used-car-price-predictor.ipynb

{
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  {
   "cell_type": "markdown",
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   "source": [
    "# <font color='green'>Used Car Price Predictor</font>\n",
    "\n",
    "**Objective**<br>\n",
    "    The goal of this model is to predict the price of used car because there is a high demand in the market for used cars but there is no such measure for fair pricing of used cars. For example, if you buy a new car and sell it the next day without any fault, the price of the car is reduced by 20-30%. There this model will help in estimating the fair price of the car based on its intended use.<br><br>\n",
    "**Steps**<br>\n",
    "1. Installing packages & Loading dataset\n",
    "2. Data Cleaning\n",
    "3. Feature Engineering (Adding derived features)\n",
    "4. Exploratory Data Analysis & Visualization\n",
    "6. Feature Engineering (Label Encoding and One Hot Encoding)\n",
    "7. Preparing the dataset for Machine Learning\n",
    "8. Training & Evaluating Different Models\n",
    "9. Predicting Price for test data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color='green'>1. Installing Packages and Loading Dataset</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Importing necessary libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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    "scrolled": true
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Name</th>\n",
       "      <th>Location</th>\n",
       "      <th>Year</th>\n",
       "      <th>Kilometers_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner_Type</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Engine</th>\n",
       "      <th>Power</th>\n",
       "      <th>Seats</th>\n",
       "      <th>New_Price</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Maruti Wagon R LXI CNG</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>2010</td>\n",
       "      <td>72000</td>\n",
       "      <td>CNG</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>26.6 km/kg</td>\n",
       "      <td>998 CC</td>\n",
       "      <td>58.16 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Hyundai Creta 1.6 CRDi SX Option</td>\n",
       "      <td>Pune</td>\n",
       "      <td>2015</td>\n",
       "      <td>41000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>19.67 kmpl</td>\n",
       "      <td>1582 CC</td>\n",
       "      <td>126.2 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Honda Jazz V</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2011</td>\n",
       "      <td>46000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>18.2 kmpl</td>\n",
       "      <td>1199 CC</td>\n",
       "      <td>88.7 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.61 Lakh</td>\n",
       "      <td>4.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Maruti Ertiga VDI</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2012</td>\n",
       "      <td>87000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>20.77 kmpl</td>\n",
       "      <td>1248 CC</td>\n",
       "      <td>88.76 bhp</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Audi A4 New 2.0 TDI Multitronic</td>\n",
       "      <td>Coimbatore</td>\n",
       "      <td>2013</td>\n",
       "      <td>40670</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Second</td>\n",
       "      <td>15.2 kmpl</td>\n",
       "      <td>1968 CC</td>\n",
       "      <td>140.8 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.74</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                              Name    Location  Year  \\\n",
       "0           0            Maruti Wagon R LXI CNG      Mumbai  2010   \n",
       "1           1  Hyundai Creta 1.6 CRDi SX Option        Pune  2015   \n",
       "2           2                      Honda Jazz V     Chennai  2011   \n",
       "3           3                 Maruti Ertiga VDI     Chennai  2012   \n",
       "4           4   Audi A4 New 2.0 TDI Multitronic  Coimbatore  2013   \n",
       "\n",
       "   Kilometers_Driven Fuel_Type Transmission Owner_Type     Mileage   Engine  \\\n",
       "0              72000       CNG       Manual      First  26.6 km/kg   998 CC   \n",
       "1              41000    Diesel       Manual      First  19.67 kmpl  1582 CC   \n",
       "2              46000    Petrol       Manual      First   18.2 kmpl  1199 CC   \n",
       "3              87000    Diesel       Manual      First  20.77 kmpl  1248 CC   \n",
       "4              40670    Diesel    Automatic     Second   15.2 kmpl  1968 CC   \n",
       "\n",
       "       Power  Seats  New_Price  Price  \n",
       "0  58.16 bhp    5.0        NaN   1.75  \n",
       "1  126.2 bhp    5.0        NaN  12.50  \n",
       "2   88.7 bhp    5.0  8.61 Lakh   4.50  \n",
       "3  88.76 bhp    7.0        NaN   6.00  \n",
       "4  140.8 bhp    5.0        NaN  17.74  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Importing the train dataset\n",
    "df_train = pd.read_csv('train-data.csv')\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6019 entries, 0 to 6018\n",
      "Data columns (total 14 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Unnamed: 0         6019 non-null   int64  \n",
      " 1   Name               6019 non-null   object \n",
      " 2   Location           6019 non-null   object \n",
      " 3   Year               6019 non-null   int64  \n",
      " 4   Kilometers_Driven  6019 non-null   int64  \n",
      " 5   Fuel_Type          6019 non-null   object \n",
      " 6   Transmission       6019 non-null   object \n",
      " 7   Owner_Type         6019 non-null   object \n",
      " 8   Mileage            6017 non-null   object \n",
      " 9   Engine             5983 non-null   object \n",
      " 10  Power              5983 non-null   object \n",
      " 11  Seats              5977 non-null   float64\n",
      " 12  New_Price          824 non-null    object \n",
      " 13  Price              6019 non-null   float64\n",
      "dtypes: float64(2), int64(3), object(9)\n",
      "memory usage: 658.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Unnamed: 0</th>\n",
       "      <th>Name</th>\n",
       "      <th>Location</th>\n",
       "      <th>Year</th>\n",
       "      <th>Kilometers_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner_Type</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Engine</th>\n",
       "      <th>Power</th>\n",
       "      <th>Seats</th>\n",
       "      <th>New_Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Maruti Alto K10 LXI CNG</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>2014</td>\n",
       "      <td>40929</td>\n",
       "      <td>CNG</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>32.26 km/kg</td>\n",
       "      <td>998 CC</td>\n",
       "      <td>58.2 bhp</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Maruti Alto 800 2016-2019 LXI</td>\n",
       "      <td>Coimbatore</td>\n",
       "      <td>2013</td>\n",
       "      <td>54493</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Second</td>\n",
       "      <td>24.7 kmpl</td>\n",
       "      <td>796 CC</td>\n",
       "      <td>47.3 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Toyota Innova Crysta Touring Sport 2.4 MT</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>2017</td>\n",
       "      <td>34000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>13.68 kmpl</td>\n",
       "      <td>2393 CC</td>\n",
       "      <td>147.8 bhp</td>\n",
       "      <td>7.0</td>\n",
       "      <td>25.27 Lakh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Toyota Etios Liva GD</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2012</td>\n",
       "      <td>139000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>23.59 kmpl</td>\n",
       "      <td>1364 CC</td>\n",
       "      <td>null bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Hyundai i20 Magna</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>2014</td>\n",
       "      <td>29000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>18.5 kmpl</td>\n",
       "      <td>1197 CC</td>\n",
       "      <td>82.85 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                                       Name    Location  Year  \\\n",
       "0           0                    Maruti Alto K10 LXI CNG       Delhi  2014   \n",
       "1           1              Maruti Alto 800 2016-2019 LXI  Coimbatore  2013   \n",
       "2           2  Toyota Innova Crysta Touring Sport 2.4 MT      Mumbai  2017   \n",
       "3           3                       Toyota Etios Liva GD   Hyderabad  2012   \n",
       "4           4                          Hyundai i20 Magna      Mumbai  2014   \n",
       "\n",
       "   Kilometers_Driven Fuel_Type Transmission Owner_Type      Mileage   Engine  \\\n",
       "0              40929       CNG       Manual      First  32.26 km/kg   998 CC   \n",
       "1              54493    Petrol       Manual     Second    24.7 kmpl   796 CC   \n",
       "2              34000    Diesel       Manual      First   13.68 kmpl  2393 CC   \n",
       "3             139000    Diesel       Manual      First   23.59 kmpl  1364 CC   \n",
       "4              29000    Petrol       Manual      First    18.5 kmpl  1197 CC   \n",
       "\n",
       "       Power  Seats   New_Price  \n",
       "0   58.2 bhp    4.0         NaN  \n",
       "1   47.3 bhp    5.0         NaN  \n",
       "2  147.8 bhp    7.0  25.27 Lakh  \n",
       "3   null bhp    5.0         NaN  \n",
       "4  82.85 bhp    5.0         NaN  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Importing the test dataset\n",
    "df_test = pd.read_csv('test-data.csv')\n",
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1234 entries, 0 to 1233\n",
      "Data columns (total 13 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Unnamed: 0         1234 non-null   int64  \n",
      " 1   Name               1234 non-null   object \n",
      " 2   Location           1234 non-null   object \n",
      " 3   Year               1234 non-null   int64  \n",
      " 4   Kilometers_Driven  1234 non-null   int64  \n",
      " 5   Fuel_Type          1234 non-null   object \n",
      " 6   Transmission       1234 non-null   object \n",
      " 7   Owner_Type         1234 non-null   object \n",
      " 8   Mileage            1234 non-null   object \n",
      " 9   Engine             1224 non-null   object \n",
      " 10  Power              1224 non-null   object \n",
      " 11  Seats              1223 non-null   float64\n",
      " 12  New_Price          182 non-null    object \n",
      "dtypes: float64(1), int64(3), object(9)\n",
      "memory usage: 125.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df_test.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color='green'>2. Data Cleaning</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "      <th>Engine</th>\n",
       "      <th>Power</th>\n",
       "      <th>Seats</th>\n",
       "      <th>New_Price</th>\n",
       "      <th>Price</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Maruti Wagon R LXI CNG</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>2010</td>\n",
       "      <td>72000</td>\n",
       "      <td>CNG</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>26.6 km/kg</td>\n",
       "      <td>998 CC</td>\n",
       "      <td>58.16 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Hyundai Creta 1.6 CRDi SX Option</td>\n",
       "      <td>Pune</td>\n",
       "      <td>2015</td>\n",
       "      <td>41000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>19.67 kmpl</td>\n",
       "      <td>1582 CC</td>\n",
       "      <td>126.2 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Honda Jazz V</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2011</td>\n",
       "      <td>46000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>18.2 kmpl</td>\n",
       "      <td>1199 CC</td>\n",
       "      <td>88.7 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.61 Lakh</td>\n",
       "      <td>4.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Maruti Ertiga VDI</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2012</td>\n",
       "      <td>87000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>20.77 kmpl</td>\n",
       "      <td>1248 CC</td>\n",
       "      <td>88.76 bhp</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Audi A4 New 2.0 TDI Multitronic</td>\n",
       "      <td>Coimbatore</td>\n",
       "      <td>2013</td>\n",
       "      <td>40670</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Second</td>\n",
       "      <td>15.2 kmpl</td>\n",
       "      <td>1968 CC</td>\n",
       "      <td>140.8 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.74</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                              Name    Location  Year  \\\n",
       "0           0            Maruti Wagon R LXI CNG      Mumbai  2010   \n",
       "1           1  Hyundai Creta 1.6 CRDi SX Option        Pune  2015   \n",
       "2           2                      Honda Jazz V     Chennai  2011   \n",
       "3           3                 Maruti Ertiga VDI     Chennai  2012   \n",
       "4           4   Audi A4 New 2.0 TDI Multitronic  Coimbatore  2013   \n",
       "\n",
       "   Kilometers_Driven Fuel_Type Transmission Owner_Type     Mileage   Engine  \\\n",
       "0              72000       CNG       Manual      First  26.6 km/kg   998 CC   \n",
       "1              41000    Diesel       Manual      First  19.67 kmpl  1582 CC   \n",
       "2              46000    Petrol       Manual      First   18.2 kmpl  1199 CC   \n",
       "3              87000    Diesel       Manual      First  20.77 kmpl  1248 CC   \n",
       "4              40670    Diesel    Automatic     Second   15.2 kmpl  1968 CC   \n",
       "\n",
       "       Power  Seats  New_Price  Price  \n",
       "0  58.16 bhp    5.0        NaN   1.75  \n",
       "1  126.2 bhp    5.0        NaN  12.50  \n",
       "2   88.7 bhp    5.0  8.61 Lakh   4.50  \n",
       "3  88.76 bhp    7.0        NaN   6.00  \n",
       "4  140.8 bhp    5.0        NaN  17.74  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Merge both train and test data for processing all the features together and then separated them at the end using price feature.\n",
    "df = df_train.append(df_test)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 7253 entries, 0 to 1233\n",
      "Data columns (total 14 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Unnamed: 0         7253 non-null   int64  \n",
      " 1   Name               7253 non-null   object \n",
      " 2   Location           7253 non-null   object \n",
      " 3   Year               7253 non-null   int64  \n",
      " 4   Kilometers_Driven  7253 non-null   int64  \n",
      " 5   Fuel_Type          7253 non-null   object \n",
      " 6   Transmission       7253 non-null   object \n",
      " 7   Owner_Type         7253 non-null   object \n",
      " 8   Mileage            7251 non-null   object \n",
      " 9   Engine             7207 non-null   object \n",
      " 10  Power              7207 non-null   object \n",
      " 11  Seats              7200 non-null   float64\n",
      " 12  New_Price          1006 non-null   object \n",
      " 13  Price              6019 non-null   float64\n",
      "dtypes: float64(2), int64(3), object(9)\n",
      "memory usage: 850.0+ KB\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Year</th>\n",
       "      <th>Kilometers_Driven</th>\n",
       "      <th>Seats</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7253.000000</td>\n",
       "      <td>7253.000000</td>\n",
       "      <td>7.253000e+03</td>\n",
       "      <td>7200.000000</td>\n",
       "      <td>6019.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2601.948435</td>\n",
       "      <td>2013.365366</td>\n",
       "      <td>5.869906e+04</td>\n",
       "      <td>5.279722</td>\n",
       "      <td>9.479468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1826.365869</td>\n",
       "      <td>3.254421</td>\n",
       "      <td>8.442772e+04</td>\n",
       "      <td>0.811660</td>\n",
       "      <td>11.187917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1996.000000</td>\n",
       "      <td>1.710000e+02</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.440000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>906.000000</td>\n",
       "      <td>2011.000000</td>\n",
       "      <td>3.400000e+04</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2392.000000</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>5.341600e+04</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.640000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4205.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>7.300000e+04</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>9.950000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6018.000000</td>\n",
       "      <td>2019.000000</td>\n",
       "      <td>6.500000e+06</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>160.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0         Year  Kilometers_Driven        Seats        Price\n",
       "count  7253.000000  7253.000000       7.253000e+03  7200.000000  6019.000000\n",
       "mean   2601.948435  2013.365366       5.869906e+04     5.279722     9.479468\n",
       "std    1826.365869     3.254421       8.442772e+04     0.811660    11.187917\n",
       "min       0.000000  1996.000000       1.710000e+02     0.000000     0.440000\n",
       "25%     906.000000  2011.000000       3.400000e+04     5.000000     3.500000\n",
       "50%    2392.000000  2014.000000       5.341600e+04     5.000000     5.640000\n",
       "75%    4205.000000  2016.000000       7.300000e+04     5.000000     9.950000\n",
       "max    6018.000000  2019.000000       6.500000e+06    10.000000   160.000000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Basic info\n",
    "df.info()\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Location</th>\n",
       "      <th>Year</th>\n",
       "      <th>Kilometers_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner_Type</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Engine</th>\n",
       "      <th>Power</th>\n",
       "      <th>Seats</th>\n",
       "      <th>New_Price</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Maruti Wagon R LXI CNG</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>2010</td>\n",
       "      <td>72000</td>\n",
       "      <td>CNG</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>26.6 km/kg</td>\n",
       "      <td>998 CC</td>\n",
       "      <td>58.16 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hyundai Creta 1.6 CRDi SX Option</td>\n",
       "      <td>Pune</td>\n",
       "      <td>2015</td>\n",
       "      <td>41000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>19.67 kmpl</td>\n",
       "      <td>1582 CC</td>\n",
       "      <td>126.2 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Honda Jazz V</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2011</td>\n",
       "      <td>46000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>18.2 kmpl</td>\n",
       "      <td>1199 CC</td>\n",
       "      <td>88.7 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.61 Lakh</td>\n",
       "      <td>4.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Maruti Ertiga VDI</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2012</td>\n",
       "      <td>87000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>20.77 kmpl</td>\n",
       "      <td>1248 CC</td>\n",
       "      <td>88.76 bhp</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Audi A4 New 2.0 TDI Multitronic</td>\n",
       "      <td>Coimbatore</td>\n",
       "      <td>2013</td>\n",
       "      <td>40670</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Second</td>\n",
       "      <td>15.2 kmpl</td>\n",
       "      <td>1968 CC</td>\n",
       "      <td>140.8 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.74</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Name    Location  Year  Kilometers_Driven  \\\n",
       "0            Maruti Wagon R LXI CNG      Mumbai  2010              72000   \n",
       "1  Hyundai Creta 1.6 CRDi SX Option        Pune  2015              41000   \n",
       "2                      Honda Jazz V     Chennai  2011              46000   \n",
       "3                 Maruti Ertiga VDI     Chennai  2012              87000   \n",
       "4   Audi A4 New 2.0 TDI Multitronic  Coimbatore  2013              40670   \n",
       "\n",
       "  Fuel_Type Transmission Owner_Type     Mileage   Engine      Power  Seats  \\\n",
       "0       CNG       Manual      First  26.6 km/kg   998 CC  58.16 bhp    5.0   \n",
       "1    Diesel       Manual      First  19.67 kmpl  1582 CC  126.2 bhp    5.0   \n",
       "2    Petrol       Manual      First   18.2 kmpl  1199 CC   88.7 bhp    5.0   \n",
       "3    Diesel       Manual      First  20.77 kmpl  1248 CC  88.76 bhp    7.0   \n",
       "4    Diesel    Automatic     Second   15.2 kmpl  1968 CC  140.8 bhp    5.0   \n",
       "\n",
       "   New_Price  Price  \n",
       "0        NaN   1.75  \n",
       "1        NaN  12.50  \n",
       "2  8.61 Lakh   4.50  \n",
       "3        NaN   6.00  \n",
       "4        NaN  17.74  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Dropping 'Unnamed: 0' column as it is repeated index which is not useful.\n",
    "df.drop('Unnamed: 0',axis=1, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Location:  ['Mumbai' 'Pune' 'Chennai' 'Coimbatore' 'Hyderabad' 'Jaipur' 'Kochi'\n",
      " 'Kolkata' 'Delhi' 'Bangalore' 'Ahmedabad']\n",
      "Fuel_Type:  ['CNG' 'Diesel' 'Petrol' 'LPG' 'Electric']\n",
      "Transmission:  ['Manual' 'Automatic']\n",
      "Owner_Type:  ['First' 'Second' 'Fourth & Above' 'Third']\n"
     ]
    }
   ],
   "source": [
    "## Looking at the unique values of Categorical Features.\n",
    "## All other features are being looked during Feature engineering.\n",
    "\n",
    "print('Location: ',df['Location'].unique())\n",
    "print('Fuel_Type: ',df['Fuel_Type'].unique())\n",
    "print('Transmission: ',df['Transmission'].unique())\n",
    "print('Owner_Type: ',df['Owner_Type'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name                    0\n",
       "Location                0\n",
       "Year                    0\n",
       "Kilometers_Driven       0\n",
       "Fuel_Type               0\n",
       "Transmission            0\n",
       "Owner_Type              0\n",
       "Mileage                 2\n",
       "Engine                 46\n",
       "Power                  46\n",
       "Seats                  53\n",
       "New_Price            6247\n",
       "Price                1234\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dropping NULL values containing rows since they are so few in number in their corresponding column.<br>\n",
    "In New_Price column many of the cells contain NULL values, so we cannot ignore it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of dataset,\n",
      "-> Before dropping any Row : (7253, 13)\n",
      "-> After dropping Rows : (7198, 13)\n"
     ]
    }
   ],
   "source": [
    "print(\"Shape of dataset,\")\n",
    "print(\"-> Before dropping any Row :\",df.shape)\n",
    "df = df.dropna(subset = ['Mileage','Engine','Power','Seats'])\n",
    "print(\"-> After dropping Rows :\",df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, after dropping 55 rows, there are now 7198 rows to work on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Resetting the dataset index.\n",
    "df = df.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color='green'>3. Feautre Engineering (Adding derived features)</font>\n",
    "`A variety of data could be extracted from the present dataset. That's where Feature Engineering comes in.`\n",
    "- Add Company/Brand name of car using Name column.\n",
    "- Add Mileage(km/kg) feature\n",
    "- Add Engine(CC) feature\n",
    "- Add Power(bhp) feature\n",
    "- Add New_Car_Price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Extracting Company name and numerical value of Mileage, Engine and Power features.\n",
    "for i in range(df.shape[0]):\n",
    "    df.at[i, 'Company'] = df['Name'][i].split()[0]\n",
    "    df.at[i, 'Mileage(km/kg)'] = df['Mileage'][i].split()[0]\n",
    "    df.at[i, 'Engine(CC)'] = df['Engine'][i].split()[0]\n",
    "    df.at[i, 'Power(bhp)'] = df['Power'][i].split()[0]\n",
    "    \n",
    "    if pd.isnull(df.loc[i,'New_Price']) == False:\n",
    "        df.at[i,'New_Car_Price'] = df['New_Price'][i].split()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Converting string dtype to float dtype\n",
    "df['Mileage(km/kg)'] = df['Mileage(km/kg)'].astype(float)\n",
    "df['Engine(CC)'] = df['Engine(CC)'].astype(float)\n",
    "df['New_Car_Price'] = df['New_Car_Price'].astype(float)\n",
    "\n",
    "# df['Power(bhp)'] = df['Power'][i].astype(float)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An error occurred while converting Power(bhp) to float<br>\n",
    " `(Can't convert str to float : null)`<br>\n",
    " \n",
    "Then, after looking at the dataset again, I discovered some Power features had a 'null bhp' value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Dropping the rows containing Power(bhp) == null\n",
    "df = df[df['Power(bhp)']!='null']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Now converting Power(bhp) string values to float dtype\n",
    "df['Power(bhp)'] = df['Power(bhp)'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of updated training dataset:  (7073, 18)\n"
     ]
    }
   ],
   "source": [
    "df = df.reset_index(drop=True)\n",
    "print('Shape of updated training dataset: ', df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Location</th>\n",
       "      <th>Year</th>\n",
       "      <th>Kilometers_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner_Type</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Engine</th>\n",
       "      <th>Power</th>\n",
       "      <th>Seats</th>\n",
       "      <th>New_Price</th>\n",
       "      <th>Price</th>\n",
       "      <th>Company</th>\n",
       "      <th>Mileage(km/kg)</th>\n",
       "      <th>Engine(CC)</th>\n",
       "      <th>Power(bhp)</th>\n",
       "      <th>New_Car_Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Maruti Wagon R LXI CNG</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>2010</td>\n",
       "      <td>72000</td>\n",
       "      <td>CNG</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>26.6 km/kg</td>\n",
       "      <td>998 CC</td>\n",
       "      <td>58.16 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.75</td>\n",
       "      <td>Maruti</td>\n",
       "      <td>26.60</td>\n",
       "      <td>998.0</td>\n",
