kucharczyk_predicting_car_prices.ipynb

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    "# **Predicting Car Prices**\n",
    "\n",
    "# 1. Introduction\n",
    "\n",
    "Discount Motors will soon be losing its car-pricing expert to retirement. The sales team would like to know if car pricing can be automated, ideally such that predicted prices are within 10% of actual prices. An automated system would benefit the organization in terms of efficiency and sales. Currently, manual estimates made by the team are about 30% off.\n",
    "\n",
    "Therefore, the goals of this analysis were to train and evaluate predictive models, then make recommendations for steps forward. This technical report includes code, code output, and written summaries organized in the following sections: 2. Data Validation, 3. Exploratory Analysis, 4. Model Development, 5. Model Evaluation, 6. Key Performance Indicator, and 7. Summary and Recommendations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9a67e87a-86d9-42cf-81ba-d83e8ef7e928",
   "metadata": {
    "editable": true,
    "executionCancelledAt": null,
    "executionTime": 10,
    "lastExecutedAt": 1711728534426,
    "lastScheduledRunId": null,
    "lastSuccessfullyExecutedCode": "# import libaries and modules\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import MinMaxScaler, OneHotEncoder\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.metrics import mean_absolute_error",
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# import packages and modules\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy.stats import iqr\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "from sklearn.pipeline import make_pipeline, Pipeline\n",
    "from sklearn.model_selection import train_test_split, KFold, cross_val_score, GridSearchCV\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import MinMaxScaler, OneHotEncoder\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor\n",
    "from sklearn.metrics import root_mean_squared_error, r2_score\n",
    "\n",
    "# set pandas display precision to 3 places after decimal\n",
    "pd.set_option('display.precision', 3)\n",
    "\n",
    "# ignore warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4d78772b-673e-4188-a6ef-7eacaf9f4391",
   "metadata": {
    "editable": true,
    "executionCancelledAt": null,
    "executionTime": 50,
    "lastExecutedAt": 1711728534476,
    "lastScheduledRunId": null,
    "lastSuccessfullyExecutedCode": "# import data\nfilename = 'toyota.csv'\ndata = pd.read_csv(filename)",
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# import data\n",
    "filename = 'toyota.csv'\n",
    "data = pd.read_csv(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "88a8a173-3070-468d-96b4-77bc81cb74d4",
   "metadata": {
    "executionCancelledAt": null,
    "executionTime": 53,
    "lastExecutedAt": 1711728534530,
    "lastScheduledRunId": null,
    "lastSuccessfullyExecutedCode": "# preview data\ndata.head()",
    "outputsMetadata": {
     "0": {
      "height": 198,
      "type": "dataFrame"
     }
    },
    "tags": []
   },
   "outputs": [
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     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>year</th>\n",
       "      <th>price</th>\n",
       "      <th>transmission</th>\n",
       "      <th>mileage</th>\n",
       "      <th>fuelType</th>\n",
       "      <th>tax</th>\n",
       "      <th>mpg</th>\n",
       "      <th>engineSize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GT86</td>\n",
       "      <td>2016</td>\n",
       "      <td>16000</td>\n",
       "      <td>Manual</td>\n",
       "      <td>24089</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>265</td>\n",
       "      <td>36.2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GT86</td>\n",
       "      <td>2017</td>\n",
       "      <td>15995</td>\n",
       "      <td>Manual</td>\n",
       "      <td>18615</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>145</td>\n",
       "      <td>36.2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GT86</td>\n",
       "      <td>2015</td>\n",
       "      <td>13998</td>\n",
       "      <td>Manual</td>\n",
       "      <td>27469</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>265</td>\n",
       "      <td>36.2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GT86</td>\n",
       "      <td>2017</td>\n",
       "      <td>18998</td>\n",
       "      <td>Manual</td>\n",
       "      <td>14736</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>150</td>\n",
       "      <td>36.2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GT86</td>\n",
       "      <td>2017</td>\n",
       "      <td>17498</td>\n",
       "      <td>Manual</td>\n",
       "      <td>36284</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>145</td>\n",
       "      <td>36.2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   model  year  price transmission  mileage fuelType  tax   mpg  engineSize\n",
       "0   GT86  2016  16000       Manual    24089   Petrol  265  36.2         2.0\n",
       "1   GT86  2017  15995       Manual    18615   Petrol  145  36.2         2.0\n",
       "2   GT86  2015  13998       Manual    27469   Petrol  265  36.2         2.0\n",
       "3   GT86  2017  18998       Manual    14736   Petrol  150  36.2         2.0\n",
       "4   GT86  2017  17498       Manual    36284   Petrol  145  36.2         2.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# preview data\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "55ef3cbc-6017-40bf-b68c-8de9df321245",
   "metadata": {
    "executionCancelledAt": null,
    "executionTime": 52,
    "lastExecutedAt": 1711728534582,
    "lastScheduledRunId": null,
    "lastSuccessfullyExecutedCode": "# number of rows and columns\ndata.shape",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6738, 9)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of rows and columns\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "80e9b3cf-5f9a-49ed-a156-c3429f54abbd",
   "metadata": {
    "editable": true,
    "executionCancelledAt": null,
    "executionTime": 52,
    "lastExecutedAt": 1711728534634,
    "lastScheduledRunId": null,
    "lastSuccessfullyExecutedCode": "# check for duplicate rows\ndata.duplicated().sum()",
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "39"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# check for duplicate rows based on all columns\n",
    "data.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e94d2325-f11d-4d79-847e-49947b3f20f5",
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    "executionCancelledAt": null,
    "executionTime": 51,
    "lastExecutedAt": 1711728534685,
    "lastScheduledRunId": null,
    "lastSuccessfullyExecutedCode": "# create filter to display duplicate rows including the first occurrence\nduplicated = data.duplicated(keep=False)\n\n# display duplicate rows including the first occurrence\ndisplay(data[duplicated])",
    "outputsMetadata": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>year</th>\n",
       "      <th>price</th>\n",
       "      <th>transmission</th>\n",
       "      <th>mileage</th>\n",
       "      <th>fuelType</th>\n",
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       "      <th>177</th>\n",
       "      <td>Corolla</td>\n",
       "      <td>2019</td>\n",
       "      <td>17960</td>\n",
       "      <td>Manual</td>\n",
       "      <td>4500</td>\n",
       "      <td>Petrol</td>\n",
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       "      <th>285</th>\n",
       "      <td>Corolla</td>\n",
       "      <td>2019</td>\n",
       "      <td>15991</td>\n",
       "      <td>Manual</td>\n",
       "      <td>10</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>145</td>\n",
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       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>Corolla</td>\n",
       "      <td>2019</td>\n",
       "      <td>15991</td>\n",
       "      <td>Manual</td>\n",
       "      <td>10</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>145</td>\n",
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       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>RAV4</td>\n",
       "      <td>2015</td>\n",
       "      <td>13500</td>\n",
       "      <td>Manual</td>\n",
       "      <td>45757</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>125</td>\n",
       "      <td>57.6</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>5560</th>\n",
       "      <td>Aygo</td>\n",
       "      <td>2019</td>\n",
       "      <td>10750</td>\n",
       "      <td>Manual</td>\n",
       "      <td>32</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>145</td>\n",
       "      <td>57.7</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6356</th>\n",
       "      <td>Avensis</td>\n",
       "      <td>2017</td>\n",
       "      <td>10595</td>\n",
       "      <td>Manual</td>\n",
       "      <td>35939</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>145</td>\n",
       "      <td>67.3</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6357</th>\n",
       "      <td>Avensis</td>\n",
       "      <td>2017</td>\n",
       "      <td>10595</td>\n",
       "      <td>Manual</td>\n",
       "      <td>35939</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>145</td>\n",
       "      <td>67.3</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6569</th>\n",
       "      <td>Hilux</td>\n",
       "      <td>2015</td>\n",
       "      <td>14995</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>72100</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>260</td>\n",
       "      <td>32.8</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6570</th>\n",
       "      <td>Hilux</td>\n",
       "      <td>2015</td>\n",
       "      <td>14995</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>72100</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>260</td>\n",
       "      <td>32.8</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>74 rows × 9 columns</p>\n",
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      ],
      "text/plain": [
       "         model  year  price transmission  mileage fuelType  tax   mpg  \\\n",
       "177    Corolla  2019  17960       Manual     4500   Petrol  145  39.2   \n",
       "178    Corolla  2019  17960       Manual     4500   Petrol  145  39.2   \n",
       "285    Corolla  2019  15991       Manual       10   Petrol  145  39.2   \n",
       "286    Corolla  2019  15991       Manual       10   Petrol  145  39.2   \n",
       "344       RAV4  2015  13500       Manual    45757   Diesel  125  57.6   \n",
       "...        ...   ...    ...          ...      ...      ...  ...   ...   \n",
       "5560      Aygo  2019  10750       Manual       32   Petrol  145  57.7   \n",
       "6356   Avensis  2017  10595       Manual    35939   Diesel  145  67.3   \n",
       "6357   Avensis  2017  10595       Manual    35939   Diesel  145  67.3   \n",
       "6569     Hilux  2015  14995    Automatic    72100   Diesel  260  32.8   \n",
       "6570     Hilux  2015  14995    Automatic    72100   Diesel  260  32.8   \n",
       "\n",
       "      engineSize  \n",
       "177          1.2  \n",
       "178          1.2  \n",
       "285          1.2  \n",
       "286          1.2  \n",
       "344          2.0  \n",
       "...          ...  \n",
       "5560         1.0  \n",
       "6356         1.6  \n",
       "6357         1.6  \n",
       "6569         3.0  \n",
       "6570         3.0  \n",
       "\n",
       "[74 rows x 9 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create filter to display duplicate rows including first occurrence\n",
    "duplicated = data.duplicated(keep=False)\n",
    "\n",
    "# display duplicate rows including first occurrences\n",
    "display(data[duplicated])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a1914fc3-9cef-48de-a8c2-d0edf6305e83",
   "metadata": {
    "executionCancelledAt": null,
    "executionTime": 50,
    "lastExecutedAt": 1711728534735,
    "lastScheduledRunId": null,
    "lastSuccessfullyExecutedCode": "# drop 39 duplicate rows from dataset, keeping the first occurrences\ndata = data.drop_duplicates(keep='first')\n\n# original number of rows was 6738. now, it should be 6699\ndata.shape",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6699, 9)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop 39 duplicate rows from dataset, keeping the first occurrences\n",
    "data = data.drop_duplicates(keep='first')\n",
    "\n",
    "# original number of rows was 6738. now, it should be 6699\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "83c5d1d6-98ca-4c85-991d-a1a5c77f3a08",
   "metadata": {
    "editable": true,
    "executionCancelledAt": null,
    "executionTime": 50,
    "lastExecutedAt": 1711728534785,
    "lastScheduledRunId": null,
    "lastSuccessfullyExecutedCode": "# column data types and number of non-null values\ndata.info()",
    "outputsMetadata": {
     "0": {
      "height": 337,
      "type": "stream"
     }
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 6699 entries, 0 to 6737\n",
      "Data columns (total 9 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   model         6699 non-null   object \n",
      " 1   year          6699 non-null   int64  \n",
      " 2   price         6699 non-null   int64  \n",
      " 3   transmission  6699 non-null   object \n",
      " 4   mileage       6699 non-null   int64  \n",
      " 5   fuelType      6699 non-null   object \n",
      " 6   tax           6699 non-null   int64  \n",
      " 7   mpg           6699 non-null   float64\n",
      " 8   engineSize    6699 non-null   float64\n",
      "dtypes: float64(2), int64(4), object(3)\n",
      "memory usage: 523.4+ KB\n"
     ]
    }
   ],
   "source": [
    "# check column data types and number of non-null values\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9c05143a-6d6d-44cd-a3ba-5ba5ba66881a",
   "metadata": {
    "editable": true,
    "executionCancelledAt": null,
    "executionTime": 53,
    "lastExecutedAt": 1711728534838,
    "lastScheduledRunId": null,
    "lastSuccessfullyExecutedCode": "# numeric column descriptive statistics\ndisplay(data.describe())",
    "outputsMetadata": {
     "0": {
      "height": 273,
      "type": "dataFrame"
     }
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
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   "outputs": [
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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>year</th>\n",
       "      <th>price</th>\n",
       "      <th>mileage</th>\n",
       "      <th>tax</th>\n",
       "      <th>mpg</th>\n",
       "      <th>engineSize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6699.000</td>\n",
       "      <td>6699.000</td>\n",
       "      <td>6699.000</td>\n",
       "      <td>6699.000</td>\n",
       "      <td>6699.000</td>\n",
       "      <td>6699.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2016.743</td>\n",
       "      <td>12529.799</td>\n",
       "      <td>22889.589</td>\n",
       "      <td>94.550</td>\n",
       "      <td>63.079</td>\n",
       "      <td>1.472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.205</td>\n",
       "      <td>6358.563</td>\n",
       "      <td>19109.289</td>\n",
       "      <td>73.943</td>\n",
       "      <td>15.861</td>\n",
       "      <td>0.436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1998.000</td>\n",
       "      <td>850.000</td>\n",
       "      <td>2.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.800</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2016.000</td>\n",
       "      <td>8266.500</td>\n",
       "      <td>9488.500</td>\n",
       "      <td>0.000</td>\n",
       "      <td>55.400</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2017.000</td>\n",
       "      <td>10798.000</td>\n",
       "      <td>18571.000</td>\n",
       "      <td>135.000</td>\n",
       "      <td>62.800</td>\n",
       "      <td>1.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2018.000</td>\n",
       "      <td>14995.000</td>\n",
       "      <td>31059.500</td>\n",
       "      <td>145.000</td>\n",
       "      <td>70.600</td>\n",
       "      <td>1.800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2020.000</td>\n",
       "      <td>59995.000</td>\n",
       "      <td>174419.000</td>\n",
       "      <td>565.000</td>\n",
       "      <td>235.000</td>\n",
       "      <td>4.500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           year      price     mileage       tax       mpg  engineSize\n",
       "count  6699.000   6699.000    6699.000  6699.000  6699.000    6699.000\n",
       "mean   2016.743  12529.799   22889.589    94.550    63.079       1.472\n",
       "std       2.205   6358.563   19109.289    73.943    15.861       0.436\n",
       "min    1998.000    850.000       2.000     0.000     2.800       0.000\n",
       "25%    2016.000   8266.500    9488.500     0.000    55.400       1.000\n",
       "50%    2017.000  10798.000   18571.000   135.000    62.800       1.500\n",
       "75%    2018.000  14995.000   31059.500   145.000    70.600       1.800\n",
       "max    2020.000  59995.000  174419.000   565.000   235.000       4.500"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check numeric column descriptive statistics\n",
    "display(data.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "736a1f1c-b580-49ce-b9d0-57c99847d054",
   "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>model</th>\n",
       "      <th>year</th>\n",
       "      <th>price</th>\n",
       "      <th>transmission</th>\n",
       "      <th>mileage</th>\n",
       "      <th>fuelType</th>\n",
       "      <th>tax</th>\n",
       "      <th>mpg</th>\n",
       "      <th>engineSize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6711</th>\n",
       "      <td>Supra</td>\n",
       "      <td>2019</td>\n",
       "      <td>59995</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>9909</td>\n",
       "      <td>Other</td>\n",
       "      <td>150</td>\n",
       "      <td>34.5</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6670</th>\n",
       "      <td>Land Cruiser</td>\n",
       "      <td>2019</td>\n",
       "      <td>54991</td>\n",
       "      <td>Semi-Auto</td>\n",
       "      <td>1000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>145</td>\n",
       "      <td>30.1</td>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6695</th>\n",
       "      <td>Land Cruiser</td>\n",
       "      <td>2020</td>\n",
       "      <td>54550</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>4000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>150</td>\n",
       "      <td>30.1</td>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6688</th>\n",
       "      <td>Land Cruiser</td>\n",
       "      <td>2020</td>\n",
       "      <td>52990</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>1244</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>145</td>\n",
       "      <td>30.1</td>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6672</th>\n",
       "      <td>Land Cruiser</td>\n",
       "      <td>2020</td>\n",
       "      <td>52291</td>\n",
       "      <td>Semi-Auto</td>\n",
       "      <td>3104</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>145</td>\n",
       "      <td>30.1</td>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              model  year  price transmission  mileage fuelType  tax   mpg  \\\n",
       "6711          Supra  2019  59995    Automatic     9909    Other  150  34.5   \n",
       "6670   Land Cruiser  2019  54991    Semi-Auto     1000   Diesel  145  30.1   \n",
       "6695   Land Cruiser  2020  54550    Automatic     4000   Diesel  150  30.1   \n",
       "6688   Land Cruiser  2020  52990    Automatic     1244   Diesel  145  30.1   \n",
       "6672   Land Cruiser  2020  52291    Semi-Auto     3104   Diesel  145  30.1   \n",
       "\n",
       "      engineSize  \n",
       "6711         3.0  \n",
       "6670         2.8  \n",
       "6695         2.8  \n",
       "6688         2.8  \n",
       "6672         2.8  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# range of years appears appropriate\n",
    "\n",
    "# lowest price appears appropriate.\n",
    "# investigate highest prices\n",
    "data.sort_values(by='price', ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2ec92b0a-51d9-4408-b882-d308a05540e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\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>model</th>\n",
       "      <th>year</th>\n",
       "      <th>price</th>\n",
       "      <th>transmission</th>\n",
       "      <th>mileage</th>\n",
       "      <th>fuelType</th>\n",
       "      <th>tax</th>\n",
       "      <th>mpg</th>\n",
       "      <th>engineSize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1630</th>\n",
       "      <td>Yaris</td>\n",
       "      <td>2020</td>\n",
       "      <td>14990</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>2</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>150</td>\n",
       "      <td>47.9</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6264</th>\n",
       "      <td>Prius</td>\n",
       "      <td>2020</td>\n",
       "      <td>25990</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>3</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>135</td>\n",
       "      <td>64.2</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5193</th>\n",
       "      <td>Aygo</td>\n",
       "      <td>2019</td>\n",
       "      <td>10995</td>\n",
       "      <td>Manual</td>\n",
       "      <td>3</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>145</td>\n",
       "      <td>57.7</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5211</th>\n",
       "      <td>Aygo</td>\n",
       "      <td>2019</td>\n",
       "      <td>9391</td>\n",
       "      <td>Manual</td>\n",
       "      <td>3</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>145</td>\n",
       "      <td>56.5</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6583</th>\n",
       "      <td>Hilux</td>\n",
       "      <td>2019</td>\n",
