYwaGwUKlk3YxiyM7TyJk1ERETUyfFMG1EAENBe2XFD4UlJ46PCXCv5rzM0ofhEDZbmpGPOxhKoAKcFSTTqCCzNSZcVtuwVKuEKGxEREZE1hrYurqiiGucbWn09DVKICu0haqA2Bh+WunaNyrom3DqkJ9bNGIZl28rNtmNq1RF4cvJAxEWHux22bBUqISIiIiJrDG1dHAuRdB4dtyk2unCeTSSec+RqGBEREZF/YGjr4hKjw309BVJIx22KBUerXLqGZTERroYRERER+R5DWxfXJrBJW6D7U2Yv3JyhNVsFE0vry+23xsqNRERERP6H1SO7uK8rXFuRIf9xc4YWmX0TzMKWWFpfTvxac9dQVm4kIiIi8kMMbV0eV1UClbPeaGJp/fhoaS0dEmK4VZaIiIjIH/l1aFuxYgWuvfZaxMTEICkpCbfddht++uknszGCIODpp59GSkoKIiMjceONN+KHH34wG9Pc3IwHH3wQiYmJiI6OxtSpU/HLL7+YjampqUFubi7UajXUajVyc3Nx/vx5szEnT55ETk4OoqOjkZiYiPnz56OlpcUjr91bRjpphkz+zdl2xuwMLZ6ccpWka7EoDREREZF/8uvQtnfvXsybNw+FhYXYuXMnLl68iIkTJ6K+/lKD4RdeeAGrVq3CmjVrcODAAWg0GmRlZaGurs40ZsGCBdi6dSs2bdqE/Px8XLhwAVOmTIHReKnC3vTp01FaWoq8vDzk5eWhtLQUubm5pvuNRiMmT56M+vp65OfnY9OmTdi8eTMWLVrknTfDQ4JUXGkLRPHRoZIbUWtiI5yOAS5VjSQiIiIi/6IShMCpRPHrr78iKSkJe/fuxQ033ABBEJCSkoIFCxZg8eLFANpX1ZKTk7Fy5Urcf//9MBgM6NGjBzZs2IA777wTAHDmzBmkpqbi008/xaRJk3Do0CGkp6ejsLAQI0eOBAAUFhYiMzMTP/74IwYMGIDPPvsMU6ZMwalTp5CSkgIA2LRpE2bOnInKykrExsZKeg21tbVQq9UwGAySH+NJH5WexkObSn09Da8ZclksSn+p9fU03BIbEYKlOVchpXukpBL8xjYBY1buht7QZLNRttjbLX/xeBYhISIiIvIiqdnAr1faLBkMBgBAfHz7lr6Kigro9XpMnDjRNCY8PBxjx47F/v37AQDFxcVobW01G5OSkoKMjAzTmIKCAqjValNgA4BRo0ZBrVabjcnIyDAFNgCYNGkSmpubUVxcbHfOzc3NqK2tNfvyJ11tdcXfA5tWHYFZ16c5HFPbdBGL/vMdpr1ZiDErdyOvTOdwvFiUBLA+wdixtxsDGxEREZF/CpjQJggCHn74YYwZMwYZGRkAAL1eDwBITk42G5ucnGy6T6/XIywsDHFxcQ7HJCUlWT1nUlKS2RjL54mLi0NYWJhpjC0rVqwwnZNTq9VITU2V87I9TiwNT771wLh++GDWKDw5OR2ffO84hHWkNzRhzsYSp8FNLEqisfisNeoIydssiYiIiMg3AqZP2wMPPIDvv/8e+fn5VvepLM5lCYJgdZslyzG2xrsyxtKSJUvw8MMPm76vra31q+AmrsLM3lji66l0SeLWxIVZV2BnuR7z3i+xuYXRHuG3ayzbVo6sdI3ToiRZ6RoUVVSjsq4JSTERkrZXEhEREZFvBcRK24MPPoiPP/4Ye/bswWWXXWa6XaPRAIDVSldlZaVpVUyj0aClpQU1NTUOx5w9e9bqeX/99VezMZbPU1NTg9bWVqsVuI7Cw8MRGxtr9uVvsjO0WDt9KPi7O3DDFYlefT4BMG1dXLatXFZg63gNnaEJRRXVTscGB6mQ2TcBtw7padXbjYiIiIj8k1+HNkEQ8MADD2DLli3YvXs30tLMz/qkpaVBo9Fg586dpttaWlqwd+9eXHfddQCA4cOHIzQ01GyMTqdDWVmZaUxmZiYMBgOKiopMY77++msYDAazMWVlZdDpLm1D27FjB8LDwzF8+HDlX7yX3TI4BX+/c6ivp+FzY/v38MnzFh6rgs7gXsl9luwnIiIi6pz8envkvHnz8P777+Ojjz5CTEyMaaVLrVYjMjISKpUKCxYswPLly9G/f3/0798fy5cvR1RUFKZPn24ae88992DRokVISEhAfHw8HnnkEQwaNAgTJkwAAAwcOBDZ2dmYNWsW1q9fDwC47777MGXKFAwYMAAAMHHiRKSnpyM3Nxcvvvgiqqur8cgjj2DWrFl+uXomV16ZDs99dsjX0/Cp7lGhyM3sjX/kV7gdoKRSAViy5SDaFCjiKqeojLFN4DZJIiIiogDh16Ft3bp1AIAbb7zR7PZ33nkHM2fOBAA89thjaGxsxNy5c1FTU4ORI0dix44diImJMY1fvXo1QkJCcMcdd6CxsRE33XQT3n33XQQHB5vGvPfee5g/f76pyuTUqVOxZs0a0/3BwcHYvn075s6di9GjRyMyMhLTp0/HSy+95KFX7z15ZTrM2SjvLFVnpIL3z/gJAGoaWt26hnguboTERul5ZTos21ZuFky16ggszUlnQRIiIiIiPxRQfdo6A3/r0yb28PLWypK/+2DWKBgaWwKmMIu4Nia1AqS9gC73OkRERETkvk7Zp42UV1RRzcDWgb62Ccu2lft6GpLJKdlvbBPsFjsRb1u2rRzGNv47DhEREZE/8evtkeR5LF5hrvpCs2IhVquOwJOT0/HM9nLoDU1ubz8VV8MWTLgCvROjZJ9FcxbQO1ahzOyb4OZsiYiIiEgpDG1dXGJ0uK+n4De06gh0jwpT7HpPTh6IWwZrERQEzNlYAhXgVnDTuHnuTGpAZ5AnIiIi8i8MbV0dCwaaTL1ai/MNLYpcSwXgme2HMClDi+wMLdbNGIbHtxzEeZlFR+KjQ/HklKugiXW/wqPU6pJyqlASERERkefxTFsXd+5Cs6+n4Dc+/k6H7pGhilzLsuF1VroGESHSf9xUv30tv30Qbh+qTCPsEWnx0Koj7OZ0FdpXG6VWoSQiIiIi72Bo6+K4qnKJztCE843uld+3JG41LKqohr5WekCWU2BEKrGdAWC9wCp+vzQnnf3aiIiIiPwMt0d2cSPS4tE9KlT2tr3OKr5bOLTqCMWKkYihWOo5sT9l9sLNGVqPNbsWt2pa9mlz97wcEREREXkOQxtRB5rYCEWaa1s2vJa6onlzhtbjlRuzM7TIStegqKIalXVNsqtQEhEREZF3MbR1cUUV1V1mlc3ZiqJ4nmtnuV6R5+u41VA8T2av9L9lyPO04CAVy/oTERERBQieaeviulJ592t7xzm8f+rV7VsD3W2ure1wHs3YJqDgaBU++f4M7ro2FQJ4noyIiIiI5OFKWxfXlQqRfH6o0uH9H3+nww39k1w+zzb2ih6YPbavaathXpnO6uxY96j26pQdV/x4noyIiIiIHGFo66KMbcJvFQ2b0C08GBeajb6ekse1OelsrTM0oeDYOZevP3tsX9OWw7wyHeZsLLHaCmn4LawtnHAFeidG8TwZERERETnF0NYF2VoBIpFr4SlIBQzv1b790tgmYNm2cptn18TtkZsOnET+4vEMa0RERETkFM+0dTHiChADm22ZfRNMWxjlaBOA4hM1ANqLuzh6fy0bbxMREREROcLQ1oU4WgHqClQOFrVUaC8gcm1v16s3ikVdpBZ36UpFYIiIiIjIdQxtXYizFaDOLjI0GCo4rt5YfKLG5RYIYlEXqcVdulIRGCIiIiJyHUNbF9LVV3YaWoxYMOEKaNTmYUnToUS/K++RuEon9lgTe7LZW9izHE9ERERE5AgLkXQhXNkBeidGIX/xeBRVVKOyrsmqeqPc98hWj7XgIBWW5qRjzsYSqACz7ajsyUZEREREcnGlrQtxtgLUFSTFRCA4SIXMvgm4dUhPZPZNMAtPct+jjqt0HWVnaLFuxjCHq3pERERERFJwpa0LcbYC1JkLlKjQHpicbUmUskq2QGKPtewMLbLSNXZX9YiIiIiIpFAJgtCZf1f3O7W1tVCr1TAYDIiNjfXJHGz1adOqIzD1ai3e2FfR6cKbGJHEFS6xsbijIGXvPVqak85VMiIiIiJShNRswNDmZf4Q2gDYDS55ZTo8+r/fo67pos/m5q7ukaE433ipAmTHsCUnjEkJd0RERERErmJo81P+Etoc2fzNKSz63+99PQ2XvXfPSAQFqWwG0jkbS6xWEi1X4oiIiIiIvEFqNuCZNrKSEhfl6ym4ZVivOESGBZvd5qixuID24LZsWzmy0jVcTSMiIiIiv8LqkWRFrKAYqDYWnkDB0Sp8VHoaBUerTNscHTUWFwDoDE0oqqj23kSJiIiIiCTgShtZ6VhB0d7e2bioUDwzNQNPbStDdX2rnVG+sXrnz2hoNZq+16ojcEuGRtJju3oDciIiIiLyP1xpI5vEPmOWK27dI0OxcEJ/PHfbIDz32SG/C2wAzAIbAOgNTXjrq+OSHssG5ERERETkb7jSRnbZ6zO2s1zvcBXO34jzDFIBgmC7H53UPm5ERERERN7G0EYOBQepkNk3wfS9o4Ie/q7tt0nba5q9NCedRUiIiIiIyO8wtJFDlr3K2gTBYUEPf/eX0b3xWZne7DVo2DSbiIiIiPwYQxvZZasRdffIUB/OyH1Z6Rr8v8npbJpNRERERAGDoY1ssteI+nyjdwuPXNc3HgdP16Ku6aLDcZZbHm3dL55Zs9zySURERETkz1g9kqz407m1/UernQY2oD2Q3X9DGlS4dEZNxDNrRERERBTIuNJGVgqPVgXEubX46FDcPqQnJqRrTCtoQy+Ps9rSyTNrRERERBTIGNrITF6ZDo9vPujradikAhAfHYa/Th7Y3k9NBZy70Gw2xl6bAq6wEREREVGgYmgjE3vn2PyFAKCqvgUnqxvwwv/9ZLaapu2wmsYza0RERETUmfBMGwHwr3Nszqzeddhq+6be0IQ5G0uQV6bz0ayIiIiIiDyDoY0AAEUV1QFxjs0eMWwu21YOY1sgRE8iIiIiImkY2ggAUFknLbB1jwzF6zOG4fUZwxAfHebhWckjANAZmlBUUe3rqRARERERKYZn2ggA2gt7SPDaH4dhdL9EAMD4K5MxasUuVNd7vnebsz5sHUkNoEREREREgYArbQQAGJEWD606wqrHmUiF9mIfo/pcKvARFhKE5bcP8sr8NOoILJxwhaSxUgMoEREREVEgYGgjAEBwkApLc9IByGtOnZ2hxR+GXeaxeT0wrh8+mDUK+YvH44Hx/SQFyxFp8R6bDxERERGRtzG0kUl2hhbrZgyDRm2+UqVRR2DdjGF2m1OP7p/osTn1T+6GzL4JCA5SuRwsiYiIiIgCGc+0kRlXmlNrYj23HdFyq6MYLJdtKzerdqnp0KeNiIiIiKgzYWgjK3KbU49Ii0f3qFCcb1CuIIkK7UHM1lZHV4IlEREREVGgYmgjvyNlq6PcYElEREREFKgY2shtRRXViq6ycasjEREREdElDG3kNiX6osVHh+L2IT0xIV3DrY5ERERERB0wtJFsxjbB7DxZfFSY7GuoAMRHh+GvkwdCo45kUCMiIiIisoOhjWTJK9NZVW6MiwqVdQ0xmj13ewa3QBIREREROcHQRpLllekwZ2MJBIvba2SeZ+OZNSIiIiIi6RjaSBJjm4Bl28qtApscD4zrh9H9ErkVkoiIiIhIBoY2kqSootpsS6RcWnUEFmZdwbBGRERERCRTkK8nQIHB3QqRU6/WMrAREREREbmAoY0kSYqJcOvxH3+ng7HNnc2VRERERERdE0MbSTIiLR5adQRcXSvTGZpQVFGt6JyIiIiIiLoChjaSJDhIhaU56QDgcnBTogk3EREREVFXw9BGkmVnaLFuxjBo1OZbJeOjpfVpc3eLJRERERFRV8TqkSRLdoYWWekaFFVUo7KuCUkxERjeKw5jX9wDvaHJZksAFdp7s41Ii/f2dImIiIiIAh5DG8kWHKRCZt8Es9uW5qRjzsYSqACz4KbqcD+rRxIRERERycftkS5Yu3Yt0tLSEBERgeHDh+PLL7/09ZR8zt7WSY06AutmDEN2htZHMyMiIiIiCmxcaZPpX//6FxYsWIC1a9di9OjRWL9+PW6++WaUl5fj8ssv9/X0fMrW1skRafFcYSMiIiIicoNKEAQ2z5Jh5MiRGDZsGNatW2e6beDAgbjtttuwYsUKp4+vra2FWq2GwWBAbGysJ6dKRERERER+TGo24PZIGVpaWlBcXIyJEyea3T5x4kTs37/f5mOam5tRW1tr9kVERERERCQVQ5sM586dg9FoRHJystntycnJ0Ov1Nh+zYsUKqNVq01dqaqo3pkpERERERJ0EQ5sLVCrzM1qCIFjdJlqyZAkMBoPp69SpU96YIhERERERdRIsRCJDYmIigoODrVbVKisrrVbfROHh4QgPD/fG9IiIiIiIqBPiSpsMYWFhGD58OHbu3Gl2+86dO3Hdddf5aFZERERERNSZcaVNpocffhi5ubm45pprkJmZiTfeeAMnT57E7NmzfT01IiIiIiLqhBjaZLrzzjtRVVWFv/3tb9DpdMjIyMCnn36KXr16+XpqRERERETUCbFPm5exTxsREREREQHs00ZERERERNQpMLQRERERERH5MYY2IiIiIiIiP8bQRkRERERE5McY2oiIiIiIiPwYS/57mViss7a21sczISIiIiIiXxIzgbOC/gxtXlZXVwcASE1N9fFMiIiIiIjIH9TV1UGtVtu9n33avKytrQ1nzpxBTEwMVCqVr6dDTtTW1iI1NRWnTp1iX70uhJ9718PPvGvi59418XPvmvz1cxcEAXV1dUhJSUFQkP2Ta1xp87KgoCBcdtllvp4GyRQbG+tXP+DkHfzcux5+5l0TP/euiZ971+SPn7ujFTYRC5EQERERERH5MYY2IiIiIiIiP8bQRuRAeHg4li5divDwcF9PhbyIn3vXw8+8a+Ln3jXxc++aAv1zZyESIiIiIiIiP8aVNiIiIiIiIj/G0EZEREREROTHGNqIiIiIiIj8GEMbERERERGRH2Nooy5v7dq1SEtLQ0REBIYPH44vv/zS7tgvvvgCKpXK6uvHH3/04ozJHfv27UNOTg5SUlKgUqnw4YcfOn3M3r17MXz4cERERKBPnz54/fXXPT9RUpTcz50/64FvxYoVuPbaaxETE4OkpCTcdttt+Omnn5w+jj/vgc2Vz50/74Fv3bp1GDx4sKlxdmZmJj777DOHjwm0n3WGNurS/vWvf2HBggX4f//v/+Hbb7/F9ddfj5tvvhknT550+LiffvoJOp3O9NW/f38vzZjcVV9fj6uvvhpr1qyRNL6iogK33HILrr/+enz77bd44oknMH/+fGzevNnDMyUlyf3cRfxZD1x79+7FvHnzUFhYiJ07d+LixYuYOHEi6uvr7T6GP++Bz5XPXcSf98B12WWX4fnnn8c333yDb775BuPHj8ett96KH374web4gPxZF4i6sBEjRgizZ882u