       "      <td>58.16</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hyundai Creta 1.6 CRDi SX Option</td>\n",
       "      <td>Pune</td>\n",
       "      <td>2015</td>\n",
       "      <td>41000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>19.67 kmpl</td>\n",
       "      <td>1582 CC</td>\n",
       "      <td>126.2 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.50</td>\n",
       "      <td>Hyundai</td>\n",
       "      <td>19.67</td>\n",
       "      <td>1582.0</td>\n",
       "      <td>126.20</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Honda Jazz V</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2011</td>\n",
       "      <td>46000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>18.2 kmpl</td>\n",
       "      <td>1199 CC</td>\n",
       "      <td>88.7 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.61 Lakh</td>\n",
       "      <td>4.50</td>\n",
       "      <td>Honda</td>\n",
       "      <td>18.20</td>\n",
       "      <td>1199.0</td>\n",
       "      <td>88.70</td>\n",
       "      <td>8.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Maruti Ertiga VDI</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2012</td>\n",
       "      <td>87000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>20.77 kmpl</td>\n",
       "      <td>1248 CC</td>\n",
       "      <td>88.76 bhp</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.00</td>\n",
       "      <td>Maruti</td>\n",
       "      <td>20.77</td>\n",
       "      <td>1248.0</td>\n",
       "      <td>88.76</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Audi A4 New 2.0 TDI Multitronic</td>\n",
       "      <td>Coimbatore</td>\n",
       "      <td>2013</td>\n",
       "      <td>40670</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Second</td>\n",
       "      <td>15.2 kmpl</td>\n",
       "      <td>1968 CC</td>\n",
       "      <td>140.8 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.74</td>\n",
       "      <td>Audi</td>\n",
       "      <td>15.20</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>140.80</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Name    Location  Year  Kilometers_Driven  \\\n",
       "0            Maruti Wagon R LXI CNG      Mumbai  2010              72000   \n",
       "1  Hyundai Creta 1.6 CRDi SX Option        Pune  2015              41000   \n",
       "2                      Honda Jazz V     Chennai  2011              46000   \n",
       "3                 Maruti Ertiga VDI     Chennai  2012              87000   \n",
       "4   Audi A4 New 2.0 TDI Multitronic  Coimbatore  2013              40670   \n",
       "\n",
       "  Fuel_Type Transmission Owner_Type     Mileage   Engine      Power  Seats  \\\n",
       "0       CNG       Manual      First  26.6 km/kg   998 CC  58.16 bhp    5.0   \n",
       "1    Diesel       Manual      First  19.67 kmpl  1582 CC  126.2 bhp    5.0   \n",
       "2    Petrol       Manual      First   18.2 kmpl  1199 CC   88.7 bhp    5.0   \n",
       "3    Diesel       Manual      First  20.77 kmpl  1248 CC  88.76 bhp    7.0   \n",
       "4    Diesel    Automatic     Second   15.2 kmpl  1968 CC  140.8 bhp    5.0   \n",
       "\n",
       "   New_Price  Price  Company  Mileage(km/kg)  Engine(CC)  Power(bhp)  \\\n",
       "0        NaN   1.75   Maruti           26.60       998.0       58.16   \n",
       "1        NaN  12.50  Hyundai           19.67      1582.0      126.20   \n",
       "2  8.61 Lakh   4.50    Honda           18.20      1199.0       88.70   \n",
       "3        NaN   6.00   Maruti           20.77      1248.0       88.76   \n",
       "4        NaN  17.74     Audi           15.20      1968.0      140.80   \n",
       "\n",
       "   New_Car_Price  \n",
       "0            NaN  \n",
       "1            NaN  \n",
       "2           8.61  \n",
       "3            NaN  \n",
       "4            NaN  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Location</th>\n",
       "      <th>Year</th>\n",
       "      <th>Kilometers_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner_Type</th>\n",
       "      <th>Seats</th>\n",
       "      <th>Price</th>\n",
       "      <th>Company</th>\n",
       "      <th>Mileage(km/kg)</th>\n",
       "      <th>Engine(CC)</th>\n",
       "      <th>Power(bhp)</th>\n",
       "      <th>New_Car_Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mumbai</td>\n",
       "      <td>2010</td>\n",
       "      <td>72000</td>\n",
       "      <td>CNG</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>Maruti</td>\n",
       "      <td>26.60</td>\n",
       "      <td>998.0</td>\n",
       "      <td>58.16</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Pune</td>\n",
       "      <td>2015</td>\n",
       "      <td>41000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.50</td>\n",
       "      <td>Hyundai</td>\n",
       "      <td>19.67</td>\n",
       "      <td>1582.0</td>\n",
       "      <td>126.20</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chennai</td>\n",
       "      <td>2011</td>\n",
       "      <td>46000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.50</td>\n",
       "      <td>Honda</td>\n",
       "      <td>18.20</td>\n",
       "      <td>1199.0</td>\n",
       "      <td>88.70</td>\n",
       "      <td>8.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Chennai</td>\n",
       "      <td>2012</td>\n",
       "      <td>87000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.00</td>\n",
       "      <td>Maruti</td>\n",
       "      <td>20.77</td>\n",
       "      <td>1248.0</td>\n",
       "      <td>88.76</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Coimbatore</td>\n",
       "      <td>2013</td>\n",
       "      <td>40670</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Second</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.74</td>\n",
       "      <td>Audi</td>\n",
       "      <td>15.20</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>140.80</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Location  Year  Kilometers_Driven Fuel_Type Transmission Owner_Type  \\\n",
       "0      Mumbai  2010              72000       CNG       Manual      First   \n",
       "1        Pune  2015              41000    Diesel       Manual      First   \n",
       "2     Chennai  2011              46000    Petrol       Manual      First   \n",
       "3     Chennai  2012              87000    Diesel       Manual      First   \n",
       "4  Coimbatore  2013              40670    Diesel    Automatic     Second   \n",
       "\n",
       "   Seats  Price  Company  Mileage(km/kg)  Engine(CC)  Power(bhp)  \\\n",
       "0    5.0   1.75   Maruti           26.60       998.0       58.16   \n",
       "1    5.0  12.50  Hyundai           19.67      1582.0      126.20   \n",
       "2    5.0   4.50    Honda           18.20      1199.0       88.70   \n",
       "3    7.0   6.00   Maruti           20.77      1248.0       88.76   \n",
       "4    5.0  17.74     Audi           15.20      1968.0      140.80   \n",
       "\n",
       "   New_Car_Price  \n",
       "0            NaN  \n",
       "1            NaN  \n",
       "2           8.61  \n",
       "3            NaN  \n",
       "4            NaN  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Dropping useless features as they are repeated.\n",
    "df.drop(['Name','Mileage','Engine','Power','New_Price'], axis=1, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color='green'>4. Exploratory Data Analysis & Visualization</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7073 entries, 0 to 7072\n",
      "Data columns (total 13 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Location           7073 non-null   object \n",
      " 1   Year               7073 non-null   int64  \n",
      " 2   Kilometers_Driven  7073 non-null   int64  \n",
      " 3   Fuel_Type          7073 non-null   object \n",
      " 4   Transmission       7073 non-null   object \n",
      " 5   Owner_Type         7073 non-null   object \n",
      " 6   Seats              7073 non-null   float64\n",
      " 7   Price              5872 non-null   float64\n",
      " 8   Company            7073 non-null   object \n",
      " 9   Mileage(km/kg)     7073 non-null   float64\n",
      " 10  Engine(CC)         7073 non-null   float64\n",
      " 11  Power(bhp)         7073 non-null   float64\n",
      " 12  New_Car_Price      1005 non-null   float64\n",
      "dtypes: float64(6), int64(2), object(5)\n",
      "memory usage: 718.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    5872.000000\n",
       "mean        9.603919\n",
       "std        11.249453\n",
       "min         0.440000\n",
       "25%         3.517500\n",
       "50%         5.750000\n",
       "75%        10.000000\n",
       "max       160.000000\n",
       "Name: Price, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Price'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Price**<br>\n",
    "Let's start with a look at our target column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dell\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 160.0)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(15,8))\n",
    "sns.distplot(df['Price'])\n",
    "plt.xlim([0,160])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Fuel Type**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count       7073\n",
       "unique         4\n",
       "top       Diesel\n",
       "freq        3783\n",
       "Name: Fuel_Type, dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Fuel_Type'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Creating a new dataframe by concatinating Price and Fule_Type columns\n",
    "data = pd.concat([df['Price'], df['Fuel_Type']], axis=1)\n",
    "\n",
    "plt.subplots(figsize=(12, 8))\n",
    "fig = sns.boxplot(x='Fuel_Type', y='Price', data=data)\n",
    "fig.axis(ymin=0, ymax=165);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As, we can see the price of diesal cars are high, followed by a petrol cars."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dell\\anaconda3\\lib\\site-packages\\seaborn\\categorical.py:1296: UserWarning: 19.7% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\Users\\dell\\anaconda3\\lib\\site-packages\\seaborn\\categorical.py:1296: UserWarning: 64.8% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\Users\\dell\\anaconda3\\lib\\site-packages\\seaborn\\categorical.py:1296: UserWarning: 72.3% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Fuel_Type', ylabel='Price'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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A3HnnnXz729+mqamJMWPGcNNNN52uYYmIiIiIdJoRBMGHohBZNdUiH15BspnU5lcJEg04Q6dhFQ/o6iGJiMhZ6IyoqRYReT8C36PlyR/h1x4AIL1hIbnzvoVVNrSLRyYiItJGy5SLyBnNO7glE6gB8D3SW17tugGJiIicgEK1iJzRDOcE/X7VA1hERM4wCtUickazyoZilY/PbBuxAiJjL+3CEYmIiHSkFxVF5IwXBD7e/g0E8UbsARMxonldPSQRETkL6UVFEflAMwwTu91stYiIyJlG5R8iIiIiIp2kUC0iIiIi0kkq/xAREXmX1lZtYH31ZspyS5jVbzpRLUsuIq0UqkVERN6FxQff5M9bHslsb63dwT9O/EIXjkhEziQK1SIiIu/C4oNvZW1vPrqNQ02VvLL/DbbW7qC8oC/XD5tH92hhF41QRLqSQrWIiMi7kGvnZG1bhsWCPS+xonINAFXxGuqSDfzz5C91wehEpKvpRUUREZF34cpBlxBpV0N92YAL2Va7M+uYXfUVJNzE6R6aiJwBNFMtIiLyLgwuHMj/nfYtttbuoFdeKX3ze7O7fi8NqcbMMUWxHkStaBeOUkS6imaqRURE3qX8SB6TyybQN783AB8bfi1luaUAFEa68elRN2AYRlcOUUS6iJYpFxER6YQgCKhPNVDg5GOZVlcPR0ROIS1TLiIicooYhqGOHyKi8g8RERERkc7STLWIiMj71JBq5M9bHmbr0bBP9SdHXk+vvLKuHpaIdAHNVIuIiLxPD219nPXVm0n5aXbWV/C7jX/u6iGJSBdRqBYREXmfdtTvzto+0HSIuPpUi5yVFKpFRETep0HdBmRt98otJceOddFoRKQrKVSLiIi8TzcMv5bh3YcA0C+/D58d88kuHpGIdBX1qRYREekkP/AxDc1TiXzYvV2fav0NICIi0kkK1CKivwVERERERDpJoVpEREREpJMUqkVEREREOkmhWkRERESkkxSqRUREREQ6SaFaRERERKST7K4egIjIO/Gq95Ba+Th+ohFnxAVERs7u6iGJiIhkUagWkTNakIrT8szPINkMQLJyB0YkB2fweV08MhERkTYq/xCRM5p3aGsmUB/j7l7VRaMRObG4m+BDskCxiLxPmqkWkTOaUVjaYZ9ZWNYFIxHpqDpew2833Mfexv2U5BTxN6M/waDCAV09LBHpApqpFpEzmtW9D5FJ14Jphdu9hhMZd1kXj0ok9OC2x9nbuB+AqngNf9z0gGasRc5S72qm2vd9fve737F9+3a+853vcN999/GFL3wBy7JO9fhERIhO+QjO2Esg2aJZajmj7Gs8kLVdFa8h4SXJsWNdNCIR6Srvaqb6Zz/7Gdu2bWPdunUAvP766/z4xz8+pQMTEWnPjBUoUMsZZ0SPoVnbAwrKFahFzlLvKlQvXbqUn/zkJ0SjUfLz8/nd737H4sWLT/XYREREzmgfH34d55adQ7dIAaOLRvC5sZ/s6iGJSBd5V+Uftm1jmm35OxKJYNt6x1FERM5uuU4unx1zY1cPQ0TOAO8qGQ8fPpz77rsPz/PYtWsXf/jDHxg5cuSpHpuIiIiIyAfCuyr/uO2229i4cSM1NTV88pOfpKWlhVtvvfVUj01ERERE5APBCN5l7599+/ZRXl5OU1MTe/fuZfTo0ad6bO9JTU0Tvq82RiIiIiJyapimQVFR/om/9m4ucM899/ClL30JgNraWm655Rb+8pe/nLwRioiIiIh8gL2rUP3ggw9y//33A1BeXs7jjz/On/70p/d90yeeeIKrrrqKq666ip/+9KcALFmyhHnz5nHZZZdx1113ve9ri4iIiIicbu8qVHueR35+21R3QUEBhmG8rxvG43HuuOMO7rnnHp544glWrFjBokWLuPXWW7n77rt59tln2bBhA6+++ur7ur6IiIiIyOn2rkL14MGDufPOO9m3bx/79u3jP/7jPxg4cOD7uqHnefi+Tzwex3VdXNclPz+fAQMGUF5ejm3bzJs3jwULFryv64vI2UVLQouIyJngXbXU+/73v8/3vvc9rrvuOmzbZvr06Xzve997XzfMz8/ny1/+MldccQU5OTmce+65HDlyhJKSkswxpaWlVFZWvqfr/rWicRH5YDn6yv3UL38Gw7LoPuOjdJ86D99N0bx5CV5LA/kjp2EXlpA8tJOqp/+T1JG95AweT8m8W7Dze3T18EVE5Cz1rkJ1cXExv/rVr07KDbds2cIjjzzCyy+/TEFBAV//+tepqKjIKicJguA9l5eo+4fIB1+6YhWJxQ8DEABHX/wD8by+JJc9iF+5A4Cjrz5IzjW3klj4K4KGIwDEd63l4FO/IeeSL3XV0EVE5Czwdt0/3jZU33HHHdx2223cfPPNJ/z6f//3f7/nwbzxxhtMmzaNoqIiAObPn89vf/tbLMvKHFNVVUVpael7vraIfLD5R3Z22OduX5YJ1ACkE6TXPZ8J1Md4JzhXRETkdHnbUD1t2jQA5s6de9JuOHLkSH7+85/T0tJCTk4OixYtYsKECTz11FPs2bOHfv368fTTT3P99deftHuKyAeD1WtYh31mz/KOB5o2ZmEv/PrD7c4dfiqHJiIi8rbeNlRffPHFADz++OP88Y9/PCk3nDlzJps2bWL+/Pk4jsO4ceO45ZZbmDFjBrfccgvJZJLZs2dz+eWXn5T7icgHh91/IpFzP0p6wwtgWkTOuQZn1CzS217DP7Kr9aAokbGXwNhLSLz+B/yafdjlY4lO/2TXDl5ERM5q72pFxfnz53PvvfeSm5t7Osb0vqimWuTDK3BTuDvfxI834Aw+D7NbyTufJCIicpK975rqY3JycrjooosYMWJEVrB+PzXVIiLvlWFHcEZc0NXDEBER+aveMVRv27aNOXPmMHPmTHr16nU6xiQiIiIi8oHytqH6kUce4ac//SkDBgxg79693HnnnVxwgWaLRERERETae9tQfc899/DUU09RVlbG6tWrueuuuxSqRURERESO847lH2VlZQCcc8451NbWnvIBicjZzY834O58E+wIzuDzMCI54YuKu1cQxBuxB0/BzA/73Lv7N+BVV2D3GY1VOriLRy5nqz0N+9h6dAflBX0ZVaTWjiJnq7cN1cevath+gRYRkZPNbzpKy2PfI4g3AJBe/zw5191O4tk78Sq3A5Bc+Ri5134bd8cyUmueBiAFxGZ/Xi8zymm39NAK7t38UGZ77oCLuWaIWsKKnI3M93Lwe106XETkvUhvfT0TqAH82oOk1j6bCdThQQlS6xeSWv9C1rmptc+ermGKZCzc83LW9qJ9r5P23S4ajYh0pbedqd66dSuTJk3KbCcSCSZNmkQQBBiGwapVq075AEXkLBL472Ffdl/6d9FyX+SkCzj+z10A+rMoclZ621C9cOHC0zUOERGckbNIb3yJINkEgFHYi8jEK/EObGy3omKEyLhLMWJ5pNctyJwbmXBFVwxZznKX9J/Nn7c8ktme3W8GjuV04YhEpKu8qxUVPwi0oqLIh4PfXIu7cxlYEZxh0zAiuQRuEnfHm/iJBuz+EzF79MEwTNw9a/Cq92D1HY3da1hXD13OUjvrKthau53ygr6MKx7d1cMRkVPo7VZUVKgWkQ+EINVCfNGv8fauw8gtJDrjMziDJnf1sERE5CzydqH6Pb2oKCLSVZIrn8DbuxYICFrqSLz8G4JUvKuHJSIiAryLPtUiIl0hSDSR2vIqQaIRZ+g0/OqK7APcJH7dIfWnFhGRM4JCtYiccQLfpeXJH+HXHQQgvWEhzvCZeIe2Zo4xYgWYPft11RBFRESyqPxDRM443sEtmUANgO8R+B7O2EsxcgoxS4eQM/fLGHak6wYpIiLSjmaqReTMY0c77DKcGHa/cZg9+mIPmIiZ2/1dXy7wffBdhXA56ZrTLTy07fHMMuU3DL+Oktyirh6WiHQBdf8QkdPGjzfg1x7AKhmE4cTe9tiW5/4Nb996oLXUo2RgZhsnRu68b2EVD3jHe6a3vk5y2YMEqRbsIecRm/U5hWs5aX6/8c+sqFyT2e5f0Jd/PffLXTcgETml3q77h2aqReS0SG9bTOK134PvQiSXnMu/it1rWBi0K3diFpVjFhQDEHhpnDGXYJePx4jkYnQrI/7kD9tdLEFq3QJyLv77rHt4dQdJr11AkGrBGXUhZvfe4T1bV2V0dywj1bM/0YlXnrbnlg+3rbU7srb3Nh4g7ibIsd/+h0YR+fBRqBaRUy7wXZJL7w8DNUCqheSbD8Kka4m/8P/AS4NhEL3gs9j9xtLy5I8ImmoAsIfNINKjb8eLeunse6RaiD/xo8xqjG7FSiLnfrTDMud+9e6T/4By1upf0I+NNVsy26U5xcSsjuVLIvLhpxcVReTUc9MEyeasXUFzLcm3Hm4Lx0FA8s2HSK5bkAnUAO72xWAYWGXtVkw0TIwefWn68z/T+NsvknjtD6T3rMkE6mPX8+sOgZVd6mH1GXXSH0/OXjcMv44B3cqBMFDfNPoTGIbRxaMSka6gmWoROeWMSA5W/wl4e9dk9jlDp5HeviT7wFScoKW+w/l+Uw3W4CkQzcHM7YE16FwSz/97ZuY7veUVHLPjHIHZrZScy24h+dZfCOINOMNn4oy68CQ+mZztinN68i9TbiHhJolaEQVqkbOYQrWInBY5F/89qbXP4lXvwe43BmfMpWCapFY9mTnGHnwezogLiO9aDoQvHhv5xSRXPkFQswcAz4pg9ujXVkrSKog3YA+dhrtjKQBmz3Iioy/GiOVjl487PQ8pZ63YCTrWiMjZRd0/RKTLBIFPestreAc2YZUMxBlzCYYdwT2wifS2xRg5BVglg0m8dHfWefbQabi7l4PXFqyj599IZPxc3OoKSCexeg3DMFThJiIiJ4+6f4jIGckwTCKjLoR2JRmB7+M3VmHYDlbxQIzcwo7nRXKIzfkHksseIojX4wybgT36orC2etvrYEeJTvkIkbGXnr6HERGRs5pCtYicUZJL7iW9aREA6c2v4Ey+Dqv3iLYlyp0cnDFzsHr0xRk4OXNeasurpLe80rrRQnLJfVh9RmP1PEHnEBERkZNMoVpEzhiB75Le8lrWPnfzK+R+/GekVj1G0FSLc87VWK0t9rwju/CqK7D6jMSv6tgqz6+uUKgWEZHTQqFaRM4chglOFJJttdJGJIfEwv+Ht38DAO7+9eRd+x3Su5eTWv5I60EGzphLOlzL6j38dI1cRETOcnqLR0TOGIZhEp3ykXY7TOyh0zKBGoBkM8n1z5Na/XTbviDAO7CZyLnXY+T1xOzRh9icf8AsKDl9gxcRkbOaZqpF5LQIggC/cgdYNlbJoL96XGTMJVh9RuFXVWD1Go7fWNXxIDfVsaWelyJ6zjyi58w72UMXERF5RwrVInLKBekELc/8DP/ILgCs8vHkzP0yhmmd8HirR99M3bSRX4TZsx/+0f3hF02byJg5GE4080IjhGFcRESkqyhUi8gpl972RiZQA3j71uHuXYMzcDJ+wxGMnEIM58SLZximSe68b5He+jpBohF76PlYPcsxSwZilQ3Fq6rA7jcGu/8EIJwRDxqOYOT1wLDblij3m45i2BGM2In7i4qIiHSGQrWInHInXHq89iDNKx/Hr9kHTozYjE/jDJ9Jetdy0htfBMshMvEq7D6jwHIwonkEvo9hx8LzqypIrXkGv/YAfu1+zOIB4KaIL/h3/LqDEMklNutvsftPIP7i3eES6YZFZPxcolNvOM2fgIiIfNhpRUUROeW8o/tpefR7bXXQTgyrfBzeruVtB9kRYpf+I4nn7uLYEuVYNrkf+xGJRb/GP7Kz9bgoOdfcRmLhLwna1Vvbg88D38OtWNl2zWgekXPmkVr2QNZ4cq/7Llbp4JP/oCIi8qGmFRVFpEtZPfuRO++bpDa+hGHZOOMuI/HaH7IPclO4O98iE6gBPJf0xpfaAjWAmyS9fkFWoAbwjuzsWEKSbMav2dthPH7dIYVqOWm21e5ka+0Oygv6MqF4DIZhdPWQRKQLKFSLyGlhlQ0lp2xoZtseMJFUu7BsdCvFLBsK297IOs/I63GCizmY3Xvj1x1q29V7BGZud1K1BzP7zKJynKHn425fknWu1Xf0SXgiEXj9wFIe2PpYZvvi8gu4fpg60IicjRSqRaRLRCZcBYGPu3slZrdSoud9FCO/CH/fOtyKVeGCLqMuIjLuMtzdK8J2fABOjMjYS2DspSRf/yPe0X3Y/cYSm/ZJcGJgmLj71mJ270v0vI9iFhQTu/ALpDYtwnByiEy6BvNEQV3kfVi09/Ws7dcOLOWawZfjWE4XjUhEuopqqkXkjBK4SdLb3sCwotjDzscwbQI3hbtrOX68HmfweZgFxV09TBEAfrDsTipbjmS2HdPh57O+j2Nqzkrkw0g11SLygRAkmmh+/AcEDWFIMTe9RO41t+LuXkHyrb8QJJoI6g8TnXkThkKLnAHmDriIezY/RND6LsAl/WcpUIucpfR/voicMdJbX8sEagC/ajfpra+TXHwfBF54zJbXMLv3JTJ+blcNUyRjau/J9MnvxdbaHfQv6MvwHkPf+SQR+VBSqBaRM0aQTnbY59UeyATqzL723UBEulh5QV/KC/p29TBEpIuZXT0AEZFjnGHTw5cNWxk5hURGz4Hjfp1u9R5xwvODwMc9vB2vek/2vv0bSO9YRpCKn5qBi4jIWU8vKorIGcWvO0R66+tg2TgjL8TM70m6YhWpNx/CTzTiDJ9JdOrH8esP4+5ZhVlQgj1oCrhJWp76KX5NGKjtQVOIzvkSiefvwtu3HghDeu5139GLjiIi8r7oRUUR+cAI0kncg5vx6w7h11cSu+BvcAZOwhk4KXOMe2AT8ef+DfywLMQeOBmrbEgmUAO4u1dgbnghE6gBgng9qY0vEjv/E6fvgURE5KzQJeUfixYtYv78+VxxxRX88Ic/BGDJkiXMmzePyy67jLvuuqsrhiUiXSzwfeILf4lftRvSCdydb5J88y8djkutfz4TqAHcipV47RZ9yVyvubbjTVKJkzpmERER6IJQvW/fPm6//XbuvvtunnzySTZt2sSrr77Krbfeyt13382zzz7Lhg0bePXVV0/30ETkFAuSzbgHtxCkWrL3pxMEvk/QVE3QVJP1Ne/wVtK73qLp/q/T+Lu/J/HGPVkrmR9j958IRru/0qJ5RMZfgVFY1rbPtHBGzjqJTyQiIhI67eUfCxcu5Morr6RXr14A3HXXXezZs4cBAwZQXl4OwLx581iwYAGzZ88+3cMTkVPE3bOG+Et3g5sCJ0bOJV/CKh1CfNGv8fatw8jpRmTapzByuxO01GXOM3v0I7Ho15mZ6fSml3DGzMEzrazyD2fwFIzYN0hveDFcdXHiVZh53cm95jbSm18hSDbjDJuOVTygKx5fREQ+5E57qN6zZw+O43DzzTdz6NAhLrzwQoYNG0ZJSUnmmNLSUiorK0/30ETkFEosuS8M1ADpBMml92OVj8fbtw6AIN5A8rXfknPJ/yG57AH8usNY/cdj95+Iu+utrGsF8UZyr/+/pLcvwezeC2foNALfJb3ltXCJc9PCzC3EmnoDZk43opOuOd2PKx9iLekWNh/dTnFOTwZ0K+/q4YjIGeK0h2rP81ixYgX33HMPubm5/MM//AOxWAzDMDLHBEGQtf1u/LU3MUXkzNDYkl3fHDTXYjXsJ91+p5uie1EhiYkXkao+QN6Ic4n1HcHe134Hvps5rGDAcBLL7yddsR4jEqMgZmDYEZp2LA0P8F1Sa5+leMJ0YuWjTv3DyVmjonY/33/9FzSnw/aMc4fO5vOTwxdfa1pqybFj5EZyunKIItJFTnuoLi4uZtq0afTs2ROASy65hAULFmBZVuaYqqoqSktL39N11VJP5MxmD5mKu21x1rafXwR7Nmb2GbECKhfdj39oKwDNG14lesFniV38dyTffIgg3oAz/AKajh4lXRF29QhSCaqf/x32iAs63LNm13YisX6n+MnkbPLghqczgRrghR2vMaXnZB7e/iRba3fgmDZXDryUywZe1IWjFJFT5e1a6p32FxUvuugi3njjDRoaGvA8j9dff53LL7+c3bt3s2fPHjzP4+mnn2bWLL1MJPJhEpv5N0QmX4fVbyyRKfOJzvg0kYlX4oybi5HXA6tsGNELPpsJ1Mekt76GVTQAu+9o7L5jsPqNxj++00fgYbZ/IRHAtLD7jTnFTyVnmxY3ewGhgIDXDixha+0OANK+y5O7FnCkpaorhiciXei0z1RPmDCBL3zhC3zyk58knU4zY8YMbrzxRgYPHswtt9xCMplk9uzZXH755ad7aCJyChl2hOjk6zrsj027EabdCECQaIJ2LyACGJFcWp76ceblRXfPapzRF5O1cHk0j8joizFj+aQ2vAi2Q/SceZjd3ttvvETeyYw+U9l8dFtme1C3/rSkOwbtypYqSnNLjj9dRD7EtKKiiLwvfrwBI5aPYZzcX3glVzxGatUT4YaTQ3TydSSX3Z91jDXkfKye/XB3LMHI7UH03OuxSgeftDEEiSaI5GCY1jsfLGedzUe3sfrIOnrGejK73zQ2VG/hD5va/ozm2DH+7/RvkWOrtlrkw+btyj8UqkXkPfHqDpJYeDd+7X6MgmJyLvp7rF7DCBJNuPvWYeT1xO4zsnP3OHoAv/4wdp+R+E1HaXnkO1lfj0y8iuh5H+vUPU7Eb6kjsfA/8Sq3Y+R0I3rBZ7NWchQ53q76PaypWk9tvI4j8Wq6RQq4avClDOzWv6uHJiKngJYpF5GTJrn4Xvza/QAEjdXEX/1fci79R1qe/BGkwl+D28NmkHPRF9/X9YNEE+lNi/Br9uLX7CVyzjycMXNIb3wJALNHH5xxc0/Owxwn+dbDeJXbw3HEG0i88r/Yn74Lw46ekvvJB9v66k38et0fCVpXIxrZYxj/Z+Lnu3hUItJVFKpF5D3xa/ZlbQf1lSRXPZUJ1ADu9sX458wj8NJ4BzZg9uyfeWnQb6nD3bUCI5aHPWgKhuVkXS/+0n/hHQg7gniV2wnSCWIzPkNk7GUEiUbM0sEYholfd4jkWw/jN1ZhD5pC5Jyr37YUJUg2k961HMMwsQefixHJIfBd3IpVBC312AMndXg2Ui34TTVY3ft05iOTD6lX9y/JBGqALbXbOdx8hF55quUXORspVIvIe2L1G4O7Y1nbdq/hGF66w3Hp3StILX+EY2uKRyZciTNiFs2P/wBalyk3y14i95pbM2E4SLVkAvUx7q7l+OPmktq0iCDRiJNOYvUZScuzd2aWNE/V7MWwHCITriAIAtLrniO9403M/J5Ezr0eI6cbLY9+j6D5KADG2mfJm/894gt/hbd/AxDOUtuDz8Wv2ZO5t1FQglnY6+R8cPKhE7EiJ9y/o243JTnFFEYLTvOIRKQrKVSLyHsSm/EZEqaNd3AzVvFAotM/iX/0AO6eNRwL0GbJINI738xsA6Q2vICfTmQCNYBfuQPv4BbsvqPDHXYUI6cbQbwhc4xRUEzLk3dkArS7fSnRmTdlto9x96wmMuEK0pteIvnmQ+H1a/bgVe3GGXtpJlADBPWHSa1bkAnU4QWSBL6PM/ZS3D2rMQt7ET3/xpP+IqZ8eFzafzaba7aS8sMfKscXj+bfV/83jakmLMPiY8Ov4YK+07p4lCJyuihUi8h7YkTzyLnwC1n7zPwicuZ9E3fXWxh5RURGX0jLEz8i69XhIMhqlXeM31RD/MX/xK+vxB44iej0T5F47feQTmDkFOIMPpfkkvvaXwj34GawHGg3Q2527w3QGu7bHd1Sh99Y3eG+ged22GcQEJv+KZj+qXf8HOTstL12FwsqXiLhJZnZ93xun/YvbKzeQlFOT17a+xqNqSYAvMDj0R3PcF6vyUT/yoy2iHy4KFSLyElh9x6B3XtEZjsyfi6JV3+b2XZGzsYZfXFYOuImATB79iO5+mloqATCMo7IlI+Q/6m78BsqMXv0y7wU2Z6Z3xN7xqdJLr0f0gnMov6YJYNw96zBLCzLnoE2LZxRF+LtXkGQaATAyC8iMuEK/ENbMy8mYtpExsw52R+LfIjUJxv4z7W/Jd06M13RsJcvTfgcM/pOBeDRHU9nHZ/yUrSkWxSqRc4SCtUicko4Iy7AKCzD27ces6g/9qDJGIZJ3kf/L+kdyzCieZglg4k//v2s89w9a4hOuhZMhyDVglU8EHvYDNzt4RLnRkExkXFzMfN64AyZil9fSfyl/yL5+h/Cr7cGbL9qN9gRolNvwC4eQO71PyC9fSmGaWEPn44ZzSPnqm+Q3rGUoLkOe/C5WD30QqL8dVuObs8E6mPWV29mZI9hHGquZELxGA40Hcp8bXDhQHrEup/mUYpIV1GfahHpMkEqTtO9X8nMXANYg6YQNBzBr9kLpkVk8nVEz5mHV72HIN5AYJi4mxYB4Iyfi39kd4fFYWIX34xVOiRcnCaiBTjk5NjTsI+frfhl1r455bNYUbma+lQjUTPKlLIJHE3W0TuvjLkDLiY/ktdFoxWRU+Ht+lTrDRwR6TJGJIfYBX8DTgwIy0GMSG4YqAF8j9SKR/Ebq7GKB2DkF5FY8AvcipW4FSuJP/Mz/KaqDtcN0gnMbiUK1HJSDehWzqX9L8RsfXl1bNEodtXvoT4VlhUl/SQbarbwpQmf4/ph8xSoRc4yKv8QkdPCq95DcvnDBE212EOnEpl4FYZh4gybjj1wMkG8HrNbKfHn/yP7xCDAb6zCLCjGrViZ/bKj52JYkTCUpxMAGDndsAdNPo1PJmeT64ZeySUDZpP20vSIdefWN36Y9fX6VAMpL01MCwaJnHUUqkXklAvcFPFn78y8KJhavh/DySEy9hL8xiqCeANm8SAA7EFTcPeszpxr5HYn8DzSFaswcrt3uLZZPJC8j9xOasurGKaNM/oizNh77w8c+D5+1S6MvB6Y+UXv70HlrJDv5EHrmkUTS8fy6v4lma+N6jlcgVrkLKVQLSKnnFe1OxOoj3H3rSVoPkpq7XNAgFnYi5yr/xVn+AwCN4W7YylGXg/8ljoSz90ZnpTXE6vPKLyDmwGwB5yDPWgShmkTO/8T73t8flMNLU//jKChEgyDyMSriZ57/fu+npw9PjL0anLsHLYe3UF5QR+uGnxZVw9JRLqIXlQUkZPKO7qP5OJ78WsPYvWfQGz6pwjcFM1//lpW6YY94gLcra9nneuMvTTsE93KPbiF+NM/yTomMvFq7GHTACPTrSNItUAQYETbaljTu1eSWvEoQSqOM+pCopOuCY91UySX3o9bsRKjWymxaZ8kve0N0q0vP4YM8m78GWZByUn6VERE5MPg7V5U1Ey1iJw0QeATf+FX4Ywv4G57g6QdITbzJqLTP03yzQchncDqPQJ7wDkdQnXQdJT0ruW4FSsx/8oS4UGqBatH39b7BSSX/pn0xkVAgDNiFtELbiJoOkrixbshCEN8asWjmN1KcYaeT2rVk6Q3vxyeH28g/vx/YBYPOP4u+M21CtXSged7HGg6RM9Yj7d9EXF77U4ONVcysucwSnP150jkbKBQLSInTdB0NBOoj/EObiYIfKzSweRe/wMMy8HM60HguRh5PbOWDzdyC0m8+J9t2yUDMfKL2pYkNyzM0iG4Bzdj9RqOt38j6Q0LM8ent7yC1W9MuNJikL16o3dwM87Q88PVGNuPOV4fXmvfurb7FpRglQ7p9OchHy6VLVX855r/pSZRi21YXD9sHrP6Te9w3OM7nmXh3lcAMA2Tvxt3E+OKR5/m0YrI6aZQLSInjZHXHSOnkCBe37avex9aHv42fu1BMEwik68lOulaDMsmd943Sa15Gr+5DmfYdNKbX8m6XlBVQezqf8Xfv5Eg2YxfX0nylf8BwOzeB2vwuR3G4NcewB58Xof9ZvFAAKzigfhHdrZ9IZJDZOylmPk9Se9YhpnXk8g5V2GYVuc/EPlQeXrX89QkagFwW5chn1J2Dk3pZrbX7aS8oC8lOcW8vK/tNzB+4PN8xSKFapGzgEK1iJw0hmkTm3MziVd/R9BYhdVnFEYkB6/2YHhA4JNa+TjO8Jng+7j7N2APOR+7bxg43IqVx18Qq3tvnD6jcA9tJf7UjzNf8usOYsYbwDAg82qIgV0+DiOSgzNuLumtr4ObxB44mfSOpSSX3IdZNgSr90i8Q1sx8noQm3kThhPFGTYdZ1jHWUeRY44F6mPSfpo3D6/k0R1P4wc+AFcOvASv9b/bjnNP2xhFpOsoVIvISWX3GUXeJ34GXgrDjtKy4K7sA4KA9O4VpN78C7SGDWf0HGIzP0PknGvwDmwmSDaF1xpyPsm3/oIRycMsLOtwL8MwiF34RVLrFgAGkfFz8Q5tJfnWwxD4kNeT3GtuJfn6H/ErtwPgH9qK1XcM+Z/7NVgOhmGc0s9DPjwmlY5nT8O+zHbf/N4sOfhWJlADvLTvNaaUTWR5ZVtbyAvLZ57WcYpI11CoFpGTzjAMaO3V6ww+D2/v2rav5Rfh7lmTCdQA6c0vE5l8LVZROXmfvBPv4Bb8VAvJV/43DMcAud0xcroRxBuO3QQMi8TrfwQ3idV/Alav4TQ/+K9t5zQfJbn6abzWQH2Md3gbhh05Zc8vH05zymdhGRZrqzZQmlvClYMu4a6V/5V1jOt7fHzERxjVczgHmw8zpmgEw3sM7aIRi8jppFAtIgS+B4aZNWsbBD4EYJhmp67tDJ8Bvkd65zKMvB5Ez7mGxCv/e/wA8GoPkFr5OPguzuiL8fesaQvHAC11ODNvIjh6gCDVgl0+jsTL/wOEpR/e3rUkC0qyV1wEaKnFLBuKX7kjs8sqO3UhJ/BcDKvtr1b3wCbcXcsxCoqJjL5YS6d/gBmGwUXlM7mo3czzrH7TeXTH05ntab2nkGPHmNpbq3qKnG0UqkXOYoGbIvHa73F3voWRU0D0/E/gDD2f5JpnSK1+GgKPyJhLiJz3MQzDIPB98N33PMvrjJyFM3JW2/bYS/Eqd3AsEFv9J5B44ZeQagEgvX0p9vAZHa5j5vUMSzbyiwjSycz5meeJN2D26ItfeyCzzx4yFbvPaOKv/i/+kZ1YvYYTm/W59zT+4/kNR0iueBS/sRpn0Lk44y4jaKgkvug3+FW7MIsHELvo7/DrD4fP1crds5q8a7/dqXvLmWVO/1mU5BSxtXYH/Qr6MrXXpK4ekoh0EYVqkbNYat0C3B1LAQha6sIZZDtK6q2/tB2z9lnM0iGQaiG57EGCVDP2oHOJXfiF8MXDtc/iVe/B7jMKZ+xl72pm2xlyHkZed7w9azALe+G7yawSEbw0hh3F6FZK0HAEAKvvGBKLfg3peLg9cDI4MUgn2q7bfwLWtBtJrX4Kv+EI9qApREZdCPC+w6xfd5jUumcJEs04I2dh9RtHyzM/J2isAiBZuQNME3f3CvyqXeE51XtIvPK/GMctl+5X7sCr2YdVVP6+xiJnpvElYxhfMqarhyEiXUyhWuQs5lftPm6Hi7dvbYfjvIObSW96OdP72d31Fqmi/vhVuzMdO7y9a/Fb6omd/3G8owfwjuzAKhuaWajFb6nH27cOI78Iq88o7F7DsXsNByC9a3mHe5rdSsj72I/wDm7GiOaR2vhiJlADeBUriV74RdytrxHEG3FGzMQeNh3v4GaskkFEJl+HmdMtPPbofrwjO088noJirN4jMQyD5Konw77XpkVk0jU4Q6fR8tSPMnXcbsUqojM/kwnUx7i7V+Idyf4s/aoK7A4t/wyMSOyvfj/kzHeouZJd9RUM6jaAPvkdFycSkbOXQrXIWczqPQJ3T1uXAuwI9pDzOvSLNmIFHRdTqdqFV7E6a5+7YympwjKSr//h2JlEZ30Wq3ggLU/9ODOrbA+bQc5FX8Q7eoD01tfAtLLqns2i/tgDJ2NYNnb5uPBSa5Idx9+9N5F538psxxf9N+6OZeGGk0PuNbfiHdmZNZ7Y7M9hFpXT8tRP2sYzfAbO4HNJrXg0c63kG38iSCfbXowEIMA7tA1MO+tFS7OwFCwbb/+Gdp/tcCITr8Ldvx5S4Q8DzqjZWqXxA2zpweXct+VhgtayoxtHzGdm3/MBaEo3k2vnYBqdewdBRD64FKpFzmLO2Evxm47i7liKkVtIdOrHsfuMJjrrb8Oaat8lMu4ynOEXkFr7DLipzLl2n9H4h7cTJBoz+4y8HqRWPNbuDgGplY9j9RqeVabhbl+MO/R84gt/2XbNWDdyLv8q7r51pLe+QfOfvxaWmVz0RQzLwRl9Ee6eVZme1GbxgLA139pnsQdNxiwe0BaoAdLxsDTlwMas8SRXPBa+qNh+PNsWY1gd68SD5toO+8zCMqLnfyJcct1LY/boS2TSdWAYJF7/A96hbVhlQ4hd8FnMgmLyP/Fz3P0bMAuKT+kLknLqPb37hUygPrY9vMdQfrvhXvY3HaQo1oO/GX0jQ7oP7LpBikiXUagWOYsZpkVs+idh+iez9kdGziYycnbWvpzLvkzyrb8QxOtxhs3AGXMxRjSXxKu/C2dtnRyiUz9O/Pl/zzovSCdbXyrMlt61PCukk2jAq9lHeuMijr2A6O56i3TpEOyhU/EObcXqfw5YNnbpYFLbl5Fe+2x43O4VOBOv7viAboqg/T2AwE0SuB3HY3Tv3WGfM3QqpOPhIjKA2aMfzthLMGMFOMOm4bfUYXbvk+maknv5VzteN5aPM/T8jmOTD5yUl+qw/dC2x9nfFC5uVJOo5U+bHuB70/5V/c9FzkIK1SLyrtj9xmD3y34Zyxk2HavfWPyj+7FKBmFEcoiMmUNqzTOZYyKjL8bqNZz4vrWZWWarbBhmYcd61CDZzPEdPbzqClKbXsq8sIhhYg+YSFBTkXWcf2grZumQtiXIDQNn9EWYhWWkWsN3ZjxlQ4nvXZe5l9VrONFxl0GymdT6FzBaa6qt0iFYpUNwJlwBiWbMsiEYrb/eN6J5WNG89/oxygfYrH7TWVDxUtt23+m8eTh7FdDqxFESXpIcW7XzImcbhWqRs5jfUodhRzAiue/7GmZON8zWZcYBIud+FLNnOV7lDqxew7AHn4dhGORecxvpnW9hFhThjJwNnkt6y6sEDZXhdcqG4oy9lPTGheC11SsbuT3aAjVA4OPuXQOmldWT2sgtJGf250lveQ2/+SjO4HPDFxP7jsEs6p8ZjzNkKgC519xKetdyzILiTLu/6JSPEJ3ykQ7PaHXv874/H/nwuHrQZfTJK2Nn/R4Gd+vP5LKJ1KcaeOvwqswxA7v1V6AWOUsZQRAE73zYma+mpgnf/1A8isgpF7gpEi/9V/iSomUTmXg10cnXESSaSG9fTOCmcIZOwywozhwfJBox84ve8738ljpIJ048M+2mcPetx7AdrL5jMUwTd/8GkssfJUg24Yychd1nNC2P/yDrPGfc3LAjyIrHgAAjVkDO1f+C1fPEreoC3yVINGHmdg+3Uy0kVz6BX12B1WcUkYlXYVjOe3+2eAPuntWYOd2wyidgmGbYgq/1BwNn1EVYJQPf83Xlg6MlHefh7U+ytXYH5QV9+diwaynK6dHVwxKRU8Q0DYqK8k/4NYVqkbNQat3zJJfdn7Uv59pvk3j5N22zwpFc8uZ/D+/ILhJv/AlSLZhFA8iZ+2XM/J7v6j6JZQ+SXv88BD5W7xHkzP3KO64oGPg+qVVPkN75JmZ+EdGpHyO15hnc1rZ7Rl4Pcq+5DbOgGL/hCH7DEaxewzBal0U/nrt3LYlXfxsuDFM8gJxLbyGx+J6svtjOmDnEZnzmXT3TMV7dQVoe/2FmwRqrfByxC79I80PfgmRzeJBlkzv/+5k2fiIi8sH2dqFa5R8iZ7AgFSe9Yxmk49hDpmLmFxEEPm7FKvzaA9jl47FKBgHgHdkZdpno2Q97wCQMw8BvrCa98y2MaC7O0PMxnPDX0n7dgQ73crcvzS6zSLWQ2vwK6U2LMp0y/Jo9JJc/Qmz23+LuWo7fUIU9cBJWz34drudV7yG97rm27UNbSW16icjIC0nvWAqBjz10WqaX9DHpDc+TWvVEeE79YeLP7SPvk/+GP/Yy/EQDdr+xmQBtdivF7Fba9nkFAe6eVfhH92P3G4tZNIDEK/+b6VDiV+8hsfR+vL3rsp9913JoDdV/bdVIv6Ued+cyMCycoeeTXr8wE6gBvH3rwx8gjgVqAM/F3bEM69zr2+51cHOmQ4jdb2zr9+MQ6d0rMfN6YA85D8Nywt8avM3nJCIiZxaFapEzVOClaXniDvza/QAkVz9N3kduJ7X2WdJbXgUgteJxYpf8A3guiZd/kznXGT0HZ+wcWh77QSYQpzctIvcjt2OYFlb/CaS3vNZ2MyuCWdS/4yDcZFbrOQC/7iCJF+/GrQjrSFMrnyDnyn/G7NmP5NL7w9rl3sOxe43ocDm/9iDNj95O0FQTnrv2OXKv/0FWYHT3rc/+HOIN+NV7MArLsGIFWTPSXvWecOXEvqMxonkkl9xLeuNLmc8mOu3GrJZ/4RgOYOQWErTUZfYZrb2j0zuWkVz6Z4JEI/bAycQu/CKGE8VvqaPlkdsJ4vXhtdcvOHF7vBO05TPaPVtq3QKSyx7IbEemzMfuO5qWp3+SqSO3ti8m55L/846fk4iInFkUqkXOUO6+dZlADYQzxxsWhoulZASk1i4AP511bnrLKwSBnxWI/Zq9ePs3YvcfjzNwMsHMvyG9+RWMaC6RSddilQ0hvfFF/KP7ADByCnEmXIm7b0PmZUIAq3RwJriGQ/BIrX8eggBvXzgD7DZWEcSbwMnJWgURJycTFCFcGt3dvpTI+LmZfWaPfngHNrWdY9mkdy8nvf5FCDzMsqHkXv7VcPXD9c+Hx0Ryybn8K6Q3vZL92Wx9DbNHH/zag5m9dvl4rD4jSLz8P5BOYMQKiE27ET/eEC7T3rqoi7t7BakefYlO+Qju9iWZQA0QNFZjDJmatQiMWdQfZ+IVeIe24B3cHO7rWY4zfGbbt3Bt28z9sW2/9kDWi5negU0k1z73jp+TiIicWRSqRc5QBidYmc0wAIOstnOGASc8tuM+v+UoLQvuwq8/jD3gHHKv+w54aZLLHiTx2u8we5YTHXEBWA72gHPw6w4Snf4p3C2vhucMnIQ9bHp2qG4drbc/u6TCO7iJ3GtuJfnWwwTpBJExc8IXE487M3ATxBf9OlxGvNcwoudcg390XxhMI7lEJlxBavkjbc9QuYPkyifCLiHHpFpIrVtwgo/GIufSW0guewCv9gB2/wlEz/sohh3BvL6c5OJ78OoOk9q0CHvgpKxVEiFs59f2GWezupXhzL8dd8ebECsgSLcQf+R7ECsgOuMmzO69CFJxWp75OfhpImMv63idzPfz+N0n6HGstsciImc0hWqRM5RVPg6zqBy/Jpw5JppHZOyl4HthnTOAYRCZcEVY/rHo1xxLlM7oOURGX4y7Y0lmiWyzeCDJ1c9AYxUA6XULMCwHv7kOd1u4uInXcATiDcTm3EzLkz8kaKwOrzdmDnmX3ZIZmz1oCu7uFeGGaREZP5dkU01mlhvA7NGX5IpHwxUNDQOvcgDR8z5Kas0zBK1jMPJ64h7cin8wnJl2G45AKkHu1f+KH2/AiOTg7W+/ImLIrz+c6XmdkU7ijJ5DesMLrTvCz8YsLCM64zMklz+Md2gbqdVPEZl0Laml9+O1lpq4jVUEiSZwYlmz+3afUeH3Ysj5GOuez5SMGN1KMcuGYOYUhs+05VVS7V78TNbsJffqfyH+7J0Q+AAkXv0tzpg5WT+QRCZeid1nNG7FSvDC3zZY/cYSmXAF6R3Lsj4ne9j0Dp+DiIicOdT9Q+QMFqSTuLveIkjFsYech5nbnSAI8PauDWdey8dhtdZCe9UVuPs3YvXsh1U+PnxRsbkWd9dbGNE8zJ79aXn0u1nXN4sHEDTXZZU2ANgjZ+O21m0fk3vDjzDzizP1zd6hrQSNVdgDzsHs3hvvyC7iL90dlkZ0K8UedC7ptc9kXSNn3rcwu/clte45jEiMyKiLaPrTLWRNL1sRCj7fVh8epOI03f/1rBcAY5f8H1LrFrQt9ALELr4Ze8hUvH3r8I7swiwoxh4yFcOO0Pzo9/CPzToDkQlXhiUr7fpcY1rkXPl1km8+RNBciz30fJyxl5F89bd4BzZidCvDHjARs6CY9J7V+Ac2gWERmXAFfv3hth8yWjmjLiK9+eWsfZEJV2INmIh3eBtW6ZBMaPcbjuDuXomR1wN78BQM0yZINpPe+RYEPs6QqRixE79tLiIip49a6okIgZuk6d6vZGauAexhMwha6sLZ5FZGQTFm0QC8iuyV4qIzPk1yxWNhuLUixC78As6Q87LvEfgELfUYud1JLnugrea5VWTqDaQ3ty74YphEJl8bdhE52lY7bpYMIjLmErzWHtLOwEl41XtIrXqSINGI1XsE/tH9+MnmsO+0HcUZPAW7/wQA0tsWk3j99+C5GDndiM7+AokFv8gah9m9N1gR/Jo9bfuKBpB3/fezjosv+jXujqVtn01eT5yxl5J688Gs45xRF5Le/ErbDsMgdvHNJF76r6zjYhf9HY5mnD+UPN/joW2P8+bhleQ7+cwfdjWTSsd39bBE5CR7u1B9gkJMEfkwMuwosdmfx4gVAGCWDCZ63keJzvgUZuuKgUZeD2KzP0+k3ct1EHbHSG1f2jZb7KVILrkXP9lC4tXf0nTPP9Hy9E/xaw9h5vXAMIywRrl9IbAVwa/e1/bSY+CTWvkEkSnXY7QuMmN0K8UsKCbxyv+Q3rCQxAv/j9TaZ7GKB5Bz2S3ELv1HUhsW4u5ZjX94G+6ut3CGTs0E6sBLk1j658yLf0G8gdS6Z+G45cTNwl7EZv1tpuuHUVBCbNbfklz1JE1//meaH/kO7p41eEd2ZZ0XNB/NCuKZ65UMxipvDVBOjOi0T+EMmUpkwpXhy4yGgT18JvaQ89/Dd0w+SF7Zv5g3Dr5J2nepTdbxx433U59sfOcTReRDQzXVImcRZ9AU7P4TCZJtqwtCD/Ju+BF+cy1GTjcM0wIgZ+6XSW9fipFbSGT8FbQ88X+zrhXEG0m++SDu1tZ67HgD8YX/j7wbfoJ3cDPu7hXYw6aHdcGRHCITryK1+qnsAQU+Zk4BeZ/4GUFLPUTzaP7Dl7IOSW18KQyngLd/Q4cWf27Fqky/5yCdyO4TDQRNtcQu+CyJ134HqThGtzKiUz9OkGwKy2ScKJHxl+Pt30BqxaOt59QQX/gr7EGTcdt1PjG6lWEPnoq7Y1nbDewI9oCJREbOCuvAnVimx3V06g1EzpkHgY9xXLCXD5fd9dk/bLmBx77G/RRGR3XRiETkdFOoFjnLGJaNkQnUbcy87KWV7QHnYA84p2178HlZ5RxW//H4h7dlnRPUV5LeuIjkknvajuszitzLvxp+ffB5mZcDIZyZDgIfv6oCq3Qwge+B5WR14TCcKH7TUfy6gxg5hR2fx4nR8szP8KoqsPuMwuw1PGtczpDzMHILcUbMwojl44ybi39kJ/Fnfp55idDbuw7z+OXEfRerz2gIAtx96zF79iU282+wisph9udJbX4ZIxK2IzzWP/pEfaTfaQVJ+WCraqmhJnGUAd3KWV3V9mfbNiz6d+u4KJKIfHipplpE3pXAd0mtXYB3cBNmUX+ik64h8cY92TXHOYUY3XvjH9qSdW7eDT/B7N4LgPTW10nvfBMjtzve0X0E1eEMn9V3DDlXfJX0+oUkj9UsGwb28Atwt70RBmAnB7vfWNyKFRAEWH1G4SebCWr2Zu5lDTgHs1tpZlVF8nqQXPTfma/bg6aAZWfPNgPO2MvadQ4BMMi74Udh/bXICTy3+0We2b2QgIB8J4+RPYaxvmYTBZECPjL0KiaWjO3qIYrISaZlykWk0wzTJnrO1XDO1Zl90fM/TtB8FO/QVoyCYmKzPkd6w0L8rBMN3ENbST1/F0G8EWfEBeRc/jXSW18Lw3Ir78BG3N2rwjZ4pYPwaw9hlQ2l5ckfZWaUSccJEg3kffIX4CYxcrvT9Pubs8bpV+4gd+6XM9stT9yR9XV39wrsodM6PJ899HyCVAvu9qXgRIlOma9ALX9VY6qJZyteJGjtXNOUbsbH5xezf9jFIxORrqJQLSLvm5nbndx53wprme0ohmFgODHcg5sztc/28JkkF/8p074uvf55zMKyDsuHA/j1lbQ89eMwpOd2Jzr149krMgJ+Sz1+7QHcvWsxe/TF6N6boO5Q25iKB5Bc8wz+0X3Y/caBddxfc4aJM2YO3v4NmTHYg6Zglw7GLh1MMPMmMK1MbbnIiTSlm/GDrB8faUg1srFmCysr19I9WshF5TMpiKgVosjZokvLP376059SW1vLT37yE5YsWcKPf/xjkskkV1xxBV/96lff07VU/iHy3vj1h/Fq9mH1Go6Z27FWuVPXjjfg7d8QlmHE60m88Musr9tDzic6+VqaH/lO2xLdTgy7fDzurrfaDozkYpUODl9QPHbugEm4e1Zltq2+Ywha6vBrD2CVDQMnkrVgjDNyNultizN12s7oOcRmfiacld67DiO3EKv3yBOvYvgeedV78BursfuOztRS+0014WqRJYMxW7ucyIfDT5f/B3sbD2S2L+g7jdcPtJVD9cnrxbfO+wrmCVY3FZEPpjOy/GPp0qU89thjXHjhhSQSCW699Vbuueceevfuzd///d/z6quvMnv27K4ansgZIQh8gnQaMxI9qddNbVhIcsmfgQAsh5y5X8500HgvvNqD+LUHsPuMylqcxMzphtnaj9loOgqGBUHbQitW2RDM7r3JnXcrqY0vhouojLuUxKu/PW6gLUSmXE+6WwlBfSX2oHNJbcpeUMU7sIm8z/xHGGLTydbFZNp9/fA28m74UfiyYffe2H1Hh+OK5OIMfecWd+7edaS3L2ntgnI5Zl4P0tuXkFr9NIHvERl3GZExc0gs+XNbTXY0j9x538SvO0zipf8On90wic3+PM7wGe/68/1rwrmQAENh7bRbV7WRFZVr6B4r5DOjPsFbh1dSFa9mYsk4Vh5Zk3XswebD7G3cz8Bu/btmsCJyWnVJqK6rq+Ouu+7i5ptvZsuWLaxbt44BAwZQXl4OwLx581iwYIFCtZzV4q/+Dnfra+GG5ZBzza3YJYM6fd3AS5Nc/iiZVQxbt99rqE6uejLTgg4nRs4V/4zdaxhBspnkisfwqiuwe48kMukaYhd9geSyB8M2dv3Gkd7xJqkNC3GGTSd24Rcy4dDuO4ZUdVtrMiO/iOTKx/D3rQt3WA6GEyXrd1KmFXY0sZwwbDrRrLZ7RqwAs1spkTFz3vGZ/JY6/KP7sUoGYUTzcPeuIb7g3zNfdytWEbvkSyRe/p/M55dcfA9E80hvWNjuw2kmtfoZvKrdbT9MBD7Jt/7S6VAdf+NPuJtfCdsRFg8k59pvYx5f4iKnxOoj6/nfDW2dbTZUb+HbU7+WmYneUru9wzl5tlopipwtuuRv4u9+97t89atf5dChsA7yyJEjlJSUZL5eWlpKZWXlXzv9hP7aVLzIB1HqyF4ajwVqAC9N4tmfMfCf76Fx1Qu07FxFpKScwmkfwYqF/2j7qXgmXL4dPxmnyU1m7TPdFoqL8wncFKbzzrPifqKZPe17TqcTsP4pSsbdzuEHf0l6R7gaY6pyB1EjSZ8rb4Zpl+Elmtn7y78naF3VMbXycbqVlJI3ahqNaxdh5ecSGTuL+J6NOEV9yB00nqMv39f2MexdS+G0j1BftTtTztH9/HkU5AbULf0zQbyBgjEzaVzzEhBgOFFKL/kUOSUFbWN3U9S/+RTJ/VuJ9R9N4XlXY1g2jetfperpu8F3MZwYZR/7Fxr3tCtFAYLGKpy9y4hnx3oiNdtJHrfPCeL4qebsvanwc36nUhO3qY7GNS8SpJPkj7+ISFEf0rWHOfL0f+Lu3dT2LNUVGG/dR8k1//i215OTY/WWNVnblS1HqDOr2VN3gA2VWykq6E63o/k0JJsAmDt0NqMHDDz9AxWRLnHaQ/Vf/vIXevfuzbRp03j00XCWy/f9rH9kgiB4z/WNqqmWD5Pk2iUd9gXJOPtfuI/0iscAaNm+gsY9W8m5/KskXvnfsBbZiRE996PvOCtrD5qSXbtcNpyKX/4DQWMVVp9RxC7+ewzTJvHa73H3r8fsWU7sgs+GPZoBv7mWwEtnj7mhniOHj9KyY1XW/qbNy+DcTwHg7t+QCdTH1G56k5rFj4WLxAA4OeTN/x5mYRlNa57uMPa4ZxO7+GbcvWvCxVn6jmH/H/6FoKUuc0x09ucxornYvUbQkGqh9o1nMYv6YxUPIP7K/2a6jrTsWEnj4QNEZ3ya5oV/yAT1IJ3gyPN/xOo1rMP9kwXlHfalS0ZiluzCr9qd2RcMmoGV3xt/3YK2z334TKqrmzqc316QTtD8l9sImmoAqHvrGXI/cjuJhb/Eb/dC5jHNezZRVaWV+06HaJDdc9zAYOGWJbx2oO3/1wklY5nWewrdo90pL+ij743Ih8wZVVP97LPPUlVVxbXXXkt9fT0tLS0cOHAAy2p7076qqorS0tLTPTSRM4Yz5DxSb/3luJ0xvJ3ZM6fegU2kVj+Nu/PNcEcqTnLxvdjl4zC7lRKkWsCJZdXeBukk0Qs+G74AWLMXq89oUssfzoRS7+BmksseDFvhVYQzzv6RncRf/E/ybvhx2OEjpxCrfDzesbIMwBk5C0wbI79nJhACGN1KcPeuw6/dj1E0AAyzrUUegGm3BWqAdJz01teJnvdRnIGTSa14vG0xGNPGrz2Y+WzcnW8RmXJdVqAOn2ELORd9kfTulSRevDtTghE574YO/anT25cSPf8TBInssBu01BGZcAXuntWZ53FGXUhk+Ez8usNh/XTg44y6GHvQFKySQaS3vo7fVIMz+Fzs/hOw+o/HLCjGO7ILq3QwzqiLCHw/bAd43KIwQbIZIrlZ9wsfMkl6/fMnDNQAVtnQE+6Xk++yARex6ehW6pL1AFxYPoM1RzZkHbOuaiN/M/oTRK1IVwxRRLrQaQ/Vv//97zP//eijj/LWW2/x/e9/n8suu4w9e/bQr18/nn76aa6//vrTPTSRM4ZZUIIz/grS654Ld1gOsSu/QXrFo1Db1m0AJ4bXIWwFuAc3477yv3iHt2Hk9SQ262+x+o4m+fofw04YtkPknHnkXPR3+I3VJI8LpX51RYeZ6KD+MN7BLSSX3Y9fsxezdCjOhCsJWmohnSS9YSHuvnVEJl5F8q2/hEuC53TDyC8mvuAX4UUMA3vk7PCHgFQCq/8E7IGT8FrDe/tnSLzxp7BrxoCJ4LkYpoU9fAaJhb9qO8xL4Vas4njHVodMrXws6wXJ1OonMXK6ETQfbXdsdww70mH23iofS+LV3xIA1qApRCbOw4zEaH7s+/hVuzG69yE2+3NYPctJLPxVOI5oLrHzP4HdfwLJFY+SWrugNXhfiDN6Du6eVSRf/xNBvD5c7GbOPxAkGom/dDd+zT6MwjKckRd2eB5yuoGT06G9oFnUn+isv+14vJwSJblFfO/8f2FH3W66xwrpnVfGzroKapN1mWNy7RxsQ+0YRc5GZ8TbLdFolJ/85CfccsstJJNJZs+ezeWXX97VwxLpUrHzP07s/I8TeOlMnbR57vW0VFdAshkMk+h5H8VwYnjtSzksB3fverzWpbqD5qMkXv4NzpSPkD5Wp532SL31F+x+YzGLysPlwhuOtF2izyiCZDNuY3Vmn1FQQmLJvQStod4/sgMjGra8S618PDyo/jB+7UHybvwFQeMRjLweNN/brj1mEOBX7yHnqn8htfppcJNgWpjFA/BbX1A0crvjVe9pa6NXvQd76DRyLv57/Maq7FluwDDMsG3ellfD7cIyrLKhBKk4QTq7dhw3TfSCj5F49XfgpcGOYJYNpen+b4AdwR4yFdwUZu8RpNY9Dy21AHhNNXg9+pKq3J4p8QjqDpJ8/Q9hGD/2Q0GymcRrfwAnRmrVk5nbpje+iFkyKHypsfUlSu/ARpIrHsWvPYhfsy+8Zn0l6a2vYZYOxj+yK3yevJ5ExszB6tmPxOt/hFQLRo++5Fz2T1iFZSf8syOnjmM5jCoantm+dsgV/HrdH0j5aUzD5LqhV2Gpx7nIWUnLlIt8wATpJN6RnZiFvTDzexIEAanVT5He+jpGLJ/oudeTXHo/fvsZbcAeOh13R3atdmzW53BGzsI7uo/k4nvxaw9i9R9PbPqnCdwUiVf+B2//RsyefYnO+Azxp36cPZhoHma3MvyqXVm7cz96B1bPvgTJ5rDFXbsgbBaV4zcdDX8wODaOuV8JA3Y6iTVwMs33/CO0/6vJjlLwuV8D0PLcL7LKTmIX34wz9Hy82oO4+9aF5SJuIux7Pfi8tg4qhDXNdr8x+PEGzPxignSC5Cv/03YfwyTv4z8hSCdoeeS7Wc9klg0NP9PjasKt/hPx9q7J2ueMuoj05uzWf+Fy66+/q2vm/e1/4x3YCOkk9oBzMqUigZsiaKnH7FaCnDl21O3mrcOrGNVzOOeUjuvq4YjIKXRG1VSLSOcYTjTTaxnAMAyik64hOumazD637+isUG10K8MaNCk7VBsWfqolXAbciRE972OZ+twgnQxfBOw3Jqy/bl20xCweiF9dkbmE3XsERjQvO1Q7MbzD20kuvgcjpwB74CTc3SuO3RSr90j89u3nAK9iFbHZn8NvqsGIRMOgXn8483WzsIzEG38ivfUNiOVjD78Aw4lgD5yM2b03gZvC6tEnLA1xW9vppRN4h7cSu/QWvAObMHv2JbXxpcxLikZeD6y+Y7I/3MDHO7gFe/C54MSyWvNZPcsxc7pllZuYZUOx+43NDtVODHv4dNKbX4F2vT/sIefh7V+fVf9t9x2Nn9u93edDuBiPE8McOJnjGXYEQ4H6jLL88Gr+uOkBAgIWH3yTqwZdypWDLu3qYYlIF1CoFvkQip73UfDS4VLe3fsQnfZJrJ594fwbSW1aFAbS/ueQWvZA5pyWQ1vJu/HnGNFcWp68A79mb/iFVU+Sd93tmN17kTPnZhKv/QGvugKr90iiM28CwKs9EJYrRPNwhs8k+cYf2gYTySN24RfxG45gl48D08ru6QzgRGn+y634tQcxcrrhjJtLet0CgkRj+FJk7xFt5zQfxd2xhJzrf0Dq9T+GZS5ODtFpn8Bvqs66bNBYgzNoMs6gyaR3LMuUrgAEzbUdOpFAuMw5hklk0jWk1z5HkGjE6j2CyJSPtB5h4B7aglU6mNjMmzDyiwji9a0LxHQnet7HsMuGEbv471sXiHGJjLsMp3wc5uVfCX+L0FCFPWgykXPmQTpJwrTxDm3BKhlEdMan39f3XLrGgj2LCNr98LRw76tcOuAiHFP/vIqcbfR/vcgZKrluAanlj4DnYhaVk3f9D054XGrTItLbFmPmFhKZfB1WUX+8yp34DVWY3UpxRl0YBmrALB1ENHYNVvm4toVbjnGTeAc2YkRy2wI1QCpOavPLxKbdiFnYi9x53+wwhrzrvovfUocRzTvBqojNGNFczB59SW9fglU8EGf0HNKbFgEBZskgvLpD+LUHAQjiDaQ3LCT3xp8TNB3FLCwlsejX2df0PVJv/iVTN046TnLxPdgDJ7d1QoFwxrndOccze/TBjOWT3vYGWA7RSdcQxOtpevonYUlGNJ/Y5V/B6T8xc05k0jXYTdOx+7QtRR6Z/JGwjjudwCoZCIAz9HycoeeT3vkm6Y0v4e5ZTWTi1eTO+xZeVQXJlY/T8vRPcUZcQM6cmzuM7a9JbXktfBHUS+MMOY/YrM+963Plr1t6cHlmpcQrBs6hOKfoXZ3nHetM08r3vezSJRE5ayhUi5yBvKajWbPIfs1emp/8EXnX3Jp1XHr7EpJv/Ck8BvAObSNn3jeJP/eLTBs679A2zGtvI719SWuQBZwYzogLOtzXLOxFEG/oOCDfI/7Sf+PuXYPZvTexGZ8JW/JVV5De8jpGJIYz+mIMy8Hs1rEdprt/I+mNL4ZjBpzRF5N3488JUi1YRf1p+vM/Zx0ftNRBvAHv0Fa8/esxe/QFlrcbqB22C8z60FycURdiFpbhHd6OVTaUyDlX4zfV4O58C+xIOKt8rF1dJDecAV/9FNhRrH5jcUbOpvnR29tqnJNNpFY8lgnViSX3ZWbMjWg+OfO+hdm9N/Fnfop3aGu4P68nudd9BzOvB+7BzSRe+q/MEOMHt5A7//u0PPPTzD2SlTswonk4g6Z0/NyP/zbUHSb52u8y2+ktr2HkdCd67vx3PFf+umWHVnDvlrYWlttrd3L7+f/yrl44vKj8Ah7a9nhme2bf83FOsABTY6qJB7Y+yrbanfQv6MeNI+e/6+AuIh8MCtUiZ6D2nSOOOdYNor3j28kFyaYwOGfNngWtgbrdi3PpBH5DFVa/sWGXDcPEGTETI78Is6gcs2gAfk3rcuGRHIJEE+7OsL+zX7Wb+MJfEZv7T8Sf+CF44b3S2xaTd8OPiYybi7t/QzhewyQy4cpwJrid9JbXiE7/JNiRsCyk/4S2wE9YgtHy3C8IjtVVR/Oxh83A3b0CI6cb0ak3EDQdJXlsphowcrqR3voG3v4NmD37YQ+dht9YTcvj/zdTG20U9Scyeg74LvbQaSQW/CLT/9nb9RYJO5LdIxrwG8Ie2n5TDekNL2Z91qk1T+EMm54J1BB2W0lvfoXolI90bPfnpUlvfKnDi4luxap3DNVBOklidcc/F+ldbylUd9KqI+uytmsStVQ07KN3XhmNqUbK8tp+UHR9lyMt1ZTkFuOYNrP7Tacst4SttTvol9+HSaXjAfADnyMtVXSPdidmR3lg66OsqQo72myp3c4fNt7P16doJUyRDxOFapEzkNVvDO6WV7J32h0XkzALex23x8AsGQwsyt6b1wOOW0YbL03uVd/Aq9lH4vU/kt7yGumti4lMuILca27F3fkmQaoFe8hU4s/+W9apQfPRMGB6beE9aKnD3bs27AGdTmGWDCY66RrsARNxd6/IvrsTJbn8MdLrFkDgYfYagTPqIryDmzGLyrF6DSO55M9txyebMAtLKfjcrwmCAHfnMry6Q9gDJ+PXHcIoKMZworjbF4ePdqCexMJfYvUZnfWyYVCzF2vqx8MOIC11HRZU8Q5txR4wqa1FHmTCbrg4TPZnGCSaCNpdP7O/dV/H7w+YpYNh00vZ+05wXHvpXcvDNoDpjjXgRqzgBGfIe1EU65G1bWCw9eh2frnmN6R9l375ffjShM9RHT/K/2z4E42pJvKdPL4w9tMM6zGEkT2HMbJn2+qblc1HuHvd76mO1xCzotw48nq21e7Musfuhr2kvfQJZ7VF5IPJfOdDROR0O2EJRipO80O30viHL5FYfE/4Atz4yzGPrahn2kSmfARn+AycUReCYQBgD5xMZPwVWH1GZV3OGXUhAO7etfhHdrTe2CO15mn8hiM4I2eF18/rgVU2OOtcI6cQI7/jr669qt0k33wIv3Y/ftUu4ot+TZCKE5kyP1xJ8di9R15Ieu0zmYVZ/MNbMQqKyPv4T8i55P+8bVBMrXiUxKJf4255BbdiJVb5OHKv+Bpea6/nY/y6QwRu8q9cBYjmd3gGq2QQsQu/gDNuLmbZUJyxl+J7KZoe/CbJ9Qsxi7KXKHdGzsIun5B9HcshSLXQ/NC3SO9dg9l7ROuHZuCMuYTI8BlEJl0DraUFVu8RRMa2dYvwju6n5dk7abr/GySW3o+fSoQlPicI1ECH1ony3s0deDFlueFstGmYzOk/iwV7FpFu/Y3P/qaDPFfxEg9ue4zGVLjyZlO6mQe2PkbCTfLA1sf4/rKf8bsN91GXrOfxnc9RHQ9/45Hwwq+XF/TNumefvF4K1CIfMpqpFjkDuXvXnWBvgF8XvsyX3vgSZkExzuiLcYZNx+9Zjj34POy+YXCOXfBZIpOvA9/DbA18OXO/QnrzK3g1ewkSjaQ3v0KQasGvr+xwJ7/+MFa7ABk97wb8lga8vWsxu/ciesFnMbv3xt2+JLPEuNV3NH5jdvcN0nHcw9vANMMabjuKM3IWft0hstdrBL+uEvfgZrx96zG698Io7JUp/zBiBTjDwxrwrDIWIL35ZaLnfwKrqD9uuzZ8Rm73sBRl94rMbLVZMgiv8QiJe8Kwb5WPx7dsgvpKzN4jsPqMIrX6KezyccSm3UjLgrvw9q4FwKs/jFU+gciASfhN1TiDz8PuPwHv6AHsQZPx6w6HS8P7Hu6xHtV1hzAKSsj9+M8wnChmbmH4eU6ZjzP2UkjFs2rQA98nvuCuTAlKev3zEAQEicYT/Hk49gG8zQ8O8q50jxby7alfY3/jQbpFC6hL1vPi3lezjjnSUsWRluw/30fi1Ty8/UmWHlreekw1dcl6mt3sH4DibpyrBl1K0ktR0bCX3nll3DT6E6f2oUTktFOoFjkDRafdSHzf2rc9xju0DXfPmkw9b3rLa+Rc9XWs0iGkN72EV703XDFx+Awg7G/tjL2U1EPfImgIg7R3YCP2mDnZF3Zy8JuqSbzxJ+zy8dgDJmLE8sm55Et4NXuxuvfGiOYB4SIv6c2LMHMLsYecT2rVE9lLjhsG3p7VrT2bQ2Zed5wRszr0gcaJEH/6p5lNe9gMrPGX41ftxq3aTfzZO3FGXwSRGCSb2m7hxMLjR8zEb6wKlxDPLyIy+TrcnW+GL1DaEcz8Isziga2LuoRlHF7FSqIXfBZn2HSSbz5E8o0/hhdd+yzR6Z/G27c++zM/uJHcK9pWiPSO7KTlyR9natiD4oGZ3xAcEzRWhas3miZe9R7Mov4YhoEZK4BYAekdS3H3rsPsWY7Vd1SHmm6vcjtm6RD8I9nlA5nPs2zICffLe2MaJv279QOgwMmnR7R71vLjE0rGUhDJZ0XlmrZ9xWPYWLMl6zo76yuYUz6Lw81tP6z2L+jLkO6D+MaUf1TJh8iHmEK1yBnI7t4Le8KVuGufAwLILQxXIGxXw2zk9cTds7rtpMAjveFF0ubLuK3Llrs7luI3HyV6zjwA/OqKTKDOnFZ3mNjsz5Pe8hpE88BNkVr2IADpTYuIzrwJq6g/8ef/I5wxtSPELvwiVu8RxJ/9ebjEtmHgVO8lOukavMod4cuPdpTIlPmkVjySdb/U+heITLgyvOeGhQRBQGTUbFJrn8s6zt2xjMjEK8OlvX2PAEguuQ9n7KVhJ5EgAAyc8VfQ8sh38I/uD8cxbi728BnEH/8heKnws+pWSt5Hf9haK51dF+0f2QXDpmcFf4DUhhcwe/TFP9pWVhJ2IWl3zHEvhfrVFVjl47O/mU6M1MYXcVsXgzGLB5J75dcxYvmk1j1HsvWzhqVY/SeAk5NV6mH1LCdy7nySbz2CX7ufwE0T1O4Pn6uwV4eOMNJ5lmlxy8Qv8PTuFziaqGNS6Xhm9Z3G1F6TKHDy2Vm/m0GFA5g3eC7/u/5eGlJtv0koivVg3uC5RO0oG6o30zuvjGuGXJ75ugK1yIeXQrXIGcrM7Q6WHfapzivCmXojqbceIojXYw89H3vkLNLHvfCGYeDuXp61K7319UyoNvJ7hrXN7ZcNLyjGGXEBzogLCBJN4bLi7c/ftIi0HW0rQXBTJBffiz18RhioAYKA9LrncEbMJDL+clJ2FCOvO/bgKaRWPZ49Rssh/spvM0t2m0UDsAecQ+r4BWFMI+xDfXx/ad8j72M/zszgpre+FgbqY+NY/zxBsikTqAGChiO4+9aHK0Ye9/xW7+HhPtPKCsiGZROb9bfEX/xPgqYajIJiojP/htTmV/AObcUqHZKpi27PGX0xQaIRv2o3RPOITLiS1Ftt7dr86gpSG18kOvk60luyly339q4jduEXSC57IFx0ptdwIudej5lbSM6Fnwcg/trvcWsPAMEJ7y8nR1leKZ8fm70QT8yO8dHh12Tt+9jwa/jN+j9R2VJFYaSAT4/6GI7lcNWgS7lKKyuKnFUUqkXOQH7TUVJL27pf+FW7SG95mfxP/YIg8DFaX/qzB05u61RhR3HGzcXdvzFrptOI5pFY9gDpra9jxAqwh0wNF0gJfIyCYrBskm8+hDNyFkYmyLereHZiBMfVSgfxhkyrufbc3StJrXiMTHnF3nVEJlxFavnDx0aDM+Q8UmueaXu2mj2kNr5EZOLVJF66O7NwRmTc5VglgzmeEetGctXjBM212L7XYWzhjTsu9IJh4B3ahjNqNu6+DZCKYw+fjtdch/fmg+FCLVuO1dEaRM6Zh99Si1U+PvzBY9ylpFc8Tmrts+Gz7lgaLi4Tycm0yLP6jcUZMBHDjpDevhizRz/MvB4dhnKs9tyI5Wd/wY5gD56CPXQqQSoeloi0k96/EXdLW61vUHuA5qd+St68f+34vHJa9Mor4ztTv05dsp5ukYJ31dtaRD6cFKpFzkCJ41c7BPxDYU9m/+iBcOW+siHELvk/ePvWhasn9hqKXTKI6LnXk1xyHxCA5WCVDg1b1wFBshm3sYqcj9xOkGgk8dJ/h32TgdSml8n76A+ITLyK1MrHw5uaNtFJ1+LuW5e1tLjVfzzOsPPx2s2KGzndWjtwtJVXBI1VmMX9iZxzDd6RnThDzwc72uHZguajOJOuwexxB+7eNXj7N4UvVR7ehjPmEtJbXwMvjT34PFKbX4Z4PRC2wHNGZ9eEG7ndiUy+Du/gZoLW48yyoSTffKjtxceCEnJv+BHxZ37eVt5hmESmfwoDsPqMwtu/keSy+9s+/7qDeHuy69zdPavJ/cTP8fasxsgpxB4wkfSOZSQW/XfmGLPPKIxofjh73soZfB5B4OOMm4tXXQFuCjCITLomrLsuKMmE8SAIwnpqyya59L4On51/eEuHfXLyHGg6RMJNMqiwP6Zx4oZZhmHQI9YdgKZUM/uaDtC/oB95Ti4Aad9ld30FPaI9KMlt6xRT0bAXA4MB3cpPdFkR+YBRqBY5AwXu8b0xAALiL96dqZc2i8rJvfqbEASkVj9JkGjELOpPzmW3YJePwzu6D6vXcJLtZryBsD65sToMnO2CHuk46e1LscvHh8HdSxOZfC12ySCsPqMwYgV4BzZh5BbijLkEu9cwuPhm0tvewIjmExl3Kekdyzh+jji97gW8A+GiF96BTURnfy6s3U42tx5hYA+ZCoDVow/pjS+1HX94G35zLXmf+Q+MIMCr3pO1DDmEgTx20d+R3r4EI6cwLHWJxMLFX2r2YpWPw3BySL7xh7ZzGqtIrX4qq16awMev3EHOnH8AIL7wl1n3cbcvDWf2278kGcnFP7QVt2IVRk5hOP7N2d1J/IObybnia6S3LyVINuGMuAAjmkvzA/9K0FiFUdgLZ/TFWN17kXjt92GpiGESPe9jOKMvpuWZn7W9pNga0rKpM+qpEAQBv9/4Z1YeCX+Q6pvfm6+c8/fknvB7EFpTtYE/bPwzad/FMR0+P/ZTlOWW8B+rf0Ndsh4Dg7kDLuLygXP4z7W/ZXtduKDTiB5D+dKEz2Gb+idZ5INM/weLnIGMgpIT7j8WqAH8mn2kNr5Eev0LmVlQv2YvyTf/Qs4lX8IsLAPAKh2Cu2NZu4ubWCUDs1YBPCZINtPy5B2ZOubE0X3k3fBjDDtCZMwcWnavwDu0BXfnm9iDzyU250sELXUkVz6Ou/NNrP7js5YCz5SatN0Bd8tr5F5zK6m1zxIkW8AwiC/8FWasgOjUj+Md2pw9psYqiDdhdCvBLCgOu2sE7V42jOWHtdemjd1/AkZ+T5of+lZmDN7hrTjjL+fdMJwYge8CZvjf7b9oOUSnzCfxyv+En49hYg8+N2tW2tu/AbO4/3EXNcIfdtq9wNj84DczrQiD+sN4Favwc7oRNNe2PrRPcvkjcGyW+pj0cUuzA+R0e1fPJu/N9rpdmUAN4Yz16weWMXfgxX/1nEe2P5XpbZ320zy642kGdRtAXTL8jUlAwPN7XibXyc0EaoCttTtYc2Q9U3qdc4qeRkROB4VqkTOQccKFPkzAz9rjNxzJKisA8Gv341VVkN7ySlhnPerCcLGVbW9gRHPDMov8IoxBUzDXP49fHS5HbhT2Iki1ZL0YGDTV4O7fgDNwEqnNr+DX7M18zd21HLd8McllD2T2eXvXEjnvBqzi/hg53TByu4c/CLQPwZaN1aMvORd+kdTGF0kuvjccd7KZ+Iv/iT3wHPzag22fRU4hyY0LcbctxszUhC8PV2LsWR7WR7fUtt5/Dc74K7Lb0vkeQUsDRm53gpa68JqxAiITriRorg37WANE8wjSSZp+dzM4UexBU/Br9mcWqIlMmoc9cBJ5N96JV7kDq2QQyTcfyvrsg3g9Vt8x4Q8sbviipDPmEszc7vj1hwmSLRg9+uG366cNhL9V6N4n+9vtu3iNHevWj2dGYu94jLx3DcmOCzDVJRtYemgFW4/uoH9BHy7oNx2n3ezy8efUJxuyOoNAGKyPLQyTde3UCRZ8EpEPFIVqkTOQM+U63OOXsi4egN94pK1swjCIjLoQv2p31qp6ZskgWp78Yab9nrt9CXkf/wnkF5Fe/SSpxffiH9hE7OKbyb32O7j71oHvYfefQGrVkx3GYkTCX3cfC6TtedW7O+zz6w4APunNr2JEc7HKx+EdW8zGtIhMvKrt/NY68baTXezyCQSJJryDmzEKSrAHnEN6/Qvhl5PN+I3V5F7/PcDAjzeQeOZn2WOq7NjP2cwtJDr/e6S3vREG20guXtVuohf/A87hrQQtdQTJFpJLwoBP0sXd8iqxK74GLfVgOaRWP03qrYcxSweTc/HNmAXF4cz5cez+43GGnh/OWhf2wiobSuLV34V14YTfR7NsKH7ljrZz+o3F6jUMr3J725iL+hMZczHu1tfaftCxIlldTQCMnqrHPRVGF40kz8mlufW3AwYGnu9y7+bwB6nllavY23iAz465kSAIqEvWM6XsHJYdXpG5xrm9JjG42wA2H237c94rr4zZ/Waw9NDyzKy2YzpMLBl3Gp9ORE4FhWqRM1G7mdoMO0LutbeRXreAIJXAGXUhVtlQcuZ+meSyB/BqD2L3nxAe266fdZBoJLVxEel2/aLdilWkNy3C6jcGd9ti/Hg9QaIRZ8wc0tuXEDQfBcAqH4/Vusy2M/T88KXGY+3oonk4o+eE/a3bdQsx7Bipt8JuH0EjYFjELr4Z7+BmvLqDpDe+hOHEsMqGhqUp7UpaMCwC38NwYtjDZxCZOI/UyseyPwffDZdRHzgZ47gZXwCzuD9GJJpZuMXI64kzZg5mbnfs/hNpeeKOTHcUq3w8uVd8DYDEG3/q+Jm31OOMuIDmh7+N39ob2j+yi8TrfyT3qm/gjL8cd9+6TI/syPgr8I/sJrn8EYJUC86oCwkCPxOoAfzqPTjjr8DM7Y5XtRur90hi0z8ZLqhjObi7V2J2KyVyztWYud3JufqbYevE1hlRd9sb2R/HkV3IyREEAUbr4j25Tg5fm/QlFu17jYSbZHqf87hvy8NZx688spY55Rfw+00PUNlyhG5OAdN7n0tjuokhhYO4qHwmtmljmRarjqyjKNaDS/rPpjDaja9MuplX9i3BNAxm95tOcU7PrnhkETmJFKpFzkDeCYKSf2QnVvc+mFM/TuCmMt0hzG6l5Fz2T5njkmue7XBucIJyEq9mL6k1z2T6Tycrd2A4MfI+/mPcnctJ7VyKV3uAxEv/RXT6J7FKh5Bz5ddJb34FIxLDGXc5Vo8+Yahf8RhBsplI6xLk2Tf38OsOh+GbAB9w928k7xM/xRkzB7/uAOltSzBi+dhDzyf5+u/bxrh/I864udC+LtswCZqO0vzEDzEsB3vwebi7lgMBRmEZ0YlXhWUnBzbhbn0dt2o3iZf+i8h5H8Xd+kZWu0Fv37ow2JYMwuozkvSmRe3uY2D1Gk7gpdv6YB87r2o3XuUOEovvwW+oxiqfQPT8T2BYFs0P/mum3CW9bkGmDCRLKk7Opf/YYXdk5GycweeBHcUwwxcQraJygv4TwLRJrXuh4/e29Qcgef921+/lz1se5lBzJSN7DuNvRn+Cgkg+vfJK+eTIj2aOy7NzOEptZjtqRXlsx7NUthwBoCHdyNbaHXx/2jcz4RxgStlEppRNzLrnwG79+eTI3vx5yyPcufI/6R4t5KPDrmFCyZhT+7AicsooVIucgdy9J1ii3HdJLn8k7JPse1j9J5JzyZcw7EjY8m7rGxixfJwRs3C798GvC2e7rb5jcMZeRnrDi1mlA2Z+EW4iu97T3b0SZ9h03H1r8fdvDPc11RAkm8i58hv4DUfAdzFyCjHzugNh6QJBQHrr6/iN1Rh5HWfc/KZqslYy9FK4e9dilQ3F7NmfnKsvwO41jPiiX2edF7TUYRaW4Yyc1Ra8h89sbRl47EFscuZ9M+zdndMNTAvDMAiaqnF3hi9oeg2VxJ/7BXb/41Y7BPx4A+llDxI0HMEedC7ekR1gR7FKB5Nc9gBmySDMksH4VW0/6Fi9RxB/4f8RxMM6WG/fWtI9emMV9c+uHweCdKJDtxOjoJj4i3e31nZfjllQQpBoIv7Sf+Ed2IiR253YzL/B7DWUlse+f+Je3Jkb+H/9a/KO/MDndxvv42giDMubj27j4e1P8qmRH+Olva+xt3E/w3sMYXa/6cwbcjm/Wf8nXN/FwOCawZfzXMWLWderSdSS9JLE7I617m8dXsXaqg0U5xRxSf/ZvLZ/CcsrVwFwNFHL7zf+mR/NuO1tO4yIyJlLoVrkDOQfPXDC/anVT2X+29u7hvTmlzF7lhN/7i6OhVa3YhW5N/wI7+Bm/Jp9mMWDMHO6kXPFV0mtfJwg2YwzcjZ2/4mkVj9N+7B7rGOI21o6kbnXgU0k1zxD+tgiLhWr8I7sIveqb+Ae2ET8uV+0XSenEHvI+WFZhx0lOuUjYFodyxYaqki+/sfMeZFzrz/hQilBS33Y/zq3O87IWQSZcHrsQi5+zV7cvWvD5dENi8j4ueEPAO2lE5jFg2H3qszKiWbJoHCWvboic1j0gs/i1x/O9PZmz2qsAROx+o3Fq9qN3XskzthLiT/9k+zP6NBWIqMu6tCdxCoZhNmjL97BLRiWjVk8oN1iOGGv67yP/4TkikfxDmxsfeY64i//hsiEK94+UEunNaQaM4H6mIqGfdy7+aFM94911RupTdYxf+jV/NPEv2NF5RrGF49mVNFw9jTuY9mhtjrqwYUDidkx0r7LnoZ9lOQUURjtxhsHlnH/1rb+89tqd2b6WB+T9tMcaDrEsB5DTuETi8ipolAtcgYyyscR7Fjyjsf5tYfwavaTteBKSx1uxWpSKx/LBDKr13Byrv5XIlPmh32gj+zCKhtG5Nz54UIvvodZOpjIhCvD43uWZ78016MPXvu2fIB3YCN+Sx3p7Uuz7k+8HnvwFLBs3J1vktr0EtHzPhZ2xTiwETBwRsxs7brRdl5qzTPkfixc/OVY9w97xKywz3ZrzXZqxaPYw2Z0/BzqDoaBGiDwSK19FmfUhcd9qAbOkHOxevYltWEhZvc+2EOnEn/s+1mHuduXdOzOsXcd+Z/7DYYV/pUZuKkOC7pYpYMxCkqITP4I6U2LCNIJ7IGTSS5/NFNyYg+bgV9fmXXtoPko3sEteK1dWDLScfzm7LAnJ19hpBvFOUVZHTkGFw5gReWarOPeOryKvnm9uWfzQwQEvH5gKZ8ceT0fG3YNtmmz5eh2+hf05fph8zjUXMkvV/8P9akGTMNk/tCrWX1kXdb19jbu58J+M7JeYoxaEfoVHNcFRkQ+MBSqRc5AhpfO7pF8zHHdH+wBE/HadZE4xqvcnjXD6R3eRnrTy+EKga2dJNyKleTd8GOs3qNILvszQf0REkvuJTbjM0Qv+BsSC3+FX38Yo6CE2OzPh+Gwtl1tsR3FcHIwcws73N/dvykzMx3UV5J4+Tfkf+qusFzCcjALimm6/+vZJ/kuBAHO2LkEXgq73zj82v1h94t2At9tW57dMHHGXHLCumWzZAhWcx3e3jXg5BA976P4tQeIP/8f4Ht4+9aF7fIMK9M2D8DILcRIxTOlHdC6nLhp4dcdIvnmQ/j1lVj9xuBV7SJorMbuPxFn2EyaHwwXdMGOEJvxGdyDm7NquN3ti3FGzOowViO3ELvvaFLtelIbud2JjLss7DF+7BrH9+iWTjMMgy+O/QwPbH2UA82HGd1zBPOHXs3mmm00ptt+aCqMdOOpXc8TtP6fGRDw1K7nGd5jKPsaDlAdr8EAahN1LNz7KvWtLfL8wOeJnc8yqueIrPtahsUl/WfjBh4rDq+he6yQjw6dR46dc9qeXUROLoVqkTNQ+xZ57TkjZuLXHSJIxTG7FZPa8AJGQTFGtzKChnAG1B4+E8Pp+A+ze3BzVg9q3FTYBWTzy5mZYXfHMpKWQ3TqxzH7jYWcbtgDJmIWh8uft1RXhLXBhknknHlg2ThjL8XdvSIzu2sPn0nQfFwfXjeFV70Hu+/o8PkSjThjLiHVrse1PXAyLQ9/G9KJ8JhhM4hOvhYwaD+jbZcMDLtsNNfi1x7EP9bLuf1q3XYUe8AEzIIi0rE8jG6lOMOmh2Uq7T6D9KaXiUy8MlMGY+R0IzLpOoKWWuIv/BLcJJgW0Wk3AgEtC+4iaC0r8esOEpl0LZFJ12KYJvEXfplZ0AU3RWLJfVi9R3b4PtiDp+Ae3krQOmNtDZxMau2z+KkWrP4T8Kv3YOQXYeYXkVx8D5ExcwgI83Rq9TMdried16+gD1+fkv3i6PxhV3Pv5r/gBR4RK8J1Q6/ktxuyl4lPeEke3PYYexrDlTmr4jX8cdMD5B4XjNO+y4w+U9ndsIfGVBMGBlcMvIQese7cOGI+N46Yf2ofUEROC4VqkTPRgElQ17GtXnrTIiKTrwuXJl/1RGa/2WsEsVl/C2467GVtWlmz2kZeD6zeI/EqVmZf0I5mLbQCYfj2F/4ys+Ji6vA2SCeJTvkI+Z/8N9y9a0mtW0Bq+cOkN7xA7IK/Jfdjd+Ad3kbQUo9VMoj0zjfx2r9saUWwivrj11cSf/FX+DX7MPKLiEy9gSDeiFUykPSuFZlADeGsbnTSNURnfJrk8oehtZzCGT0n/Cy2L8607sMwcUbPCRenieQQnXQN3uHtJNotNe7tW99xRtv3cMZdhjPiAty96wg8FwIPu99Y8j/1C9J71xI012JE8/BrD2UCdWaM+9aFNeMQfu5Z36xwvOHnEP5QYBYPwE80EZlwdbg4TiRGfMG/t5vNNsi55v+zd56BdZx11v9Nu1W992ZJtmy5926n90J6CHUpSy8LLOwC2xcW2IUFFgg1BUIJCenFcWI77t2yZFmW1Xvvum3a+2GkuRpdh5ddmoF7PkWP5s4888yVc57/nP85nyF85Oe21aDeXY9r7R0zbyTiVer/LTRDQxIkhxvHb4J1OatYmFpBz1QvxUmF+BQvW/M3sKs9GkO/NW8Dx2YaDWcxGBzm5rJraZ+MvtUpSiygOmMR/7zxM7SMt5HhTSPDm/7b3VgcccRx2SFOquOI4zKEMNz2hvRJaz4aIwEw+i5ghqcIvfoduwlPLl2LkJCGoLiRStYgJqSht52wybJUsDQmVhxATC2wJBPzruleczuC4kHrrMUYtEJfzOAEwb3fw3/nvxI+/BjGsFWxkxduR1m4DbXpMII3Gff6OxE8CQT3PGgfY04No9bvwX/vlxAEAfVirIbc1CK4llyJmFlMaM/30FpPEJgYxLPj3TPV5dkDDfS+Rvx3/os9FH7xP51r1N+Ea/VtROakQsoL1iF6Eok0HrKkMUDkKLi3vQMpJY/w6w/ZGxNpwXpQPA7iL6ZG9a9y6RoiM/cGIKYXo5SuQvQno3XUIHiTUZsOEd7zXQCExAxcy653yEPARG143RlNDqhNRzAv5V0OMNNcGocTQS3Eo/U/5+xQPSnuZO5ZeBtLMxZzsOcor3bsRxAEri3eybqcVQCE9QiqrpLg8tvnSHYnkuxOtH++pew6cv3ZNI+3UZZUzNqclYxHJhz666LEAq4tvgK/4uPsYD1ZvgyuK7E2gi5JYVFaxW8w9yDH+k4T0SOsyV5Bqifld7MoccQRx+8VcVIdRxyXIZSCZURmG+/mQUhIt0j13GY6l49I/Ws2oQbQ2k7gu+NfCR981EpKdHnxbLgP14Z7UWteRGs9SeAnf4NUvBxDlDEn+pFyKnFvvI9A3wWIRMmeMCc50JhDHAGIBIicec4xrl3Yh/e2zwOgNh4gtOd76AOtMZ81JwctUuny4Vp8JcHOs/aGQcqpREq30gLDrz9kyyWM4XZCBx8GTXWeSwujj/Wgd9QipuReQgIjoFRuQcoutwi8riGXb8AwDMKnnUmSkVPPIGUUO/TrevNR3JveTPjkUxCeRswowb3W8jDWhzswNRWpZBVmYBwxJRd0lalHPmQ1SC6+EjEhDXMs+szMyaGYtwQAYnJODHkXEjIgNOnQec9CyomVmMQBL7bupmbIclMZDY/xo3OP8a7qt/BYQzQE6ZH6n5Prz+bCaBPPt+wiYqgszVjMO5fcj0tyxZxTEATW5ayyiTjAPZW3AXBhtInCxHzuqbwNQRDYmr+RrfkbHZ8PaiEujjaT4U0nLyEHgMnIFCf7a5BEiTXZK5AEiS+f+Cb9AUtKtKt9D59e+xHS4+EwccRx2SNOquOI4zKEmHLp6qPgTca97i4Agi/+50zjn4xn0/2oF5yWdZgmkdqX0XtnxMaRIKEDj+DZ9g5HiqHedgrvNR/BlGTUulcI738IuWITWsM+0DVLpuBPY/pX/4yUXoiUU4ExHHWqEBIzMdTYRkGt+Wg0SdAEtfZlywFkTty5mFlK+NQz6L0XkLLK8F7/N1YlfGoYY3qE4CvfRFl5S0z4ijHcibJwK2rDXntMzltM4PHP2U2HcukacHntzYGyeCdiYgbGSJfV/GcaaC3HkKuucCRCWouiWlKQeZByKlEqt6B11yNlliJIClrPeYLPf8W+rpRXhVy4jNCr34ree90uqL465nxCQipy5Ra7qVPMLse15EoETwLhgz+2PMF9KXjW34kx1mu9iZjnS603HYbt74g59186ZnXOswjrEU7Nc+AwMTnRf4bdHfvssdqhevZ2HeSa4p2/0XV8io93LLn//3tc52QPXz/9IAHN+j5eU7yTHQWb+eLx/2YiYvnFv9bxOteWXGETaoCAFuRQ73FuLrv2N5pPHHHE8cdDnFTHEcdlCPXikUuO++//CoKkzPz3f2IMtSMm51juFIpnRtphVXrl4pUOWQcAhobWd5H50PoaUc+9Eo0372nAe+1HETx+IhePoNVbARfGYAtiToXVnNh2CjElF/eGey1f5YtRUi94EkEQY64j5S5CTEizSGl6EaYo2n7QxmAr5tQIcskq1NqXrXnRgt53ETGnAmPOvOX8Jbi3vBUpp3ymAXIJkbMvOlw8tLaT+O6a8eue6EfKKMXUNSJnnncQU61hrxXPXveKPaZUX4OUXkiwqzZaOS+oRj2/B/X8XusZjXRiTA4iyG7HdfWe8whJWTH3LngSEHwpmLObCrcfpXwTYkIaxsqbMbWwFR4DuKp2IJeswpwYQMwoQZBkpLRCQgd/DPOr1folEhvjoCJlAU1jrfbPfsVHVVoFh3uPO45ziUrMZ3um+jncc5xjfadIcidyfcmV5PizMUyD2qF6+gODVKdX2dXmaTVA63g7+Qm5tlRjODjKmcFaEhQ/q7KW8VLbbptQA+zu2IcANqEGGAgO0THh3EACCAg0jjbROt7BgpRSylNKf5uliSOOOH5PiJPqOOK4HHGJEBSA4CvfREzJs6K4PQlI2eWWG4ihoZSuQbz179HaTlkBLEXL0Ttq7EARAMGbhFK52apC26ptAUQxSqgBMNF6G/BsuAf91W875mD0XcS78z0IigdjtAd9oBnXou14rvkwasPrCKKEa8VNmIaGevbF6AcFEblkFVJavj00+aP3Oc6tdZzBnJ9IGBzHtel+NMWHMdiClLcI9+a3WKYgkmKRWsUNxrxkQdPSfIePPQ6RICpWpdmcQ4BnD1RW3ISUU4k+0IKcu9CKOW87ad2HrlmpjhWbmJ5nA6h31VkV8XmQchfOrPHsvQuWhr1gKXpPvdUgWbkFwZeMPtaDmJiJOLNZUpsOozUfQ/Cn4VpxI5gGoX0/RG0+Atr8uYO1EHHMx7XFO5lUpzg9cJZ0Txp3VNzMguQSWsc72N99GEEQ2Fm4le0Fm9nd+TqROZsTr+zhxw2P2z83jjbzzxs/zc8u/IojfVbQy7MtL/PepW9DFmUerH2YiB5BFETuW3gHxUkF/OfJ/yE8c84DPUcR5z0nwzSIXOJtSH5CLnn+HHqmLalQguJHNzT++/R37WPurLiFnYVbfneLFUcccfxOECfVccRxGUKf16hmj3fUoHfUYAy24L32owRf/ppVnRYElKXX4tlwL2rrCdSjvyBy5KeIBdW4Vt6E1noSwZeCsuRq5JwKPFe+z4o7B1zLr0dwJ6Cecdq1zaYrisnZ6HMq3oI3ieC+H2L0nAesijCRAHLpWiKTg+ij3WhddXg2P4Bn53uInH0ZBAExOZvI6WeQi5ajVGyyz23MSTMUEjMRU3LQo72EIAiYk0NWfHgkCIKEoHgIH3gU9fyME8OZ55CrdsIchwy5fCbVcY42XO9rRFl1K8ZA65zjNiH5kpHK1qKUrUXrOEvgqX+KVqiLluPZcI8136Rs9Ln+1f40lOXXo3WetZ1F5JLVuCo2IYB176KIlFlG8PkvWamOmaV4r/0I5tQIgaf+BTMwhuBJxHPV+zGnRwntiZInrfscyoINURnNJRF3BLkUFEm5pF3dnZW3cMuC6wAB18xG5v3L3slzrS8zrQbYlLuW5nFnEM9EZJKzQ+c42hd1zzFMg90d+wjrEZuQG6bBU03Pszyz2ibUAC3jbVxTvJOm8WjlvDSpmKuKt3Gi/7Tth53hTWd19nLW5KzkVH8NYSPC6qzl/NORLzvm80r7njipjiOOyxBxUh1HHJcjgmO/9td67wXCp5+znTwwTdSzLyGlFUbjtQGjqw6hcCnKkisJH/kFes95IhnFeK/7GL6SlURqX0FrPoqYXYG8aBtaw37ARMpfgpRdDoB7w30EX/oq5vQIKF5ca+8k/PoPHfNRGw+hD3VE/bW1MKGDPybhga+hVGwi8MJXLNcSLK21GZrEtfRaPJsfILjr65Y23O3Hs+VtiOmF6L0XLIcRUUZZeo0V6z1DcrXmI0RSc1EbnETT6LuA77bPoXWcQUzJRS5bZ+mS50HOqUS59e9RO87MJFJ2EHz127jX3YmYmEmkbpfDXUXvqJkJwclCWXotxuSgJeFw+Syru46zVtXZk4CUUQq+JAIvfAUzMIZSsQl54Tamf/IxW7dtDLYSOfkU+lC7LQUxQ5OE9j+EmOTU0pvjfRZhj+P/hJrBc7zSvhcDgysLt7I6ewU9U3083fwiw6ERVmYu5frSqyhIzCXbl0XTWCtdU70kuRIc5xEQSHWnXPIak5Epx88BLYiBEXPc4rSFFCcVcmagjkxfOjsLtuBTvPzd+o9xou80siizJnslHtlDzWAdB3uOYWKSqCQwf+NkxDdSccRxWSJOquOI4zKEVL4F/eQTb3yA4olqc+fgUhVufbjDasybCT0xhtqJnHoWdDVaAW07hbL4Svxv/i/Cxx5HazpM4JefRcpfjPeaj+C769+I1L4MahgxJQdkl8PzWfAlx8Rvo4UxpkcRtHA0QnwG6oUDSLmLCJ/4FabsQV60EveG+xFdbstGb9l1GBMDKAvWY4x0o5ovOO9pqGNGsjJHDiHKSFllSFll6AMtRE4+heBPdcxVTC9GyqtCEEW0zrO2b7c20okx0oX/rn8DMfafRX1igPALX7FSKl0+3JseQC5dTeDJf8QMjtvPxHfL3xN47otWQA5Y6YuRYEwjpDHagzHmjEI3xwcQcpypewiCJfEZbImZUxy/Hj1TfXy/7lGMGf38j879lBR3Mj889xhjYeuZ9U7345JcdE52c3LA8lXvDwxQmbqAgoQ8uqZ6EAWRa4uvoCylhLU5KznWZ/lSCwhcWbSNtvEOXmp/zb7uiqyl7CzYwqn+GiKG9dxLk4ooTymlY7KLdG8qaZ5Uu0quGzqqoWFgops63VO9fK/2UTu58UfnHmNL3nr290T7LK4u2vH7Xbw44ojj/4Q4qY4jjssQet1Lb/xLQcK97i7ExAy0iwej44rXarhrPOAgvFJ6EVrjQccpjPG+aJV7BurFQyjlG9Dm+EXr3fWoDXvROmttYqzWvoyyaAdqwx6rouv2I1duxhzrJTKH/InJOZa1nBqKiVcXPAlR9xJAaxhATMrGtfRqAs/8G8aQ9fpdrd+D9/qPx8azFy5FSsklcmbGq1oQcK28yfpM60lCr3yT2eqeVFBtOZaM9aEPdxB4+l9wrbwZrf20c01GuzEmBnAtv55gd52tMZfLN6KefSka+z5jIYgkRwk1gBqymiVnCLV93pmgm7lNo1LRCoTEDMdaS0XLca+8Gb373MyxAq4VNyGXb8ScHkFrP+OwTIzj16N+5IJNqMFy+jjSe9Im1LOoGz5P56QzwfTiaAufX/8pnmh6mvHwJC5JwTAN3lJ1N0szFjMQGGRJehWFiXksSV9EsjvZttS7onArLknhM2s/yt6ug2T5MtmUt4664fM8ePZhmyzXDNZxT+VtfOH415hWAwDs7TzIpry19jGz8050J/LBFe+ibbyDBSklVKaW/76WLY444vgtECfVccRxOUJyA9Mxw54bPoUx2GJVjQUBZdF2jPF+BLcfZdm1CN4kvDd8ksjpZzAjQVxVO5HL1xM5+7Il35iBXLzSanCcMyb4kjHmJQYC6AMtzkqzaWBMj+C/98to3ecIn3qG8GsPguRCLl2DMTmE4PYjJmaint+DUrkF95rbCR/9BWCC4kUu30D49R85r9NVi5aUaRNqwCKTbafwXvthwscexwxOWA4imWXIVTuQCpagddVhBqcwJ4cxw9OWfd0cUqJ31Vm2dSd+ZZ0TCL3yTaSCapjrm614UTvOol3Yj5hWiJS1ALmwGqlwGdOPORsUzcDYJd1NxIR0a3wOmZPSCnCvv5vIsccxJgeRS9fiWnYd6BHCbj96byNSViliWiHhIz9DXrAeOacSISWHyPEnCTz+d9YzK1tnNaHGifVvhFx/TsxYWXIRx/tPoc5Zw1x/Dpqh0zYRFfJn+zJ5sO4h+qatty+dU91gwjUlO8nwpKEZmi0REQWRbQUb2VYQ9aTumerjf2p+wFh4HEWUcctuTvSddpDl2qF6srwZNqEGy0972hEGNDvHbKrSKqlKq/wtViSOOOL4fSNOquOI43JESg4ERmKGjYEmIieetH9Wx/vx3fZ59L4LBF/4CmgR5OKVeK/+IGZgnNChnxA+/kvEjFLIKMYMjCHnVWECyqLtRE4/a5E0SbF8nJOzQXaDFravIRcstfXQsxAkGTExA739DMxWYPUIWvc5PNvfRWj3N9FnNdCtJ/Dd+Clw+VCbDiGlFyPlVIKkOGQRYlpBrF80gK4iF1QjphUQeOYLaE2H0ZoOI1duwbX0GtTaXaCraIB64XUEX4rz84IQU5XHNJCyyjCnZgJYFC9K1XYih6IabGOkC9fKmxAEEbl4JWr9q/bvpNyFyEXLUDNK7EZLISkLMb0IZdF2K1xGCyNmlaNPDKAfeAS5fAPeqh0YgTFCr34