       "      <td>26995</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>4</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>265</td>\n",
       "      <td>2.8</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6684</th>\n",
       "      <td>Land Cruiser</td>\n",
       "      <td>2020</td>\n",
       "      <td>47885</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>4</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>145</td>\n",
       "      <td>30.1</td>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1397</th>\n",
       "      <td>Yaris</td>\n",
       "      <td>2019</td>\n",
       "      <td>11888</td>\n",
       "      <td>Manual</td>\n",
       "      <td>4</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>145</td>\n",
       "      <td>47.9</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4032</th>\n",
       "      <td>Aygo</td>\n",
       "      <td>2019</td>\n",
       "      <td>10495</td>\n",
       "      <td>Manual</td>\n",
       "      <td>4</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>145</td>\n",
       "      <td>57.7</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4229</th>\n",
       "      <td>Aygo</td>\n",
       "      <td>2019</td>\n",
       "      <td>9295</td>\n",
       "      <td>Manual</td>\n",
       "      <td>5</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>145</td>\n",
       "      <td>56.5</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6634</th>\n",
       "      <td>PROACE VERSO</td>\n",
       "      <td>2019</td>\n",
       "      <td>27998</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>145</td>\n",
       "      <td>38.7</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              model  year  price transmission  mileage fuelType  tax   mpg  \\\n",
       "1630          Yaris  2020  14990    Automatic        2   Petrol  150  47.9   \n",
       "6264          Prius  2020  25990    Automatic        3   Hybrid  135  64.2   \n",
       "5193           Aygo  2019  10995       Manual        3   Petrol  145  57.7   \n",
       "5211           Aygo  2019   9391       Manual        3   Petrol  145  56.5   \n",
       "6583          Hilux  2019  26995    Automatic        4   Diesel  265   2.8   \n",
       "6684   Land Cruiser  2020  47885    Automatic        4   Diesel  145  30.1   \n",
       "1397          Yaris  2019  11888       Manual        4   Petrol  145  47.9   \n",
       "4032           Aygo  2019  10495       Manual        4   Petrol  145  57.7   \n",
       "4229           Aygo  2019   9295       Manual        5   Petrol  145  56.5   \n",
       "6634   PROACE VERSO  2019  27998    Automatic        5   Diesel  145  38.7   \n",
       "\n",
       "      engineSize  \n",
       "1630         1.5  \n",
       "6264         1.8  \n",
       "5193         1.0  \n",
       "5211         1.0  \n",
       "6583         2.4  \n",
       "6684         2.8  \n",
       "1397         1.5  \n",
       "4032         1.0  \n",
       "4229         1.0  \n",
       "6634         2.0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# highest prices appear appropriate.\n",
    "\n",
    "# highest mileage appears appropriate.\n",
    "# investigate lowest mileages\n",
    "data.sort_values(by='mileage', ascending=True).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "dee6af1c-0f8b-4201-9ea4-0d2746db0e6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1787"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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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>model</th>\n",
       "      <th>year</th>\n",
       "      <th>price</th>\n",
       "      <th>transmission</th>\n",
       "      <th>mileage</th>\n",
       "      <th>fuelType</th>\n",
       "      <th>tax</th>\n",
       "      <th>mpg</th>\n",
       "      <th>engineSize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6732</th>\n",
       "      <td>IQ</td>\n",
       "      <td>2012</td>\n",
       "      <td>2495</td>\n",
       "      <td>Manual</td>\n",
       "      <td>72498</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>0</td>\n",
       "      <td>64.2</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6730</th>\n",
       "      <td>IQ</td>\n",
       "      <td>2013</td>\n",
       "      <td>4849</td>\n",
       "      <td>Manual</td>\n",
       "      <td>41142</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>0</td>\n",
       "      <td>64.2</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5054</th>\n",
       "      <td>Aygo</td>\n",
       "      <td>2014</td>\n",
       "      <td>4495</td>\n",
       "      <td>Manual</td>\n",
       "      <td>35863</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>0</td>\n",
       "      <td>65.7</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5355</th>\n",
       "      <td>Aygo</td>\n",
       "      <td>2012</td>\n",
       "      <td>3950</td>\n",
       "      <td>Manual</td>\n",
       "      <td>55000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>0</td>\n",
       "      <td>65.7</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4992</th>\n",
       "      <td>Aygo</td>\n",
       "      <td>2013</td>\n",
       "      <td>4695</td>\n",
       "      <td>Manual</td>\n",
       "      <td>20000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>0</td>\n",
       "      <td>65.7</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      model  year  price transmission  mileage fuelType  tax   mpg  engineSize\n",
       "6732     IQ  2012   2495       Manual    72498   Petrol    0  64.2         1.0\n",
       "6730     IQ  2013   4849       Manual    41142   Petrol    0  64.2         1.0\n",
       "5054   Aygo  2014   4495       Manual    35863   Petrol    0  65.7         1.0\n",
       "5355   Aygo  2012   3950       Manual    55000   Petrol    0  65.7         1.0\n",
       "4992   Aygo  2013   4695       Manual    20000   Petrol    0  65.7         1.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lowest mileages are associated with young cars\n",
    "# this seems plausible\n",
    "\n",
    "# highest tax appears appropriate.\n",
    "# investigate tax=0\n",
    "display((data['tax']==0).sum())\n",
    "data[data['tax']==0].sort_values(by='mpg', ascending=True).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "616338e7-31c2-4a5a-89d1-245e6e724e52",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>year</th>\n",
       "      <th>price</th>\n",
       "      <th>transmission</th>\n",
       "      <th>mileage</th>\n",
       "      <th>fuelType</th>\n",
       "      <th>tax</th>\n",
       "      <th>mpg</th>\n",
       "      <th>engineSize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6594</th>\n",
       "      <td>Hilux</td>\n",
       "      <td>2020</td>\n",
       "      <td>39257</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>1259</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>265</td>\n",
       "      <td>2.8</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6583</th>\n",
       "      <td>Hilux</td>\n",
       "      <td>2019</td>\n",
       "      <td>26995</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>4</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>265</td>\n",
       "      <td>2.8</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6575</th>\n",
       "      <td>Hilux</td>\n",
       "      <td>2019</td>\n",
       "      <td>26500</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>7123</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>260</td>\n",
       "      <td>2.8</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6562</th>\n",
       "      <td>Hilux</td>\n",
       "      <td>2020</td>\n",
       "      <td>18495</td>\n",
       "      <td>Manual</td>\n",
       "      <td>50</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>260</td>\n",
       "      <td>2.8</td>\n",
       "      <td>2.4</td>\n",
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       "    <tr>\n",
       "      <th>6595</th>\n",
       "      <td>Hilux</td>\n",
       "      <td>2019</td>\n",
       "      <td>27850</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>10250</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>260</td>\n",
       "      <td>2.8</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6596</th>\n",
       "      <td>Hilux</td>\n",
       "      <td>2020</td>\n",
       "      <td>30500</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>260</td>\n",
       "      <td>2.8</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6552</th>\n",
       "      <td>Hilux</td>\n",
       "      <td>2019</td>\n",
       "      <td>28495</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>3350</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>260</td>\n",
       "      <td>2.8</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6576</th>\n",
       "      <td>Hilux</td>\n",
       "      <td>2019</td>\n",
       "      <td>20500</td>\n",
       "      <td>Manual</td>\n",
       "      <td>5190</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>260</td>\n",
       "      <td>2.8</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6617</th>\n",
       "      <td>Hilux</td>\n",
       "      <td>2020</td>\n",
       "      <td>36995</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>150</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>260</td>\n",
       "      <td>2.8</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6563</th>\n",
       "      <td>Hilux</td>\n",
       "      <td>2020</td>\n",
       "      <td>23495</td>\n",
       "      <td>Manual</td>\n",
       "      <td>100</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>260</td>\n",
       "      <td>2.8</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5966</th>\n",
       "      <td>C-HR</td>\n",
       "      <td>2018</td>\n",
       "      <td>16690</td>\n",
       "      <td>Manual</td>\n",
       "      <td>16429</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>145</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6694</th>\n",
       "      <td>Land Cruiser</td>\n",
       "      <td>1998</td>\n",
       "      <td>19990</td>\n",
       "      <td>Manual</td>\n",
       "      <td>100000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>265</td>\n",
       "      <td>23.9</td>\n",
       "      <td>4.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6693</th>\n",
       "      <td>Land Cruiser</td>\n",
       "      <td>2004</td>\n",
       "      <td>6450</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>143000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>325</td>\n",
       "      <td>27.2</td>\n",
       "      <td>3.0</td>\n",
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       "    <tr>\n",
       "      <th>6698</th>\n",
       "      <td>Land Cruiser</td>\n",
       "      <td>2014</td>\n",
       "      <td>44990</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>60000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>540</td>\n",
       "      <td>29.7</td>\n",
       "      <td>4.5</td>\n",
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       "    <tr>\n",
       "      <th>6675</th>\n",
       "      <td>Land Cruiser</td>\n",
       "      <td>2019</td>\n",
       "      <td>44935</td>\n",
       "      <td>Semi-Auto</td>\n",
       "      <td>15200</td>\n",
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       "      <td>145</td>\n",
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       "    <tr>\n",
       "      <th>6680</th>\n",
       "      <td>Land Cruiser</td>\n",
       "      <td>2019</td>\n",
       "      <td>48995</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>789</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>145</td>\n",
       "      <td>30.1</td>\n",
       "      <td>2.8</td>\n",
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       "    <tr>\n",
       "      <th>6651</th>\n",
       "      <td>Land Cruiser</td>\n",
       "      <td>2020</td>\n",
       "      <td>44995</td>\n",
       "      <td>Semi-Auto</td>\n",
       "      <td>200</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>150</td>\n",
       "      <td>30.1</td>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6649</th>\n",
       "      <td>Land Cruiser</td>\n",
       "      <td>2019</td>\n",
       "      <td>39498</td>\n",
       "      <td>Semi-Auto</td>\n",
       "      <td>11712</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>145</td>\n",
       "      <td>30.1</td>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6667</th>\n",
       "      <td>Land Cruiser</td>\n",
       "      <td>2019</td>\n",
       "      <td>50995</td>\n",
       "      <td>Semi-Auto</td>\n",
       "      <td>6254</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>145</td>\n",
       "      <td>30.1</td>\n",
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       "    <tr>\n",
       "      <th>6684</th>\n",
       "      <td>Land Cruiser</td>\n",
       "      <td>2020</td>\n",
       "      <td>47885</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>4</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>145</td>\n",
       "      <td>30.1</td>\n",
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      ],
      "text/plain": [
       "              model  year  price transmission  mileage fuelType  tax   mpg  \\\n",
       "6594          Hilux  2020  39257    Automatic     1259   Diesel  265   2.8   \n",
       "6583          Hilux  2019  26995    Automatic        4   Diesel  265   2.8   \n",
       "6575          Hilux  2019  26500    Automatic     7123   Diesel  260   2.8   \n",
       "6562          Hilux  2020  18495       Manual       50   Diesel  260   2.8   \n",
       "6595          Hilux  2019  27850    Automatic    10250   Diesel  260   2.8   \n",
       "6596          Hilux  2020  30500    Automatic     5000   Diesel  260   2.8   \n",
       "6552          Hilux  2019  28495    Automatic     3350   Diesel  260   2.8   \n",
       "6576          Hilux  2019  20500       Manual     5190   Diesel  260   2.8   \n",
       "6617          Hilux  2020  36995    Automatic      150   Diesel  260   2.8   \n",
       "6563          Hilux  2020  23495       Manual      100   Diesel  260   2.8   \n",
       "5966           C-HR  2018  16690       Manual    16429   Petrol  145   6.0   \n",
       "6694   Land Cruiser  1998  19990       Manual   100000   Diesel  265  23.9   \n",
       "6693   Land Cruiser  2004   6450    Automatic   143000   Diesel  325  27.2   \n",
       "6698   Land Cruiser  2014  44990    Automatic    60000   Diesel  540  29.7   \n",
       "6675   Land Cruiser  2019  44935    Semi-Auto    15200   Diesel  145  30.1   \n",
       "6680   Land Cruiser  2019  48995    Automatic      789   Diesel  145  30.1   \n",
       "6651   Land Cruiser  2020  44995    Semi-Auto      200   Diesel  150  30.1   \n",
       "6649   Land Cruiser  2019  39498    Semi-Auto    11712   Diesel  145  30.1   \n",
       "6667   Land Cruiser  2019  50995    Semi-Auto     6254   Diesel  145  30.1   \n",
       "6684   Land Cruiser  2020  47885    Automatic        4   Diesel  145  30.1   \n",
       "\n",
       "      engineSize  \n",
       "6594         2.4  \n",
       "6583         2.4  \n",
       "6575         2.4  \n",
       "6562         2.4  \n",
       "6595         2.4  \n",
       "6596         2.4  \n",
       "6552         2.4  \n",
       "6576         2.4  \n",
       "6617         2.4  \n",
       "6563         2.4  \n",
       "5966         1.2  \n",
       "6694         4.2  \n",
       "6693         3.0  \n",
       "6698         4.5  \n",
       "6675         2.8  \n",
       "6680         2.8  \n",
       "6651         2.8  \n",
       "6649         2.8  \n",
       "6667         2.8  \n",
       "6684         2.8  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# tax=0 is associated with high-mpg cars.\n",
    "# this seems plausible (zero-tax incentives for high-efficiency vehicles)\n",
    "\n",
    "# investigate lowest mpgs\n",
    "data.sort_values(by='mpg', ascending=True).head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e59a46e6-812d-4b35-b90d-604ed7a51756",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>6694</th>\n",
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       "      <td>100000</td>\n",
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       "      <th>6693</th>\n",
       "      <td>Land Cruiser</td>\n",
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       "      <td>60000</td>\n",
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       "    <tr>\n",
       "      <th>6687</th>\n",
       "      <td>Land Cruiser</td>\n",
       "      <td>2020</td>\n",
       "      <td>45950</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>27</td>\n",
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       "      <td>145</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>6667</th>\n",
       "      <td>Land Cruiser</td>\n",
       "      <td>2019</td>\n",
       "      <td>50995</td>\n",
       "      <td>Semi-Auto</td>\n",
       "      <td>6254</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>145</td>\n",
       "      <td>30.1</td>\n",
       "      <td>2.8</td>\n",
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      ],
      "text/plain": [
       "              model  year  price transmission  mileage fuelType  tax   mpg  \\\n",
       "6694   Land Cruiser  1998  19990       Manual   100000   Diesel  265  23.9   \n",
       "6693   Land Cruiser  2004   6450    Automatic   143000   Diesel  325  27.2   \n",
       "6698   Land Cruiser  2014  44990    Automatic    60000   Diesel  540  29.7   \n",
       "6687   Land Cruiser  2020  45950    Automatic       27   Diesel  145  30.1   \n",
       "6667   Land Cruiser  2019  50995    Semi-Auto     6254   Diesel  145  30.1   \n",
       "\n",
       "      engineSize  \n",
       "6694         4.2  \n",
       "6693         3.0  \n",
       "6698         4.5  \n",
       "6687         2.8  \n",
       "6667         2.8  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# rows with mpg < 10 are not plausible (I looked up mpg of 2019/2020 Hilux and 2018 CH-R)\n",
    "# need to replace these mpg values with null.\n",
    "data.loc[data['mpg'] < 10, 'mpg'] = np.nan\n",
    "\n",
    "# confirm change\n",
    "data.sort_values(by='mpg', ascending=True).head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8ea907c4-ede0-4bcc-b4c4-fd66c4556cec",
   "metadata": {},
   "outputs": [
    {
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       "      <td>Prius</td>\n",
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       "      <td>Prius</td>\n",
       "      <td>2018</td>\n",
       "      <td>20495</td>\n",
       "      <td>Automatic</td>\n",
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       "      <td>235.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>6236</th>\n",
       "      <td>Prius</td>\n",
       "      <td>2019</td>\n",
       "      <td>21500</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>4000</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>135</td>\n",
       "      <td>235.0</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6256</th>\n",
       "      <td>Prius</td>\n",
       "      <td>2018</td>\n",
       "      <td>18150</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>59191</td>\n",
       "      <td>Other</td>\n",
       "      <td>135</td>\n",
       "      <td>235.0</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6116</th>\n",
       "      <td>Prius</td>\n",
       "      <td>2017</td>\n",
       "      <td>19998</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>13476</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>140</td>\n",
       "      <td>235.0</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6118</th>\n",
       "      <td>Prius</td>\n",
       "      <td>2019</td>\n",
       "      <td>24987</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>6552</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>135</td>\n",
       "      <td>235.0</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6223</th>\n",
       "      <td>Prius</td>\n",
       "      <td>2018</td>\n",
       "      <td>18699</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>34502</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>140</td>\n",
       "      <td>235.0</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6134</th>\n",
       "      <td>Prius</td>\n",
       "      <td>2017</td>\n",
       "      <td>20998</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>17681</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>135</td>\n",
       "      <td>235.0</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6200</th>\n",
       "      <td>Prius</td>\n",
       "      <td>2018</td>\n",
       "      <td>19072</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>43584</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>135</td>\n",
       "      <td>235.0</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6216</th>\n",
       "      <td>Prius</td>\n",
       "      <td>2018</td>\n",
       "      <td>22890</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>31786</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>135</td>\n",
       "      <td>235.0</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6198</th>\n",
       "      <td>Prius</td>\n",
       "      <td>2019</td>\n",
       "      <td>29995</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>3754</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>135</td>\n",
       "      <td>217.3</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6176</th>\n",
       "      <td>Prius</td>\n",
       "      <td>2019</td>\n",
       "      <td>26995</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>12998</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>135</td>\n",
       "      <td>217.3</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6253</th>\n",
       "      <td>Prius</td>\n",
       "      <td>2019</td>\n",
       "      <td>27999</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>9975</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>140</td>\n",
       "      <td>217.3</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6244</th>\n",
       "      <td>Prius</td>\n",
       "      <td>2020</td>\n",
       "      <td>31995</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>100</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>135</td>\n",
       "      <td>217.3</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6286</th>\n",
       "      <td>Prius</td>\n",
       "      <td>2019</td>\n",
       "      <td>26700</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>6053</td>\n",
       "      <td>Other</td>\n",
       "      <td>135</td>\n",
       "      <td>217.3</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6151</th>\n",
       "      <td>Prius</td>\n",
       "      <td>2019</td>\n",
       "      <td>27999</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>3650</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>140</td>\n",
       "      <td>217.3</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6291</th>\n",
       "      <td>Prius</td>\n",
       "      <td>2019</td>\n",
       "      <td>26990</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>7000</td>\n",
       "      <td>Other</td>\n",
       "      <td>135</td>\n",
       "      <td>217.3</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6287</th>\n",
       "      <td>Prius</td>\n",
       "      <td>2019</td>\n",
       "      <td>28590</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5000</td>\n",
       "      <td>Other</td>\n",
       "      <td>135</td>\n",
       "      <td>217.3</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6240</th>\n",
       "      <td>Prius</td>\n",
       "      <td>2013</td>\n",
       "      <td>13495</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>35726</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>0</td>\n",
       "      <td>134.5</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6195</th>\n",
       "      <td>Prius</td>\n",
       "      <td>2014</td>\n",
       "      <td>15362</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>50647</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>0</td>\n",
       "      <td>134.5</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6180</th>\n",
       "      <td>Prius</td>\n",
       "      <td>2015</td>\n",
       "      <td>17495</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>19350</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>0</td>\n",
       "      <td>134.5</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6177</th>\n",
       "      <td>Prius</td>\n",
       "      <td>2015</td>\n",
       "      <td>15995</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>51896</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>0</td>\n",
       "      <td>134.5</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6250</th>\n",
       "      <td>Prius</td>\n",
       "      <td>2012</td>\n",
       "      <td>13199</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>37870</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>0</td>\n",
       "      <td>134.5</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6188</th>\n",
       "      <td>Prius</td>\n",
       "      <td>2014</td>\n",
       "      <td>15995</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>47221</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>0</td>\n",
       "      <td>134.5</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6314</th>\n",
       "      <td>Prius</td>\n",
       "      <td>2016</td>\n",
       "      <td>15795</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>47000</td>\n",
       "      <td>Other</td>\n",
       "      <td>0</td>\n",
       "      <td>134.5</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6160</th>\n",
       "      <td>Prius</td>\n",
       "      <td>2013</td>\n",
       "      <td>13522</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>52217</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>0</td>\n",
       "      <td>134.5</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6289</th>\n",
       "      <td>Prius</td>\n",
       "      <td>2014</td>\n",
       "      <td>15990</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>23523</td>\n",
       "      <td>Other</td>\n",
       "      <td>0</td>\n",
       "      <td>134.5</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6201</th>\n",
       "      <td>Prius</td>\n",
       "      <td>2018</td>\n",
       "      <td>20950</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>10549</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>135</td>\n",
       "      <td>94.1</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6183</th>\n",
       "      <td>Prius</td>\n",
       "      <td>2016</td>\n",
       "      <td>17995</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>32988</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>0</td>\n",
       "      <td>94.1</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6135</th>\n",
       "      <td>Prius</td>\n",
       "      <td>2016</td>\n",
       "      <td>18390</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>12075</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>0</td>\n",
       "      <td>94.1</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6136</th>\n",
       "      <td>Prius</td>\n",
       "      <td>2016</td>\n",
       "      <td>16995</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>31432</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>0</td>\n",
       "      <td>94.1</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6138</th>\n",
       "      <td>Prius</td>\n",
       "      <td>2016</td>\n",
       "      <td>16390</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>30125</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>0</td>\n",
       "      <td>94.1</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6153</th>\n",
       "      <td>Prius</td>\n",
       "      <td>2017</td>\n",
       "      <td>18495</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>25200</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>0</td>\n",
       "      <td>94.1</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6154</th>\n",
       "      <td>Prius</td>\n",
       "      <td>2017</td>\n",
       "      <td>18999</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>19870</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>0</td>\n",
       "      <td>94.1</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6193</th>\n",
       "      <td>Prius</td>\n",
       "      <td>2017</td>\n",
       "      <td>17995</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>23056</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>0</td>\n",
       "      <td>94.1</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6191</th>\n",
       "      <td>Prius</td>\n",
       "      <td>2018</td>\n",
       "      <td>17995</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>44512</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>135</td>\n",
       "      <td>94.1</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6156</th>\n",
       "      <td>Prius</td>\n",
       "      <td>2016</td>\n",
       "      <td>16890</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>31055</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>0</td>\n",
       "      <td>94.1</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6157</th>\n",
       "      <td>Prius</td>\n",
       "      <td>2017</td>\n",
       "      <td>18490</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>18460</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>0</td>\n",
       "      <td>94.1</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6172</th>\n",
       "      <td>Prius</td>\n",
       "      <td>2016</td>\n",
       "      <td>13690</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>78500</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>0</td>\n",
       "      <td>94.1</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6158</th>\n",
       "      <td>Prius</td>\n",
       "      <td>2017</td>\n",
       "      <td>19313</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>28031</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>0</td>\n",
       "      <td>94.1</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6182</th>\n",
       "      <td>Prius</td>\n",
       "      <td>2017</td>\n",
       "      <td>19000</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>11996</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>0</td>\n",
       "      <td>94.1</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6181</th>\n",
       "      <td>Prius</td>\n",
       "      <td>2017</td>\n",
       "      <td>20100</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>30687</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>0</td>\n",
       "      <td>94.1</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6161</th>\n",
       "      <td>Prius</td>\n",
       "      <td>2017</td>\n",
       "      <td>15698</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>68812</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>0</td>\n",
       "      <td>94.1</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6165</th>\n",
       "      <td>Prius</td>\n",
       "      <td>2017</td>\n",
       "      <td>17542</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>40205</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>0</td>\n",
       "      <td>94.1</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6167</th>\n",
       "      <td>Prius</td>\n",
       "      <td>2016</td>\n",
       "      <td>15999</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>33501</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>0</td>\n",
       "      <td>94.1</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6169</th>\n",
       "      <td>Prius</td>\n",
       "      <td>2016</td>\n",
       "      <td>17998</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>32868</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>0</td>\n",
       "      <td>94.1</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6171</th>\n",
       "      <td>Prius</td>\n",
       "      <td>2017</td>\n",
       "      <td>17498</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>21824</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>0</td>\n",
       "      <td>94.1</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       model  year  price transmission  mileage fuelType  tax    mpg  \\\n",
       "6207   Prius  2019  22495    Automatic    13772   Hybrid  135  235.0   \n",
       "6124   Prius  2020  27990    Automatic     1500   Hybrid  135  235.0   \n",
       "6267   Prius  2018  19699    Automatic    43678   Hybrid  135  235.0   \n",
       "6098   Prius  2017  20795    Automatic     3881   Hybrid  135  235.0   \n",
       "6103   Prius  2018  20495    Automatic    16347   Hybrid  140  235.0   \n",
       "6236   Prius  2019  21500    Automatic     4000   Hybrid  135  235.0   \n",
       "6256   Prius  2018  18150    Automatic    59191    Other  135  235.0   \n",
       "6116   Prius  2017  19998    Automatic    13476   Hybrid  140  235.0   \n",
       "6118   Prius  2019  24987    Automatic     6552   Hybrid  135  235.0   \n",
       "6223   Prius  2018  18699    Automatic    34502   Hybrid  140  235.0   \n",
       "6134   Prius  2017  20998    Automatic    17681   Hybrid  135  235.0   \n",
       "6200   Prius  2018  19072    Automatic    43584   Hybrid  135  235.0   \n",
       "6216   Prius  2018  22890    Automatic    31786   Hybrid  135  235.0   \n",
       "6198   Prius  2019  29995    Automatic     3754   Hybrid  135  217.3   \n",
       "6176   Prius  2019  26995    Automatic    12998   Hybrid  135  217.3   \n",
       "6253   Prius  2019  27999    Automatic     9975   Hybrid  140  217.3   \n",
       "6244   Prius  2020  31995    Automatic      100   Hybrid  135  217.3   \n",
       "6286   Prius  2019  26700    Automatic     6053    Other  135  217.3   \n",
       "6151   Prius  2019  27999    Automatic     3650   Hybrid  140  217.3   \n",
       "6291   Prius  2019  26990    Automatic     7000    Other  135  217.3   \n",
       "6287   Prius  2019  28590    Automatic     5000    Other  135  217.3   \n",
       "6240   Prius  2013  13495    Automatic    35726   Hybrid    0  134.5   \n",
       "6195   Prius  2014  15362    Automatic    50647   Hybrid    0  134.5   \n",
       "6180   Prius  2015  17495    Automatic    19350   Hybrid    0  134.5   \n",
       "6177   Prius  2015  15995    Automatic    51896   Hybrid    0  134.5   \n",
       "6250   Prius  2012  13199    Automatic    37870   Hybrid    0  134.5   \n",
       "6188   Prius  2014  15995    Automatic    47221   Hybrid    0  134.5   \n",
       "6314   Prius  2016  15795    Automatic    47000    Other    0  134.5   \n",
       "6160   Prius  2013  13522    Automatic    52217   Hybrid    0  134.5   \n",
       "6289   Prius  2014  15990    Automatic    23523    Other    0  134.5   \n",
       "6201   Prius  2018  20950    Automatic    10549   Hybrid  135   94.1   \n",
       "6183   Prius  2016  17995    Automatic    32988   Hybrid    0   94.1   \n",
       "6135   Prius  2016  18390    Automatic    12075   Hybrid    0   94.1   \n",
       "6136   Prius  2016  16995    Automatic    31432   Hybrid    0   94.1   \n",
       "6138   Prius  2016  16390    Automatic    30125   Hybrid    0   94.1   \n",
       "6153   Prius  2017  18495    Automatic    25200   Hybrid    0   94.1   \n",
       "6154   Prius  2017  18999    Automatic    19870   Hybrid    0   94.1   \n",
       "6193   Prius  2017  17995    Automatic    23056   Hybrid    0   94.1   \n",
       "6191   Prius  2018  17995    Automatic    44512   Hybrid  135   94.1   \n",
       "6156   Prius  2016  16890    Automatic    31055   Hybrid    0   94.1   \n",
       "6157   Prius  2017  18490    Automatic    18460   Hybrid    0   94.1   \n",
       "6172   Prius  2016  13690    Automatic    78500   Hybrid    0   94.1   \n",
       "6158   Prius  2017  19313    Automatic    28031   Hybrid    0   94.1   \n",
       "6182   Prius  2017  19000    Automatic    11996   Hybrid    0   94.1   \n",
       "6181   Prius  2017  20100    Automatic    30687   Hybrid    0   94.1   \n",
       "6161   Prius  2017  15698    Automatic    68812   Hybrid    0   94.1   \n",
       "6165   Prius  2017  17542    Automatic    40205   Hybrid    0   94.1   \n",
       "6167   Prius  2016  15999    Automatic    33501   Hybrid    0   94.1   \n",
       "6169   Prius  2016  17998    Automatic    32868   Hybrid    0   94.1   \n",
       "6171   Prius  2017  17498    Automatic    21824   Hybrid    0   94.1   \n",
       "\n",
       "      engineSize  \n",
       "6207         1.8  \n",
       "6124         1.8  \n",
       "6267         1.8  \n",
       "6098         1.8  \n",
       "6103         1.8  \n",
       "6236         1.8  \n",
       "6256         1.8  \n",
       "6116         1.8  \n",
       "6118         1.8  \n",
       "6223         1.8  \n",
       "6134         1.8  \n",
       "6200         1.8  \n",
       "6216         1.8  \n",
       "6198         1.8  \n",
       "6176         1.8  \n",
       "6253         1.8  \n",
       "6244         1.8  \n",
       "6286         1.8  \n",
       "6151         1.8  \n",
       "6291         1.8  \n",
       "6287         1.8  \n",
       "6240         1.8  \n",
       "6195         1.8  \n",
       "6180         1.8  \n",
       "6177         1.8  \n",
       "6250         1.8  \n",
       "6188         1.8  \n",
       "6314         1.8  \n",
       "6160         1.8  \n",
       "6289         1.8  \n",
       "6201         1.8  \n",
       "6183         1.8  \n",
       "6135         1.8  \n",
       "6136         1.8  \n",
       "6138         1.8  \n",
       "6153         1.8  \n",
       "6154         1.8  \n",
       "6193         1.8  \n",
       "6191         1.8  \n",
       "6156         1.8  \n",
       "6157         1.8  \n",
       "6172         1.8  \n",
       "6158         1.8  \n",
       "6182         1.8  \n",
       "6181         1.8  \n",
       "6161         1.8  \n",
       "6165         1.8  \n",
       "6167         1.8  \n",
       "6169         1.8  \n",
       "6171         1.8  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# investigate highest mpgs\n",
    "data.sort_values(by='mpg', ascending=False).head(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c38d992d-bc06-43a3-b782-7626e068fac1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\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>model</th>\n",
       "      <th>year</th>\n",
       "      <th>price</th>\n",
       "      <th>transmission</th>\n",
       "      <th>mileage</th>\n",
       "      <th>fuelType</th>\n",
       "      <th>tax</th>\n",
       "      <th>mpg</th>\n",
       "      <th>engineSize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5233</th>\n",
       "      <td>Aygo</td>\n",
       "      <td>2019</td>\n",
       "      <td>8000</td>\n",
       "      <td>Manual</td>\n",
       "      <td>8531</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>145</td>\n",
       "      <td>56.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5960</th>\n",
       "      <td>C-HR</td>\n",
       "      <td>2017</td>\n",
       "      <td>14300</td>\n",
       "      <td>Manual</td>\n",
       "      <td>46571</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>145</td>\n",
       "      <td>47.1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5257</th>\n",
       "      <td>Aygo</td>\n",
       "      <td>2019</td>\n",
       "      <td>8000</td>\n",
       "      <td>Manual</td>\n",
       "      <td>5354</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>145</td>\n",
       "      <td>56.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2545</th>\n",
       "      <td>Yaris</td>\n",
       "      <td>2016</td>\n",
       "      <td>11000</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>39909</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5126</th>\n",
       "      <td>Aygo</td>\n",
       "      <td>2019</td>\n",
       "      <td>9800</td>\n",
       "      <td>Manual</td>\n",
       "      <td>3635</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>150</td>\n",
       "      <td>56.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2535</th>\n",
       "      <td>Yaris</td>\n",
       "      <td>2016</td>\n",
       "      <td>12300</td>\n",
       "      <td>Manual</td>\n",
       "      <td>6148</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3984</th>\n",
       "      <td>Aygo</td>\n",
       "      <td>2018</td>\n",
       "      <td>9295</td>\n",
       "      <td>Manual</td>\n",
       "      <td>11000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>150</td>\n",
       "      <td>56.5</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3960</th>\n",
       "      <td>Aygo</td>\n",
       "      <td>2017</td>\n",
       "      <td>7480</td>\n",
       "      <td>Manual</td>\n",
       "      <td>22744</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3961</th>\n",
       "      <td>Aygo</td>\n",
       "      <td>2015</td>\n",
       "      <td>5498</td>\n",
       "      <td>Manual</td>\n",
       "      <td>33248</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3962</th>\n",
       "      <td>Aygo</td>\n",
       "      <td>2018</td>\n",
       "      <td>9488</td>\n",
       "      <td>Semi-Auto</td>\n",
       "      <td>15212</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>150</td>\n",
       "      <td>67.3</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       model  year  price transmission  mileage fuelType  tax   mpg  \\\n",
       "5233    Aygo  2019   8000       Manual     8531   Petrol  145  56.5   \n",
       "5960    C-HR  2017  14300       Manual    46571   Petrol  145  47.1   \n",
       "5257    Aygo  2019   8000       Manual     5354   Petrol  145  56.5   \n",
       "2545   Yaris  2016  11000    Automatic    39909   Hybrid    0  86.0   \n",
       "5126    Aygo  2019   9800       Manual     3635   Petrol  150  56.5   \n",
       "2535   Yaris  2016  12300       Manual     6148   Hybrid    0  86.0   \n",
       "3984    Aygo  2018   9295       Manual    11000   Petrol  150  56.5   \n",
       "3960    Aygo  2017   7480       Manual    22744   Petrol    0  69.0   \n",
       "3961    Aygo  2015   5498       Manual    33248   Petrol    0  69.0   \n",
       "3962    Aygo  2018   9488    Semi-Auto    15212   Petrol  150  67.3   \n",
       "\n",
       "      engineSize  \n",
       "5233         0.0  \n",
       "5960         0.0  \n",
       "5257         0.0  \n",
       "2545         0.0  \n",
       "5126         0.0  \n",
       "2535         0.0  \n",
       "3984         1.0  \n",
       "3960         1.0  \n",
       "3961         1.0  \n",
       "3962         1.0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# based on my research, these mpg values appear to be lab-derived (i.e., NEDC- and WLTP-reported values).\n",
    "# therefore, the high values are plausible.\n",
    "\n",
    "# highest engine size appears appropriate.\n",
    "# investigate lowest engine sizes\n",
    "data.sort_values(by='engineSize', ascending=True).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e5217593-2587-4a52-8cdb-5bfab57bf5b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>year</th>\n",
       "      <th>price</th>\n",
       "      <th>transmission</th>\n",
       "      <th>mileage</th>\n",
       "      <th>fuelType</th>\n",
       "      <th>tax</th>\n",
       "      <th>mpg</th>\n",
       "      <th>engineSize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5261</th>\n",
       "      <td>Aygo</td>\n",
       "      <td>2018</td>\n",
       "      <td>7300</td>\n",
       "      <td>Manual</td>\n",
       "      <td>14521</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>145</td>\n",
       "      <td>69.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3960</th>\n",
       "      <td>Aygo</td>\n",
       "      <td>2017</td>\n",
       "      <td>7480</td>\n",
       "      <td>Manual</td>\n",
       "      <td>22744</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3961</th>\n",
