project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 305,
   "id": "42d64ca5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import pylab as pl\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "id": "1ee6b765",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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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>Area</th>\n",
       "      <th>Room</th>\n",
       "      <th>Parking</th>\n",
       "      <th>Warehouse</th>\n",
       "      <th>Elevator</th>\n",
       "      <th>Address</th>\n",
       "      <th>Price</th>\n",
       "      <th>Price(USD)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>Shahran</td>\n",
       "      <td>1.850000e+09</td>\n",
       "      <td>61666.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>Shahran</td>\n",
       "      <td>1.850000e+09</td>\n",
       "      <td>61666.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>79</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>Pardis</td>\n",
       "      <td>5.500000e+08</td>\n",
       "      <td>18333.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>95</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>Shahrake Qods</td>\n",
       "      <td>9.025000e+08</td>\n",
       "      <td>30083.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>123</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>Shahrake Gharb</td>\n",
       "      <td>7.000000e+09</td>\n",
       "      <td>233333.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Area  Room  Parking  Warehouse  Elevator         Address         Price  \\\n",
       "0   63     1     True       True      True         Shahran  1.850000e+09   \n",
       "1   60     1     True       True      True         Shahran  1.850000e+09   \n",
       "2   79     2     True       True      True          Pardis  5.500000e+08   \n",
       "3   95     2     True       True      True   Shahrake Qods  9.025000e+08   \n",
       "4  123     2     True       True      True  Shahrake Gharb  7.000000e+09   \n",
       "\n",
       "   Price(USD)  \n",
       "0    61666.67  \n",
       "1    61666.67  \n",
       "2    18333.33  \n",
       "3    30083.33  \n",
       "4   233333.33  "
      ]
     },
     "execution_count": 306,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"housePrice.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "id": "477671d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Area           0\n",
       "Room           0\n",
       "Parking        0\n",
       "Warehouse      0\n",
       "Elevator       0\n",
       "Address       23\n",
       "Price          0\n",
       "Price(USD)     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 307,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "id": "fe8a7d76",
   "metadata": {},
   "outputs": [],
   "source": [
    "cdf = df.dropna()\n",
    "# delete null value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "id": "a2eb928f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        63.0\n",
       "1        60.0\n",
       "2        79.0\n",
       "3        95.0\n",
       "4       123.0\n",
       "        ...  \n",
       "3474     86.0\n",
       "3475     83.0\n",
       "3476     75.0\n",
       "3477    105.0\n",
       "3478     82.0\n",
       "Name: Area, Length: 3402, dtype: float64"
      ]
     },
     "execution_count": 309,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdf['Area'] = cdf['Area'].str.replace(',', '').astype(float)\n",
    "cdf = cdf[cdf['Area'] < 350 ]  # Remove rows with unrealistic house sizes\n",
    "cdf['Area'].round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "id": "9a82869f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>Area</th>\n",
       "      <th>Room</th>\n",
       "      <th>Parking</th>\n",
       "      <th>Warehouse</th>\n",
       "      <th>Elevator</th>\n",
       "      <th>Address</th>\n",
       "      <th>Price</th>\n",
       "      <th>Price(USD)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Shahran</td>\n",
       "      <td>1.850000e+09</td>\n",
       "      <td>61666.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>60.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Shahran</td>\n",
       "      <td>1.850000e+09</td>\n",
       "      <td>61666.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>79.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Pardis</td>\n",
       "      <td>5.500000e+08</td>\n",
       "      <td>18333.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>95.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Shahrake Qods</td>\n",
       "      <td>9.025000e+08</td>\n",
       "      <td>30083.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>123.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Shahrake Gharb</td>\n",
       "      <td>7.000000e+09</td>\n",
       "      <td>233333.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3474</th>\n",
       "      <td>86.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Southern Janatabad</td>\n",
       "      <td>3.500000e+09</td>\n",
       "      <td>116666.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3475</th>\n",
       "      <td>83.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Niavaran</td>\n",
       "      <td>6.800000e+09</td>\n",
       "      <td>226666.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3476</th>\n",
       "      <td>75.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Parand</td>\n",
       "      <td>3.650000e+08</td>\n",
       "      <td>12166.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3477</th>\n",
       "      <td>105.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Dorous</td>\n",
       "      <td>5.600000e+09</td>\n",
       "      <td>186666.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3478</th>\n",
       "      <td>82.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Parand</td>\n",
       "      <td>3.600000e+08</td>\n",
       "      <td>12000.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3402 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Area  Room  Parking  Warehouse  Elevator             Address  \\\n",
       "0      63.0     1        1          1         1             Shahran   \n",
       "1      60.0     1        1          1         1             Shahran   \n",
       "2      79.0     2        1          1         1              Pardis   \n",