LfaAZKbsC7/UfR2s9Tvjgo9Fb7mvEVX2Nw09cazkGsjdOqn9DLE6rZHvBJvZ3H8E0TdbnrmZ97hoEQeTxxmcI6SFKkoq4ofQqxsLjfL/2UYZDo6S4k7mu5Eoeqv+p43xnhuoI62Fb6qGICh9Y/k7yE/L45cVnZirVedxVcStPN79gV8RVQ+OXjc9QmlzkOJ+AgCxKMfMuTMhjW/5GDvQcxTRNNuSuYUVm9e9pleKII47fJeKkOo44LkMIhnrJVqQYcgioLcdnmhNnSGz7aSJ1r6I1H7VDWvTpUZSFW5GrthN8+RvRgJTFVyDnLSJ8+jkih39KBEuGIAgiRmgKISENY3IQqXAZ+mzDnOSyKq0QW9mOBInUveJs9OuuJ1z7CpHDP7E+03sBvfsc7q1vI3z4p1Y6YdYCBE8SiLJTKqF4URZute6zdhfmRFS3rTUesMj/HCJujPXiKt+I3tdoa8iVhVuRssvRZp1CZiBlV4AvBWOgGXnBBvRWp38xuoreXY/h8mGKIlLRCszpEcTUfIzAGNM//ii4fCjVVyNmlBKpe5nQ7v+xnl9aIZ4r30foxf/EGGiaeXYNCIKI2nLMrvxrLccwtTBmaNJxaaO/CT37Eq/444T6N4YgCNxdeRs3ll6DaZp2/PiG3DWsylrGtBqwPaWTXIn848a/ZTQ0Too7iYih4hIVWxMNkOZO4ZU5ITGqofJC26sku5I43m/prMfC40xGpgnr0U0pQEgPsTF3LRdHm9Fm/vbW5axiZ+FWjvWdZjQ8BkCOL4uV2cvYkLeGm8qudcw7jjjiuPwRJ9VxxHEZwnTNj9i2IJeudvhOW4NuYtwBhlodqYcAWtc59LFeZ0BKwz4EdyLmcPTVt95Rg/eGTxB6/UcY/RfRZ67h3vp2jLFetN4Ggru+jly6Bql4RdTxAyshUfQkxHgf6JfQL0vJOSQ88DW0zjpCu/+HyEyTpVS6BmnxlRiTgxhD7QSe+hekouXzfLRnTxTrsiBll+O/699R20+j9zSgtp1GG+5CKlqO3lkLoohr6bWo9a9acgpAu3AAuXJzzLn0wVZnKMzSa60UxabD1kAkgFq/B9eGLMy5spWRTrTGAzHhO2rrcfTueuc1us4hFy51rpkkI5esRq19Jfq8BMmKLw9fopofxxvCr/hixlySKyaGXBRE0r2WP7xXlLir8lZ+0fg0qqGS7cvkyqJtnB6sdXwmpIVsicgs2iY6uKZ4J71zxkuTilmdvZzipAJqh86T6U1ncfpCREHk79Z9jDODtUiCxIqspbhn5nWpeccRRxyXN+KkOo44LkcMdl5y2JgYQFm4DbXpCAjgWnY9ruqrUM++4GxOLF6JPtDiIHVieiHm9JjzhKaBOT0vdRHQ2k45CaEWxpgYQGs6bDcXqvWvoay8CdeaN6F11CC4vIgZJYgpOU7f5opNCKLM/NgSwZuEIClW1PicCqzeegL3+nsIvvAVuzKtNR5AKlqBFXRibSAEf5pladfXaFd6pdyFSHmLEAQRIkH0jjPWbYYmMTyJ+N/8VQTFjRmeZvqxv5m7EBjjfcglqy3fbUnGtfxG1Av7HXNW619Dyls076FomGO9MWuIIMboq8XETMz0Ioc3t5hehGvN7ej9TTP3IaAsuQq9owZ5wTpL9y4pCP40tF/rVx3H7xKb8taxMmspo6Fxcv3ZCILA0ozF1A5FN0Vb8zdweqCW+pHoG6QMbzo3lV6DT/ZybriBXH8215deZf9uvr+0T/GyKW/dH+am4ogjjt8r4qQ6jjguR7xBSJ569iVQvPju+GeMqSEix59AbdhnSRMCYxCeQi5bj+hOwL3xfsKHH8OcGkbMKMGz6QH03gZC+35gn09esMFKWGw8iF3tdvkQMy8Rg6yGbEI9C72nAf+tn7UaBGtfnpE1CLg33Y+pWRprfbgTKbMUwZtkf14u34AxMWhZ3hmxKYHm9IhD6gGWZ7P3hr9BvXAAU49Y3tznduO97uMYI51o3efQuuuZ/vlncK+5HX0mnMb+fGjSkm9klmKqYRAEh0xFUDx4r/kQRmAcQVYQXD4rxXAuJBkpf4lV8Z6F249SfQ1q0+Fo0IziwVW1A8HlIXL8V2DqiKl5KCtuRBrpJHz4Z5jjfYgpeUj5iwkfeBQxpxK5cBliSg7Bl74GMw1rgjcJ77UfI/DUP8c+kzj+z1ANjV9efIZT/TWkeVK5s+JmKlIXcHbwHK92vo6AwFVF26nOqAKs2PEri7ZRmbqA/sAgS9OrqM6oojyljB/U/ZiuqR7SPWm8bfE9SKLE1cU7uLp4xxtev2W8jccbn2YwOMLKzGruqryNoBbksYYnaBprpTipgPsX3UGGN/0PtCJxxBHHb4s4qY4jjssRnkQIjF76d2oQrekIkdqXLLs6QG85hnvDfYhp+QR3fd2qEgsS7u3vRM4uJ3zyVwSe/QJSZimeK9+PPthqJRUGJ9F7zuO+4j1oTUcRFDdiehGYJmJ6EcaMLETwp6EsvdZqwJu5JoCUUYKphlDPvTpngqbVMKh4MfoaAcsHWq7agVK8ErXxIFrTEbSmIwgJ6bhW32ZJImYqulLRCqTscgRfisOLW8wsQS6oBtlN8Nl/B9NEx2qEdK27y26mNEOThPY8iFyxGWauD4DiQR9qJ3zkZwgun1WVbj0xcyOyFQkOiL5kAIypYVxVOwkf+TmzGw5X1U6LHC+/Aa3lGKI/DblyC3pHDe4tb8MYaAFMpLzFRM6+BIKA7+ZPg+zCFESCz33J2ix4EvFc/SGIBBybHGOgGWXRdptQgxW1Hmk8wHyJTxy/HXa17+FAt7VpCkwFebD2Ed6/7B18t/YR26WjebyNz6z9KMf6TrG7Yx8mJjm+LD688j0ku5M41neKhpGLrM9dzfuy3kmSKwHxEq4w86EaGg+efZgp1XL4OdR7nARXAr3TfdQNW5vBC6NNPHTuZ3xizQd+TysQRxxx/K4RJ9VxxHE5Yqz/1/7ajEw7yC2A1lOPeXGOR7WpEzn6c7TsimjIyfQIZngK14obCb74VWaJmpiah/eWzxF85l+iTh8JGXi2vRNEEcGfhjnRj2f7uwgf/gnm9BhSbiVS1gLMSIAYwqfr6MONzqHOWlxLnY4W5tQwxmAbvts/j9Z2CmNqBK2jhqmH3o9UtAxjsBVzcggxqwy5aAWmFrb0zHMqzGZwIsadBNNEyizBDE2id5xF8KegVG4lvP+h6DGKB881H8acHgW3H8Hlm/moQWjfD9AaDwEmUu4i5JLV6COdRGpehJoXrOrxTX+L3t/kSJd0rbsLpWwt07/8nO2gojYexH/3vxN+9TvR6ntokvDhxyzHk7nTDoxhhpwJfQBSeiGa4nWQ7Th+OzSNtTp+DmpBjvSedNjeGabBkb4TvNoRld30BQbY3bGPRCWBp1teBOBo30naJzp5x5L7f6Nr903324Q6Op8Weqacf/etE+3oho50CZeQOOKI4/JDnFTHEcfliMR0GO+55K/E1HyUJVeh1u9xaJGljBLUQSdRMENTMY2Neu8FIm4/c4mwMdqDWvOcZS83i6khTDWE3t9kE2EhMRPvbZ9Drd+DevpZy41ktuo7hywry68ncuyXjnAUMTkHM+CUjwCYwXGkjBKQXQR+8ff2vPSW47i3vwtjoh/19HOEXvsOgicRuWxt7JpkFMekNkq5VbiWXIWphUFSnIQabDmLWv+qfd9y2bo5cpjZ9WpALl1luY3MzM0MThA5+TT6vKTDyJnnLDnLHEtCIgG0lhMYY87naU4NIxYtd2rNBQGlaoflvT1DwMX0YpSKTUgzloJzG03j+L+jJKmQxtEm+2eX5KIspZiDvc4Nmkdyx3x2NDTG2SFnw+nJ/hruW/gmPLKH8fAkh3qOEtYjbMhdQ44/i6AWYk/nfvoDgyxKq8Qrewhq0Y1xcVIhLtFFw+hFe6wgIS9OqOOI408IcVIdRxyXIYSkTMxLkGrXhvvQB5uJHPkZytKr0RoPYoYmrejt5GzksnWo53bbx1uNbqMOKz4xNQ/RkxTTOGhe4n/exnifs7I8OYha8xJqfTSWmUgAQw3huebDGMOdYGjoXeesYJb2MxAJICSkIxUtA0lGSMrCnGPFp1RYrhtGfzPzK956bwPaxcP2uBmaxJgaRkzNixLh0jW4Vt8GathqepTdKGVrMScHMFOyEWT3zJpmx9yfPtTm2EhoLccQvEmxx430OBoOwdoMxOjBDQPhEhZogtuHXLTc0fgo5S60pC8DzVbgjSijLLsOfaAZ9/q7MXUVQVLQhzqY+vFHrKZHxQ2RQMz54/jf49riKxgMDnNmoJYUdzL3LLyNJemLaBhp4kS/5VazPnc1VxZt52DPMUcS4+rsFQyHnPIst+RGEmVCWogvn/iGbZO3r/sQf7vmw/y88SmbxJ/oP8O2/I2cG25gJDTGsozF3Fh6NdNqkIfO/ZTWiXYKEvJ46+J7/jCLEUcccfxO8Ech1d/85jd58UXrtdn27dv51Kc+xaFDh/jCF75AOBzm+uuv52Mf+9gfY2pxxHFZwBy9dJU6cvKpqASgQ8B782fQ2s+g1rxg6ZJlN8qy6zAmBi1nD11HzCrHDE1hjHYjJGagLNqBmFmK1n4q2jhYuQV39TVoDa9Hdcxuv1X9nj+3wFisX3JoCqVkFeGxHiInf2UPS0UrUKp2EtrzXSKHrTANuWIzQvFKzOlRTEMnuPd7CG4fypKrmOvuAdYGIKYyGwniu+Nf0XrqUeteQWs7hfbjj+Jeeyf+B75B4Nl/Qz2/F/X8XsS0Qny3/j2C4kEuXoHWfgaj/yIIIsrSaxyOKbMQkrJBlKP3KAi4qrZbNoVzbPOUis2YkekZzbUFV/XVyOUbUBv2YoxYDi5iZhlGcBLTNJEKl2FODiGk5GAGxpl+9COIWWV4r/sYiDLBV75hy3pmq+bq6acv+V2I47eDR3bzruoHYuQVb19yL7eX3wAIJLsTAfjoyr/m5fbXGA2P4RbdnOg/zYLkEnqm+9AMDQGBG0uvQhFlzgzU2oQaIKJH2Nt10FEVB2ib6OSfN33GcX2v7OUTaz4Ql3zEEcefKP7gpPrQoUMcOHCAX/3qVwiCwLve9S6ee+45vvKVr/Doo4+Sm5vLe9/7Xvbt28f27dv/0NOLI47LG3M1taaJdvEw6lybNS2MMdqDmJKLWvsyADpWeqJr5U2E9v6A8OHHQJRxb307gsuLPtiCMdBC6MhP8Vz9Qcs1w9ARUnItrbE/1dIdgyVPqL4KMzjuqH7bAS0XDjimq3fUWMmJkah+VLt4CP+9X0Lrqbejys1IgMjRX+DacA9q3SuYaghl0Xbkis2WdGKOzMKy6BMxR3us8wOEpwkfeAQzPI050mUfa4x0Wo2RnWetYwUJeeFW3GvvQPSloPddRD2/x9ZoC94klPINiN5E1IsHwTBwVV9t2fetugW9txFzahi5dA2IEnpfI8rCbdYGJLscvf8i0z/5GIiSNf+ydUTO7yVyJJrO5978gKUfn/HlNgaaCZ96GjExy6GT11qOgTse/PH7xqXIa7Lb+bYi05fOA1V38c0z36dmKCozuq74SlLcSRzsOc4TTc9xuPcEG3JXx5zPJ3mRBckOfgHsUJdLXT9OqOOI408Tf3BSnZmZyac//WlcLsvgfsGCBbS1tVFcXExhYSEAN998My+99FKcVMfxl4vg5P//GEDwJMRIEMzwNGqjk9yqjQfRehuj1VdDI3LiCVyrb0M987x9nN7biO9N/0TwhS9jnHwKsAJd5NI1dmVZPfsy8qLtSHmLMUY6MQ0dreMMYCJ4EjHH+6IXVtyYMc11JmZkGr3v4rxhA9GfSsL9/4nafJTQgUdQa15AzCxDWXIVxtQIYKK1n7bSDufpx8HEuIRftNZ1Lkq+TR3twn5cVTtQ+xrRmo7M+F+D6E9FzCgh8PjfYYYmEZNz8Fz7YYyBVsvT2tDA5cV73ccwRroIH3jEvoZUuAwps3Qm2RLQQW3Yh7xgPfqsw8jss6jfizHpTKI0Blpi49UBKa2AeIbi7w6maRIxVDtgZRZhPYJLVBCEN/CyBCYik5wfcTbf1gzVkeFNo3PK2sj1TPdxoPsYpUlFtE5Yzjmp7hS2F27GJSs822JtdL2yhxtLr/5fzTGOOOK4/PEHJ9UVFRX2f7e1tfHiiy/ywAMPkJmZaY9nZWXR3//r3Q/mIz094Xc2xzji+GNj0rw0lUpYup2pWisq2V2wkNwr76F/tI1gW9Q3OW3tNYwffho1HK0OS75EzPCkQ7FsBicROk85zm9ODiI3voIxh7Aag62kb7iJkb2PoU9agTBa20my7/4M48NNhNqta+sdNSQsv4rpkU5MNQQIpO+8Hzkxg/45TYSunDKyF1UzGexlaC75F0QyFy1F8kq0v/4ju2prDLbgLyhHVwQCTafsa3nL1zgJpySTte02+jrPYISsexc9frxJScz305A6jxM4+ZL9s5yaQ/69f0vn/7zfDpIxxvswjv+CSF9zdDMSCWKcegJTc4pi9M6z+DJycPqxgDc0QEhWMOfITBS/HzG1imBLjT3mLlhI6qab6Gs/Y2u3PYVV5O24jTGXydjRZxBECX3q0jaLmZmJlxyPI4pzA418+9gjDEwPU5VZwUc3/hWmafLfR37A+cEmsv0ZvH/9W6nKrLjk5xM1F27ZTXhOE2pGQgod492O4waCg3z/2i/TONRMSIuwJm8pHsVDecFtXLlwAz2T/SzOrMSjuKnrv8BYaIJVudUkuP1cGGrmf449TN/UIJXpZXx041+R4U/7va5LHHHE8bvDH61R8eLFi7z3ve/lU5/6FJIk0dbWZv/ONM1fWzG4FIaHpzCMuI9rHH8mkD0QibVWM1feib/qerSuOvTuc3Q99U2U5bfgylqIMdoLksTYxVqEvGoYG7DIoCAir7gFY6wbo25OE2P5BnRp3j8BgkggEGY+xprqbEI9i6GjL6G3O2ObAx3n8d/3FbTuerS2kwzv+zliYgauDfdijnYDAmrfRVq/cDdS4TKURdtRLx62GvlK1zDU2gKehBlSHsV0dxPGwDwLtL42XKtuRb2wH8HtR0zJofeXX0FIyUNOykbwJOKq2oE+MQC1e6MflFwE+roc59JG++irOYY+NeIYD/W3Q9D5HCJjQ4jJ85oeJRk1tWzeqgkEk0pQll5L5PSz9nFm7lJ0XUXMnsYYakPMLMPMWsjggaeRKzcjKF7E5CxMdwLtj/4zgjsBz42fRkrNY/K7b+dSGBz8zd5s/KVCN3S+dugHjEesHoLzgxf53tGfoZsG5wctrXP/9BBfPfgD/mXTZ97Qa/rm0mt54uKzmJh4ZQ/XFlzFa+Z+ToXO2scUJOQRnjApdpWBCybHVCZnOhMU/BS7ypge1/jq2R9QM2Q58/hlHx9f9T6+dfZHDIes72DjcAsPHnmM9yx72+9tXeKII47/PURReMNC7h+FVJ88eZIPf/jD/N3f/R033ngjx44dY3Bw0P794OAgWVlZf4ypxRHH5YFLEGqA4Mv/jXv9PYQP/YTZhj6towb/vV8ifOgnaA1RKzhlxU2IiZlE6l8jfOBh8KWiVO3ECI6DrlsuGklZCMnZmOP9IEgoS65ALl5pOYjMVlcVD1LZWkt7PAdiYga64nHogAVfCoInAWO0y/aONsLTqJPD+O77MoFffMZuhNQ7ziAu2o7//q8QeOpfUM/tRj23GzFnoeWLPR0luHLhUiLj/TC3+u5Pxb3mdtxrbid8+lkix58AwBzvx5wcwn/flzGGO9G66pDL1mNMDiC4/bhW3ozWdATdUWAUkFJyELMW2FpnAKVkJcbUiB13DqCUb0QqWkawv9m2znMtvQ4M3ZJ79DSA7ELKLif43H+AFkIqqLZ+19dI5PgvZy4p4b3mQ+iDrURORZsRpcJlyEXLCL7wFXtM7zyL77bPX/I7Ecf/HxORSZtQz6JjsgvDcDq6jIXHGQmN0THZRUSPsDxzCV7Za/9+Z+EWlmUsoS8wwILkYjyyh7u96eimwYWRJgoT87hv4ZswTRMT00HOm8Za6ZnqY1FaBSE9ZBNqgGktwK72PTahjs7RWQWPI444Lm/8wUl1b28vH/jAB/jqV7/Kxo0bAVi+fDmtra20t7dTUFDAc889xx133PGHnloccVw+ENxgxlaMjf4m1IbXcVjPRYJorSdjAlC05qNImaWYwzOOFYFRtLZTyIu2oc5UTo2e84i5i/BsfQehw4+h1r2Cem43cvlGBMllEXCs4Ba5csuMV7PlkCFXbEJMySV88CeWQ4c7AWXZtdbxc5oYAcyw5Zc9NyERQO9vRj2/B3Myuqk2+i7g3vwWtI4zGOMDSFllmCYoFZssKz9DB5cX1/q7o+fpdFbMzelR1AsHLN3zjJxCzCjGe/3HEQQRwe1H66rFnBwCBFzLr8dUQ8gF1eiK2wq3KViCsmgHauvxmUZGA7mgGiG9CLV2F1J+FVJmGVLuQkJ7vzdzLhDTCvHsfA+BJz5vPye9qw4pp2ImUGZ2kjqRM89jzF+TzrOo8xoUzdAkkdpd/CkirEfQDQ2f4rPHDNMgpIXxKd5f88nfHVLcyWT5MhgIDNljlanlGIbBUG/UMjLbl8kP6n5Mx6T1JuPZlmQ+tebD6KbGi627GQqOsCJrKdvyNyIIAjWD5zjQfQS35OLDK99NcVIhh3qO8V+nvk1IC7Ehdw13V97Gc6272NVubUpFQeSm0mti5mhgkp+QS/dUtC+gMnXB72tJfmNMqwEUUcYV13jHEcf/F39wUv2DH/yAcDjMF7/4RXvs3nvv5Ytf/CIf+tCHCIfDbN++neuuu+4PPbU44rh8IEvEeNkBKB7E5MyYYTExE2S3s2rs9qPPxIzPwgyOozUdcYwZvQ1oHWcwZ481TbSLh3BvfTvqgYftOHAhMQPfnf+K1nyMSN0ugr/6J8TMUry3fx69u55I7cuEXvwvImkFiJmlMJdYKx6k3EUxFWgppxwzEpsSKLj9+K7/GyIN+2yHEAC5aidK2VqEhHTChx8j2F2PlFGCkDBPdyq70DrrHN7SxlA7evd51PpX0dpOg8uLsux6XIt3ovc3EXjiH7DjyFffhlyyisBT/2RX7MWsBUgF1dZxMxprvasOl3qNTahhxnHkwryND6APdwHzvK4NDdGfij5nU4HLh5CYEbMmYmpezNjljudbX+GV9j1ohs6a7BW8pepuGkab+Mn5xxmPTFCWXMJfVb+ZFHcynZM9XBxrpiixgPKUUvscvwt7OUEQeHf1W3m88Wl6pvtYkr6I2xfcgAnops654QbyEnJZlr6YXzY9Y39uLDzOwZ4jnOivoT9gNZc2jjVjYpLvz+V7cyLNz4808qGV7+GxhifssQM9R8nyZvLanERGwzQ4O3jOQaBFQWRL/gYSXQn84sJTdE31sCitgjsrbvmt7ns+ptRpzg01kOxOYmFquUNm2R8YpH74Ajm+LBalVaAZGg/X/4wzg3UooswNpVdzdfGO3+l84ojjzw1/cFL92c9+ls9+9rOX/N0zzzxzyfE44viLwxvEUbtW3ISycCtae40tU5DL1iFlluBee8eMLASQZNxr3oTWUYM6x41DTMkDX4qjMozLixEYZz7UpiPOOPDJIYzxPiK1L9lE0xhsRa1/Fb2zDmYqrsZIF3iTrZTF9lMIvlTEzBKCL/0XYnY55lgvxngfUk4FKF4rHlxSQLd2EYI/FblouTWHmhcdc9Ia9+PZeB+h1x60HT30/osIkTyknEr0vkZw+/FsfgC9+3xMwI3aegKtbaY5MxJArd2Fa+k1RE4/x1wSHKl5AWNq2OFjbQw0Ezn7stOjW9diNi4AQkK6457AkpIIsgvt4hyJzsJtiAnpBF/7jhXqIkq4191p3UtHDcbMueWqnciFS4l9d3H5on2ikxdaX7F/Pt5/mtLkYl5ofcWO6G4Zb+OpphdZmFbOT84/bpPRm8uu44rCrTzW8AQnB86QqCRwZ+UtrMpaxmud+3mp7VUM0+CKwq3cUHo102qAPZ37GQqOsiprKcsylwAwFZmmfbKLosR88hJy+Miq98bMc27AyvG+0zG/Hw2N2YR6FqcHzjKQMOSINA/pYY70nnCMgSUz0ecFB6mmxkdX/jUHe44yEZlkTfYKipMs96sPrXw3YJHvtokO/IqfbJ+1kdYNawOgGirVGYtxSy6Gg6M837qLweAQyzKWcGXRNsbDEzx6/hc0jjZTlFTAA4vuQhQE/vPktwho1r8tKzKX8u6lbwHg3PAFvnP2Rxgz89yWv5FMXwanB603QBFD5anmF1iSvoi8hJyYNYojjjgsxBMV44jjssSlm24jx39JpOYFPFveipB0P5FTz6K1HGeq9SRK9VX47/4CakeNpd2tfRm5fD3Koh1onWetVMPsBYi+VCIjnZbLhWiRb8GXgt50OHohtx8xNR+jt8E5AV2LCUwxhrsc1WcAc6wX75v/C9PQCO39AdrsuQdakMvW4l7zJoKvfN0KrAGk/MWIqfmYaghjuIvpn3/asvGLWQGrsqb3Oa3NzNEevG//NqYWwQxOWMTZnwIun51AKJeshuC8zYOpY4x0Yc5PRpxHguyr+5JjxuSilegDLfZ1BH8qUloB7s0PoJ7fixmaQi5aYVkAun24N9yLEZrEGO0hfPBR6xwVm5DLN2KMdBA++gtQQ4hZ5Xhu+CSoQcKHf8r0PE375Y7uqb6YsbaJDptQR4/roW2i3UFGX2nfg2HqHO+3NkDjkQkerv8ZLtHFExeftY97vvUVihILeL51l60/Pt5/incsuR+XqPDDcz9BNTRkUeYdi++jOqOKJy4+y8mBGtLcKdxRcTMVMxKLsB6hKq2CNE8qIzNpiR7Jzea8DRzrO+XwmE73pJHhSY25v4Wp5RzsOWqTU4BF6ZWIosixvqjTzo6CLfgUr1357Z7q5ZX2veT4s6hOr2JKneZrp75D3wyZ35q/kbsqbuGrp75tW/Wle1L5xOoP8q2zP6Rv2nLLahm3pF5NY61cmAmbaZ/o5KH6n1KcWGgTaoAzg7V0TvZQmJg3s97ROR/oOcqqrGUx99c73Rcn1XHE8WsQJ9VxxPGnhkiA0P6HcK+/J9pAZ+qotS8jFyxBPfO8bQund5/Dc82HkUtWEnz561aaICCVb8a1cDORsy8RPvQYyC7k8g2WzZ7bZ0k1JAV9jlxDLl2LmFWOkJCOORV1ApFLVqJhOmz45MJqAARRjlaGZ6C1nsQMTTmq4Hp3Pe4tbyP47Bds3bVa/ypS0Qr0OZV2pWonZnACKbvc8quegZiaj+DyYoz3E3jm36JV7/QiXNVXI/pTkfKXoNa/5pyP4kHMLMG15CrCh38SHa7caiUwthy3JTVidoXV8DjQHN0M5C7CNDTcG+/DmBxCMA3U1pN2k6FcuRX3qpuZfuLz9nk0tx/3hvsc/uBa4wGkvEVEjj1hV8KNgSb0jjN24MyfGhamliMJEvocMroycxmt4+0MBqP3szCtnDMDdY7P6qZB+0SnY0wzNGrnNPfNombwXExD3+Ge4wyHRlBn1lIzNJ5seo7e6QFe77Y2eNNqgAdrH+FfNn2GJy8+x5G+E8iizI6CzXhlDxE9QnV6FSmeJG4tv4Gnml5AN3XSPWmsz1lNcWIBNUPnaBprRUBgQ+4aVmYt5Z28mWdbXmJaDbAqaznL0qtYl72SytRyeqf6qEqrJN2bimpoKKJMzeA5h4xkS/4GfLLXJtQA+7sPk+FNtwk1wHBolF3te2xCPYvTg7UMBZzfl+6pXrvaPRcRPTKzPvO87k2T8pQyTvSfsccUUaY85Y+v8Y4jjssZcVIdRxx/ilBD6HNcKmahtZ+2CbU91nIMc3rMEfetNx1CT0xH75yxAtPCaE1H8d3xT4RefwitxaqgCsm5eK56P1r7GbSmo2itxxFzFyKmFWBODCIkZqKP9SGXrkH3p2IMdyKlF6KP9DD100+iLFiPmJRpSUJmICRlQYxlmYAxORjTyGiGJvHd9nm0nnqMsV7U+tdQ63ZZ+ubchei9jYgZRXi2v8talvrXHJILc7gDMTETOW8R+nAnRmgKqXQNxmAbgj8FKa2Q6Z9+CnQVqWglYloeZmgKtfEA6vk9CKkFKKWrweVFbXid4BOfBcWLa92dCO5EwgcfQZ+p5iuLtoE/DXMsGjGvNe5HkCSH1p3wNNq8QBjACsOZF/9ujPZgjP5pOkCke1N5z9K38mLbq0T0CFvzN7IsczGZvnR+2fgMfYEBlmYs5pay60jzpDoq0NsKNpLkSqRuOPqmxCW5WJG5jAM9zobc8pRSDvced1S6E1x+WifaHcdNRCa5OOb8mwlqQV5p38uhmWbFiB5hV/sePrXmQxzvO81/nvoWhmmwPLOaf9z4txztPcEr7Xv5+pnvkuvP5n3L3sFgcJhnml/kcO9x2ic6eevie/jkmg/y4NmHeb37EId6jnJ96dVcV3IF3VO9PHj2YYZDI/gVH29bfC+7O/Y65n6o5xgrMqtj1nM8HCvREgQRRVRQjeh3PtObTpIrkdqhenusMCGPrfkbOTNYZ1ekCxLyKE0uAixXk7ZzHfY8VmcvZ2v+BlRD5WDPMXyyhxtLr7Fj2+OII45LI06q44jjTxBCUpYV4e0IT5GQilai1s+3vstEn40ZnwN9DvmzYKJeOOCIBDfHe9H7W9AuRl0rjN4LuDe9GWO837LeYyYKfdn1eK94L1M/+bhtfRc58xzK4isxA+MW2Xf7URZuRZDdFhmdqZDJFZuQMktB8Tr05FJGCVJWGYLbx/TPP8OsLMYYaMa1+jY8O99LaP9DBJ79AlJ2OcIlYr0FUULrPEvwpa/Zsg65bB2uFTcQePIfo+vRcRq5oJrwHN20OdqFmVtp2fTNrpcaJHL6OWu+cyp8asN+5PL1Mdc3jVgpiZS9AL2zJlqtFwSUhVvR289gzpGoyEXLEBQPWtvJmHP8KaA6o4rqjCrHWK4/29YNz+KKwq3k+rNpHG2mOLGA5ZnVGKbBaGiM432nSXYncXv5jVSlV3BX5a283Paaralen7ua7qleXu20mgETFD/XFl+BV/ayvzsqaVqXswq/4rdlEQAuUWFaDcTM+2R/DXu6on9bNYN1VKYsYHfHPsKGVd3tne7nmZaXCOsR2iejiYo/OvdTVmcv5+KY9XekmTrPtbzMqqyl/PLis7Zt3rQa4LGGJ0hxOyVFpmmyInMppwai3tdJrkSuLtrOqYGzjM2Qa5fkYkv+enJ8mTx+8RlUQyXLm8HNZdciizK6odM42kRRUgFvXnQXOf4s/mb1+znZX0OSK5HNeesRBZH64Quc7D/DotQKUj3JVKQuYHXWcvu5XFG49f/7nOOIIw4LcVIdRxyXI+Y1uc1CTM1DSMhEzCjC6L+Ia/09aC3HQJSQchdi9F9EXrABreUomCZiagGCN8mSUfQ12iROLl+PXLTcGaEtuRD8KTHXdDQ1zsAY60W9MC8KvWEfcvEKh5c0gDE5gP/N/4XWd5HQ6w8ROfa4dbnCpUj51eh9jWjNR9FaTyAXr0TvbcAMjCFmlYPssiLXFS/zdebGWC+hfT9A77YkAXrnWcSsBQ5iLuVVIWaXW3KMOZpRreUYYkZRzH1pfRdiq8VjvZjhecRLDcUE1ICJXLwSremoPVfBk4hrxY0Ygy12tV7MKMY0rY2EMdINoohrydUYI11IJaswRnsgPIW8YD1CYibC1AhSwVLMqaFLxrD/uSBRSUAWJIJ6GM3UUUSZuypv5fqSqxAFwbbk21GwmR0Fmx2ffVPFTazPXc1IaJSKlAV4ZDd3VdxChjeNlrE2SpOL2Vm4Bc3QGQwMcWawjmR3EndX3oooiBzoiTriiIKIV/bEzK9zqpuQ7mwV7Q8M2iQ3OjZAzzw9uYlJf2CQ/mlnw+NYeJyby66lfaLTrhKvz13N6uzlKKLMkd4TJLj8XFW0gyR3Ep9c80EO9hxD1VU25K4h25dJti+TlVnLGA2PkevPtr2xP7Dir2LuwSt58MlePLIHSZRoGW/jWzU/tK/tkTzcsuD639ptJY44/lIRJ9VxxHE5Qr90TLn31s8RfObfUDtnIq5lN77bPo967hWHRte94V6QXYQPPWY7gsgLtyImpKMPtqF1nEXvb7aa44baENwJKNVXIyZlonoSoxISxYOy/Hq0zlrQow2KcvFKSxsdjI5ZqYa5IMoOYiqlFSJICkbPeZiMkgq9sxYpZyH6bBXW0NCaj+C58VMQCRLa/T8YA02ogFiw1NF0ODuH0J7vOdbHGGzDf/9XUBsPWFpkjJjQGnteWeUgCA5tt1K2DqO/yRk8U7wCMxwgMhyVE4gpebiW30ho9/8wS6Cl4pUI3iTcV7wXveU4KG7M6VECP/80eJOsMJ60fMIHf4JxwgqqEfxp+O74Z0K7vh5tvhRlfDd/Gq2jhtCurzueafjIzy55L3/qOD/SyLdqfmhLE04N1PC+Ze/gx+cf51jfKQRBYFv+Ru6suIWh4Ai72l9jIjLJuhyLgAKkeVLRDR15hhBKosRVRduhaLt9HVmUedfSt6AbOqIg2pZyd1bcwutdh3BJLq4vvYrChHxean8Nbc73eF32KtrGOxxa56UZi+mb7ndUlQsS8liZWc2Zwah3ulf2Up5SxtLMxRzojhL4ipQyNuSuIdefzbnhBnL82bb0Y1nmEtvFZBYp7mQ25a4lpIfJ8VsBaaZp0jrRbhP5/IRcQlqIJy4+x/mRRvITcrmr8ham1QBfPfVtW2d+uPc4pUlF8xxMQpwbbmBD7pr/3QOMI444gDipjiOOyxO+FAjESjbUmhcc+mS0sJVE2LDfeVzjQQSX10FutYuHcW+8z25uNCMBtKlhfPf8B+qZZwm9+m3ARMpbjJhRhBmYwAyMEj78GErVdozRHsxIADExE63lGHLZOtT63RYpFUTk4hVEal5EWXwF6sWDEJ5GyqkEbxL6QDNmMDZK2xhujxkzh9vRus87Paa7avFc/UHUC/sxp8cQ0wsQPAmImSWOBEQxqxTRn4rWehJjqA2AcNc5lKod6D2SrSuXS9dgjPUgL9xmBdXoKsqi7ZjBMeTCpRgTA5iRAHLJGkzDRB/vQypajjk9anltqyFCr//Q0nZnloDiRq17lWD7aZBkPDvegz7YGpXNBMdR63bhWn4DhKNpmeb0CJGzLzrdTAyNcN0r6O1nHOsSOftSzFr9uWBv50GH+0T98AVe69jP0T5rw2WaJnu7DrIotYKfNf7Krg7XDTdYVVXT5JHzPyesR0hyJfLXy95OfkIuz7e+Qt3QebL9Wdy24AYyvGlMRaY5M1iLR/awPLMaRZTZWbiFxWmVBLQgxUmFiILI+5e9k5fbXyOiq+wo2MTCtHLet/wdPNP8Ev2BQZZlLOa64isIaEF0Q+fCaBOFifnct/BNZPuzCGhBjvSeJMHl58bSq/HKHu4ovxlVV2mf7KQksYhby6+nYeQir7TvRTM18vw5dqV5PDzJyf7TyKLC2pwVeGUvv2h8mte7DmFisiC5hPctfyfPtrzMvi7LpvHp5hd5V/UD1I9csHXio+ExxmvHyU/Mswk1WK4gxYkFMc8izZPye3nGccTxl4A4qY4jjssRc0IZHBAv8ScrSjMV17ljIqY6z9XY0GOs6DCtSq6V0mhB76lHLltDpPGgXbE2Blrw7HgXatMRS24yA9faOxGTMtG6z6PWRT2JlTV3IPqSCe//kWXvB8gLtzkqw4IvBXnhVsthYw6k3Cq0HmciI4CUXoy0oYDA0/+KNtyO1njQkndklGAMtSGmF+HZ8jaMsT6bUNv3NNSB785/Rms/g+BNInz8SbtZUEjKwn/nvxB48b8w+mf0toKE98ZPojbuR2uM+kq7Vt6MPtBsWw0aA00IihszPAXajBxE1wgf+olVtZ8LLWK5nsyD8EbPVBRjxy4jnOyvoX7kAvn+HLbmb0SRlP/zuaSYxlUYmhfZDVA3fD5GbnGi/wxNYy2EZ96kTEQmebLpOcqSS+wUw57pPvqnB/jrZe/gSye+btv6FScW8jer389jF57gSK/1fchPyOUjK9+LIimMhyfoCwzg7nVRmVZOguLHK3sI62H6AgNMqlOkuJN5oOpu2iY6yE/IJdmdhGZoTEamEAWRVHcyaZ5UptUA/1PzA9onOhEQWJq+mJAW5ls1P7QdUprH2vib1e8nxZ3MF4//tz3PPV37uW/hnTZ5Bmgeb+O19tcdunETk10de5mMOL9nnVM95PpjrfCWZ1bTMdlN24yryNrsVVTEHT7iiOP/jDipjiOOyxHTY5cctqQYNRgDM82Ebj+u6qtBklFnK5mCgGvFjZiRoDONsGwNUv5iZ5y5ICBcggzpPRdiXETUpqPoXU7rM63tFL5bP0to7w+c4+dfQ3D5HdIKrfko3uv/BrXpMGbIIgt6dz2utXdYpF4QkLIr0DpqkMvWoHfX2TIYecEGxKRMQvsftgisPc/zeG/5LJFzu9FbjhF45t9wLb8xRpMuJqQhpeYjpeYTqX3Z8RbAnBggfOaFKKEGK0L83G70NmcYiHphf0zFXe9pQPDMjxWfQMrdYTuDANazWnY9WtspW14iJOfgWn49+mBLNGpdduNeei1aah6RY7+0P+5aeTPh/Q9xOWB3xz5+1RSVG10YaeJ9K975fz7fFUXbODdywZZbrMpaxrqclRzoPmLLE0RBpDpjcYz7R5IrIYZEDgVHCMwLUOqZ7mNX+x6HT3b7ZCd7ug7YhBos+7l9XQc51HOc0fAYAA2jF3ni4rPIMzpngMHgMGPhCW4uu4bvnH2IsB5BFETevOhOeqb7eHUmRbF1op2+wAAVKQtsm0ATk1c69iKAw3LQxKR26DyKKDvmORAY4vRATcy6DYdj32aZpkF+Qq7ttQ2Q6k7hyqJtnB06Z+vCK1PLWZRWwaK0Cjomu3CLLrJnJCX/V4yFx/nisf9mUp3CJ3v52Kr3xX2t4/iLQpxUxxHHZYlLh4+EXvwv5JJVCEuugkgAMTkHre0kckE1csFSjJFOxPzFCIaB4E1EvP7jaB01iKn5KAu3gSBiDHeiNuxDcHlxr7sLKXcRkTPPz5GKCMgL1qG1HnOQYjE5B72n3uF4Ibh9Vh6LJOGILxRlTN0ZEoOhIeVUWtKJV74JgN5hndd39xcJPvvv0bRBScZzxV9jjHaj911EnxwkfPLpSzQHgtZyBL15RqeqhoiceAJlxY2oNS+BqSP4U3GteVP0A6YZc45Yiz9AUhDcPsfmQvAkICRmOAi4mFWKlLUAtfZle0wuWY1r5c2Y4Wm0lmMICRmWzl0A35v+Aa3tNIIoIZWsxhjrw735LRhD7ZiBMaT8JRgjnUipBXhv+jTGUCtILocDyx8bzzQ7pSh1Iw08XP9z7lt4Oy7JxdnBczSONlOYmM/anJW2pOGNUJ5SyufW/w1nh+pJ96SxNKMKURB52+J72dN5AFEQuaZ4B0szqriycBuvde7HxCTHl8W1xVfSPz1Iw+hF+3wrs5YyHp6gZzraMOiTvchCbLV/IhIrSxoIDNmEehZtE522r/MsWsbbePLi83aV3DANnmx6Do/kdhzXNNZKoivWjk6+xFuKLF8Go6GxmPE8fy4eyUNIj/4NrM1eiSxKHOyx3h4JCFxZuI2S5GLGwuN0TnaT6k7hrYvvpiAxj89t+ARnBupIdCWwfI5eu+gSMpC5GA2NkeBKQJmZ74n+MxzsPopX8XJt8U6Kkwo5O3iOB2sftj8T0IJ84fjX+MbOL/7ac8cRx58T4qQ6jjj+hKD3NqD3NuBa8ybExAyCL/wns7oP1/IbUJZdR/D5L2OMdAICyrJr8Wx+C+HTzzL1448gIOBaeSMJ7/wuWutxwkd+jhmcRCpcalV2dRUhOQe9qw65fJPlIqJriJmlSLmVIBCVechu5PKNmMEJ3CtvIXz05zOzFHCvusWqlB9+zJ67snCbFdM9R04BYIz3oV543aGNRtcsz+2JQVuyEhloRlqwHoSoNlpMLcCMxIZ3S2kFiFveYm0eEjMcGwG5fCORmhdt6zrBn4Z72bWYo13RYBjFg7JgPUZKHpGTT1pEXJKRyzciuHyoWgRjuAMxrRAxKQt9tBupZJW1fr4U9IEWpn74XuSiZfjv+nfM8BTBXV/HGO1B8Cbh2fEuxIwSgs/++4xGXkBZdh2uZdcSeOpf7LAXKacS9xXvtZodL+EG88fC3OrqLI71nSRB8ZHsTnJUsVsnOri78lZebtvDif7TpLiTuXXB9RQlFTAUHOZAt1V53pS3zrZvm4xM2fHda3NWOq7zpoqb2FawicnIFMVJBYiCyDur38zzra/QNdnNwrQKri3eyVh4gr7pAXqm+/DJXu5bdAf5/hyO9J2wq7XZvkyuKtrGkd4TtrWegMDG3LW0T3QyEByyr1uRUsZoaMxBwtM9qTGkPKAGyffnMjynUpyg+FmdtZzTcxoa/YqPKwq3Mh6Z5FDPMUxMqtOrCOsR/IqfBMVvV6uzfZmsz11FSXIhL7e9RlALsTl/PVXplSxMK6cqbSE9030sTltoe09/eu1HmIpM41O8CAiYpkmKO5kdhZuZVgPs7nidicgka7NXUppchGEaHOk9QftkF5UpZazOXsFoaIwHzz5E51QPftnHvYvehEdy86Nz0b/rCyMX+eiq9/H9uh/HfCcM0yCoBfHK3tgvURxx/BlCMM1LlW3+9DA8PIVh/FncShxxMPndt//a34tphWCaGKNzmhYlxWoSnFMxBXBvfXuMbMBzzYcI7f6Wg2y6N96H3t/s1EyvvxsxMYPQ6z+CSBAEEWXFjYgJ6UTqdmGO9oAg4Vp1C3JhNWrrSSvERAujVGxESMlD76rDmBzCmBpBSi8EQ4/Gls/O56oPzDhpRCGXb4w5TkjMxHvV+1GbjiB4k3BV7UDrrCX02nfmLI6MZ8dfEXrtwTmL4Md/z3+g1r6M2ngQPAlIWQsQU3Iwp0fRmo6A4kUuXYXoT0PrOW/bDYq5i1CqdqCe32trqYXkHHw3/a1FlOckSbrW341at9vhHqIs3IoRnEDviL6+F3wpyOUbopKd2WOXXIl67lXn2LLrYo6bj8T3PPRrf/+7xgde+9Qlx2edJ+YSSlmQuKXsep5sfs4eS1D8fHL1B/nSiW8wrVlk1it7+ft1H+N4/2mebXkZwzTI9WfzwRXvwjANfnrhSVrHO1iQXMJ9i96EX/HzxMVnOTVQQ7onlTsqbqE8pZS9XQd5teN1BOCa4p1UpVXSHxjgmeaXGA2PsyR9EWmeVMCkZbydjsluChJySXIlopk6mZ50BoNDuCQX/YFBBgKDVKQsoDAxH9VQOTNYx2BwiGRXEm9fch/nRxpt7TbAisylXF9yJd8++yPGwuO4JRcbctZYbiMIdE334pM9CAi0jLeR5cvk+pKrkASJ79Y9bJP74sQC1uSsRNU1Low20THZRXlKCfctvAPd1H/j9Xi+ZRevdr6OgFXtv7p4B1849jW7ii8KIh9e8W5ODpx16LNvKr2W/sAAx/ujEiiv7GFFZjWH58hlALblb7STKufj69u/gCRdXv0AccTx20AUBdLTEy75u3ilOo44/gQhJmZgjDvjiTFNjEvEWes9DTFjWttpB6GePU6b5zihNR5C8CRYhBqsxsa6V5Art1iEGiz98cmnkMvWoDW8bmuew8MduLe9A0QJfSZSXBtuR0gvtBw0ZuPPKzcj+FKQ8hZb8hKwKsVLr0HvqnPIL8TkbKTMUqTMUvT+JoJ7vgvhAHLZWvTBNgS3D/ea22Oi0QlPEz72OFrDPuvn6RG08T7c6++d46oxhlrzEu7t73D4dxu9DejphTahBjDH+4icfdlBqAHUi4cchBpA72/CnGeRaAbGMMadnsXW+CVS8zyXX4pdmjuFkXnyCIDCxHza5sWLy6LM+VFng+yUOs2rXfttQg1WuuGBnqPsat9jO4H0Tvfzctseeqf77DCVuuHz/OT8LylJLrJJ4LQa4LtnH+YdS+7j8can7XP+9MKTfHjFu/lB3WO2bOJo30luXXA99cMX7HNeHGthcdpCVmcv59Hzv7A/n+fP4XPrP8kXj3+N2mHru5nsSuKz6z5OQAvxYttuJiNTrMxcimEauGU37eOdfPXUd1ibvYKNeWvZ332Ufd1R6c7t5TcyEZ60w2rGI5MMNzzO8swljiCa9skubllwPS+27aZpzPqe1Q6dxzB/SUSP/Ebr8eaqu3ihbbd9zmdaXkIRFYcsxjAN9ncfpWaOBSDAgZ4jJCjOXoGgFsI/4xc+F+UpZZck1RmetDihjuMvCnFSHUccf2IQ/GnIi7ZhTg7ZHtQAyuKdSLmLHIRQ8CYhl69Hm9Ucz0AuWWVVZ+dYbInZ5dBV55QZuDwx0eGoIcyJ+YTQROs462giBNDbz6CPOpMbzeFOfPd9BXO8D635KOqFA5YkJDEL95a3Yoz2oPU2Etr1DaScCrTuc6CGLW109dWYpokZniLwwlcc8d/e6z6KXLTCuu5QR8y6xcxZi8QmFZr6JTch5kRsAI5pqLENkYlZGJGgLd8AkLIrQFJQ66MVaDGzFKVyE3p7lPwLvhRcK25C6zhre4IL/jSUhVvB0Imcfs7hFf7HRFCPldxUpizgtgU3cHGshR/W/cRuMLyu5Eqm1GnOj0SJtSRIZHjSY86hG5rDWg9gKDRsk8pZXBxrdkRzA0xrAU4NOIkhwMmBsw4dMsDF0ZZLnnP+tXum+9jduZdJNfq9Ho9McH6kkedbd9vn7Zrq4b6Fd/BE07O27np/zxGS3Ukc73N+x17vOhQjhxgJjTIVcYYmAUR0NWaejSNNaPPkN2+0HnOjymcxFIrdeHtkNy7JhaZFmzvdkpsl6Yvomor+/Wb5Mriu+CraJjppGmtFQGBHwWZWZy9nIjLJLy8+4zjv3DcWccTxl4A4qY4jjj8heK77GOGjjxN6+b8tuUf11QguH8Z4L2rD66gXDiCVrQM1iKB4EVw+K6lwJgIbQUDKq0JrOY5cvt4KSAlNIpetRUjIQFm03Y4eR5RRllyJOdZH5FS0+icVLkUp34DeGdWHCr4U5JJVVlriHGIipuRiGjr6RLSqLngSEX0pmKYxk8o4I9uaHMAYakdtPmqTZW16BNf6u0EQiZx8muBLX0VMyUWp2ukg1GA5kQguv+XQISuI6UUYwx2AgLL4CgR/KnrP+egHBBEpb5GdyDgziFKxyXJImSXLgoBr2bWEBlswgxP22rgWbUdKziF8+GdWQ6Q3Gbl4BULlZiKnnsEY7UbKqcA0DTANK0FxoMVKSfQmWc4n6++25iQpoKmEDjyCsuQKQLBIf28D049+GDG9CO8tnyb4q3/+P3xrfvcIasGYse35m7gwchGv4uOz6z9O01grRYkFFCUVEFADdEx20zjahEfy8KbyG1mTs5KjfSfonrJSIvP8OVxdtJMT/TWOJsGVmUuZjgRon4xWwIsSCyhOKrSrtWDFdlelVdj+zLNYnFbJsb6TDo/moqQCptXYcya5nW8FrHTFWD3wUGgkhqif6D8d08jYOt6OLMpoepQEuyQXhYn5DrLqV3zsLNhCzWAdkRlynOvPZkn6QgoT8+mc7LaPLUkuIqxH6JiMSr9m13n+eizLWMLhXqdl5ZrslYyHJ+1wmgTFz1VF28hLyLGr/KIgclPZNSzLWIxhGtQO1ZPty+S28hvwKh4+tup99E8P4Jbddsz6zsItMaTaxCSoBvEqcU11HH8ZiGuq44jjMsQbaarlhdvQLkQ9pRElPDveQ+i1bzuO89zwSSKHf2JFXs/Ae+1H0YfbiZz4VfR8CzagVG0n+NJXQbMIgbL0GoTEDNQLBzCHOyytcdkaCE9jCqJ1TkNDyiixSKbiBgQwNARvElrLCdAjiJllyCWrEdw+IjUvWHHnLi+u6quR8qoACD73H455i9nlTms7QCpajt7f5Ig/l3Kr0HvPO45TllyJWr/XbmIU/Kl4rvogoi8ZY7wffbAZveeCJTFx+awYcLcPY7jLsgpUPMj5VWCaCN5kS0qjq6B4MMf7EZIyEXzJoGuY0yPo/c2IqXm417wJMzxF6PDPIDgOCLhW3YJSvpHpJz4frS67fHhv/KR1z7MbAtmN703/RPD5LzlkI+5ND6A2H5nnMlIWtVKch8tFUz2LRakVfHDFuzjUe4znW3YR1iNszd/IzsIt9AUGeeLiM/RND7A4fSGrs5YhCCIXR5tpHG0mw5uOIikE1ABLMxaT5EokYkR4vesQXVO9FCUWsLNgM4rk4ljfKWqH6klxJ3NV0XZS3Em0jLexv+coAnBV0XZ2FGyhYaSRX158lonIJCuzlvJA1d2MhEZ56NxP6ZrqoSixgLcvuQ8B+O/T32UsPI6AwHUlV7KjcDNfPPbfNtHP8mbwziX38x8nvuFII7y6aDt7uw46yPtNpdciixJPNb8AWE2Q71hyP5WpC/hh3U9oHGsmxZ3M/YvuZEn6QgYCgxzvP8NEeJKGkUbGIhNUpVYyHBqhZ7qP4sRC3r7kXjRD5+H6n9E11UN+Qi4bc9dSmVLO8227ODt4jmR3EutyVrEis5rm8TZ2t+9DEASWZ1azNKOKypQFNI+30jPVT9dUDxORSVZnLacwMZ+LY800jrTQM91LaXIxbyq/CZfk4mcXnuTkQA2p7mTuqryVJemLeKV9L6907AUTrireztPNL8Z8Fz67+hPkJv92Vn1xxHE54ddpquOkOo44LkO8EakWcyox5gW4yIu2R7XCs2NVO9HmxXPLJavRh9sxJ6OOBggCYuYCjIE5RFZ2W/rouS4dshvfzZ8m8NQ/OyzpPFd/kPCBR6IVXMB9xXtBlAnvedD2mZbKN+Guvprgvu9jjlpVNzG7AnN6FHMqOh/31rcTPvioQ++tLL02pvlSTMlFLl5J5OzLYOpI+UsQEtPR5oTYzM7PGGwjcma2SU7Ave3t6MMdaHMaAl2b34ox2o02R6KhLNoBkuRoHBSzFiClFaI27I0uYUK6JVNpmiOxEUSUpdfENBhKJaujseyzS7v4CrT615zH5S+xPK7n6t7nuJ7Mx+VGqgHevOguHmv4pYN4vrXqHp5peckR4LI5bx0CgsN/ujipkHcsvp+vnPym7YCxNKOKty++nwdrH6Zx1Pq+5viz+ciKd7Ov6xAvtVtr6JE8fHDFX+GVvTx07jE6p3rI9Kbz1qp7KUrK58JoE8f6TpHkSuTKom34FT9t4x0c6j2GV/awNW8jk+okoiBydrCeoBZkZdYyy+ZOAAmJodAwU+o0R3pPoBoaZcnFrMpcRthQOdB9hInIJAtTy0lyJeBX/FSkljEeniDTm0Hd8HlCWpiNeWvJ82djmAZ7uw7SM93PkvRFrMys5u8P/pvtUAJwfcmVbMvfyL7uwwwEBlmWsYS1OSs50XeGR87/HN20YtcfWHQXRYn5fO30g/a6bc5bx10Vt/Lfp79L64SVYFqYkMeHV76XL5/8BgOB6N/fA1V3Uzt4jpqh6NubpRlVFCbkO7TZbsnFO5a8me+cjfrgvxE+v+4TZCfESXUcfz6INyrGEcefCZQF6wjPIdVCQjpy2boYUi3lVsaQasGTiOBOcJJqxYsZnqfl1CIxOmi0MGrbqRiPZ7XxkINQA+jtNZhq0CbUAHrTIbTkLJtQAxj9F3FvegvGaBfG9Chy/mLM8DTKwq2orScgPI1cvAr3mjeh95yfkXJYkItX4l5/N8qy60ANIyZlEj7xZMx6CYrHCnuxYRI5+1KMRlqt2xXTJKhePIDgTXaMGQPNMetlTg1jTKY6L2wal0xAFH3JzKfFYlKWdewcAi0mZ4MgOMJ2YqUqlzc6J7sdhBqgYeRiTCJiy3g7Ic2p0W6f6GRXx2uOAJTaofPs7z5sE2qAvul+DnQfZVfHXnsspId4qe01dFOnc0ZiMRgc5tGGn3NH+c18ew4RPDtUz1uq7uLrZ75r66lPDZzl02s+ypdOfJ3xiPXdPtJ3ko+vej+He4/ZntAA9y18E7Ig85MLv6Rl3CKs1xTvZGn6Yr56+tv2OU/0n+Fv136E/zj+NcZnLPiO9J3gE6s/wHMtu6gfsRJETw+cpWOi00GoZ9fowmgzLeNt9hxDephXO/bZ9oaGafBU8wtUp1c51u1gzzGyvBk2oQYrYXFX+x4HoQY42X/GISEBODd8AXVeo21Yj1A79Jt9F7P8mb/RcXHE8eeAX+/IH0cccVxWMNUwrnV3IWaVIZeuwbXiJqSENNwb70PwpViNbStvRkrKRl603f6c4E1GWbwT97o7Lf2uNYp73Z24qrY5riGXrEIpdnoDCwnpyEXLY+YjZZfFjInzfKHtuYdiG7EQBTxb34Z75U2Ej/2CyLHHUc/vRUwvJuHt30HKW0Tg2X8HlxepoBoxrRC5dA1a30Wmn/5X9L5GxCTrf9rK4isRkqIVMbl4JVJOZWzYi2HExMALoojgTXKOeZIQU/OcY95kpGxnjLPgT7WaCefeVlohruU3IqYX2WNSTiXK6tsRs6JrJmaWIhetQKm+xn4uYnoxyrLrUBbtQMpfAm4/UtFyXOvujF2/ywjSnGCVDE8am/PWIuBc56q0CluDO4uy5BJyE7IdYynuZHQ9NgBpfnIiwIQ6FdNgOK0GHDpksAJdDs3TFw8Fh3m1Y7/j85ORKV7t3GcTarAI66GeozFWcge6j3Cg56jj8691vM7h3uOOsfHIBK927LMJ9ew593cfsQn1LC6MNsWEx+T4smxCPYujvSdiUiMDWtDhqDKLschEzJgJMc8n1Z1Mrt/5LHL92ZSllDjG3DN67d8Ewry/tTji+HNGvFIdRxx/Qogcexy5ZDWebe8g+NyX0FpPEMaKsPa/+auEXnsQ9fSzqKefRcwsw3vzZ9C7zxE5v4/Ak/+AlLsQ3+3/iN7bgHphP+GDP7YCRjbejz7UBrqGPt6LPj2CVLoWc7QbwZeM4PYTOf0s8oL1aB01oKtIBdWYuo68YIMVEmOaiKl5iKl5CCm5VlV1htBKhctRllyBemGfrd3G7UcuWW3dV+0uR2Xb6D5HpPZlInOrzy4f3us/TvCZf7ebIUO7/wfx9n9AH2ghcvo5TF1FrtyKq2o7UnY5phZBqdrukHC4llyJMTWEWrtrZkRAWbgdJJnwkZ9a8xAllKrtCJ4kjPEBzIl+cCcgL1gHkoKYsxCjrxEhORsxrQD1wn4r/MU0ERMzMbUIgSf/ASEpC/fWt1vJlx01BB77GxAE5MotVkNkdz2Bxz9j6biTc/BsfyeC7CL41L9YVoKihHvzW5AySwg884Xf19fqd4KtuesRJQmf7GNL/noSXQncv+hOnm/dRVgPszV/I2tzVpHpy+CnF56kf3qA6ozF3LbgBibVKb4beoS+6X6SXIk8UHUXbsnF8YHTNjnN8WdzZfF2jvadtCuxsiizLX8j/dMDNI5Fw4M25a2lYeQiJ+dEe5cmFcUQeoAUd1LMWKo7JWbMr/hiSKgoSEQMZ3Oibhp4Zc9vdJ1EVyIuUbGbEwGS3cncVXErj198mpHQKKuzVnB18Q4O9Bx1hO4kuhJZkFLK7o7oW6oNuWtYml7F2cFz9luCXH82VxZaATdBzdLyuyQXW/PX45YUXmjdjYlJuieVa0uuJKAG+H7dowyHRknzpPLmRXeS589hJDTKyf4aUj3J3FlxK0vSF3J7+Y280r4XsPTrs9rxuZgITZJ0GdpCxhHH7wNxTXUccVyG+PXhLwJS8Qrb+9kaEvFc8deEXv2W40jX2juJnH3R0eSnLNyKPtbraIKT8qpwrbndIqxzzul70z8SfOlrzia6K99nuXTURP8H6lp3l+XQceyXlu5XlHCtvg3UMMbEAFpXHRi61RwoyZhqCGNqBILjyCWrMSb60VqcVUQpf0mM3EGu3ILWeMAxpiza4dA4A3iu+zh69znLxs4UkIuWI6TlYYx0obfXACZSQbVlQdh3Eb3DWksxr8oK0Dm/B6N7xo4sMQvvlX9N+NjjGLPuIaKE5/pPoJ590eGCoiy+wt6A2MuYkI574/2EXvmGY47une8lvOe7MEcioVRfgzHW45B9oHiQi5ahNTtdLebictBUS0h8dsPHyfJlcrzvtJ2eeE3xFaR7U2kaa2Vv10FEBK4o2kpJUhHj4UkO9BwhpIXYkLsGn+wlyZVI+2QnLePt+GQvPdN9JCoJrMtZTdNYCyOhMUbDY5iYth67b3qA/sAgI6FRlmUuYXHaQtomOjjQfYSmsRYKEwu4s+JmxsITPFz/M7vpcEveenYWbuW7tQ/TH7AkQSsyl/LmRXfyyPmfUTtkPe8MbzofXvFuXu3cz74uq9fAajq8j2k1wM8bn7LXYW32Km4rv56vnvw2QyHr76Y6vYq3Vd3Dw+d/Tt2wdc5MbzrvX/ZO6kYaePLic5iYeCQ3b118D9XpVUgz8qGIrnJh9CK1Q+ft5EWf7OWmsmvJ9WUzEBykaayNkqRCMn3p6Ialrz49WEuSK5E0dwqaqVOQkMvpwTpM02BF5lJ6A/34ZC8liYVMadMkKAkc7j2OaZpszF2LS1II6xF2d+xjSp1mY+4almdW0z89wLOtu2YI/3KuKNxK60QHP7vwpO3kMhefWv1hipN/fQx6HHH8KSGuqY7jt4JhmojxV3iXDxR3jB80poEx0R9zqDHW6yDUYHk4z9UngxVQonecdYxhGqjn98aEmWgtx9A7nH7AasM+qyo9W0kzdNTze/Fe9QGruXH2sxdex3PlBwgf/Znt5Rw58xzyom0gydHGxoJqpKwFMXZ3UvaCGFJtXiK+W7uwH601StK1thO4Mt6EPicURu88i5hRbBNqAKPnPEZaQZRQA0wOoDYeiBLq2fur3YXe6VwHrfUEQoLTf9mcGrY2FfNgNZya844dillv1BCmenn4U/866Og8Uv9zdhRs5qH6n9rjDSMXec+yt/H109+1K61nh+r52zUf4ltnf8TIjJfx692H+dSaD3F6sJYnLkY3JXeU38SW/I18+cQ37NCSFHcyf7v2w7zY+iqvzwSrJCh+PrbqfYyFx/n7g/9KQAvil328a+kD+BU/3zjzfYZDI/hlH7eUXUduQg7PNr/EgZ6jpLiSuaPiZnL92bzc9hqf3P8PpLiTuX3BDWT5Mjk1cJZ/PPIlFEFhc956snwZTEameKzhSSJGhEWpFeT4swhpIeqGGjgzeJYNuWtYkr6Ijoku694O/BNL0hfxgeV/xUBgiNe7DvFPR79Mnj+H9y//K4ZDI7zasY/v1j5CsiuRty6+lyxfBv958lu2Dr06vYr1Oat4tuVlfjFD5BelVvDXy97ON858n+Zxy9M6y5fBx1e9n+/WPmLLRjySh0+u+QCSIPOlE18nMGOLWJRYwHuWvpUvHP+aHT5zoOcIn1j9Ab52+kFbclM7VM97l72dX1x4yt6UtE90Igoiuzv2xWjlZxEvdcXxl4Q4qf4LxbnWEV492YUkCVy3rogF+bGvRbuHpvnBc/W09U1SXpDMu29aTGZK3G/0jwsB1/LrETyJhPsu2qNiZinKou1Eal6Iph8KAkrVDvTeBmcYSf5ikBVnpTq7HDEtP+ZqUmYp8ymr6E9Fv8Qea27yIYAZnEAfaI45TuusccwHwBjpxn/nv6K2nkLwJYFhYEyNIGYtwBhoBklBXrAeIzCBVLwSveMMAHLFZuSFW9EuHnReRIhtF9HnWfUBGMOdsWOXCHqx13QuJAnBn+ogwUJSFlJKnjNpUfEiF62IdWOp3IzWUeP4vFy2DmNiwCF7kXIX4Vp2HcHO2jd0/7hc0DrRga/Pmbg3FBphb+cBh3RBNVR2d7xuE2oAzdA43HPcEYsN8HL7HpLcSY4UwLHwOLvbX7cJNVgpjbva99A20WkTxmktwC8anyZB8TM8UzWe1gLs7z5CljfDPudYZJx9nQcpTymzG/XGwuPsat/L9SVX2XMKm2EO9hzlvUvfxq+anrev3TB6kbLkYvZ1HbJlF/u7j5Dly2BXx160GZu9uuHz5PqzOT/SSH/Q+p71TPfxYttuXKLCYND6uxiPTPLj81bK4lyyWjd8ngxvGgPBIce1X2zbbRNqsPTjz7a87NBhh/QQ+7oOo4iyvT4AHZNdvNz+miPNMaiF2NW+N0bDfqDriMNDHOBUf80bEmoAr+x+w9/FEcefG+Kk+i8Q7X2TfPUXNRgzyp/a5mH+9d3ryUh2EubvP1dPe59FlJq6xnnoxQY+ed/KmPPF8YeDmLvQ8pmWXMjlG22XCX2onemffQoprwpBcWPO+CCHDzyMlF6MkZCGOTmEmFaAMd6PmJhpEdehdsTMYoSEdLTexihhFUSkwuWWK0f5xhm7ONMijeklsFBAPfeKPS/XsuswRrsd2mWlcovVKDgPcuEyS+oxx/FBSstHTM7BveIGAs/9RzSkRXLhufajaF21Dgs896Y3W3NtOYZ2fq+l9e6sBdPAtfQapIJqS+dtwwp10btq5zQuCiiLtlvV8LlBL0uvITTUFk2SFCWUqp2YatAK0AGL5Bcuw0wvIlLzohW240lEyigG02o2NIbbrUTLsnVozUeQS1ahD7WDKCFllhI++ChCYqbVzGhoiElZRE4+haGGkYpWgGkguP3owx0EX/gyUk6FZbN3GaM0qYg0r9MJRUAgyxvrAHEpfbNHdsc4hhimYZPSuQjpsRudaTXAUNC5YRsKDjsII8BoeAx13huOodAIrkmX83xawEFWZzG/uRCsqPP5c78w0hwz987JbkfwC0DXZHeMDns0PHbpxszwZMzYpY4Lz3NUATAwYuYIIAuxVCDZFauDzvRlII1Kjg1SfkIuI+GxNyTWbsF1yfE44vhzRJxU/wXiZOOATagBIppBTdMwkiRw9Fw/qYlubtxYbBPqWbT0xnaQx/GHhTFLqvQIWtMRfHf+C8Hn/sOuEuvtp3EtvwEjMIZ20ariGSNdSIVLcW1+gNCuqK5XSMzEd9+XCPzyc9FQEcmF79bPoTYdRq17xY7Rdq27GwSIHH+S0L7vgyChLLseQXGj9zdZcemeJOTKLaCFMU3QBlsxJgdxrbgRtekI6CpiVjl6bwNK1Q7UxgMQnkZML0ZMLUAfbAXTdKYeztyn1uZ0XVDP70HvaXDEjLvX34O89Bq0C/tR6/cgL1hvkVhBQMooRWs7iVy+CWOsx2oMTMpErXkBqaDaItWmieBLInzkZ4hp+QgF1SCImNMjBJ/7Isgu5IXbEFPz0DvOEn79h9Y6puTi3vRBwgd/HN1USDLeGz6J1teIOieNUi7fgJy/hNC+H0Rvxu3Hd+OnCPzqH23Cr3eM4r7qA0SO/ty2QLzcCbWExJrslRgYpHvSGA6NICCwIXcNSe5EFiSX2gR1YWo5hYl5LEguoXmmmpriTqY4sZBt+fBiW3QDtbNwK9XpVaR5Uu3Ktlf2cm3xFbRNdDp0vJvy1uKR3ZzoP2OPrchciiIqjqTFipQyUj0pHOuLyoHKkospTS52VMTTPKksz1zC6cGozEcSJDbkrOFQz3EHuVyRuZTm8TaH68eyjMW0jLc5KsOL0iowMbkwxxpwYVoFiYrf4U5SllzMlvz1nBo4axPhDE8aVxfv4OxwvU3WExQ/1xTvpHbovB2nrogKVxfvYCA4SMeMC4pLVNiUs5aIoXKo55ht25frz2ZT7joujjXTNbOW2b5MipIKWJO9wl7LHF8WOb5MNuet43DvcVRDI8+fQ5o3lZWZS2kYvUjvdKwErW2ykxW+2A1UHHH8OSLeqPgXiH1nunn4JWel5eo1BbxyIhp7m5roJi3RTXNPlEgvW5DOR++KtVWL43ePX9+oGIVrw71EjvzMMSZml1uph5G51TkBqWSlQ1MMoFRfjVr3imPMtfIWq7lxTiVPTMkFQXAkNAreZOTFV6CejCY0Iim4N7/FJpzWRTwk3PcVQod+gtZ02DF30Z9KaO/3bC21XLkVrXG/Yz7ygg0WqZ7jDiIm52CM9zNXsWkHwsxpoFQW7QCXxxHCIlduRfQlETkTfX0v5VQil6wkfOTn0fvzp6EsvWbe+gq4t7+D8L4598cbNFBW7UDrrHVKXQQBqWhekymXDriRKzc7A3h+DS6HRsW5SHWncO/C26kdqrdDXdySi3sqb2coOMxL7a9hmAaKIHN96VUYpsGezgNMawFcosLOwq1WA2Kgn3PDDUiCxI6CzXgVL6OhMZrH2+ie6qUsqZjCxHym1WkmIlO0TXSQ5kkl15/NaHgMv+xjUp1CEiT8ip+h4DCpnhQkQURAQDd1eqb7Sfek4pHcSKKEYZq0T3SS5kkl3ZOKZuoookzLeDtuyUWWLwPTNEn1pNI81kJAC1GUmI8sKghAy3gbQT1ERcoCJEFEEiT6AwOMRyYpTynFMAxUQyOoBxkIDJHnz8Eje2yC3h8YIMubiVfxElSDFCTkMR6ZwDB1eqcHmJg5j1t0YWDSO9VPx1QXBQl5FCbmIwoiI8Exzo9eIElJpDqjilRPMqIgsqt9D0EtRGFiPovTFqKbOif6zjAWGSfNncqOwk0IiLzU9qptzbclbwMLU8v52YUn7bHq9CpuLL2aH517zJaiJCoJNqmfi/cufCvL8qt/V1+9OOL4o+PXNSrGfar/ArGpOofFJdFXtOuqshgYdb5KHZ0Mc9WaAqqKU3ErEkvL0nnbdYv+0FON49dB8aAsWA9uv2NYyiyzSPAcCElZiP55ASUQ01QHxJxvFmZwnmY6PBWrmdZVVIfsAlBDaB01aM1HHMNawz7U+tccZFlrOuyUjEgu5PINFjmeA2XZdSA7XysL7gTUeYmKauMB1AtOsqs1HUKd5zSi9zWitjqTDs3pEWfV3BrFGGpnPkw19lU7shvBM+8fXsUb82wQBEvnPg9S7iKExIzY8/4JYDQ8Rut4u6PyGtYjnOw/w96ug3Y1VzU1zgzWcW74gk3YIobK4d7j5CXkcGawDtXQCOlhXmp/jbLkYlrG2+mc7MYwDZrGW+kPDpLsTqZh9CIhPUzPdB8XRpu4uew66oYbaJvopHm8jbrh89xRcTONo02cHaqnZugcDSMXuX/hm2ib6KB2+DxnBuuoH27gA8v/ikl1ihMDZzgzWMvx/tPcXn4jbsnNmcE6aobOsbfrABvy1rIhdzWnB2s53n+KY/2nKE0u5u7K2zgzWMvJgRqO9Z9CNTQ+tOLdnBmo5dTgWWqH62mf6OTd1W+lbaKDM4O11A7Vc264gbdW3UPfdD/H+05RN3yel9pfpTy5lHPDF+iY7GIsPM6J/jOkzlTu2yY7MEyDjskueqb6SHQlcG7kPIZpMBYZ52jfSVZnLeeF1ldsS73OyW6CWoj64QuMRSzZxkh4lJP9Z2mb6HB4XR/qPUbd8HnHWN3weeqGzzu03Zci1ABB8xJ/G3HE8WeKuPzjLxCKLPGJe1fSPTiFLIlkp/l4bLcz+loQYEF+MusX5/yRZhnHpSCVrcMc6wHFg+BJILT3+yjlG6yK6OQgYk4lZiSImF5oRYBPjyB4kpBLViF4EhAS0u3KqVy2DsGbHG0GBMTUAsSkLCvVcE50tlJ9NcZ4v6OaKpdvREzJIzLHUg7ZhZS1AKPLaYUnpOZZ4SbaHBcLxWNrv20YOu6d78Xou2BZ8XXWEnr5a5aGuXILYlKW5T8dnrYaFM+9CpgguXCtfROh/Q/DHGcUwe1DcCdgOMYSEBMz0ScGotd1+xFT8x3Nm0gyctFy9I4ax5iy+Eq05mPRxkxBQlm8E8JTNgkXPIkIydnI3iQio90zGwcBZfFORH8aYlaZJbmRFOSydegdNUhl6ywrPT2CVLAUrescYtYCTNmNMd6HlFN5CZJ/+SKohWJCWQJa0CZ2s5iMTGHMa8CcjEzRNhHbRNo63kHHZJdjrGWsneC879GUOs3xvlMO/bBhGuzrOog6R+OsmTqvdR6IGdvbdTBGm32g+4gjlRDgWN+pGI3zyYGaGI1zz3Qf+7oOojmaNTVe7XzdkZ5omAZ7ug7YVnyzONhzzJGSCHBh9GLMGrVOtMdos1VD5fzIRcc9AvRM9cbINXqm+3DPayw0TCNGkw5cUud+KWS644mKcfzlIF6p/gtGfmYC2WlWp/6NG4opyLSqapIocPvWMkfjYv9ogLPNQwTDv9k/pHH8fqC3HMO18c0QsZrm9O5zqOdexVV9NZ4bPonR14jWuB/t/F6Q3Xhv+wdMlwf17ItEjj2OaRh4rv4QcsUmtJZjhPc8iDHajXvr23GtvwdjcojQrv9GPb8PpfpqXKtvQ8pfQvjgo6gNryOXrUMuW2s1CQ61oXWcQSpeBS4fQnIOctk6UCOI2RXWhEUZuXwjRm+DFbAyC1FCqdhkHT8HculqpMR0lIpNYJo22UdX0RoPImVXEHrtO4R2/w/aud1IhcvwXP1B/Pd9GbA8uO14cEFEWXIVcuVmy64PQJCQF25DyluE4EuZuagbpWILoi8VMa3AHpPLN2GMdCGXrkFIyEBML0JesJHQa99BSC1AXrAeuXwjUlYZoee/jD7Qily1E9fGN2PKCpEDjxA59jhiVjmeqz6Asuxa1DPPEz74KMZQO+5t78S18ha0iwdR619DbzmGVLwKz7UftZ5t8xH05qOYkSAJb/82ZvCNHRYuN3gkNzsLt7IotcIxvjV/IyuyljrGNuSuZlWWU1a2JH0hVWnOzwoILEwtpyjR6XlcllJMaXKRY8wreylNLo6ZV64/tkiQ7k2LGcv0xr7BSfUkI4vOOlSyK4kExel24pbcJLmcbygEBDIucc6sS45lxoTMZPkzcImKY6wgMY+yefdYmlRMeUrpvPm4WJlVTbLLGT5TnVHF4vSFjrEl6YvYkLPaMVaYmM/VxTsQ5zjq5Pqz2VG4hUxv9E1KonLp1+GNw7EOQHHE8eeKuKY6DlTNoHd4mqwUL0PjIZL8LpL80Vfrzx1q41evt2ACfo/MJ+5dSXFOPCHr94lfp6mWF25Du+CUOUjZFQiJ6TMuHVEoy65HPfuiY8y18paZcJLo34tcthZjetRZqXX7cS2/gcixx6NjgoBr0wNEDj465+IufHf/G8HnvoQ5OWNHJ8+4djQdic5VEHGvvwdEicj5vZijXVhBNssR/WmYahittwFBi6AsuRJjtDs2EKZwmSNsBcB7y99bRHXGe1vMW4Sr6grUpiN2o6WQnIN79a2oLcejunKXD8+2d6BePBTVOEsy7h3vxRhoRq2N6rCVZdchJmYQPvjj6FLMRL9HTj7lWB9lyVUxOnXPtR8l9Oq3HY4nYkYJZiSAObdiLkjIC9Y5tOcAnh3vIrT3+7wRLhdN9Q0lVzGpTtE+3knXdC9lySUsSC5mIjLFlDpN23gHSe4kihMLCOohBKBjogtFVMhLyGEiMolP8TEenkA3NDJ9mbSOtyOJEgUJeRimQbYvk4tjLXRMdlGcWEB+Qi4e2UvvdB/1wxdI9aRQkbIAj+xmIDBIw8hFBEFgecYSlmUs4WjfSRpGLTvKBcklbM3bwKHe43YiY0VKGVcUbuPMYC1H+yxJUJonhTvKb6FtooPdHfswMUlQ/Dyw6G7GwmP8sulZNENDQOD6kivJTcjhF41P2RXrTblrWZezmpfaXrWvXZlazjVFO9jfc4SaQcvHvDAxny1562kd7+Bo38mZpMM0tuVvZCw8xon+GibVKSpSyihMyEc1NTonu+mY7KIosYDixAICWpCAFqRlvI0UdzKlycX0Tw+QoPgJ6iHGw5Pk+bMJqEEUScYEBgNDZPuzCKpBxiIT5PlzkEWJBCWB4eAwnVM95PqzyfCmk+RKoG2ii4aRRpJdSSzNrCLLm0lfoJ+DPbEBRWuVVbx9672/y69fHHH8UREPf/kTRjCs4XZJv7fwlaaucb7x5FkmAypet8xf37qEgqzol2UqqPLMwVabfk2HNJ4+0MqH71z2e5kPwNB4EMMwyUr1/f8P/guElFVmOXvMef0qJGYguC7hIS7F/ombWpiY0JFwIGohN4twAH1onp2YaaLP0x6jR1BrX4kSagAtgt56wtm8ZxqorceRC6pnCDWAid5+BuXqDxJ65X9g5oV95ORTKEuucl7H5YNLeN5qTUccYTZGTwNGwVKbUAOY433ofU3ORs1IALXpsLNpUNfQm4+gzXPaUOv3IOU4K6dmcBy9d561mmliTMb6XJvhKUfjJ4CphhAUj/NJyAqCNzbOWkjOQfAmYQYvcwceQaBzsoeOKctxommsBUWUWZhazuFma4M0qU4xHBzhzYvu5AfnopuU/uAgH1j+Lr5V8wO7aa9zqocPrngXR3tPcbw/+ux2Fm7hrVV388UTX6d1wnr2ya4k/n3z5/ja6e9wtM9yixEFkfcuext1Q+c50HOU04O1eCQ3b198H+PhCZ5peYnm8TZEQeTmsuvI9mXy8wu/4sHahwDYnLeexWkLebblJb5X9wgA63NWsy57FQd7j/Od2h8BUJZUzPaCTZwerOWFtt2AZTV3d8Wt1A2f53DvCQ71HifHl8UHl7+brqlunm/dxTdrvo9X9vCWRXejmwa/vPg0P71geZRvzF3Lsowl/OzCk/yq2WqqzfPn8JGV7+Frpx+0/bS9spd/3vhpvlv7CPvm+Ha/c8mbGQoO80xLdHOYn5DL3ZW38o0z37PHPJKbz63/BF888d/2JmAoOMytZddTP3LBvs5EZBKP5MYlKZyfsRQci4xzvO809yy8nYNNl078HFIv4fseRxx/poiT6ssUo5NhHny6jsaucdKT3LzjhioWl8S+pvxt8djuRiYD1v/sg2GNR1++wJfetwlNNwhFdAJhDU13ErCJwO8m3W06pHLm4hB+j8LSBWkIgsAPnjvP4XOWpdWK8gzef3s1shRXKc1CKlyGMTWCUrkFtXE/GDqCPw0xKWtGZ51oa32louVImaVo/jQ7YETwWrZ3+kCzoyotL9yGOdrliNeWi1cgFyxDb5ljZycpSLkL0XvmJA4CYnLWJSarEJOnZhgYk0Mxh2rd9bHHYvlRq40HELzJuFffhhEJoLedsK3nxJQ8UDwxn7vUNYzAaMwYkVDsmGkgyG7MOUmUguJGTMl1xoeLElLRcqfOWfHgWnodwc46e9Mj+FORMkttv2r70MotCN4kwvsftkNdlMrNiCk5CAkZmFPWPUjFK9G765EXrI+pgF9uaJ/opG3CmdbZPN4WI2cI6SFODdQ4xgzT4EjvMYdNHUDd0HlODpxxjB3rO4VLdBHRo/8WjUcmeLVzH/2BaOXfMA2O953m1ED07UZID3N64Cw90332tQzT4PWuQ1Skljka7g71HENEpG/OOY/2naQ0qZhTc+bUMtFO6WQRZwaj34/uqV6ax9s5Ose2ry8wQP1IAyf6z9ga56AWYl/3IRJcfiKG6rhOoiuB8Uh0I9Uz3cfujtcd+uqgFmR3x74Yrfnh3uMxOuzuqV4O9zrf/oT0MId6j8fowM8NN9A0z6O7YfQiBWpezOcbR99Y4uEhXhyJ4y8HcVJ9meLxPU00dlkayuGJMN99tp4v/fVGnj3UxvHzA6Qne7hr5wJKcmKrWv8bDI45XT+GJ0Icre+3yXZlQTJluUkOj+rN1f/75sWhsSBnW4bJSvGypDSN4fEQ//rICSZmCP2iohSuWVdoE2qAM01DHDnXz5ZluW902r846J1nbfmDsvwGpOwFhPb9iMiMF7KQVohn85vRuurRLuy3muw8iSjLbwRTR2s/TfCJz0FCOsrinZi6hjE1Qnjvdy2f6YXbIDSJKUrok0Po9XuQy9aiD7QguP1IWWUY4Wmk3EWWb7KkIJeuwcSSMxhDbdY8fMlIBdWYwYk5UgYBZfEOBJffUcEW3Ako5RvR5jRGglWRVyo24aq+Gq3rHMHXHsScHETKrrTcTBLTUZZciRkYsxooZyv3bj/u5dejt56Yk/IooFRfgzk1Ys8RQUBZfAWIYpQsCxJy6WrErDIix5/EIvoCcvlGBG8iYnqRVRWXXcglqzDG+y0/7ME2qxHUl0J4/0NIuQsRfCkILi9abyOBx//e0mlXbLKq02rYema6ipi1wAqm6Tkf9bl2+XBvfTuC4ia070cxFnyXKypTFzClTtM+p4GuLKmYoqQCR2CKIspUpCxw+D8DlKcs4Pgcj2mwqrOJipNcJruScEuxoSI+OZbASaIUE3gS0IJMzWu+m9YCMU2HJqYj9XEWvXO8rGfRH4jdyA0GY8dGQ7GhLqPhMZR5em3DNBybhlmIl0gLTVD8CAiO+/QrPjySm84Zn2oAl+Qixxe7AS5NKkQRFdQ5pL4gMY+gHnL4gBck5FORWmZ7i89ee1XW8hiyPotpLvO3K3HE8TvEXzyp7uif5Oj5fpJ8LrYuy8PnuTyWpL3f+Y/7xHSEZw618sJhqwo0MBbka7+o4cvv3wRYMo6MFO//OkZ8VWUm+89G/9FcWpbO/2vvPgPjKK+Fj/9nZpukXfXemy3JluQiGXdcKDbgYIpDNyVcbgqEBBJCkmu4CVy/JCTBJCSQdlMIJMEEjGk2tgFjW5aL3OQmuUhW79Kqbt95P4y00ngNIdcEG/z8PtmzM7szs6vdM8+c55w/r6/C6dZGcI419jK7MJGJWdG0dg8xZVwsMyb+86Da4/VjNGhf/sca7Pz07/vx+rRKALOLErGFmAIBNUBVvZ3k2OBSbu32M7SH/hS4PT6qG+zERlhIijlziblzzXP0fSRJ1lW7ULsbUH2+4frGwz+wzn7UgU7UITtq7/Bs/4EufM3V2oTF6uG60EM9eE9sJ2Txgzje/klgNNjbeYqQax7Rmpsc3aytKylYLvs6nuqtWtB8olyrwDH9RvB7cB9+D+e6n4Fiwlh4GRjN+NtrcW35YyC4VB192qRCVcW9+x8YcmfgaziE6nWjpEzA192IVF+JkpyP491nYXjk2NdajTE2HWPOdJwbf4mvsw4lPhspLApkA/7+Dgb/8QhydIpWqs7vQ/V7cW74BVJoBIa8uUiKAX93M85Nvxren5nItnh87TWB3GU5Phfj+Nn4WqtHq57ICub59+BrOqJrjW666AbUgc7Riim9rchxWShxWajdwyO3Xhfeml2EXP1fONY8Fnh//O0n8cdl4a0dc0fAPYSvpRpJMcIZAqvzVa29jsSQeFRVpXGgmTRrClZjGD0OO3lRuRzrOYnVFMaEqPG0DrVTFDOBI93VKLJCUcwEupzdTI2fRGXHIXyqn7yoXDocXRTFTaB8uNmKSTYyJb4Ii2Im2hJJt9MOoOVX25IoiBrP0R6tmpHNaGVO8nS6HT26UdeZSdNoGGji/YbRi7sZSaVkh2cEcqsB0qzJzE2dweHu0XSgcJONBWlzKG+tCAS9EhIL0uZQ21sXKD0nIbEgdTaN/c26C4JpiVNRUXWj2qUJk0m3puqC1YLo8UxPLGFn655AxZRoSxSzk6dxqq8+0KQmITSeNFsqFyVOCYyKhxlCAznodf2NdDt7MMoG5iRPx6iYyLClUdffgITEpLhCjtlrmJ44lb3tlQx5HWSFZ2CSjYyPzMHtc9Ph6CIhNI4ocwStg21MjMmnrq+BKEskqdZktjaVMzW+WHdHYIQkwgzhAnJBT1Q82dTLEy/sYWSzhKgQVt4zA1n+9+Qv/yte2FDNe3tHRxgSokKIspmpqrfr1vvy1RP4+3sn6B1wIwFL52Rx9Zws1u2oY8PuBgyKxJJZmcybnAKA1+dn874malv6yUuPZHpBPG9sr6O6oYespHAuyo/n/72gbxCSnRzOzZeOY1tlCyEmA5eUpBITYWHA4WHt1lrq2vuZkBHFklmZOFxefvfGEQ7VdhMXaeGOxfls3N3AgZOj5akkYMbERN2oNMCdi/N4YePxQPAtSfDIHaVnPRr/r2rpGuTxP1cELizmTU7+1Gt0f6zmL+YwTPnzdM1OAEwzb8Fd/lfdMiUpD19XPbj1FylK+mStLfkYhry5o4H2MGP+PDxVH+i3TS3S2n6P3TZ3BqprEF/DmOXmMExTvhDUpCb0uh/iWL9Kl8ttmX8Pvu5G3eRKY+HleA5t0G0rx+eAz6PLpTZklaJ63bqJjHJCLsbMqbh2rh6zk2ZMxYsDo/sASBLmuXfpm9YAphk34975ktYOfuQ5E8drVUn8o2kKki0WVFXf6AWQE8fhbz2uW2aeeTOu8r/plikpE/A16VNqlPRJyKFReKo288+cLxMVR8SFxHDf5Ht4YteqQMk4o2zkO6X38ffqNYHgUULirom3cNJeq8sHvib7SmxmKy8cfTkw+joxJp/5qbPZcOp9jvdqeb5hhlCuyr6MfvcgG+s241W9GCSFyzMWoMgK25p20uOyE2WOpCi2ALffQ4/TznF7DSGKhcLYAvyqH7/q53hPDR7VS35U7vBosETbYDsOn4OM8HTcPjcmxUi/e4D2oQ6SrckYZUVL41BV2h2dxIXEEGIIwev3YVAMNPQ3YTNaCTdZ8al+DLJCbW89IQYLydZEHF4nNpOVY90n6fcMkBuZRVxIDJIkcaD9MH2efpLDEsmPHocsyRzoOEyHo5NQJYQ5KTOIDY1hY937dAyX/5scV8jU+EmsP/VuIOiOMIWzvOBGKtr2smN44qVBUrgp73q8qoeXql8LnOPJcYVcnrGAZ/b/LhDIx1iiuW/yPfx8368DrcglJO6b/B9saSoPTLL8MCYUVi184mN9rgThs0A0f/kQf3v3OGPj8LYeBwdrujjR1Evlya5AcAdavnF9Wz8+v/8Mz/TJWzY/h4snJRERZqIgI4r7risiPUFfccNokNld1UHvgDZaogJvbD9F+eFWXt58kt5BN119Lv68vjrQcvwv71Tz103HKT/cyp/WVfHWjjqWzc/hiukZ+PwqjR2DRNv0k8FS4sL40Qt7+WB/M+t31bPyLxU43V5++/ph3t3byInGXl4vO8Xq90/wj80nOVSr5e922J385vXDuL36c6YCpflxulzphOhQZhUl8a0bJ1GcE8PErGi+saz43xJQe31+3B593mZb9xB7qjsYcHj49drDgYAa4IP9zXSeoxHzj2KaeAlycj4YR+9OSJFJGHNnIMfoS20ZcmdiSNV3NVOSCzAk6UtqoZgwJAc3IpEik4OWBUrXjeXz4h9bzQLANYi/ozZoVU9tRdDkSE9tBZ4j7+qXndyJZNF/9pX4bF1ADVoDF1+rvt66v+0E3ubTJhN6XfhazzDBcKRV+9jFA526gBpA9fvgtNQDyWBBjkrRLwuNxJChL08moPFxRwAANSdJREFUhUVjGDdbm3Q5hiF3FnKcvhSaIasEJb34jDnj57sORxfvN2zV1WD2+D2UNe/SjcaqqGxv3kVZi36S27bmHexoqdClMxzuqsLhdQQCatBSNjod3exp349X1dJ/vKqPna17ONBxmB6XHdDSK07Ya0kKS6C654RWe9k7xO62fUxPLGFP+wH6PP04vA72dRykIHo8lZ2HaRxspsvZw972A0yKm0iXo4fj9hp63f0c7a7GrFgIN9k42nOcLmcPVT0nsLv6yIpIZ197JZ2OLmr76jjVV09hbAH7Ow7R6+6jdaidys4jXJ/7Bfa1V2J39+JTfVT3nCDcZONAhxZQg5ZH3eO00zbUEUgnGfI52N6yi9bBtkBADbC/4xDdzh5dq/Vedx/VPSd0ud1e1UdF2z52t+7TneP9HYfY1bpXV0u8y9nNtqbyQEA98r6VN++mskNfj/5MrJyfd/rOV6rXi7O+Dr/zDPM9/gW+gQHsm9+jt2wrftcn24Bn8Mhhut9Zh6shuJb8he6Cvi8zEmiO9cKGarr6tA9gfGQI31teQlVdD39aV4XL4yM63Mw3l00iOtzCixurOVjTTUpsGLddPp6UOCuvl9Xywf5mQs0GrpmbTUleHCebe3mz7BQOl5d5U1KYOTERp9vLxt0NNHUOUpQdw+wiLW+4ur6HgzXdpMaFcfuifOQrtFFzVVVZMiuThvYBjtb1EGYxBEaPx/L5VY7WBecAHmuwkxIXxvZD+tHhLQdasFqM/P290UlrU8bFkhgTSnuPg6nj4/B4/fjGXH3YB9zsqe4IBM8j9lR3YA3R11LtH/JQmh9PVX3PSDYBxTkxTBkXx6N3llJ+uJUwi5Fp+fHsPtqOxaRw33VFGBSZIad2jgadHmZOTCQhOpTeARevbqmhsWOAwqwYvjA7E4C122qpPNlFcmwYy+blEBNh4Y2yWjbvbybEbOCaOVmU5sezqaKBNVtrcXt8zJiQwB1X5LOpopGX3z+BCpiNCq7TAm6Afcc7uWxaWtDyT5uhYCFKeCzezlPapEJVRY7LwpBWjOoewnNqL4N/uR8pLhND/jwtxUKS8VR9gBQejyGrFF9HrTayag7D19+hpYDU7UMKCddypXtbtdbgNVqgY8iYrOVTpxWNjkAbQzAVXobLPTg6EispGCYsRG48pBs9VxLGYcicivfkmE6LigFDxmQ8+95E12rcFofvtGBdMpiwLLgHV9kL+PvaMWSVYJ62DF/TYV3bdDkuG3xu3YivHJuJkpCDr0HfwEXJLMHXPKbCh8GMccJCPMe3j6ZbSDLG8bNRh3oD5wIkTHlzUQftuPe+FljPkDkFDGb8fe2ova1IFhuGjCn4uuowZJbg72tHtsWiZJbgKv8bSmohOHpR3U6kiAQ8letRZVnL3TaYUD1OXFv+pE2ajEnHmH0Rqs+LZ+Q1PwNiLMETq8NNtqDcX4vBHJQnrMgGFOm0zwESPjV4UMPtc9Pr0uft9rr66Dstb7llsI36Pv1kPr/q50Bn8EjrgY7DuvxigMNd1dT164OI6h79XQjQGrCcXq2p190fFIB6/V72th8Iaspywl5Ln1v/2zQ2r3nEgGfwjPnep+drg3ZBc3peuVf1opyWxy0hYTytHjaAzRQ8KhdpiSDUEKLrtHgmg3z048IoZ30dTb9Yhc9uR7ZYSLjrP7CVlOJqaqR/9y4MEZGEz5qNbA6ugjSW126n7vEf4Ou1A9DzznrSV/w3sil4HsJH8btc9O3YjrenG1vJNMxp6XT8YzU967Xv905pNUn3fAXbRdP/T8f7eXRBB9W+M6SLjATUoOXzbtrdwOb9TYFAq7vPxerNJ4i2WSg/rOWnVjfY+dWaQ3xhVgavbdVG5Hr6Xfx67SFW3FHCT/+2P7D9scZerCFGNlU0crBGG2HYdbSd/iEP1hAjf3h7tJLA0boebl+cx4sbj7OtsoWwEAM3LMjlq9cUYjEpGBQZr0/VpYSkxVuZlBMTFGxnJ4cjyxIhZgMDjtEfC2uIkff2NenWrTzZxS+/eTFmk/aj9trW4BG8yDATEWEmegdH8z3jIiykJ9poaB/9Uo8ONzNvUjIZCTb2HusgJtxMc+cQD/96OwnRody4cByhZgP/83wFPf3auc9NieBbN03iiRf20NSp5dGu31XPiuWl/PmdKk42aT+gtS39eLx+JAnW7dRGLRvaB2jpGuSK6RmsGfNe/Ob1w4SaDfx10+iPYNmhVlLjrby2rSbwc3OmgFp73uALsHPBe2wrxisfwjcmncHfUQsZU/DW7oHhKh9qxykIi0GJSsG9/01txY5a5LgsQi79GkNrVwZGYKWQcMJufBLHG0/gHUnxMJgJ+cL38dbswnNoA95Te0E2YLroi+D34zm+Tcu7DonAOPEykNDykd/6CVJUMsb8+fh6GrVqJG4H7sp3tAmPXQ1aJY2oVLwnd2GcsADP0Q9A9SFHJiNHJmmpJpWjZcBMxYtQ4rIIu/4x7Ry0VOPa/qIWRAP+nmbkuGxkawygorqG8HeeQo5JR0nKxz9k11JVmo4ghYajpEzE116DIbMEX3cDktkK5jCc7z6Lklyg5R2hIplCGXrjR4CkdZ+0xeLvbcW17XlQVZS0YgxZJXhP7BytmmIKxXLVd/AcK8Nz9P3RY5jyBZS0YhxvPDF63kMjMc+5A+eGn4++v91NhCy6H8f6VYFlalc9ZEwJqs99PrsoYSqR5ghyIjIDI9Op1mSiQ6IoSZhExfBkRLNiojhuIjEh0bxbr9Uzl5CYlVRKmDGUE/aaQBfC0oTJxFqiSQiNo21IK9NmkBRKE6YAsK159KJtWuJUHF6HLm95Qkwe46Ky2TOm6ohBNjAlrpitTfr67hNixnOo66guEM2wpdI40KzrtDiStzw2CI6xRJNiTdaNyBtlI7mRWbrJmiP5zJsatugmJBZEj6ffM6DrdpgfPR6DrPBew2haVpothVnJ03XHGGmOYFbyRezrqAzkmocYLMxMKqXb2U1l55HAa0+Nm4Qiy5y01waqoExPLCE3MoudrXsCx5QclkiMJYrJcYWB14oLiSU5LIEZSaW837gNv+rHrJhx+YJHRF2IhmEfV8dLf8NntwPgdzppf/F5FJuNpqd+gurVzmNfeRlp31uBJEmoqop0hnK7vWVbAwE1gLu5iYH9ewm/aMZHvr6nuwvfwADmtHQkSaLp50/hOKZ9ZrvXvU3yfd/AvmlMKp6q0r3uLRFUj3FBB9UWo4zT89HpHPZBF4NO/ZdCR4+D9h59OkBr9xCVNfp8Sp9fZcv+lqBAbceRtkBAPaLsUEvQ6EbZwVYSo0PZPBz09g64+d83j/Ljr8xkW2ULG3bXI8sSs4sSGXJ6ibKZCQ81UdPcx5ziJHYfbUeRJYpzYthd1U7fkJtl83P48/oqVFXrnHj9xdms3aa/NW8wyLq88osnJVN+uC1QKaQ4JwaL2cCCKSms21mPy+MjPNRIblokLreXwuxoapv7SIwJZXxqJL9ac5CMRBtfmJ3Jm9tP8e5ebbSow+6krfsA0yckBAJqgBNNvazbUR8IqAHcHj8bdzcEAuoRe491oCj681bfNsCBE/pZ9z6/yp5jp6UlAKda+3D/k88AQGF2cOezc8LnwW9vDlrs72kKlM0LrNpVF7Suv6MW95H3dCkNqqMP94G39et6XVqnv7FNZvxerUqGYhid8OjoxXtqD3LCuED3Q7WnCa/XTcjS/2Lo798JtCb3d9Rguew+XDtfHp3kJxsIWfwA/v42XNv/imvbnwGtLboUFo2v4SCushdx7foH5mnXI0en4Xjrx4FJlJItjpAvPoFj7eP4O7SLP8liI+Tmp3Btema0gYskYVn8gNaMZkzlEVPJtfg7T+Edqa7R24ohcyrG/Hm6wNZbswvTjBvxjRmB9zVUoiSO05cXdA/hPVGO77QmPJ7qrVolkrHnfcgelKeO6sNTt5/T+fvaUc9QJvB88+WiOznYeYTtLbvY1baXUCWE5QU30OPsZV3tRv50+G/IkszlGQsIN9nY0lTO80deQpZkpieWkmpN5HB3NWtOauc5NyKL4riJtA22U95Swe62fURborgsfT5un5u6/gae3vdrIkzhzEgsZdA7CEg09jcTagylKKaAtqEOYizRePweKlr3Myl2IjV9dYQZQkkMS2BL03amJ5ZwpLsaj89LfvQ4OhxdzEyaxr6Ogzi9TvKicul3D1AUU0Bl52G6nD0khyWSEBqHT/XR7bTT7uggwhxOQfR4FEkOTAYMNYQwJa6YPnc/+VHjOGY/SYjBwsToPN6u3UhRzAQaB5rodfUzLjKL+v5Gkq2JWI1hdDl7SLOm0Onowul1MTEmnx6nnUhLBB6vh9XVayiOnaCNMCtGGvqbeHznT4kyRzA3ZSZhhhDq+hv50e6fY5JNTEuYSqQ5nIb+ZlYffw2AzPB0psQX0+XopqxpBztaK4g0R7AkexGq6mdT3Rb+9/CLAFycMpOC6PG8evxNnj+qXdhnh2dwSfo83q7dQNMZqqIIH5+nQ/8b5evrw/7upkBADeCsOcnQsWr6t5fRt2M7hogI4m68GVvJtNENz5Sm6tPHIV67HdXvxxit3VHqWP13eja+A6qKOT2DuC/eFAioR7bv/eB9Tp+Gp35KKbGfFRd0UP3lqyfy81dGJ1SZjDKhZgP24RxlWZKYPzmFth4HJxpH88mm5sXR1evUBdZRNjPjUiPZeUT/R5GfEcn7p40EJ0eHYjLKumAuPNSE060P3mUZTrXog0i/qrK1spnXy04FlpUdbOX7y0t4ceOxQEqLIkt8+6bJ7D3WwcYKLYjdsLuBZfNz+NGXZ7JxdwP2ARdtPQ6unJnB7944Ehi5/8KsTIwGGa/Pz/Prqyk/3EqIWeHS0lRK8uJYv6OelX/RJryMpL6s31XP2+V1gX36ytKJNLQP8Nbwsn3HO2lsHwiq5tFhd2LvDx7dONOkU7NJwRpi1I20x0eHYFRkWrpGbzGGWQzkpkaw40ibbvuS/Hi2HGjR3aGYlBOLy+1n/5ggfFJuNAdOjAaoIWaFiwoSgvbnXFDSijFmT8O18yXdpEPjuJn4u5vw94ze3jYkT8Dv6AP7mLsW5jAka/AFgmQ5w6QLSdJNxgOtYYnaq2+XrQ5242vX3wZX+zvw1e4NBNQjPEc3o/aNeV/8Xrx1e/F11Opey1O9FeOkq0YDVo8T1/a/YsicGgioR17Hc+BtcI++/6qzH++hDfo8blXFc2Rz0KRMT9UHQe2/vfUHgvKjAXyn52YD/t7gIEL1upFMIVrDl2GS2XrGpi5ydGrwRNGsEi3dZEydbENWKZIpNCjf/HzTPKivgzzkc3C4q4qmgVZ8aN93ftVPectuSuIn0T484uxX/exq3UNW3rVUdY9+lk701jI5vogdrXvwD2/f7eyhz92PQVY4NVy6r9fdR2XnYa7LXcILVaMdQEMMITww5as8uecXeEfqhiPxwNSv8uqJN9k/pqTfjeOvYdDj4M3adwLLLkm7mPFROfzm4J8DnRAzbWncN+kentr3LO83ahdoYYZQvl1yH38+/De2NWsXVEbZyANTvsrW5nLKWkZH0W8afy2DniHeGPM6BdHjuSV/GU/v/XVgdNxqDOPbJffxxO5VuMaMZH+t+EusPvYanU7tO6rT2c2ijIXU9TUERvB7XL0c7T7GzMRSjnZr8wxcfhcVbfu4reAGNtZvDjzfqb56psQVUd6yK/Ae2V29NA+04PS5cPlHv5+3N+8izBhKh3N0UKimr46M3hoRUH8CrFNLdSPBIfkFyJbgORWDFbvpK9PuWni7u2n93W8IGZ+HJMkMHqrEmJSEHBaGf1D7DjHGxWGdos3vUFWVtuf/SN+2raCqWEunEX3FEno2jN4ddNXX0bd7R9DrymYzERfPo/f90fKnUZct+mQO/nPigg6qJ42L46dfm8lr204RHxHCVbMy6O5zsWlPA0NOL3OLk8lJieDea4tYu7WGxo5BCrOjuXJGBv1DHnoH3FQ32ImPCuFLVxaQkxJOfVs/ZQdbsZgUrpmbzbT8BGou6mPj7kb8qsqEzCguLU3DaJB56T0tj9diUrh2bjZ9Q26eXXMI/3DQcFlpGrGRIeyuHu1IpcgSQ67g22nlh1p1OeI+v8rm/U3sPaYf3Xp3TyOyJLFpjxZ8VVR3UDI+jv/3nzNYt6OO5q4hunqddPU62Xusg20HtYBswOHl3T2NpMdbdZU8mjoHOVzbzYETXUGv09mrn2ix51gH0wsSqG8bDTZCzQYWlqSyq6o9cJERG2HhipkZVDXYAxcz1hAjl5amMj4tkj+uO4rD5SM2wsKNC3IxDAfVrd1DhJoN3LE4nynjY2loH2BbZQtmo8I1c7OYkBHNvdcV8dqWGgadXuZNTmbGxESmjI9jU8VofvvMiYk0tPfz6gc1ZCbaWDo3++N9oD4Fvt5WBl58AEPKRO2n1z0EFhvuw++hpE5ACo3A392AHJOBKsvIUSn4exq1UU6jBeOEhSixGXgjElGHA0IlrRg5tQi5bl+gIYxksWEsWIA62DM6igsYC+bjb6/VVaSQ47ORIxK1Lo/DpIgE5Hj9xDsAKTwu+KBOa7QCgNeFv7v+tBVVVDU4PedMFwSS+QwNJ0wWMIXoglXJYkWyhOHvGs2VlaNSUJLyYEwjHNAuXHwNBwONWkDCWLAAf1cj/q7hC0pJwZg9DX9UCu49a7QLANmAceJCUIxI4fGBtuRKWhGSNVqrwNJQCbKCIWMK3iPvYUjMQ/V5wOdGjsvGc/Ad/GdqVHOe6XP1B+XuDniGGDytCYnD46Dboc8HVlFpHggOzFoH24IawnQ7e4JSDYa8jqCSbg6vg23N5YGAeuR1ylt2BzWp2dW6VzfpD2Br8w66nD34x9xhONXfQFnzTl3u8qB3iC1N23XBpsfvYXfbXva0HQh6Tt9pF6tHh1t+jz13A55BNjdu0wXUADtaKgIB9Ygj3dXYnfqLw05HV6Cz5dhjr+s7/e8K2oY6gnK7u5w9Qf2YvKqPfvdpf6vAoPvD86atfHT+rzAqbtkNyCEhDB05jDktndil1+K12xnYszswcTG0sBhPj/79V71eBvbsofPVl/EPae+FtWQalowMXC3NuBobaXzqJ0RfcSWSwUjf1tE7kAMVuzFERQXti+pyEzZlKoP7tAmuktlM1GWLMaenE5pXgKuxnrCJRYSMG//vOh2fSRd0UA0QHa4FxCNiIizcuFDfjjgizMTtp5VUi7KZefjWqbg9PkzG0Qk1d15RwK2X5aHIUiCF4saF41g8PQOX2xtovX35RelMHhdLc9cQ41MjCLVok0Meu/siDp/qJjXOSkFGFH6/SnvPEFsPtGANNfLF+TkoisymCv2Em/TE4MDCZNDyrj1jqm8YDTJbK/UpAXuPdVCUE83m/dryYw12DtZ0kZOiH1lTVTh1hsmdgy4vkqQbQMRkkIkIM+nSOsIsRq6fl02H3cHJ5j7CQ43cvjifzMRw/vvOaYGLkYsnJRNiMvCdm6ew73gngw4PU/PiCA81ER8VSlF2DJ29DpJiwgLneOU902m3O4iymgPvxx2L87n1svHI0uh7MTk3lsm5sbr9NxsVrpqZqVuWFm/jG1+cFHSs59xwQOat26elLvS3D9ek1tIRjAXzMRVeiuOdXzDyiygnjMNyyb04t/4Rz7438KDVYDYvfgBv3QG81R/ga6hEik7DPPdO1IEuPCd3MfTKI0jRaRinfAHV0Y862I179ysQFq3lI/c0IltjhoNaWctVbjuOHJmEHJWK5+AGDDkXabnefh9KcgFK8gTUnhatcQxaPrccl4lBkrQR52GGnOkYUgvx1YxpKGEwYS69HkdnfSDVRUmfjJI4Hm/dvsBFghyVquVTZ0wdbVVuCsWYMhHZGotn3xvauVEMKGlFgITqHEAd7EGyRiPHZ+Op2oIh+yJtP2UFJbkAb+0eDDkXaW3IVRUpLFqrc20MwTB+DpLFijrQpS1TVeT4HEwTL8HX06TlYQMYzJhn3AyqimvXy4GJn8bJS1Dis3Fu+EXgcKWQcEKWfJehVx7VtaQ/ny1Im0Ndf6MuYJ2ZWErzYKtudPSixBLGR+VQ2TWaOhNpjmBe6izKW3YFAjxZkpmdMp3j9lpdp8Sp8cV0O+3Uj2lsEmWOJN2WEpS3nBGeBk3ljJUUloBBUgL52gDh5nDsp014NMqGMzaZMSvBgaJZDl5mUkwokqx7HZNsxGgI1XVpNCsmIs0RQdsnhQX3BEixJXO055iuQkdKWBKJoQm6Vu7ZERkUxuTrSt6ZFBNzU2awo6Ui0L1RQmJ2ykXU9TfoJkROjS/GrJioqx694MyLyuXilJnsaK0IXKjYTFYWZy7kcHcVg57g4DoN0bzr45IMBmKXXgtLrw0sU2w2Mv/nCQb27cMQGUFY8WR6Nm1gcP/oYIdkMjFUdTgQUAMM7NmNtXQanWteCfw4Nz/7S6IWXRH8uoqCYrPh6x/9fbdNu4iw4kkMVh7A29NN2KQpgVQRW+k0bKXTgp5HuMDrVH+Wvbqlho0VDciSxFUzM7hyRga/+EdlII0hxGzg+8tLOHiyi9XvD48+Av+xZALv72/SpbOYjQr56ZG6EWiAxdPTWb9zTB1gReK/75zGj17cG8gzV2SJFbeXsq2yJZArbVBkHrhBC0h/8UolLrcPRZa484r8QJWTviE3oWaDaEH+IT5OnWoltVCrYDE24DJaUBLHB01qM065Gs++13XLzDNvwbXjb7qrIWPRInzNR3Qjt0rieJTkfNx7R7eXLDZCrnqYodd+CL7hdBxjCKHLHsfx9k8DAS6AZcFXUB29Wq1o1QeyEdPUJaAYcR9YD84+kCSUrIu0yhdeF/7uJiSjCTkicbibYwhyZAqS0YIhuxTcTrytx7WmLKoPTKGYJl0JkqxNzHQPATLGwkuRIxJxH96EOpwzrmRMwZg7E9eBt1FHuiuarYQs+gauva/jH1N72zzjRi0AHjMxVEmfhDH7Ipybfzd6MmUFyyVfw7nxGd05Nk1bFuicGNg+ZSKqe0ifnqKYMGSX4D2uD/6MEy75WCkf50udagmJpLAEsiIycPvcDHgGOW6vIVQJYWJsHh6/F5/fx7Gek3hUL7kRWfhUH2bFzJDXgd1pJy08FZ/fi9fvQ5KgdbCDhNBYQgwh9HsGCTOE0O7oJMQQQpghhFN99URZIkkMS8AoGajrb6S+v3F4EmIRSaEJNAw0s7/jICoq4yKzKYqdQLujk7KmnaioWI1hXJ6xkH53P5vqPwiMGF+ReSkp1iReOPoyTp8WxBbGFHBp+sW8VP0aLUNaKlNcSAw3513PGzXvUNun3bWwmawsz7+BQ11VbBmuwS1LMldmXoZRVlhf9x4OrxNZklmQNocYczSbm8oCKTETovOYGJtPTe8p9rUfxK/6yY7IZGJMHv3uASra9jPgGSTFmkRSaAI+1Yfb56F5sJWksASsxjB6XX0YFSNtQ+2EGUMJM4TSOtRBjCUKo6IN5FiNYZzqq8eiWAg323B5XYQaQznWcwKf6iMvahxmxYxBljlur6HbaSc3MovYkBgUZBoHmjnV10BiWLxucuWICYzj3oX3fIKfPkH1eml/6W/079iOISKS2BtupHfLB7pAGyB87jz6turnbUQtvlLLnR7JsZYk0r73CLLFTPdbb+Ib6Cd89px/OqnxQvZRdarPq6D6jTfe4LnnnsPr9XLHHXdw6623fuxtL7SgGrT8agkCs3/9qsqhmi7sA24m58YSHqaNsJxq7aO2pZ/xaZGkxIZxtK6Hn798IFA/+oYFuXT0Onh/TLMZCVj55RmUVbawtbIFW4iR6+ZlM2VcHC1dg2yqaMTl8TF/cgq5qdoIS1VdD609QxRlxRAToeWBDTk91DT3kRpvJdIqbgN+XO6mo7je+vFHrmOauhTP8TLdBDY5Mgk5OhXv2BFetGA50BVwZNnES/Ec3qRbdqZmMBjMKLEZQTWgDfnz8Z7WmORMjVqUrFL8rce08n4j+xmThhyRNKZUHVpguvAr2kjvCEnWAvUNz4wG6pKM5cpv4dzwDHhGR+u0yh0yvqYxpcuMFkxFi/SNXgDT9Btwj20Iw5kDWDkqFVR/0IRPQ84MvCf1OYfGgoV4jupbrRvGzcF7fJtumRyTrtXzHvuckoyx6HJd1RMA8/y7cW3+Xz6ahO0///hP1vlk/bPmLwmhccxNnsk/ToxeiMmSzNeKv8QvD/xet+4dE27i7dqNutSLy9LnM+AZ1OVnZ4anMz91Nn86Mto4x6yY+P60B/nZ3l8FqlUYZSNfKb6T7c27ApU+ZEnmtvwvMuR18MrxNwJB88LUOaTaUnntxFuButD5UeOYFFfI4a4qDnVp1ZiywzOYljiF1qF2tjXuwIefKHMkC9Pm4PZ5+KCxjD7PAGbZzLzUWUSYw9nevJOmwVYkJEoSJpETkcmOlj2BsnxxITEszbmCo93HKGvW/g6MkoFrc5fg8jl5veadwH5elj6f3Mgs/vfwi4FKIVnhGdycfx2r9j4XGLVWJIXvlH6dvxxdTePA6Ofr+twlNAw0s2tMreqciExmJE3jxTE56GbFxFeK7uLn+3+je4/umnALLx9fy8CYNJ55qbPocvQEztGHuTL+Uq4qvPwj1xHO3kDlAZqfeTowSGLOzCLq8sW0/vY53Xop33gQZJme9etQfV4iL70c29SSMzyj8GE+Kqg+b9I/2traWLVqFa+++iomk4mbbrqJ6dOnk5ube6537bx1erUQWZIozokNWi8zMVzXRKUgI4onvzaLY/Va7eqkmDC6+5wcrumm3e5AAhbPSCcxKpTr5+Vw/bwc3fMlxYSxfNFpTUOA/Iwo8jP0uVmhFuP5UznjM8SUUoArPhfaR+uHmy75Cp7da1AHuzDkTMc0+SqU2Ewc7z6n1VY2mDHPvBkpJBxvw8FAwGnMm4tp4iVaibeRiYPGEIxFl+M9rQGLMbsUPM5AegaAIWUCki1OH1QbLciRwbd1JWtwbWLJbEV16mvnqkO9qIbTJuD4fXjr9fmnqH48R97XjXyj+vEc3aILqLXntA+XwxvD48R/WpdDQBfgB5ahgsGkm1wphdi0ih32MSsazMhxmaALqiWM42biOV4G3tGUJ+P4WagOu1Y1ZWRZ3lyQZFxlfxl9ynEzMU26El/DQfw92sWtMX8+pvFzUe1tuCvXg9+HIXsa3rYaGOwMvG7Yf4wZMT9PtA11cGJMkxYYrgl9hu57R7uPBeUyH+muZuC03N1TffUc6NCnSLh8bt5v3Koraefxe6jsPKzLr/arfna0VNDvGdDlLW9r2cVM/IGAGqCq5zhFsQW6YLGmr45J8YVsb94dmMzX47JT01tHn3uAPo/2+Xb5Xexs3cP0pJLAxD0VlYq2/WSGp+vqXHc4umjqb6G8ZbQ9vUf1cqj7KN1Ou24/P2jazqBnUFd6r7avLqhRi0/1saWpXBdQA1S0H6DLoc/DPdl7Cutp9addPjd720/7G2T4/TgtL762t/6MdbJPNzEh+LdC+ORZiyeR+tB36d+9C2N0DBHz5iNbLAwdOURf2TaQZSLnzSe0sAhJkgibWPjPn1T4l503QfX27duZMWMGkZGRACxatIj169dz3333ndsd+5wKDzVRmh8f+H90uIWV/zmdk019RNrMxEeGfMTWwqfBds2KoGXmHP0tOUPmFKy3rcLXVY8Sm4E03Kkv7KYn8TUcRLLFBromhi59BM+R97RR0YmXoITHE7LkO7j3rEUd7MGQOxPj+DkoyRNwbnsef0cNSuJ4zHNuR5IV/H1t+OorkcIiscy+HSWlAO+JcvzDKRRKUj6miZei9rbiOboZACksCvPkq7QSfSdGUxuM4+cg2WLxtY1WepBjMzAkF+hK3oFWIeN0cmg4pEzUjUobcmeCJGl538OUlAkY8y/WSviNlOEzWzFNugJv3f7RYN1gwjRBOyeuHS8BqjbKXXodqH6tvJ7HCUiYp12PceJC1J5mLYg2WjBPux4lcRyhS76De9+bqB4nxgkLMKRMQInPxn1oI/6eZgzpkzDmau+hbIvF23gIJToNw/jZSLJC6LLH8bfXIJmtyJFaPq35omWYJl+J6vMin6GCyPkoKSyB/OhxuhrKiqQwNWES25p36gLGgug8DnUeZcg7Ws0mxZrEgHuQ3u7Ri59oSxQp1mT2janYISERHxo8+dVyhpxnFVU3YRHA7/fhOO3iDAgK8gFaB9uDGsJ0OXuCG8+4++gYCt7+9KAWYMA7pJsECeD0us64n2e6DxtqCP6ejrFEY5QNuomH0ZYoLIqZ6p7Ri/TYkBjSrMm6Cx0JiaLYCWxt1t+FKYop4Eh3tW5yZm5kFl3OyH/apjwzLuMjHxc+OaHj8wgdr7+ISbzzbuKW3ail2IWJ7pb/budNUN3e3k5c3OiXY3x8PJWVH7/ZwYcNxQv/msSE4MkywvnOBqmnT2iyQfppZeHiJkD+aS3I4/Jg3Gm38uNskPVI8MssfxS/x4VkMCINd8BT//MnOOuPIMkK5tR8LRXpuq/javsCvv5uLJmFyAYT/oz76duVjavlJJaMQsKnXo4kK/RHhTNYvRNjVBKRM69BDrHS0X2CgYMfIMkKETOuJvriL9DafpShaq0smRIWSeK861AsYdjLX8PdUU9ozhRsU7XSTv0xMQyd3IspLp2ImdegWMIYCnuE/v0bkc1hRMy4GlNMCr67f0x/5Xv4nYNYC+dhikmGvHw8U+fi7mjAkj4BxaL9CPnG/QZnw1FMcWkYo4bP9bJvoHq/BrKMNNIFMm4yTJwc/F4k3xJ8PuPmQMmc4OXxU4KXYTvDsnPnp4se4dvvPB74/6KcuXQ5eqlsO0pmZBpfnnYrKbZEBtQ+3q/djs1s5Zbia5iRNhmPcTmrD72Jy+ti0bj5XFV4MQnREfxm94tareboTL407Yu4fG5+WvYbGnqbiQmJ4r4Zd5ITlU6Ts5F9LYcxKUa+OHEJV+VdwsGeQxzt0ALGRGscN0y5kiEG2VqnpVXIksy1hYvoGOzij/tG034uyZnDrPQSKtr3B4LbBGscyyYtprxld6DyhiRJXDVhPi2OFk7ZRyeJz82aRtdQD+tPbA4sm5RYwCU5M9nXMfr7FW62csOUK9nbcYBe13CaimLk2qLL6Pf1sr91dMLmlfnzGXAP8qd9o2kZl+bMZWH2bPa2Hwjs07iYLG6aehWNzib2NmsXGtlR6Vw/+XJiImw8f+AVfH4fMaFR3F6iTXz7WdlvaexrISY0ivtn3ElmVBotrhb2NB8kxGDh5uKlzB83DZ/JxT+OvI3b5+HKcQu4fOJsMhIT+d89f6dloINpKZO4s+Q6nF4Xv979Aofaq8mOSmdR9jx+vusPgf1+YPo9xMWdX5/dC5J4Dz41501O9XPPPYfL5eKb3/wmAKtXr+bQoUM89thjH2v7CzGnWhA+r1T3EEgKklEbcVRVFV9LNaqzD0NaMZIxuHar8Nnm8/sY8jqCWmL3ufuxGsN0rcx7Xf2YFSOW4RQiv+qnuvsEbr+bCdF5GBUjftXP/o5DtA91UBhTQKotGYCjXceo6jlOmjWZqQmTkCWZk/ZT7Grdg9VkZV7qLMJNNk711bOx7gM8fg8Xp8ykMLYAu6uXdbWbaB/qpDhuIvNSZ+FT/ayv3UR1zwnSbClclX05VmMYFa37KG+pwGaysihzIUlhCXQ5utnSVI7H72FW0kWk2pJx+9yUNe+ibaiD4tgJTIjRRhqPdFUHnnNqfDGyJNPp6GZv+wFsRislCZMxDU82rO9rxO33kB2REThPfe5+uhzdpNtSUWRFd+5sJv35HPAMYpZNgcmLgiB8uM/ERMU1a9ZQUVHBypUrAfjVr36FqqofO/1DBNWCIAiCIAjCv9NHBdXnTT2zWbNmUV5eTnd3Nw6Hgw0bNnDxxRef690SBEEQBEEQhH/qvMmpTkhI4IEHHuD222/H4/GwbNkyiouLz/VuCYIgCIIgCMI/dd6kf5wtkf4hCIIgCIIg/Dt9JtI/BEEQBEEQBOGzSgTVgiAIgiAIgnCWRFAtCIIgCIIgCGdJBNWCIAiCIAiCcJZEUC0IgiAIgiAIZ0kE1YIgCIIgCIJwlkRQLQiCIAiCIAhnSQTVgiAIgiAIgnCWRFAtCIIgCIIgCGdJBNWCIAiCIAiCcJZEUC0IgiAIgiAIZ0kE1YIgCIIgCIJwlkRQLQiCIAiCIAhnyXCud+CTIsvSud4FQRAEQRAE4XPso+JNSVVV9VPcF0EQBEEQBEH43BHpH4IgCIIgCIJwlkRQLQiCIAiCIAhnSQTVgiAIgiAIgnCWRFAtCIIgCIIgCGdJBNWCIAiCIAiCcJZEUC0IgiAIgiAIZ0kE1YIgCIIgCIJwlkRQLQiCIAiCIAhnSQTVgiAIgiAIgnCWRFAtCIIgCIIgCGdJBNUXiIGBAX74wx+yZMkSli5dyvLlyzl8+DCNjY3k5eVRVlamW3/hwoU0NjYC4PV6ee6557jiiiu48sorWbRoEb/+9a8RHe6F/6vGxkYKCwtZunQpS5cuZdGiRXzve9+js7OTgwcP8l//9V+f6GstXLjwE3s+4fNv7Ofzmmuu4aqrruKuu+6itbX1Q7dZvXo1b7755r/0OsuXL2fnzp1nu7vCBWznzp0sX75ct+zjfH7Ly8u57bbbWLRoEZdddhn333//R36+hY9HBNUXAL/fzz333ENERASvvfYaa9eu5d577+Wee+7BbrdjNBp55JFHGBgYOOP2P/zhD6msrOSll17i7bff5pVXXqG8vJy//vWvn/KRCJ8n8fHxrF27lrVr17J+/XpiY2O5//77KSoqYuXKled694QL3Mjn87XXXuOtt94iLy+PJ5988kPX37t3L263+1PcQ0H4cB/1+a2oqOChhx7i29/+Nu+88w4bN25k+vTp3Hvvved4rz/7DOd6B4R/v507d9LS0sL999+PLGvXUTNmzOCJJ57A7/cTHx/PrFmz+PGPf8zjjz+u27a1tZXXX3+dLVu2EB4eDoDVauXRRx/lxIkTn/qxCJ9PkiTx9a9/ndmzZ/P888+zceNG/vKXv1BXV8cPfvAD7HY7FouFRx55hAkTJvDGG2/w+9//HkVRSE1N5Sc/+Qlms5nf/va3rFu3Dp/Px5w5c3jooYfO9aEJnxPTp0/nqaeeorKykieeeAKn00lUVBQ//OEPaWho4L333mPHjh3ExcXx1ltvYbfbqaur46GHHiI6OpqVK1ficrmIioriscceIyMj41wfknABGfn8Ajz77LN89atfZfLkyYHHb731VpxOJ263G5PJdI728rNPjFRfAI4cOUJ+fn4goB4xb948oqOjAfjud7/Ltm3bgtJAKisrycnJISIiQrc8JyeHRYsW/Xt3XLigmEwmMjIyiI2NDSx7+OGHeeihh1izZg2PP/44DzzwAABPP/00f/jDH3j11VdJSUmhpqaGLVu2cOjQIf7xj3/w2muv0dbWxuuvv36uDkf4HPF4PLzzzjsUFhayYsUKfvazn7FmzRruuusuHnnkEWbNmsXChQu5//77mTt3LgCRkZGsW7eOOXPm8OCDD/LII4/w+uuvc9NNN/Hggw+e4yMSLiQjn9+RIHr//v1MmzYtaL27775bBNRnSYxUXwBkWcZsNn/kOlarlccffzzwxT+WJEmBf69fv57nnnsOv9+PyWTilVde+bfss3BhkiQJi8UCwODgIIcOHeJ73/te4PGhoSF6enpYsGABN998M5deeimLFi2ioKCA119/ncrKSq677joAnE4nycnJlJSUnJNjET7b2tvbWbp0KQBut5vi4mKuv/561q9fz1e/+tXAeh+WNldcXAzAqVOnCA8PD/z/iiuu4NFHH6W/v//ffATChexMn99vfetbgcdHftfdbjdf/OIXAejt7eWpp55i6tSpn/4Of06IoPoCUFhYyF//+ldUVdUFyE899ZTuFuScOXMCaSBjtz158iQDAwNYrVYWL17M4sWLaWxs5Pbbb/9Uj0P4fHO73dTW1tLV1QUQuHBbu3ZtYJ3W1lYiIyNZsWIFVVVVfPDBBzz00EPcd999+Hw+7rjjDu666y4A+vr6UBSFnp6ec3I8wmfbSE7qWFVVVaSmpgaW+3w+Ojs7z7j9yMWh3+8PekxVVXw+3ye8x4Iw6kyf3xFFRUXs3buXcePG6b5jly9fjsfj+TR383NHpH9cAEpLS4mJieGXv/xl4It869atvPrqq+Tm5urWHUkDaW9vByA5OZmrr76ahx9+mL6+PkCrBrJ58+agdBJB+L/y+/0888wzTJo0ifT0dABsNhuZmZmBL/yysjJuvfVWvF4vl19+OVFRUXz5y19m6dKlHD16lBkzZrB27VoGBwfxer3ce++9vPPOO+fysITPmezsbHp7e6moqADglVde4dvf/jYAiqKcMVDOzs7GbrdTWVkJwNtvv01ycjKRkZGf2n4Lwlhf//rX+dWvfsWBAwcCy6qqqmhoaEBRlHO4Z599YqT6AiBJEs8++yxPPPEES5YswWAwEBUVxW9/+9vA5MMRI2kgd999d2DZD37wA/74xz9y++234/P5GBwcZPr06fzud7/7tA9F+BwZe3vS7/dTUFDAU089RVVVVWCdn/zkJ/zgBz/g97//PUajkVWrVmE0Grn//vv50pe+hNlsJiYmhh/96EfExMRQVVXFDTfcgM/nY+7cuVx77bU0NTWdq0MUPmdMJhM///nPA5MOrVZr4M7erFmzeOqpp7DZbEHbrFq1iscffxyHw0FERASrVq06F7svfE5VVFQwZcqUwP//WfpGaWkpq1at4umnn6azs5OhoSGSkpJ4+OGHKS0t/Xfv7ueapIpiw4IgCIIgCIJwVsT9e0EQBEEQBEE4SyKoFgRBEARBEISzJIJqQRAEQRAEQThLIqgWBEEQBEEQhLMkgmpBEARBEARBOEuipJ4gCMJ5JC8vj/Hjx+vqwBcWFrJy5cr/0/M988wz9PT08Oijj37oOv/zP//D7t27ATh58iQpKSmB5iUvvfRS4N+CIAjChxNBtSAIwnnmz3/+M9HR0Z/a661YsSLw74ULF/LTn/6UoqKiT+31BUEQPg9EUC0IgvAZkZeXR3l5eSDgHvv/9957j+eeew6Px4PFYuHhhx/WNYT4vxgaGmLevHmsXr2arKwsAO68805uu+02Nm3ahNlspqqqiq6uLmbPns2KFSswGo2cPHmSlStXYrfb8fl8LF++nGXLlp318QuCIJzPRFAtCIJwnrnjjjt06R9/+MMfiImJ+dD1T506xapVq3j++eeJiori+PHj3HXXXWzYsOGs9iM0NJRrrrmGl19+me985zvU19dz6tQpFixYwKZNm6isrOSFF17AaDTypS99iZdeeombbrqJ+++/nyeffJKJEyfS39/PjTfeSG5uLpMnTz6r/REEQTifiaBaEAThPPOvpn+UlZXR3t7OnXfeGVgmSRL19fVnvS+33HILt912Gw888AAvvfQSy5YtQ1EUAK699lrCwsIAWLp0Ke+++y4zZsygvr6e73//+4HncDqdHDlyRATVgiB8romgWhAE4TPI7XYH/u33+5k5cyZPP/10YFlLSwvx8fFs3LjxrF4nKyuLvLw83n33Xd58801Wr14deGwkuAZQVRVZlvH5fNhsNtauXRt4rLOzE5vNdlb7IQiCcL4TJfUEQRA+I6Kjozl48CAAb775ZmD5zJkzKSsr4+TJkwB88MEHXH311Tidzk/kdW+55RaefPJJiouLSUhICCxft24dbrcbl8vFmjVrWLBgAVlZWVgslkBQ3dLSwpIlSzh06NAnsi+CIAjnKzFSLQiC8BmxYsUKHnvsMcLDw5k1axZxcXEA5Obm8thjj/Hggw+iqioGg4HnnnsukJpxthYsWMCKFSu46aabdMstFgu33HILfX19LFq0iOuvvx5Zlnn22WdZuXIlv//97/F6vXzjG9+gpKTkE9kXQRCE85Wkqqp6rndCEARBOH/t27ePFStW8OabbyJJEgDf/e53GTduHHffffc53jtBEITzgxipFgRBuAB885vfpLa29oyPrVq1iuzs7DM+9vDDD7Nr1y5WrVoVCKgFQRCEYGKkWhAEQRAEQRDOkpioKAiCIAiCIAhnSQTVgiAIgiAIgnCWRFAtCIIgCIIgCGdJBNWCIAiCIAiCcJZEUC0IgiAIgiAIZ+n/AyD3kA9tRxZoAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x612 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(12, 8.5)\n",