       "      <td>Aygo</td>\n",
       "      <td>2015</td>\n",
       "      <td>5498</td>\n",
       "      <td>Manual</td>\n",
       "      <td>33248</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3962</th>\n",
       "      <td>Aygo</td>\n",
       "      <td>2018</td>\n",
       "      <td>9488</td>\n",
       "      <td>Semi-Auto</td>\n",
       "      <td>15212</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>150</td>\n",
       "      <td>67.3</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3963</th>\n",
       "      <td>Aygo</td>\n",
       "      <td>2019</td>\n",
       "      <td>7890</td>\n",
       "      <td>Manual</td>\n",
       "      <td>8441</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>145</td>\n",
       "      <td>56.5</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3964</th>\n",
       "      <td>Aygo</td>\n",
       "      <td>2019</td>\n",
       "      <td>8790</td>\n",
       "      <td>Manual</td>\n",
       "      <td>12143</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>145</td>\n",
       "      <td>57.7</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3965</th>\n",
       "      <td>Aygo</td>\n",
       "      <td>2017</td>\n",
       "      <td>6490</td>\n",
       "      <td>Manual</td>\n",
       "      <td>10967</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3966</th>\n",
       "      <td>Aygo</td>\n",
       "      <td>2015</td>\n",
       "      <td>5995</td>\n",
       "      <td>Manual</td>\n",
       "      <td>26200</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3967</th>\n",
       "      <td>Aygo</td>\n",
       "      <td>2019</td>\n",
       "      <td>8788</td>\n",
       "      <td>Manual</td>\n",
       "      <td>3305</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>150</td>\n",
       "      <td>56.5</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3968</th>\n",
       "      <td>Aygo</td>\n",
       "      <td>2016</td>\n",
       "      <td>7000</td>\n",
       "      <td>Manual</td>\n",
       "      <td>14347</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      model  year  price transmission  mileage fuelType  tax   mpg  engineSize\n",
       "5261   Aygo  2018   7300       Manual    14521   Petrol  145  69.0         1.0\n",
       "3960   Aygo  2017   7480       Manual    22744   Petrol    0  69.0         1.0\n",
       "3961   Aygo  2015   5498       Manual    33248   Petrol    0  69.0         1.0\n",
       "3962   Aygo  2018   9488    Semi-Auto    15212   Petrol  150  67.3         1.0\n",
       "3963   Aygo  2019   7890       Manual     8441   Petrol  145  56.5         1.0\n",
       "3964   Aygo  2019   8790       Manual    12143   Petrol  145  57.7         1.0\n",
       "3965   Aygo  2017   6490       Manual    10967   Petrol    0  69.0         1.0\n",
       "3966   Aygo  2015   5995       Manual    26200   Petrol    0  69.0         1.0\n",
       "3967   Aygo  2019   8788       Manual     3305   Petrol  150  56.5         1.0\n",
       "3968   Aygo  2016   7000       Manual    14347   Petrol    0  69.0         1.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# these hybrid and petrol vehicles cannot have 0.0 L engine sizes.\n",
    "# need to replace these 0 values with null.\n",
    "data.loc[data['engineSize'] == 0, 'engineSize'] = np.nan\n",
    "\n",
    "# confirm change\n",
    "data.sort_values(by='engineSize', ascending=True).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "823af549-4e51-4131-adf0-02ce69018d3e",
   "metadata": {
    "executionCancelledAt": null,
    "executionTime": 52,
    "lastExecutedAt": 1711728534890,
    "lastScheduledRunId": null,
    "lastSuccessfullyExecutedCode": "# unique values: 'model'\ndisplay(data['model'].value_counts())\nprint('Number of unique values: {}'.format(data['model'].nunique()))",
    "outputsMetadata": {
     "1": {
      "height": 37,
      "type": "stream"
     }
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "model\n",
       "Yaris            2117\n",
       "Aygo             1940\n",
       "Auris             709\n",
       "C-HR              479\n",
       "RAV4              467\n",
       "Corolla           265\n",
       "Prius             232\n",
       "Verso             114\n",
       "Avensis           114\n",
       "Hilux              85\n",
       "GT86               73\n",
       "Land Cruiser       51\n",
       "PROACE VERSO       15\n",
       "Supra              12\n",
       "Camry              11\n",
       "IQ                  8\n",
       "Urban Cruiser       4\n",
       "Verso-S             3\n",
       "Name: count, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of unique values: 18\n"
     ]
    }
   ],
   "source": [
    "# get unique values: 'model'\n",
    "display(data['model'].value_counts())\n",
    "print('Number of unique values: {}'.format(data['model'].nunique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "15a1d538-ea04-4e52-a47e-8f4b9d1d6493",
   "metadata": {
    "executionCancelledAt": null,
    "executionTime": 51,
    "lastExecutedAt": 1711728534941,
    "lastScheduledRunId": null,
    "lastSuccessfullyExecutedCode": "# unique values: 'transmission'\ndisplay(data['transmission'].value_counts())\nprint('Number of unique values: {}'.format(data['transmission'].nunique()))",
    "outputsMetadata": {
     "1": {
      "height": 37,
      "type": "stream"
     }
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "transmission\n",
       "Manual       3793\n",
       "Automatic    2651\n",
       "Semi-Auto     254\n",
       "Other           1\n",
       "Name: count, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of unique values: 4\n"
     ]
    }
   ],
   "source": [
    "# get unique values: 'transmission'\n",
    "display(data['transmission'].value_counts())\n",
    "print('Number of unique values: {}'.format(data['transmission'].nunique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9107666d-ec02-4ddc-ab09-3277e136ee72",
   "metadata": {
    "executionCancelledAt": null,
    "executionTime": 53,
    "lastExecutedAt": 1711728534994,
    "lastScheduledRunId": null,
    "lastSuccessfullyExecutedCode": "# unique values: 'fuelType'\ndisplay(data['fuelType'].value_counts())\nprint('Number of unique values: {}'.format(data['fuelType'].nunique()))",
    "outputsMetadata": {
     "1": {
      "height": 37,
      "type": "stream"
     }
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fuelType\n",
       "Petrol    4058\n",
       "Hybrid    2040\n",
       "Diesel     496\n",
       "Other      105\n",
       "Name: count, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of unique values: 4\n"
     ]
    }
   ],
   "source": [
    "# get unique values: 'fuelType'\n",
    "display(data['fuelType'].value_counts())\n",
    "print('Number of unique values: {}'.format(data['fuelType'].nunique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "74f8fc18-953d-4743-8886-27585d919ab0",
   "metadata": {
    "editable": true,
    "executionCancelledAt": null,
    "executionTime": 55,
    "lastExecutedAt": 1711728535049,
    "lastScheduledRunId": null,
    "lastSuccessfullyExecutedCode": "# unique values: 'engineSize'\ndisplay(data['engineSize'].value_counts())\nprint('Number of unique values: {}'.format(data['engineSize'].nunique()))",
    "outputsMetadata": {
     "1": {
      "height": 37,
      "type": "stream"
     }
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "engineSize\n",
       "1.0    2038\n",
       "1.5    1419\n",
       "1.8    1289\n",
       "1.3     600\n",
       "2.0     394\n",
       "1.2     300\n",
       "2.5     247\n",
       "1.6     166\n",
       "2.4      70\n",
       "2.2      58\n",
       "1.4      40\n",
       "2.8      36\n",
       "3.0      34\n",
       "4.2       1\n",
       "4.5       1\n",
       "Name: count, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of unique values: 15\n"
     ]
    }
   ],
   "source": [
    "# get unique values: 'engineSize'\n",
    "display(data['engineSize'].value_counts())\n",
    "print('Number of unique values: {}'.format(data['engineSize'].nunique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "53053412-d326-430a-9c65-f912a79008f9",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6699, 9)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# display number of rows and columns\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6176f99d-1691-4be9-8c46-a827c429e8e3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model            0\n",
      "year             0\n",
      "price            0\n",
      "transmission     0\n",
      "mileage          0\n",
      "fuelType         0\n",
      "tax              0\n",
      "mpg             11\n",
      "engineSize       6\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# display number of null values per column\n",
    "print(data.isna().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "186cbcdf-4aa4-4f17-8593-9f31aee29acb",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17\n"
     ]
    }
   ],
   "source": [
    "# number of rows with null value in any column\n",
    "print(data.isna().any(axis=1).sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27ca8304-5066-48a7-b75b-95a72571d8a8",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "# 2. Data Validation\n",
    "\n",
    "The dataset provided by the sales team contains used car characteristics and final sales prices from the past 6 months. Validation was performed to understand the dataset's dimensions, identify any duplicate rows, identify any null values, identify any abnormal values, and ensure each column matches its corresponding description in the data dictionary.\n",
    "\n",
    "Overall, the original dataset comprised 6738 rows and 9 columns, 39 duplicate rows, and no null values. The 39 duplicate rows were removed (with first occurrences retained) because it is very unlikely that there were cars that had identical attribute values across all columns (especially 'mileage'). These rows were likely to have been erroneously duplicated. It is important to remove them from the perspective of model evaluation. If duplicate rows are retained, they can end up being part of both the training data and the test data. This can lead to an optimistically biased estimate of model performance on unseen data.\n",
    "\n",
    "During range validation, abnormal values were found for 'mpg' and 'engineSize'. There were 11 cars with mpg values under 10. Based on research, these mpg values should be higher, so they were marked as null. Also, six cars had engine sizes of 0.0 L. Based on research, these engine sizes should be higher, so they were marked as null.\n",
    "\n",
    "The following list summarizes the inspection and resulting data cleaning of each column:\n",
    "\n",
    "- **model**:\n",
    "  - Correct data type (object/character)\n",
    "  - 18 unique values as described in data dictionary\n",
    "  - No cleaning needed\n",
    "- **year**:\n",
    "  - Correct data type (integer/numeric)\n",
    "  - Values range from 1998-2020 as described in data dictionary\n",
    "  - No cleaning needed\n",
    "- **price**:\n",
    "  - Correct data type (integer/numeric)\n",
    "  - Minimum and maximum values appear appropriate\n",
    "  - No cleaning needed\n",
    "- **transmission**:\n",
    "  - Correct data type (object/character)\n",
    "  - 4 unique values as described in data dictionary ('Manual', 'Automatic', 'Semi-Auto', 'Other')\n",
    "  - No cleaning needed\n",
    "- **mileage**:\n",
    "  - Correct data type (integer/numeric)\n",
    "  - Minimum and maximum values appear appropriate\n",
    "  - No cleaning needed\n",
    "- **fuelType**:\n",
    "  - Correct data type (object/character)\n",
    "  - 4 unique values as described in data dictionary ('Petrol', 'Hybrid', 'Diesel', 'Other')\n",
    "  - No cleaning needed\n",
    "- **tax**:\n",
    "  - Correct data type (integer/numeric)\n",
    "  - Minimum and maximum values appear appropriate\n",
    "  - No cleaning needed\n",
    "- **mpg**:\n",
    "  - Correct data type (float/numeric)\n",
    "  - Maximum value appears appropriate\n",
    "  - There were 11 values under 10. Based on research, they were deemed inaccurate and changed to null.\n",
    "- **engineSize**:\n",
    "  - Correct data type (float/numeric)\n",
    "  - 16 unique values as described in data dictionary\n",
    "  - Maximum value appears appropriate\n",
    "  - There were 6 values of 0. Based on research, they were deemed inaccurate and changed to null.\n",
    " \n",
    "The cleaned dataset comprised 6699 rows and 9 columns, 0 duplicate rows, and 17 rows with null values.\n",
    "\n",
    "# 3. Exploratory Analysis\n",
    "\n",
    "Exploratory analysis of the cleaned dataset was performed to understand the distribution and descriptive statistics of each variable as well as the relationships between numeric variables. The analysis is organized in the following subsections: 3.1 Numeric Variables, 3.2 Categorical Variables, and 3.3 Numeric Variable Relationships.\n",
    "\n",
    "## 3.2 Numeric Variables\n",
    "\n",
    "Table 1 and Figure 1 show descriptive statistics and a multivariate boxplot of the numeric variables, respectively. The target variable, price, has a median of 10798 GBP. In other words, half of the cars in the database sold for this price or lower. Moreover, 75% of cars sold for 14995 GBP or less. The data are right-skewed with exterme values marked by circles, showing that some cars sold for as much as 59995 GBP (Figure 1).\n",
    "\n",
    "Table 1: Descriptive statistics for numeric variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "093d8e86-f7e4-49a4-9189-187fff213cf7",
   "metadata": {
    "executionCancelledAt": null,
    "executionTime": 49,
    "lastExecutedAt": 1711728535098,
    "lastScheduledRunId": null,
    "lastSuccessfullyExecutedCode": "# generate numeric column descriptive statistics table\ndata.describe()",
    "outputsMetadata": {
     "0": {
      "height": 273,
      "type": "dataFrame"
     }
    }
   },
   "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>year</th>\n",
       "      <th>price</th>\n",
       "      <th>mileage</th>\n",
       "      <th>tax</th>\n",
       "      <th>mpg</th>\n",
       "      <th>engineSize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6699.000</td>\n",
       "      <td>6699.000</td>\n",
       "      <td>6699.000</td>\n",
       "      <td>6699.000</td>\n",
       "      <td>6688.000</td>\n",
       "      <td>6693.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2016.743</td>\n",
       "      <td>12529.799</td>\n",
       "      <td>22889.589</td>\n",
       "      <td>94.550</td>\n",
       "      <td>63.177</td>\n",
       "      <td>1.473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.205</td>\n",
       "      <td>6358.563</td>\n",
       "      <td>19109.289</td>\n",
       "      <td>73.943</td>\n",
       "      <td>15.686</td>\n",
       "      <td>0.434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1998.000</td>\n",
       "      <td>850.000</td>\n",
       "      <td>2.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>23.900</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2016.000</td>\n",
       "      <td>8266.500</td>\n",
       "      <td>9488.500</td>\n",
       "      <td>0.000</td>\n",
       "      <td>55.400</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2017.000</td>\n",
       "      <td>10798.000</td>\n",
       "      <td>18571.000</td>\n",
       "      <td>135.000</td>\n",
       "      <td>62.800</td>\n",
       "      <td>1.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2018.000</td>\n",
       "      <td>14995.000</td>\n",
       "      <td>31059.500</td>\n",
       "      <td>145.000</td>\n",
       "      <td>70.600</td>\n",
       "      <td>1.800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2020.000</td>\n",
       "      <td>59995.000</td>\n",
       "      <td>174419.000</td>\n",
       "      <td>565.000</td>\n",
       "      <td>235.000</td>\n",
       "      <td>4.500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           year      price     mileage       tax       mpg  engineSize\n",
       "count  6699.000   6699.000    6699.000  6699.000  6688.000    6693.000\n",
       "mean   2016.743  12529.799   22889.589    94.550    63.177       1.473\n",
       "std       2.205   6358.563   19109.289    73.943    15.686       0.434\n",
       "min    1998.000    850.000       2.000     0.000    23.900       1.000\n",
       "25%    2016.000   8266.500    9488.500     0.000    55.400       1.000\n",
       "50%    2017.000  10798.000   18571.000   135.000    62.800       1.500\n",
       "75%    2018.000  14995.000   31059.500   145.000    70.600       1.800\n",
       "max    2020.000  59995.000  174419.000   565.000   235.000       4.500"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# generate numeric column descriptive statistics table\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6e720b2c-7aed-40be-b844-456a7d1cabe0",
   "metadata": {
    "executionCancelledAt": null,
    "executionTime": 195,
    "lastExecutedAt": 1711728535293,
    "lastScheduledRunId": null,
    "lastSuccessfullyExecutedCode": "# generate boxplot: numeric columns\ndata.plot(kind='box')\nplt.title('Figure 1: Multivariate boxplot of numeric variables', pad=10.0)\nplt.xlabel('Variable', labelpad=8.0)\nplt.ylabel('Value', labelpad=8.0)\nplt.show()"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# generate boxplot: numeric columns\n",
    "data.plot(kind='box')\n",
    "plt.title('Figure 1: Multivariate boxplot of numeric variables', pad=10.0)\n",
    "plt.xlabel('Variable', labelpad=8.0)\n",
    "plt.ylabel('Value', labelpad=8.0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ce5ebe7-a678-4bf5-9df0-f8ddfd9d1121",
   "metadata": {},
   "source": [
    "Table 1 and Figure 1 also show that the numeric variables have different value ranges. To examine their distributions in greater detail, Figures 2-7 display histograms for each variable. Most variables (Figures 3-7) have right-skewed distributions, while 'year' is left-skewed (Figure 2)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "cba0fcae-d828-40e8-b4de-8dfa52522981",
   "metadata": {
    "executionCancelledAt": null,
    "executionTime": 708,
    "lastExecutedAt": 1711728536001,
    "lastScheduledRunId": null,
    "lastSuccessfullyExecutedCode": "# set up figure with subplots\nfig, ax = plt.subplots(nrows=3, ncols=2, figsize=(8,8))\n\n# generate histogram: year\ndata['year'].hist(ax=ax[0,0])\nax[0,0].set_title('Figure 2', pad=10.0)\nax[0,0].set_xlabel('year', labelpad=4.0)\nax[0,0].set_ylabel('Number of cars', labelpad=4.0)\n\n# generate histogram: price\ndata['price'].hist(ax=ax[0,1], xrot=0)\nax[0,1].set_title('Figure 3', pad=10.0)\nax[0,1].set_xlabel('price', labelpad=4.0)\nax[0,1].set_ylabel('Number of cars', labelpad=4.0)\n\n# generate histogram: mileage\ndata['mileage'].hist(ax=ax[1,0], xrot=0)\nax[1,0].set_title('Figure 4', pad=10.0)\nax[1,0].set_xlabel('mileage', labelpad=4.0)\nax[1,0].set_ylabel('Number of cars', labelpad=4.0)\n\n# generate histogram: tax\ndata['tax'].hist(ax=ax[1,1])\nax[1,1].set_title('Figure 5', pad=10.0)\nax[1,1].set_xlabel('tax', labelpad=4.0)\nax[1,1].set_ylabel('Number of cars', labelpad=4.0)\n\n# generate histogram: mpg\ndata['mpg'].hist(ax=ax[2,0])\nax[2,0].set_title('Figure 6', pad=10.0)\nax[2,0].set_xlabel('mpg', labelpad=4.0)\nax[2,0].set_ylabel('Number of cars', labelpad=4.0)\n\n# generate histogram: engineSize\ndata['engineSize'].hist(ax=ax[2,1])\nax[2,1].set_title('Figure 7', pad=10.0)\nax[2,1].set_xlabel('engineSize', labelpad=4.0)\nax[2,1].set_ylabel('Number of cars', labelpad=4.0)\n\n# adjust subplot spacing\nfig.tight_layout(pad=2.0)\n\n# display subplots\nplt.show()"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# set up figure with subplots\n",
    "fig, ax = plt.subplots(nrows=3, ncols=2, figsize=(8,8))\n",
    "\n",
    "# generate histogram: year\n",
    "data['year'].hist(ax=ax[0,0])\n",
    "ax[0,0].set_title('Figure 2', pad=10.0)\n",
    "ax[0,0].set_xlabel('year', labelpad=4.0)\n",
    "ax[0,0].set_ylabel('Number of cars', labelpad=4.0)\n",
    "\n",
    "# generate histogram: price\n",
    "data['price'].hist(ax=ax[0,1], xrot=0)\n",
    "ax[0,1].set_title('Figure 3', pad=10.0)\n",
    "ax[0,1].set_xlabel('price', labelpad=4.0)\n",
    "ax[0,1].set_ylabel('Number of cars', labelpad=4.0)\n",
    "\n",
    "# generate histogram: mileage\n",
    "data['mileage'].hist(ax=ax[1,0], xrot=0)\n",
    "ax[1,0].set_title('Figure 4', pad=10.0)\n",
    "ax[1,0].set_xlabel('mileage', labelpad=4.0)\n",
    "ax[1,0].set_ylabel('Number of cars', labelpad=4.0)\n",
    "\n",
    "# generate histogram: tax\n",
    "data['tax'].hist(ax=ax[1,1])\n",
    "ax[1,1].set_title('Figure 5', pad=10.0)\n",
    "ax[1,1].set_xlabel('tax', labelpad=4.0)\n",
    "ax[1,1].set_ylabel('Number of cars', labelpad=4.0)\n",
    "\n",
    "# generate histogram: mpg\n",
    "data['mpg'].hist(ax=ax[2,0])\n",
    "ax[2,0].set_title('Figure 6', pad=10.0)\n",
    "ax[2,0].set_xlabel('mpg', labelpad=4.0)\n",
    "ax[2,0].set_ylabel('Number of cars', labelpad=4.0)\n",
    "\n",
    "# generate histogram: engineSize\n",
    "data['engineSize'].hist(ax=ax[2,1])\n",
    "ax[2,1].set_title('Figure 7', pad=10.0)\n",
    "ax[2,1].set_xlabel('engineSize', labelpad=4.0)\n",
    "ax[2,1].set_ylabel('Number of cars', labelpad=4.0)\n",
    "\n",
    "# adjust subplot spacing\n",
    "fig.tight_layout(pad=2.0)\n",
    "\n",
    "# display subplots\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "119242dc-5f9d-4b44-949b-b700c8293147",
   "metadata": {},
   "source": [
    "Due to the differing scales of the numeric variables, each numeric predictor variable will be normalized, i.e., rescaled to a range of 0 to 1. This preprocessing technique is important because some machine learning algorithms (e.g., linear regression, k-nearest neighbors, support vector machine) perform better when numeric variables are on same scale.\n",
    "\n",
    "## 3.2 Categorical Variables\n",
    "\n",