       "3      95.0     2        1          1         1       Shahrake Qods   \n",
       "4     123.0     2        1          1         1      Shahrake Gharb   \n",
       "...     ...   ...      ...        ...       ...                 ...   \n",
       "3474   86.0     2        1          1         1  Southern Janatabad   \n",
       "3475   83.0     2        1          1         1            Niavaran   \n",
       "3476   75.0     2        0          0         0              Parand   \n",
       "3477  105.0     2        1          1         1              Dorous   \n",
       "3478   82.0     2        0          1         1              Parand   \n",
       "\n",
       "             Price  Price(USD)  \n",
       "0     1.850000e+09    61666.67  \n",
       "1     1.850000e+09    61666.67  \n",
       "2     5.500000e+08    18333.33  \n",
       "3     9.025000e+08    30083.33  \n",
       "4     7.000000e+09   233333.33  \n",
       "...            ...         ...  \n",
       "3474  3.500000e+09   116666.67  \n",
       "3475  6.800000e+09   226666.67  \n",
       "3476  3.650000e+08    12166.67  \n",
       "3477  5.600000e+09   186666.67  \n",
       "3478  3.600000e+08    12000.00  \n",
       "\n",
       "[3402 rows x 8 columns]"
      ]
     },
     "execution_count": 310,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdf['Parking'] = cdf['Parking'].astype(int)\n",
    "cdf['Warehouse'] = cdf['Warehouse'].astype(int)\n",
    "cdf['Elevator'] = cdf['Elevator'].astype(int)\n",
    "cdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "id": "a91e188a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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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>Area</th>\n",
       "      <th>Room</th>\n",
       "      <th>Parking</th>\n",
       "      <th>Warehouse</th>\n",
       "      <th>Elevator</th>\n",
       "      <th>Price</th>\n",
       "      <th>Price(USD)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Area</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.793324</td>\n",
       "      <td>0.266271</td>\n",
       "      <td>0.137016</td>\n",
       "      <td>0.219015</td>\n",
       "      <td>0.807132</td>\n",
       "      <td>0.807132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Room</th>\n",
       "      <td>0.793324</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.274952</td>\n",
       "      <td>0.129691</td>\n",
       "      <td>0.220497</td>\n",
       "      <td>0.565638</td>\n",
       "      <td>0.565638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parking</th>\n",
       "      <td>0.266271</td>\n",
       "      <td>0.274952</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.431811</td>\n",
       "      <td>0.440636</td>\n",
       "      <td>0.225514</td>\n",
       "      <td>0.225514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Warehouse</th>\n",
       "      <td>0.137016</td>\n",
       "      <td>0.129691</td>\n",
       "      <td>0.431811</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.198571</td>\n",
       "      <td>0.133268</td>\n",
       "      <td>0.133268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Elevator</th>\n",
       "      <td>0.219015</td>\n",
       "      <td>0.220497</td>\n",
       "      <td>0.440636</td>\n",
       "      <td>0.198571</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.182581</td>\n",
       "      <td>0.182581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Price</th>\n",
       "      <td>0.807132</td>\n",
       "      <td>0.565638</td>\n",
       "      <td>0.225514</td>\n",
       "      <td>0.133268</td>\n",
       "      <td>0.182581</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Price(USD)</th>\n",
       "      <td>0.807132</td>\n",
       "      <td>0.565638</td>\n",
       "      <td>0.225514</td>\n",
       "      <td>0.133268</td>\n",
       "      <td>0.182581</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Area      Room   Parking  Warehouse  Elevator     Price  \\\n",
       "Area        1.000000  0.793324  0.266271   0.137016  0.219015  0.807132   \n",
       "Room        0.793324  1.000000  0.274952   0.129691  0.220497  0.565638   \n",
       "Parking     0.266271  0.274952  1.000000   0.431811  0.440636  0.225514   \n",
       "Warehouse   0.137016  0.129691  0.431811   1.000000  0.198571  0.133268   \n",
       "Elevator    0.219015  0.220497  0.440636   0.198571  1.000000  0.182581   \n",
       "Price       0.807132  0.565638  0.225514   0.133268  0.182581  1.000000   \n",
       "Price(USD)  0.807132  0.565638  0.225514   0.133268  0.182581  1.000000   \n",
       "\n",
       "            Price(USD)  \n",
       "Area          0.807132  \n",
       "Room          0.565638  \n",
       "Parking       0.225514  \n",
       "Warehouse     0.133268  \n",
       "Elevator      0.182581  \n",
       "Price         1.000000  \n",
       "Price(USD)    1.000000  "
      ]
     },
     "execution_count": 311,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Correlation analysis\n",
    "correlation_matrix = cdf.corr()\n",
    "correlation_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "id": "49353329",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram analysis\n",
    "cdf.hist(figsize=(10, 10))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "id": "838d29c4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Area', ylabel='Count'>"
      ]
     },
     "execution_count": 313,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load dataset\n",
    "# penguins = sns.load_dataset(\".\")\n",
    "import seaborn as sns\n",
    "# Plot histogram\n",
    "sns.histplot(data = cdf, x = \"Area\", kde = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "id": "59ff3c99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(cdf['Area'], cdf['Price'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "id": "6fcada29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Area', 'Room', 'Parking', 'Warehouse', 'Elevator', 'Address', 'Price',\n",
       "       'Price(USD)'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 315,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdf.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "id": "cf208217",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = cdf[['Area','Room','Parking']]\n",
    "y = cdf[[\"Price\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "id": "e31de4b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test , y_train , y_test = train_test_split(X,y, test_size=0.2, random_state=57)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "id": "72ebb2cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-25 {color: black;background-color: white;}#sk-container-id-25 pre{padding: 0;}#sk-container-id-25 div.sk-toggleable {background-color: white;}#sk-container-id-25 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-25 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-25 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-25 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-25 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-25 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-25 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-25 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-25 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-25 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-25 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-25 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-25 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-25 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-25 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-25 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-25 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-25 div.sk-item {position: relative;z-index: 1;}#sk-container-id-25 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-25 div.sk-item::before, #sk-container-id-25 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-25 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-25 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-25 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-25 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-25 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-25 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-25 div.sk-label-container {text-align: center;}#sk-container-id-25 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-25 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-25\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</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-25\" type=\"checkbox\" checked><label for=\"sk-estimator-id-25\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 318,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "clf = LinearRegression()\n",
    "clf.fit(x_train , y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "id": "7f954876",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.29818478e+08 -1.80423000e+09  4.56839926e+08]]\n",
      "[-4.85996071e+09]\n"
     ]
    }
   ],
   "source": [
    "print(clf.coef_)\n",
    "\n",
    "print(clf.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "id": "db763c3c",
   "metadata": {},
   "outputs": [],
   "source": [
    " prediction = clf.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "id": "329d3a52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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79O3bFyNGjMDLly+xcOFCdRJkqA8//BATJ07Ehx9+iNTU1BxLOGfMmIFHjx6hdevWKFeuHOLi4rB48WLY2toiJCREfZyNjQ1CQkJw4MCBfMWTly+//BL79+9H48aNMW7cOFStWhUpKSm4d+8e9uzZgxUrVqBcuXIYOHAgvv/+ewwcOBBz5sxB5cqVsWfPnlw/aFV8fX0xffp0fPXVV3j79i0+/PBDuLm5ISoqCi9evFAvea1Rowa2bduG5cuXo27duupv/X369MH69evRqVMnjB8/Hg0aNICtrS0ePXqEQ4cOoVu3bnjvvfdQvXp19O/fH4sWLYKtrS3atGmDq1evYuHChSYpTmZlZYVvvvkG/fr1Q5cuXfDxxx8jNTUVCxYsQFxcHObNm6c+tnfv3pgxYwb69OmD//3vf0hJScGSJUsgl8s1zqnPz0doaCi2bt2K5s2bY8KECahZsyYUCgUePHiAffv2YdKkSQVenyg0NBSbN29Gt27dMG3aNDRo0ABv377FkSNH0KVLF7Rs2VLv93HFihU4ePAgOnfujPLlyyMlJUXde5TbHBgqIqSdy2saAITt27erb2/ZskUAIDg5OWn8s7GxEXr16pXj8YMGDRK6deuWo71y5crC119/rdF2/PhxAYAQExNj7JdRZHXv3l2ws7MTnj17pvOYPn36CDY2NkJsbKwgCILw8OFDYejQoYKnp6dga2sreHt7C7169RKePn2qfszGjRuFatWqCba2tgIAYebMmer7fvvtN6F69eqCg4ODEBAQIGzevFnrqotVq1YJVatWFezt7YWKFSsKc+fOFVauXCkAEKKjo9XH6bPqJ6u+ffsKAIQmTZrkuG/Xrl1Cx44dhbJlywp2dnZC6dKlhU6dOgnHjh3TOA6AXs+pWsGRlwoVKgidO3fWet/z58+FcePGCX5+foKtra3g7u4u1K1bV/jss8+EpKQk9XGPHj0SevbsKTg7OwsuLi5Cz549hZMnT+a56kdlzZo1Qv369QUHBwfB2dlZqF27tsbjXr16Jbz//vtC8eLFBZlMpnGO9PR0YeHChUKtWrXUj69WrZrw8ccfC7du3VIfl5qaKkyaNEkoXbq04ODgIDRq1EiIiIjIsUpFFwDCmDFjtN73+++/a6z6UdmxY4fQsGFDwcHBQXBychJat24tnDhxIsfj9+zZIwQFBQmOjo5CxYoVhaVLl+a4Vvr+fCQlJQmff/65ULVqVcHOzk5wc3MTatSoIUyYMEH9e6SLMX5mtF3P169fC+PHjxfKly8v2NraCqVLlxY6d+4s/Pvvv+pj9HkfIyIihPfee0+oUKGCYG9vL3h4eAghISHCX3/9lWfMVPjJBMGASlZmTiaTYfv27eqVO5s3b0a/fv1w7dq1HF3uzs7OOSbaDR48GHFxcTnmJjRv3hy1a9fWmMC1fft29OrVC2/evIGtra1JXg8REZGlsoihn9q1a0Mul+PZs2do1qyZwecJDg7Gzp07Ndr27duHevXqMUkhIiIygSKTqCQlJamrKALKpcSRkZFwd3dHlSpV0K9fPwwcOBDffvstateujRcvXuDgwYOoUaMGOnXqBACIiopCWloaXr16hcTERPVOvKpN7kaOHImlS5di4sSJGD58OCIiIrBy5cp8LSMlIiIi3YrM0M/hw4fRsmXLHO2DBg3Cr7/+ivT0dMyePRtr1qzB48eP4eHhgeDgYISFhaFGjRoAlBP8VOW7s8p6iY4cOYIJEybg2rVr8Pb2xtSpUzFy5EjTvTAiIiILVmQSFSIiIip6LKaOChERERU+TFSIiIjIbBXqybQKhQJPnjyBi4uLQVuYExERUcETBAGJiYnw9vbW2MBTm0KdqDx58sSgfUKIiIhIeg8fPsxz88pCnaioNqx6+PChScphExERkfElJCTAx8dHY+NJXQp1oqIa7nF1dWWiQkREVMjoM22Dk2mJiIjIbDFRISIiIrPFRIWIiIjMFhMVIiIiMltMVIiIiMhsMVEhIiIis8VEhYiIiMwWExUiIiIyW0xUiIiIyGwxUSEiIiKzxUSFiIiIzBYTFSIiIjJbTFSIiIgop8REqSMAwESFiIiIstu1C6hUCdixQ+pImKgQERHRf968AUaPBrp2BZ49A4YNA2JiJA3JRtJnJyIiIvNw/jzQrx9w40ZmW+PGgI20qQJ7VIiIiCyZXA7Mmwc0apSZpDg6AitWAH/+CZQqJWl47FEhIiKyZOnpwLp1QEaG8nbdusD69UDVqtLG9R/2qBAREVkyBwdgwwZlL8qnnwInT5pNkgKwR4WIiMiyxMUB8fFAhQqZbTVrAnfvAp6ekoWlC3tUiIiILMXRo0CtWkCPHkBamuZ9ZpikAExUiIiIir60NOWwTosWwIMHwIULwOzZUkelFw79EBERFWU3biiXHZ8/n9nWvDnw0UfSxSQCe1SIiIiKIkFQLjGuXTszSbGxAebOBQ4e1JyjYsbYo0JERFTUqKrK7tyZ2Va1qnLZcd260sVlACYqRERERUlCAhAUpFn6fuRI4NtvgWLFJAvLUBz6ISIiKkpcXYFBg5T/L1VK2auyfHmhTFIA9qgQEREVPWFhypU+U6YAZcpIHU2+MFEhIiIqrORy5ZCOgwMwblxmu52dsr0IYKJCRERUGD18CAwcCBw+rExMQkKUxdyKGM5RISIiKmw2b1aWvT98WHk7PR04dkzSkEyFPSpERESFRUICMHYssHZtZpuPj/J2SIh0cZkQExUiIqLC4PhxYMAA4N69zLYPPwSWLQOKF5cqKpPj0A8REZE5S08HPv9c2WOiSlJcXYF164ANG4p0kgKwR4WIiMi8paYq56QoFMrbzZoBa9YAvr6ShlVQ2KNCRERkzpydlb0njo7A118Dhw5ZTJICsEeFiIjIvLx4Abx9q5wkq9KwoXLYp3RpycKSCntUiIiIzEV4OFCjhnKSbEaG5n0WmKQATFSIiIik9/atsrJsx45AbCxw4gTw3XdSR2UWOPRDREQkpUuXgH79gGvXMts6dFBWnSX2qBAREUlCoQAWLgQaNMhMUhwcgB9+APbsATw9pY3PTLBHhYiIqKA9egQMGgQcPJjZVquWsi5KQIB0cZkhJipEREQF6cULZVLy6pXytkwGTJoEzJ4N2NtLG5sZ4tAPERFRQSpZUjknBQDKlQP++QdYsIBJig7sUSEiIipo8+crE5Pp04ESJaSOxqyxR4WIiMhU0tOBmTOBn37SbHd0VPaiMEnJE3tUiIiITOH2baB/f+D0aWVi0rw5UK2a1FEVOuxRISIiMiZBAFauBIKClEkKAKSlAadOSRpWYWU2icrcuXMhk8kQGhoqdShERESGefkS6NkTGDYMSE5WtlWqBJw8CQweLGlohZVZJCpnz57FTz/9hJo1a0odChERkWH271fu07N9e2bb8OHAxYvKom5kEMkTlaSkJPTr1w8///wzSnBSERERFTYpKcCECUC7dkBMjLLNw0OZsPz0E+DsLG18hZzkicqYMWPQuXNntGnTRupQiIiIxEtJAbZty7zdvj1w5QrQvbtkIRUlkq762bRpEy5cuICzZ8/qdXxqaipSU1PVtxMSEkwVGhERkX6KFwfWrAE6dQLmzgXGjgWsJO8HKDIkS1QePnyI8ePHY9++fXBwcNDrMXPnzkVYWJiJIyMiIsrF48fKsvfe3pltISHA/fvKqrNkVDJBEAQpnnjHjh147733YG1trW6Ty+WQyWSwsrJCamqqxn2A9h4VHx8fxMfHw9XVtcBiJyIiC7V1KzBiBFC7NrBvH3tODJSQkAA3Nze9Pr8l61Fp3bo1rly5otE2ZMgQVKtWDVOnTs2RpACAvb097LkXAhERFbTERGD8eGD1auXtAweA5cuBMWOkjcsCSJaouLi4IDAwUKPNyckJHh4eOdqJiIgkc+qUssLsnTuZbT17An36SBeTBWGfFRERkTYZGcCsWUDTpplJirOzslfl99+VS5DJ5Mxqr5/Dhw9LHQIREZEyMenfX7PsfaNGwLp1gL+/dHFZILNKVIiIiCT36JFyn56kJOVta2vgiy+Azz4DbPixWdA49ENERJRVuXJA797K/1esCBw/DsycySRFIrzqRERE2S1aBJQqBUyfDri4SB2NRWOPChERWa7UVGDSJGDtWs12Z2dllVkmKZJjjwoREVmmq1eBfv2Ay5eVCUnTpoCfn9RRUTbsUSEiIsuiUACLFwP16imTFABISwPOn5c2LtKKPSpERGQ5YmKAIUOAv//ObAsMBDZsAGrUkC4u0ok9KkREZBl27FAmI1mTlAkTgLNnmaSYMfaoEBFR0ZaUpExIfvkls83LC/jtN6BtW+niIr2wR4WIiIq2t2+BXbsyb/foAVy5wiSlkGCiQkRERVupUsr9eZydgZUrgT/+4D49hQiHfoiIqGiJjgacnIDSpTPbOnQA7t1jglIIsUeFiIiKBkEA1qwBatUCPvpIeTsrJimFEhMVIiIq/F69Avr0AQYNAhITlXNS1qyROioyAg79EBFR4XbwIDBwIPD4cWbb4MHAe+9JFhIZD3tUiIiocEpNBf73P6BNm8wkpUQJ4PfflZNnXV2ljY+Mgj0qRERU+Fy7ptyn59KlzLZWrZS1UcqVky4uMjomKkREVLjcvq3cpyclRXnbzg74+mtlUTcrDhQUNXxHiYiocKlUKXP+SUAAcOYMMGkSk5Qiij0qRERU+CxbBvj7A9OnA46OUkdDJsT0k4iIzFdyMvDxx8DWrZrtxYsDX33FJMUCMFEhIiLzdPYsULs28NNPwPDhmsuPyWIwUSEiIvMilwNz5gCNGwO3binbUlM1V/iQxeAcFSIiMh/37gEDBgDHj2e21a8PrF8PVK4sWVgkHfaoEBGR9AQBWLdOuU+PKkmxsgI++ww4cYJJigVjjwoREUkrLg4YNQrYtCmzzdcXWLsWaNpUqqjITLBHhYiIpPXmDbBvX+btAQOAyEgmKQSAiQoREUnN2xv4+WflkuNNm5S7Hru5SR0VmQkO/RARUcG6fh3w9FRuIKjSowfQsqVmGxHYo0JERAVFEID/+z+gTh3lnBRB0LyfSQppwUSFiIhM7+lToEsXYOxY5WaCmzcD27ZJHRUVAkxUiIjItHbtAmrUAPbsyWwbOxbo2FG6mKjQ4BwVIiIyjTdvlLsar1iR2VamDLB6NZMU0hsTFSIiMr7z54F+/YAbNzLbunYFfvkFKF1aurio0GGiQkRExnXpEtCoEZCRobzt6Ah8/z0wYgQgk0kbGxU6nKNCRETGVbMm0KmT8v/16gEXLwIff8wkhQzCRIWIiIxLJlMO8Xz1FXDyJFC1qtQRUSHGRIWIiAwXFwf076+5ogcASpUCPv8csLWVJCwqOpioEBGRYY4eVe52vH49MHQo8OyZ1BFREcREhYiIxElLA6ZPB1q0AB48ULalpABRUZKGRUUTV/0QEZH+btxQLjs+fz6zLSREuZFg+fLSxUVFFntUiIgob4KgLNxWu3ZmkmJrC8ybBxw4wCSFTIY9KkRElLvnz4GPPgJ27sxsq1oV2LBBucEgkQmxR4WIiHKXnAwcOZJ5e9Qo4MIFJilUIJioEBFR7nx9gf/7P+WS4507gWXLgGLFpI6KLAQTFSIi0hQZCSQmarb16wfcvAl06SJJSGS5mKgQEZGSXA7Mnw80aACEhmreJ5MBxYtrHq4QEHHnJf6MfIyIOy8hVwgFFipZDk6mJSIiZT2UgQMz56KsWgV88AHQoYPWw8OvxiBsZxRi4lPUbV5uDpjZNQAdAr0KImKyEOxRISKydJs2KTcSVCUpMhkwdSrQqpXWw8OvxmDUugsaSQoAxManYNS6Cwi/GmPqiMmCMFEhIrJU8fHKfXo+/FD5fwDw8QEOHVLWR7Gzy/EQuUJA2M4oaBvkUbWF7YziMBAZDRMVIiJLdOxY5j49Kh9+CFy+rKw0q8OZ6Fc5elKyEgDExKfgTPQrIwZLloxzVIiILM3Jk8p9ehQK5W1XV+WS43798nzos0TdSYohxxHlhT0qRESWplGjzPknzZoBly7plaQAQGkXB6MeR5QXJipERJbGygr47Tfgm2+U81F8ffV+aAM/d3i5OUCm434ZlKt/Gvi5GyNSIiYqRERF2vPnQI8ewOHDmu3e3sD//gdYW4s6nbWVDDO7BgBAjmRFdXtm1wBYW+lKZYjEYaJCRFRUhYcrlx1v366skfL6tVFO2yHQC8v714Gnm+bwjqebA5b3r8M6KmRUnExLRFTUvH2rrIPyww+abbduKavOGkGHQC+0DfDEmehXeJaYgtIuyuEe9qQUHXKFYBbvLxMVIqKiJDJSOTE2KiqzrWNHZaVZT0+jPpW1lQzB/h5GPSeZB3OqPMyhHyKiokChABYuBBo2zExSHByApUuB3buNnqRQ0WVulYclTVSWL1+OmjVrwtXVFa6urggODsbevXulDImIqPB5/Bho21Y5OTYtTdkWFAScPw+MGaMsiU+kB3OsPCxpolKuXDnMmzcP586dw7lz59CqVSt069YN165dkzIsIqLCJSkJOHVK+X+ZTJmwnDoFBARIGxcVOuZYeVjSRKVr167o1KkTqlSpgipVqmDOnDlwdnbGKdUvHBER5a1qVeC774By5YADB5T1UeztpY6KCiFzrDxsNnNU5HI5Nm3ahOTkZAQHB0sdDhGR+TpzRrmKJ6sRI4Br14CWLaWJiYoEc6w8LHmicuXKFTg7O8Pe3h4jR47E9u3bEaCjuzI1NRUJCQka/4iILEZ6OjBjBhAcDEyZonmfTKbcs4coH8yx8rDkiUrVqlURGRmJU6dOYdSoURg0aBCisi6ry2Lu3Llwc3NT//Px8SngaImIJHL7NtC0KfDVV8oVPkuXKndAJjIic6w8LBMEoeCm7uqhTZs28Pf3x48//pjjvtTUVKSmpqpvJyQkwMfHB/Hx8XDlNwkiKooEAVi5EggNBZKTlW3W1sCsWcC0aYANy2GR8Zm6jkpCQgLc3Nz0+vw2u59wQRA0kpGs7O3tYc8JYkRkKV68AIYPB3bsyGyrXBlYt85oFWaJtDGnysOSJirTp09Hx44d4ePjg8TERGzatAmHDx9GeHi4lGEREUlv3z5g8GAgJktxreHDlat7nJ0lC4ssh7lUHpY0UXn69CkGDBiAmJgYuLm5oWbNmggPD0fbtm2lDIuISFr79wPt22fe9vAAfvkF6N5dspCIpJLvOSoJCQk4ePAgqlatiurVqxsrLr2fW98xLiKiQkMuB1q0AI4fVyYsq1cDXtyRmIoOMZ/folf99OrVC0uXLgUAvH37FvXq1UOvXr1Qs2ZNbN261bCIiYgok7U1sHatcvfjPXuYpJBFE52oHD16FM2aNQMAbN++HYIgIC4uDkuWLMHs2bONHiARUZH25AnQqVNmCXwVX19g7FjASvIqEkSSEv0bEB8fD3d3ZaGX8PBw9OzZE8WKFUPnzp1x69YtowdIRFRkbd0K1KgB7N0L9O8PJCZKHRGR2RGdqPj4+CAiIgLJyckIDw9Hu3btAACvX7+Gg0PBldQlIiq0EhOBoUOB998HXv23udvbt0B0tLRxEZkh0at+QkND0a9fPzg7O6N8+fJo0aIFAOWQUI0aNYwdHxFR0XLqlLL35M6dzLb33wd+/BFwL7iy5EWNXCGYRc0PMj7Ricro0aPRoEEDPHz4EG3btoXVf+OnFStW5BwVIiJdMjKAOXOUJfDlcmWbs7NywuygQcq9esggpq6iStIyeHlyWloaoqOj4e/vDxuJSjhzeTIRFQp37yp7USIiMtuCg5Ure/z9pYurCAi/GoNR6y4g+weZKu1b3r8OkxUzZNLlyW/evMFHH32EYsWK4Z133sGDBw8AAOPGjcO8efMMi5iIqChLSgLOn1f+X7VPz9GjTFLySa4QELYzKkeSAkDdFrYzCnKFWW1pRyKJTlQ+/fRTXLp0CYcPH9aYPNumTRts3rzZqMERERUJNWsC8+YpE5Pjx4GZM7mZoBGciX6lMdyTnQAgJj4FZ6JfFVxQZHSiE5UdO3Zg6dKlaNq0KWRZxlQDAgJwJ+vkMCIiS3XsGJCWptk2fjwQGQk0aiRJSEXRs0TdSYohx5F5Ep2oPH/+HKVLl87RnpycrJG4EBFZnJQUYOJEoHlz4IsvNO+zsuJmgkZW2kW/khj6HkfmSXSiUr9+fezevVt9W5Wc/PzzzwgODjZeZEREhcmVK0CDBsD33ytvL1iQOS+FTKKBnzu83Byg6yuyDMrVPw38uOy7MBM9SDp37lx06NABUVFRyMjIwOLFi3Ht2jVERETgyJEjpoiRiMh8KRTAkiXAtGlAaqqyzd5eOSeldm1pYyvirK1kmNk1AKPWXYAM0JhUq0peZnYNYD2VQk50j0rjxo1x4sQJvHnzBv7+/ti3bx/KlCmDiIgI1K1b1xQxEhGZpydPgA4dgAkTMpOUwEDg7FkgNJT79BSADoFeWN6/DjzdNId3PN0cuDS5iDC4joo5YB0VIpLMtm3A8OGZJfABZXIydy7A7UQKHCvTFi5iPr9FD/3s2bMH1tbWaN++vUb733//DYVCgY4dO4o9JRFR4bJtG9CzZ+ZtLy/gt9+Atm2li8nCWVvJEOzvIXUYZAKi+yWnTZsGuar8cxaCIGDatGlGCYqIyKx16QKohrp79FBOpGWSQmQSontUbt26hYCAgBzt1apVw+3bt40SFBGRWbOzA9avh+LYcZxu0Q3PHqagdNxLixpuKKihFg7pkOhExc3NDXfv3oWvr69G++3bt+Hk5GSsuIiIzEN0NPDRR8B33wFBQerm8HRXhD33Rcwvp9VtlrIRXkFtAsjNBgkwYOjn3XffRWhoqEYV2tu3b2PSpEl49913jRocEZFkBEE576RWLeDQIaBfP+DtWwCZG+FlL98eG5+CUesuIPxqjBQRF4iCeu2WfI1Jk+hEZcGCBXByckK1atXg5+cHPz8/VK9eHR4eHli4cKEpYiQiKlivXgG9ewODBwOJicq2lBTg0SOL3givoF67JV9jysmgoZ+TJ09i//79uHTpEhwdHVGzZk00b97cFPERERWsgweBgQOBx48z2wYPVhZ1c3HBmTsv9d4Ir6itQhGzCWB+XntBPQ8VDgZt3ymTydCuXTu0a9fO2PEQEUkjNRX47DPg228z20qUAH76CXj/fXWTJW+EV1Cv3ZKvMeWkV6KyZMkSjBgxAg4ODliyZEmux44bN84ogRERFZjr14EPPwQuXcpsa90a+PVXoFw5jUMteSO8gnrtlnyNKSe9EpXvv/8e/fr1g4ODA75XbbilhUwmY6JCRIVPYiJw9ary/3Z2wNdfK8viaymBr9oILzY+RescChmU5duL4kZ4BfXaLfkaU056TaaNjo6Gh4eH+v+6/t29e9ekwRIRmUSDBsCsWcA77wBnzgCTJuncp0e1ER6AHLv2FvWN8ArqtVvyNaacRK36SU9PR8WKFREVFWWqeIiITO/gQSAjQ7Pt00+VmwnWqpXnwy15I7yCeu2WfI1Jk+hNCcuWLYt//vkH1atXN1VMeuOmhEQkSnKyckjn55+BsDBgxox8nc6Sq6ayMi3lh5jPb9GJyrx58/Dvv//il19+gY2NQYuGjIaJChHp7exZZdG2W7eUt62tlXv0mMGXLiJLY9Ldk0+fPo0DBw5g3759qFGjRo6y+du2bRN7SiIi05HLgXnzlHNQVMM9xYoBixcD1apJGhoR5U10olK8eHH0zLq9ORGRuYqOBgYMAE6cyGyrXx9Yvx6oXFm6uIhIb6ITldWrV5siDiIi4xEEYN06YMyYzBL4VlbKgm5ffAHY2kobHxHpTe9VPwqFAgsWLECTJk3QoEEDTJ8+HSkprApIRGZo7VplGXxVkuLrCxw9Cnz5JZMUokJG70Rl/vz5mDZtGpycnODl5YXvvvuOxd2IyDz16gUEBir/P3CgsuJskybSxkREBtF71U/VqlUxfvx4jB49GgAQHh6O7t274+3bt5DJpFkqxlU/RARAOdST/e/Q5cvAv/8qkxYiMitiPr/17lG5f/8+unTpor7dvn17CIKAJ0+eGB4pEVF+Xb8ONGumTEqyqlmTSQqpyRUCIu68xJ+RjxFx5yXkClGVOUhCek+mTUtLg6Ojo/q2TCaDnZ0dUlNTTRIYEVGuBAFYtgyYPBlISVHWSImIUO7VQ5RF+NUYhO2MQkx85rxKLzcHzOwawAq3hYCoVT9ffPEFihUrpr6dlpaGOXPmwM3NTd323XffGS86IiJtnj4Fhg4F9uzJbHv7FoiNBcqXly4uMjvhV2Mwat2FHJsbxsanYNS6CyzHXwjonag0b94cN27c0Ghr3LixxkaEUs1VISILsnMn8NFHwPPnmW1jxgDffKMs5Eb0H7lCQNjOKK07MAtQbnAYtjMKbQM8WZbfjOmdqBw+fNiEYRAR5SE5Wbmr8Y8/ZraVKQOsWgV06iRdXGS2zkS/0hjuyU4AEBOfgjPRrxDs71FwgZEo0m7WQ0SkjwsXgL59gay9ul27Ar/8ApQuLV1cZNaeJepX60vf47Thpommx0SFiMxfUhJw86by/46OwPffAyNG5FySTJRFaRcHox6XHSfpFgy9lycTEUmmeXNg2jSgbl3g4kXg44+ZpFCeGvi5w8vNAbp+UmRQJhYN/NxFn1s1STf70JJqkm741RjxAZNWTFSIyPz8/TegUGi2hYUBJ08CVatKExMVOtZWMszsGgAAOZIV1e2ZXQNED9XkNUkXUE7SZa0W42CiQkTmIy5OORelQwfl8E5WtraskUKidQj0wvL+deDppjm84+nmYPDSZDGTdCn/9JqjcvnyZb1PWLNmTYODISILduSIcl+eBw+Ut6dPB95/H6hQQdq4qNDrEOiFtgGeRpv0WhCTdCmTXolKUFAQZDIZBEHIs1aKXC43SmBEZCHS0oCZM4H585XVZgHAzQ1YsYJJChmNtZXMaEuQTT1JlzTplahER0er/3/x4kVMnjwZ//vf/xAcHAwAiIiIwLfffotvvvnGNFESFRJcqijSv/8qS99fuJDZ1qIF8NtvrDBLZks1STc2PkXrPBUZlENLhkzSpZz0SlQqZPlW88EHH2DJkiXolKXAUs2aNeHj44MvvvgC3bt3N3qQRIUBlyqKIAjKwm0TJypL3wPKOSizZyuLullbSxsfUS5Uk3RHrbsAGaCRrORnki5pJ3oy7ZUrV+Dn55ej3c/PD1FRUUYJiqiw4VJFkZYtA0aNykxSqlUDTp0CpkxhkkKFgikm6ZJ2MkEQRK2fqlOnDqpXr46VK1fCwUH5BqWmpmLo0KG4fv06LmTtwjWxhIQEuLm5IT4+Hq6urgX2vERZyRUCms4/qHMVgKob+PjUVvyGpZKUBNSuDdy+DYweDSxYwH16qFDicK9hxHx+i65Mu2LFCnTt2hU+Pj6oVasWAODSpUuQyWTYtWuXYRETFWLcT0QPgqBZoM3ZGdiwAXj2DOjcWbq4iPLJmJN0STvRiUqDBg0QHR2NdevW4d9//4UgCOjduzf69u0LJycnU8RIZNa4VDEPFy8CI0cCGzcCFStmttevL11MRFRoGLTXT7FixTBixAhjx0JUKHGpog5yOfDtt8DnnwPp6cCAAcpaKTbcYoyI9GdQZdq1a9eiadOm8Pb2xv379wEA33//Pf7880+jBkdUGJhyPxFzIVcIiLjzEn9GPkbEnZd5lwZ/8ABo0waYOlWZpABASgrw8qXpg80n0a+ViExK9Feb5cuXY8aMGQgNDcXs2bPVBd5KlCiBRYsWoVu3bkYPksicFfWliqKXXW/apBzqiY9X3pbJlAlLWJjZl8DnEnMi8yO6R+WHH37Azz//jM8++ww2Wbpw69WrhytXrhg1OKLCoqguVRS17Do+Xjm88+GHmUmKjw9w6BAwd26hSFK4xJzI/IjuUYmOjkbt2rVztNvb2yM5OdkoQREVRsbeT0Rqee0QK4Nyh9i2AZ6wPhWh3Ezwv6FgAMqEZdkyoHjxggk4H0S91kL6fhIVVqITFT8/P0RGRmpUqwWAvXv3IiAgwGiBERVGRWmpoqhl14mJmUmKq6syQenXr2ACNQIuMafCyhLquIhOVP73v/9hzJgxSElJgSAIOHPmDDZu3Ii5c+fil19+MUWMRCQBUcuu27cHxo1TLkVeswbw9c31MebwxzVrDLeeJur1GItdYk5myVLmVIlOVIYMGYKMjAxMmTIFb968Qd++fVG2bFksXrwYffr0EXWuuXPnYtu2bfj333/h6OiIxo0bY/78+ahatarYsIjIyHQupxYEtLh7Docr1gNkMpR0sle2f/ONculxHiXwzeGPq7YY9GFxS8zJbKnmVGUfrlTNqSrMc+OyM2h58vDhw3H//n08e/YMsbGxePjwIT766CPR5zly5AjGjBmDU6dOYf/+/cjIyEC7du0414XIDGhbdu3+Jh4/b5uNX/8IQ7/IvQCASb9fUk40tbfXK0mResKqrhhyUxSWmFPRkdecKkA5p6qoLK0Xnai0atUKcXFxAICSJUuidOnSAJR1+1u1aiXqXOHh4Rg8eDDeeecd1KpVC6tXr8aDBw9w/vx5sWERkZGpll0Dyg/qFnfO4e9VY9D29mkAwOcHV6Jk8ms8TdAvyTCHP665xaBLUVhiTkWLmDlVRYHooZ/Dhw8jLS0tR3tKSgqOHTuWr2Di/1vS6O6u/VtLamoqUlNT1bcTEhLy9XxElLsOgV748f3qiP9kIj44lVnQ8UUxN0ztOA4vnEoA0G9VjDlMWM0rBm08zXzM3xzm+1DBsrRtO/ROVC5fvqz+f1RUFGJjY9W35XI5wsPDUbZsWYMDEQQBEydORNOmTREYGKj1mLlz5yIsLMzg5yAikSIj0W5QPyAqSt10sGI9TO04Hs+dS6jb9EkyzOGPq77nHtuyEiqXcTb7D35zmO9DBc/Stu3QO1EJCgqCTCaDTCbTOsTj6OiIH374weBAxo4di8uXL+P48eM6j/n0008xceJE9e2EhAT4+PgY/JxEpINCAXz3HTB9uroEfoqNHea0HIq1tTtr7oScRW6JgDn8cdX33E0qlTT7ZciWNJmSNKnmj8XGp2gdxpRB2RNYVOZU6Z2oREdHQxAEVKxYEWfOnEGpUqXU99nZ2aF06dKwzmMinS6ffPIJ/vrrLxw9ehTlypXTeZy9vT3s7e0Neg4iEmH+fGWS8p/k6jXQrfEo3C5ZPteH5ZYImMMfV3OIwRhYoM6yFfVtO7LTezJthQoV4OvrC4VCgXr16qFChQrqf15eXgYlKYIgYOzYsdi2bRsOHjwIPz8/0ecgIhMYNQooX17ZczJlChwunEWyf5V8bbyYfXJu9scDpv/jag4xGIOlTaaknIrqth3aiJ5MO3fuXJQpUwZDhw7VaF+1ahWeP3+OqVOn6n2uMWPGYMOGDfjzzz/h4uKinvfi5uYGR0dHsaERmUyRn7AoCJrDOcWLAxs2AGlpQMuWsAaM8g1O9cc1+7yKgpywag4x5Jc5zPch6RW1bTt0kQmCIGotoK+vLzZs2IDGjRtrtJ8+fRp9+vRBdHS0/k+uY5x79erVGDx4cJ6PT0hIgJubG+Lj4+Hq6qr38xKJUeQnLJ44AYwfD+zYAeQy9AoY71qYQ+JnDjEYKuLOS3z486k8j9s4vJHZz7UhyyTm81t0ouLg4IDr16/nGKa5e/cuAgICkJJScBk8ExUyNV0TFlUfZ4W6izU9HfjyS+Drr5WTZ1u3BvbtA6xyHxEuzB/wRYVcIaDp/IN5zrU5PrUV3xsyS2I+v0UXfPPx8cGJEydytJ84cQLe3t5iT0dktsyhQJnJ3LoFNG0KzJ6tTFIAICUF+K+WUW5UGy92CyqLYH8PfhBKoKjMtSHSh+hEZdiwYQgNDcXq1atx//593L9/H6tWrcKECRMwfPhwU8RIJIkiOWFREIBffgFq1wbOnFG22dgoE5YjR4ASJXJ/PJkNS5pMSZZN9GTaKVOm4NWrVxg9erS6Qq2DgwOmTp2KTz/91OgBEkmlyE1YfPECGD5cORdFpXJlYP16oH59ycIiw1nKZEqybKITFZlMhvnz5+OLL77A9evX4ejoiMqVK7O+CRU55lCgzGj++QcYMADIUlEaw4cri7o5O0sXF+WbaiiOqKgSnaioODs7oz6/hVERVlSKgwEAkpIykxQPD2DlSqBbN2ljIiLSg16JSo8ePfDrr7/C1dUVPXr0yPXYbdu2GSUwIqkVqeqP3bsDw4YBjx4Bq1YBXobPX+CqHyIqSHolKm5ubuqaJ25ubiYNiMicFMriYAoFsHs30KWLZhG3pUsBOzud+/Too8jXlCEisyO6joo5YR0VKiiFphfh8WNg8GDlnJTffgMGDjTaqYt0TRkiKlAmLfhmTpioEGWxdSswYgTw6r/l0m5uwL17ynL4+aQqMKZruXZhKTBWaBJOoiJOzOe3XkM/tWvX1lnuPrsLFy7odRwRGUliorIE/urVmW1lywJr1hglSQHE1ZQx1xUoHLYiKpz0SlS6d++u/n9KSgqWLVuGgIAABAcHAwBOnTqFa9euYfTo0SYJkoh0iIgA+vcH7t7NbPvgA2DFCsDdeKuRCntNGV3DVrHxKRi17gKHrYjMmF6JysyZM9X/HzZsGMaNG4evvvoqxzEPHz40bnREpF1GhrKa7OzZgFyubHN2Vk6YHTgwXxNmtSnMNWXy2gpBBuVWCG0DPDkMRGSGRJfQ//333zFQywS9/v37Y+vWrUYJiojyMHMmEBaWmaQ0bgxcugQMGmT0JAXIrCmj68wyKIdRzLGmTJHcCoHIgohOVBwdHXH8+PEc7cePH4eDg/l9myIylFwhIOLOS/wZ+RgRd17muvlg9mPTMhR6P9YgEyYAnp6AtbVyB+QjR4CKFY37HFkU5k3wCvuwFZGlE12ZNjQ0FKNGjcL58+fRqFEjAMo5KqtWrcKMGTOMHiCRFMRMvNR2rJUMyJqb5HvSpiBo9pSULAls3Ag4OgINGxp2TpEKZU0ZFO5hKyIycHnyli1bsHjxYly/fh0AUL16dYwfPx69evUyeoC54fJkMgV964XIFQKWHryF7/+5lec59ak1onPp7D//AFOmAOHhQOnSBr8uYzHGEt+CXCasWlqd11YIhiyt5nJn8VTXLDYhBa+SUuHuZAdPN0deOwvDOipEBsqrXgig7B35onMAvtwVhdgE/YcLcvtA1NYrU6GYFX67vQO+a39SNnTuDOzcaZI5KAVJimXCquQT0L4VgiGrfrjcWTxt10yF186yiPn8Fj1HBQDi4uLwyy+/YPr06Xj1X3GpCxcu4PHjx4acjshs5DXxElBOvBy94YKoJAXQPWlT9SGa9XmrPL+HH5eOzkxSACAtDUhOFvWc5kbbawUylwmHX40xyfOqhq083TSHdzzdHAxOUqR4HYWZrmumEsNrRzqInqNy+fJltGnTBm5ubrh37x6GDRsGd3d3bN++Hffv38eaNWtMESdRgSiICZXPElMyu7/j3+Kr3dfV3/JlggKDz+/EtMO/wl6eDgBItbGF7TfzYTV+PGBl0HeLApPbUIjUy4Q7BHqhbYCnUYatuNxZnNyuWVYCeO0oJ9GJysSJEzF48GB88803cHFxUbd37NgRffv2NWpwRAWtICZU3nuRrHV4qXTiSyzcswjN711Ut10v5YvQrpMx690+CDZBkqItsQBg0Id5XkMh5lDd1tpKlu9zm8PrKGz06alU4bWj7EQnKmfPnsWPP/6Yo71s2bKIjY01SlBEUmng5w53J1u8Sk43+rllAIoXs9U6+bb17dNYsGcx3N8mqNt+qdcNC0IGIdXGziQ9PdoSi+LFbAEAcW8yX78+cwf0qfyamqHQKy5zXybM5c7iib0WvHaUleivaA4ODkhISMjRfuPGDZQqVcooQRFJxdpKhm61vI1+XlV/hK6ub6e0t+ok5amzO/r3+gqzWw9Hqo0dAOP39OiaLxD3Jl0jSQHynneR11AIoOzOL+lsr1ds5r5MmMudxRN7LXjtKCvRiUq3bt3w5ZdfIj1d+cdMJpPhwYMHmDZtGnr27Gn0AIlMLXuxNu/ixfJ9zuwjJZ5uDghtUyVHEqDyV0AL7AgIwd4qjdF+6FIc96sNwDQVX/WdL6CSNdnQVrhO36EQCCi01W2zKsxVeqWS1zXLiteOshM99LNw4UJ06tQJpUuXxtu3bxESEoLY2FgEBwdjzpw5poiRyGS0DX+4OYr+tdAwoU0VjGrhj/P3X2vM89h1+QkAwFohR6s7Z7G/ciONx03pGIo0axv18mNjVHzVNgdFzHwBldzmXejbTf8iORUzuwZg1LoLkEH7MmFzrW