    "sns.swarmplot(x = 'Fuel_Type', y ='Price', data = df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Year**<br>\n",
    "Let's see how the price of the car varies with its age."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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n7xEoAQAAAOhWIpFSWVZKkpRKpZilBHQTpix1dcvpewRKAAAAALoVt+w/gsOZ8oYf7rRkydN6772/asmSp51upcPccPoegRIAAACAbsUt+4/gcOxthc4Sj8dVWVkuSVq1qtzzoWUwGNTcufMdm50kESgBAAAAsMGEGSRu2X8E6djbyp1MeM5LTbOTLMuSJFmWZcQsJacRKAEAAADImgkzSNyy/wjSsbeVO5nwnJek1asr0upYrKKVK5EtAiUAAAAAWTFpBokb9h9BOva2cp+m53yZLMtSeXmZp5/zyWQyYw37CJQAAAAAZMWkGSRu2H8E6djbyn0ikdK0kM/Lz3nkHoESAAAAgKwwgwSdib2t3Keysjxt36FVq8od7qj98vLyMtawj0AJAAAAQFaYQYLOxN5W7jNwYGHG2ktGjx6bVhcXj23lSmSLQAkAAABAVphBgs7G3lbu0tBQn7H2ksmTp7aopznUiTkIlAAAAABkhRkk6Gwm7W0Vj8c1f/5cT29kPWbMuLT6nHPGtXKl+5WVvSqfzydJ8vl8Kit7xeGOvI9ACQAAAEDWmEECZCcSKdWGDes9vZF106zEz0IYLz/vY7Fo2n5Q7AHXcQRKAAAAALJm0gwSoLPE43FFoytlWZai0ZWenqV0aKDkZewBl3sESgAAAEAnM2HpC4DsRSKlSqWSkqRkMunZWUqRSGlaoOTVcUjsAdcZCJQAAACATmbC0hcA2YvFokomPwuUvLq8ypRxSOwB1xkIlAAAAIBOZNLSFwDZGTFiVFo9cuSoVq50N9OWibEHXG4RKAEAAACdKBIplWWlJEmpVIpZSgA8w7RlYuwBl1sESgAAAEAnisWiSiQSkqREIuHpJSMmYV8rdKa1a99Iq9eseaOVK92NZWLIhEAJAAAA6ESmLRkxBftaoTMVF4eUl5cnScrLy/P0855lYmgNgRIAAADQiUxbMmIC9rVCZwuHS+T3fxYoefl5zzIxtIZACQAAAOhEpi0ZMWGpmGn7WplwT0wTDAY1alSxJGnUqGJPP+/XrXtbl19eonfeWed0K3AZAiUAAACgk5m0ZMSEpWKm7Wtlwj2Bey1a9JAsK6Wf/3yB063AZQiUAAAAgE5mypIRU5aKmbSvlSn3xDTxeFxvvBGTJL3xRsyz92Xdure1a9cuSdKuXbuYpYQ0BEoAAAAAsmLKUjGT9rUy5Z4cZMryPVPuy6JFD6XVzFLCoQiUAAAAAGTFlKViJu1rZco9OciU5Xum3JeDs5Naq9G9ESgBAAAAyIpJS8VM2dfKpHti0vI9U+5L3759M9bo3giUAAAAAGTFpKVipuxrZdI9MWWZmGTOfbn++lvS6htuuNWhTuBGBEoAAAAAsmLSUjFTmHRPTFkmJplzX0477XT16tVbktSrV299+cunOdwR3IRACQAAAEDWTFkqZhJT7okpy8QOMuW+9OnTtMyN5W5oyWdZluV0E7nQ0LBTqZQRQwEAAACAbicej+vmm2fqwIH96tGjhxYuXOzZmT2mqKraqDlzPlvm9qMfPaRhw4Y71xC6lN/v08CB/Vr/fBf2AgAAAADAEZmyTMwkixc/nFY/+ujPnGkErhRwugEAAAAAAKSmZWI1NR96fpmYKWpqPsxYo3tjhhIAAAAAwBVMOX3PFEOGDM1Ye008Htf8+XPV2Bh3uhUjECgBAAAAAFyBN/zuMnPmjWn1rFk3OdNIjkQipdqwYb0ikVKnWzECgRIAAAAAwBVMesNvQjjWv39Bi7q/M43kQDweVzS6UpZlKRpd6en74hYESgAAAAAAx5n2ht+EcCwSKZXf3xQb+P1+z4/FslKSpFQq5emxuIUjgVJZWZlmzJihyZMn67777pMkxWIxTZs2TRdccIEWLlzoRFsAAAAAAIeY9Ia/KRwrk2VZKi8v82w4FotFlUp9dk8qK6MOd9R+sVhUiURCkpRIJDw9Frfo8kDpww8/1D333KPFixdr2bJl+utf/6ry8nLdddddWrx4sV566SW98847Ki8v7+rWAAAAAAAOMekNfyRSmjYWr4ZjxcUhBQJNh8MHAgGNGRNyuKP2Ky4OKS8vT5KUl5fn6bG4RZcHSq+++qqmTJmiwYMHKz8/XwsXLlTv3r01bNgwDR06VIFAQNOmTdPy5cu7ujUAAAAAgENMCi8qK8tlWZYkybIsrVrlzQkT4XCJfL7PlryFwyUOd9R+4XCJksmkJCmZTHp6LG7R5YFSdXW1ksmkrrvuOk2fPl2///3vVVdXp8LCwuZrioqKVFtb29WtAQDQJUzYpBMAgFwzKbwYOLAwY+0VwWBQodB4+Xw+hULjVVAQdLqldtu+vbFFvd2ZRgwS6OovmEwmtWbNGj311FPq06ePvve976lXr17y+XzN11iWlVZnY+DAfrluFQCATvHMM09qw4b1Wr78ec2ePdvpdgAAcIXCwqM0ceIF+uMf/6iJEyfqi188wemW2m3btq2H1YWFRznUTcdcc823VFf3sa655ioNGODNMUjS7ben79W8ePFCPfHEEw5103ENDQ360Y9+pDlz5mjAgAGO9NDlgdIxxxyj0aNHNw/4X//1X7V8+fLmtYySVF9fr6KiIluP29CwU6mUldNeAQDItXg8rhUrXpFlWVqxYoUmTZru6b/2AQCQS5MmXaS///0DTZo0XfX1O5xup92Ki0N67bUVzfWYMSEPj6eH7rjjh0om5eExSDU1NWn1Rx995OnxPPHEb/TOO+/o8cef1FVXfbdTvobf78s4eafLl7yNHz9eq1at0ieffKJkMqmKigpNmjRJGzdubF4O9+KLLyoU8u56WQAAWmPSCTYAAORaMBjU3LnzPf/HlgkTzm9RX+BQJzBR0ymCK2VZlqLRlY5to9DlgdLpp5+ua665Rt/4xjc0ZcoUHXfccbr00kv14IMPavbs2ZoyZYpOPPFETZo0qatbAwCg05l0gg0AADiysrJXW9SvONQJDjrjjBFp9ZlnjmjlSvdzyx8ou3zJmyR97Wtf09e+9rW0j40ePVrLli1zoh0AALpMcXFI5eWvKZFIeP4EGwAAcGSxWPofjCoro522LAnZ6dfvqIy1lxzpD5ROfH91+QwlAAC6M5NOsAEAAEc2YsSotHrkyFGtXImusnbtG2n1mjVvtHKl+xUXhxQINM0PcvIPlARKAAB0IZOO3wUAAOaLx+OaP3+uY/v05Epxcaj5MLC8vDxPzxJ3yx8oCZQAAOhi4XCJTjrpFGYnAQBgKJNmwyxZ8rTee++vWrLkaadb6ZBwuER+v/MhTC645Q+UBEoAAHQxU06wAQAAR2bKkrd4PN58gEhlZdTTs5SCwaAGDjxGknTMMYWe/z3MDX+gJFACAAAAALiCKcurTLFkydNpp4l5eZZSPB5Xbe0WSdKWLZs9/z3mhj9QEigBAAAAAFwhEinVhg3rHTsGPVdMWfK2enVFWh2LVbRypfs1hWOWJMmyLE+HY25BoAQAAAAAcFw8Hlc0ulKWZSkaXenpGSSnnnpGWn3aaWc60wiamRSOuUXA6QYAAAAAAIhEStOWV0Uipbrqqu863FX7bNpUnVZXV1c500gHFRYWacuWzc11UdEgB7vpHioqXld5eVmb1zU2NkqSCgoK2rx23LgJGjv23I60dUTMUAIAAAAAOC4WiyqRSEiSEolE82bQXrRly8cZa69oOUssHt/mUCcdV1g4KGPtNdu3x7V9u7Oz+JihBAAAAABwXHFxSGVlr8iyLPl8Po0ZE3K6pXbr27evdu3alVZ70Zgx4/Taayua63POGedgNx3T2LgtY+0WY8eem9VsovvumydJuvvuezu5o9YxQwkAAAAA4LgJE85P2zR5woQLHO6o/Q7OtGqt9ooJE85vUXv3nowZkx6GeTkccwsCJQAAAACA48rKXm1Rv+JQJx33la+MTqv/5V9Gt3Klu5WVvSqfzydJ8vl8nr4n4XCJAoF8SVJ+fr7C4RKHO/I+AiUAAAAAgONisfQ9k7y8h5IpYrFo2qwxL9+TYDCoceMmyOfzady4CSooCDrdkucRKAEAAAAAHDdixKi0euTIUa1c6X5r176RVq9Z80YrV7rbqaeekVafdtqZzjSSI+FwiU466RRmJ+UIgRIAAAAAADlUXByS39/0dtvv93t2g/FNm6rT6urqKmcayZFgMKi5c+czOylHCJQAAAAAAI4zZVaP1DQT5tOVYs21F23Z8nHGGt0bgRIAAACArMXjcc2fP1eNjXGnW4FhTJnVc9Cne1l72pAhQzPWXsPPr9wiUAIAAACQtUikVBs2rFckUup0K/iUKW+Sw+ESpVKfbQDt1Vk9UtPz5NDT0bz6fJk588a0etasm5xpJEf4+ZVbBEoAAAAAshKPxxWNrpRlWYpGV3o+wDCFWW+SPwuUvCwWiyqZTEqSksmkZ09HGz78c+rbt68kqW/fvho2bLizDXUAP79yj0AJAAAAQFYikVJZVkqSlEqlDAkwvK3pTXKZLMtSeXmZp98kL1nydMbaS4qLQwoEApKkQCDg2eV78Xhc+/btkyTt27fP099fkUipUqnPQj5+fnUcgRIAAACArMRiUSUSCUlSIpHw7KwLk0QipWn3xMtvklevrkirY7GKVq50v3C4RD7fZ/tBeXX5XsvvJy9/f5kya8xNCJQAAAAAZMWUWRcmqawsb14eZlmWVq0qd7ij9mu5zM3Ly96CwaBCofHy+XwKhcZ79ph6k0LkESNGpdUjR45q5Upki0AJAAAA6GQmbZpswqwLyZx7MnBgYcbaS4qKBqXVgwYNdqiT3AiHS3TSSad4+nliUoi8f/++jDXsI1ACAAAAOpkpmyabMutCMueebN1al7H2kpbhXjy+zaFOcJBJIfLatX9Oq9es+XMrVyJbBEoAAABAJzLtZCETZl2YdE+OOaYoY+0lY8aMk8/nkyT5fD6dc844hzvqGBNCS5NCZOQegRIAAADQiUw7GS0YDGru3PmefmNp0j1paKjPWHtJ02yYzwIlr4eW5eVmnL5nQogsSSNG/EtazR5KHUegBAAAAHQikza1NYVJ98S0WT0e3oc7TSRSqmTSjNP3TAiRJalHj54t6h4OdWIOAiUAAACgExUXh5SXlydJysvL8/SmtqYwaaPhcLhEfv9n319enkUSiZTK7/8sHPNyCGPS6XumWLv2jbR6zZo3WrkS2SJQAgAAADpROFyS9sbSy2/4TWHSRsPBYFCDBx8rSRo06FhPzyKJxaJKJpOSpGQy6emZYyadvmcKwv3cI1ACAAAAOpkpy3hMYdJGw/F4XHV1WyRJdXVbPL1Xj0lv+OvqatPq+vraVq50v3g8rvnz53r6e0s6OJvPjCDZLQiUAAAAgE5k0jIek5iy0XDTBuOfzYDz8vdXOFyiVOqzzdK9fG9SqWRafXDmlReZcFqd1BQkFxUNliQVFQ32dJDsFgRKAAAAQCcyaRkP3MekDcYlpYVjXtYyQPJqoBSPxxWNrpRlWYpGV3p6llI8HldtrRmz+dyCQAkAAADoRCYt4zGJKbMuTNpgfMmSpzPW6HpNM+A+mzXm5edLU+9mzOZzCwIlAAAAoBM1LeNhU243MWnWhUkbjMdi6bOrvDzb6uij+6fV/fsXONNIB5k0A86ksbgFgRIAAADQyXw+pzvAoUyadWHSBuMH909qrfaS/Pz8jLVXmDTD0qTZfG5BoAQAAAB0okikVD4fm3K7iWkzFUzZYNwkDQ1b0+qtW+sd6qRjTJphadJsPrcgUAIAAAA6EZtyu49pMxWCwaDmzp3v6dlJcC9TZliaNJvPLQiUAAAAgE5kWnhhAmYquJPf789Ye8m//MvotHrUqNGtXOlups2wnDDhfPXq1VsTJlzgdCtG8O4zFAAAAPAAwgv3YaaCOxUXp4etXg5fr7zy2xlrrzBthmVZ2avau3ePyspecboVIxAoAQAAAJ2I8MKd2HfIfS655PKMtZcEg0ENGDBQkjRw4DGefd6bFPLF43GVl78my7L0+uuvefp0R7cgUAIAAAA6GeGF+7DvkDsdurzKy+LxeHNgEY9v82x4MXLkqLT6K18526FOOi4SKU3bjN/ry/fcgEAJAAAA6GQmhRdVVRt1zTVXqLq6yulWOiQWW6XLLrtYf/pTzOlWOsyUe2LSfj1LljytVColSUqlUlqy5GmHO2qfp59+Mq3+7W+fcKiTjlu16vW0uqLi9SNcBTsIlAAAAABkbfHih7Vnz249+ujPnG6lQx577BFJ0i9+8bDDnXScKfckFoumhTBe3q9n9eqKtDoWq2jlSnerqfkwY+0lBw9HaK2GfQRKAAAAALJSVbWx+Q1lTc2Hnp0RE4utSlv64uVZSqbcE0kaMSJ9eVXL5VZeYllWxtorBg8+LmPtJbt27cpYwz4CJQAAAABZWbw4fTaPV2fEHJyddJCXZymZck9MEwwOyFh7xQknDEurhw0b7kwjOcAMpdwjUAIAAACQFVOWvxycndRa7SWm3BNJWrv2jbR6zZo3WrnS/bZta8hYe8X//d9f0up1695yppEcMOl57xYESgAAAACyMmTI0Iy1V+Tl5WWsvaSoaFDG2ktY8uY+Jt0TU35+uQmBEgAAAFwpHo9r/vy5nj1u20QzZ96YVs+adZMzjXTQySd/Ka0+5ZQvtXKl+7XMKTyaW0iS9u/fl7H2kl69emesvcKke2LKzy83IVACAACAK0UipdqwYb2njw43zfDhn2v+q/6QIUM9u5/KBx/8La3+xz/+1sqV7ldfX5ux9pK1a/+cVq9Z8+dWrnQ/U4KYlssO33zTu8sQTfn55SYESgAAAHCdeDyuaHSlLMtSNLqSWUouMnPmjerdu4+n/7pfXBxKq8eMCbVypfsNHnxsWn3ssd49hSuZTGasvcTn82WsvSKVSmWsvcaEn19uQqAEAAAA14lESmVZTW9cUqkUs5RcZPjwz+nxx5/y9F/3w+ES+f1Nb4X8fr/C4RKHO2q/E04Y3qIeduQL0aVGjx6bVhcXj23lSnczJRg7yISfX25CoAQAAADXicWizSfwJBIJVVZGHe4IpvHyXkOHOvwUrr8c8Tp0rcmTp7aopznUSceYsrk4OgeBEgAAAFynuDikQCAgSQoEAp5ekgT3iURK5fc3zbTw+XyengFXXBxKm23l5eeK32/O6Xsvv/xii/oFhzrpmIPfW63VXsNhD7nl7e8GAAAAGCkcLpHPZ8aSJLhPLBZt3p8nmUx6egZcy+eGl58rRUVFLepBDnXScbFY+veUV7/HTNpvTOKwh1wjUAIAAIDrBINBhULj5fP5FAqNV0FB0OmWYBDTZsClUk3LkLy+HKnlrJF4fJtDnXScKZtZt3xunHPOOIc66TgOe8g9AiUAAAC4UjhcopNOOsXTMy7gTk3fU58tefPy91jTTIvPAiUvz7wYMyY9rPByeNFyuZ5Xl+89/fSTafVvf/uEQ510HIc95B6BEgAAAFwpGAxq7tz5zE5CzgWDQQ0aNFiSVFQ02NPfY5WV5Wn1qlXlrVzpfuFwiQKBfElSfn6+p4M+U055q6n5MGPtJRz2kHuOBko//vGPdccdd0iSYrGYpk2bpgsuuEALFy50si0AAAC4AJunorPE43HV1m6WJNXWbvH091hBwYCMtZcEg0GNGzdBPp9P48ZN8HTQd8kll6dtln7JJZc73FH7FBaas69VcXGoeaZYXl6e55e6uoFjgdLq1asViUQkSXv37tVdd92lxYsX66WXXtI777yj8nLvJusAAADoODZPRWeJREoP2ZQ74envsfr62oy110yYcL569eqtCRMucLqVDgkGg80bWo8ZE/JsOObz+ZxuIWfC4ZLmvaxSqZSnZ8C5hSOBUmNjoxYuXKjrrrtOkrRu3ToNGzZMQ4cOVSAQ0LRp07R8+XInWgMAAIALsHkqOlNlZXnzBtaWZXl6mZhpXn75Re3Zs1svv/yC06102OTJU9W7dx9NnjzN6Vbara6uNmPtNYc+79FxjgRK8+bN080336yjjz5aklRXV6fCwsLmzxcVFam21tvfqAAAAGg/Nk91LxOWIg4cWJix9hJT9uqRmr63YrGmfW0qK6Oe/h6TzAjHBg8+Nq0+9tjjHOqk4x5//BcZa9gX6OovWFpaqmOPPVajR4/Ws88+K6npl4RDp9JZlmV7at3Agf1y2icAAACcs3p1RdrmqbFYhW6//RaHu4IkPfPMk9qwYb2WL39es2fPdrqddtm2bethdWHhUQ510zGzZl2rVateP6S+TgMGeHMsTz75i7QlSc8999+67bbbHO6qfRoaGprvS0XF65/eF+/tb/XFL35BW7ZsTqu9+lz5y1/WptVvvbXWs2ORpPz8pv2gnBxDlwdKL730kurr6zV9+nRt375du3fvVk1NTdoxivX19SoqKsrwKIdraNipVIppawAAACYYPXqsystfUyKRUCAQUHHxWNXX73C6rW4vHo9rxYpXZFmWVqxYoUmTpntyb5ji4pDKyl5p/kP2mDEhz35/VVXVpNUffFCjZDLfoW46ZuXKlWl1WVmZvvWt6xzqpmN+8YvH0upHH/1PXXed9wLYNWvWpNVvvvmmZ58rR+LlsRw40LQPXGeOwe/3ZZy80+VL3p588km9+OKLev7553XDDTdowoQJevzxx7Vx40ZVV1crmUzqxRdfVCjEjusAAADdVThcIp/vsxOS2DzVHUxZihgOlygvr+lv64FAwNPfX4888lDGGs6orEzfl8ur+3Qd3Fj8IE5Gw6EcO+XtUD179tSDDz6o2bNna8qUKTrxxBM1adIkp9sCAACAQ4LBoEKh8fL5fAqFxntyFoyJYrFo2lLEysqowx21TzAY1Nlnj5EknX32GE9/fx26HEmSNm/+2KFOOq6wcFDG2ktabvrs1U2gR44clVZ/5StnO9QJ3KjLl7wdasaMGZoxY4YkafTo0Vq2bJmT7QAAAMBFwuES1dR86OnZI6YpLg6lLUVktgJyqbFxW8YaXe+//uvxtPrJJ3+lhx56xKFuOqZ//wJt397YXHs5SHYLV8xQAgAAAFoKBoOaO3c+v/S7iClLEePxuN54IyZJeuONmKdPEzvjjBFp9ZlnjmjlSvf7/Of/Ka3+whf+qZUr3c+U09G2bPk4Y+0ll19+VVp9xRVXO9SJOQiUAAAAAGTFlKWIkUipUqmmDW2TyaRn94KSpH79jspYe8l7772bVq9f/24rV7rf7Nm3ZKzR9Z577g9p9bPPLnWoE3MQKAEAAADIWjhcopNOOsWzs5Okpr2gksnPAiWv7gUlSWvXvpFWr1nzRitXut/Be9Ja7SXDh39ORUVNe0AVFQ3SsGHDnW0Iqqn5MGMN+wiUAAAAAHQrI0akbzTccuNhLykuDikvL0+SlJeX5+l9rQKBQMbaa4477nhJ0pAhxzvcCSSpb9++GWvYR6AEAABgkHg8rvnz53p6Txi425IlT+u99/6qJUuedroVqGnGmN//WaDk5Zlj1147O63+3vdudKiTjovH4/rLX9ZKkt56a61nfyYfDCtbq73k4AmVrdWwj0AJAADAIJFIqTZsWO/pPWHgXvF4XLFY0/KwysqoZ98km7RMzJR9rSSpuPictPrss4sd6qTjWgauXg1gTVqGeM4556bVY8eee8TrkD0CJQAAAEPE43FFoytlWZai0ZWefbMP91qy5GmlUilJUiqV8uybZJOWvElm7GslSevWvZ1Wv/POOoc66bjKyvK0etWq8laudLdgcEDG2kvC4RLl5TUtowwEAp5/vrgBgRIAAIAhIpFSWdZnb/aZpYRcW726Iq2OxSpauRKwb9Gih9Lqn/98gUOddJxlWRlrr9i+vTFj7SXBYFDnnnuefD6fzj33PE/P5nMLAiUAAABDxGLR5j0hEomEp0+uAjqTSUveJHP2tdq1a1fGGl3v4IzE1mqvMWU2n1sQKAEAABiiuDjUfCpSIBDw9GlPcKcRI/4lrR458l9audLdiotD8vub3gr5/X5PP1fi8Xjz8qpVq8o9vdTVpA2g4U7BYFBz585ndlKOECgBAAAYIhwukc/32Ztk/gKLXOvRo2fG2ivC4RIdugLJy8+VJUuebl5OZVmWp2cpmbQBNNAdECgBAAAYwqTTnuBOJi0V8/mc7iA32NcKgFMIlAAAAAzC/hDoTMXFoeZlSHl5eZ5dKta0gf1ns3q8vIG9SXvc9O7dO2MNdFQ8Htf8+XM9vTTUTQiUAAAADML+EOhM4XCJ/P7PAiWvBpeVleVpgZJXj3SX1LwXVGu1l7DkDZ0tEinVhg3rPR0iu4l3f9oAAAAA6FKmLKts2XcwOMChTjrOlI3SJWnAgIFp9cCBxzjUCQ7q0aNHxtpL4vG4otEyWZal8vIyZinlAIESAAAAgKyZsKyyrq42ra6t3eJQJx1nykbpklRfX5dWt7xP6Hr79+/PWHtJJFKqRCIhSUokEsxSygECJQAAAABZM2FZpa/Fjtwtay8xaaN069Cj945QAx1h0lJXtyBQAgAAgCtVVW3UNddcoerqKqdbwSFM2NR29OixaXVx8dhWrnS/4uL0jdG9ulG6dPjsqp49vTvbqmXvXh5LOu+GryYtdXULAiUAAAC40uLFD2vPnt169NGfOd0KDmHCpraTJ09tUU9zqJOOGzlyVFr9la+c7VAnHbd37560es+ePa1c6X779u3LWHvF4UsqvbuHkklLXd2CQAkAAACuU1W1UTU1H0qSamo+ZJaSSzRtartSlmUpGl3p2VlKL7/8Yov6BYc66bgnn3wsrf71r//ToU5gov3792WsvSSVSmWsYR+BEgAAAFxn8eKH02pmKblDJFIqy2p6E5ZKpTw7S2n16oq0OharaOVK92s564KNrN3B7/dnrAET8F0NAAAA1zk4O6m12mtM2HdIkmKxaNopSZWVUYc7Atzp8KWIo1q5EvAuAiUAAAC4zpAhQzPWXmPCvkNS0wbQgUBAkhQIBDy7AbRJm3LDnQ7fe8iUTbmBzxAoAQAAwHUuv/yqtPrKK692qJOOM2XfIUkKh0vk8zW9hfD7/QqHSxzuqH0uueTy5iVIfr9fl1xyucMdtZ9JS6sKC4vS6qKiQQ510nFr1vwprX7zzT+1cqW7tdyE28ubcufn57eovTsWt/DuTxsAAAAYa82aN9Jqr74Zk5pmJyWTny0T8/IspWAwqDPPHCFJOvPMEYcdw+0VwWBQxcVNs6vGjAl5dhySWUehX331dWn1t799XStXut9RR/XPWHvF/v37M9ZeEgpNSKvHjRvvUCfmIFACAACA68Ri6XvzeHmvnlgs2nyaUCqV8vRYJGnTpqpP/7fa2UY66JJLLtfJJ/+zp2cnSdK2bQ1pdUPDVoc66biWz41Vq8od6qTj6utrM9boeuFwifLy8iRJeXl5np1h6SYBpxsAAAAAWhoxYpRWrXq9uW65wa2XnHrqGfrzn1c316eddoZzzXRQVdVGbdmyWZK0efPHqq6u0rBhw51tCsY4UpB83XWzHeoGXlJR8brKy8vavK5nz17avXuXjjrqaC1atDDjtePGTdDYsefmpD9TMUMJAAC0myknVwGd6eCMns9q787sWbz44bT60Ud/5kwjOfD447/Qe+/9VY8//gunW8GnDs7ka60GOsqyUvL7/SosLHS6FSMwQwkAALTboSdXXXXVd51uBwY50oa2Xp2pcHBGz0GbN3/sUCcdV1PzYcbaK+LxuP7yl7WSpLfeWqvGxrin91ECuruxY8/NajbRfffNkyTdffe9ndxR98AMJQAA0C4mnVwF9xk4sDBj7SVDhgzNWHuJKadwtZyVxCwl4Mh69OiZsUb3RqAEAADaJRIplWV9ttGwl0+ugvs0NNRnrL3k8suvSquvvPJqhzrpOJ/P53QLOXFwdtJBb721tpUrge5t//59GWt0bwRKAACgXWKxqBKJz45C9/rJVXCXMWPGNYcXPp9P55wzzuGO2m/NmjfS6jff/FMrV7pfXV1txhoA0H0QKAEAgHYpLg4pEGjajjEQCGjMmJDDHcEk4XCJLMuSJFmW5enjnY90cpVX9e3bN2PtFb1790mr+/Tp08qV6Eq9e/fOWHtJjx49MtaACQiUAABAu4TDJfL5mn6V8Pv9nn7DD/fZvr2xRb3dmUZyYMSIUWn1yJGjWrnS/Q4cOJCx9op/+7fL0upLLrnSoU5wqIOzXlurvWT//v0Za8AEnPIGAADaJRgMKhQar7KyVxQKjeeEJOTUkY6n/8lPfuZMMx10+B4k3n1jWVg4KO1kt8JCb27KvXz5H9Pql15apvPOO9+hbjpmwIBjtG3b1uZ64MBjHOymYwKB/LSQMj8/38FuzFdR8brKy8ts/3sHT0o7knHjJmR12hrMwAwlAADQbuFwiU466RRmJyHnTDmeXpLWrv1zWt1yTyUvMWWz9C1bPs5Ye0lj47a0Oh7f1sqV7rdnz+60evfu3a1cia7Sr99RafVRRx3VypXojpihBAAA2i0YDGru3PlOtwEDDRkyNC1EGjJkqIPd4KAxY8bptddWNNde3izdFKlUKmMNZ/TvX5C2dNeNs3jHjj23zdlE8Xhc119/TXP94IMLXTkWt3vqqSdUXV2V08esrt4oKfOMMbuGDRuuK67I/iRSAiUAAAC4zsyZN2rOnFub61mzbnKumQ4qLByUNgPGq8vEJGnChPPTAqUJEy5wsJv2CwYHpM3kGTBgoIPd4KCjjz5an3zyySF1fwe76ZgdO3ak1YeOy0uCwaD69TtKO3fu0KhRowmT2qm6ukrVf/u7TuhfmLPH7O/vKUmy6nKzx+Cm7fZnnBIoAQAAwHWGD/+c+vbtq127dqlv374aNmy40y21W8slSS1rLykre7VF/Yquuuq7DnXTfo2N8bTay8vETNIydPnkE+9uxp9KJTPWXjJ48LGqqUnqyiu/7XQrnnZC/0LdPebrTrfRqvsql9r+d9hDCQAAAK4Tj8e1b1/TZtb79u07LADwkjFj0peFeXmZWGVleVq9alV5K1e6m2VZGWsAn8nPz9fw4Z9jdhIOQ6AEAABgkHg8rvnz53o6gJGkSKQ0Y+0lEyac36L25jIxSSooGJCx9opevXplrIGO8vl8GWvABARKAAAABolESrVhw3pPBzCSFItFlUgkJEmJREKVlVGHO2q/srJXm99M+nw+lZW94nBH7VdfX5ux9or9+/dnrIGOYhYcugMCJQAAAEPE43FFoytlWZai0ZWenqVUXBxSINC03WcgENCYMSGHO2q/WCza/GbSsixPh2Om4GQ0AOg4AiUAAABDRCKlsqymN8apVMrTs5TC4RL5fE2/qvr9foXDJQ531H4mhWMjRvxLWj1y5CiHOgEAOI1ACQAAwBAmLRMLBoMKhcbL5/MpFBrv6c1gm8Kxz5a8eTkc69GjZ4u6h0OdAACcRqAEAABgCJNmwkhNQcxJJ53i6QBGagrHiooGS5KKigZ7Ohxbu/aNtHrNmjdauRIAYDoCJQAAAEOYtExMagpi5s6d7+kARmra26q2doskqa5ui6f3thoxIn2Jm1eXvHECFwB0XMDpBgAAAJAbB5eJlZW94vllYiY50t5WV131XYe7ap/9+/e1qL15OlqPHj21b9/etBqdq6LidZWXl9n+9+67b16rnxs3boLGjj233T0B6BhmKAEAABjElGViJonFokomk5KkZDLp6b2tWi5xe/PNPznUScccGiYdqYYz+vU7Kq0+6qijWrkSgBswQwkAAMAgB5eJwT1GjBilVateb669ukxMapphlakGWjN27LltziaKx+O6/vprmusHH1zITEvAxZihBAAAYJB4PK758+d6ep8eAN1TMBhsnqU0alSxp8Mkv9+fsQZMwHc1AACAQSKRUm3YsF6RSKnTreBTJp2MxmbW6GyDBx+r3r376Morr3a6lQ7Jy0tfDHTwBE7AJARKAAAAhojH44pGV8qyLEWjK5ml5BJDhw5Pq084YfgRr/MCy7Iy1l7Ro0ePjDWck5+fr+HDP+fp2UmSVFQ0KK0uLBzUypWAdxEoAQAAGOJIp4nBee+/vz6t3rBhfStXoqu0PJ3Oq6fVwb0aGuoz1oAJCJQAAAAMEYtFlUgkJEmJRMLTp4lJ7AcFwLvGjBnXvCTU5/PpnHPGOdwRkHss5AQAADBEcXFI5eWvKZFIKBAIaMyYkNMtdciSJU/rvff+qiVLntZ11812uh2oaX+bLVs2N9fHHnucg90AXa+i4nWVl5e1ed2BAweal4RalqXq6o267755rV4/btyENk/BA9yGGUoAAACGCIdL5PM1/Xrn9/sVDpc43FH7xeNxxWJNM6wqK6OenqV04olfSKu/8IV/cqiTjvvmN7+TVn/rW99p5Uqge8vPz1deXp4kqaAgqEAg3+GOgNxzZIbSokWL9PLLL0uSxo0bp9tvv12xWEwPPPCA9u3bp8mTJ+vmm292ojUAAADPCgaDGjWqWKtWve75I7eXLHlaqdRn+0F5eZbScccdrw8++Htz7eVZPS2XUa5aVa4vf/k0h7oBut7YsedmPZPonnvuVE3NR7r//v/w9M9joDVdPkMpFotp1apVikQieu655/Tuu+/qxRdf1F133aXFixfrpZde0jvvvKPy8vKubg0AAAAusXp1RVodi1W0cqX7rV37Rlq9Zs0brVzpfibdF6CzmXJiHdCaLg+UCgsLdccdd6hHjx7Kz8/X5z//eVVVVWnYsGEaOnSoAoGApk2bpuXLl3d1awAAAJ4Wj8f1xhsxSdIbb8Q8vUzMJCNGjEqrR44c1cqVAAB4R5cHSl/84hd1xhlnSJKqqqr08ssvy+fzqbCwsPmaoqIi1dbWdnVrAAB0CU6uQmeJREplWZ8tE4tESh3uqP1Gjx6bVhcXj23lSnSlwsJBGWuvOOGE4Wn1sGEnOtMIAHiYY6e8/e1vf9O1116r22+/XXl5eaqqqmr+nGVZzUcsZmvgwH457hAAgM7xzDNPasOG9Vq+/HnNnu3NPWHgTqtXVyiRSEiSEomEYrEK3X77LQ531T6zZl2rWCyqVColv9+vWbOu04ABRzndVrscaclbYeFdDnXTMfF4w2F1YaH37ovf37K2XDmOV199VStWrLD97/34x//e6ucmTpyo888/vyNtdar8/KaNrN14P+wyZSymjENybiz5+Xna36VfsX3y8/Ns/bdxJFBau3atbrjhBt1111366le/qj//+c+qr69v/nx9fb2KiopsPWZDw06lUlauWwUAIKfi8bhWrHhFlmVpxYoVmjRpOnsrIGdGjx6rsrJXmv84V1w8VvX1O5xuq5166LTTztRf/rJWp59+lpLJfM+OpX//Au3Zs6e5LigIenYsh/N5ciyH/jFbkjZu3OjKcezYsVcHDiRt/3uZ/p0dO/a6cqwHHezdzT1my5SxmDIOybmxtOd57IQDB5Jp/238fl/GyTtdHiht3rxZs2bN0sKFCzV69GhJ0umnn66NGzequrpaxx9/vF588UVdfPHFXd0aAACd7khLkq666rsOdwWpKexbtOinmj37+54N+SZMOF+vvdY0m8GyLE2YcIHDHXVMTc1HkqSPPvrQ4U46pq4ufSuH2totDnXScfv27WtR73Wok+4hmxPFfvObx/Xqqy831xMnTtOVV36rcxsDADmwh9Kvf/1r7du3Tw8++KCmT5+u6dOn69lnn9WDDz6o2bNna8qUKTrxxBM1adKkrm4NAIBOF4tF05YktTyCG86JREq1YcN6T+87VFb2aov6FYc66biqqo2qr28KYurra1VdXeVsQx3QcisHu1s7AJl861vXpNWESQC6SpfPULr77rt19913H/Fzy5Yt6+JuAADoWsXFIZWXv6ZEIqFAIKAxY0JOtwQ1zU6KRlfKsixFoysVDpd4cpZSZWV5Wr1qVblnZ8A98shP0+qf//whPfTQIw510zGnnnqG/vKXtc31aaed4VwzMFL//gXavr1REydOc7oVAN1Il89QAgCgOwuHS+TzNb38+v1+hcMlDnfUMaacWGfK6Wh9+6bvc9Cvn3c3UN2y5eOMtZccPpbNDnUCUx133BCdcsqXmJ0EoEs5dsobAADdUTAYVCg0XmVlrygUGu/JWTCHOnSZmFdnwkhHXoroxfE0NGxNq7durW/lSnSllgHS5s3eDce8oKLidZWXl9n+9+67b16rnxs3bkKbexkBQHfDDCUAALpYOFyik046xYjZSYcuE/PyLKXi4pACgaa/s7EU0R169eqdsfaSgQOPaVEXOtQJAAC5wwwlAAC6WDAY1Ny5851uo8NMOrEuHC5RNLpSkreXIvbu3TvtePrevb0bwuzduydj7SU7d+7IWCO3sjkZ7ZZbZqctRRw8+Djdffe9ndwZAJiFGUoAAKBdTDqx7uBSRJ/P5+mliDfccFtafdNNtzvUSccVFQ3KWHvJvn37WtR7HeoEB82e/f20+oYbbnGoEwDwLmYoAQCAdjHtxLpwuEQ1NR96dnaSJJ122unKy8tTMplUXl6evvzl05xuqd0sy3K6BRhs+PDPKT8/XwcOHNDgwcdp2LDhTrcE4AieeuoJVVdX5ezxqqs3Ssq8Z5pdw4YN1xVXXJ2zx/MSAiUAANAupiwTO8iEpYjxePo+Vo2Ncc/Otqqvr0ur6+pqHeoEphoyZKiqq6uYnQS4WHV1lar/9g+dcPTgnDxef18fSZJVuysnj7fpky05eRyvIlACAADtYtqJdSaIRErl8/kkST6fz9P7Wg0ZMlQ1NR+m1UAu9e7dWyeffAqzkwCXO+HowZpz9jVOt3FE9//pcadbcBR7KAEAgHYz5cQ6U5i0r9XMmTem1bNm3eRMIwAA4IgIlAAAQLsdXCZmwuykeDyu+fPnqrEx3vbFLlVcHFIg0DQB3ev7WvXvX3BI5VP//v2daqXDDt6T1moAALyIVzMAAAA1LRfbsGG9a5eJVVS8rvLysozXHDhwoHmGUjKZVHX1xjY3Hh03bkKbR6w7IRIpPWSDcb9r70s2/H5/xhoAAC8iUAIAAN1ePB5XNLpSlmUpGl2pcLjEk7Ou8vPzm0OY/v0LFAjkO91Su8ViUSWTSUlN4VhlZdSzgdLYseP12msrmutQaLyD3QAwnRdORpO69+lopiBQAgAA3V4kUirLSkmSUqmUK2fDjB17blYzie65507V1Hyk++//D0+GYgeNGDFKq1a93lyPHDnKuWYyqKjIbubYodqaOebWWWMAvKHpZLQPdMJRx+fk8frrKEmStWV/Th5Pkjbt+ChnjwXnECgBAIBu70ibWbstUMpWfn6+hg//nKfDJNMcOnOsoCDoypljFRVtB2OtIRwD3OeEo47XXV+5xek2WvWjNx9yugXkAIESAADo9oqLQ3r99f/9dL+ePE9vZm2KNWv+lFa/+eafdN11sx3qpnXdbeZYjx49tH///rQaANA9ESgBAIBuLxwuUVnZq5Iky7IUDpc43BEGDixUTc2HabWXuX3mWLbBWFXVRs2Zc2tz/cMfPqBhw4Z3XmMAANfiiAkAAABJPp/THeBQdXVbMtZwxvDhn2uelTRkyFDCJADoxgiUAABAtxeJlGas0fVSqVTGGs457rjj5fP5NWvWTU63AgBwEIESAADo9mKxaHNgkUqlVFkZdbgjJJPJjDWc07t3b5188inMTgKAbo49lAAAQLfnlSPqAcBtnnrqCVVXV+Xs8aqrN0rKfHqgXcOGDdcVV1yds8cD0IRACQAAAADQLtXVVfrg7xs1aMCwnDxer0B/SdKubblZ5lq7rTqr63IdjEmEYzAfgRIAAF2sqmqj7rtvnubOne/5JSOmjMUrR9QDgBsNGjBMV15wt9NtHNFvX7kvq+uqq6tU9beNGtL/hJx97X7+pnDsQF1uluzWbN+Uk8cBcoVACQCALrZ48cPas2e3Hn30Z/rJT37mdDsd8sgjD2nPnt165JGHtGDBI063026mHVEPALBvSP8TdMPYOU630aqfV9zvdAtop8bGRjVur9d9lUudbqVV1dvrVNDDsvXvECgBANCFqqo2NgcXNTUfqrq6yrMze6qqNmrLls2SpM2bP/b0WOrqatPq+vraVq5ELlRUvK7y8jLb/16mZSPjxk3Q2LHntrsnAABgD4ESAMAzTFhetXjxw2m1l2cpPfLIQ4fVXp2llEpxohgAAOgcBQUF6r/fp7vHfN3pVlp1X+VS+Qr62/p3CJQAAJ5hwlKxQ5dVHan2koOzkw7avPljhzrpOI6o71pjx57b5myiWGyVHn10YXM9e/YtOvvs4k7uDOganIwGwAQESgAATzBlqdiQIUPTQqQhQ4Y62A3gXsXF5zQHSnl5eYRJMEp1dZX+8Y+NGjAwNyej5ec3zSqIN+bmZLRtDdmdjAageyNQAgB4gilLxWbOvFFz5tzaXM+adZNzzXRQYWGR6uvrmuuiokEOdgMTHXvsEG3eXKOZM29yuhUg5wYMHKapF851uo0jenHZfKdbAOABfqcbAAAgG6YsFRs+/HMaPPhYSdKxxx7nyVlWBzU2xtPqeHybQ510XO/efdLqPn36tHIlulJBQYFOOeVLzE4CAMCFmKEEAPAEk5aKnXDCcG3ZslknnJCbpQ5OsSwrY+0le/bsTqt3797dypUAnOKFfYck9h4C0H0QKAEAPMGUpWLxeFxvvbVWkvTWW2vV2BhXQUHQ4a7aJ5FIZKy9ZPDgY9M2GT/22OMc7AbAkVRXV+n9f3ygfseckJPHs3ocLUn6eHvufnbt3LopZ48FAG5HoAQA8IThwz/XPEtpyJChnl0qFomUyrKaNk1NpVKKREp11VXfdbir9unbt6927dqVVnvV4MHHpQVKgwcTKAFu1O+YE3Ta9DudbqNV655/wOkWAKM0Njaq8ZOtuv9PjzvdyhFVf7JZBT2PcboNx7CHEgDAM2bOvFG9e/fx7OwkSYrFos0zeRKJhCorow531H7XX39LWn3DDbe2cqX7rVv3lxb1W840AgAA4BHMUAIAw8XjcS1a9FPNnv19zy6tOmj48M/p8cefcrqNDikuDqm8/DUlEgkFAgGNGRNyuqV2O+2005tnKfXt21df/vJpTrfUbqlUMq1OJpOtXAkAgLs1Njaqccc2/ejNh5xupVXVOz5SQa8BbV5XUFCg/vvyNefsa7qgK/vu/9Pj8hV4d4Z2RxEoAYDhIpFSbdiw3tNLq0wSDpeovLxMkuTz+RQOlzjcUcdcf/0t+slP7vP07CTAZLneyFrqnM2s2cgaALyHQAkAjsCUWT3xeFzR6EpZlqVodKXC4RJPj8cEwWBQgwYNVk3NhyoqGuza+1FR8Xpz8JVJY2OjBg8+Vs899wc999wfMl47btwEjR17bk76MwknV7mTF+5LNvekurpK6//xD+UNHJKzr5vK7ydJer9xb04eL9lQk5PHAdCkoKBA/ff20V1fuaXtix3yozcfkq+gh9NtoIMIlADgCEyZ1WPSBtCmiMfj2rLlY0nSli2bPX3KmyRt3x6X1PTLq1tVVGQXjrWU6Y1/LsOxpjf878s3sO2p/9mw8pt+vXuvcWtOHk+SrIZtOXssr2i6L3+Xf+CgnDyeld9LkrShcUdOHi/VUJv1tXkDh6jfhbNy8nU7w85ljzrdAgCgHQiUAKAFk2b1HGkDaAIlZ0Uipc378ySTCdeGfGPHnptVYHIwdLn77ns7uSOz+QYOUP60SU630aoDLyx3ugVH+AcOUu8LL3e6jSPas+xpp1sAjNLY2Kj49m36ecX9TrfSqo+2VyvYIzd/fEDX27S9XvdVLs3Z423f13TSbv+eudnDadP2eg0r6m/r3yFQAoAWTJrVY9IG0KZYter1tLqi4nXPfn95RTbh2C9+8UjavRk79lxdd93szm0MAAC0adMnW3T/nx7PyWNt37dTktS/Z7+cPN6mT7Zo2KDPt3ndsGHDc/L1DrW9umn2cEHRcTl5vGFF/W33SaAEAC2YNKsnHC5RNGrOBtAmCAQC2rdvX1oN511yyeXNgZLf79cll7hzVgoAuE1jY6O2bYvrt6/c53QrR1S7rVoD/G3PNC8oKFDf/UfphrFzuqCr9vl5xf3KL8hzuo0ulesgZnt1nSSpYFBuljMPG/T5rHrsjD0I3TBLnN9iAaAFk2b1BINBFRW5fwPo7mTXrl0ZazgjGAzq6KP765NPtmvMmBDPlXYyZSNrAPC6TTs+0o/efCgnj7V93yeSpP49j87J40lN/Q0bfGKb1+X657UbQhiTECgBQAtNs3pWSmqaqeDlWT3xeFy1tVskSXV1Wzy/AbQJhgwZqpqaD9NquENR0SAdOHCA2Ukd0LSR9d/kG3hMTh7Pym86Aei9xnhuHq8hdxuVAx3R2Niohoa4Xlw23+lWjqihoVo+Zff7QkFBgfJTR+vKC+7u5K7a57ev3Ke+BX6n2+hSuZ/V03SYQMHg3Pxsl6Rhg0/slGVg6FoESgDQQjAYVCg0XmVlrygUGu/pACYSKZVkSZIsy/L0flCmmDnzRs2Zc2tzPWvWTc41gzT5+fkaPvxznn7Ou4Fv4DHqOS3sdBtHtO+FiNMtAECnY1YPugqBEgAcQThcopqaDz09O0nyxn5QFRXZH+ne2NgoKbsj6nN5rHu2KiqyG0teXkDJZEI9evTQU089kfFaJ8YBAG7U2NionQ3btO75B5xupVU7t25So9X2KVwFBQWydLSmXji3C7qy78Vl81XQzWb1SFLN9k05PeXtk33bJUlH97R3clZrarZv0vCiz+XksYBcIFACgCMIBoOaO9ed09DtMGk/KEnavr1p2Us2gZKb5ec3BUpDhhzvdCsAAHRY7bbqnG3KvXNPoySpX++CnDxe7bZqnTig7RCmM5Zf7axuCpQGFrUdMmZjeNHnWCYGVyFQApAz8Xhcixb9VLNnf58lIy7hhf2gsjnS/SC3T7nOdixuHwcAczQ2NirZ0KCdyx51upVWJRtq1KiBbV5XUFCg3b5+Om36nV3QVfuse/4BFfTvXm+xch1wbP00hBk0IDchzIkDsgthTD2FC+hM3eunHYBOFYmUasOG9ezT4yIm7QcFAIBJtjVU52xT7j27GyVJvfsU5OTxtjVUK1iQ3dIq9usBuq9uHShVVGS314WdPTsk9rtA9xSPxxWNrpRlWYpGVyocLvF0eOGF2VYVFdn9DGtoaFAgkK/q6o1tHr3Nzy/zmXKse67HIXFEPdI1NjYq1bBVe5Y97XQrR5RqqFWjkm1eV1BQoDr1Ur8LZ3VBV+2zc9mjKijo5XQbXSrXs3o+2d40q+e4gtzM6gkWsLQKQNu6daCULVP27AA6UyRSKstKSZJSqZTnZymZNNtq585PlJ8fUCCQ73QrcIGmY903SMf0y80DNp3qrvXba3LzeFt3ZnVZ0zjel29gbjY6lSQrv2kD2vcaa3PzeA3bc/I4AD6zc+umnG3KvX9303O0R5/c/RzZuXWT1P/ENq9jVg8AE3TrQIm9LoDc8cJpYtnyymwrfoah3Y7pp8D0s5zu4ogSz/+/rK/1DeyvwIXjOrGbjkksK3e6BXRAQUGBapWn3hde7nQrR7Rn2dMqKDgqq2uTDTU53UMptXuHJMnfJ7uv35ZkQ41U8Pk2r8v1jJnqxk8kSccd2/b+TVnrfyIzewB0G906UAKQOyadJmbabCunmLQkyQvLxCSWV3lVY2OjrIZtOvDCcqdbaZXVsE2NWfza2DSWrdr3QqQLurLPatiqRvmcbqNLdUa4Ub19c9NjFxTm5gELPu/Ipsn8wQUwR0VFdltB2PkdjK0g2kagBCAnvHCaWLZMmm3lpOrqKm34x7vqlcM//CY+XbVX3fhuTh5vb0N21zUtr1ovHdMjJ19XPZr2PVm//R+5eTxJ2ro/d4/lAU3BRaOrZwFZDY1qVE+n20AHpBpqc7aHkrW7aTmnr09ulpumGmqlLGYocXIVAHymf3/3rTrwMgIlADnhldPEKira/utFz569tGfPnrQ6018xcv3XCy/Mhsl2JkyvgdKJF/pz9nVz7YNlqewvPqaH8sLHdV4zHZSMfOx0C2ingoICbW6oy9njWbubfn75+vTO2WNK2e0l2TSW+px9TWv3bkmSr0+fnD1mtnti5nx51fatTY9bcGxuHrDgKJZWAcCnst0KArllZKDkhTdjEksTWlNRkfvT95iu2DXC4RLV1Hzo6dlJknTMMYVqbGzajN/n86mwMEdT+rNUXV2lv/39XR2dm4Na5Pv0J33tttzM6vlkW04eBg5pbGyUGnbY2quoS23doUarsc3LCgoKtEX7XL+HUjavUbkPLpp+bxlWcEzuHrTgmKz6zP1YGpset2BIbh6wIJh1jyyvAgA4oaLCO8v3jAyUqqurVP339zWsf27ejfX3f/qfqX5rTh5Pkqq3d793ZNkGfY2Njc0n62Wyd+9eScrq2ueffzarJyUh35FVVNgL+RYtWtjmtW6e1ZOfn6+8vDwlk0n171/Q5ulo5eVlOf/+OnqANGqyO/f5eONly+kWulxTCLPP3bOAtu7LKogxidWwPadL3qzdTa8rvj65Ob7catguFQxq8zqTgguTxgIAR1JRkd3vxZI73vDDXG5YvmdkoCRJw/oP0N1jJzrdRqvuq1jhdAtdrrq6SlV/e1fH98+8/KWfpH5ZzNL/xN/0pvbonnvbvnj/ZiXqNme85KPtNpa+ZKGiIvsXG6dmW3kh5JOyC2Kqq6v097+/q2COfq726iXt3Svl5cXV0ND2eNoSt/EQjY2N+mSbe4ObT7ZJPf2NbV7X2NiovQ02l5V1sb0NUqMas7v4gCVt3ZebL5z89H/zcvNwkpr6y0LTkqTa3H3d3Z/u3dQnR/tLyefIrB7p0Jk9bYdAWSkYxJIkAPhURYV3Zl3kihve8GdSUdH97okXeGn5nrGBEtznYGiSK0f3zP3sjWx6zHUIIzk326q6ukr/+Pu7KizI/Fh+ScG+bX5J7f70zXGfXrvbvjixW59srWnzsvrGth9Kyv33V9++Tf/kkp0eE4ncLS1LfZrn+HO0ldGn+5V3K6effkbW+65k4+AvZsOGfS5nj9n0eMNzco0d1Y2fjuXYHC1J6p9dj2w0DKA1FRW8SfYyt4cwXnqznytuvydwjqsCpRdeeEG/+MUvlEgk9M1vflOXXXaZ0y05rqLidf32t79u87p9+/Yrmcztu7y8vIB69mz7L85XXvntbvVD9e23/6LNm9sOQtpj9+62g5jdu3dn9fUbGxt1xRVtX3MgkX1o05aDwcWuLCaNZetAIvsgJpGwNxMoEydDGC+EF9m84S8oKFBtQ26fK4lPnyKB3O3Pm9V/a5OW8WQ7loqK7GdZZos3ZB1TUZH7N8kS9wXdlxfeJFdUmBOOdccgxu24J+go1wRKtbW1WrhwoZ599ln16NFDl1xyiUaNGqUvfOELth+rsbFRtQ11+s4Lz2S8LpFKKZHK/VKMgN+vQBvvQvcmExqU75r//F2ioKBAjfVtv7n8ZJ+lT/bmdqnP0b18Wc1oyuaN5THHHJPVTKJEItF89HwuBQIBBQJtf+8cc0zbm7HmeiypT59PfqvtFCbbcQTysxtLtiGM3eV7+flt76XSv38wp0t0THnDn+147czmS3x6XwIHcnRfCnI7Y6eiwpxf/LPFGzL34Z50rYoKM8ZSUWHOvjDd8U2yF573AMzjmkQjFovp7LPPbv7lf+LEiVq+fLmuv/5624+V7ZtkJRJSohP2KAnkSW28Ue6l/KzeJJv0gpjtmzZ/Y6N8qRxNMzn4mEcFFWjjjeXwoux6vOOO3J7256Rsx1JR4f6T93Idwph0iqBTv2R2RjBmyn3xwi/+Jr3+ZMvt94V74m2MBe3RHZ/3ALzDZ1mWK3Z9feyxx7R7927dfPPNkqTS0lKtW7dO8+fPz+rfj8d3KZVyxVAAAAAAAAA8ze/