+3KK68UHn/8cZvj9+zZIwAQampqvDA78jQAwtatWx2Oeeyxx4Qrr7zS7Lb7779fGDVqlAdnRp4k5XPnz3rnU1lZKQAQ9u7da3cMf947HymfO3/eO6e4uDjhH//4h837AvFnnStt1GW1tLSguLgYEydONLt94sSJ2L9/v8PHDh06FFqtFjfddBP27NnjyWmSjxUUFFj9HZk0aRK++eYbtLa2+mhW5C38We88DAYDACA+Pt7uGP68dz5SPncRf947B6PRiE2bNqG+vh6ZmZk2xwTizzpDG3VZ586dg9FoRHJystntycnJ0Ov1Nh+j1WrxxhtvYPPmzdiyZQsGDBiAm266Cfv27fPGlMkH9Hq9zb8jFy9exLlz53w0K/I0/qx3LoIg4OGHH8aYMWOQkZFhdxx/3jsXqZ87f947h4MHD6Jbt24IDw/H7NmzsXXrVqSnp9scG4g/6yG+ngCRr6lUKrPvBUGwuk00YMAADBgwwPR9ZmYmTp06hZdeegk33HCDR+dJvmPr74it26nz4M965/LAAw/g+++/R35+vtOx/HnvPKR+7vx57xwGDBiA0tJSnD9/Hps3b8bdd9+NvXv32g1ugfazzpU26rISExMRHBxstapWWVlp9a8vjowaNQqHDx9WenrkJzQajc2/IyEhIUhISPDRrMgX+LMemB588EF8/PHH2LNnDy677DKHY/nz3nnI+dxt4c974AkLC0O/fv1wzTXXYMWKFbj66qvxyiuv2BwbiD/rDG3UZYWFhWH48OHYuXOn2e07d+7EddddJ/k63377LbRardLTIz+RmZlp9Xdkx44duOaaaxAaGuqjWZEv8Gc9sAiCgAceeABbtmzB7t27kZaW5vQx/HkPfK587rbw5z3wCYKA5uZmm/cF4s86t0dSl/bwww8jNzcX11xzDTIzM/HGG2/g5MmTmD17NgBgyZIlOH36NP75z38CAF5++WX07t0bV111FVpaWrBx40Zs3rzZv0vEkpkLFy7gyJEjpu8rKipQWlqK+Ph4XH755Vaf+ezZs7FmzRo8/PDDmDVrFgoKCvDWW2/hgw8+8NVLIBfI/dz5sx745s2bh/fffx8fffQRYmJiTP+qrlarERkZCcD6/+P58x74XPnc+fMe+J544gncfPPNSE1NRV1dHTZt2oQvvvgCeXl5ADrJz7rP6lYS+YnXXntN6NWrlxAWFiYMGzbMrCzw3XffLYwdO9b0/cqVK4W+ffsKERERQlxcnDBmzBhh+/btPpg1uUos7Wz5dffddwuCYP2ZC4IgfPHFF8LQoUOFsLAwoXfv3sK6deu8P3Fyi9zPnT/rgc/W5w1AeOedd0xj+PPe+bjyufPnPfD95S9/Mf0u16NHD+Gmm24SduzYYbq/M/ysqwTht1N3RERERERE5Hd4po2IiIiIiMiPMbQRERERERH5MYY2IiIiIiIiP8bQRkRERERE5McY2oiIiIiIiPwYQxsREREREZEfY2gjIiIiIiLyYwxtREREREREfoyhjYiIyAV6vR4PPvgg+vTpg/DwcKSmpiInJweff/65x59bpVLhww8/9PjzEBGRfwjx9QSIiIgCzfHjxzF69Gh0794dL7zwAgYPHozW1lb83//9H+bNm4cff/xR9jWNRiNUKhWCgvjvqUREZI7/ZSAiIpJp7ty5UKlUKCoqwh/+8AdcccUVuOqqq/Dwww+jsLAQALBq1SoMGjQI0dHRSE1Nxdy5c3HhwgXTNd599110794dn3zyCdLT0xEeHo4TJ07gwIEDyMrKQmJiItRqNcaOHYuSkhLT43r37g0AuP3226FSqUzfExFR58XQRkREJEN1dTXy8vIwb948REdHW93fvXt3AEBQUBD+/ve