    "The categorical variables were examined to explore the proportion of cars in each category. Figures 8, 9, and 10 shows the proportion of cars in the database for each model, transmission type, and fuel type, respectively. Yaris is the most popular model (32%), followed closely by Aygo (29%) (Figure 8). The majority (57%) of cars have manual transmissions, although a large percentage have automatic transmissions (40%) (Figure 9). Most cars have petrol fuel types (61%), followed by hybrid (30%), diesel (7%), and other (2%) (Figure 10)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "eefe3ee1-b4dc-4984-a1ce-50ddd199d26f",
   "metadata": {
    "executionCancelledAt": null,
    "executionTime": 52,
    "lastExecutedAt": 1711728536053,
    "lastScheduledRunId": null,
    "lastSuccessfullyExecutedCode": "# display proportion of cars by model, transmission, and fuelType\ndisplay(data['model'].value_counts(normalize=True).round(2))\ndisplay(data['transmission'].value_counts(normalize=True).round(2))\ndisplay(data['fuelType'].value_counts(normalize=True).round(2))",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "model\n",
       "Yaris            0.32\n",
       "Aygo             0.29\n",
       "Auris            0.11\n",
       "C-HR             0.07\n",
       "RAV4             0.07\n",
       "Corolla          0.04\n",
       "Prius            0.03\n",
       "Verso            0.02\n",
       "Avensis          0.02\n",
       "Hilux            0.01\n",
       "GT86             0.01\n",
       "Land Cruiser     0.01\n",
       "PROACE VERSO     0.00\n",
       "Supra            0.00\n",
       "Camry            0.00\n",
       "IQ               0.00\n",
       "Urban Cruiser    0.00\n",
       "Verso-S          0.00\n",
       "Name: proportion, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "transmission\n",
       "Manual       0.57\n",
       "Automatic    0.40\n",
       "Semi-Auto    0.04\n",
       "Other        0.00\n",
       "Name: proportion, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "fuelType\n",
       "Petrol    0.61\n",
       "Hybrid    0.30\n",
       "Diesel    0.07\n",
       "Other     0.02\n",
       "Name: proportion, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# display proportion of cars by model, transmission, and fuelType\n",
    "display(data['model'].value_counts(normalize=True).round(2))\n",
    "display(data['transmission'].value_counts(normalize=True).round(2))\n",
    "display(data['fuelType'].value_counts(normalize=True).round(2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "96adcd00-f0a3-4ab6-9ed4-598ac7a9362c",
   "metadata": {
    "executionCancelledAt": null,
    "executionTime": 387,
    "lastExecutedAt": 1711728536440,
    "lastScheduledRunId": null,
    "lastSuccessfullyExecutedCode": "# set up figure with subplots\nfig, ax = plt.subplots(nrows=1, ncols=3, figsize=(15,3))\n\n# generate bar plot: model\ndata['model'].value_counts(normalize=True).plot(kind='bar', rot=90, ax=ax[0])\nax[0].set_title('Figure 8: Car models', pad=10.0)\nax[0].set_xlabel('Model', labelpad=4.0)\nax[0].set_ylabel('Proportion of cars', labelpad=4.0)\n\n# generate bar plot: transmission\ndata['transmission'].value_counts(normalize=True).plot(kind='bar', rot=0, ax=ax[1])\nax[1].set_title('Figure 9: Car transmission types', pad=10.0)\nax[1].set_xlabel('Transmission type', labelpad=4.0)\nax[1].set_ylabel('Proportion of cars', labelpad=4.0)\n\n# generate bar plot: fuelType\ndata['fuelType'].value_counts(normalize=True).plot(kind='bar', rot=0, ax=ax[2])\nax[2].set_title('Figure 10: Car fuel types', pad=10.0)\nax[2].set_xlabel('Fuel type', labelpad=4.0)\nax[2].set_ylabel('Proportion of cars', labelpad=4.0)\n\n# display subplots\nplt.show()"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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VygrHjsLtX7lyRQghRFpamjAwMBDDhw9XqJednS3s7e1Fr169XtpmYUOHDlVZLy8vT9SoUUMEBgYqlAcEBAhXV1fpeF7w+SrqPKldu3ZSmb+/v6hWrZq4f/++QpvDhg0TxsbG0veKzp07i8aNG7+076QZjmijMrFq1Sr88ccfCg9V94h5+PAhjh8/jq5duypcdlmhQgW1fyUpTo8ePRSe79ixAzKZDB9++KHCr1v29vZo1KjRS2cgS01NRZcuXWBtbY1NmzbhwIEDmDFjBmJjYxESEqJQt3///sjNzS3yMqPS2LhxI1q0aIEKFSrAwMAAhoaGWLZsGc6ePatU97333tPovnGNGzdG1apVpef16tUDAKVLsQrKC4Z9nz9/HtevX0e/fv2gp/ffoadChQro0aMHfvvtNzx69AgPHz7EH3/8ge7du8PY2FiqZ25urvSel+b9eueddwAAvXr1woYNG3Dt2jW19wHR2+BNPU4PHz4cFy5cwLBhw3Dt2jVcvXoVoaGh0rHqxeNTeT1OF6fw/iywaNEiNGnSBMbGxlJ/9u7dq7I/nTp1gr6+vvS8YDREwT60srKCq6srZs6ciejoaCQnJxc542pJY0pRbRkZGeHjjz/GypUrVV5i1bRpU9y7dw99+vTB1q1bVY6WKEzd+PWi999/X+G5u7s7njx5gps3b750faqYm5vjo48+QmxsrDT67pdffsGZM2cwbNgwpfp9+/ZVGHni5OQEHx8f7Nu3DwBw8eJFnDt3Dh988AEAKPwvdezYEenp6Th//jyA5/vsp59+wtixY7F//36t3GuQ6HXwpsY5TRU3Su3F1/bu3auTKz8ePHiAzz//HDVr1oSBgQEMDAxQoUIFPHz4UGUMKiqOaVtCQoL0HeDF98HY2BitWrXS6vugp6eHYcOGYceOHUhLSwPwfMTh7t27ERYWpvQeFXWedPDgQeTl5eHJkyfYu3cvunXrBlNTU6Vj/JMnT6RbPjRt2hQnT55EWFgYEhISkJWVpbXtepsx0UZlol69evDy8lJ4qHL37l0IIWBnZ6f0mqoyTTk4OCg8v3HjhrQ+Q0NDhcdvv/320i/kY8eORVZWFhISEtCjRw+0bNkSn332GWJiYrB8+XIcOHBA4z5Wr14dAHDlyhW16m/evBm9evVC1apVsWbNGhw9ehR//PEHBg0apDD8u0DhffAyVlZWCs8LvnAUVV6wzoJLTFStr0qVKsjPz8fdu3dx9+5d5Ofnw97eXqle4bLSvF8tW7bEli1bpABarVo1NGjQgPecIfp/b+pxetCgQZg2bRpWr16NatWqoXr16jhz5gxGjx4NAApJH3W9bsfp4qhqKzo6Gp988gmaNWuG+Ph4/Pbbb/jjjz/QoUMHlUkVa2trhecFl90W1JXJZNi7dy/8/f0xY8YMNGnSBLa2thgxYgSys7MVli1pTFHF1dUVP//8MypXroyhQ4fC1dUVrq6uCveZ6devH5YvX45//vkHPXr0QOXKldGsWTMkJiYW2a668etFL9tHJTF8+HBkZ2fjhx9+APD80qFq1aqpnMm2qBhasC0Fl56NHj1a6f8oLCwMAKT/pXnz5uHzzz/Hli1b0KZNG1hZWaFr165Kl7ESlTdvapxTV8FxStVl4Hfu3IFMJkPFihU1brd69eq4devWSy/JL9C3b1989913CAkJQUJCAo4dO4Y//vgDtra2Ko+Z2oyJxSk4Tr7zzjtK70NcXJzW3ocCgwYNgomJiXT58Pz582FiYqLy/tFFHeOfPn2KBw8e4Pbt28jNzcW3336r1PeOHTsC+O8YHxkZiVmzZuG3335DQEAArK2t0bZtW5X32ib1lc9ppuitUalSJchkMpX3IsjIyFB4XpDVL3xDzOLuIVL41wEbGxvIZDIcOnSo2Hv4FCUlJQVubm4K92ID/hs9derUKbRq1arYNgpr06YNDA0NsWXLFoSGhr60/po1a+Di4oK4uDiF7VN1o1Cg+F+xtKkgmKu60fj169ehp6eHSpUqQQgBmUym9P4Cyu95ad+vwMBABAYGIicnB7/99humTp2Kvn37wtnZGd7e3ppsHtFbq7wdpwHg888/x6hRo/D333/D3NwcTk5OGDJkCMzMzODp6fnS5QsrT8dpVW2tWbMGrVu3xsKFCxXKCyfFNOHk5IRly5YBAC5cuIANGzYgKioKT58+lU4idMHX1xe+vr7Iy8vD8ePH8e2332LUqFGws7ND7969AQAfffQRPvroIzx8+BAHDx7ExIkT0blzZ1y4cAFOTk5Kbaobv3StZs2aCAgIwPz58xEQEIBt27Zh0qRJCqMLCxQVQwu2xcbGBsDzE6zu3burXF+dOnUAAGZmZpg0aRImTZqEGzduSKPbunTpgnPnzmlr84heW+UxzqnD1dUVJiYmCvcmLfDXX3+hZs2aCqOm1OXv7489e/Zg+/bt0nG3KPfv38eOHTswceJEjB07ViovuJepKqWNiS++Ry/uy8KJs4Lj5KZNm1TGBm2ztLTEgAEDsHTpUowePRorVqxA3759VSY7izrGGxkZoUKFCjA0NIS+vj769eundH+3Ai4uLgAAAwMDREREICIiAvfu3cPPP/+McePGwd/fH1evXi31LLhvK45oo9eamZkZvLy8sGXLFulGxsDzIcYFs5IVsLOzg7GxMf7880+FclWzuBWlc+fOEELg2rVrSr9weXl5oWHDhsUuX6VKFZw+fRoPHjxQKC+4MWVRM+8Ux97eXvqFR9VNsoHnQ4sLtlsmk8HIyEghCGVkZGi0H3ShTp06qFq1KtauXaswo87Dhw8RHx8vzeRWMLvc5s2bFUYuZGdnY/v27Qptlvb9KiCXy9GqVStpCvOSzCRE9LYqb8fpAnK5HA0aNICTkxPS0tIQFxeHwYMHw8TERO2+FHidjtNyuVzjUVMymUzpxO3PP/8s0Y2jValduza++OILNGzYECdOnNBKmy+jr6+PZs2aSbOrqVqvmZkZAgICMH78eDx9+hSnT59W2Za68UsbXvb+jRw5En/++ScGDBgAfX19DB48WGW9devWKfT1n3/+wZEjR9C6dWtpm2rVqoWTJ0+q/D/y8vKCubm5Urt2dnYYOHAg+vTpg/PnzytdMkv0Jiqvce5lDAwM0KVLF2zevFnhh5W0tDTs27evyCT8ywQHB8Pe3h5jxowp8tYsmzdvBgBpZuXCMWjp0qXIy8sr0fpfpmA27sLvUeHzDH9/fxgYGODSpUtFHic19bLRzSNGjEBmZiZ69uyJe/fuqbw1AIAiz5N8fX2hr68PU1NTtGnTBsnJyXB3d1fZ98IjrwGgYsWK6NmzJ4YOHYo7d+4ozcBK6uOINnrtTZ48GZ06dYK/vz9GjhyJvLw8zJw5ExUqVFD4paPgXgbLly+Hq6srGjVqhGPHjmHt2rVqr6tFixb4+OOP8dFHH+H48eNo2bIlzMzMkJ6ejsOHD6Nhw4b45JNPilx+1KhR6Nq1K/z8/BAeHg4bGxtppJSbmxsCAgKkuqtWrcKgQYOwfPnyl97/Jzo6GpcvX8bAgQORkJCAbt26wc7ODpmZmUhMTMSKFSuwfv16uLu7o3Pnzti8eTPCwsLQs2dPXL16FV999RUcHBzK9DIPPT09zJgxAx988AE6d+6MIUOGICcnBzNnzsS9e/cwbdo0qe5XX32FDh06wM/PD59++iny8vIwffp0mJmZKbznpXm/vvzyS/z7779o27YtqlWrhnv37mHu3LkwNDTUeNQh0duuPB2nT506hfj4eHh5eUEul+PkyZOYNm0aatWqha+++kqhbnk8Tjds2BDr169HXFwcatSoAWNj45eelHXu3BlfffUVJk6ciFatWuH8+fOYPHkyXFxcSnQ/nj///BPDhg3D//73P9SqVQtGRkb45Zdf8OeffyqMWNC2RYsW4ZdffkGnTp1QvXp1PHnyBMuXLwcAtGvXDgCkZGqLFi3g4OCAjIwMTJ06FZaWltLo88I0iV+l1bBhQ2zevBkLFy6Ep6cn9PT0FE7m/Pz84Obmhn379uHDDz9E5cqVVbZz8+ZNdOvWDYMHD8b9+/cxceJEGBsbIzIyUqqzePFiBAQEwN/fHwMHDkTVqlVx584dnD17FidOnMDGjRsBAM2aNUPnzp3h7u6OSpUq4ezZs1i9erVWE4xEr7vyFOeA57NVPnz4UEqgnTlzBps2bQLwfFbogv/dSZMm4Z133kHnzp0xduxYPHnyBF9++SVsbGzw6aefKrTZtm1bHDhw4KVxwdLSElu3bkXnzp3h4eGBYcOGwdvbG0ZGRvj777+xZs0anDx5Et27d4eFhQVatmyJmTNnwsbGBs7Ozjhw4ACWLVtWostW1dGxY0dYWVkhODgYkydPhoGBAWJjY3H16lWFes7Ozpg8eTLGjx+Py5cvo0OHDqhUqRJu3LiBY8eOSaN9NVEQj6dPn46AgADo6+vD3d1duj1C7dq10aFDB/z0009499130ahRI5Xt6Ovrw8/PDxEREcjPz8f06dORlZWl0J+5c+fi3Xffha+vLz755BM4OzsjOzsbFy9exPbt2/HLL78AALp06YIGDRrAy8sLtra2+OeffxATEwMnJyfUqlVLo+2jF7z6+RfobVYwm8sff/yh8vWiZur58ccfRcOGDYWRkZGoXr26mDZtmhgxYoSoVKmSQr379++LkJAQYWdnJ8zMzESXLl1EampqkbP83Lp1S2U/li9fLpo1aybMzMyEiYmJcHV1Ff379xfHjx9/6Tb+8ssvon379sLe3l6YmJiI2rVri08//VRkZmaq3BeqZiVSJTc3V6xcuVK89957wsrKShgYGAhbW1sREBAg1q5dqzAT2rRp04Szs7OQy+WiXr164vvvv1c5Gw5UzMRTHCcnJ9GpUyelclXtFLyXM2fOVCjfsmWLaNasmTA2NhZmZmaibdu24tdff1Vqc9u2bcLd3V3hPS9qRh913q/Cs47u2LFDBAQEiKpVqwojIyNRuXJl0bFjR3Ho0CG19wfRm+hNP06fP39etGzZUlhZWQkjIyNRs2ZN8cUXX4gHDx4UuS/K03E6NTVVtG/fXpibmwsA0nGvYDbKjRs3Ki2Tk5MjRo8eLapWrSqMjY1FkyZNxJYtW5SOm0Ud1wv6WfD+3bhxQwwcOFDUrVtXmJmZiQoVKgh3d3cxZ84chdlKSxtTCu+vo0ePim7dugknJychl8uFtbW1aNWqlcJMpStXrhRt2rQRdnZ2wsjISFSpUkX06tVL/Pnnn1KdwrOOFlAnfhX1uS08m11R7ty5I3r27CkqVqwoZDKZypgXFRUlAIjffvtN6bWCvq9evVqMGDFC2NraCrlcLnx9fVX+b5w8eVL06tVLVK5cWRgaGgp7e3vx3nvviUWLFkl1xo4dK7y8vESlSpWEXC4XNWrUEOHh4Urfa4jKizc9zgnx/PiKImZWLXwcOn78uGjbtq0wNTUVFhYWomvXruLixYtKbbZq1UqtmTULZGRkiM8//1zUr19fmJqaCrlcLmrWrCmGDBki/vrrL6nev//+K3r06CEqVaokzM3NRYcOHcSpU6eEk5OTGDBggFTvZe+bKkXF0GPHjgkfHx9hZmYmqlatKiZOnCiWLl2qcv9s2bJFtGnTRlhYWAi5XC6cnJxEz549xc8//yzVUXfW0ZycHBESEiJsbW2lY3zh9cXGxgoAYv369UrLF3w2p0+fLiZNmiSqVasmjIyMhIeHh0hISFBZf9CgQaJq1arC0NBQ2NraCh8fH/H1119LdWbPni18fHyEjY2N9NkODg4WqampL90eKppMiBfGlROVE8+ePZNmKtuzZ09Zd4eIiArhcZpIN7y8vCCTyfDHH38ovbZ//360adMGGzduRM+ePcugd0RvD8Y50oWC2axTU1NhaGio8FpqaipcXFwwc+ZMaRInej3x0lEqF4KDg+Hn5ydd5rFo0SKcPXtWYSYxIiIqOzxOE+lOVlYWTp06hR07diApKQk//vhjWXeJ6K3DOEe6kpOTgxMnTuDYsWP48ccfER0drZRko/KFiTYqF7KzszF69GjcunULhoaGaNKkCXbt2iXdc4WIiMoWj9NEunPixAm0adMG1tbWmDhxIrp27VrWXSJ66zDOka6kp6fDx8cHFhYWGDJkCIYPH17WXaJS4qWjREREREREREREWqBX1h0gIiIiIiIiIiJ6EzDRRkREREREREREpAVMtBEREREREREREWkBE21ERERERERERERawEQbERERERERERGRFjDRRkREREREREREpAVMtBEREREREREREWkBE21ERERERERERERawEQbERERERERERGRFhiUdQdeR/n5+bh+/TrMzc0hk8nKujtEROWeEALZ2dmoUqUK9PT4Gw/AWENEpE2MM8oYZ4iItEvdWMNEmwrXr1+Ho6NjWXeDiOiNc/XqVVSrVq2su/FaYKwhItI+xpn/MM4QEenGy2INE20qmJubA3i+8ywsLMq4N0RE5V9WVhYcHR2l4ysx1hARaRPjjDLGGSIi7VI31jDRpkLB0GoLCwsGJSIiLeKlK/9hrCEi0j7Gmf8wzhAR6cbLYg1vYEBERERERERERKQFTLQRERERERERERFpARNtREREREREREREWsBEGxERERERERERkRYw0UZERERERERERKQFTLQRERERERHp2IIFC+Di4gJjY2N4enri0KFDxdbPycnB+PHj4eTkBLlcDldXVyxfvvwV9ZaIiErKoKw7QERERERE9CaLi4vDqFGjsGDBArRo0QKLFy9GQEAAzpw5g+rVq6tcplevXrhx4waWLVuGmjVr4ubNm8jNzX3FPSciIk0x0UZERERERKRD0dHRCA4ORkhICAAgJiYGCQkJWLhwIaZOnapUf/fu3Thw4AAuX74MKysrAICzs/Or7DIREZUQE20acB67U616qdM66bgnREREqqkbq14HjJdE9DZ4+vQpkpKSMHbsWIXy9u3b48iRIyqX2bZtG7y8vDBjxgysXr0aZmZmeP/99/HVV1/BxMTkVXS7SIwzRETFY6KNiIiIiIhIRzIzM5GXlwc7OzuFcjs7O2RkZKhc5vLlyzh8+DCMjY3x448/IjMzE2FhYbhz506R92nLyclBTk6O9DwrK0t7G0FERGrjZAhEREREREQ6JpPJFJ4LIZTKCuTn50Mmk+GHH35A06ZN0bFjR0RHRyM2NhaPHz9WuczUqVNhaWkpPRwdHbW+DURE9HJMtBEREREREemIjY0N9PX1lUav3bx5U2mUWwEHBwdUrVoVlpaWUlm9evUghMC///6rcpnIyEjcv39fely9elV7G0FERGpjoo2IiIiIiEhHjIyM4OnpicTERIXyxMRE+Pj4qFymRYsWuH79Oh48eCCVXbhwAXp6eqhWrZrKZeRyOSwsLBQeRET06jHRRkREREREpEMRERFYunQpli9fjrNnzyI8PBxpaWkIDQ0F8Hw0Wv/+/aX6ffv2hbW1NT766COcOXMGBw8exGeffYZBgwaV+WQIRERUPE6GQEREREREpENBQUG4ffs2Jk+ejPT0dDRo0AC7du2Ck5MTACA9PR1paWlS/QoVKiAxMRHDhw+Hl5cXrK2t0atXL3z99ddltQlERKSmMh/RtmDBAri4uMDY2Bienp44dOhQkXUPHz6MFi1awNraGiYmJqhbty7mzJmjVC8+Ph5ubm6Qy+Vwc3PDjz/+qMtNICIiIiIiKlZYWBhSU1ORk5ODpKQktGzZUnotNjYW+/fvV6hft25dJCYm4tGjR7h69Spmz57N0WxEROVAmSba4uLiMGrUKIwfPx7Jycnw9fVFQECAwq85LzIzM8OwYcNw8OBBnD17Fl988QW++OILLFmyRKpz9OhRBAUFoV+/fjh58iT69euHXr164ffff39Vm0VERERERERERG+hMk20RUdHIzg4GCEhIahXrx5iYmLg6OiIhQsXqqzv4eGBPn36oH79+nB2dsaHH34If39/hVFwMTEx8PPzQ2RkJOrWrYvIyEi0bdsWMTExr2iriIiIiIiIiIjobVRmibanT58iKSkJ7du3Vyhv3749jhw5olYbycnJOHLkCFq1aiWVHT16VKlNf3//YtvMyclBVlaWwoOIiIiIiIiIiEgTZZZoy8zMRF5eHuzs7BTK7ezskJGRUeyy1apVg1wuh5eXF4YOHYqQkBDptYyMDI3bnDp1KiwtLaWHo6NjCbaIiIiIiIiIiIjeZmU+GYJMJlN4LoRQKivs0KFDOH78OBYtWoSYmBisW7euVG1GRkbi/v370uPq1asabgUREREREREREb3tDMpqxTY2NtDX11caaXbz5k2lEWmFubi4AAAaNmyIGzduICoqCn369AEA2Nvba9ymXC6HXC4vyWYQEREREREREREBKMMRbUZGRvD09ERiYqJCeWJiInx8fNRuRwiBnJwc6bm3t7dSm3v27NGoTSIiIiIiIiIiIk2V2Yg2AIiIiEC/fv3g5eUFb29vLFmyBGlpaQgNDQXw/JLOa9euYdWqVQCA+fPno3r16qhbty4A4PDhw5g1axaGDx8utTly5Ei0bNkS06dPR2BgILZu3Yqff/4Zhw8ffvUbSEREREREREREb40yTbQFBQXh9u3bmDx5MtLT09GgQQPs2rULTk5OAID09HSkpaVJ9fPz8xEZGYkrV67AwMAArq6umDZtGoYMGSLV8fHxwfr16/HFF19gwoQJcHV1RVxcHJo1a/bKt4+IiIiIiIiIiN4eZZpoA4CwsDCEhYWpfC02Nlbh+fDhwxVGrxWlZ8+e6Nmzpza6R0REREREREREpJYyT7S9rZzH7lSrXuq0TjruCRERERERERERaUOZTYZARERERERERET0JmGijYiIiIiIiIiISAuYaCMiIgKwYMECuLi4wNjYGJ6enjh06FCRdffv3w+ZTKb0OHfu3CvsMRERERERvW6YaCMiordeXFwcRo0ahfHjxyM5ORm+vr4ICAhQmPlalfPnzyM9PV161KpV6xX1mIiIiIiIXkdMtBER0VsvOjoawcHBCAkJQb169RATEwNHR0csXLiw2OUqV64Me3t76aGvr/+KekxERERERK8jJtqIiOit9vTpUyQlJaF9+/YK5e3bt8eRI0eKXdbDwwMODg5o27Yt9u3bV2zdnJwcZGVlKTyIiIiIiOjNwkQbERG91TIzM5GXlwc7OzuFcjs7O2RkZKhcxsHBAUuWLEF8fDw2b96MOnXqoG3btjh48GCR65k6dSosLS2lh6Ojo1a3g4iIiIiIyp5BWXeAiIjodSCTyRSeCyGUygrUqVMHderUkZ57e3vj6tWrmDVrFlq2bKlymcjISEREREjPs7KymGwjIiIiInrDcEQbERG91WxsbKCvr680eu3mzZtKo9yK07x5c/z9999Fvi6Xy2FhYaHwICIiIiKiNwsTbURE9FYzMjKCp6cnEhMTFcoTExPh4+OjdjvJyclwcHDQdveIiIiIiKgc4aWjRET01ouIiEC/fv3g5eUFb29vLFmyBGlpaQgNDQXw/LLPa9euYdWqVQCAmJgYODs7o379+nj69CnWrFmD+Ph4xMfHl+VmEBERERFRGWOijYiI3npBQUG4ffs2Jk+ejPT0dDRo0AC7du2Ck5MTACA9PR1paWlS/adPn2L06NG4du0aTExMUL9+fezcuRMdO3Ysq00gIiIiIqLXABNtREREAMLCwhAWFqbytdjYWIXnY8aMwZgxY15Br4iIiIiIqDzhPdqIiIiIiIiIiIi0gIk2IiIiIiIiIiIiLWCijYiIiIiISMcWLFgAFxcXGBsbw9PTE4cOHSqy7v79+yGTyZQe586de4U9JiKikmCijYiIiIiISIfi4uIwatQojB8/HsnJyfD19UVAQIDCRDuqnD9/Hunp6dKjVq1ar6jHRERUUky0ERERERER6VB0dDSCg4MREhKCevXqISYmBo6Ojli4cGGxy1WuXBn29vbSQ19f/xX1mIiISoqJNiIiIiIiIh15+vQpkpKS0L59e4Xy9u3b48iRI8Uu6+HhAQcHB7Rt2xb79u0rtm5OTg6ysrIUHkRE9Oox0UZERERERKQjmZmZyMvLg52dnUK5nZ0dMjIyVC7j4OCAJUuWID4+Hps3b0adOnXQtm1bHDx4sMj1TJ06FZaWltLD0dFRq9tBRETqMSjrDhAREREREb3pZDKZwnMhhFJZgTp16qBOnTrSc29vb1y9ehWzZs1Cy5YtVS4TGRmJiIgI6XlWVhaTbUREZYAj2oiIiIiIiHTExsYG+vr6SqPXbt68qTTKrTjNmzfH33//XeTrcrkcFhYWCg8iInr1yjzRpsk015s3b4afnx9sbW1hYWEBb29vJCQkKNSJjY1VORX2kydPdL0pRERERERECoyMjODp6YnExESF8sTERPj4+KjdTnJyMhwcHLTdPSIi0rIyvXS0YJrrBQsWoEWLFli8eDECAgJw5swZVK9eXan+wYMH4efnhylTpqBixYpYsWIFunTpgt9//x0eHh5SPQsLC5w/f15hWWNjY51vDxERERERUWERERHo168fvLy84O3tjSVLliAtLQ2hoaEAnl/2ee3aNaxatQoAEBMTA2dnZ9SvXx9Pnz7FmjVrEB8fj/j4+LLcDCIiUkOZJtpenOYaeB5QEhISsHDhQkydOlWpfkxMjMLzKVOmYOvWrdi+fbtCok0mk8He3l6nfSciIiIiIlJHUFAQbt++jcmTJyM9PR0NGjTArl274OTkBABIT09HWlqaVP/p06cYPXo0rl27BhMTE9SvXx87d+5Ex44dy2oTiIhITRpfOnrixAn89ddf0vOtW7eia9euGDduHJ4+fap2O6WZ5rpAfn4+srOzYWVlpVD+4MEDODk5oVq1aujcuTOSk5PV7hcREb3+tBWLiIiIVNFFnAkLC0NqaipycnKQlJSkMKlBbGws9u/fLz0fM2YMLl68iMePH+POnTs4dOgQk2xEROWExom2IUOG4MKFCwCAy5cvo3fv3jA1NcXGjRsxZswYtdspyTTXhc2ePRsPHz5Er169pLK6desiNjYW27Ztw7p162BsbIwWLVoUe+PQnJwcZGVlKTyIiOj1pa1YREREpArjDBERlZTGibYLFy6gcePGAICNGzeiZcuWWLt2LWJjY0t0zwBNprl+0bp16xAVFYW4uDhUrlxZKm/evDk+/PBDNGrUCL6+vtiwYQNq166Nb7/9tsi2pk6dCktLS+nBabCJiF5v2o5FREREL2KcISKiktI40SaEQH5+PgDg559/loYwOzo6IjMzU+12SjPNdVxcHIKDg7Fhwwa0a9eu2Lp6enp45513ih3RFhkZifv370uPq1evqr0dRET06mkrFhEREanCOENERCWlcaLNy8sLX3/9NVavXo0DBw6gU6dOAIArV668NEH2opJOc71u3ToMHDgQa9euldZdHCEEUlJSip0KWy6Xw8LCQuFBRESvL23FIiIiIlUYZ4iIqKQ0nnU0JiYGH3zwAbZs2YLx48ejZs2aAIBNmzYVmyBTRdNprtetW4f+/ftj7ty5aN68uTQazsTEBJaWlgCASZMmoXnz5qhVqxaysrIwb948pKSkYP78+ZpuKhERvaa0GYuIiIgKY5whIqKS0ijRlpeXh7t37+LAgQNKM33OnDkT+vr6Gq1c02muFy9ejNzcXAwdOhRDhw6VygcMGIDY2FgAwL179/Dxxx8jIyMDlpaW8PDwwMGDB9G0aVON+kZERK8nbcciIiKiFzHOEBFRaWiUaNPX14e/vz/Onj2rFHSMjY1L1IGwsDCEhYWpfK0geVbgxSmvizJnzhzMmTOnRH0hIqLXny5iERERUQHGGSIiKg2N79HWsGFDXL58WRd9ISIiUgtjERER6RLjDBERlZTGibZvvvkGo0ePxo4dO5Ceno6srCyFBxERka4xFhERkS4xzhARUUlpPBlChw4dAADvv/8+ZDKZVC6EgEwmQ15envZ6R0REpAJjERER6RLjDBERlZTGibZ9+/bpoh9ERERqYywiIiJdYpwhIqKS0jjR1qpVK130g4iISG2MRUREpEuMM0REVFIaJ9oKPHr0CGlpaXj69KlCubu7e6k7RUREpA7GIiIi0iXGGSIi0pTGibZbt27ho48+wk8//aTydd6vgIiIdI2xiIiIdIlxhoiISkrjWUdHjRqFu3fv4rfffoOJiQl2796NlStXolatWti2bZsu+khERKSAsYiIiHSJcYaIiEpK40TbL7/8gjlz5uCdd96Bnp4enJyc8OGHH2LGjBmYOnWqLvpIRESkQBexaMGCBXBxcYGxsTE8PT1x6NAhtZb79ddfYWBggMaNG5dovURE9PrhOQ8REZWUxom2hw8fonLlygAAKysr3Lp1CwDQsGFDnDhxQru9IyIiUkHbsSguLg6jRo3C+PHjkZycDF9fXwQEBCAtLa3Y5e7fv4/+/fujbdu2mm8EERG9tnjOQ0REJaVxoq1OnTo4f/48AKBx48ZYvHgxrl27hkWLFsHBwUHrHSQiIipM27EoOjoawcHBCAkJQb169RATEwNHR0csXLiw2OWGDBmCvn37wtvbu0TbQUREryee8xARUUlpPBnCqFGjkJ6eDgCYOHEi/P398cMPP8DIyAixsbHa7h8REZESbcaip0+fIikpCWPHjlUob9++PY4cOVLkcitWrMClS5ewZs0afP311y9dT05ODnJycqTnWVlZGvWTiIheHZ7zEBFRSWmcaPvggw+kvz08PJCamopz586hevXqsLGx0WrniIiIVNFmLMrMzEReXh7s7OwUyu3s7JCRkaFymb///htjx47FoUOHYGCgXiidOnUqJk2apFHfiIiobPCch4iISkrjS0cLMzU1RZMmTRhwiIiozGgjFslkMoXnQgilMgDIy8tD3759MWnSJNSuXVvt9iMjI3H//n3pcfXq1RL3lYiIXi2e8xARkbo0HtHWs2dPeHl5KV1iM3PmTBw7dgwbN27UWueIiIhU0WYssrGxgb6+vtLotZs3byqNcgOA7OxsHD9+HMnJyRg2bBgAID8/H0IIGBgYYM+ePXjvvfeUlpPL5ZDL5Wr3i14fzmN3lnUX1JY6rVNZd4HojcBzHiIiKimNR7QdOHAAnTopf4nr0KEDDh48qJVOERERFUebscjIyAienp5ITExUKE9MTISPj49SfQsLC/z1119ISUmRHqGhoahTpw5SUlLQrFkzzTaGiIheOzznISKiktJ4RNuDBw9gZGSkVG5oaMgbOxMR0Suh7VgUERGBfv36wcvLC97e3liyZAnS0tIQGhoK4Plln9euXcOqVaugp6eHBg0aKCxfuXJlGBsbK5UTEVH5xHMeIiIqKY1HtDVo0ABxcXFK5evXr4ebm5tWOkVERFQcbceioKAgxMTEYPLkyWjcuDEOHjyIXbt2wcnJCQCQnp6OtLS0UvebiIjKB57zEBFRSWk8om3ChAno0aMHLl26JN2DZu/evVi3bh3vVUBERK+ELmJRWFgYwsLCVL4WGxtb7LJRUVGIiooq0XqJiOj1o4s4s2DBAsycORPp6emoX78+YmJi4Ovr+9Llfv31V7Rq1QoNGjRASkpKidZNRESvjsaJtvfffx9btmzBlClTsGnTJpiYmMDd3R0///wzWrVqpYs+EhERKWAsIiIiXdJ2nImLi8OoUaOwYMECtGjRAosXL0ZAQADOnDmD6tWrF7nc/fv30b9/f7Rt2xY3btwozSYREdEronGiDQA6deqk8uagRERErwpjERER6ZI240x0dDSCg4MREhICAIiJiUFCQgIWLlyIqVOnFrnckCFD0LdvX+jr62PLli1a6QsREemWxvdoIyIiIiIiIvU8ffoUSUlJaN++vUJ5+/btceTIkSKXW7FiBS5duoSJEyeqtZ6cnBxkZWUpPIiI6NVjoo2IiIiIiEhHMjMzkZeXBzs7O4VyOzs7ZGRkqFzm77//xtixY/HDDz/AwEC9i5CmTp0KS0tL6eHo6FjqvhMRkeaYaCMiIiIiItIxmUym8FwIoVQGAHl5eejbty8mTZqE2rVrq91+ZGQk7t+/Lz2uXr1a6j4TEZHm1Eq06XLY8YIFC+Di4gJjY2N4enri0KFDRdbdvHkz/Pz8YGtrCwsLC3h7eyMhIUGpXnx8PNzc3CCXy+Hm5oYff/xRZ/0nIqJXg5fAEBGRLukqztjY2EBfX19p9NrNmzeVRrkBQHZ2No4fP45hw4bBwMAABgYGmDx5Mk6ePAkDAwP88ssvKtcjl8thYWGh8CAioldPrURbpUqVcPPmTQDAe++9h3v37mll5QWz74wfPx7Jycnw9fVFQEAA0tLSVNY/ePAg/Pz8sGvXLiQlJaFNmzbo0qULkpOTpTpHjx5FUFAQ+vXrh5MnT6Jfv37o1asXfv/9d630mYiIyoauYhERERGguzhjZGQET09PJCYmKpQnJibCx8dHqb6FhQX++usvpKSkSI/Q0FDUqVMHKSkpaNasmVb6RUREuqHWBf8VKlTA7du3UblyZezfvx/Pnj3Tyso1nX0nJiZG4fmUKVOwdetWbN++HR4eHlIdPz8/REZGAng+hPrAgQOIiYnBunXrtNJvIiJ69XQVi4iIiADdxpmIiAj069cPXl5e8Pb2xpIlS5CWlobQ0FAAz89Zrl27hlWrVkFPTw8NGjRQWL5y5cowNjZWKiciotePWom2du3aoU2bNqhXrx4AoFu3bjAyMlJZt6ihzIUVzL4zduxYhfKXzb7zovz8fGRnZ8PKykoqO3r0KMLDwxXq+fv7KyXpXpSTk4OcnBzpOS9PIiJ6/egiFhERERXQZZwJCgrC7du3MXnyZKSnp6NBgwbYtWsXnJycAADp6elFXtVDRETli1qJtjVr1mDlypW4dOkSDhw4gPr168PU1LRUKy7J7DuFzZ49Gw8fPkSvXr2ksoyMDI3bnDp1KiZNmqRB74mI6FXTRSwiIiIqoOs4ExYWhrCwMJWvxcbGFrtsVFQUoqKitNYXIiLSHbUSbSYmJtKw5uPHj2P69OmoWLGiVjqg7uw7ha1btw5RUVHYunUrKleuXKo2IyMjERERIT3PysridNhERK8ZXcYiIiIixhkiItIGtRJtL9q3b5/0txACgHJiSx2azr7zori4OAQHB2Pjxo1o166dwmv29vYatymXyyGXyzXcAiIiKivaikVERESqMM4QEVFJqTXraGGrVq1Cw4YNYWJiAhMTE7i7u2P16tUataHp7DsF1q1bh4EDB2Lt2rXo1KmT0uve3t5Kbe7Zs6fYNomIqPzRRiwiIiIqCuMMERGVhMYj2qKjozFhwgQMGzYMLVq0gBACv/76K0JDQ5GZmak0EUFxNJl9B3ieZOvfvz/mzp2L5s2bSyPXTExMYGlpCQAYOXIkWrZsienTpyMwMBBbt27Fzz//jMOHD2u6qURE9JrSZiwiIiIqjHGGiIhKSuNE27fffouFCxeif//+UllgYCDq16+PqKgojYKOprPvLF68GLm5uRg6dCiGDh0qlQ8YMEC6gaiPjw/Wr1+PL774AhMmTICrqyvi4uLQrFkzTTeViIheU9qMRURERIUxzhARUUlpnGhLT09XeRmmj48P0tPTNe6AJrPv7N+/X602e/bsiZ49e2rcFyIiKh+0HYuIiIhexDhDREQlpfE92mrWrIkNGzYolcfFxaFWrVpa6RQREVFxGIuIiEiXGGeIiKikNB7RNmnSJAQFBeHgwYNo0aIFZDIZDh8+jL1796oMRkRERNrGWERERLrEOENERCWl8Yi2Hj164Pfff4eNjQ22bNmCzZs3w8bGBseOHUO3bt100UciIiIFjEVERKRLjDNERFRSGo9oAwBPT0+sWbNG230hIiJSG2MRERHpEuMMERGVhMYj2oiIiIiIiIiIiEgZE21ERERERERERERawEQbERERERERERGRFjDRRkREREREREREpAVMtBEREREREREREWmBxrOOPnz4ENOmTcPevXtx8+ZN5OfnK7x++fJlrXWOiIhIFcYiIiLSJcYZIiIqKY0TbSEhIThw4AD69esHBwcHyGQyXfSLiIioSIxFRESkS4wzRERUUhon2n766Sfs3LkTLVq00EV/iIiIXoqxiIiIdIlxhoiISkrje7RVqlQJVlZWuugLERGRWnQRixYsWAAXFxcYGxvD09MThw4dKrLu4cOH0aJFC1hbW8PExAR169bFnDlztNofIiIqOzznISKiktI40fbVV1/hyy+/xKNHj3TRHyIiopfSdiyKi4vDqFGjMH78eCQnJ8PX1xcBAQFIS0tTWd/MzAzDhg3DwYMHcfbsWXzxxRf44osvsGTJEq30h4iIyhbPeYiIqKQ0vnR09uzZuHTpEuzs7ODs7AxDQ0OF10+cOKG1zhEREami7VgUHR2N4OBghISEAABiYmKQkJCAhQsXYurUqUr1PTw84OHhIT13dnbG5s2bcejQIXz88ccl2CIiInqd8JyHiIhKSuNEW9euXXXQDSIiIvVpMxY9ffoUSUlJGDt2rEJ5+/btceTIEbXaSE5OxpEjR/D1118XWScnJwc5OTnS86ysrJJ1mIiIdI7nPEREVFIaJ9omTpyoi34QERGpTZuxKDMzE3l5ebCzs1Mot7OzQ0ZGRrHLVqtWDbdu3UJubi6ioqKkEXGqTJ06FZMmTdJKn4mISLd4zkNERCWlcaKtQFJSEs6ePQuZTAY3NzeFS2iIiIheBW3GIplMpvBcCKFUVtihQ4fw4MED/Pbbbxg7dixq1qyJPn36qKwbGRmJiIgI6XlWVhYcHR1L3F8iItI9nvMQEZGmNE603bx5E71798b+/ftRsWJFCCFw//59tGnTBuvXr4etra0u+klERCTRZiyysbGBvr6+0ui1mzdvKo1yK8zFxQUA0LBhQ9y4cQNRUVFFJtrkcjnkcrna/SIiorLDcx4iIiopjWcdHT58OLKysnD69GncuXMHd+/exalTp5CVlYURI0booo9EREQKtBmLjIyM4OnpicTERIXyxMRE+Pj4qN2OEELhHmxERFR+6eKcZ8GCBXBxcYGxsTE8PT1x6NChIusePnwYLVq0gLW1NUxMTFC3bl3MmTOnpJtDRESvkMYj2nbv3o2ff/4Z9erVk8rc3Nwwf/58tG/fXqudIyIiUkXbsSgiIgL9+vWDl5cXvL29sWTJEqSlpSE0NBTA88s+r127hlWrVgEA5s+fj+rVq6Nu3boAnp8QzZo1C8OHD9fC1hERUVnTdpyJi4vDqFGjsGDBArRo0QKLFy9GQEAAzpw5g+rVqyvVNzMzw7Bhw+Du7g4zMzMcPnwYQ4YMgZmZGWe3JiJ6zWmcaMvPz1ea3hoADA0NkZ+fr5VOERERFUfbsSgoKAi3b9/G5MmTkZ6ejgYNGmDXrl1wcnICAKSnpyMtLU1h/ZGRkbhy5QoMDAzg6uqKadOmYciQISXfKCIiem1oO85ER0cjODhYmjQnJiYGCQkJWLhwIaZOnapU38PDQ+F+cM7Ozti8eTMOHTrERBsR0WtO40Tbe++9h5EjR2LdunWoUqUKAODatWsIDw9H27Zttd5BIiKiwnQRi8LCwhAWFqbytdjYWIXnw4cP5+g1IqI3mDbjzNOnT5GUlISxY8cqlLdv3x5HjhxRq43k5GQcOXIEX3/9tUbrpvLDeezOsu6CWlKndSrrLhC99jS+R9t3332H7OxsODs7w9XVFTVr1oSLiwuys7Px7bff6qKPREREChiLiIhIl7QZZzIzM5GXl6c0wY6dnZ3SRDyFVatWDXK5HF5eXhg6dKg0Ik6VnJwcZGVlKTyIiOjV03hEm6OjI06cOIHExEScO3cOQgi4ubmhXbt2JerAggULMHPmTKSnp6N+/fqIiYmBr6+vyrrp6en49NNPkZSUhL///hsjRoxATEyMQp3Y2Fh89NFHSss+fvwYxsbGJeojERG9XrQdi4iIiF6kizgjk8kUngshlMoKO3ToEB48eIDffvsNY8eORc2aNYuc3Xrq1KmYNGlSiftHRETaoXGirYCfnx/8/PxKtXJNbwqak5MDW1tbjB8/vthZdywsLHD+/HmFMibZiIjePNqIRUREREXRRpyxsbGBvr6+0ui1mzdvKo1yK8zFxQUA0LBhQ9y4cQNRUVFFJtoiIyMREREhPc/KyoKjo2Op+k5ERJpTK9E2b948fPzxxzA2Nsa8efOKravJdNea3hTU2dkZc+fOBQAsX768yHZlMhns7e3V7gcREb3+dBWLiIiIAN3FGSMjI3h6eiIxMRHdunWTyhMTExEYGKh2O0II5OTkFPm6XC6HXC5Xuz0iItINtRJtc+bMwQcffABjY+NiR5LJZDK1g442bgpalAcPHsDJyQl5eXlo3LgxvvrqK4VZewrLyclRCFq8nwER0etHF7GIiIiogC7jTEREBPr16wcvLy94e3tjyZIlSEtLQ2hoKIDno9GuXbuGVatWAQDmz5+P6tWro27dugCAw4cPY9asWZyIh4ioHFAr0XblyhWVf5dGaW4KWpy6desiNjYWDRs2RFZWFubOnYsWLVrg5MmTqFWrlspleD8DIqLXny5iERERUQFdxpmgoCDcvn0bkydPRnp6Oho0aIBdu3bByckJwPN7UaelpUn18/PzERkZiStXrsDAwACurq6YNm0ahgwZotV+ERGR9mk86+jkyZPx6NEjpfLHjx9j8uTJGnegJDcFLU7z5s3x4YcfolGjRvD19cWGDRtQu3btYmcHioyMxP3796XH1atXS7x+IiLSPW3HIiIiohfpIs6EhYUhNTUVOTk5SEpKQsuWLaXXYmNjsX//fun58OHDcerUKTx8+BD379/HiRMn8Mknn0BPT+PTNyIiesU0PlJPmjQJDx48UCp/9OiRRqPCSnNTUE3o6enhnXfewd9//11kHblcDgsLC4UHERG9vrQVi4iIiFRhnCEiopLSONFW1IizkydPwsrKSu12Xrwp6IsSExPh4+OjabeKJIRASkoKHBwctNYmERGVLW3FIiIiIlUYZ4iIqKTUukcbAFSqVAkymQwymQy1a9dWCDx5eXl48OCBdDNPdWl6U1AASElJAfB8woNbt24hJSUFRkZGcHNzA/D816fmzZujVq1ayMrKwrx585CSkoL58+dr1DciInr96CIWERERFWCcISKi0lI70RYTEwMhBAYNGoRJkybB0tJSes3IyAjOzs7w9vbWaOWa3hQUgMLsoUlJSVi7di2cnJyQmpoKALh37x4+/vhjZGRkwNLSEh4eHjh48CCaNm2qUd+IiOj1o4tYREREVIBxhoiISkvtRNuAAQOQm5sLAGjXrh2qVaumlQ6EhYUhLCxM5WuxsbFKZUKIYtubM2dOsdNxExFR+aWrWERERAQwzhARUelpdI82AwMDhIWFIS8vT1f9ISIiKhZjERER6RLjDBERlYbGkyE0a9YMycnJuugLERGRWhiLiIhIlxhniIiopNS+dLRAWFgYPv30U/z777/w9PSEmZmZwuvu7u5a6xwREZEqjEVERKRLjDNERFRSGifagoKCAAAjRoyQymQymTQFNodYExGRrjEWERGRLjHOEBFRSWmcaLty5You+kFERKQ2xiIiItIlxhkiIiopjRNtTk5OuugHERGR2hiLiIhIlxhniIiopDROtAHApUuXEBMTg7Nnz0Imk6FevXoYOXIkXF1dtd0/IiIilRiLiIhIlxhniIioJDSedTQhIQFubm44duwY3N3d0aBBA/z++++oX78+EhMTddFHIiIiBYxFRESkS4wzRERUUhqPaBs7dizCw8Mxbdo0pfLPP/8cfn5+WuscERGRKoxFRESkS4wzRERUUhqPaDt79iyCg4OVygcNGoQzZ85opVNERETFYSwiIiJdYpwhIqKS0jjRZmtri5SUFKXylJQUVK5cWRt9IiIiKhZjERER6RLjDBERlZTGl44OHjwYH3/8MS5fvgwfHx/IZDIcPnwY06dPx6effqqLPhIRESlgLCIiIl1inCEiopLSONE2YcIEmJubY/bs2YiMjAQAVKlSBVFRURgxYoTWO0hERFQYYxEREekS4wwREZWUxok2mUyG8PBwhIeHIzs7GwBgbm6u9Y4REREVhbGIiIh0iXGGiIhKSuNEW4GbN2/i/PnzkMlkqFOnDmxtbbXZLyIiopdiLCIiIl1inCEiIk1pPBlCVlYW+vXrhypVqqBVq1Zo2bIlqlSpgg8//BD379/XRR+JiIgUMBYREZEuMc4QEVFJaZxoCwkJwe+//46dO3fi3r17uH//Pnbs2IHjx49j8ODBuugjERGRAl3EogULFsDFxQXGxsbw9PTEoUOHiqy7efNm+Pn5wdbWFhYWFvD29kZCQkJJN4eIiF4zPOchIqKS0vjS0Z07dyIhIQHvvvuuVObv74/vv/8eHTp00GrniIiIVNF2LIqLi8OoUaOwYMECtGjRAosXL0ZAQADOnDmD6tWrK9U/ePAg/Pz8MGXKFFSsWBErVqxAly5d8Pvvv8PDw6NU20ZERGWP5zxERFRSGo9os7a2hqWlpVK5paUlKlWqpJVOERERFUfbsSg6OhrBwcEICQlBvXr1EBMTA0dHRyxcuFBl/ZiYGIwZMwbvvPMOatWqhSlTpqBWrVrYvn27xusmIqLXD895iIiopDROtH3xxReIiIhAenq6VJaRkYHPPvsMEyZM0GrniIiIVNFmLHr69CmSkpLQvn17hfL27dvjyJEjarWRn5+P7OxsWFlZFVknJycHWVlZCg8iIno98ZyHiIhKSuNLRxcuXIiLFy/CyclJupwmLS0Ncrkct27dwuLFi6W6J06c0F5PiYiI/p82Y1FmZiby8vJgZ2enUG5nZ4eMjAy1+jN79mw8fPgQvXr1KrLO1KlTMWnSJLXaIyKissVzHiIiKimNE21du3bVQTeIiIjUp4tYJJPJFJ4LIZTKVFm3bh2ioqKwdetWVK5cuch6kZGRiIiIkJ5nZWXB0dGx5B0mIiKd0UWcWbBgAWbOnIn09HTUr18fMTEx8PX1VVl38+bNWLhwIVJSUpCTk4P69esjKioK/v7+Wu8XERFpl8aJtokTJ+qiH0RERGrTZiyysbGBvr6+0ui1mzdvKo1yKywuLg7BwcHYuHEj2rVrV2xduVwOuVxe6v4SEZHuafuch5PuEBG9PTROtBVISkrC2bNnIZPJ4ObmxgM+ERG9ctqIRUZGRvD09ERiYiK6desmlScmJiIwMLDI5datW4dBgwZh3bp16NSpU4n6T0RErzdtnfO8OOkO8HxSnYSEBCxcuBBTp05Vqh8TE6PwfMqUKdi6dSu2b9/O8y4iotecxom2mzdvonfv3ti/fz8qVqwIIQTu37+PNm3aYP369bC1tdWoPU2GUKenp+PTTz9FUlIS/v77b4wYMUIpCAFAfHw8JkyYgEuXLsHV1RXffPONwsnTm8Z57E616qVO44kgEb0ZtB2LIiIi0K9fP3h5ecHb2xtLlixBWloaQkNDATy/7PPatWtYtWoVgOdJtv79+2Pu3Llo3ry5NBrOxMRE5Sx1RERUvmgzzhRMujN27FiFcl1MupOTkyM956Q7RERlQ+NZR4cPH46srCycPn0ad+7cwd27d3Hq1ClkZWVhxIgRGrVVMIR6/PjxSE5Ohq+vLwICApCWlqayfk5ODmxtbTF+/Hg0atRIZZ2jR48iKCgI/fr1w8mTJ9GvXz/06tULv//+u6abSkREryltxiIACAoKQkxMDCZPnozGjRvj4MGD2LVrF5ycnAA8/6Hnxdi0ePFi5ObmYujQoXBwcJAeI0eO1No2EhFR2dFmnHmVk+5YWlpKD94HlIiobGg8om337t34+eefUa9ePanMzc0N8+fPR/v27TVqS9Mh1M7Ozpg7dy4AYPny5SrbjImJgZ+fHyIjIwE8H4Vw4MABxMTEYN26dRr1j4iIXk/ajEUFwsLCEBYWpvK12NhYhef79+8v0TqIiKh80EWc4aQ7RERvB41HtOXn58PQ0FCp3NDQEPn5+Wq3UzCEunCg0mQItSpHjx5VatPf37/YNnNycpCVlaXwICKi15e2YhEREZEq2owz2ph0Z8OGDWpNumNhYaHwICKiV0/jRNt7772HkSNH4vr161LZtWvXEB4ejrZt26rdjjaGUKuSkZGhcZscZk1EVL5oKxYRERGpos048+KkOy9KTEyEj49PkcutW7cOAwcOxNq1aznpDhFROaJxou27775DdnY2nJ2d4erqipo1a8LFxQXZ2dn49ttvNe5ASYdQa7PNyMhI3L9/X3pcvXq1VOsnIiLd0nYsIiIiepG240xERASWLl2K5cuX4+zZswgPD1eadKd///5S/YJJd2bPni1NupORkYH79+9rbRuJiEg3NL5Hm6OjI06cOIHExEScO3cOQgi4ubm9dChzYaUZQl0ce3t7jduUy+WQy+UlXicREb1a2opFREREqmg7zgQFBeH27duYPHky0tPT0aBBA7Un3Rk6dKhUPmDAAKX7hhIR0etFo0Rbbm4ujI2NkZKSAj8/P/j5+ZV4xS8Ooe7WrZtUnpiYiMDAwBK36+3tjcTERISHh0tle/bsKXZYNhERlR/ajEVERESF6SrOcNIdIqK3g0aJNgMDAzg5OSEvL08rK4+IiEC/fv3g5eUFb29vLFmyRGkI9bVr17Bq1SppmZSUFADAgwcPcOvWLaSkpMDIyAhubm4AgJEjR6Jly5aYPn06AgMDsXXrVvz88884fPiwVvpMRERlS9uxiIiI6EWMM0REVBoa36Ptiy++QGRkJO7cuVPqlQcFBSEmJgaTJ09G48aNcfDgwWKHUAOAh4cHPDw8kJSUhLVr18LDwwMdO3aUXvfx8cH69euxYsUKuLu7IzY2FnFxcWjWrFmp+0tERK8HbcYiIiKiwhhniIiopDS+R9u8efNw8eJFVKlSBU5OTjAzM1N4/cSJExq1p8kQauD5xAYv07NnT/Ts2VOjfhARUfmh7VhERET0IsYZIiIqKY0TbYGBgaWeFZSIiKg0GIuIiEiXGGeIiKikNE60RUVF6aAbRERE6mMsIiIiXWKcISKiklL7Hm2PHj3C0KFDUbVqVVSuXBl9+/ZFZmamLvtGRESkgLGIiIh0iXGGiIhKS+1E28SJExEbG4tOnTqhd+/eSExMxCeffKLLvhERESlgLCIiIl1inCEiotJS+9LRzZs3Y9myZejduzcA4MMPP0SLFi2Ql5cHfX19nXWQiIioAGMRERHpEuMMERGVltoj2q5evQpfX1/pedOmTWFgYIDr16/rpGNERESFMRYREZEuMc4QEVFpqZ1oy8vLg5GRkUKZgYEBcnNztd4pIiIiVRiLiIhIlxhniIiotNS+dFQIgYEDB0Iul0tlT548QWhoKMzMzKSyzZs3a7eHRERE/4+xiIiIdIlxhoiISkvtRNuAAQOUyj788EOtdoaIiKg4jEVERKRLjDNERFRaaifaVqxYoct+EBERvRRjERER6RLjDBERlZba92gjIiIiIiIiIiKiojHRRkREREREREREpAVMtBEREREREREREWmB2vdoo7eD89idatVLndZJxz0hIiIiIiIiIipfmGgjnWHSjoiIiIiIiIjeJrx0lIiIiIiIiIiISAuYaCMiIiIiIiIiItICJtqIiIiIiIiIiIi0gIk2IiIiIiIiIiIiLWCijYiIiIiIiIiISAuYaCMiIgKwYMECuLi4wNjYGJ6enjh06FCRddPT09G3b1/UqVMHenp6GDVq1KvrKBERERERvbaYaCMiordeXFwcRo0ahfHjxyM5ORm+vr4ICAhAWlqayvo5OTmwtbXF+PHj0ahRo1fcWyIiIiIiel0x0UZERG+96OhoBAcHIyQkBPXq1UNMTAwcHR2xcOFClfWdnZ0xd+5c9O/fH5aWlq+4t0REVB5x5DQR0duBiTYiInqrPX36FElJSWjfvr1Cefv27XHkyBGtrScnJwdZWVkKDyIiejtw5DQR0dujzBNtmvyyAwAHDhyAp6cnjI2NUaNGDSxatEjh9djYWMhkMqXHkydPdLkZRERUTmVmZiIvLw92dnYK5XZ2dsjIyNDaeqZOnQpLS0vp4ejoqLW2iYjo9caR00REb48yTbRp+svOlStX0LFjR/j6+iI5ORnjxo3DiBEjEB8fr1DPwsIC6enpCg9jY+NXsUlERFROyWQyhedCCKWy0oiMjMT9+/elx9WrV7XWNhERvb44cpqI6O1iUJYrf/GXHQCIiYlBQkICFi5ciKlTpyrVX7RoEapXr46YmBgAQL169XD8+HHMmjULPXr0kOrJZDLY29u/km0gIqLyzcbGBvr6+kqj127evKk0yq005HI55HK51tojIqLy4VWOnJ40aZLW2iMiopIpsxFtJfll5+jRo0r1/f39cfz4cTx79kwqe/DgAZycnFCtWjV07twZycnJxfaFv/4QEb29jIyM4OnpicTERIXyxMRE+Pj4lFGviIjoTcOR00REb4cyS7SV5JedjIwMlfVzc3ORmZkJAKhbty5iY2Oxbds2rFu3DsbGxmjRogX+/vvvIvvC++YQEb3dIiIisHTpUixfvhxnz55FeHg40tLSEBoaCuD5yUv//v0VlklJSUFKSgoePHiAW7duISUlBWfOnCmL7hMR0WvsVY6ctrCwUHgQEdGrV6aXjgKa/7Kjqv6L5c2bN0fz5s2l11u0aIEmTZrg22+/xbx581S2GRkZiYiICOl5VlYWk21ERG+RoKAg3L59G5MnT0Z6ejoaNGiAXbt2wcnJCQCQnp6udP9QDw8P6e+kpCSsXbsWTk5OSE1NfZVdJyKi19yLI6e7desmlScmJiIwMLAMe0ZERLpQZom2kvyyY29vr7K+gYEBrK2tVS6jp6eHd955p9gRbbxvDhERhYWFISwsTOVrsbGxSmUFP/QQERG9TEREBPr16wcvLy94e3tjyZIlSiOnr127hlWrVknLpKSkAIDCyGkjIyO4ubmVxSYQEZGayizRVpJfdry9vbF9+3aFsj179sDLywuGhoYqlxFCICUlBQ0bNtRe54mIiIiIiNTEkdNERG+PMr10VNNfdkJDQ/Hdd98hIiICgwcPxtGjR7Fs2TKsW7dOanPSpElo3rw5atWqhaysLMybNw8pKSmYP39+mWwjERERERERR04TEb0dyjTRpukvOy4uLti1axfCw8Mxf/58VKlSBfPmzUOPHj2kOvfu3cPHH3+MjIwMWFpawsPDAwcPHkTTpk1f+fYREREREREREdHbo8wnQ9D0l51WrVrhxIkTRbY3Z84czJkzR1vdIyIiIiIiIiIiUkuZJ9qI1OE8dudL66RO6/QKekJEREREREREpBoTbURERERERET0RlFnsMbrgoNG3ix6Zd0BIiIiIiIiIiKiNwETbURERERERERERFrAS0fprcP7vRERERERERGRLnBEGxERERERERERkRYw0UZERERERERERKQFTLQRERERERERERFpARNtREREREREREREWsBEGxERERERERERkRYw0UZERERERERERKQFTLQRERERERERERFpARNtREREREREREREWmBQ1h0gIiIioreP89idZd0FtaVO61TWXSAiIqJygiPaiIiIiIiIiIiItICJNiIiIiIiIiIiIi3gpaNEpaDOZS/qXm6izbaIiIiIiIiI6NXjiDYiIiIiIiIiIiItYKKNiIiIiIiIiIhIC3jpKNEbiJehEhEREREREb16TLQRUbGYtCMiIiIiIiJSDy8dJSIiIiIiIiIi0gKOaCOiV4aj44iIiIiIiMovdc7pXgdleV5Z5iPaFixYABcXFxgbG8PT0xOHDh0qtv6BAwfg6ekJY2Nj1KhRA4sWLVKqEx8fDzc3N8jlcri5ueHHH3/UVfeJiOgNoYt4REREVIBxhojo7VCmiba4uDiMGjUK48ePR3JyMnx9fREQEIC0tDSV9a9cuYKOHTvC19cXycnJGDduHEaMGIH4+HipztGjRxEUFIR+/frh5MmT6NevH3r16oXff//9VW0WERGVM7qIR0RERAUYZ4iI3h5lmmiLjo5GcHAwQkJCUK9ePcTExMDR0RELFy5UWX/RokWoXr06YmJiUK9ePYSEhGDQoEGYNWuWVCcmJgZ+fn6IjIxE3bp1ERkZibZt2yImJuYVbRUREZU3uohHREREBRhniIjeHmV2j7anT58iKSkJY8eOVShv3749jhw5onKZo0ePon379gpl/v7+WLZsGZ49ewZDQ0McPXoU4eHhSnWYaCN6s2jzfm+8d9zbTVfxiIiICGCcISJ625RZoi0zMxN5eXmws7NTKLezs0NGRobKZTIyMlTWz83NRWZmJhwcHIqsU1SbAJCTk4OcnBzp+f379wEAWVlZCvXycx69fMNULKcK29J+W+q0w7bYlq7bajAx4aV1Tk3yf+vaKth/Qgi11vEq6SoeFaZurCktdY+9rwNtb7uucJ/qBvcraRPjDOOMKuXpf7e87FfuU93gftU+XexTdWNNmc86KpPJFJ4LIZTKXla/cLmmbU6dOhWTJk1SKnd0dCy648WwjCnRYmzrNWmHbbEttqW7trKzs2Fpaam9FWmRLuLRi7Qda94E2vzM0XPcp7rB/Vp+MM4wzryI/7vax32qG9yv2qfLffqyWFNmiTYbGxvo6+sr/Ypz8+ZNpV9vCtjb26usb2BgAGtr62LrFNUmAERGRiIiIkJ6np+fjzt37sDa2rrY4JeVlQVHR0dcvXoVFhYWRdZTB9tiW2yLbb3JbQkhkJ2djSpVqpRqXbqgq3hUWEljTVnT5meF/sP9qn3cp7pRXvYr40z5jTNA+fmclSfcp7rB/ap95WmfqhtryizRZmRkBE9PTyQmJqJbt25SeWJiIgIDA1Uu4+3tje3btyuU7dmzB15eXtJ9Cry9vZGYmKhwn7Y9e/bAx8enyL7I5XLI5XKFsooVK6q9LRYWFlr7QLAttsW22Nab2tbrOsJAV/GosNLGmrKmzc8K/Yf7Vfu4T3WjPOxXxpnyHWeA8vE5K2+4T3WD+1X7yss+VSfWlOmsoxEREVi6dCmWL1+Os2fPIjw8HGlpaQgNDQXw/FeZ/v37S/VDQ0Pxzz//ICIiAmfPnsXy5cuxbNkyjB49WqozcuRI7NmzB9OnT8e5c+cwffp0/Pzzzxg1atSr3jwiIiondBGPiIiICjDOEBG9Pcr0Hm1BQUG4ffs2Jk+ejPT0dDRo0AC7du2Ck5MTACA9PR1paWlSfRcXF+zatQvh4eGYP38+qlSpgnnz5qFHjx5SHR8fH6xfvx5ffPEFJkyYAFdXV8TFxaFZs2avfPuIiKh80EU8IiIiKsA4Q0T09ijzyRDCwsIQFham8rXY2FilslatWuHEiRPFttmzZ0/07NlTG90rllwux8SJE5WGaLMttsW22BbbKn90EY/eBG/K+/u64X7VPu5T3eB+1R7GmaLxc6Z93Ke6wf2qfW/iPpWJ13EObCIiIiIiIiIionKmTO/RRkRERERERERE9KZgoo2IiIiIiIiIiEgLmGgjIiIiIiIiIiLSAibaiOiVyMvLw4EDB3D37t2y7spbJzc3F5MmTcLVq1fLuitECmQyGbZs2VLW3XhjOTs7IyYmpqy7oVP8DOlObGwsKlasWNbdIFJb69atMWrUqLLuRplJTU2FTCZDSkqKxstGRUWhcePGxdYZOHAgunbtWqK+lRevOqa87THsTY4zTLRp4OrVq/j333+l58eOHcOoUaOwZMmSErV37949zJ49GyEhIRg8eDCio6Nx//59jds5ceIE/vrrL+n51q1b0bVrV4wbNw5Pnz4tUd8Ke/LkCWbNmlWqNt577z38888/WulPaTx+/BiPHj2Snv/zzz+IiYnBnj17StTepUuXMHz4cLRr1w5+fn4YMWIELl26pHE7K1euxM6dO6XnY8aMQcWKFeHj46PxfpswYQLy8vKUyu/fv48+ffpo3LcX5ebmlmg5fX19+Pv74969e6Va/4vu3buH48ePIykpSavtlsbAgQNx8OBBrbT1+PFjbNu2DbNmzcK8efOwe/dule/ryxgYGGDmzJklWvZFd+7cUTgGAsDp06fx0UcfoVevXli7dm2p2qeSGzhwIGQyGUJDQ5VeCwsLg0wmw8CBA199x15jR44cgb6+Pjp06KDxsuqckLxKRX1R/eOPP/Dxxx+/kj7cvHkTQ4YMQfXq1SGXy2Fvbw9/f38cPXpUp+tNT09HQEDAS+s9fvwYlSpVgpWVFR4/fqzxevbv3w+ZTPbaxBpNXL16FcHBwahSpQqMjIzg5OSEkSNH4vbt21KdtyEpS7pREH9kMhkMDQ1Ro0YNjB49Gg8fPnzpsuX5/0rbikpivap9NHr0aOzdu1en6yhLhT+ndnZ28PPzw/Lly5Gfny/VUzemkCLGGWVMtGmgb9++2LdvHwAgIyMDfn5+OHbsGMaNG4fJkydr1Nbx48fh6uqKOXPm4M6dO8jMzMScOXPg6uqq8TTeQ4YMwYULFwAAly9fRu/evWFqaoqNGzdizJgxareTmZmJnTt3Ys+ePdIJ+bNnzzB37lw4Oztj2rRparWzbds2lY+DBw9ix44d0nNN/fHHHxgzZgx69+6N7t27Kzw0ERgYiFWrVgF4nqhp1qwZZs+ejcDAQCxcuFCjthISEuDm5oZjx47B3d0dDRo0wO+//4769esjMTFRo7amTJkCExMTAMDRo0fx3XffYcaMGbCxsUF4eLhGba1atQotWrRQSPjt378fDRs2RGpqqlpt7N69W0rg5ufn4+uvv0bVqlUhl8tRrVo1TJs2DZpOWtywYUNcvnxZo2VUSU1NRadOnWBjY4NmzZqhadOmsLGxQefOndXevsI2bdqEXr16oXnz5mjSpInCQxPZ2dlo3749atWqhSlTpuDatWsl6s+2bdvg5OSErl27YsyYMRg1ahQ6duwIZ2dnhUTelStX1GqvXbt22L9/f4n6UmDo0KGIjo6Wnt+8eRO+vr74448/kJOTg4EDB2L16tWlWgeVnKOjI9avX6+QRHjy5AnWrVuH6tWrl2HPXk/Lly/H8OHDcfjwYaSlpZV1d3TC1tYWpqamr2RdPXr0wMmTJ7Fy5UpcuHAB27ZtQ+vWrXHnzh2drtfe3h5yufyl9eLj49GgQQO4ublh8+bNOu3T6+Ty5cvw8vLChQsXsG7dOly8eBGLFi3C3r174e3trfP3R5Vnz5698nWSbnXo0AHp6em4fPkyvv76ayxYsACjR4/WWvvaGjhAyoQQyM3NRYUKFWBtbV3W3dGpgs9pamoqfvrpJ7Rp0wYjR45E586dpYEE6sYU+g/jTBEEqa1ixYri3LlzQggh5s6dK3x8fIQQQiQkJAgXFxeN2nr33XfFwIEDxbNnz6SyZ8+eiQEDBghfX1+N2rKwsBAXL14UQggxbdo00b59eyGEEIcPHxbVqlVTq41ff/1VVKxYUchkMqGnpyeaNm0qTp8+LWrVqiVcXV3Ft99+Kx4+fKhWWwVtyGSyIh96enoabeO6deuEoaGh6NSpkzAyMhKdO3cWderUEZaWlmLgwIEatWVtbS1OnTolhBDi+++/F+7u7iIvL09s2LBB1K1bV6O2GjduLD7//HOl8s8//1x4eHho1JaJiYn4559/hBBCjBkzRvTr108IIcSpU6eEjY2NRm3du3dPBAUFiQoVKoglS5aI0aNHC0NDQzFhwgSRm5urVhtubm7i119/FUIIMWXKFGFtbS2io6PFTz/9JGJiYoSdnZ2YNm2aRv1KSEgQjRs3Ftu3bxfXr18X9+/fV3ioIy0tTdjZ2Ylq1aqJKVOmiB9//FFs3rxZfPPNN6JatWrC3t5eXL16VaN+zZ07V1SoUEEMHTpUGBkZiSFDhoh27doJS0tLMW7cOI3aEkKIzMxMERMTIxo3biwMDAxEhw4dxMaNG8XTp0/VWv7XX38VhoaGokePHuLIkSPi7t274u7du+LXX38V3bt3F8bGxuLs2bNizJgxYtKkSWq1uWjRImFvby8+/fRTsXbtWrF161aFhzqcnZ3Fvn37pOczZ84Urq6u0nFs5syZolmzZmq1Rdo1YMAAERgYKBo2bCjWrFkjlf/www+iYcOGIjAwUAwYMEAIIcRPP/0kWrRoISwtLYWVlZXo1KmTFEOEEOLKlSsCgIiPjxetW7cWJiYmwt3dXRw5ckSqM3HiRNGoUSOFPsyZM0c4OTlJz48dOybatWsnrK2thYWFhWjZsqVISkpSWAaA+PHHH7W2H9T14MEDYW5uLs6dOyeCgoIU/o9WrFghLC0tFer/+OOPouAr04oVKwQAhceKFSuEEEL8888/4v333xdmZmbC3Nxc/O9//xMZGRlSOwX7bdmyZcLR0VGYmZmJ0NBQkZubK6ZPny7s7OyEra2t+PrrrxXWP3v2bNGgQQNhamoqqlWrJj755BORnZ0thBBi3759Sv2ZOHGiEEIIJycnMWfOHKmdu3fvisGDB4vKlSsLuVwu6tevL7Zv317q/Xn37l0BQOzfv7/IOvfu3RODBw8Wtra2wtzcXLRp00akpKSUet+o+xlq3bq1WLRokVi4cKFo06aNwmsFn/nk5GSlbdq3b5/0+ouPgv+nJ0+eiOHDhwtbW1shl8tFixYtxLFjx16+016RDh06iGrVqolHjx4plKenpwtTU1MRGhoqWrVqpbR9Qvz3v7B7925Rt25dYWZmJvz9/cX169cV2lq+fLmoW7eukMvlok6dOmL+/PnSawX7Li4uTrRq1UrI5XKxfPly3W84vTIF8edFISEhwt7eXuTn54vp06cLFxcXYWxsLNzd3cXGjRuFEKLY/6tWrVqJoUOHivDwcGFtbS1atmwphBBi//794p133hFGRkbC3t5efP755wrnUq1atRIjR458FZutdar2oxD/HeP//fdfYW5uLu2/Atu2bROmpqYiKytL2qfr1q0T3t7eQi6XCzc3N4XvbgXt7d69W3h6egpDQ0Pxyy+/KMX13NxcER4eLn1X+Oyzz0T//v1V9rE8KGr/7t27VwAQ33//vRBCOab8+++/olevXqJixYrCyspKvP/+++LKlSvS6/v27RPvvPOOMDU1FZaWlsLHx0ekpqZKr2/btk00adJEyOVy4eLiIqKiohQ+s2X1PUibGGdUY6JNA2ZmZtI/VpcuXaQkwz///COMjY01aqvgRLmw06dPCxMTE43aMjc3FxcuXBBCCNGuXTsRExOjcb/ee+89ERQUJP766y8RHh4uZDKZcHFxEStXrhT5+fka9adDhw6iU6dO4saNGwrlBgYG4vTp0xq1VaBhw4biu+++E0IIUaFCBXHp0iWRn58vBg8eLL788kuN2noxofW///1PREVFCSGeJ3A03fdyuVza9y86f/68kMvlGrVla2srTpw4IYR4nsBbuXKlEEKIixcvCjMzM43aKjBu3Dghk8mEoaGh+PnnnzVa1tjYWKSlpQkhhGjQoIGIi4tTeH3Hjh2iZs2aGrVZONla8NAk+frRRx+Jli1bisePHyu99ujRI9GyZUsxaNAgjfpVp04dsXbtWiHEf58vIYSYMGGCGDp0qEZtFXbixAkxbNgwYWxsLGxsbMSoUaNUfmZeFBAQID7++OMiX//444+FjY2NsLa2VjhRLY42Et/GxsYKXx4CAgLE6NGjpefnz58XVlZWarVF2lXwBTI6Olq0bdtWKm/btq2YM2eOQqJt06ZNIj4+Xly4cEEkJyeLLl26iIYNG4q8vDwhxH9fWOrWrSt27Nghzp8/L3r27CmcnJykL4fqJNr27t0rVq9eLc6cOSPOnDkjgoODhZ2dncjKypLqlNUXzGXLlgkvLy8hhBDbt28Xzs7OUqx7WaLt0aNH4tNPPxX169cX6enpIj09XTx69Ejk5+cLDw8P8e6774rjx4+L3377TTRp0kS0atVKamfixImiQoUKomfPnuL06dNi27ZtwsjISPj7+4vhw4eLc+fOieXLlwsA4ujRo9Jyc+bMEb/88ou4fPmy2Lt3r6hTp4745JNPhBBC5OTkiJiYGGFhYSH1pyAJ92KiLS8vTzRv3lzUr19f7NmzR1y6dEls375d7Nq1q9T789mzZ6JChQpi1KhR4smTJ0qv5+fnixYtWoguXbqIP/74Q1y4cEF8+umnwtraWty+fbtU+0adz9DFixeFXC4Xd+7cEbdv3xZyuVw6zgvx8kRbbm6uiI+PFwDE+fPnRXp6urh3754QQogRI0aIKlWqiF27donTp0+LAQMGiEqVKknbVZZu374tZDKZmDJlisrXBw8eLCpVqiQyMzNFtWrVxOTJk6XPkBDP/xcMDQ1Fu3btxB9//CGSkpJEvXr1RN++faU2lixZIhwcHER8fLy4fPmyiI+PF1ZWViI2NlYI8d++dXZ2lupcu3ZN9xtPr4yqBMbw4cOFtbW1GDdunKhbt67YvXu3uHTpklixYoWQy+Vi//79xf5ftWrVSlSoUEF89tln4ty5c+Ls2bPi33//FaampiIsLEycPXtW/Pjjj8LGxkb6YaFguTc10VbwQ0nHjh0VXu/WrZvo37+/EOK//7dq1aqJTZs2iTNnzoiQkBBhbm4uMjMzFdpzd3cXe/bsERcvXhSZmZlKcX369OnC0tJSaic4OFiYm5u/cYk2IYRo1KiRCAgIEEIoxpSHDx+KWrVqiUGDBok///xTnDlzRvTt21fUqVNH5OTkiGfPnglLS0sxevRocfHiRXHmzBkRGxsrnWfu3r1bWFhYiNjYWHHp0iWxZ88e4ezsLJ17Fl5fecQ4UzQm2jTQtGlT8fnnn4uDBw8KY2Nj6QT36NGjomrVqhq1VblyZZGQkKBUvnv3blG5cmWN2mrTpo3o37+/WLVqlTA0NBR///23EOL5rz4vnvQU58VRXg8fPhR6enpiw4YNGvXjRdHR0aJ69eoKv5SXJtFmamoqJTmtra3Fn3/+KYQQ4syZM8Le3l6jtho2bCjmzp0r0tLShIWFhTRK4/jx48LOzk6jtqpVq6ZyP8XFxQlHR0eN2urbt69o0qSJCA4OFqamplJA3Lp1q6hfv75GbQkhxLx584SJiYkUENzc3NROygghhIODg3QyY2dnJyUBC1y4cEHjxOT+/fuLfajbr0OHDhX5+oEDB4SDg4NG/TIxMZESSLa2ttJ+unDhQqkSR9evXxfTpk0TtWvXFmZmZqJ///7Cz89PGBgYiOjo6CKXq1ixovQZV+XkyZNCJpMpnBS+CpUrV1b4DFlbW4tNmzZJzy9cuFDipDCVTsEXyFu3bgm5XC6uXLkiUlNThbGxsbh165ZCoq2wmzdvCgDir7/+EkL894Vl6dKlUp3Tp08LANIPROok2grLzc0V5ubmCnGhrL5g+vj4SD9KPXv2TNjY2IjExEQhxMsTbUKo3v49e/YIfX196QcKIf7bbwUjnCZOnCiNPCjg7+8vnJ2dpUSnEM+T/1OnTi2y/xs2bBDW1tbSc1V9FkIx0ZaQkCD09PTE+fPni2y3NDZt2iQqVaokjI2NhY+Pj4iMjBQnT54UQjxPulpYWCgl4VxdXcXixYuFECXfN+p8hsaNGye6du0qPQ8MDBTjx4+Xnr8s0SaE4slugQcPHghDQ0Pxww8/SGVPnz4VVapUETNmzCi2T6/Cb7/9Vuz+iY6OFgDEjRs3lEY/CvHf6M0XR7zOnz9f4buSo6Oj9ENVga+++kp4e3sLIf7btwX/b/TmKZzA+P3334W1tbXo2bOnMDY2VhgNLYQQwcHBok+fPkII1f9XQjxPmDVu3FihbNy4caJOnToKAwDmz58vKlSoIB0jynuiTV9fX5iZmSk8jI2NpX30+++/C319fSmJcOvWLWFoaCh9hy74f3vxipNnz56JatWqienTpwsh/tvnW7ZsUVh/4bjm4OCgsp03MdEWFBQk6tWrJ4RQjCnLli1T+szl5OQIExMTkZCQIG7fvl3saG5fX1+lBNTq1asVzlPKe6KNcaZovEebBqZPn47FixejdevW6NOnDxo1agTg+b2UmjZtqlFbQUFBCA4ORlxcnDTJwvr16xESEqLxzepjYmJw4sQJDBs2DOPHj0fNmjUBPL/nlI+Pj1pt3LlzB7a2tgAAU1NTmJqawsPDQ6N+vCg8PBzbtm3D559/jiFDhihMPlASVlZWyM7OBgBUrVoVp06dAvD8Hmuatv3ll19i9OjRcHZ2RrNmzeDt7Q0A2LNnj8bbPHjwYHz88ceYPn06Dh06hMOHD2PatGkYMmSIxjegnj9/Pry9vXHr1i3Ex8dL90lISkrS+DMREBCASZMmYdWqVfjhhx+QnJyMli1bonnz5pgxY4ZabXTr1g3ffPMN8vLyEBgYiAULFijck+27777T+GbgrVq1Kvahjtu3b8PZ2bnI12vUqKFw40112NvbS8s4OTnht99+A/D8/mcvbrM6nj17hvj4eHTu3BlOTk7YuHEjwsPDkZ6ejpUrV2LPnj1YvXp1sfd1fPLkCSwsLIp83dLSEnK5vMQ3Y3/y5EmJlmvatCnmzZuH/Px8bNq0CdnZ2Xjvvfek1y9cuABHR8cStU3aYWNjg06dOmHlypVYsWKFdC/DF126dAl9+/ZFjRo1YGFhARcXFwBQuk+Zu7u79LeDgwOA5/flU9fNmzcRGhqK2rVrw9LSEpaWlnjw4EGZ3w/t/PnzOHbsGHr37g3g+WQhQUFBWL58eanaPXv2LBwdHRX+B9zc3FCxYkWcPXtWKnN2doa5ubn03M7ODm5ubtDT01Moe3Ff79u3D35+fqhatSrMzc3Rv39/3L59W62bjRdISUlBtWrVULt27ZJuYrF69OiB69evY9u2bfD398f+/fvRpEkTxMbGIikpCQ8ePIC1tTUqVKggPa5cuaJwL9GS7JsXBQQESG3Xr18fwPMZr1euXIkPP/xQqvfhhx9i5cqVpZ4g5tKlS3j27BlatGghlRkaGqJp06YK7/nrqiC+yWSyIuuYmprC1dVVeu7g4CDt/1u3bkk3wH7xff3666+VJoXy8vLSwRbQ62LHjh2oUKECjI2N4e3tjZYtW2L06NF48uQJ/Pz8FD4fq1atUmvSsMKfmbNnz8Lb21vh89qiRQs8ePBAaaKm8qpNmzZISUlReCxdulR6vWnTpqhfv750r+nVq1ejevXqaNmypUI7Bec2wPMY5+XlpXRMKu5/8v79+0hPT1fZzptICKHyOJiUlISLFy/C3Nxc+vxaWVnhyZMnuHTpEqysrDBw4ED4+/ujS5cumDt3LtLT0xWWnzx5ssLnf/DgwUhPTy/1uXF58TbHGYOy7kB50rp1a2RmZiIrKwuVKlWSyj/++GONbzY8a9YsyGQy9O/fX7r5oqGhIT755BO1Jx0o4O7urjDraIGZM2dCX19frTZkMhmys7NhbGwsHWwePXqErKwshXrFnfwX1qhRIxw/fhzh4eFo3LixxgmLF/n6+iIxMRENGzZEr169MHLkSPzyyy9ITExE27ZtNWqrZ8+eePfdd5Geni4lSwGgbdu26Natm0ZtTZgwAebm5pg9ezYiIyMBAFWqVEFUVBRGjBihUVsVK1bEd999p1Q+adIkjdoBns8M+ueff6JKlSoAABMTEyxcuBCdO3dGSEiIWpNkTJkyBe3atUPdunXh7e2NjRs3IjExEbVr18bFixdx+/btEs3UeujQISxevBiXL1/Gxo0bUbVqVaxevRouLi549913X7p8lSpVcPr0aVSrVk3l66dOnZKSAup67733sH37djRp0gTBwcEIDw/Hpk2bcPz4cY0n23BwcEB+fj769OmDY8eOqUyG+fv7FzuVde3atfHLL7/go48+Uvn63r17UatWLY36lZeXhylTpmDRokW4ceMGLly4gBo1amDChAlwdnZGcHDwS9v46quv0K5dO6xZswa5ubkYN26cwrFw/fr1aidMSXcGDRqEYcOGAXiewC+sS5cucHR0xPfff48qVaogPz8fDRo0ULrZtKGhofR3wRekgpm59PT0lI7phW88O3DgQNy6dQsxMTFwcnKCXC6Ht7d3md/UetmyZcjNzUXVqlWlMiEEDA0NcffuXbW2TZWivqgXLn9xvwKQZkArXFawr//55x907NgRoaGh+Oqrr2BlZYXDhw8jODhYo5v9Fky2o0vGxsbw8/ODn58fvvzyS4SEhGDixIkICwuDg4ODyglZXjwWarpvClu6dKk0GUjBcgkJCbh27RqCgoIU6ubl5WHPnj0ICAiQEnkvvu/qvucFfSpcXtxJxatSs2ZNyGQynDlzRuVshufOnUOlSpWUkvEvUrX/C7a74H34/vvv0axZM4V6hb9/mpmZlWQTqJxo06YNFi5cCENDQ1SpUgWGhob4/fffAQA7d+5UON4CUOtm84U/M6r+r9Q5iS9PzMzMpAETBQonEUNCQvDdd99h7NixWLFiBT766CO1tr9wHf5P/ufs2bPSj44vys/Ph6enJ3744Qel1woGqKxYsQIjRozA7t27ERcXhy+++AKJiYlo3rw58vPzMWnSJJXnEsbGxtrfkDLAOFM0Jto0pK+vr3BiCaDY0TVFMTIywty5czF16lRcunQJQgjUrFlTq7ODafIPLIRQ+JVbCKEwuqsguGn666+JiQkWLVqEbdu2Yd++fcX+kxXnu+++k0bhREZGwtDQEIcPH0b37t0xYcIEjduzt7eHvb29QpmmoxKB5weC8PBwhIeHSyPuXvw1/mX+/PNPNGjQAHp6evjzzz+Lrfvi6JKXKWrG006dOqlMyqpiaWmJI0eOYNmyZdi+fTucnZ2Rn5+Pp0+fok+fPvjkk0+KTHYVJT4+Hv369cMHH3yAEydOICcnB8DzmTqnTJmCXbt2vbSNwMBAfPbZZ2jSpIkU5ArcvHkTn3/+ucoDfXGWLFkiHchDQ0OlE9kuXbogNDRUo7aio6PRq1evYv//KlWqVOxsoQMHDsTo0aNhZ2eHjh07Kry2c+dOjBkzBuPGjdOoX9988w1WrlyJGTNmYPDgwVJ5w4YNMWfOHLUSbY0bN8bZs2dx5MgR2NvbKwW83r17w83NTaN+kfZ16NBBSmb5+/srvHb79m2cPXsWixcvhq+vLwDg8OHDGq/D1tYWGRkZCic+KSkpCnUOHTqEBQsWSJ/hq1evIjMzU+N1aVNubi5WrVqF2bNno3379gqv9ejRAz/88ANcXV2RnZ2Nhw8fSl/aCm+bkZGRUjx0c3NDWloarl69Ko1qO3PmDO7fv4969eqVuM/Hjx9Hbm4uZs+eLSWENmzY8NL+FObu7o5///0XFy5c0NmotsLc3NywZcsWNGnSBBkZGTAwMCjRdyZ1FT6ZB54nVnv37o3x48crlE+bNg3Lli1DQECAFEvS09Ol7z6q3nMACvu5Zs2aMDIywuHDh9G3b18AzxN0x48fx6hRo7S1WSVmbW0NPz8/LFiwAOHh4QrJ1oyMDPzwww/o378/ZDKZWp+hwuzs7FC1alVcvnwZH3zwgba7T+WIqgSRm5sb5HI50tLSivwRTtX/VVHc3NwQHx+vEHeOHDkCc3Nzlf/7b6oPP/wQY8aMwbx583D69GkMGDBAqc5vv/0mjXLLzc1FUlKS9AOcOiwtLeHg4KCynSZNmmhnQ14Tv/zyC/766y+Eh4crvdakSRPExcWhcuXKxQ428fDwgIeHByIjI+Ht7Y21a9eiefPmaNKkCc6fP6/0v/EmYZwpGhNtL9GkSRPs3bsXlSpVgoeHR7G/GJw4cULj9k1NTdGwYUONl7OyssKFCxdgY2ODSpUqFdsvdabU3bdvn8Z9KEpMTAz69+8PKysrqez999/H+++/X+I2X2xLT08PY8aMUWtUlipt2rQpdn/98ssvarf1+PFjCCFgamoKc3Nz/PPPP1i2bBnc3NyUTuJUady4MTIyMlC5cmU0btxYIYMP/JfRL0mSsyiaJDsNDQ0RGhqqcbKpKF9//TUWLVqE/v37Y/369VK5j49PsZdSvmjixInYtWsXXF1d8eGHH6Ju3boAnp/Qrl27Fvb29vjyyy/V7lNubi6++eYbDBo0SDo57tWrF3r16qXBlv3X1qBBg9CkSRM0aNBA4+ULjBw5EkeOHEHnzp1Rp04d6ST9zJkz+PvvvxEYGKjxSdyqVauwZMkStG3bVuH9dHd3x7lz59Rux9bWFoGBgdLzf//9F1WqVIGenh46deqkUZ9IN/T19aVLRAr/2lepUiVYW1tjyZIlcHBwQFpaGsaOHavxOlq3bo1bt25hxowZ6NmzJ3bv3o2ffvpJ4YtozZo1sXr1anh5eSErKwufffbZKxlVVZwdO3bg7t27CA4OhqWlpcJrPXv2xLJly7B3716Ymppi3LhxGD58OI4dO4bY2FiFus7Ozrhy5Yp0Oaa5uTnatWsHd3d3fPDBB4iJiUFubi7CwsLQqlWrUl3O4OrqitzcXHz77bfo0qULfv31VyxatEipPw8ePMDevXvRqFEj6RYQL2rVqhVatmyJHj16IDo6GjVr1sS5c+cgk8nQoUOHEvcPeJ7A/d///odBgwbB3d0d5ubmOH78OGbMmIHAwEC0a9cO3t7e6Nq1K6ZPn446derg+vXr2LVrF7p27aqzyz1u3bqF7du3Y9u2bUrH5AEDBqBTp064desWbG1t0bx5c0ybNg3Ozs7IzMzEF198oVDfyckJMpkMO3bsQMeOHWFiYoIKFSrgk08+wWeffQYrKytUr14dM2bMwKNHj9T68eJV+O677+Dj4wN/f398/fXXcHFxwenTp/HZZ5+hatWq+OabbwA8/wwdPHgQvXv3hlwuV/u7QsEIfgsLCwQEBCAnJwfHjx/H3bt3ERERoctNo9ecubk5Ro8ejfDwcOTn5+Pdd99FVlYWjhw5ggoVKmDAgAFF/l+pEhYWhpiYGAwfPhzDhg3D+fPnMXHiRERERChcXv6mq1SpErp3747PPvsM7du3V/mj9/z581GrVi3Uq1cPc+bMwd27dzFo0CCN1jNy5EhMmzZNaic6Ohr37t3T0laUjZycHGRkZCAvLw83btzA7t27MXXqVHTu3Bn9+/dXqv/BBx9g5syZCAwMxOTJk1GtWjWkpaVh8+bN+Oyzz/Ds2TMsWbIE77//PqpUqYLz58/jwoULUltffvklOnfuDEdHR/zvf/+TBlb89ddf+Prrr1/15usM44xqTLS9RGBgoDS8WdNRMsXp1q2bymSPTCaDsbExatasib59+6JOnToql58zZ440ciomJqbU/dHm5V6TJk3C2LFj8f777yMkJAR+fn4lGtJd+LLV4mhySWvhS/mePXuGlJQUnDp1SuWvQsUJDAxE9+7dERoainv37qFp06YwMjJCZmYmoqOj8cknnxS7/JUrV6Rf0osb4aQpPT29Yvd5aZJ2N27cgBBCaUSgOs6fP690Hwng+funbvCuVKkSfv/9d4wbNw7r16+XlqtYsSL69u2Lb775RiEx+zIGBgaYOXOmxu99UW05OTmVOimqp6eHjRs3Ii4uDuvWrZMSYXXq1EFUVJR0bylNXLt2TeUvavn5+RpdflaYm5sbUlJSUKNGjRK3QdpX1DFRT08P69evx4gRI9CgQQPUqVMH8+bNQ+vWrTVqv169eliwYAGmTJmCr776Cj169MDo0aOxZMkSqc7y5cvx8ccfw8PDA9WrV8eUKVMwevTo0mxWqS1btgzt2rVTSrIBz0e0TZkyBampqVizZg0+++wzLFmyBO3atUNUVJTCfTd79OiBzZs3o02bNrh37x5WrFiBgQMHYsuWLRg+fDhatmwJPT09dOjQAd9++22p+ty4cWNER0dj+vTpiIyMRMuWLTF16lSFkwIfHx+EhoYiKCgIt2/fxsSJExEVFaXUVnx8PEaPHo0+ffrg4cOHqFmzpsa3q1ClQoUKaNasGebMmSPdt8zR0RGDBw/GuHHjIJPJsGvXLowfPx6DBg3CrVu3YG9vj5YtW8LOzq7U6y/KqlWrYGZmpvIWE23atIG5uTlWr16NiIgILF++HIMGDYKXlxfq1KmDGTNmKPxgVrVqVen7zUcffYT+/fsjNjYW06ZNQ35+Pvr164fs7Gx4eXkhISFB6eqHslKrVi0cP34cUVFR0ufD3t4eXbt2xcSJE6V4OXnyZAwZMgSurq7IyclR+3YfISEhMDU1xcyZMzFmzBiYmZmhYcOGr8WIPip7X331FSpXroypU6fi8uXLqFixIpo0aSKNyi/q/0qVqlWrYteuXfjss8/QqFEjWFlZITg4WCkp/jYIDg7G2rVri0yeTZs2DdOnT0dycjJcXV2xdetWja8q+vTTT5Geno6BAwdCT08PgwYNQrdu3XD//n1tbEKZ2L17NxwcHGBgYIBKlSqhUaNGmDdvHgYMGKAyWWtqaoqDBw/i888/R/fu3ZGdnY2qVauibdu2sLCwwOPHj3Hu3DmsXLkSt2/fhoODA4YNG4YhQ4YAeH5VwY4dOzB58mTMmDEDhoaGqFu3LkJCQl71pusU44xqMlGaG2e9RfLy8nD48GG4u7tr5ctTwRfyihUrwtPTE0IIJCcn4969e2jfvj1OnjyJ1NRU7N27V+Emu4Xl5ubihx9+gL+/f4kSHwXUTWqpk9DKycnBpk2bsGLFCuzbtw9Vq1bFRx99hIEDB6q8/r0oL0sWASW/pFWVqKgoPHjwALNmzVJ7GRsbGxw4cAD169fH0qVL8e233yI5ORnx8fH48ssvy+xmyFu3blV4/uzZMyQnJ2PlypWYNGmSWr+037lzB4MHD8bx48fRuXNnzJs3D0OGDMHy5cshk8nQrFkzxMfHa3Q/NFdXVyxevBjt2rWDubk5Tp48iRo1amDVqlWYNm0azpw5o9F2CiFw69YtAM9HWpX0Hh1du3ZF165dMXDgwBIt/6IVK1Zg48aNWLNmjUYJP13z8vLCqFGj8OGHHyrs+0mTJuHnn3/GoUOHStTui20RERERkW798MMPGDlyJK5fvy5dfktErxcm2jRgbGxc5M0SNTV27FhkZWXhu+++kzLo+fn5GDlyJMzNzfHNN98gNDQUp0+ffun9c0xNTXH27Fk4OTmVuD8vS2qVNKGVmpqKFStWYNWqVbh69Spat26NkJAQdOvW7aU3Qj1w4IDa69HGiLyLFy+iadOmal1qW8DU1BTnzp1D9erV0atXL9SvXx8TJ07E1av/196dh9Wcvn8Af5/TvipbNJOyFWUr32zNj8mgwcg2tqaUsq8l64yxmzGMZRgMo80WWTJixjojUwkhZSZFVBrZJSWUnt8fLudyKjqnDkd5v67rXOPzeT6f+9znDKe6e57nvg4bGxulOsqEhITIOgYCwLRp07BhwwbY2toiNDS0Qv9/X9q2bRt27NhRohBXGm9vb5w5cwajRo3Crl27YGpqiqtXr2Lt2rWQSqWYNGkSmjZtipCQEIWff8mSJQgJCUFgYCC6du2K33//Henp6fDz88Ps2bOV2j+iuMjISOTl5aF9+/ZKF8PXr1+PuXPn4quvvkLr1q1LbKapzLJne3t7XLlyBQUFBbC0tCwRS5El5qosfL8UEREBDw8PzJw5E/Pnz8e8efOQnJyMTZs2Yf/+/ejatavCsV7FQhsRERHR2/f48WNcu3YNgwYNQu/evWVL8ojo/cNCmxIcHR2xePFipbtclqZWrVqIjo4usSFxSkoKOnTogLt37yIxMRH/93//V+aSOmdnZ0yaNKlCS1tfLWoJIdCjRw9s3LixxOaiFSloHT16FEFBQdi7dy90dXVx7969csd6GzZv3ozp06fjxo0bCt/TokULWeGwWbNmOHjwINq3b4+zZ8+iZ8+euHnzpsKxbGxssG7dOnTu3BknT57EZ599hpUrV2L//v3Q1NTEnj17yvOy5KSmpqJFixbIy8sr81pzc3Ps2rULHTp0wK1bt1C3bl0cOnRIVpCJjo7GoEGDlG6p/s0332DFihWy5hY6OjqYMmUKFixYoND9S5cuRW5urqwbqxAC3bt3l3VArV27No4dOwY7OzuFc3rT3h7KFpjL6hI7Z84chfJ5G4XvQ4cO4bvvvsPZs2dRVFQEBwcHzJ49W6H9BF/n+++/x5gxY97YRZWIiIiIKmbu3LlYtGgROnbsiN9+++21+9kRkfpxjzYlLFq0SFYQKG3WizKzSwoLC3Hp0qUShbZLly7JfnjW1dVVaCnc2LFj4e/vj8zMzFLzUqRbZfECmoaGBtq1a6fSWSoviwdCCFmHxzcpqwvnq5TpyFm8xbIQAllZWYiLi1O6g+ns2bPh5uYGPz8/fPbZZ2jfvj0A4PDhw3JdWxVx/fp12R5ae/fuxZdffomRI0fCyclJ6f2TSpOfn4/Vq1cr3Cn04cOHskKrmZkZNDU15ZaJmpubl2tT1EWLFuGbb77Bv//+i6KiItja2ir1jUJoaCimT58uO961axdOnDiBv//+G02bNsXQoUMxb968El353kSRv4+KUqSQVhZVNid5lYuLS4kulBU1c+ZMlcYjIiIiopLmzp1b6v6bRPT+YaFNCS+7crm6usoVwMozu8TDwwM+Pj74+uuv4ejoCIlEgtOnT+O7776TbXD8cu+vsgwaNAgAMHHiRNm5t9GtsjzS09MRHByM4OBgXL9+HR07dsSvv/6K/v37l3lvaV04S6Psayy+CbZUKoWNjQ3mz5+v9MyeL7/8Ep988gmysrLQsmVL2fnPPvsMffv2VSqWoaEh7t27h3r16uHw4cOyNtO6urrIz89XKlbxTrRCCDx69Aj6+vrYsmWLQjEaN26M/fv3Y9y4cfjjjz+gq6uLw4cPyzq3HTp0qNzLqPX19WWdCI8ePSrXWbMs165dkyus/v777+jfv79sL8NZs2ZhwIAB5cpL1XJzc0sU8RQpyKuyOclL169fh0QikRVaT58+jW3btsHW1lZuk3dFpaamYuXKlUhKSoJEIkHTpk0xadIkNGzYUNWpExERERERVRostClBlbNMVqxYATMzMyxZsgS3bt0C8GLWkJ+fn2y2Trdu3WTFvTdRZbdKVXjy5Al2796NwMBAREZGom7duvD09IS3t7dSM+Te1usKCgpSSZzCwkLo6uoiPj6+xOy1Nm3aKB2va9euGD58OOzt7ZGSkiLbq+2ff/6BlZWVUrFWrFghV2iTSqWoVasW2rZtq/D+ZVOnToWnpydWrlyJzMxMbNmyBRMnTsSpU6cglUqxZ88eLF++XKm8Bg4ciI4dO2L8+PHIz8+Ho6Mjrl27BiEEtm/frlABtqCgQG5/v5MnT2LSpEmyY3Nzc9y9e1epvADg2LFjWLFihaxw1KRJE/j6+qJLly5Kxbl27RrGjx+P48ePy5bHAqpt3FEebm5uGDlyJDw8PHDz5k106dIFzZo1w5YtW3Dz5k3Mnj1b4ViHDh2Cq6srWrVqBScnJwghEBMTAzs7O0RERJR7vzciIiIiIqLKjnu0vQdebnyuzNLTt83IyAgJCQnlmrFkYmKCJ0+e4IsvvoCPjw9cXFzeuAdWZdawYUPs2bNHbjZbeWVnZ2PWrFm4fv06xowZIyuyzpkzB9ra2vjmm28q/BzKioqKwqlTp9ChQwe0b98e//77LxYvXozHjx+jV69e8PT0VCpenTp1cOjQIbRs2RLbtm3DnDlzcOHCBYSEhGDDhg04f/58mTFatWoFX19feHl5ISMjA1ZWVrh48SJsbW0BADExMRg4cKBSe8f9/PPP8PPzw5dffilb/hsbG4tdu3Zh+fLlSjVp6NChAwBg0qRJMDMzK7H8+23MVlOEqakpYmNjYWNjg1WrVmHHjh2Ijo7G4cOHMXr0aFy9elXhWPb29nBxccHixYvlzs+YMQOHDx9WqOEDERERERFRVcRCWzk8fvwYGRkZePbsmdx5ZfYJmzt3LoYNG6aSTpKbNm164/jLpahvUnzfsoiICHTu3LnEfm+KbMi/fPlyDB06FDVr1iwx9uDBA2zZsgUBAQGIj48vM9arKrJUrXr16khJSUHNmjVLLKssTpmuo0FBQdi5cye2bNmC6tWrK3zf26DqPe1OnDiBDh06QFNTdRNf9fT0kJKSAgsLCwwdOhTm5uZYvHgxMjIyYGtri9zc3DJjrF+/Hv7+/hg0aBBiY2NhYmKC6Oho2fjChQtx6tQpREREKJzXRx99hJkzZ5YoqK1ZswaLFi1SqkGGoaEhzp49CxsbG4XveRcMDQ1x8eJFWFlZwdXVFU5OTpg+fToyMjJgY2Oj1PJkXV1dJCYmonHjxnLnU1JS0KJFC7mZfERERERERB8SLh1Vwp07dzBs2DD88ccfpY4rsyQsIiICCxcuRKdOneDj44N+/fpBV1e3XHm9umwOeLG07vHjx9DW1oa+vr5Chbbi+5a5u7uXKxcAmDx5colzR48eRUBAAPbu3YuaNWuWKOyVpaJL1VasWAEjIyMAwMqVK5V67jdZtWoVrly5AnNzc1haWpYoTCo7syc7OxunT5/G7du35fb2kkgk8PDweOO9qt7TztnZGVlZWahdu7ZiySvAwsICJ0+eRPXq1XHw4EFs374dwIsCrKJ//0eNGgVNTU3s378fHTt2LNF84MaNGxg2bJhSeeXk5JS6TLtbt25yjRcU4ejoiOvXr793hTY7Ozv88ssv6NmzJ44cOSLr8nrjxg3UqFFDqVi1atVCfHx8iUJbfHy8Sv++EBERERERVTYstCnB19cXDx48QGxsLJydnREeHo5bt25h4cKFWLZsmVKxzp49i4SEBAQFBcHPzw/jxo3D4MGD4e3tDUdHR6ViPXjwoMS5y5cvY8yYMZg6dapCMVS1b9mrMjIyEBQUhKCgIOTm5uLBgwcICwtTaB+u4mbMmAE/P79Sl6pNnz69zELbyyWOhYWFAF50X6xTp47SeRTXp0+fCsd4KSIiAl999RXy8vJgZGQkN+tOkUKbqve0exuTXX19ffHVV1/B0NAQlpaWsm6qJ06cQPPmzRWO4+PjAx8fn1LH1q5dq3Rerq6uCA8PL/Hv5bfffkOvXr2UirVx40aMHj0a//33H5o1awYtLS25cWVmvqrSDz/8gL59+2Lp0qXw9PSULXfet2+f0nsKjhgxAiNHjsTVq1fRoUMHSCQSREVF4YcffoC/v//bSJ+IiIiIiKhS4NJRJdStWxe//fYb2rRpA2NjY8TFxcHa2hr79u3DkiVLEBUVVa64hYWFiIiIQFBQEA4ePAgbGxsMHz4cXl5eJWaaKSMuLg7u7u64dOlSue4PDQ2Fq6triVlaZQkLC8PGjRsRHR2NHj16wN3dHd27d4eBgQEuXLgg20tLGapcqqavr4+kpCSVLNtVJWtra/To0QPfffcd9PX11Z0OpFIpbt26hVq1aqk0blxcHK5fv46uXbvC0NAQAHDgwAGYmJjIOodWxLlz5zB79mzs379f4XsWLlyIH3/8EU5OTnJ7tEVHR8Pf319u/8RXu/uWJjY2Fm5ubkhLS5Ode1+6AD9//hw5OTlyDTHS0tKgr6+v1Ew0IQRWrlyJZcuWyZbVmpubY+rUqZg4ceIbl2YTERERERFVZSy0KcHY2BgJCQmwsrKClZUVtm7dCicnJ1y7dg12dnZ4/PhxueI+e/YM4eHhCAwMxJ9//okOHTrg1q1buHHjBn799VcMGjSoXHHPnz+PTp06yZotKMvY2Bjx8fFKdQoFAE1NTUybNg0zZ86ULdcEAC0trXIX2iwsLLB8+XIMGDBA7nxYWBimTJmCjIwMhWM5Oztj0qRJKpuNlp2djV27diE1NRVTp05F9erVce7cOZiZmeGjjz5SOI6BgQESExOVfr9f2rdvH7p37w4tLS3s27fvjde6urqWGU8qlWLkyJFlFv2U7TyqCkeOHMHhw4ehpaWF4cOHo0GDBrh06RJmzJghW0p88OBBheMp2vRDIpGU2TTA1tYWTZs2xbRp00pthvC+FXgr6tGjRwAg92+diIiIiIjoQ8Wlo0qwsbFBcnIyrKys0KpVK6xfvx5WVlb45ZdfULduXaXjnT17FkFBQQgNDYWOjg6GDh2KNWvWoFGjRgCAZcuWYeLEiWUW2ooXVYQQyMrKws8//1yhGULlrcF6e3tj7dq1iIyMhIeHBwYNGiQ3g6Y8VLlUbezYsfD390dmZiZat25dYsaeMkv7EhIS0KVLF1SrVg1paWkYMWIEqlevjvDwcKSnp5fZqOJVLi4uiIuLK3ehrU+fPrh58yZq1679xiKiMrOqEhMToa2t/cZYZZk8eTIWLFgAAwODUvfve5UiRbuQkBAMGzYM1atXx/3797Fx40YsX74cY8eORf/+/XHhwgU0a9aszDivUuWy2/T0dOzbt0/271idHBwccOzYMZiamsLe3v6N/7+U2U8wPz8fQgjo6+vDyMgI6enpCAgIgK2tLbp166aK1ImIiIiIiColzmhTwOrVqzFhwgRs3boVBQUF8PLywvnz5+Hi4oJ79+5BW1sbwcHBSs08a9GiBZKSktCtWzeMGDECvXr1goaGhtw1d+7cgZmZmdym+KWRSqVyxxKJBLVq1ULnzp2xbNmychUBgRczVC5cuFCuwk9+fj7CwsIQGBiIU6dOwcXFBQcOHEB8fLzSRRBAtUvVir9fQPmX9nXp0gUODg5YsmSJ3PsVExNTYvlgWQICAjB//nwMGzYMzZs3L7G3lyKz0FRJKpXKCncV8XI/QxMTEzg7O7/2OolEgj///LPMeK1atcLgwYMxY8YMhIWFYfDgwbC3t0dYWJhCHWjftl69esHLy6tcexGq2rx58zB16lTo6+tj3rx5b7y2eFOJN+nWrRv69euH0aNHIzs7GzY2NtDW1sbdu3exfPlyjBkzpqKpExERERERVUostCmgevXqaN26NQIDA2FhYSE7//jxY1y6dAn16tVDzZo1lYq5YMECeHt7l1hamJiYiICAAJV2xiyvqKgoODo6QkdHp0JxLl++jMDAQGzatAm5ubno2bMnvvzyS4U7jxYWFmLr1q2yBgYVXaqWnp7+xnFllvZVq1YN586dQ8OGDeUKbenp6bCxsVFq77jSCoAvqWNvLw0NDZV3HVUFIyMjJCQkoH79+igqKoKOjg6OHj2KTp06lTumt7f3G8cDAwMVjrVhwwYsXLgQ3t7e70XB9G2oWbMmIiMjYWdnh40bN2L16tU4f/48du/ejdmzZyMpKUndKRJRGYKDg+Hr64vs7OwKxTl+/DicnZ3x4MEDmJiYqCQ3IiKqmvg1gz4ULLQp4MaNGxg5ciSio6OxatWqMrs/KisnJwehoaEICAhAXFwcWrRogfj4+ArHVaZol5+fjyNHjsDZ2blEASsnJwfHjx+Hi4tLhYpuRUVFOHDgAAICAvDHH3/g6dOnCt+rqgYGjx49QmxsLAoKCtCmTRulC6TFmZmZ4eDBg7C3t5crtB0+fBg+Pj64fv16heIra9WqVQpdV9aG/oDqZrSpWvG8KjLz8qW+ffvKHRcUFODixYvIzs5G586dsWfPHqXyex11N0NQFX19fdkvGQYOHAg7OzvMmTMH169fh42NTbn3qyR635U1e9rT0xPBwcHvJpkKys/Px6NHjyr8Gf/s2TPcv3+/1D0pVenTTz9Fq1at3otfRBIRVVVeXl4ICQkpcf7y5csq2RZFkUKbRCJBeHi4yvbTJlIH7tGmAHNzc+zfvx/BwcGYNGkSwsPDMWvWLGhqyr99yuztBQCRkZEICAjA7t278eTJE0ydOhXbtm2r0IdYaUU7RWzYsAH79u0rdbaNsbExVq1ahevXr2PcuHHlzk0qlaJXr17o0qULfv75Z6Xubdu2Lc6fP1+hQltCQgK6d++OmzdvQggBY2Nj7Nq1C126dCl3zN69e2P+/PkICwsD8OILQ0ZGBmbMmFGhpYNPnjyBrq6u0vetWLFC7vj69euoW7eu3N9ViUSiUKEtKCioQl1vS+Ps7PzGH8QUWToKAIcOHZLlVlRUhGPHjuHixYty1ygzcyw8PLzEuaKiIowdO1bpAl5ZS73VRSqVvvG9V6YA2KhRI+zduxd9+/bFoUOH4OfnBwC4ffu2XIdWoqomKytL9ucdO3Zg9uzZSE5Olp3T09OTu76goKDErNb3hZ6eXol8y0NbWxt16tRRQUZERPQ++PzzzxEUFCR3rlatWmrKhqiSEqSUI0eOCA0NDSGVSoVEIpE9pFKpQvffuHFDLFq0SDRs2FDUqVNH+Pn5iTNnzghNTU3xzz//lDuv48ePCw8PD6Gvry+kUqmYPn26uHz5ssL3Ozo6in379r12PCIiQjg6Oioc786dO2L//v3i0KFDorCwUAghxLNnz8TKlStFnTp1RI0aNRSOJYQQYWFhokGDBmL16tUiJiZGXLhwQe6hiO7du4t27dqJ6OhocfbsWeHq6ipsbGyUyqO4hw8fCicnJ2FiYiI0NDSEhYWF0NLSEh07dhS5ublKxSosLBTz588X5ubmQkNDQ6SmpgohhJg1a5bYuHFjufIzNDSUxVHWlStXxLBhw2THFhYWwtTUVPaoWbOmuHTpklIxfX195R7jxo0TTk5Oolq1amLixIkKxXj1393rHor+eyzLpUuXRJ06dVQSS9327t0r99i5c6f4+uuvxUcffaT036+dO3cKLS0tIZVKRdeuXWXnv/vuO/H555+rOnWi91JQUJCoVq2a7PjatWsCgNixY4fo1KmT0NHREYGBgeLu3bti8ODB4qOPPhJ6enqiWbNmYtu2bXKxOnXqJCZMmCCmTp0qTE1NhZmZmZgzZ47cNXPmzBEWFhZCW1tb1K1bV0yYMEE2ZmlpKRYsWCA8PDyEgYGBqFevnti7d6+4ffu2cHV1FQYGBqJZs2bizJkzr80/Pj5efPrpp8LQ0FAYGRkJBwcH2fVpaWniiy++ECYmJkJfX1/Y2tqKAwcOCCGE+OuvvwQA8eDBA1msXbt2CVtbW6GtrS0sLS3Fjz/+KPdaLC0txaJFi8SwYcOEoaGhsLCwEOvXr3/te+3p6SkAyD2uXr0qGjZsKJYuXSp3bWJiopBIJOLKlStCCCEAiLVr14rPP/9c6OrqCisrKxEWFiZ3T2Zmphg4cKAwMTER1atXF66uruLatWuvzYeIqKry9PQUvXv3Vnhs0qRJolOnTrLjoqIi8cMPP4j69esLXV1d0aJFC7Fz507ZeGlfM15laWkp91lvaWkprl27JiQSidzXMCGEWLVqlahXr54oKiqSxd2/f79o0aKF0NHREW3atBEJCQly90RHR4v/+7//E7q6uuLjjz8WEyZMUPpnNiJFsNCmhGXLlgl9fX3h6ekprly5ItLS0uQeitDR0RHu7u7i4MGD4vnz57Lz5Sm0qbJoZ2JiItLT0187np6eLkxMTBSKFR0dLUxMTGQFjzZt2oh//vlHNG7cWDRs2FCsXr1a5OXlKZXf64opyhRVatWqJfcBfffuXSGVSsWjR4+UykWIF19UEhMTZcfHjh0TS5cuFT/88IM4cuSI0vGEEGLevHmiQYMGYsuWLUJPT09WINuxY4do165duWJWpNDm6+srZs6cKRdryZIlIjg4WAQHB4vu3buLUaNGlSt2cXPmzBH+/v4qiaVKBw4cEDVr1lT6vtzcXHHgwAGxbt068dNPP8k93jdbt24Vrq6uSt+XlZUlzp07J/c5durUKZGUlKTK9IjeW68rtFlZWYndu3eLq1eviv/++09kZmaKpUuXivPnz4vU1FSxatUqoaGhIWJjY2X3durUSRgbG4u5c+eKlJQUERISIiQSiTh8+LAQ4kVx29jYWPz+++8iPT1dnDp1SmzYsEF2v6Wlpahevbr45ZdfREpKihgzZowwMjISn3/+uQgLCxPJycmiT58+omnTpqKoqKjU/O3s7IS7u7tISkoSKSkpIiwsTMTHxwshhOjZs6fo2rWrSEhIEKmpqSIiIkJERkYKIUr+0BQXFyekUqmYP3++SE5OFkFBQUJPT08EBQWVyHfNmjXi8uXL4vvvvxdSqfS1nx/Z2dmiffv2YsSIESIrK0tkZWWJwsJCsWjRImFrayt3rZ+fn+jYsaPsGICoUaOG+PXXX0VycrKYNWuW0NDQEP/++68QQoi8vDzRuHFj4e3tLRISEsS///4r3NzchI2NjXj69KkifxWIiKqMihbavv76a9GkSRNx8OBBkZqaKoKCgoSOjo44fvy4EKLsQtvt27cFABEUFCSysrLE7du3hRBCdO3aVYwdO1buWnt7ezF79my5uE2bNhWHDx8WCQkJ4osvvhBWVlbi2bNnQgghEhIShKGhoVixYoVISUkR0dHRwt7eXnh5eSn5LhGVjYU2BaSmpgonJydRp04dsXfv3grFsra2FlZWVuLrr7+W+4ayPMUxVRbtDA0NRVxc3GvH4+LihKGhoUKxOnfuLAYNGiQSExOFn5+fkEgkon79+iIkJET2Db6yihc1y1PklEgk4tatW3LnDA0NxdWrV5XOx8bGRkilUuHo6CjWr18vHj58qHSM4ho2bCiOHj0qy+tlgSwpKUnhImdxFSm02dnZiT///PO1sY4fPy4aNWpUrtjFXb58WZiamqokVnn4+fnJPXx9fcWgQYOEoaGhGDdunFKxzp07J+rUqSOMjY2FhoaGqFWrlpBIJMLAwEDUr1//Lb2C8rty5YrQ19dX+PqCggKhoaEhV2gm+hC9rtC2cuXKMu/t0aOH3C8XOnXqJD755BO5axwdHcX06dOFEC9+0WdtbS37YaE4S0tL4e7uLjvOysoSAMS3334rO3fy5EkBQGRlZZWav5GRkQgODi41fvPmzcXcuXNLHSv+Q5Obm5vcTFchhJg6dapcQax4vkVFRaJ27dpi3bp1pT6HEC/eo0mTJsmdu3HjhtDQ0BCnTp0SQryYOV+rVi251wFAjB49Wu6+tm3bijFjxgghhAgICBA2NjZy3588ffpU6OnpiUOHDr02HyKiqsjT01NoaGgIAwMD2ePLL7+Ujb2p0Jabmyt0dXVFTEyM3DU+Pj5iyJAhQoiyC21CvPjcDg8Plzu3Y8cOYWpqKp48eSKEeDELWyKRyGYfv4y7fft22T337t0Tenp6YseOHUIIITw8PMTIkSPl4v79999CKpWK/Pz8Mt8bImW8ftdukmnRogXq1KmDxMRE9O7du0KxkpOTsWXLFmRlZcHR0RGtW7eW7aul7CbClpaWiIqKwokTJ5CSklKhvOzs7HD06NHXjh85cgR2dnYKxbpw4QK+/fZbNGvWDAsXLoREIsEPP/yAoUOHlnujZEtLyzc+FCGRSPDo0SPk5OQgJycHDx8+LHEuJydHoViXLl3CiRMn0Lx5c0yZMgXm5ubw9PTEiRMnyvX6AOC///4rdX++oqIiFBQUlDtueaWnp6N+/fqy4+HDh8vt2WZlZYXMzEyVPNfJkycV3pNu7NixyM3NlR1v3rxZ7jg7Oxs9evRQ6vnPnz8v90hISAAALFu2TOmNt/38/NCrVy/cv38fenp6iI2NRXp6Olq3bo0ff/xRqVhvW35+PlavXo2PP/5Y4Xs0NTVhaWlZJZo6EL0N//vf/+SOnz9/jkWLFqFFixaoUaMGDA0NcfjwYWRkZMhdV3xP1bp16+L27dsAgAEDBiA/Px8NGjTAiBEjEB4ejsLCwtfeb2ZmBgBo3rx5iXMvYxY3efJkDB8+HF26dMHixYuRmpoqG5s4cSIWLlwIJycnzJkzR/YZWZqkpCQ4OTnJnXNycsLly5flPjdezVcikaBOnTqvze116tati549e8o6Q+/fvx9PnjzBgAED5K5r3759ieOX3ZHPnj2LK1euwMjICIaGhjA0NET16tXx5MkTufeAiOhD4ezsjPj4eNlD0WZr//77L548eYKuXbvKPk8NDQ2xadOmCn+e9unTB5qamrJ9lQMDA+Hs7AwrKyu56179vK9evTpsbGzkPu+Dg4PlcnNxcUFRURGuXbtWofyIimMzBAX88ssvcHd3V1k8JycnODk5YdWqVQgNDUVgYCCeP3+OsWPHws3NDX369FFow8nk5GRER0cjICAAjo6OsLa2luWpbEHL29sbkydPhp2dHb744gu5sYiICCxcuBDLly9XKNb9+/dl+evr60NfXx/29vZK5VOa1NRUrFy5EklJSZBIJGjatCkmTZqEhg0bKnS/EALW1tYlzr3MTQihVFfIl/8fV69ejR07diAoKAiffvopGjZsCB8fHwwdOhTm5uYKvz47Ozv8/fffJQqHO3fuVPj9K14olEgkyM3NLXFekQ3rpVIpbt++LfsCVrzRwq1bt5Te5Ltfv35yx0IIZGVlIS4uDt9++61CMdavX4+5c+fC0NAQADBu3Dg4OTnJjp8+fYpDhw4plddff/2l1PVvEh8fj/Xr10NDQwMaGhp4+vQpGjRogCVLlsDT07PEe/CumJqayn0uCCHw6NEj6OvrY8uWLUrFmjVrFmbOnIktW7agevXqqk6VqFIzMDCQO162bBlWrFiBlStXonnz5jAwMICvry+ePXsmd13xz1OJRCJrrmJhYYHk5GQcOXIER48exdixY7F06VJERkbK7nv1/pf/1ks797qGLXPnzoWbmxsOHDiAP/74A3PmzMH27dvRt29fDB8+HC4uLjhw4AAOHz6M77//HsuWLcOECRNKxHn5tbT4ueLe9HqVMXz4cHh4eGDFihUICgrCoEGDoK+vX+Z9r74frVu3xtatW0tcw82/iehDZGBgUOov/6VSaYnP81cnA7z8DD9w4AA++ugjuet0dHQqlJO2tjY8PDwQFBSEfv36Ydu2bQr/MvzVz/tRo0aV2hSuXr16FcqPqDgW2hSgyiLbqwwNDTFixAiMGDECSUlJCAgIwKxZszB27FiFZzCpqmg3cuRInDhxAq6urmjSpAlsbGwgkUiQlJSElJQUDBw4ECNHjlQop5ezxHR1dWXfcD9+/LhcxZ6XDh06BFdXV7Rq1QpOTk4QQiAmJgZ2dnaIiIhA165dy4yhymLKq/T19TFs2DAMGzYMqampCAwMxJIlSzB79uwSP0i9yZw5c+Dh4YH//vsPRUVF2LNnD5KTk7Fp0ybs379foRgmJiYliimvFumUKSa+nOXYpk2bUscPHTqEZs2aKZTXS8W7mEqlUtjY2GD+/Pno1q2bQjGKf4Ev7Qe4ioqMjEReXh7at28PU1NTpe7V0tKS/T8wMzNDRkYGmjZtimrVqpWYwfIuFf9mRCqVolatWmjbtq3Sr3HVqlW4cuUKzM3NYWlpWaKwcO7cuYqmS1Rl/P333+jdu7fse4mioiJcvnwZTZs2VSqOnp4eXF1d4erqinHjxqFJkyZITEyEg4ODynK1traGtbU1/Pz8MGTIEAQFBaFv374AXhT7Ro8ejdGjR2PmzJn49ddfSy202draIioqSu5cTEwMrK2toaGhUe7ctLW1S/3a1aNHDxgYGGDdunX4448/Sp1ZHhsbi6FDh8odv/za6ODggB07dqB27drsmkxE9Aa1atXCxYsX5c7Fx8fLfnFia2sLHR0dZGRkoFOnTuV+Hi0trVI/74cPH45mzZph7dq1KCgoKPWX17GxsbKi2YMHD5CSkoImTZoAePF5/88//5RaRCRSNRba3hNNmzbFjz/+iMWLF2Pfvn1K36+Kot2WLVvg6uqKbdu2ISUlBUII2NjYYN68eRg4cKDCuRSfOVaRYs9LM2bMgJ+fHxYvXlzi/PTp0xUqtFXkA18ReXl5iIyMRGRkJLKzs2FjY6PU/b169cKOHTvw3XffQSKRYPbs2XBwcFC4kAiotpg4bNgw+Pr6omXLlujZs6fcWEREBBYvXqzUssrnz5/Dy8sLzZs3f29mQS1duhS5ubmYN28egBd/N7t3747Dhw8DAGrXro1jx44pvGwaAOzt7REXFwdra2s4Oztj9uzZuHv3LjZv3iy3jOtdKiwsRFpaGry9vWFhYVHheH369Kl4UkQfiEaNGmH37t2IiYmBqakpli9fjps3bypVaAsODsbz58/Rtm1b6OvrY/PmzdDT01N464Sy5OfnY+rUqfjyyy9Rv359ZGZm4syZM+jfvz8AwNfXF927d4e1tTUePHiAP//887X5+/v7w9HREQsWLMCgQYNw8uRJ/Pzzz1i7dm2FcrSyssKpU6eQlpYmW94plUqhoaEBLy8vzJw5E40aNSqxTBR4MTP8f//7Hz755BNs3boVp0+fRkBAAADgq6++wtKlS9G7d2/Mnz8fH3/8MTIyMrBnzx5MnTpVqaX1RERVWefOnbF06VJs2rQJ7du3x5YtW3Dx4kXZz3lGRkaYMmUK/Pz8UFRUhE8++QQ5OTmIiYmBoaEhPD09FXoeKysrHDt2DE5OTtDR0ZH9Qrhp06Zo164dpk+fDm9vb+jp6ZW4d/78+ahRowbMzMzwzTffoGbNmrLvW6dPn4527dph3LhxGDFiBAwMDJCUlIQjR45g9erVqnmTiF56x3vC0TtUUFAgdu/e/c6f9/jx4wo9lKGjoyNSUlJKnE9OThY6OjqqSr1cIiMjhZeXlzA0NBSGhoZi2LBhIioqSq05qcrgwYOFRCIRTZs2FX369BF9+/YVTZs2FVKpVAwYMEDpeDo6OuVqPvGq4k0tijdpuHnzpsKdaO3t7eU2TQ0LCxN6enoiKipK3Lt3T/Ts2VPp13nmzBlZE4nbt2+L7t27CyMjI2Fvby/r4KcOhoaGsg1jiajiXtcM4fz583LX3bt3T/Tu3VsYGhqK2rVri1mzZomhQ4fKbShd2kb/vXv3Fp6enkIIIcLDw0Xbtm2FsbGxMDAwEO3atZM1zxHiRXOBFStWyN2PYptJF8/v1fyfPn0qBg8eLCwsLIS2trYwNzcX48ePl20OPX78eNGwYUOho6MjatWqJTw8PMTdu3eFEKVvbL1r1y5ha2srtLS0RL169cTSpUvlcist35YtW4o5c+aI10lOThbt2rUTenp6AoDc51lqaqoAIJYsWVLiPgBizZo1omvXrkJHR0dYWlqK0NBQuWuysrLE0KFDRc2aNYWOjo5o0KCBGDFihEoaHRERVSZv6joqhBCzZ88WZmZmolq1asLPz0+MHz9erutoUVGR+Omnn4SNjY3Q0tIStWrVEi4uLq/tVF2affv2iUaNGglNTU1haWkpNxYQECAAiNOnT8udfxk3IiJC2NnZCW1tbeHo6Fjie+/Tp0+Lrl27CkNDQ2FgYCBatGghFi1apNB7Q6QMiRBvYd0VkYpZWFhg+fLlJTY4DgsLw5QpU975krzMzEyEhIQgODgYqampaNu2LXx8fDB48GDZXmHKGjZsGNzd3dG5c+dyN414G7Zv347t27fLGm40btwYQ4YMweDBg5WO5ejoiMWLF+Ozzz4rdz5SqRQjR46U7cGzZs0auLu7y5alPn78GL/++qtCMyZNTU0RExMjm5kxbNgwFBYWYvPmzQBeTD8fMGAArl+/Xu583xd9+vRBnz594OXlpZJ42dnZ2LVrF1JTUzF16lRUr14d586dg5mZWYl9OYiI3qbo6Gh8+umnyMzMlDV9eEkikSA8PJwzcYmIqoBFixZh+/btSExMlDt//PhxODs748GDBzAxMVFPckSv4NJRqhRGjBiBkSNH4urVq+jQoQMkEgmioqLwww8/wN/f/53nY2VlhRo1asDDwwM+Pj5K77VTmnv37qFnz56oUaMGBg8eDHd3d5U0kaiowYMHl6uoVppFixZhypQpWLBgAVq3bl1iby9F9sfp2LEjkpOTZccdOnTA1atXS1yjiIKCArnNWU+ePIlJkybJjs3NzXH37l2FYuXn5+PIkSNwdnaGkZGR3FhOTg6OHz8OFxeXCm8GW17du3fHzJkzcfHixVLfe1dXV4VjJSQkoEuXLqhWrRrS0tIwYsQIVK9eHeHh4UhPT8emTZtUnT4RUQlPnz7F9evX8e2332LgwIElimxERFQ15ObmIikpCatXr8aCBQvUnQ5RmTijjSoFIQRWrlyJZcuW4caNGwBeFEGmTp2KiRMnvvMZYHv27IGrqys0NVVbq87OzkZYWBi2bduGv//+GzY2NnB3d4ebm1uJ9tWVkVQqlf25eNMGZfftU4VWrVrB19cXXl5eyMjIgJWVFS5evAhbW1sALzbwHjhwIDIzM8uM9dNPP2Hfvn04duxYqeNdunRB3759MW7cOJW+BkW9+t4Xp+x736VLFzg4OGDJkiUwMjLChQsX0KBBA8TExMDNzQ1paWkqyJiI6M2Cg4Ph4+ODVq1aYd++faXOpuWMNiKiys/LywuhoaHo06cPtm3bVqK5Dme00fuGhTaqdB49egQAJWYNVTWZmZmyTrKXL19GYWGhulOqsMjIyDeOK9KwwtfXV9Z1qKLWr18Pf39/DBo0CLGxsTAxMUF0dLRsfOHChTh16hQiIiLKjNWmTRt8++236NWrV6nj+/fvx/z583H69OkK561u1apVw7lz59CwYUO5Qlt6ejpsbGzw5MkTdadIRERERESkFlw6Su+10pbjvfzv+7Ac720pKChAXFycrMNaVVkOo4rOrwcPHsTq1avRunVrDB8+HIMHD1ZoyWlpRo0aBU1NTezfvx8dO3bEnDlz5MZv3LgBb29vhWJdvnwZLVu2fO14ixYtcPny5XLl+b7R1dVFTk5OifPJycmoVauWGjIiIiIiIiJ6P7x+LRHRe2DDhg346aefSp29ZmxsjFWrVmHjxo1qyOzt+OuvvzBixAiYmZnB09MTRkZGiIiIqPSb8V++fBlDhgwptTjz8OFDuLm5ldhn7XUuXbqEEydOoHnz5pgyZQrMzc0xdOhQnDhxoly5+fj4IDw8HOvWrUOdOnXkxtauXYu+ffsqFKewsBB37tx57fidO3fUMivxzz//hK2t7Wvfezs7O6Xfu969e2P+/PkoKCgA8GJpVkZGBmbMmIH+/furJG8iIiIiIqLKiIU2eq9t3boVvr6+rx339fVFSEjIu0voLfr444/Ro0cP3LlzB+vXr8etW7cQFBSELl26vHF/rcpg6dKlsLCwKHXmWbVq1WBhYYGlS5cqHM/JyQkBAQG4efMmVq9ejbS0NHz66ado3LgxFi9eLNvH712ys7PD0aNHXzt+5MgR2NnZvcOMXli5ciVGjBjx2vd+1KhRWLFihVIxf/zxR9y5cwe1a9dGfn4+OnXqhEaNGsHIyAiLFi1SVepERERERESVDvdoo/eaqakpLly4gHr16pU6npGRgZYtW+LBgwfvODPV27BhAwYMGABTU1O584mJiQgICMDKlSvVk5gKNGnSBJs3b4ajo2Op42fPnoWbm5tcN1FlpaamIjAwEOvWrUNubi6ePXtW7ljlsWHDBkyePBnbt2/HF198ITcWERGBIUOGYPny5Rg5cuQ7zcvS0hIHDx58bWfcS5cuoVu3bsjIyCgzVvH98f7880+cO3cORUVFcHBwQJcuXVSaOxERERERUWXDPdrovfZyOd7rCm3qWo73NrxagMnJyUFoaCgCAgIQFxeHFi1aqDGziktPT0ft2rVfO16zZs0KLY/Ny8tDZGQkIiMjkZ2dDRsbm3LHKq+RI0fixIkTcHV1RZMmTWBjYwOJRIKkpCSkpKRg4MCB77zIBgC3bt2ClpbWa8c1NTXfuOT1VaXtj9e5c2dVpUpERERERFTpVe71aFTlva/L8d6WyMhIDB06FHXr1sXYsWPRuXNnpKSkID4+Xt2pVUi1atWQmpr62vErV66Uq6HBiRMnMGzYMNSpUweTJk2CtbU1/v77byQlJVUk3XLbsmULtm/fDmtra6SkpODSpUuwsbFBaGgoQkND1ZLTRx99hMTExNeOJyQkoG7dugrFKm1/PE9Pz3Lvj0dERERERFTVcOkovdfe1+V4qpSVlYWgoCAEBgYiLy8PQ4YMgZubG9q3b48LFy7A1tZW3SlW2MCBA1FQUIDw8PBSx3v37g1tbW3s3LmzzFiZmZkICQlBcHAwUlNT0bZtW/j4+GDw4MEwNDRUdeqV3oQJE3D8+HGcOXMGurq6cmP5+flo06YNnJ2dsWrVKqXiPn78GDt27EBQUBCioqLQsGFD+Pj4YOjQoTA3N1flSyAiIiIiIqo0WGij9567uzu2bdv22uV46poppCq6uroYMGAA3N3d0bVrV1njAy0trSpTaDt//jzat2+PL774AtOmTZMt7bx06RKWLFmCAwcOICYmBg4ODmXG0tTURI0aNeDh4QEfH5/X7j1GL9y6dQsODg7Q0NDA+PHj5f4NrVmzBs+fP8e5c+dgZmZW7udQ9/54RERERERE7wvu0UbvvS1btsDV1RXbtm1DSkoKhBCwsbHBvHnzMHDgQHWnV2GWlpaIiopCvXr1YGlpiSZNmqg7JZWzt7fHrl274O3tXWJWW40aNRAWFqZQkQ0AwsLC4OrqCk1NfnwpwszMDDExMRgzZgxmzpyJl79bkUgkcHFxwdq1aytUZHsf9scjIiIiIiJ6X3BGG9F7IDo6GgEBAdi5cyesra3h7u6OadOmISEhoUrN2MrPz8fBgwdx5coVCCFgbW2Nbt26QV9fX92pfRAePHgge+8bN25cosOtMk6cOIGgoCDs2rULADBgwAD4+PjAyclJVekSERERERFVOiy0Eb1HcnNzERoaisDAQJw6dQqdOnWCm5sb+vTpg1q1aqk7PfrAcX88IiIiIiKiN2Ohjeg9lZSUhICAAGzevBn3799HQUGBulOiDxz3xyMiIiIiInozFtqI3nOFhYXYt28f+vXrp+5U6AO3Z88e7o9HRERERET0Biy0EVGVIoTAnTt3ULt2bXWnQkRERERERB8YqboTICJShr6+Pu7cuSM7/vzzz5GVlSU7vn37NurWrauO1IiIiIiIiOgDx/U/RPRW5eTkKHytsbFxmdc8efIEr07EjY6ORn5+vtw1nKhbupSUFBw/fhy3b99GUVGR3Njs2bPVlBUREREREVHVwUIbEb1VJiYmkEgkCl37/PlzlTynos/3Ifn1118xZswY1KxZE3Xq1JF7jyQSCQttREREREREKsBCGxG9VX/99Zfsz2lpaZgxYwa8vLzQvn17AMDJkycREhKC77//Xl0pfhAWLlyIRYsWYfr06epOhYiIiIiIqMpiMwQiemc+++wzDB8+HEOGDJE7v23bNmzYsAHHjx8vM4aGhgZu3ryJWrVqAXix3PTChQuoX78+AODWrVswNzdX2ey4qsLY2Bjx8fFo0KCBulMhIiIiIiKqslhoI6J3Rl9fHxcuXEDjxo3lzqekpKBVq1Z4/PhxmTGkUimqVasmW/qYnZ0NY2NjSKUversIIZCTk8NCWzE+Pj5wdHTE6NGj1Z0KERERERFRlcWlo0T0zlhYWOCXX37BsmXL5M6vX78eFhYWCsUICgp6G6lVeY0aNcK3336L2NhYNG/eHFpaWnLjEydOVFNmREREREREVQdntBHRO/P777+jf//+aNiwIdq1awcAiI2NRWpqKnbv3o0ePXqoOcOq6+XS2tJIJBJcvXr1HWZDRERERERUNbHQRkTvVGZmJtauXYtLly5BCAFbW1uMHj1a4RltDx48wJYtW+Dp6QljY2O5sYcPH2LTpk2ljhERERERERG9bSy0EVGlsmDBAiQkJGDnzp2ljg8cOBAtW7bEN998844zIyIiIiIiog8dC21E9E5lZ2fj9OnTuH37NoqKiuTGhg4dWub9rVq1wrJly/DZZ5+VOn7s2DFMmTIF58+fV0m+VUlmZib27duHjIwMPHv2TG5s+fLlasqKiIiIiIio6mAzBCJ6ZyIiIvDVV18hLy8PRkZGss6hwIt9whQptKWmppboWvqqxo0bIzU1VSX5ViXHjh2Dq6sr6tevj+TkZDRr1gxpaWkQQsDBwUHd6REREREREVUJUnUnQEQfDn9/f3h7e+PRo0fIzs7GgwcPZI/79+8rFENDQwM3btx47fiNGzcglfKjrbiZM2fC398fFy9ehK6uLnbv3o3r16+jU6dOGDBggLrTIyIiIiIiqhL40ygRvTP//fcfJk6cCH19/XLHsLe3x969e187Hh4eDnt7+3LHr6qSkpLg6ekJANDU1ER+fj4MDQ0xf/58/PDDD2rOjoiIiIiIqGpgoY2I3hkXFxfExcVVKMb48eOxbNky/Pzzz3j+/Lns/PPnz7F69WqsWLEC48aNq2iqVY6BgQGePn0KADA3N5dbXnv37l11pUVERERERFSlcI82InpnevbsialTp+Lff/9F8+bNoaWlJTfu6upaZoz+/ftj2rRpmDhxIr755hs0aNAAEokEqampyM3NxdSpU/Hll1++rZdQabVr1w7R0dGwtbVFz5494e/vj8TEROzZswft2rVTd3pERERERERVAruOEtE786a90yQSidwMtbKcPn0aW7duxZUrVyCEgLW1Ndzc3NCmTRtVpFrlXL16Fbm5uWjRogUeP36MKVOmICoqCo0aNcKKFStgaWmp7hSJiIiIiIgqPRbaiKhKuXfvHjZv3gxfX191p0JEREREREQfGBbaiKjSE0Lg8OHDCAgIwG+//QZjY2PcuXNH3Wm9l+Li4pCUlASJRIKmTZuidevW6k6JiIiIiIioyuAebUT0TuXl5SEyMhIZGRl49uyZ3NjEiROVipWWlobAwEAEBwfjv//+g5ubGw4cOABnZ2dVplwlZGZmYsiQIYiOjoaJiQkAIDs7Gx06dEBoaCgsLCzUmyAREREREVEVwBltRPTOnD9/Hj169MDjx4+Rl5eH6tWr4+7du9DX10ft2rVx9erVMmM8ffoUe/bswcaNGxETE4Pu3bvDzc0NQ4YMwYULF2Bra/sOXknl061bN+Tk5CAkJAQ2NjYAgOTkZHh7e8PAwACHDx9Wc4ZERERERESVHwttRPTOfPrpp7C2tsa6detgYmKCCxcuQEtLC+7u7pg0aRL69etXZoyaNWvC1tYW7u7uGDBgAExNTQEAWlpaLLS9gZ6eHmJiYmBvby93/ty5c3ByckJ+fr6aMiMiIiIiIqo6Xt8CkIhIxeLj4+Hv7w8NDQ1oaGjg6dOnsLCwwJIlS/D1118rFOP58+eQSCSQSCTQ0NB4yxlXHfXq1UNBQUGJ84WFhfjoo4/UkBEREREREVHVw0IbEb0zWlpakEgkAAAzMzNkZGQAAKpVqyb7c1mysrIwcuRIhIaGok6dOujfvz/Cw8Nlcal0S5YswYQJExAXF4eXE5nj4uIwadIk/Pjjj2rOjoiIiIiIqGrg0lEieme6desGLy8vuLm5YfTo0Th//jwmTpyIzZs348GDBzh16pRS8VJTUxEUFISQkBD8999/GDJkCLy8vNC5c2fOdgNgamoqV4DMy8tDYWEhNDVf9MF5+WcDAwPcv39fXWkSERERERFVGSy0EdE7ExcXh0ePHsHZ2Rl37tyBp6cnoqKi0KhRIwQGBqJVq1bliltUVISDBw8iMDAQERERMDIywt27d1WbfCUUEhKi8LWenp5vMRMiIiIiIqIPAwttRFSl3LlzB5s3b8bkyZPVncp7o7CwEFu3boWLiwvq1Kmj7nSIiIiIiIiqLBbaiEjtLly4AAcHBzx//lzhe86cOYPQ0FCkpKRAIpHA2toaQ4YMwf/+97+3mGnlpa+vj6SkJFhaWqo7FSIiIiIioiqLzRCI6L2gTM1/2rRpaNu2LTZu3IjMzExkZGRgw4YNaNu2LaZPn/4Ws6y82rZti/Pnz6s7DSIiIiIioipNU90JEBEBULhraEhICFavXo1Vq1Zh1KhR0NLSAgAUFBRg3bp1mD59Ouzs7DB06NC3mW6lM3bsWPj7+yMzMxOtW7eGgYGB3HiLFi3UlBkREREREVHVwaWjRKR2yiwdbdOmDYYMGQI/P79Sx5cvX47t27fj9OnTqk6zUpNKS05glkgkEEJAIpEotWyXiIiIiIiISsdCGxG9dTk5OW8cT0hIQKdOnRQq9hgYGCAxMRENGjQodfzq1ato3rw58vLyypVrVZWenv7Gce7dRkREREREVHFcOkpEb52Jickbl4a+nFWlCA0NDTx79uy14wUFBdDQ0FA6x6qOhTQiIiIiIqK3j4U2Inrr/vrrL5XFat26NbZu3YoFCxaUOr5582Y4ODio7Pkqu02bNpV6vlq1arCxsUGTJk3ecUZERERERERVF5eOElGlsn//fvTp0weTJ0+Gv78/zMzMAAA3b97EsmXLsHLlSoSHh+OLL75Qc6bvB1NT01LP5+bmoqioCD169MC2bdtgZGT0jjMjIiIiIiKqelhoI6JKZ/Xq1ZgyZQoKCwtRrVo1AMDDhw+hoaGBJUuWwNfXV70JVgJFRUU4e/Yshg8fjq5du+LHH39Ud0pERERERESVHgttRFQpZWZmYufOnbh8+TIAwNraGv3794eFhYWaM6tcjh49inHjxiE5OVndqRAREREREVV6LLQRUZXz+PFj6OvrqzuNSiEtLQ12dnbs0kpERERERKQCUnUnQESkKk+ePMGyZcvQoEEDdadSaaSmpuLjjz9WdxpERERERERVAgttRFSpPHv2DN988w0cHR3RoUMH7N27FwAQFBSEBg0aYPny5Zg0aZJ6k6wEhBA4d+4c/P390atXL3WnQ0REREREVCVw6SgRVSpff/011qxZg65duyI6Ohp3796Ft7c3jh8/jq+//hpubm7Q0tJSd5rvDVNTU0gkkhLnc3Nz8fz5c3z++efYsWMHDA0N1ZAdERERERFR1aKp7gSIiJQRFhaG4OBg9O3bFxcuXIC9vT1ycnLwzz//QFOTH2nFrVy5stTzxsbGaNKkCZo2bfpuEyIiIiIiIqrCOKONiCoVHR0duX3FdHV1ERsbi1atWqk3MSIiIiIiIvrgcY82IqpUCgoKoK2tLTvW0tJCtWrV1JgRERERERER0QtcZ0VElc7s2bOhr68P4EVzhIULF5Yoti1fvlwdqREREREREdEHjEtHiahS+fTTT0vd3P9VEokEf/755zvKiIiIiIiIiOgFFtqIiIiIiIiIiIhUgEtHiajSefToEWJjY1FQUIA2bdqgZs2a6k6JiIiIiIiIiIU2IqpcEhIS0L17d2RlZQEAjI2NsWvXLnTp0kXNmREREREREdGHjktHiahS6dGjBx48eIBly5ZBV1cX8+bNQ3JyMi5duqTu1IiIiIiIiOgDx0IbEVUqtWvXxu+//47//e9/AIB79+6hdu3aePjwIQwNDdWcHREREREREX3IpOpOgIhIGXfv3kW9evVkxzVq1IC+vj7u3LmjxqyIiIiIiIiIuEcbEVUyEokEjx49gq6uLgBACCE7l5OTI7vO2NhYXSkSERERERHRB4pLR4moUpFKpZBIJHLnXhbbXv3z8+fP1ZEeERERERERfcA4o42IKpW//vpL3SkQERERERERlYoz2oiIiIiIiIiIiFSAzRCIiIiIiIiIiIhUgIU2IiIiIiIiIiIiFWChjYiIiIiIiIiISAVYaCMiIiIiIiIiIlIBFtqIiIiIiIiIiIhUgIU2IiIiIiIiIiIiFWChjYiIiIiIiIiISAVYaCMiIiIiIiIiIlIBFtqIqqDjx49DIpEgOztb4XusrKywcuXKt5YTERERERERUVXHQhuRGnh5eUEikWD06NElxsaOHQuJRAIvL693nxgRERERERERlRsLbURqYmFhge3btyM/P1927smTJwgNDUW9evXUmBkRERERERERlQcLbURq4uDggHr16mHPnj2yc3v27IGFhQXs7e1l554+fYqJEyeidu3a0NXVxSeffIIzZ87Ixfr9999hbW0NPT09ODs7Iy0trcTzxcTEoGPHjtDT04OFhQUmTpyIvLy8t/b6iIiIiIiIiD40LLQRqdGwYcMQFBQkOw4MDIS3t7fcNdOmTcPu3bsREhKCc+fOoVGjRnBxccH9+/cBANevX0e/fv3Qo0cPxMfHY/jw4ZgxY4ZcjMTERLi4uKBfv35ISEjAjh07EBUVhfHjx7/9F0lERERERET0gWChjUiNPDw8EBUVhbS0NKSnpyM6Ohru7u6y8by8PKxbtw5Lly5F9+7dYWtri19//RV6enoICAgAAKxbtw4NGjTAihUrYGNjg6+++qrE/m5Lly6Fm5sbfH190bhxY3To0AGrVq3Cpk2b8OTJk3f5komIiIiIiIiqLE11J0D0IatZsyZ69uyJkJAQCCHQs2dP1KxZUzaempqKgoICODk5yc5paWmhTZs2SEpKAgAkJSWhXbt2kEgksmvat28v9zxnz57FlStXsHXrVtk5IQSKiopw7do1NG3a9G29RCIiIiIiIqIPBgttRGrm7e0tW8K5Zs0auTEhBADIFdFenn957uU1b1JUVIRRo0Zh4sSJJcbYeIGIiIiIiIhINbh0lEjNPv/8czx79gzPnj2Di4uL3FijRo2gra2NqKgo2bmCggLExcXJZqHZ2toiNjZW7r7ixw4ODvjnn3/QqFGjEg9tbe239MqIiIiIiIiIPiwstBGpmYaGBpKSkpCUlAQNDQ25MQMDA4wZMwZTp07FwYMH8e+//2LEiBF4/PgxfHx8AACjR49GamoqJk+ejOTkZGzbtg3BwcFycaZPn46TJ09i3LhxiI+Px+XLl7Fv3z5MmDDhXb1MIiIiIiIioiqPhTai94CxsTGMjY1LHVu8eDH69+8PDw8PODg44MqVKzh06BBMTU0BvFj6uXv3bkRERKBly5b45Zdf8N1338nFaNGiBSIjI3H58mX83//9H+zt7fHtt9+ibt26b/21EREREREREX0oJEKRDZ6IiIiIiIiIiIjojTijjYiIiIiIiIiISAVYaCMiIiIiIiIiIlIBFtqIiIiIiIiIiIhUgIU2IiIiIiIiIiIiFWChjYiIiIiIiIiISAVYaCMiIiIiIiIiIlIBFtqIiIiIiIiIiIhUgIU2IiIiIiIiIiIiFWChjYiIiIiIiIiISAVYaCMiIiIiIiIiIlIBFtqIiIiIiIiIiIhUgIU2IiIiIiIiIiIiFfh/op1bX6lhxIgAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1500x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# set up figure with subplots\n",