3KZhhM13XzFBKpVLy+T5bkmRZVlrdlkyDBAAAAAAAQO7kaLvZjhs8eLDq6+ub6/r6ehUVFTnYEQAAAAAAAI7ENYFScXGxVq9erW3btmnPnj165ZVXFAqFnG4LAAAAAAAALbhmydugQYN0880368orr9SBAwf0ta99TaeddprTbQEAAAAAAKAF12zKDQAAAAAAAG9wzZI3AAAAAAAAeAOBEgAAAAAAAGwhUAIAAAAAAIAtBEoAAAAAAACwhUAJAAAAAAAAthAoAQAAAAAAwBYCJQAw3O7du51uAfCEeDzudAsdcuDAAdXX16uxsdHpVgB0MV7rgezwWp9bBEo2ePmbz23feHbt2bNHCxYs0L/+67/q1FNP1emnn67zzz9f8+fP144dO5xur9vavHmzZs6cqRkzZmjx4sVKJpPNn7v22msd7MyeHTt2aOHChXriiSdUW1urSy65RGeddZa+853vqLa21un2Ouyyyy5zuoV2+dnPfiZJ+uSTT3Trrbdq1KhRGjNmjO655x7t3LnT2eZsqqmp0fe//31t2rRJmzdv1hVXXKEzzzxTl19+uTZt2uR0e1k766yz9NJLLzndRk5s3rxZt912m+bNm6cPP/xQ06ZN05QpU3T++efrvffec7o9WxoaGvS9731PZ555pkKhkCZPnqxRo0Zp3rx5nnqTyWu9O5nyWi+Z/XrPa73zTHmtl8x5vee1vvP5LMuyHPvqLrZ582b99Kc/Ve/evfWd73xHM2fO1NatW9WvXz898sgjOvnkk51uMSsNDQ26++67VVFRoWQyqYKCAqVSKU2cOFF33HGH+vTp43SLWZk1a5a+9KUvacaMGSosLJQk1dfX67nnntPatWv1q1/9yuEOs7do0aKMn7/++uu7qJOOu+qqqzR16lSddNJJWrRokZLJpH7xi18oEAjooosu0nPPPed0i1mZOXOmPv/5z6u2tlZ//vOf9b3vfU8XXnihXnrpJb366qv6z//8T6dbzNqpp56qRCIhSbIsSz6fTwd/zPt8Pq1fv97J9mwJh8OKRCK67bbbdOyxx+qaa65RKpXS008/rfXr1+vRRx91usWsfeMb39D06dMVDod14403asKECZo2bZrKysr01FNP6ZlnnnG6xaycd955GjJkiPr06aNbb71VX/jCF5xuqd2uvPJKXXDBBdq9e7d+85vf6Ic//KEuuOACrV27Vg899JB+//vfO91i1q677jpNnz5d48eP14svvqidO3fqwgsv1BNPPKGamhotXLjQ6RazYtJr/Z133pnx8w888EAXddJxprzWS+a83vNa706mvNZL5rze81rfBSwc0RVXXGE99dRT1mOPPWaNHj3aWrFihWVZlrVmzRrr0ksvdbi77F177bXWSy+9ZO3Zs8cqLS21nnzySauhocH6j//4D+umm25yur2sTZ48udXPffWrX+3CTjpu4cKF1hlnnGE9/PDD1iOPPHLYP15y0UUXNf//VCpl3Xzzzc3fV9OnT3eoK/umTZtmWZZl7d+/3youLk773KFj9IJ3333XuvTSS5t/ZlmWt+7FoQ7+t586dephn5syZUpXt9Mhh34fXXzxxWmfO9L43Oqiiy6yUqmUtXTpUuu8886zvv3tb1vPPvustWnTJmvfvn1Ot2fLoc+LsWPHpn3uwgsv7OJuOqZlv+FwuPn/Z3r9dBuTXuuXLl1qnXnmmdYzzzxjPfvss4f94yWmvNZbljmv97zWu5Mpr/WWZc7rPa/1nY8lb6345JNPdPnll+u73/2uAoGALrjgAknSiBEjtGvXLoe7y97mzZs1efJk9erVS1/72te0bNkyDRgwQLfeeqs2bNjgdHtZGzBggF5++WWlUqnmj1mWpT/+8Y8KBoMOdmbfTTfdpClTpqh37966/vrrD/vHSwKBgP72t79JavqL2I9//GNt27ZN8+bNS5sS73aBQEAffPCB8vPz9eSTTzZ//K9//at8Pp+Dndn3z//8z3riiScUi8V05513ateuXZ4bw0H19fV66aWXNHjwYP3lL39p/vi6devUs2dP5xprh6KiIi1dulSSNHLkSJWXl0uSKioqVFBQ4GBn9vl8PpWUlOiVV17RFVdcof/3//6fZs6cqVGjRjndmi39+vXTkiVL9PjjjyuZTGrlypWSpLVr13ru+ys/P19vvvmmJCkWi6lv376SpP/7v/9Tr169nGzNFpNe60tKSvSNb3xDH330kcLh8GH/eIkpr/WSOa/3vNa7k0mv9ZIZr/e81ncBx6Isl7vsssusZ555xvrVr35lFRcXW2VlZZZlNc1QKikpcbi77F188cXWn//8Z8uyLKuystK6/PLLLcuyrHXr1qWlmm738ccfW9dee6111llnWePGjbPGjRtnnXXWWda1115r1dTUON2ebTt27LAikYjTbXTYmjVrrPHjx1vLli1r/tiuXbus733ve9bJJ5/sYGf2vPnmm9YFF1xgJRKJ5o+9+uqr1tixY621a9c62FnH/O///q/1ta99zTr//POdbqVdIpGINX/+fOvrX/+6df3111uWZVlPPvmkNWbMGGvNmjUOd2dPXV2d9a1vfcsaPXq0FQ6HrZNPPtkaOXKkNXXqVGvjxo1Ot5c1r/4F/Eg++ugj67bbbrNuueUWa9OmTdall15qjRo1yho3bpy1bt06p9uz5e2337bOPfdc6+yzz7YmTJhgvfPOO9Z7771nhcNhT42l5Wv9ueeea40YMcKzr/X79u2zVq1a5XQbHWbKa71lmfl6/9prrxn5Wn/OOefwWu8gU17vTXytHz16tDVhwgTr3XffdcVrPXsotaKmpkYPP/ywUqmUbrzxRv3gBz/QBx98oF69eumRRx7Rqaee6nSLWVm3bp1uvPFG7d27V3369NHPf/5zBQIB3Xnnnfr3f/93z4zjoEQioXg8rlQqpYEDByoQCDjdEtS06Xt+fn7ax9avX69TTjnFoY46bv/+/QoEAvL7vT2Rc+vWrXr99df1ta99zelWcmLnzp3q06ePZ+9LPB7Xhx9+qEQiocLCQg0dOtTplmzZtm2bBgwY4HQbncbr4/N6/wfxWu9O+/fvV48ePdI+5vXXesmM1/utW7dq5cqVKikpcbqVnOC13nmmvJ4cidfH5rb+CZRscNvNs8PLvUtSKpXS0qVLtXz5cm3ZskV+v19FRUUaN26cLr/88sPCDDdrbSyhUEhXXHGFEWPx2n05OI6XX35ZtbW1nh2H1PpY+P5ylilj4fvLnUwaiyna2qj6oosu6pI+AADoTARKrdizZ48eeughlZWVqb6+Xvn5+TrhhBM0ZcoUffvb31ZeXp7TLWZlz549+ulPf6rXXnvN0+OYO3euUqmUwuGwioqKZFmW6uvrtWzZMu3evVsLFixwusWsMRb3MWUcEmNxK1PGYso4JMbiRiaFMD/4wQ/0yiuvaNKkSUf8vJdOeTPpvpgyFlPGITEWtzJlLKaMQ3LvWJhD3Ip58+bppJNO0q9+9avmjeJOPvlk/eY3v9H999+vefPmOd1iVkwZx5tvvqnly5enfWzYsGEaOXKkpkyZ4lBX7cNY3MeUcUiMxa1MGYsp45AYixutXr06YwjjpV/8f/zjH2v79u0aMWKE55ccm3RfTBmLKeOQGItbmTIWU8YhuXcsBEqteP/99/Uf//EfkqTZs2erpKREpaWlWrBggSZPnuxwd9kzZRx9+/bVunXrdNppp6V9/K233mre4d4rGIv7mDIOibG4lSljMWUcEmNxI5NCGEm699579cILLzjdRoeZdF9MGYsp45AYi1uZMhZTxiG5dywESq2wLEsffPCBTjzxRG3YsKH5KM6Dy8a8wpRx3Hfffbr99tu1b98+FRYWyufzqa6uTj179vTMVP6DGIv7mDIOibG4lSljMWUcEmNxK1NCGKnpCPFvf/vbTreREybdF1PGYso4JMbiVqaMxZRxSO4cC3sotaK8vFx33nmnhgwZos2bN+snP/mJBg0apKuvvlr33nuvxo0b53SLWTFlHAd9/PHHqqurUyqV0uDBg3Xcccc53VK7MRb3MWUcEmNxK1PGYso4JMaCzlNRUXHEAzgmTpzodGsAAOQEgVIGO3bsUFVVlYYPH66jjjpKB/9THZzl4xWmjONIv5iNGzdOF1xwgdOt2cZY3MeUcUiMxa1MGYsp45AYixuZEsI8/PDDWrdunS688MK0jdJffPFFfeELX9APfvADp1u0xZT7IpkzFlPGITEWtzJlLKaMQ3LnWAiUWpFKpfTb3/72sNPRvvrVr3pqg0tTxmHSL2aMxX1MGYfEWNzKlLGYMg6JsbiRKeOQpIkTJ+rll1+W3+9P+3gymdTUqVP18ssvO9SZfSbdF1PGYso4JMbiVqaMxZRxSC4ei4Ujuv/++60f/vCH1uuvv27deeed1n/9139ZL7/8snXZZZdZixYtcrq9rJkyjgsuuMBKJpOHfTyRSFiTJk1yoKP2YyzuY8o4LIuxuJUpYzFlHJbFWNzIlHFYlmVNmzbNqqmpOezjmzZtsi688EIHOmo/k+6LKWMxZRyWxVjcypSxmDIOy3LvWNiUuxV/+tOftGzZMknS2LFjddlll+mZZ57RhAkTdOGFF2rWrFkOd5gdU8bRs2dPbdmy5bD9ID7++GP16NHDoa7ah7G4jynjkBiLW5kyFlPGITEWNzJlHJJ0xx136LLLLtPw4cPTNkqvqqrSAw884HR7tph0X0wZiynjkBiLW5kyFlPGIbl3LARKrUgmk2poaNDAgQNVX1+vvXv3SpIOHDigQMA7/9lMGYdJv5gxFvcxZRwSY3ErU8ZiyjgkxuJGpoxDkoqLi3Xrrbdq48aNysvL0/HHH6/Bgwfr9NNPVyQS0dlnn+10i1kz6b6YMhZTxiExFrcyZSymjENy71jYQ6kVzz77rB5++GGdeeaZevvtt3XLLbfo1FNP1be+9S1df/31uvjii51uMSumjEOS/vjHP7b6i9m//du/Od2eLYzFfUwZh8RY3MqUsZgyDomxuJEp41iwYIHeffddnXjiiXr55Zf1gx/8QNOnT5ckhcNhRSIRhzu0x5T7IpkzFlPGITEWtzJlLKaMQ3LnWPxtX9I9zZgxQ7/5zW80adIkPfnkk5o6daqOPfZYRSIRT4UwpoxjwYIF+sMf/qB4PK6nnnpKqVRKX/nKV9SjRw8tWbLE6fZsYSzuY8o4JMbiVqaMxZRxSIzFjUwZhySVl5frV7/6lebOnavf//73evjhh5s34vba33JNui+mjMWUcUiMxa1MGYsp45BcPBbHdm9yuZqamoz/eIUp45g6dap14MABy7Isa+PGjdb48eOtl156ybIsy5o+fbqDndnHWNzHlHFYFmNxK1PGYso4LIuxuJEp47Asy/rqV79qJRKJ5vr999+3xowZY/3pT3+yLrroIgc7s8+k+2LKWEwZh2UxFrcyZSymjMOy3DsW72yi08WuvfZaVVVVNR/Jdyifz6fXXnvNoc7sMWUclmXJ5/NJkoYPH67HHntMV111lQYMGND8ca9gLO5jyjgkxuJWpozFlHFIjMWNTBmHJE2aNElXXHGF7rjjDp122mn64he/qIcffljXX3+99u/f73R7tph0X0wZiynjkBiLW5kyFlPGIbl4LF0aX3nIjh07rGnTpllr1qxxupUOMWUcjzzyiHXppZdab7/9dvPH1qxZY5199tnWWWed5WBn9jEW9zFlHJbFWNzKlLGYMg7LYixuZMo4DorFYtbf//73tI99/PHH1n333edQR+1j0n0xZSymjMOyGItbmTIWU8ZhWe4dC4FSBm+//bZ19913O91Gh5kyDlN+MbMsxuJGpozDshiLW5kyFlPGYVmMxY1MGYdpTLovpozFlHFYFmNxK1PGYso4LMudY+GUNwAAAAAAANjCKW8AAAAAAACwhUAJAAAAAAAAthAoAQAAdMC///u/6+tf/7qSyWTzx5LJpC655BItXLjQwc4AAAA6D4ESAABAB9xxxx3as2ePHnvsseaPPfbYY8rLy9MNN9zgYGcAAACdh025AQAAOmjDhg269NJL9bvf/U6WZem73/2u/ud//kfRaFTPPPOMUqmUCgoKNHfuXH3+85/Xxo0bde+992rXrl2qr6/XySefrJ/97Gfq2bOnvvzlL+u8887Te++9pwULFujUU091engAAACHCTjdAAAAgNeddNJJuvnmmzVnzhylUindf//9qq6u1nPPPaff/e536t27t1atWqXrr79eL7/8spYuXaqLLrpI06dP14EDBzRjxgy9/vrrmjhxog4cOKDx48fr4YcfdnpYAAAArSJQAgAAyIErrrhCK1as0Oc//3mNGzdOP/nJT1RdXa1LLrmk+ZpPPvlEjY2Nuu2221RZWalf/epXqqqqUl1dnXbv3t183ciRI50YAgAAQNYIlAAAAHLk+OOP1wknnCBJSqVSmj59um677bbmuq6uTv3799fNN9+sZDKpyZMn69xzz9XmzZt16C4Effr0caR/AACAbLEpNwAAQCc455xz9Mc//lF1dXWSpGeeeUbf/OY3JUmrVq3SrFmzNGXKFEnS22+/nXZKHAAAgNsxQwkAAKATnHPOOfrOd76jq6++Wj6fT/369dOiRYvk8/l08803a9asWerTp4/69eunr3zlK9q0aZPTLQMAAGSNU94AAAAAAABgC0veAAAAAAAAYAuBEgAAAAAAAGwhUAIAAAAAAIAtBEoAAAAAAACwhUAJAAAAAAAAthAoAQAAAAAAwBYCJQAAAAAAANhCoAQAAAAAAABb/j8opTiBriCetwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Creating a new dataframe by concatinating Price and Year columns\n",
    "data = pd.concat([df['Price'], df['Year']], axis=1)\n",
    "\n",
    "f, ax = plt.subplots(figsize=(20, 10))\n",
    "fig = sns.boxplot(x='Year', y=\"Price\", data=data)\n",
    "fig.axis(ymin=0, ymax=165);\n",
    "plt.xticks(rotation=90);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is an obvious a general trend that the price of the newer cars is higher than the price of the older cars."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Company**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1440x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 10))\n",
    "sns.catplot(x=\"Company\", kind=\"count\", palette=\"terrain\", height=6, aspect=2, data=df);\n",
    "plt.xticks(rotation=90);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Maruti is the most popular and widely used car brand, followed by Hyundai and Honda."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Owner Type**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Owner_Type', ylabel='Price'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 842.4x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(11.7, 8.27)\n",
    "sns.stripplot(x = 'Owner_Type', y ='Price', data = df) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is clear that the majority of cars about 90% are either first hand or second hand cars.<br>\n",
    "And the price of first-hand cars is higher than that of a second-hand car, which is followed by a third-hand car."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color='green'>5. Feature Engineering</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Working with Categorical Data\n",
    "We need to change all categorical columns to numerical columns primarily so that a machine learning algorithm can understand.<br>There are several methods to handle Categorical features, but the two most commonly used techniques are:\n",
    "1. Label Encoding\n",
    "2. One-Hot Encoding\n",
    "\n",
    "One-Hot Encoding is used when the categorical features present in the dataset are not ordinal and when there are fewer categorical features one-hot encoding is very effective. And when the categorical features present in the dataset are in order then there Label Encoding is used."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, currently we have only 5 categorical features remaining in our dataset:\n",
    "1. Location\n",
    "2. Fuel_Type\n",
    "3. Transmission\n",
    "4. Owner_Type\n",
    "5. Company\n",
    "\n",
    "In order to determine which encoding to use, plots will be used for each feature."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Encoding Location**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mumbai        934\n",
       "Hyderabad     850\n",
       "Kochi         764\n",
       "Coimbatore    762\n",
       "Pune          734\n",
       "Delhi         650\n",
       "Kolkata       633\n",
       "Chennai       569\n",
       "Jaipur        484\n",
       "Bangalore     422\n",
       "Ahmedabad     271\n",
       "Name: Location, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Location'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can observe from the above values that Mubmai has the most cars for sale, followed by others."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x14a52ff1310>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Price vs Location plot\n",
    "sns.catplot(y='Price', x='Location', kind=\"boxen\", height=6, aspect=3, data= df.sort_values('Price', ascending=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll be using One-hot encoding here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Location_Bangalore</th>\n",
       "      <th>Location_Chennai</th>\n",
       "      <th>Location_Coimbatore</th>\n",
       "      <th>Location_Delhi</th>\n",
       "      <th>Location_Hyderabad</th>\n",
       "      <th>Location_Jaipur</th>\n",
       "      <th>Location_Kochi</th>\n",
       "      <th>Location_Kolkata</th>\n",
       "      <th>Location_Mumbai</th>\n",
       "      <th>Location_Pune</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Location_Bangalore  Location_Chennai  Location_Coimbatore  Location_Delhi  \\\n",
       "0                   0                 0                    0               0   \n",
       "1                   0                 0                    0               0   \n",
       "2                   0                 1                    0               0   \n",
       "3                   0                 1                    0               0   \n",
       "4                   0                 0                    1               0   \n",
       "\n",
       "   Location_Hyderabad  Location_Jaipur  Location_Kochi  Location_Kolkata  \\\n",
       "0                   0                0               0                 0   \n",
       "1                   0                0               0                 0   \n",
       "2                   0                0               0                 0   \n",
       "3                   0                0               0                 0   \n",
       "4                   0                0               0                 0   \n",
       "\n",
       "   Location_Mumbai  Location_Pune  \n",
       "0                1              0  \n",
       "1                0              1  \n",
       "2                0              0  \n",
       "3                0              0  \n",
       "4                0              0  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Location = pd.get_dummies(df[['Location']], drop_first=True)\n",
    "Location.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Encoding Fuel_Type**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Diesel    3783\n",
       "Petrol    3217\n",
       "CNG         61\n",
       "LPG         12\n",
       "Name: Fuel_Type, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Fuel_Type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x14a56590610>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Price vs Fuel_Type plot\n",
    "sns.catplot(y='Price', x='Fuel_Type', kind=\"boxen\", height=6, aspect=3, data= df.sort_values('Price', ascending=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here also, we'll be using One-hot encoding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fuel_Type_Diesel</th>\n",
       "      <th>Fuel_Type_LPG</th>\n",
       "      <th>Fuel_Type_Petrol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Fuel_Type_Diesel  Fuel_Type_LPG  Fuel_Type_Petrol\n",
       "0                 0              0                 0\n",
       "1                 1              0                 0\n",
       "2                 0              0                 1\n",
       "3                 1              0                 0\n",
       "4                 1              0                 0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Fuel_Type = pd.get_dummies(df[['Fuel_Type']],drop_first=True)\n",
    "Fuel_Type.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Encoding Transmission**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Manual       5048\n",
       "Automatic    2025\n",
       "Name: Transmission, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Transmission'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x14a52d53a90>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Price vs Transmission plot\n",
    "sns.catplot(y='Price', x='Transmission', kind=\"boxen\", height=6, aspect=3, data= df.sort_values('Price', ascending=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here also, we'll be using One-hot encoding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>Transmission_Manual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Transmission_Manual\n",
       "0                    1\n",
       "1                    1\n",
       "2                    1\n",
       "3                    1\n",
       "4                    0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Transmission = pd.get_dummies(df[['Transmission']], drop_first=True)\n",
    "Transmission.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Encoding Owner_Type**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "First             5840\n",
       "Second            1100\n",
       "Third              124\n",
       "Fourth & Above       9\n",
       "Name: Owner_Type, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Owner_Type'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case, we are going to use Label Encoding because Owner_Type has ordered data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>Location</th>\n",
       "      <th>Year</th>\n",
       "      <th>Kilometers_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner_Type</th>\n",
       "      <th>Seats</th>\n",
       "      <th>Price</th>\n",
       "      <th>Company</th>\n",
       "      <th>Mileage(km/kg)</th>\n",
       "      <th>Engine(CC)</th>\n",
       "      <th>Power(bhp)</th>\n",
       "      <th>New_Car_Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mumbai</td>\n",
       "      <td>2010</td>\n",
       "      <td>72000</td>\n",
       "      <td>CNG</td>\n",
       "      <td>Manual</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>Maruti</td>\n",
       "      <td>26.60</td>\n",
       "      <td>998.0</td>\n",
       "      <td>58.16</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Pune</td>\n",
       "      <td>2015</td>\n",
       "      <td>41000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.50</td>\n",
       "      <td>Hyundai</td>\n",
       "      <td>19.67</td>\n",
       "      <td>1582.0</td>\n",
       "      <td>126.20</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chennai</td>\n",
       "      <td>2011</td>\n",
       "      <td>46000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.50</td>\n",
       "      <td>Honda</td>\n",
       "      <td>18.20</td>\n",
       "      <td>1199.0</td>\n",
       "      <td>88.70</td>\n",
       "      <td>8.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Chennai</td>\n",
       "      <td>2012</td>\n",
       "      <td>87000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.00</td>\n",
       "      <td>Maruti</td>\n",
       "      <td>20.77</td>\n",
       "      <td>1248.0</td>\n",
       "      <td>88.76</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Coimbatore</td>\n",
       "      <td>2013</td>\n",
       "      <td>40670</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.74</td>\n",
       "      <td>Audi</td>\n",
       "      <td>15.20</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>140.80</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Location  Year  Kilometers_Driven Fuel_Type Transmission  Owner_Type  \\\n",
       "0      Mumbai  2010              72000       CNG       Manual           1   \n",
       "1        Pune  2015              41000    Diesel       Manual           1   \n",
       "2     Chennai  2011              46000    Petrol       Manual           1   \n",
       "3     Chennai  2012              87000    Diesel       Manual           1   \n",
       "4  Coimbatore  2013              40670    Diesel    Automatic           2   \n",
       "\n",
       "   Seats  Price  Company  Mileage(km/kg)  Engine(CC)  Power(bhp)  \\\n",
       "0    5.0   1.75   Maruti           26.60       998.0       58.16   \n",
       "1    5.0  12.50  Hyundai           19.67      1582.0      126.20   \n",
       "2    5.0   4.50    Honda           18.20      1199.0       88.70   \n",
       "3    7.0   6.00   Maruti           20.77      1248.0       88.76   \n",
       "4    5.0  17.74     Audi           15.20      1968.0      140.80   \n",
       "\n",
       "   New_Car_Price  \n",
       "0            NaN  \n",
       "1            NaN  \n",
       "2           8.61  \n",
       "3            NaN  \n",
       "4            NaN  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Owner_Type'] = df['Owner_Type'].map({\"First\":1,\"Second\":2,\"Third\": 3,\"Fourth & Above\":4})\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Encoding Company**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Maruti           1404\n",
       "Hyundai          1284\n",
       "Honda             734\n",
       "Toyota            481\n",
       "Mercedes-Benz     378\n",
       "Volkswagen        373\n",
       "Ford              342\n",
       "Mahindra          326\n",
       "BMW               305\n",
       "Audi              284\n",
       "Tata              224\n",
       "Skoda             200\n",
       "Renault           170\n",
       "Chevrolet         150\n",
       "Nissan            113\n",
       "Land               64\n",
       "Jaguar             48\n",
       "Mitsubishi         36\n",
       "Fiat               32\n",
       "Mini               31\n",
       "Volvo              28\n",
       "Jeep               19\n",
       "Datsun             17\n",
       "Porsche            17\n",
       "Force               3\n",
       "ISUZU               3\n",
       "Isuzu               2\n",
       "Bentley             2\n",
       "Ambassador          1\n",
       "OpelCorsa           1\n",
       "Lamborghini         1\n",
       "Name: Company, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Company'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1440x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Price vs Company plot\n",
    "plt.figure(figsize=(20, 10))\n",
    "sns.catplot(y='Price', x='Company', kind=\"boxen\", height=6, aspect=3, data= df.sort_values('Price', ascending=False))\n",
    "plt.xticks(rotation=90);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a lot of variation in the price of the car in this company's or brand's feature, so drop them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop([\"Company\"], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adding all encoded features (\"Company,\" \"Fuel_Type,\" and \"Transmission\") to the dataset.<br>\n",
    "Removing all string type features that have already been encoded by one-hot encoding and label encoding.<br>\n",
    "Also, the \"New_Car_Price\" feature is being removed because most of the values are null."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Location                0\n",
       "Year                    0\n",
       "Kilometers_Driven       0\n",
       "Fuel_Type               0\n",
       "Transmission            0\n",
       "Owner_Type              0\n",
       "Seats                   0\n",
       "Price                1201\n",
       "Mileage(km/kg)          0\n",
       "Engine(CC)              0\n",
       "Power(bhp)              0\n",
       "New_Car_Price        6068\n",
       "dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>...</th>\n",
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       "      <th>Location_Jaipur</th>\n",
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       "      <th>Location_Mumbai</th>\n",
       "      <th>Location_Pune</th>\n",
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       "      <th>Fuel_Type_Petrol</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>2010</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015</td>\n",
       "      <td>41000</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.50</td>\n",
       "      <td>19.67</td>\n",
       "      <td>1582.0</td>\n",
       "      <td>126.20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011</td>\n",
       "      <td>46000</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.50</td>\n",
       "      <td>18.20</td>\n",
       "      <td>1199.0</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012</td>\n",
       "      <td>87000</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.00</td>\n",
       "      <td>20.77</td>\n",
       "      <td>1248.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013</td>\n",
       "      <td>40670</td>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.74</td>\n",
       "      <td>15.20</td>\n",
       "      <td>1968.0</td>\n",
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       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Kilometers_Driven  Owner_Type  Seats  Price  Mileage(km/kg)  \\\n",
       "0  2010              72000           1    5.0   1.75           26.60   \n",
       "1  2015              41000           1    5.0  12.50           19.67   \n",
       "2  2011              46000           1    5.0   4.50           18.20   \n",
       "3  2012              87000           1    7.0   6.00           20.77   \n",
       "4  2013              40670           2    5.0  17.74           15.20   \n",
       "\n",
       "   Engine(CC)  Power(bhp)  Location_Bangalore  Location_Chennai  ...  \\\n",
       "0       998.0       58.16                   0                 0  ...   \n",
       "1      1582.0      126.20                   0                 0  ...   \n",
       "2      1199.0       88.70                   0                 1  ...   \n",
       "3      1248.0       88.76                   0                 1  ...   \n",
       "4      1968.0      140.80                   0                 0  ...   \n",
       "\n",
       "   Location_Hyderabad  Location_Jaipur  Location_Kochi  Location_Kolkata  \\\n",
       "0                   0                0               0                 0   \n",
       "1                   0                0               0                 0   \n",
       "2                   0                0               0                 0   \n",
       "3                   0                0               0                 0   \n",
       "4                   0                0               0                 0   \n",
       "\n",
       "   Location_Mumbai  Location_Pune  Fuel_Type_Diesel  Fuel_Type_LPG  \\\n",
       "0                1              0                 0              0   \n",
       "1                0              1                 1              0   \n",
       "2                0              0                 0              0   \n",
       "3                0              0                 1              0   \n",
       "4                0              0                 1              0   \n",
       "\n",
       "   Fuel_Type_Petrol  Transmission_Manual  \n",
       "0                 0                    1  \n",
       "1                 0                    1  \n",
       "2                 1                    1  \n",
       "3                 0                    1  \n",
       "4                 0                    0  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final= pd.concat([df, Location, Fuel_Type, Transmission], axis=1)\n",
    "df_final.drop([\"Location\",\"Fuel_Type\",\"Transmission\",\"New_Car_Price\"], axis=1, inplace=True)\n",
    "df_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7073 entries, 0 to 7072\n",
      "Data columns (total 22 columns):\n",
      " #   Column               Non-Null Count  Dtype  \n",
      "---  ------               --------------  -----  \n",
      " 0   Year                 7073 non-null   int64  \n",
      " 1   Kilometers_Driven    7073 non-null   int64  \n",
      " 2   Owner_Type           7073 non-null   int64  \n",
      " 3   Seats                7073 non-null   float64\n",
      " 4   Price                5872 non-null   float64\n",
      " 5   Mileage(km/kg)       7073 non-null   float64\n",
      " 6   Engine(CC)           7073 non-null   float64\n",
      " 7   Power(bhp)           7073 non-null   float64\n",
      " 8   Location_Bangalore   7073 non-null   uint8  \n",
      " 9   Location_Chennai     7073 non-null   uint8  \n",
      " 10  Location_Coimbatore  7073 non-null   uint8  \n",
      " 11  Location_Delhi       7073 non-null   uint8  \n",
      " 12  Location_Hyderabad   7073 non-null   uint8  \n",
      " 13  Location_Jaipur      7073 non-null   uint8  \n",
      " 14  Location_Kochi       7073 non-null   uint8  \n",
      " 15  Location_Kolkata     7073 non-null   uint8  \n",
      " 16  Location_Mumbai      7073 non-null   uint8  \n",
      " 17  Location_Pune        7073 non-null   uint8  \n",