6blapKb2F/HQUp6zXLjQy8dpST6L/Irq6uOH78OA4ePIgLFy5AoVCgTp06aNOmjSniK9JYLCqTFNdC17yK+LcZej0++1yW7HM5sn+gl3Syh09cLL7f9S3qPb6OUd2mYW+1pur702xsNY7Pb8VXXZNbOwZ6GnQ+QHtSImYoJNjfwyTVbQv656ewVumVkq5rpsI6KqSLqIJvGRkZcHBwQGRkJAIDA00Zl14Kc8E3FovKJMW10KewW2683Bxw5H8tc/Sa6PpwDL/yBGfCFmPCzh/gkvYWAPCimBuajlyJFNvMD3p3J1t80eUdeLrm78M2t+q6+anwuHF4oxwJmCGVX42ZWEj5u8QvG+KxMi0BJq5M6+/vj23btqFWrVr5CtIYCmuiom+Jdksg1bWIuPMSH/58SvTjDInrwIkopH40Ap1unFC33S/uiQldJuFC2eoGn1eXvJIwGZSjS2L2ScyrzLwpKr/qg79LRIWTSSvTfv755/j000/VFWlJHH1XSBh9t10zJOW1MHT5o9hKpvJ/DqBGpxCNJGVLjTboNHiJOkkBgDKu9kb7UNVncqvqkurz/VWfeRfGrvyqD/4uEVkG0XNUlixZgtu3b8Pb2xsVKlSAk5OTxv0XLuQ+WcrSsVhUJimvhaHLHxe+XwtNKpfM+8DUVOCzz2D97bdQbSMY5+CMT9uP1ZiXovJtryA0qaTHefWgbxL2URNf7Lkam2cdFX3nXRir8qu++LtEZBlEJyrdunWDrJBviiYlFovKJOW1UC2X1DWvQpcXyan6HTh5MrB0qfrm8Qq1MLnTBMS6ak9GXiTpeV496JuEtQnwxPTOAUarTAsYp/Krvvi7RGQZRCcqs2bNMkEYloPFojJJeS1yW2JqlFg+/RTYuBGKhER83bQ/VtbvDkGme6TVmK8xryRMNd9ElYBoSywKQw8Ef5eILIPec1TevHmDMWPGoGzZsihdujT69u2LFy9emDK2IonFojKZ8lpkL+KmbZ6CrnkVBsWSfU66tzewcSOE06exu21fQEeSYor3W5WEqc6f/fmAolGrgr9LRJZB70Rl5syZ+PXXX9G5c2f06dMH+/fvx6hRo0wZW5FkKR8i+jDVtQi/GoOm8w/iw59PYfymSHz48yk0nX9Qawn4DoFeOD61FTYOb4ShTXwNi+XPP4G6dYHXrzXb27aFde0gSd5vKSa3FjT+LhFZBr2XJ/v7+2POnDno06cPAODMmTNo0qQJUlJSYG1tbdIgdSmsy5MB1lHJypjXwpDlqllrYdx7kYyNZx4gNiFzzoiuWOSJSXgxfDTKbF4LAFB80AtWmzepK8ua6jWKYQl1Pvi7RFT4mKSOip2dHaKjo1G2bFl1m6OjI27evAkfH5/8RWygwpyoAJbxIaIvY1wLfeqHZK8Fou1DztPVAR82KA/fksV0xhKxcQ/KfjIC5V8+VrcdCWiC1DVr0a6un8leI2nHa0tUuIj5/NZ7Mq1cLoednZ3mg21skJGhX7lxyqkgV0iYO2NcC7HLVXX1vjxNSMGif25ief86OWOSy3EzdDrq/99C2AgKAMAbW3uEtR6BLTXbAb9HYbm9g9Zv8ny/TYfXlqjo0jtREQQBgwcPhr195lbtKSkpGDlypEYtlW3bthk3QiI9iVmumlexMBmUxcLaBnhmfjOPjoYwYACqnMgs3hbpVQWhXSbhnruyp1Hr4wgAez2IyDB6JyqDBg3K0da/f3+jBkOUH2KWq4ouFrZxI/Dxx5AlJgIA5DIrLA3uhR8a90GGtY3uxxEAziMhIsPpnaisXr3alHEQ5ZuY+iG7Lj/R65zqXpqkJOC/JOWBWxlM6DIJ58sF5P040jnEFhufglHrLhSZVUhEZBqiC76R/tjVXbCsrWR4t5YXfjwarfMY1XJV0cXChg0Ddu/GMysHdKrQE0n2xfR7nIUzaIiNiCgLJiomwq7ughd+NQY/5ZKkjGjup772ufW+2MrT0fLueVyp1yKzWJhMBmzZAg8bW7jMP4hkPXptiPvxEFH+id49mfKm6urO/gda1dWtrfCYpdCnYqyh59X1zV3lr0sx6ufTVSzM/8VDbF87GT9tm40f3GI0v+Xb2bHImEjcj4eI8os9KkbGrm7dtPUyuTvZoXuQN9oGeOZraCyvb+5Azm/uquqtYTujEBP3FgMu7sZnh1bBISMNAFBv3nRgWC8g27J81eNm/RWF2IQs9VfYY5YD9+MhovxiomJk7OrWTteEylfJaVh14h5WnbiXr6ExQ7+5dwj0QlsPGRL6DkCJw/9k3lG9OrBhQ44kRZPmq9GzdqJFETPBmYhIGw79GBm7unPSZ1gGUCZwhg6NGfzNfedOWNeqqZmkjB0LnD8PBAVpPYcq6cpaZh8AYhNSLX5oLzsOlRFRfjFRMTJ2deekz7BMVmE7o0TPXRG9k25yMjByJPDuu8Dz58q2MmWAPXuAH34AHB21nievpEswMP6izBI2SCQi0+HQj5GxqzsnMb1Hhg6Nqb65j1p3ATJoDspo/eY+dizw66+ZB737LvDLL0CpUrk+jyFzYei/IbYATy7XJyLR2KNiZObW1W2qVTZiGNJ7ZMjQmJhv7vIZM5Hh7IIMBwfc+Woh5Nu255mkAEBs/Fu9YtH3OGPS9l6bw/uvotqPp1tQWQT7ezBJISK9sEfFBDRWk8RLtyrEXGq55NXLpI2hQ2M6v7lneWbldbmDqh0m4UFxT9xNKgevbw7pdV1eJafpFYe+xxmLtve6eDFbAEDcm3R1G2v5EFFhIxMK8VIFMdtES0HKyrS6Vtmonr2g5wao4gGyr5XRpBoaOz61lfGu1fr1wLffAkeOIPx+Ur6uy/aLjzFhc2SeT/l97yC8V7uswSGLoeu91kaq95+IKCsxn9/sUTEhU209nzUBKulkD8iAF0mp6mQIgEG1XEyZWOnqZcrK6ENjcXHA6NHKDQUBKD4Zh7Cq/fO8Lq2qlcH5+6+1XgdPV/16evQ9Thd93wt9V1SpWHotHyIqfJiomAExCYK2Lv6sPF0d8GGD8qJruRTEMFHWYZl/omKxPfIxXiVnDkuohsbaBngi4s5L9fWoW6GEzsRBpyNHgAEDgIcP1U0v45Lx7HUyYGWt9SGq69Jo7j8acWW9DqphrNyur1c+J0uLeS/ErqgCLLeWDxEVThz6kZiYDyUxXfz6WNwnCN2Cyko2TKQtQdsfFZvjeljJgKxzQHNNoNLSgBkzgG++AVQ/2m5uwIoV2F61mV7DNtllvw66hrGyHmfoChex78WfkY8xfpP41wRkvv9ERAWNQz8FKD/DJbo+lFR7AmX9UBLbxa+P0i4OeZb8B4BZf10zyTBB9qExXdcj+0KV7NdH9R68uXwVDb8YB+drlzMPDgkB1qxBeIItvtp+xaA4sw+X5DVZGgCazj8ounfKkO0X7r14Y9BrAiyrlg8RFV5MVPIhP8MlYj+UDOni1yVrLRd9zhubkIqlB29jfJvKRnl+bcQkYlmvj0IBfLU7CiFHdmDmgZ/hmKGsFquwsYXVnNnApEkIv/4s3z1R2YdLdK0u2h8Vq3fymZ3Y7RfkCgEbzzww6PW4O9miboUSBj2WiKggsY6KAeQKAYv/uYWR+dghWcyHEmB4yf28arnoe97v/7lp0tLwYhMx1fUZvUH5HhRLT1UnKbfdy+HdfgsQ3mkA5DIro/ZEZb1e2euCALlPYlbdr6uWidjtF85Ev9LYFFGMV8npCFlwiOX+icjsMVERKfxqDJrMO4Dv/7mp9X59PpAA8R9KhnTTVyvjDFcHzU6zMq72Gt/qxZzXWKXhtRUhy+/eR6vrdcVR39pYU7szugxehGuelRC2Mwqn7r40Wk8UkPv1Ept8ijm3tuPye830TaqJiKTEoR8R9J3Mqs+qCrEfSoYUTfv3aZKWVs0+Fn1WsagYY6WIruGyPvXL630Oh/QUNI++iH1VgtVtgswKQ9+fiQzrzB/pmPgURNx5aXCs2ZUoZpvrap78bkgpdvsFfX+GXBxskJiSkaOdS5WJqDBgj4qeDJnMmtsHl9hN9LKW5s+P2ATNb9Fiz5ufb/GqRE/bcNmif26ieDFbnddD5Z3Y29j1ayhWbP8awfcva9yXNUnJZLzpx6/fpGN/VKzO+/O7IaXY7Rf0+Rlyd7LVmqSo5NXLQ0QkNUkTlaNHj6Jr167w9vaGTCbDjh07pAwnV4ZMZs3tg8uQPYHUe9m42ouKQ5uswzgdAr0wQc+JsoauFNFndZFqM0FtH7xWCjlGnvoD29dORqVXj2AFAbP3LYOVQp7r8wZXLJnrh7kYqt4HXcNfondw1kLMfkX6/Ay9p+fy4/wOIxERmYqkiUpycjJq1aqFpUuXShmGXsT+IfdwsstzVUXbAE+EtqkCN0dbjXZtH0oqHQK9cGJaa0xoU0VUPFlp+xY9tlXlXKup5vYhq8/Gd/rM33j9Jh2dAsughJPm9fBOeIYNmz7DtCO/wk6h7B24UsYfI3p8BoWO4m2qeOv7uaNPfR+j9Kvk1ftgrA0pOwR64fjUVtg4vBEW9wnCxuGNcHxqK50/D7klNm0CPPV5aVyqTERmS9I5Kh07dkTHjh2lDEFvYv+Qv0xOQ8gC3Rvdad1EztEWQ5r4Ymyryrl+mFlbyTC+TWVU9XTGrL+iDF75kX0Fy6x3A3ItZKbtQ1bfJdr6Jnp7rj4FALg72SElXY7WkQcxZ98yuKYmAwAUkGFFo574vmk/pFvb6jyPAODdWl4IWXDIqJNpgdxfi7E2pBSz/YLOjRitZJArBFHzXoiIzI3ZVKaVyWTYvn07unfvrvOY1NRUpKamqm8nJCTAx8enQCrTyhUCms4/KGoyq65qosasBJu14NyLxFR8tfu6ntEBo0P8kZiaDgGAn4cTBgT74uC/T41WKXdCm8rqpGvxP7d0rpTSxjU1GWH7luO9qMPqtkeupTCp80ScLl9D7/OYwsbhjfJMIqTckDI7fSrpcoNCIipIYirTFqpEZdasWQgLC8vRXlAl9MOvxmDkf3/w9ZV9N2BVwpPbxnyG7h6clqFAtS/25qjkqi8rGTC8mR+mdKie54dsXq9DxdPVAd2CvPDT0WhRwy/Ltn+NTjdPqm/vCAjBjLajkODgLOYlGZVJdnYuIAWxlxMRkb6KbAn9Tz/9FBMnTlTfVvWomLPsS5XFVh8V4/z91wYnKYCyVP2PR6MBAJ92yn0lkL6Ti2MTUtTnFGNByCC0iD6PDJk1zk2Zg9B001XF1YfRd3YuYLkNDxERmbNClajY29vD3j7/K14MoVq1YijVvAZ952qcuP1C4wMFgMaHjLYdhY21cuPnY9GY1K4a7Gx0z7U29ioRmaCAIMt8vmj3svjk3Sn4t5QfWterD0TcN+rzieXp5oAvOleHm6Md/ox8nO8PeimGhsTMeyEiMheFKlGRUn732lFNxtV3Uu7SQ7fV/y9eTDlpNO5NurpN247CYoqm5UYhAGsj7uGjZhV1HmO0VSKCgL6XwtHn0t/o1XceUmwzz3ugUkMAQAX3YsZ5LgO5O9mhS00vfLX7ulGGTjgMQ0SkP0mXJyclJSEyMhKRkZEAgOjoaERGRuLBA8M2WjOl/PQgWMmA18lpAPKutaFN3Jt0jSQF0L6jsL5F0/Rx72VyrverXkd+uL+Jx8/bZuPrv/8PNWNv49PDqzXuVy0xHhDsq07WpPAqOQ0/H4s2eF+nrHIresdy9kREOUmaqJw7dw61a9dG7dq1AQATJ05E7dq1MWPGDCnD0io/PQgKARizQfkhlFutjfzQp2iaMVlbyfBF5+oGP77FnXP4e9UYtL19Wt1mJQiQCQoAhWNOiL77OqnoU/TOWPspEREVFZImKi1atIAgCDn+/frrr1KGpZUhPSFZCQCmbbuCE7deoG2Ap9YiXfmlKpo2oU1luOWzB8JKlvcrLeEkfr6QfXoqZu1fgV//mIVSyXEAgBfF3PBRzy/wRbvR6nkqWYvenYl+laNHyVyIKUGf300LiYgsEeeo6EnVEzJq3QV1r4VYcW/S0W/lafV8hONTW6knVN56moilh+4YJdYXSan57lH5LeI+XianoW1AGZ2TPcUOhwU8vYvFOxeg8suH6raDFethSqfxeOGkrOIbUtkDI0IqAQLwLCkVK4/dzXMYyhzocy3yu2khEZElYqIigqrqaH6qwQKZ8xGyFtqKuPPSaInK2lPGmeOz63IMdl1WzpnQNtlTzHDY0LN/Ytrh1eoS+Ck2dpjd8iOsq90JyNJ7c/rea5x/cB5Jqbo30jNH+lyL/G5aSERkibh7sgHyWyNP23yE/A4tmZq2yZ5iYnbISFUnKVfL+KPzoMVYV6ezRpICACnpCkmSlO61vOFkr33foNzos9GgijE2LSQisjRMVETYc1lZmfZpYmreB+ch+3yErJNszZHw37/Ptl9FWoZC3d6nfnmde8gAmUurVzTsiVM+gVjRsCfeG7AQd0qaR6E+VXLwbe8gfPtBLcig/0RksRN+jbVpIRGRJTGbEvqGEFOCN7/2XH6CsRsv5qvyqzaL+wShW1BZkz+PMbk72eKDuuXw16UYrZNDnVPfoNOL62g1dTjaBnhi0pZI7Ih8AmuFHHIdux1LQdteN7pqnLxbyyvH62UdFSIiwxTZEvpSCb8ag9EbLprk3NnnI5RwsheVpJQr7ohHcW+NHFXuXiWn6yyLX/dRFFYdWAzX57GQjWkHWHmhbHFHADBqklKjrCuuPE7Q+/gGviXw4NUbxCZk9oZp29E4t1Lz+uyBpA+Wsyci0h8TlTzkt3S+LqoN7rLPRxC74qOgkxRdbOQZGHdiI8ac+h3W/9VCEUaPxt+/7cKGM8Yv4FeplLO4RMXPHRtHBOuVHOgqNW/MEvQsZ09EpB8mKnnIb+l8bXKbj2CKFR/O9tawtpIh/q1pJqn6vnqMRbu+RVDMTXXb2bIBmBkSiqj1xu+JkgHoWqsstkc+0fsxwRVLMjkgIiqEmKjkwRQ1LbQNOaioVobExqcYVKtFm+HNKmJUi0o4f/81YuPf4qvd1/Hqv5L++SII6HPpb8w4+DOKpSuHVNKtrLGoSV+saPS+yeajCAAm/R4JJztrJKfJ8zy+eDFbNNKRoEixOSAREemPiUoejN3D8UXn6hjcxE/nh6ExCstl9/0/t7D6xD0MalwBzva2RklSSryJx7zwH9D+1il1290S3gjtOhmXvark+/x5eS2iUu28HjVyXG+5QsDSg8rrEvc281yc1EpEZF6YqORB1cNhrOGfki72uX5jlysEuDnaYUgTX+yIfGKcng8AcW/TsfjA7bwP1NPinQvR/F7msM6GWu0xu9UwvLFzFHWekSEVYWOlXBRsBWDJIXExygC4OdpqJBsqnq72mPXuOzmSjvCrMZi27YrWsvzaivEREZF0mKjkQdXDMXLdBaOcL7cemp2XnmD69itITDH/qqxzWn2Ehr9NQLKdI6Z1+AT7qgQbdJ6QKqXV80Yi7rwUnagIUCZhE9pURnkPJ7xKSoW7kx083Ry1DuOodi/W1VOl2tAxbGcU2gZ4chiIiEhiTFTyoOrhaFGlJA7ffGHweXSt8lE9R68fT+L8/TjDAzUxK4UciixzTm6U8sXYblMR6VUFz50Nq6SavQprfuYDbTr7EMentsqzt0rX7sVZZS3Gx8m3RETSYqKSC22FuQyR2yqf8KsxmLA5Em/TFTkfaAZkggIfnd2Bzv+eQK9+85Bunbkr8/7KjfJ17j71y2tcj/zMB9InsRC7goubAxIRSY8l9HVQDRGITVJkAIo72mq0ebo5aJ3zEH5VWZLfXJMUz4QXWLv5C3x+aBVqx9zApGPrjHr+lLQM9V5HQOZ8IEPllViITTy4OSARkfTYo6KFvkME2ggA/q9vHVhZyXJd8mqqQnLG0vHf45j791IUT0kCAChU/UKCkGMjQUMtP3oXOy49Ua+yybriyZBrn1diISbx4OaARETmgT0qWuS3yNuL5FQ08HNHaRcHPEtUDknIs9XFN0UhOWNwTn2DBbsXYfmf89RJSoyzB/r1mY15LYYYLUlRyb4rc4dALyzvX0dUz4q+uw6L2e2ZmwMSEZkH9qhokd+5CfdevEHT+Qdz3XTOHOc/1Hl0HYt2LUT5+Kfqtl3VmuGzdqMR7+hikufUtspG2144L5JS8cnGnFVuxew6rE+NmuLFbDGvRw10CPRiMTgiIjPAREULQ+cmyAC4FbPF9//czHFf9voc5jb/YdyJjRh/YqN6n55EO0fMbDsS295pZfRelOy0rbLRVu7e1lqWY3JzblV+tVH12GQ/T/FithjS2A9jW1WCtZWMOxwTEZkJJipaNPBzh7uTnehiawKAN2naa6Covr2reg4a+LnD09VeYzdfKdnJ09VJyrmy1RHaZRIeFfcs0Bjy6mUy1q7DeZ1HV60VFoMjIip4TFS0sLaSoXuQN1aduCf6sWkZuU8DVfUcxL9NQ0qG+az2WdSkL4LvX8bhinWxLLiXyfbpyY0+vUzG2lhQ13lym0jNYnBERAWPk2l1aBtgut6En4/dwah1F7SWcC8Ixd8moM2t0xptGdY26NVvPn5o8mGBJyn6ToYtCHlNcs46TEVERKbHREWHGmXdTHbuQzeeG21nZLGaRl9E+KqxWLZjLgKe3tW4z5QJSteanlpX24iZDGsqcoWAiDsv8WfkY5y4rV/1YXOcDE1EVBRx6EeH+eHXTXZuQYIsxT4jDVOO/IaPzv2pbpv5z4/o3W9+gTx/aRcHuBWzzdGLVLyYLeb+t8pGCoZWHza3ydBEREUVExUd7r18I3UIRlP1+T0s2rkQ1Z/fU7cd9a2NyZ1CCyyGlTrm+7yWaPgLyHuDQm1y27OJiIiMj4mKDr4exXDsltRR5I9MUGDIuZ2YeuRX2MuVCUGqtS3mtRiMX+t2hSAz/cifDMrVzQod2YBUk1MNqT5sDsNURESWhomKDpPbVcPaUw+kDsNgpRNfYuGeRWh+L7NI2vVSvgjtOhk3SvkWSAyqomq5DXVJtVOxIZWBxdZsISKi/GOiosMf5x9KHYLhBAHLd8xF3Sf/qpt+qdcNC0IGIdXGrsDCKF7MFrXLF8fBf5/neWxBT07V9/nGtvRH5TIurExLRCQRJio6RL9MljoEw8lkCGszAlvX/Q8vi7lhUueJOOEbVOBh2FnLEPkwXq9jC3pyqr7P16RSqQLt6SEiIk1MVHQobN+brRRyKLIsL77sVQVju03FaZ9AxDm6ShLT00T9KvvKALwWWQU4v1QbFMbGp2idp8JJs0RE5oF1VHSo7VNC6hD0Yq2QY9yJjdi08VNYK+Qa9/1dpbFkSYoYAoAxGzJ3UC4Iqg0KgZxJKSfNEhGZDyYqOngVd5Q6hDz5xMViy/qpmHh8PRo8isLYk5ulDilfwnZGQa5reZAJqDYo9HTTHAbydHPgfj5ERGaCQz86mNumgRoEAe9fPYBZ//wI57S3AIAMmRXkVoU375Rq9Y+xNjokIiLTYKKiw/6oWEmLkeni9jYRX/+9FJ1vnFC33S/uiQldJuFC2eoFFoe7ky1q+xTHAT1W9IghRWl6Y210SERExsdERYvwqzEYue6C1GHkEHz/Er7b9R28kl6q27bUaIOw1iOQbF+sQGIo7miL/+tXB40qKj/Ylx68hdUn7iHurXGSOpamJyKirJioZCNXCJj11zWpw9AkCJh2eDVGntmmbopzcMa0Dp8gvGqTAg0l7m06rGQy7I+KzbFHjqrAmyG4yoaIiLRhopLNmehX5jcvRSaDbZYVPccqBGFy51A8dSkpSTj7o2Kx+sS9HElJfpIUgKtsiIgoJyYq2UgxR0If34QMQr1HUfgrIASr6r1bIPv06LLhzAODkxJtWJqeiIh0YaKSjTnMkSiV9Ao1Ym/jYKUG6rZUGzu8N2ChRlE3qaSkKwx+rLO9Nb7pWQslnOy4yoaIiPLERCWbBn7ucLa3RlKqPO+DTaDdzQjMC/8BxdJT0HnQYtwp6aO+ryCTlPzMN9HF3ckOpz5tDTubwruMmoiIChY/MbKxtpLBv5RzgT9vsbS3mLt3CX7aPgfubxPgkJGGzw6tzPd5W1crpX8MdtZY0b8OVvSvA7ditvl+7qxkAL5+L5BJChERicJPDS38SjoV6PPVenIDu38dhw8v71O3hVcJxqTOE/J97mHN/DGhTWW9jv15YD31PJE4I9eQCW1ThXNQiIhINA79aFFQ0yWsFXKMOvU7Qo9vgI2gnPeRbOuAsNYjsKVmW0CWv0A8nOzQwM8dDfzcsfHMQ8QmaJ8orFoa3KiiB+QKAdO2XcnX82rjW7Jg6rwQEVHRwkQlG7lCwIHrz0z+POXiYvH9ru9Q/3GUui3SqwpCu0zCPfeyRnmOr7oFqiepzno3AKP+K2KXde5J9qXBi/+5ZfTeFMA8JikTEVHhw6GfbM5Ev0J8SoZpn0QQ8PO22eokRS6zwuLGffB+v2+MlqQMb+aLTjUzh1r02YBPrhCw+kS0UZ5fRQbAi4XciIjIQOxRyaZA6qjIZJjRdiQ2b/gUj9xKI7TLZFwoZ7x9eoY29sVnnd/J0Z7XBnxnol/lqxR+9pVCLORGRET5xUQlm5JO9iY5r7VCDnmW5cVnfQIxuvs0HPetjSQj79NTtoSj7jhy2YDPkCSteDFbzOtRAwBylNRnITciIsovJirZGfmLv11GOiYeW4t3nt7FwN5falSUNdU+PfdfvTHocfrOI2ngWwIN/NwRXLEkGvl7qHtLcuutISIiMgQTlWye6VgZYwj/Fw+xZOcCvPPsLgBg2Jkd+LlhD6OdXxefEob10DTwc4eXmwNi41N0FnsrUcwWG0cEa01AcuutISIiMgQn02bzKjkt/ycRBAy4sAu7fxuvTlJSrW2QYV0wlWWreboY9DhrKxlmdg0AoL1jSQZgbo8a7CUhIqICwx6VbFwc8ndJSia/xjd7FqPV3XPqtpse5TH+3cm4XrpifsPTy6s3hidbqtVB2eebeHG+CRERSYCJSjb7o54a/NjWt09j/t4lKPkmXt22um5XzAsZjFRb00zS1Sa/NUvyWh1ERERUUJioZJOUKr6GikxQYPa+ZegXGa5ue+ZUAv/rFIojFesaM7w8GatmCeebEBGROWCiks2Dl+JXzAgyK8iEzOmn+yo3wrQOn+BVMTdjhqaXolCzRK4Q2JtDREQAmKjk8PpNqkGP+6rVcNSMvY11QR2xqVb7fO/TI5YMwP/1rVPo55CEX43h/BgiIlLjqp8s0jIUeJOua2FuprLxz9DizjmNtrd2Dnh34HfYFNShwJMUABjXupJGyfzCKPxqDEatu6CRpABAbHwKRq27gPCrMRJFRkREUmGiksXaiHt5HtPt2iHsXTUWS/+aj/KvNT84FVYFs/w4uxLFbDGudRVJnttY5AoBYTujtNZvUbWF7YyCXJF3IklEREUHE5Uscqvo6pqShMV/LcDiXd/CNe0NnNPeYsrRNQUYnXZFpbbJmehXOXpSshIAxMSn4Ez0q4ILioiIJMc5Klm80bHip+GDK/h293col/Bc3bbtnZaY2XZkQYWmVVGau6HvPkMFsmkkERGZDckTlWXLlmHBggWIiYnBO++8g0WLFqFZs2YFHodcIWBfVKxGm608HROOr8fIU1th9d8ARIK9Ez5rNxo7A0IKPEaVsS390aRSqSK1Gkbf2i/5rRFDRESFi6SJyubNmxEaGoply5ahSZMm+PHHH9GxY0dERUWhfPnyBRrLqbsvkZAiV9/2f/kQi3YuRI2nd9RtEeVrYFLnCXjiWrpAY1ORQbkj8YS2VYtMgqKS1z5DqtdujBoxRERUeEg6R+W7777DRx99hGHDhqF69epYtGgRfHx8sHz58gKPJeLOS/X/rRRy/LL1S3WSkmZlg7ktBqNf79mSJSmAcp5GUaiTok1u+wypbhfV105ERLpJlqikpaXh/PnzaNeunUZ7u3btcPLkSa2PSU1NRUJCgsY/48n8Hq+wssaMtqMAALfdy+G9AQvxY8P3JVvVYylU+wx5umkO73i6OWB5/8JfI4aIiMSTbOjnxYsXkMvlKFOmjEZ7mTJlEBsbq/Uxc+fORVhYmEniCa5YEksPZQ7zHPOrg5HdP8XhinWRYms+8yLCdkahbYBnke1Z4D5DRESUleTLk2XZiqMJgpCjTeXTTz9FfHy8+t/Dhw+NFkcjfw8Us9PsMQmv2sSskhTAMpboqvYZ6hZUFsH+HkxSiIgsmGSJSsmSJWFtbZ2j9+TZs2c5ellU7O3t4erqqvHPWKytZPi4eUWjnc+UuESXiIgshWSJip2dHerWrYv9+/drtO/fvx+NGzeWJKaxrSrDwabgLknd8sWxrG8deLmJ67XhEl0iIrIUki5PnjhxIgYMGIB69eohODgYP/30Ex48eICRI6UppGZtJUPLaqWx96r2OTLG1KZ6afwyqD4AoH2gck7Gvmux+PXkPa3LcwEu0SUiIssjaaLSu3dvvHz5El9++SViYmIQGBiIPXv2oEKFCpLF1LdBeZMnKt9/UAvv1S2nvq2akxHs74H6viUwesPFHI/hEl0iIrJEklemHT16NEaPHi11GGpWBbDz8Tf7bsDR3lrrcttONb2xwkqGsJ1RGnvfeBahcvlERET6kjxRMTcvklNN/hyx8SkYte6CztogXKJLRESkxEQlm5LO9iZ/DgHKoZzcaqKohoOIiIgsmeR1VMyOrpmsJngaS6iJQkRElB9MVLIpiKGfrFgThYiISDcmKtkUdI0S1kQhIiLSjYlKNg383OHl5pBjB19jkwHwYk0UIiKiXDFRycbaSoaZXQPyfZ7cEh3WRCEiItIPExUtOgR6YXn/OnCyt877YC0mtKkCz1zK4nu6OehcmkxERESZuDxZh7YBnrCxuiz6cV5uDhjbqhLGtqqkroNS0tkeEJQTdVkThYiISH9MVHQ4E/0K8W8z9D5e23AO66AQERHlDxMVHcQuG2aJeyIiIuNjoqKDvsuGOwZ6YmCwL4dziIiITICJig6qZcqx8Sk6i9UWL2aLpX3rMEEhIiIyEa760SHrMmVdaci8HjWYpBAREZkQE5VcqJYpZ19q7OXmgBVcXkxERGRyHPrJQ4dAL7QN8FQvNebyYiIiooLDREUP1lYyLjUmIiKSAId+iIiIyGwxUSEiIiKzxUSFiIiIzBYTFSIiIjJbTFSIiIjIbDFRISIiIrPFRIWIiIjMFhMVIiIiMltMVIiIiMhsFerKtIKg3Nc4ISFB4kiIiIhIX6rPbdXneG4KdaKSmJgIAPDx8ZE4EiIiIhIrMTERbm5uuR4jE/RJZ8yUQqHAkydP4OLiApnMeJsEJiQkwMfHBw8fPoSrq6vRzkvi8b0wH3wvzAvfD/PB90I8QRCQmJgIb29vWFnlPgulUPeoWFlZoVy5ciY7v6urK3/ozATfC/PB98K88P0wH3wvxMmrJ0WFk2mJiIjIbDFRISIiIrPFREULe3t7zJw5E/b29lKHYvH4XpgPvhfmhe+H+eB7YVqFejItERERFW3sUSEiIiKzxUSFiIiIzBYTFSIiIjJbTFSIiIjIbDFRyWbZsmXw8/ODg4MD6tati2PHjkkdkkU6evQounbtCm9vb8hkMuzYsUPqkCzW3LlzUb9+fbi4uKB06dLo3r07bty4IXVYFmn58uWoWbOmurBYcHAw9u7dK3VYBOXviUwmQ2hoqNShFDlMVLLYvHkzQkND8dlnn+HixYto1qwZOnbsiAcPHkgdmsVJTk5GrVq1sHTpUqlDsXhHjhzBmDFjcOrUKezfvx8ZGRlo164dkpOTpQ7N4pQrVw7z5s3DuXPncO7cObRq1QrdunXDtWvXpA7Nop09exY//fQTatasKXUoRRKXJ2fRsGFD1KlTB8uXL1e3Va9eHd27d8fcuXMljMyyyWQybN++Hd27d5c6FALw/PlzlC5dGkeOHEHz5s2lDsfiubu7Y8GCBfjoo4+kDsUiJSUloU6dOli2bBlmz56NoKAgLFq0SOqwihT2qPwnLS0N58+fR7t27TTa27Vrh5MnT0oUFZH5iY+PB6D8gCTpyOVybNq0CcnJyQgODpY6HIs1ZswYdO7cGW3atJE6lCKrUG9KaEwvXryAXC5HmTJlNNrLlCmD2NhYiaIiMi+CIGDixIlo2rQpAgMDpQ7HIl25cgXBwcFISUmBs7Mztm/fjoCAAKnDskibNm3ChQsXcPbsWalDKdKYqGQjk8k0bguCkKONyFKNHTsWly9fxvHjx6UOxWJVrVoVkZGRiIuLw9atWzFo0CAcOXKEyUoBe/jwIcaPH499+/bBwcFB6nCKNCYq/ylZsiSsra1z9J48e/YsRy8LkSX65JNP8Ndff+Ho0aMoV66c1OFYLDs7O1SqVAkAUK9ePZw9exaLFy/Gjz/+KHFkluX8+fN49uwZ6tatq26Ty+U4evQoli5ditTUVFhbW0sYYdHBOSr/sbOzQ926dbF//36N9v3796Nx48YSRUUkPUEQMHbsWGzbtg0HDx6En5+f1CFRFoIgIDU1VeowLE7r1q1x5coVREZGqv/Vq1cP/fr1Q2RkJJMUI2KPShYTJ07EgAEDUK9ePQQHB+Onn37CgwcPMHLkSKlDszhJSUm4ffu2+nZ0dDQiIyPh7u6O8uXLSxiZ5RkzZgw2bNiAP//8Ey4uLupeRzc3Nzg6OkocnWWZPn06OnbsCB8fHyQmJmLTpk04fPgwwsPDpQ7N4ri4uOSYp+Xk5AQPDw/O3zIyJipZ9O7dGy9fvsSXX36JmJgYBAYGYs+ePahQoYLUoVmcc+fOoWXLlurbEydOBAAMGjQIv/76q0RRWSbVcv0WLVpotK9evRqDBw8u+IAs2NOnTzFgwADExMTAzc0NNWvWRHh4ONq2bSt1aEQmwzoqREREZLY4R4WIiIjMFhMVIiIiMltMVIiIiMhsMVEhIiIis8VEhYiIiMwWExUiIiIyW0xUiIiIyGwxUSEisyKTybBjxw6jn7dFixYIDQ01+nmJCqOjR4+ia9eu8Pb2Nuh3LiUlBYMHD0aNGjVgY2OD7t27az3uyJEjqFu3LhwcHFCxYkWsWLFCdKxMVIgs1MmTJ2FtbY0OHTqIfqyvry8WLVpk/KD0MHjwYMhkMshkMtja2qJixYqYPHkykpOTc33ctm3b8NVXXxVQlETmLTk5GbVq1cLSpUsNerxcLoejoyPGjRuHNm3aaD0mOjoanTp1QrNmzXDx4kVMnz4d48aNw9atW0U9F0voE1moVatW4ZNPPsEvv/yCBw8eFKo9lDp06IDVq1cjPT0dx44dw7Bhw5CcnKwu959Veno6bG1t4e7uLkGkROapY8eO6Nixo87709LS8Pnnn2P9+vWIi4tDYGAg5s+fr95Kw8nJSf37duLECcTFxeU4x4oVK1C+fHn1l5rq1avj3LlzWLhwIXr27Kl3rOxRIbJAycnJ2LJlC0aNGoUuXbpo3T/pr7/+Qr169eDg4ICSJUuiR48eAJRDKPfv38eECRPUPRsAMGvWLAQFBWmcY9GiRfD19VXfPnv2LNq2bYuSJUvCzc0NISEhuHDhguj47e3t4enpCR8fH/Tt2xf9+vVTd12r4li1ahUqVqwIe3t7CIKQY+gnNTUVU6ZMgY+PD+zt7VG5cmWsXLlSfX9UVBQ6deoEZ2dnlClTBgMGDMCLFy/U9//xxx+oUaMGHB0d4eHhgTZt2uTZq0NUWAwZMgQnTpzApk2bcPnyZXzwwQfo0KEDbt26pfc5IiIi0K5dO4229u3b49y5c0hPT9f7PExUiCzQ5s2bUbVqVVStWhX9+/fH6tWrkXXbr927d6NHjx7o3LkzLl68iAMHDqBevXoAlEMo5cqVU2/eGRMTo/fzJiYmYtCgQTh27BhOnTqFypUro1OnTkhMTMzX63F0dNT4w3f79m1s2bIFW7duRWRkpNbHDBw4EJs2bcKSJUtw/fp1rFixAs7OzgCAmJgYhISEICgoCOfOnUN4eDiePn2KXr16qe//8MMPMXToUFy/fh2HDx9Gjx49wK3TqCi4c+cONm7ciN9//x3NmjWDv78/Jk+ejKZNm2L16tV6nyc2NhZlypTRaCtTpgwyMjI0kv68cOiHyAKtXLkS/fv3B6AcRklKSsKBAwfUY81z5sxBnz59EBYWpn5MrVq1AADu7u6wtraGi4sLPD09RT1vq1atNG7/+OOPKFGiBI4cOYIuXboY9FrOnDmDDRs2oHXr1uq2tLQ0rF27FqVKldL6mJs3b2LLli3Yv3+/+jVXrFhRff/y5ctRp04dfP311+q2VatWwcfHBzdv3kRSUhIyMjLQo0cP9e7qNWrUMCh+InNz4cIFCIKAKlWqaLSnpqbCw8ND1LlUPa4qqmQ+e3tumKgQWZgbN27gzJkz2LZtGwDAxsYGvXv3xqpVq9Qf2pGRkRg+fLjRn/vZs2eYMWMGDh48iKdPn0Iul+PNmzd48OCBqPPs2rULzs7OyMjIQHp6Orp164YffvhBfX+FChV0JimA8vVZW1sjJCRE6/3nz5/HoUOH1D0sWd25cwft2rVD69atUaNGDbRv3x7t2rXD+++/jxIlSoh6HUTmSKFQwNraGufPn4e1tbXGfdp+J3Tx9PREbGysRtuzZ89gY2MjKuFhokJkYVauXImMjAyULVtW3SYIAmxtbfH69WuUKFECjo6Oos9rZWWVY+gj+zj04MGD8fz5cyxatAgVKlSAvb09goODkZaWJuq5WrZsieXLl8PW1hbe3t6wtbXVuN/JySnXx+f1+hQKBbp27Yr58+fnuM/LywvW1tbYv38/Tp48iX379uGHH37AZ599htOnT8PPz0/UayEyN7Vr14ZcLsezZ8/QrFkzg88THByMnTt3arTt27cP9erVy/E7mxvOUSGyIBkZGVizZg2+/fZbREZGqv9dunQJFSpUwPr16wEANWvWxIEDB3Sex87ODnK5XKOtVKlSiI2N1UhWss8POXbsGMaNG4dOnTrhnXfegb29vaixahUnJydUqlQJFSpUEPUHT6VGjRpQKBQ4cuSI1vvr1KmDa9euwdfXF5UqVdL4p0qCZDIZmjRpgrCwMFy8eBF2dnbYvn276FiIpJCUlKT+/QeUS4kjIyPx4MEDVKlSBf369cPAgQOxbds2REdH4+zZs5g/fz727NmjPkdUVBQiIyPx6tUrxMfHa5wPAEaOHIn79+9j4sSJuH79OlatWoWVK1di8uTJ4oIViMhibN++XbCzsxPi4uJy3Dd9+nQhKChIEARBOHTokGBlZSXMmDFDiIqKEi5fvizMnz9ffWzbtm2Fd999V3j06JHw/PlzQRAEISoqSpDJZMK8efOE27dvC0uXLhVKlCghVKhQQf24oKAgoW3btkJUVJRw6tQpoVmzZoKjo6Pw/fffq48BIGzfvl3naxg0aJDQrVs3nffPnDlTqFWrVo72kJAQYfz48erbgwcPFnx8fITt27cLd+/eFQ4dOiRs3rxZEARBePz4sVCqVCnh/fffF06fPi3cuXNH+Pvvv4UhQ4YIGRkZwqlTp4Q5c+YIZ8+eFe7fvy9s2bJFsLOzE/bs2aMzLiJzcujQIQFAjn+DBg0SBEEQ0tLShBkzZgi+vr6Cra2t4OnpKbz33nvC5cuX1eeoUKGC1nNkdfjwYaF27dqCnZ2d4OvrKyxfvlx0rExUiCxIly5dhE6dOmm97/z58wIA4fz584IgCMLWrVuFoKAgwc7OTihZsqTQo0cP9bERERFCzZo1BXt7e40/TMuXLxd8fHwEJycnYeDAgcKcOXM0EpULFy4I9erVE+zt7YXKlSsLv//+u1ChQgVJEpW3b98KEyZMELy8vAQ7OzuhUqVKwqpVq9T337x5U3jvvfeE4sWLC46OjkK1atWE0NBQQaFQCFFRUUL79u2FUqVKCfb29kKVKlWEH374QWdMRGQ4mSBwPR0RERGZJ85RISIiIrPFRIWIiIjMFhMVIiIiMltMVIiIiMhsMVEhIiIis8VEhYiIiMwWExUiIiIyW0xUiIiIyGwxUSEiIiKzxUSFiIiIzBYTFSIiIjJbTFSIiIjIbP0/znIlhLT0W54AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test, prediction)\n",
    "plt.xlabel(\"Actual Prices\")\n",
    "plt.ylabel(\"Predicted Prices\")\n",
    "plt.title(\"Actual vs. Predicted House Prices\")\n",
    "\n",
    "# Add the regression line\n",
    "plt.plot([min(y_test.values), max(y_test.values)], [min(y_test.values), max(y_test.values)], color='red', linestyle='--', lw=2)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "id": "e1e95a61",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             Price\n",
      "1215  5.000000e+08\n",
      "2953  5.500000e+08\n",
      "1445  1.265000e+10\n",
      "1496  1.020000e+09\n",
      "2820  3.240000e+09\n",
      "[[6.15186207e+09]\n",
      " [1.26796518e+09]\n",
      " [5.11331424e+09]\n",
      " [1.12491800e+09]\n",
      " [5.76240664e+09]]\n"
     ]
    }
   ],
   "source": [
    "print(y_test[0:5])\n",
    "print(prediction[0:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "id": "31ce9d96",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 1.097377571246491e+19\n",
      "R-squared: 0.7119667601948532\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "mse = mean_squared_error(y_test, prediction)\n",
    "r2 = r2_score(y_test, prediction)\n",
    "\n",
    "print(f'Mean Squared Error: {mse}')\n",
    "print(f'R-squared: {r2}')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}