/o6ysDP/zP/+D3bt347HHHjMb29DQgBUrVuAf//gHfvjhByQlJaGurg533303vvzySxQWFqJ///645ZZbUFdXBwA4cOAAAOCdd96BTqczfU9ERJ0Xt0cSERHJcOTIEQiCgCuvvNLhuAULFpj+nJaWhmeeeQZz5szB2rVrTbe3trZi7dq1uPrqq023jR8/3uw669evR1xcHPbu3YspU6agR48eANrDoUajUeAVERGRv+NKGxERkQyCIABoLwbiyJ49e5CVlYWePXsiJiYGf/rTn1BVVYX6+nrTmLCwMAwePNjscZWVlZg9ezauuOIKqNVqqNVqXLhwASdPnlT+xRARUUBgaCMiIpKhf//+UKlUOHTokN0xJ06cwC233IKMjAxs3rwZxcXFeO211wC0r66JIiMjrcLfzJkzUVxcjJdffhn79+9HaWkpEhIS0NLS4pkXREREfo+hjYiISIb4+HhMmjQJr732mtmqmej8+fP45ptvcPHiRfz3f/83Ro0ahSuuuAJnzpyRdP0vv/wS8+fPxy233IKrrroK4eHhOHfunNmY0NBQGI1GRV4PERH5P4Y2IiIimdauXQuj0YgRI0Zg8+bNOHz4MA4dOoS///3vyMzMRN++fXHx4kW8+uqrOHbsGDZs2IDXX39d0rX79euHDRs24NChQ/j666/xxz/+EZGRkWZjevfujc8//xx6vR41NTWeeIlERORHGNqIiIhkSktLQ0lJCcaNG4dFixYhIyMDWVlZ+Pzzz7Fu3ToMGTIEq1atwsqVK5GRkYH33nsPK1askHTtt99+GzU1NRg6dChyc3Mxf/58JCUlmY357//+b+zcuROpqakYOnSoJ14iERH5EZUgnqgmIiIiIiIiv8OVNiIiIiIiIj/G0EZEREREROTHGNqIiIiIiIj8GEMbERERERGRH2NoIyIiIiIi8mMMbURERERERH6MoY2IiIiIiMiPMbQRERERERH5MYY2IiIiIiIiP8bQRkRERERE5McY2oiIiIiIiPwYQxsREREREZEf+//00olEQRXesgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "ax.scatter(x = df2['carat'], y = df2['predicted_prices'])\n",
    "plt.ylabel(\"Predicted prices\")\n",
    "plt.xlabel(\"Carat\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " In Summary, the model is a good fit for the data, so basically  the model can give a good prediction on the average, but may predicted wrong for any particular diamond."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Serializing the best model for subsequent and easy usage\n",
    "with open('model.pkl', 'wb') as file:\n",
    "    pickle.dump(model, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.8"
  },
  "vscode": {
   "interpreter": {
    "hash": "afa288cf6f67e916b585baecb4d9c34d378a1bb369c56b53d0be2113d8cb4601"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}