      " 18  Fuel_Type_Diesel     7073 non-null   uint8  \n",
      " 19  Fuel_Type_LPG        7073 non-null   uint8  \n",
      " 20  Fuel_Type_Petrol     7073 non-null   uint8  \n",
      " 21  Transmission_Manual  7073 non-null   uint8  \n",
      "dtypes: float64(5), int64(3), uint8(14)\n",
      "memory usage: 538.9 KB\n"
     ]
    }
   ],
   "source": [
    "df_final.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color='green'>6. Preparing the dataset for Machine Learning</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_test = df_final[df_final['Price'].isnull()]\n",
    "final_train = df_final[~df_final['Price'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>6.00</td>\n",
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       "      <td>998.0</td>\n",
       "      <td>67.10</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>5871</th>\n",
       "      <td>2011</td>\n",
       "      <td>47000</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.50</td>\n",
       "      <td>25.44</td>\n",
       "      <td>936.0</td>\n",
       "      <td>57.60</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5872 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Year  Kilometers_Driven  Owner_Type  Seats  Price  Mileage(km/kg)  \\\n",
       "0     2010              72000           1    5.0   1.75           26.60   \n",
       "1     2015              41000           1    5.0  12.50           19.67   \n",
       "2     2011              46000           1    5.0   4.50           18.20   \n",
       "3     2012              87000           1    7.0   6.00           20.77   \n",
       "4     2013              40670           2    5.0  17.74           15.20   \n",
       "...    ...                ...         ...    ...    ...             ...   \n",
       "5867  2014              27365           1    5.0   4.75           28.40   \n",
       "5868  2015             100000           1    5.0   4.00           24.40   \n",
       "5869  2012              55000           2    8.0   2.90           14.00   \n",
       "5870  2013              46000           1    5.0   2.65           18.90   \n",
       "5871  2011              47000           1    5.0   2.50           25.44   \n",
       "\n",
       "      Engine(CC)  Power(bhp)  Location_Bangalore  Location_Chennai  ...  \\\n",
       "0          998.0       58.16                   0                 0  ...   \n",
       "1         1582.0      126.20                   0                 0  ...   \n",
       "2         1199.0       88.70                   0                 1  ...   \n",
       "3         1248.0       88.76                   0                 1  ...   \n",
       "4         1968.0      140.80                   0                 0  ...   \n",
       "...          ...         ...                 ...               ...  ...   \n",
       "5867      1248.0       74.00                   0                 0  ...   \n",
       "5868      1120.0       71.00                   0                 0  ...   \n",
       "5869      2498.0      112.00                   0                 0  ...   \n",
       "5870       998.0       67.10                   0                 0  ...   \n",
       "5871       936.0       57.60                   0                 0  ...   \n",
       "\n",
       "      Location_Hyderabad  Location_Jaipur  Location_Kochi  Location_Kolkata  \\\n",
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       "...                  ...              ...             ...               ...   \n",
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       "5868                   0                1               0                 0   \n",
       "5869                   0                1               0                 0   \n",
       "5870                   0                0               0                 1   \n",
       "5871                   1                0               0                 0   \n",
       "\n",
       "      Location_Mumbai  Location_Pune  Fuel_Type_Diesel  Fuel_Type_LPG  \\\n",
       "0                   1              0                 0              0   \n",
       "1                   0              1                 1              0   \n",
       "2                   0              0                 0              0   \n",
       "3                   0              0                 1              0   \n",
       "4                   0              0                 1              0   \n",
       "...               ...            ...               ...            ...   \n",
       "5867                0              0                 1              0   \n",
       "5868                0              0                 1              0   \n",
       "5869                0              0                 1              0   \n",
       "5870                0              0                 0              0   \n",
       "5871                0              0                 1              0   \n",
       "\n",
       "      Fuel_Type_Petrol  Transmission_Manual  \n",
       "0                    0                    1  \n",
       "1                    0                    1  \n",
       "2                    1                    1  \n",
       "3                    0                    1  \n",
       "4                    0                    0  \n",
       "...                ...                  ...  \n",
       "5867                 0                    1  \n",
       "5868                 0                    1  \n",
       "5869                 0                    1  \n",
       "5870                 1                    1  \n",
       "5871                 0                    1  \n",
       "\n",
       "[5872 rows x 22 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dell\\anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:4308: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  return super().drop(\n"
     ]
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       "      <th>5875</th>\n",
       "      <td>2014</td>\n",
       "      <td>29000</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>18.50</td>\n",
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       "      <td>7.0</td>\n",
       "      <td>16.00</td>\n",
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       "      <td>2011</td>\n",
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       "      <th>7070</th>\n",
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       "      <td>23.08</td>\n",
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       "      <th>7071</th>\n",
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       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>7072</th>\n",
       "      <td>2014</td>\n",
       "      <td>72443</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.00</td>\n",
       "      <td>2148.0</td>\n",
       "      <td>170.00</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1201 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Year  Kilometers_Driven  Owner_Type  Seats  Mileage(km/kg)  Engine(CC)  \\\n",
       "5872  2014              40929           1    4.0           32.26       998.0   \n",
       "5873  2013              54493           2    5.0           24.70       796.0   \n",
       "5874  2017              34000           1    7.0           13.68      2393.0   \n",
       "5875  2014              29000           1    5.0           18.50      1197.0   \n",
       "5876  2016              85609           2    7.0           16.00      2179.0   \n",
       "...    ...                ...         ...    ...             ...         ...   \n",
       "7068  2011              89411           1    5.0           20.54      1598.0   \n",
       "7069  2015              59000           1    5.0           17.21      1197.0   \n",
       "7070  2012              28000           1    5.0           23.08      1461.0   \n",
       "7071  2013              52262           3    5.0           17.20      1197.0   \n",
       "7072  2014              72443           1    5.0           10.00      2148.0   \n",
       "\n",
       "      Power(bhp)  Location_Bangalore  Location_Chennai  Location_Coimbatore  \\\n",
       "5872       58.20                   0                 0                    0   \n",
       "5873       47.30                   0                 0                    1   \n",
       "5874      147.80                   0                 0                    0   \n",
       "5875       82.85                   0                 0                    0   \n",
       "5876      140.00                   0                 0                    1   \n",
       "...          ...                 ...               ...                  ...   \n",
       "7068      103.60                   0                 0                    0   \n",
       "7069      103.60                   0                 0                    0   \n",
       "7070       63.10                   0                 0                    0   \n",
       "7071      103.60                   0                 0                    0   \n",
       "7072      170.00                   0                 0                    0   \n",
       "\n",
       "      ...  Location_Hyderabad  Location_Jaipur  Location_Kochi  \\\n",
       "5872  ...                   0                0               0   \n",
       "5873  ...                   0                0               0   \n",
       "5874  ...                   0                0               0   \n",
       "5875  ...                   0                0               0   \n",
       "5876  ...                   0                0               0   \n",
       "...   ...                 ...              ...             ...   \n",
       "7068  ...                   1                0               0   \n",
       "7069  ...                   0                0               0   \n",
       "7070  ...                   0                0               0   \n",
       "7071  ...                   0                0               0   \n",
       "7072  ...                   0                0               1   \n",
       "\n",
       "      Location_Kolkata  Location_Mumbai  Location_Pune  Fuel_Type_Diesel  \\\n",
       "5872                 0                0              0                 0   \n",
       "5873                 0                0              0                 0   \n",
       "5874                 0                1              0                 1   \n",
       "5875                 0                1              0                 0   \n",
       "5876                 0                0              0                 1   \n",
       "...                ...              ...            ...               ...   \n",
       "7068                 0                0              0                 1   \n",
       "7069                 0                1              0                 0   \n",
       "7070                 1                0              0                 1   \n",
       "7071                 0                0              1                 0   \n",
       "7072                 0                0              0                 1   \n",
       "\n",
       "      Fuel_Type_LPG  Fuel_Type_Petrol  Transmission_Manual  \n",
       "5872              0                 0                    1  \n",
       "5873              0                 1                    1  \n",
       "5874              0                 0                    1  \n",
       "5875              0                 1                    1  \n",
       "5876              0                 0                    1  \n",
       "...             ...               ...                  ...  \n",
       "7068              0                 0                    1  \n",
       "7069              0                 1                    0  \n",
       "7070              0                 0                    1  \n",
       "7071              0                 1                    0  \n",
       "7072              0                 0                    0  \n",
       "\n",
       "[1201 rows x 21 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_test.drop([\"Price\"], axis=1, inplace=True)\n",
    "final_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Final Features Selection\n",
    "We only need to look for features on which we need to work since our train and test data are ready."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Year', 'Kilometers_Driven', 'Owner_Type', 'Seats', 'Price',\n",
       "       'Mileage(km/kg)', 'Engine(CC)', 'Power(bhp)', 'Location_Bangalore',\n",
       "       'Location_Chennai', 'Location_Coimbatore', 'Location_Delhi',\n",
       "       'Location_Hyderabad', 'Location_Jaipur', 'Location_Kochi',\n",
       "       'Location_Kolkata', 'Location_Mumbai', 'Location_Pune',\n",
       "       'Fuel_Type_Diesel', 'Fuel_Type_LPG', 'Fuel_Type_Petrol',\n",
       "       'Transmission_Manual'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5872, 21)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = final_train.loc[:,['Year', 'Kilometers_Driven', 'Owner_Type', 'Seats',\n",
    "       'Mileage(km/kg)', 'Engine(CC)', 'Power(bhp)', 'Location_Bangalore',\n",
    "       'Location_Chennai', 'Location_Coimbatore', 'Location_Delhi',\n",
    "       'Location_Hyderabad', 'Location_Jaipur', 'Location_Kochi',\n",
    "       'Location_Kolkata', 'Location_Mumbai', 'Location_Pune',\n",
    "       'Fuel_Type_Diesel', 'Fuel_Type_LPG', 'Fuel_Type_Petrol',\n",
    "       'Transmission_Manual']]\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.74</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Price\n",
       "0   1.75\n",
       "1  12.50\n",
       "2   4.50\n",
       "3   6.00\n",
       "4  17.74"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = final_train.loc[:,['Price']]\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1296 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18,18))\n",
    "sns.heatmap(final_train.corr(),annot=True,cmap='RdYlGn')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-53-914d803b91a8>:3: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  selection.fit(X,y)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "ExtraTreesRegressor()"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "selection= ExtraTreesRegressor()\n",
    "selection.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## For better visualisation, created a graph of feature importances.\n",
    "\n",
    "plt.figure(figsize = (12,8))\n",
    "feat_importances = pd.Series(selection.feature_importances_, index=X.columns)\n",
    "feat_importances.nlargest(20).plot(kind='barh')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color='green'>7. Training & Evaluating Different Models</font>\n",
    "\n",
    "First we are spliting the data to train and test for the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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 = 25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on Traing set:  0.7083070284244648\n",
      "Accuracy on Testing set:  0.699101653082699\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "linear_reg = LinearRegression()\n",
    "linear_reg.fit(X_train, y_train)\n",
    "y_pred= linear_reg.predict(X_test)\n",
    "print(\"Accuracy on Traing set: \",linear_reg.score(X_train,y_train))\n",
    "print(\"Accuracy on Testing set: \",linear_reg.score(X_test,y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Not a good accuracy so, let's try something more better regressor.`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Random Forest Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-57-d4d870abb6b4>:3: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  rf_reg.fit(X_train, y_train)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on Traing set:  0.9840415931376388\n",
      "Accuracy on Testing set:  0.9094818742330318\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "rf_reg = RandomForestRegressor()\n",
    "rf_reg.fit(X_train, y_train)\n",
    "y_pred= rf_reg.predict(X_test)\n",
    "print(\"Accuracy on Traing set: \",rf_reg.score(X_train,y_train))\n",
    "print(\"Accuracy on Testing set: \",rf_reg.score(X_test,y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "`That's a Great Accuracy`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Error Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error      :  1.5063660243161097\n",
      "Mean Squared  Error      :  10.607857144963647\n",
      "Root Mean Squared  Error :  3.2569705471440247\n",
      "R Squared Error          :  0.9094818742330318\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
    "\n",
    "print('Mean Absolute Error      : ', metrics.mean_absolute_error(y_test, y_pred))\n",
    "print('Mean Squared  Error      : ', metrics.mean_squared_error(y_test, y_pred))\n",
    "print('Root Mean Squared  Error : ', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))\n",
    "print('R Squared Error          : ', metrics.r2_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One could go even further by Hyperparameter tuning. But, for now I left it  over you all.\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color='green'>8. Predicting Price for Test data</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_pred = rf_reg.predict(final_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dell\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1676: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._setitem_single_column(ilocs[0], value, pi)\n"
     ]
    },
    {
     "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>Year</th>\n",
       "      <th>Kilometers_Driven</th>\n",
       "      <th>Owner_Type</th>\n",
       "      <th>Seats</th>\n",
       "      <th>Mileage(km/kg)</th>\n",
       "      <th>Engine(CC)</th>\n",
       "      <th>Power(bhp)</th>\n",
       "      <th>Location_Bangalore</th>\n",
       "      <th>Location_Chennai</th>\n",
       "      <th>Location_Coimbatore</th>\n",
       "      <th>...</th>\n",
       "      <th>Location_Jaipur</th>\n",
       "      <th>Location_Kochi</th>\n",
       "      <th>Location_Kolkata</th>\n",
       "      <th>Location_Mumbai</th>\n",
       "      <th>Location_Pune</th>\n",
       "      <th>Fuel_Type_Diesel</th>\n",
       "      <th>Fuel_Type_LPG</th>\n",
       "      <th>Fuel_Type_Petrol</th>\n",
       "      <th>Transmission_Manual</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5872</th>\n",
       "      <td>2014</td>\n",
       "      <td>40929</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>32.26</td>\n",
       "      <td>998.0</td>\n",
       "      <td>58.20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.179600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5873</th>\n",
       "      <td>2013</td>\n",
       "      <td>54493</td>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>24.70</td>\n",
       "      <td>796.0</td>\n",
       "      <td>47.30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3.134600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5874</th>\n",
       "      <td>2017</td>\n",
       "      <td>34000</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>13.68</td>\n",
       "      <td>2393.0</td>\n",
       "      <td>147.80</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>17.778700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5875</th>\n",
       "      <td>2014</td>\n",
       "      <td>29000</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>18.50</td>\n",
       "      <td>1197.0</td>\n",
       "      <td>82.85</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.547600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5876</th>\n",
       "      <td>2016</td>\n",
       "      <td>85609</td>\n",
       "      <td>2</td>\n",
       "      <td>7.0</td>\n",
       "      <td>16.00</td>\n",
       "      <td>2179.0</td>\n",
       "      <td>140.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>12.564800</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",
       "      <td>...</td>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7068</th>\n",
       "      <td>2011</td>\n",
       "      <td>89411</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20.54</td>\n",
       "      <td>1598.0</td>\n",
       "      <td>103.60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.957400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7069</th>\n",
       "      <td>2015</td>\n",
       "      <td>59000</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.21</td>\n",
       "      <td>1197.0</td>\n",
       "      <td>103.60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6.279900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7070</th>\n",
       "      <td>2012</td>\n",
       "      <td>28000</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>23.08</td>\n",
       "      <td>1461.0</td>\n",
       "      <td>63.10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.185200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7071</th>\n",
       "      <td>2013</td>\n",
       "      <td>52262</td>\n",
       "      <td>3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.20</td>\n",
       "      <td>1197.0</td>\n",
       "      <td>103.60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4.157000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7072</th>\n",
       "      <td>2014</td>\n",
       "      <td>72443</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.00</td>\n",
       "      <td>2148.0</td>\n",
       "      <td>170.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15.486482</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1201 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Year  Kilometers_Driven  Owner_Type  Seats  Mileage(km/kg)  Engine(CC)  \\\n",
       "5872  2014              40929           1    4.0           32.26       998.0   \n",
       "5873  2013              54493           2    5.0           24.70       796.0   \n",
       "5874  2017              34000           1    7.0           13.68      2393.0   \n",
       "5875  2014              29000           1    5.0           18.50      1197.0   \n",
       "5876  2016              85609           2    7.0           16.00      2179.0   \n",
       "...    ...                ...         ...    ...             ...         ...   \n",
       "7068  2011              89411           1    5.0           20.54      1598.0   \n",
       "7069  2015              59000           1    5.0           17.21      1197.0   \n",
       "7070  2012              28000           1    5.0           23.08      1461.0   \n",
       "7071  2013              52262           3    5.0           17.20      1197.0   \n",
       "7072  2014              72443           1    5.0           10.00      2148.0   \n",
       "\n",
       "      Power(bhp)  Location_Bangalore  Location_Chennai  Location_Coimbatore  \\\n",
       "5872       58.20                   0                 0                    0   \n",
       "5873       47.30                   0                 0                    1   \n",
       "5874      147.80                   0                 0                    0   \n",
       "5875       82.85                   0                 0                    0   \n",
       "5876      140.00                   0                 0                    1   \n",
       "...          ...                 ...               ...                  ...   \n",
       "7068      103.60                   0                 0                    0   \n",
       "7069      103.60                   0                 0                    0   \n",
       "7070       63.10                   0                 0                    0   \n",
       "7071      103.60                   0                 0                    0   \n",
       "7072      170.00                   0                 0                    0   \n",
       "\n",
       "      ...  Location_Jaipur  Location_Kochi  Location_Kolkata  Location_Mumbai  \\\n",
       "5872  ...                0               0                 0                0   \n",
       "5873  ...                0               0                 0                0   \n",
       "5874  ...                0               0                 0                1   \n",
       "5875  ...                0               0                 0                1   \n",
       "5876  ...                0               0                 0                0   \n",
       "...   ...              ...             ...               ...              ...   \n",
       "7068  ...                0               0                 0                0   \n",
       "7069  ...                0               0                 0                1   \n",
       "7070  ...                0               0                 1                0   \n",
       "7071  ...                0               0                 0                0   \n",
       "7072  ...                0               1                 0                0   \n",
       "\n",
       "      Location_Pune  Fuel_Type_Diesel  Fuel_Type_LPG  Fuel_Type_Petrol  \\\n",
       "5872              0                 0              0                 0   \n",
       "5873              0                 0              0                 1   \n",
       "5874              0                 1              0                 0   \n",
       "5875              0                 0              0                 1   \n",
       "5876              0                 1              0                 0   \n",
       "...             ...               ...            ...               ...   \n",
       "7068              0                 1              0                 0   \n",
       "7069              0                 0              0                 1   \n",
       "7070              0                 1              0                 0   \n",
       "7071              1                 0              0                 1   \n",
       "7072              0                 1              0                 0   \n",
       "\n",
       "      Transmission_Manual      Price  \n",
       "5872                    1   3.179600  \n",
       "5873                    1   3.134600  \n",
       "5874                    1  17.778700  \n",
       "5875                    1   4.547600  \n",
       "5876                    1  12.564800  \n",
       "...                   ...        ...  \n",
       "7068                    1   3.957400  \n",
       "7069                    0   6.279900  \n",
       "7070                    1   3.185200  \n",
       "7071                    0   4.157000  \n",
       "7072                    0  15.486482  \n",
       "\n",
       "[1201 rows x 22 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_test.loc[:,['Price']] = test_pred\n",
    "final_test"
   ]
  }
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