    "fig, ax = plt.subplots(nrows=1, ncols=3, figsize=(15,3))\n",
    "\n",
    "# generate bar plot: model\n",
    "data['model'].value_counts(normalize=True).plot(kind='bar', rot=90, ax=ax[0])\n",
    "ax[0].set_title('Figure 8: Car models', pad=10.0)\n",
    "ax[0].set_xlabel('Model', labelpad=4.0)\n",
    "ax[0].set_ylabel('Proportion of cars', labelpad=4.0)\n",
    "\n",
    "# generate bar plot: transmission\n",
    "data['transmission'].value_counts(normalize=True).plot(kind='bar', rot=0, ax=ax[1])\n",
    "ax[1].set_title('Figure 9: Car transmission types', pad=10.0)\n",
    "ax[1].set_xlabel('Transmission type', labelpad=8.0)\n",
    "ax[1].set_ylabel('Proportion of cars', labelpad=4.0)\n",
    "\n",
    "# generate bar plot: fuelType\n",
    "data['fuelType'].value_counts(normalize=True).plot(kind='bar', rot=0, ax=ax[2])\n",
    "ax[2].set_title('Figure 10: Car fuel types', pad=10.0)\n",
    "ax[2].set_xlabel('Fuel type', labelpad=8.0)\n",
    "ax[2].set_ylabel('Proportion of cars', labelpad=4.0)\n",
    "\n",
    "# display subplots\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb0bbb4b-1a04-4df2-8242-59c4cd992aeb",
   "metadata": {},
   "source": [
    "Because machine learning models generally require all variables to be numeric, the categorical variables will be one-hot encoded. One-hot encoding will create a binary variable for each category and remove the original categorical variables.\n",
    "\n",
    "## 3.3 Numeric Variable Relationships\n",
    "\n",
    "To examine the relationships between the numeric variables, the Kendall Tau correlation coefficient was calculated for each pair (Figure 11). This coefficient was used due to the non-normal data distributions (Figures 2-7). In terms of predictor-target pairs, there is a strong positive relationship (0.60) between 'engineSize' and 'price'; cars with larger engines tend to cost more. A weaker positive relationship (0.33) exists between 'year' and 'price', with newer cars having higher prices (Figure 11). In terms of predictor-predictor pairs, there is a strong negative relationship (-0.59) between 'year' and 'mileage', with newer cars having a lower mileage. A moderate negative relationship (-0.48) exists between 'mpg' and 'tax', with higher miles-per-gallon cars having lower taxes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "759fdcc8-1c54-4000-b2e0-1f0d123fa702",
   "metadata": {
    "executionCancelledAt": null,
    "executionTime": 270,
    "lastExecutedAt": 1711728542897,
    "lastScheduledRunId": null,
    "lastSuccessfullyExecutedCode": "# generate correlation matrix: numeric columns\nfig = plt.figure(figsize=(7, 6))\nsns.heatmap(data=data.corr(method='kendall'), annot=True)\nplt.title('Figure 12: Correlation between numeric variables')\nplt.show()"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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9rW3bttDU1MTdu3cRHByM8PBw0WDnxYsXyMzMxN27d1GrVi1YW1uLPhconL/142dv3boVDMMgOTkZSUlJKCgowB9//CFRr0+fPgAk/46GhoYS2wxAif9upW0vjYGBgdhrdXV1qX9fdXV1sb+vrH+fkl75l5CQIPfVWZMnT0ZUVJRoIPv19PP3g/iXL1+iZ8+eAArn8T19+hSvXr3CypUrAUhuy5LGffnyZYwYMQKWlpY4deoUPD098erVK0yePFnqcVGSY+BHX/fNxYsXS2z3WbNmAfi2z/3+++9YunQprl69ii5dusDAwACDBg2qkLfXIPKr1HN2SkpfXx8sFkvq/JzY2Fix11+/uH6cQFrc+dsfJxMbGRmBxWLBw8ND9CX7PWllFUmvXr1w584dXL9+Hb/88kuxdb/+oxITEyPxXnR0NFRUVCR+pRY1+VpauZGREezs7LBx40apbSwsLIqM7dSpU+jcuTP2798vVp6RkVFkm5/5WX+NjIzkXrc0P+6f35d9jcXIyAgaGhpFzkcpSUzq6upo37497t69i2rVqsHMzAy2traoVasWgMKJw/fu3RPLbnxd7x9//FHklUKmpqYoKCgAm83GuHHjMHv2bKn1vg6gyoo8x7m8ZP37lPTiBGNj4yIzGz/Tq1cvWFhY4NixY+jVqxeOHTuGVq1aoWHDhqI6f//9N9TU1HDjxg2xAV1R92kqadynTp2CtbU1zp07J9amqEn7JTkGfvR1my5fvlxswvX36tWrBwDQ0tLC2rVrsXbtWsTFxYmyPP3794e/v3+J+kQqvv/EYEdLSwvNmzfH1atXsX37dtGprMzMTNy4cUOsrqmpKbhcLt69eydWXtyVAj/q168ftmzZgqioKIwYMaL0HShjTk5O+O2337BkyRJ06NABlpaWEnUuX76MIUOGoF69erC0tMSZM2ewePFi0ZdXVlYWLl26JLpCS179+vWDm5sbateuXaLU/vdYLJbEwPLdu3fw9PRE9erVRWWy/Opv06YNNDQ0cOrUKQwfPlxU/vnzZ9y/f7/YCZPy+PjxI96+fSt2KuvMmTPQ0dFBs2bNABRuo02bNsHQ0PCngwYOh1NkP7t3747ly5dDR0dHdKpKS0sLrVu3xh9//IHo6GhROQC0a9cOenp68PX1LfbGb+rq6ujSpQt8fHxgZ2cndiq5vCjiOC8pWf4+snB0dMRff/2FgIAA0T/cJfV18Llr1y54eHjAy8sLBw8eFKvDYrGgqqoqNiE8JycHf/31V6niZrFYUFdXFxvoxMbGFrnt7927h7i4OFEWXiAQ4Ny5c6hdu3aRma169eqhTp06ePv2LTZt2lTi2ExNTTFx4kS8ffsWu3btUsitREjF8J8Y7ADAunXr0LdvX/Tq1Qvz58+HQCDAb7/9Bm1tbdFVOwBEc22OHj2K2rVro0mTJnj58iXOnDlT4s9q164dpk2bhkmTJsHLywsdO3aElpYWYmJi8OTJE9ja2mLmzJnFruPmzZvIysoSZSF8fX1F97bp06eP6ABMSEgQzVt5//69qO3X+8N06tRJtM5u3brh0aNHP523w+PxcO3aNfTr1w/29vZiNxUMCgrCqVOn8PbtWwwZMgQqKirYtm0bxowZg379+mH69Ong8/n47bffkJqaii1btpR4u0mzbt06uLu7o23btpg3bx7q1auH3NxchIeHw83NDQcOHCjyC69fv35Yv349Vq9ejU6dOiEgIADr1q2DtbW12DbQ0dGBlZUVrl27hm7dusHAwABGRkaoWbOmxDr19PTg4uKCFStWYPz48Rg1ahSSkpKwdu1acLlciauKSsvCwgIDBgzAmjVrYG5ujlOnTsHd3R1bt24V7QMLFizApUuX0LFjRyxcuBB2dnYQCoWIjIzEnTt3sGjRIrRq1QoAYGtri4cPH+L69eswNzeHjo6O6B/Kbt26QSAQ4N69e6LLnYHCQdDq1avBYrHQtWtXUbm2tjb++OMPTJgwAcnJyRg2bBhMTEyQkJCAt2/fIiEhQZRV2717N9q3b48OHTpg5syZqFmzJjIyMhAcHIzr16/j/v37Ct1uP6OI47ykZPn7yGLdunW4efMmOnbsiBUrVsDW1hapqam4desWnJ2dJW64+qPJkydj69atGD16NDQ0NDBy5Eix9/v27YsdO3Zg9OjRmDZtGpKSkrB9+/ZSZ6a/XqI+a9YsDBs2DJ8+fcL69ethbm4u9dSRkZERunbtChcXF9HVWP7+/j+9/PzgwYNwdHREr169MHHiRFhaWiI5ORl+fn54/fo1Lly4AABo1aoV+vXrBzs7O+jr68PPzw9//fVXqX+okQqmXKdHy+nrFTI/Xt3wVVFXWly5coWxtbVl1NXVmRo1ajBbtmxh5s2bx+jr64vVS0tLY6ZMmcKYmpoyWlpaTP/+/Znw8PAir9JISEiQGsfRo0eZVq1aMVpaWoyGhgZTu3ZtZvz48YyXl9dP+2hlZVXkFWffX2319SoEaUunTp3E1tmpUydGlj95bGwss3TpUqZRo0aMpqYmw+FwGBsbG2b69OnM+/fvxepevXqVadWqFcPlchktLS2mW7duzNOnT8XqFLe9rKysmL59+0qNIyEhgZk3bx5jbW3NqKmpMQYGBoyDgwOzcuVKJjMzU1Tvx78Pn89nFi9ezFhaWjJcLpdp1qwZc/XqVWbChAmMlZWV2GfcvXuXsbe3ZzgcDgNAdPXIj1djfXX48GHGzs6OUVdXZ3g8HjNw4ECxK8MYpvBqLC0tLYn+fN0OP/N1m1y8eJFp1KgRo66uztSsWZPZsWOHRN3MzExm1apVTL169UQx2draMgsXLhS7kuXNmzdMu3btGE1NTYl9RCgUMkZGRgwAJioqSlT+9OlTBgDTrFkzqXE+evSI6du3L2NgYMCoqakxlpaWTN++fZkLFy6I1QsLC2MmT57MWFpaMmpqaoyxsTHTtm1bZsOGDaI6X/dnaW2lHdM/Kmofk/Z3LO1xXtTft1OnTkyjRo3Eykr69wHAzJ49W2rffoyLYQqv8Jw8eTJjZmbGqKmpMRYWFsyIESOYuLi44jaTSNu2bRkAzJgxY6S+f/ToUaZevXoMh8NhatWqxWzevJk5cuSIxLYs7viVdjXWli1bmJo1azIcDodp0KAB8+eff0o9Lr5uj3379jG1a9dm1NTUmPr16zOnT58WqyftaiyGYZi3b98yI0aMYExMTBg1NTXGzMyM6dq1K3PgwAFRnWXLljHNmzdn9PX1Rf1cuHAhk5iY+JOtRyoTFsP85KYrVVh+fj6aNm0KS0tL3Llzp7zDIYQQQogS/GdOYwGFc1F69OgBc3NzxMbG4sCBA/Dz88Pu3bvLOzRCCCGEKMl/arCTkZGBxYsXIyEhAWpqamjWrBnc3NzEJl0SQgghpGr5T5/GIoQQQkjVV6lvKkgIIYSQ8vP48WP0798fFhYWYLFYRd6H6XuPHj2Cg4OD6OHGBw4cUHqcNNghhBBCiFyysrLQpEkT7Nmzp0T1w8LC0KdPH3To0AE+Pj5YsWIF5s2bh0uXLik1TjqNRQghhJBSY7FYuHLlCgYNGlRknaVLl+Kff/4Rew7ajBkz8PbtW3h6eiotNsrsEEIIIUSEz+cjPT1dbCnqcR6y8vT0FD1z7atevXrBy8sL+fn5CvkMaSrM1Vj5iaHlHUKFpmHRobxDqNBiOtmUdwgVnmtQ6R96W5XVFZb/YywqsmGmks+DI+Kqv7pXZp+lzH8zN+85ibVr14qVrV69GmvWrCn1umNjYyUewP31GXqJiYklfqCsrCrMYIcQQggh5W/58uVwdnYWK1PkA6x/fGjs19k0JX2YrDxosEMIIYRUNkKB0lbN4XAUOrj5npmZmcST7OPj46GqqlrkU+wVgebsEEIIIaRMtGnTBu7u7mJld+7cQfPmzaGmpqa0z6XBDiGEEFLZMELlLTLIzMzEmzdv8ObNGwCFl5a/efMGkZGRAApPiY0fP15Uf8aMGYiIiICzszP8/Pxw9OhRHDlyBIsXL1bYppGGTmMRQgghRC5eXl7o0qWL6PXXuT4TJkzA8ePHERMTIxr4AIC1tTXc3NywcOFC7N27FxYWFvj9998xdOhQpcZJgx1CCCGkshHKloFRls6dO6O42/UdP35coqxTp054/fq1EqOSRIMdQgghpJJhZDzd9F9Hc3YIIYQQUqVRZocQQgipbCrIaazKgjI7hBBCCKnSKLNDCCGEVDY0Z0cmlNkhhBBCSJVGmR1CCCGkslHi4yKqIsrsEEIIIaRKo8wOIYQQUtnQnB2ZUGaHEEIIIVUaZXYIIYSQyobusyMTGuwQQgghlQw9LkI2dBqLEEIIIVUaZXYIIYSQyoZOY8mEMjuEEEIIqdIos0MIIYRUNjRnRyaU2SGEEEJIlSZTZodhGERGRsLExAQaGhrKiokQQgghxaHHRchEpswOwzCoU6cOPn/+rKx4CCGEEEIUSqbBjoqKCurUqYOkpCRlxUMIIYSQn2GEyluqIJnn7Gzbtg2//vorPnz4oIx4CCGEEPIzQqHylipI5quxxo4di+zsbDRp0gTq6uoSc3eSk5MVFhwhhBBCSGnJPNjZtWuXEsIghBBCSIlV0dNNyiLzYGfChAnKiIMQQgghRClKdVPBnJwc5Ofni5Xp6uqWKiBCCCGE/EQVnVujLDJPUM7KysKcOXNgYmICbW1t6Ovriy2EEEIIIRWJzJmdJUuW4MGDB9i3bx/Gjx+PvXv3IioqCgcPHsSWLVuUEWOZ8XrzHsfOXISvfzASkpKxe7MLunVsW95hlRlXF2dMcRoDfX0eXr70wdz5K+HrG1hk/fHjRuDokZ0S5Vo6tcDn8wEA2tpaWLtmCQYN7A0TE0O8efMRC51d4eX9Vmn9UAZu/0HQHP4LVAwNUBAejqz9e5D/4Z3UuqqNbKE9dTrY1WuAxeFCEBeL3H+vI+fyBbF6GoOHgdt/INgmphCmpYHv8RBZR/4E8vPKoEfK0XfBcLQf1Q2aPG2EvwnC3y5HEBNU9H25zOtUQ3/nkahhaw3Daia4sO447h91k6jHM9XH4GVj0ahzU6hz1REXFoNTS/Yj8kOYMrtTKq0XDoHt6C7g8rQQ4xOCBy7HkRQYVWwbG8cWaLt4GHg1TJAWGY+n2y4g5LaX6P0Ws/vDpncLGNQ2R0FuHqK9g/Bk8zmkhMZ8W0fv5rAd0xWmttbQMNDBqd4rkOAbqbR+Kor2sAHQGTsCbCND5IeGI2XHPuS9eS+1rnqTxtCbOxWqVjXA4nIgiI1D5uUbyDx7SVRHo0t76E4cDdXqloAqGwWfopBx6gKyb94tqy4pFcPQTQVlIXNm5/r169i3bx+GDRsGVVVVdOjQAatWrcKmTZtw+vRpZcRYZnJyclHPphZWOM8q71DK3K+LZ2HB/GmYt2AVWrfti9i4BNxyOwttba1i26WlpcOyelOx5etABwAOHdyO7t07YOKkeWjarDvc7z7C7Vt/w8LCTNldUhhOpy7QnjkH2Wf/QsrMqcj/8A68TVuhYmwivUFuLnKuXUGq8zwkO41H9pm/oDXRCdw+/b+ts2t3aE2Zhuy/TiDZaTwydmwFp3NXaDlNLaNeKV7PGQPRzakvzrkexdYBy5GekIp5p1aBo8Utso26BgeJkXG4uvUM0uJTpNbR1NXCr5fWQ1BQgD0TN2FtD2dc2nAS2enZyupKqTWf2Q/NpjjigcsJnOnniuyEVAw5vQxqxWwL82Y26Lt3DvwuP8Gp3ivgd/kJ+u6bA7OmtUV1qrVqgLcn3PH3oDW4NGYrVFTZGHJqKVQ1OKI6apocRHsF4smWc0rtoyJp9OgMPedZSD92BrFjp4P/5j2Md28G21T6Mcbk5CLz/FXET1+A2BGTkH70NHgzJ0FrcF9RHWFaBtKPnUbc5LmIHTUVWddvw8B1Cbitm5dVt0gFIvNgJzk5GdbW1gAK5+d8vdS8ffv2ePz4sWKjK2Md2rTAvGkT0KNzu/IOpczNmzsFm7f8jqtXb+LjxwBMmrwAmpoaGPXL4GLbMQyDuLgEseUrLpeLIYP7YPnyjfB48gIhIeFYt34HwsI/Ycb08cruksJoDB2B3FtuyL35LwSREcjavweChARo9B8otX5BSBD4D+5BEBEOYVws+Pfckef9Cmq2dqI6ag0bIf/jB/Af3IUwLhb53l7gP7gHtbr1y6pbCtd1ch/c2nsFb26/RHTgJ5xYtBfqGhy0GNi+yDYR70JwefMpeF1/hoK8fKl1es4ciJToJPz1635EvA1B8ucEBDz7gMTIOGV1pdSaOfXGyz3XEHzLC0mBn3Hb+SBUueqoP6joTLG9U29EeHzAq73XkRISg1d7r+PTU1/YO/UW1bkyfht8L3ogKTAKiX6RuLPoEHSrGcHUtqaojt/lp3ix+yoin1See6HpjB6GrGs3kXXNDQXhkUjdsQ+CuHhoD+svtX5+YDCy7zxAQWgEBDFxyL55F7nPvcBpaiuqw3/9FjkPn6IgPBKCqBhk/n0Z+cGhUG/auKy6pVx0U0GZyDzYqVWrFsLDwwEADRs2xPnz5wEUZnz09PQUGRspI9bWNWBubgr3u49EZXl5eXjs8Rxt2hT/K0hbWwshQS8QHuqFa1dOoGnTRqL3VFXZUFVVRW4uX6xNbk4u2rVtodhOKIuqKlTr1kWe9yux4jzvV1BtVLIvTdXadQoHN+/eiMryP7yHap26UK1XOLhRMTOHesvW4L/wVFjoZcmougl4Jvrw9fh2erIgrwBBL3xR26FeqdZt1705It6HYsrehdjm9SdW/LsV7X7pVtqQlYZXwxhaJnqIePztFIwgrwBRL/xh4VCnyHbmzWzE2gBA+KN3xbZR19EEAOSmZpUy6nKkqgr1+nWR+8JLrDj3hTfU7RoV0UicWl0bcOwagf9a+qllAOC0sIeqVTXwX0s/NVbp0E0FZSLznJ1Jkybh7du36NSpE5YvX46+ffvijz/+QEFBAXbs2KGMGImSmX1JFcfFJYqVx8UlwKpGtSLbBQQEY/KUhfjwwR+6OtqYO3cKHj+8hmbNeyA4OAyZmVnw9PTCyhXz4ecfhLi4BPzyyyC0bGmPoOCKO9fieyo8HlhsVQhTxG+WyaSkQEXfoNi2BmcuQIWnB7DZyP7rOHJv/it6j//wPlg8Pejt3AOwWGCpqiLnn6vIOXdGGd1QOl1jPQBARkKaWHl6QhoMqxmVat1GNUzQcWwP3Dv8L27tu4KaTWwwYs0kFOTl48XlipdN1vyyLbITxbdFdmIadCyL3hZaxnpS22ga84ps08l1DKJeBiApsPI+r1BFjweWKhuCZPHTmIKkFHANiz/GzG/8DbY+D2Czkf7nSWRdE5/vxdLSgoXbObDU1QCBEClbd4P/0lvhfSAVn8yDnYULF4r+v0uXLvD394eXlxdq166NJk2alGgdfD5fbF4HAKjw+eBwOEW0IIo0atRg7N+7VfR6wMDCU0oMw4jVY7FYEmXfe/HyNV68fC16/fTZK7x6eRuzZ03CQmdXAMCESfNw+ND/8CniNQoKCuDj8x5n/74Ce3vbolZbMf24GVgAitk2AJDqPBcsribUGjSE1pRpEERHgf/gHgBAza4ptEaPReYfO5Hv5we2pSW0Z82FZnISsk+fVE4fFKjFwPYYvWma6PW+yZsBFLUPle6zWCwVRLwPwbXfzgIAPn8Mh0Wd6ug4tmeFGOzUH9QW3TZPFr2+OnF74f9I7DMsybIf/fA+i8Uqcj/rsn4CjOpXx/mh62ULuKKS4xiLn7YAKhoaULdtAN7sqSj4FIXsOw++rTI7G3FjpoGlqQFui2bQWzgTBVEx4L+uXBdISFVFTzcpS6nus5Obm4saNWqgRo0aMrXbvHkz1q5dK1a26td5cF0yvzThkBK6fv0OXr70Eb3mcNQBAGZmxoiNjReVm5gYIS4+UaJ9URiGgZfXG9SxsRaVhYZGoGv3YdDU1ICurg5iY+Nx5vR+hId9UkBPlE+YlgZGUAAVA/FfmCw9fQhTpU+oFbWNjQUACMJDoaKvD81xE0WDHa2JTsi9e0eU7RGEhyKLy4XOgsXIPvPXT7/ky9u7u14IfxMkeq2qrgYA0DXRQ3pCqqhcx0gXGT9kK2SVFp+C2B+u6IoN+Qx7x1alWq+ihLi/RoxPiOi1Kqfwa1XTmIes+FRRuaahrkTm5ntZCakSWRwNQ11kJ6ZL1O28djxq92iG88M3IDO2cj+iR5iaBqZAALah+K1L2Ab6EtmeHwmiYyEAkB8SBraBPnSnTRAb7IBhUPA5GgCQHxgC1Zo1oDNxVNUY7BCZyDxnRyAQYP369bC0tIS2tjZCQ0MBAC4uLjhy5EiJ1rF8+XKkpaWJLUvnz5A1FCKnzMwshISEixZf30DExMShe7eOojpqamro2KE1PD29ilmTpCZNGiHmuwHTV9nZOYiNjYeeHg89e3TCP9dvl7ofZaKgAAWBgVBvJj53Sb1ZcxR8lGECKIsFlprat9ccjuSARigs/PXPYpUi4LLBz8pFQkScaIkJ+oy0+BQ0aP9tEjZbjY06rRoixDugVJ8V6h0A01oWYmUm1hZIikoookXZys/KRVpEnGhJCoxCVnwqrDp8m9OlosaGZav6iPYOKnI9Ma+DxdoAgFVHW4k2XdaNRx3H5rj4yyakf6oY26BUCgqQ5x8IbisHsWJuSwfkvftY8vX8eIwVVUf9J3UqC6FAeUsVJHNmZ+PGjThx4gS2bduGqVO/XSZra2uLnTt3wsnJ6afr4HA4Eqes8vNKnkFQluzsHER++RUAAFHRcfAPDAFPVwfmZkVcZlxF/P7HYSxbOhdBwWEIDg7DsqVzkZ2dg7N/XxHVOXZ0N6KjY7ByVeH9lFxWLcSLF68RFBwGXV0dzJk9GU2bNMK8eStFbXr26AQWi4WAwBDY1K6JLVtcEBgYguMnKs9lsTmXzkNn6UoUBAYg3+8juH36gW1igpwb/wAAtCZPhYqRMTK2bQIAcAcMgjA+HoJPEQAA1cZ20Bg+EjlXL4vWmff8GTSGjkBBcBDy/X3BtqgGrQmTwfd8WmknCN4/6obeswcjPjwGCWGx6D17MPJy+Hh17YmozoT/zUZqXDKubSs8JcVWY8O8TrUv/68KPVMDVGtoJRpMAcC9I//i10vr0XvWYHj/+ww1m9ig/ahuOL38UNl3soReH7mFFrMHICUsDqlhsWg5ZwAKcvPgf/WZqE6vndORGZuCp1sLL/LwOXobIy6sQvOZ/RByxxu1ezqgRvtGYqepum6YiHoD2+CfKTuRl5UrygTx07Mh4BdezcbhaUHX0hBapoWZEv3a5gCArIQ0ZCeULsumLBlnLsJw7TLk+QaC/94X2oP7gm1mgsxL1wEAvNlOYBsbIXlN4el37eEDURAbj4LwwvsHcZo2hs7Y4cg4d1W0Tp2Jo5DnG4iCqGiwVFXBbdcKWn17IGXL7jLvHyl/Mg92Tp48iUOHDqFbt26YMeNbNsbOzg7+/v4KDa6sffAPwuS5S0Wvt/1R+GU60LE7Nq5aVF5hlYnftu+DhgYXe37fJLqpoGPf0cjM/HaVR43qFhB+9w+xHo+H/fu2wczMGGlpGXjz5gO6dB2KV15vRHV0ebrYuH4ZqlUzR3JyKi5fcYOL61YUFBSUZfdKhf/oAVi6PGiOHQ8VA0MUhIchbeVSCOML/zFWMTSEisl3g2GWCrQmTwXbzByMUABBdDSyDh9C7r//iKpkny48VaU10QkqRsYQpqUi7/kzZB09XNbdU5g7B65BjauOUeunQJOnhbA3wfhj3Ebws3JFdQwsjcTm9fBMDbDS7TfR6x7TB6DH9AEIfP4RO38pPNUd8S4EB6Zvx6Alo9Fn/lAkforHhXUnxAZRFY3X/htQ5aqj28aJ4OhqIvZNCC6P2Yr877aFjoURGOG3bRHjHQS3OXvQdvFwtF00DKkRcXCbvQexb76dImsyvjsAYMSFVWKfd9v5IHwvegAAavdohl47pove67t3LgDAc+dlPN95GRVRjvtDpPJ0oTtlHNhGBsgPCUfiguUQfMkSs40Mwf7+ByeLBb3ZTmBbmAECAQo+xyB1z2FkXb7xrQqXC/2l88A2MQbD56Mg4hOSXDcjx/1hGfdOSWjOjkxYTHEzUKXQ0NCAv78/rKysoKOjg7dv36JWrVrw9fVFy5YtkZmZKVcg+YmhcrX7r9Cw6FDeIVRoMZ1syjuECs81yLS8Q6jQ6grVyzuECm2YaczPK/3HVX91r8w+K/flhZ9XkhO35XClrbu8yDxnp1GjRvDw8JAov3DhAuzt7RUSFCGEEEKKQffZkYnMp7FWr16NcePGISoqCkKhEJcvX0ZAQABOnjyJGzdu/HwFhBBCCCkdOo0lE5kzO/3798e5c+fg5uYGFosFV1dX+Pn54fr16+jRo4cyYiSEEEIIkZvMmZ2JEydi8uTJePTo0c8rE0IIIUTxqujpJmWRObOTkZGBnj17ok6dOti0aROio6N/3ogQQgghpJzIPNi5dOkSoqKiMGfOHFy4cAFWVlZwdHTEhQsXkJ8v/anFhBBCCFEgmqAsE5kHOwBgaGiI+fPnw8fHBy9fvoSNjQ3Gjx8PCwsLLFy4EEFBRd8llBBCCCGkLMk12PkqJiYGd+7cwZ07d8Bms9GnTx98/PgRDRs2xM6dOxUVIyGEEEK+wzACpS1VkcyDnfz8fFy6dAn9+vWDlZUVLly4gIULFyImJgYnTpzAnTt38Ndff2HdunXKiJcQQgghRCYyX41lbm4OoVCIUaNG4eXLl2jatKlEnV69ekFPT08B4RFCCCFEQhWdW6MsMmd2du7ciejoaOzdu1fqQAcA9PX1ERYWVtrYCCGEECINI1TeIod9+/bB2toaXC4XDg4OUp+08L3Tp0+jSZMm0NTUhLm5OSZNmoSkpCS5PrskZB7sjBs3DlwuVxmxEEIIIaSSOXfuHBYsWICVK1fCx8cHHTp0gKOjIyIjI6XWf/LkCcaPHw8nJyd8/PgRFy5cwKtXrzBlyhSlxViqCcqEEEIIKQcV6NLzHTt2wMnJCVOmTEGDBg2wa9cuVK9eHfv375da//nz56hZsybmzZsHa2trtG/fHtOnT4eXl1dpt0qRaLBDCCGEEBE+n4/09HSxhc/nS62bl5cHb29v9OzZU6y8Z8+eePbsmdQ2bdu2xefPn+Hm5gaGYRAXF4eLFy+ib9++Cu/LVzTYIYQQQiobJc7Z2bx5M3g8ntiyefNmqWEkJiZCIBDA1NRUrNzU1BSxsbFS27Rt2xanT5/GyJEjoa6uDjMzM+jp6eGPP/5Q+Gb6igY7hBBCCBFZvnw50tLSxJbly5cX24bFYom9ZhhGouwrX19fzJs3D66urvD29satW7cQFhaGGTNmKKwPP5L50nNCCCGElDMlXnrO4XDA4XBKVNfIyAhsNlsiixMfHy+R7flq8+bNaNeuHX799VcAgJ2dHbS0tNChQwds2LAB5ubmpeuAFJTZIYQQQohc1NXV4eDgAHd3d7Fyd3d3tG3bVmqb7OxsqKiIDz/YbDaAwoyQMlBmhxBCCKls5LwfjjI4Oztj3LhxaN68Odq0aYNDhw4hMjJSdFpq+fLliIqKwsmTJwEA/fv3x9SpU7F//3706tULMTExWLBgAVq2bAkLCwulxEiDHUIIIaSyqUB3UB45ciSSkpKwbt06xMTEoHHjxnBzc4OVlRWAwudofn/PnYkTJyIjIwN79uzBokWLoKenh65du2Lr1q1Ki5HFKCtnJKP8xNDyDqFC07DoUN4hVGgxnWzKO4QKzzVI+vlzUqiuUL28Q6jQhpnGlHcIFV71V/fK7LNybv6utHVrOM5T2rrLC2V2CCGEkMqmAmV2KgOaoEwIIYSQKo0yO4QQQkhlU4EmKFcGlNkhhBBCSJVGmR1CCCGksqE5OzKhzA4hhBBCqjTK7BBCCCGVDc3ZkQkNdgghhJDKhk5jyYROYxFCCCGkSqPMDiGEEFLZ0GksmVBmhxBCCCFVGmV2CCGEkMqG5uzIpMIMduhBl8XLifYo7xAqtJEOC8o7hApvWi4lcovDZ9E/HsVZHs8r7xAqvFPlHQApUoUZ7BBCCCGkhCizIxP6qUcIIYSQKo0yO4QQQkhlwzDlHUGlQoMdQgghpLKh01gyodNYhBBCCKnSKLNDCCGEVDaU2ZEJZXYIIYQQUqVRZocQQgipbOhxETKhzA4hhBBCqjTK7BBCCCGVDc3ZkQlldgghhBBSpVFmhxBCCKls6KaCMqHMDiGEEEKqtFJndnJzc8HlchURCyGEEEJKgubsyESuzI5QKMT69ethaWkJbW1thIaGAgBcXFxw5MgRhQZICCGEkB8IhcpbqiC5BjsbNmzA8ePHsW3bNqirq4vKbW1tcfjwYYUFRwghhBBSWnINdk6ePIlDhw5hzJgxYLPZonI7Ozv4+/srLDhCCCGESMEIlbdUQXINdqKiomBjYyNRLhQKkZ+fX+qgCCGEEEIURa7BTqNGjeDh4SFRfuHCBdjb25c6KEIIIYQUjREySluqIrmuxlq9ejXGjRuHqKgoCIVCXL58GQEBATh58iRu3Lih6BgJIYQQQuQmV2anf//+OHfuHNzc3MBiseDq6go/Pz9cv34dPXr0UHSMhBBCCPkeXY0lE7nvs9OrVy/06tVLkbEQQgghhCgcPS6CEEIIqWyq6FVTyiLXYEdfXx8sFkuinMVigcvlwsbGBhMnTsSkSZNKHSAhhBBCflBFJxIri1yDHVdXV2zcuBGOjo5o2bIlGIbBq1evcOvWLcyePRthYWGYOXMmCgoKMHXqVEXHTAghhBBSYnINdp48eYINGzZgxowZYuUHDx7EnTt3cOnSJdjZ2eH333+nwQ4hhBCiaFV0IrGyyHU11u3bt9G9e3eJ8m7duuH27dsAgD59+oiemUUIIYQQUl7kGuwYGBjg+vXrEuXXr1+HgYEBACArKws6Ojqli44QQgghkujSc5nIdRrLxcUFM2fOxIMHD9CyZUuwWCy8fPkSbm5uOHDgAADA3d0dnTp1UmiwhBBCCCGykmuwM3XqVDRs2BB79uzB5cuXwTAM6tevj0ePHqFt27YAgEWLFik0UEIIIYR8wdDVWLKQ+z477dq1Q7t27RQZCyGEEEKIwpX6poI5OTkSTzrX1dUt7WoJIYQQUpQqOrdGWeQa7GRnZ2PJkiU4f/48kpKSJN4XCASlDkwZXF2cMcVpDPT1eXj50gdz56+Er29gkfXHjxuBo0d2SpRr6dQCn88HAGhra2HtmiUYNLA3TEwM8ebNRyx0doWX91ul9aO8eL15j2NnLsLXPxgJScnYvdkF3Tq2Le+wyszIBaPQY3RPaPG0EeQTiD9dDuBT0Kci63f/pSc6D+2CGvWsAAAh74NxettfCH4bJLbOkQtHibVLiU+BU4sJyumEklSf2APWs/uDY6KHzIDP8Hc5iZQX/lLrckz0UG/tOPDsrKFZywwRh2/B3+WkWB3TPi1Qa/4gaFqbgaXGRnZoLML3/4voix5l0R2lqDmxO2xm9QPXRA8ZAVF473oSyS8CpNblmOih8Zox0LOzhlYtM4Qevo0Prn+J1dGpZ4n6vw6HXhNraFY3xnuXkwj981ZZdEVphiwYiS6je0CLp4UQnyAcd/kTUcUcY51/6Y4OQzujWr0aAICw9yE4v+00Qt8Gi+rUa9kQfacPhLVtbeibGmDn1C3wvvNS6X1ROrqpoEzkuhrr119/xf3797Fv3z5wOBwcPnwYa9euhYWFBU6ePPnzFZSDXxfPwoL50zBvwSq0btsXsXEJuOV2FtraWsW2S0tLh2X1pmLL14EOABw6uB3du3fAxEnz0LRZd7jffYTbt/6GhYWZsrtU5nJyclHPphZWOM8q71DK3OAZQ9B/ykD86XoIS/svQmpCClafXgeulkaRbRq3aYwn/zyG6y8rsXzwr0iMTsTqv9bCwNRArF5kQAQmNx8vWhb2mqvs7iiU2cA2aLB+AkJ3XcGz7suQ8sIfDmeXgWtpKLW+CkcNeUnpCNl1BRkfI6TWyU/NQsiuq3je1wVPOy9F1N+P0Hj3DBh1tlNmV5TGYmBr2K4bj8BdV/GwxwokvfBHmzNLoVHkNlIFPykDgbuvIe1jpNQ6bA0OsiLj4bvhb+TGpSgz/DLRb8ZgOE7pjxOuf8K1/1KkJqRi2enV4Gpxi2zToE1jeP7zBBt/ccWawcuRFJ2IpX+thv53xxhHk4NIv3CccP2zLLpBKii5BjvXr1/Hvn37MGzYMKiqqqJDhw5YtWoVNm3ahNOnTys6RoWYN3cKNm/5HVev3sTHjwGYNHkBNDU1MOqXwcW2YxgGcXEJYstXXC4XQwb3wfLlG+Hx5AVCQsKxbv0OhIV/wozp45XdpTLXoU0LzJs2AT06//fmavVzGoBLe87jxS1PRAZG4vdFu8DhctBxYMci2+yavwO3/rqJcN8wRIVEYf/SPWCpqMCuXROxeoICAVITUkVLenK6srujUDVn9MXnMw/w+fQDZAVFw9/lJHKjklBjYg+p9XM+JcB/1QlEX/BAQXqO1DrJz3wRf/MVsoKikRMRh4g/byLDNxJ6reorsytKYzO9DyLOPkTkmYfIDIrGB9e/kBOVhJoTJO9XBgA5nxLxweUkPl3wQEFGttQ6qW9C4bvuDKKueUKYV6DM8MtEb6d+uLbnErxuvcDnwEgcXPQ71LkctC3mGNs/fxfu/nULkb7hiAmJwuGl+6GiwkKjdt8Gxe8e+uDi9rPwuvWiLLpRdhih8hY57Nu3D9bW1uByuXBwcICHR/FZWD6fj5UrV8LKygocDge1a9fG0aNH5frskpBrsJOcnAxra2sAhfNzkpOTAQDt27fH48ePFRedglhb14C5uSnc7z4SleXl5eGxx3O0adO82Lba2loICXqB8FAvXLtyAk2bNhK9p6rKhqqqKnJz+WJtcnNy0a5tC8V2gpQb0+qm0DcxwBuPN6KygrwCfHzxEfUcGpR4PeoaHLDV2MhIzRArN7e2wOGXx7D/yZ9w/mMxTKubKip0pWOpsaFrZ43Eh+/EyhMfvYNe87oK+xyDDo2hZWOOFE8/ha2zrLDU2ODZWSPhh20U/+g9DFoobhtVZsbVTaFnoo/3Pxxj/i8+oo5DvRKvh6OhDrYaG5k/HGNEuc6dO4cFCxZg5cqV8PHxQYcOHeDo6IjISOlZSQAYMWIE7t27hyNHjiAgIABnz55F/frK+zEj15ydWrVqITw8HFZWVmjYsCHOnz+Pli1b4vr169DT01NwiKVnZmoCAIiLSxQrj4tLgFWNakW2CwgIxuQpC/Hhgz90dbQxd+4UPH54Dc2a90BwcBgyM7Pg6emFlSvmw88/CHFxCfjll0Fo2dIeQcFhSu0TKTt6JvoAgNSEVLHy1MRUGFsal3g945aNR3JsMt49/TafK/BNAH533ono0GjoGelh2NwR2HR5G+b3mFMpvrDVDXShospGXkKaWHleQho4JnqlWreqjgY6v90PFXVVMAIhfJcdRdLj96VaZ3ngGOhARZWN3B+2ET8hDVxjXjlFVbHofdlX0n44xtISU2EkwzE2ctk4pMQm4+PTdz+vXNlVoDk7O3bsgJOTE6ZMmQIA2LVrF27fvo39+/dj8+bNEvVv3bqFR48eITQ0VHQj4po1ayo1RrkyO5MmTcLbt4Vf2MuXLxfN3Vm4cCF+/fXXn7bn8/lIT08XWxgF3jNg1KjBSE0OFC1qaoVjuh8/g8ViFfu5L16+xpkzl/HunS+ePH2JX0ZNR2BQKGbP+vY09wmT5oHFYuFTxGtkZ4Zh7uzJOPv3lQo7SZv8XMdBnXDa95xoYauyv7zz4/4jUVSkQdOHoP2Ajtg2fTPy+d+uXvR5+BrPb3oiMiAC756+xcZJ6wAAXYZ1VUBPytIPG4IlebzJqiAzF8+6LoVnr5UI2nwO9deOg0HbhqVaZ7n6cROx/ru3Smk7qCMO+54WLd+OMXEsFqvEx1jf6YPQZkB77Jq+TewYI8qVl5cHb29v9OzZU6y8Z8+eePbsmdQ2//zzD5o3b45t27bB0tISdevWxeLFi5GTI/20tiLIldlZuHCh6P+7dOkCf39/eHl5oXbt2mjSpEkxLQtt3rwZa9euFStjqWiDxVbMJevXr9/By5c+otccjjoAwMzMGLGx8aJyExMjxMUnSrQvCsMw8PJ6gzo21qKy0NAIdO0+DJqaGtDV1UFsbDzOnN6P8LCiryAgFdtL95cI9Pl2lZ6aeuFhomesj5T4bxNBeYZ6SE1M/en6Bk4bhKGzh2HNGFdE+IcXW5efw0dkQATMa1rIFXtZy0tOh7BAAHVjPbFydSOeRLZHZgyD7PA4AEDGxwho1bVErXkDkfzMt3TrLWP85AwICwTgmohncdSNeOAnlnIbVVKv3V8i5LtjTFVdDQDAM9ZD6nfHmK4hD2klOMb6TBuIAbOHYsuYNfjkL33Se1XDKPHScz6fL3YhDgBwOBxwOByJuomJiRAIBDA1FT/9bmpqitjYWKnrDw0NxZMnT8DlcnHlyhUkJiZi1qxZSE5OVtq8HbkyO9/Lzc1FjRo1MGTIkBINdIDCbFBaWprYwlJR3HO0MjOzEBISLlp8fQMRExOH7t2+TXRTU1NDxw6t4enpJdO6mzRphJjvBkxfZWfnIDY2Hnp6PPTs0Qn/XL9d6n6Q8pGblYPYiBjR8inoE1Lik9GkfVNRHVU1VTRq1QgB3sXPIRk4fTCGzR2J9RPWIuR9cLF1AUBVXRXVbKohJT65tN0oE0y+AOnvwmDUyVas3KijLVK9ir6tgzxYLBZUvvyjWJkw+QKkvQuD8Q/byKRTYyS/Uuw2qixys3IRFxErWqKCPiE1PgWN23/7N4Stpor6rRohyFv65flf9Z0+EIPmDsO2CesR9j5E2aH/J2zevBk8Hk9skXY66nssFkvsNcMwEmVfCYVCsFgsnD59Gi1btkSfPn2wY8cOHD9+XGnZHbkyOwKBAJs2bcKBAwcQFxeHwMBA1KpVCy4uLqhZsyacnJyKbS9thFjURlGU3/84jGVL5yIoOAzBwWFYtnQusrNzcPbvK6I6x47uRnR0DFau2gIAcFm1EC9evEZQcBh0dXUwZ/ZkNG3SCPPmrRS16dmjE1gsFgICQ2BTuya2bHFBYGAIjp84p9T+lIfs7BxEfo4WvY6KjoN/YAh4ujowNzMpx8iU78aRfzB09jDEhEcjJiwaQ+YMBz+Xj8fXvk3In7djAZJik3F6W+HtFwZNH4JRi8Zg5/ztiP8cB70v2Y/crFzkZucCACasnIRXd18iMToRPEMehs0dAQ1tTTy8dL/M+yiv8AP/wm7PbKS9DUWqVyCqj+sObjUjRJ64CwCou/IXcMwM8H7uPlEbnUaF9x5ia3GgbqgLnUZWEOYXICswCgBQa95ApL0JRXZEHFTUVGHcrSkshneA79IjZd9BBQg+6AaHP2Yh9W0okr2CUHNsV2hYGiH85D0AQIMVI6FhboDXc/eL2uh+2UaqWlyoG+pCt5EVmPwCZHzZRiw1NnTqFs45VFFTBdfcALqNrCDIykXWl4xYZXLryA0MmD0UceExiA2LwYA5Q5CXy8ez746x6TvmISU2Cee3FV7123f6IAxbNAr75u9E4ud48L47xvhfjjGOJhemNb/dCsS4uglqNKyJrNRMJEWXPLNf4Shxzs7y5cvh7OwsViYtqwMARkZGYLPZElmc+Ph4iWzPV+bm5rC0tASP9y3b2aBBAzAMg8+fP6NOnTql7IEkuQY7GzduxIkTJ7Bt2zZMnTpVVG5ra4udO3f+dLBTHn7bvg8aGlzs+X2T6KaCjn1HIzMzS1SnRnULCL9LDerxeNi/bxvMzIyRlpaBN28+oEvXoXjl9UZUR5eni43rl6FaNXMkJ6fi8hU3uLhuRUFB5b8U9Ecf/IMwee5S0ettfxwCAAx07I6Nq6r2s9CuHLgMdS4H0zbMgJauNoLeBGLd2NXIzfr2K8TIwhjC776Aeo9zhBpHDUsOLBdb17mdZ3Fu11kAgKGZIZz/WAwdfV2kJ6cj0CcAywb/ioSoBFQWsdc8oaavDRvnoeCY6iHD/xO8R29B7ufCf0g4JvrQsDQSa9Pu/lbR//Oa1obF0PbIiUzAoxaF9xhia3LQcOtkcM0NIcjNQ1ZwNN7N3ovYa55l1zEFir72HOr62qjnPAQcEz1k+H/G8zHbkPNlG3FN9STuudPl3rdf0npNaqH60HbI/pQA9xbzC9uY6YvVqTOrH+rM6ofEZ754OmRDGfRKsW4cuAJ1rjombpgGTV0thLwJwtax65CblSuqY2RhJHb6pvu43lDjqGH+gSVi67q88xwu7yr8wVnLrjZWnlsvem+s62QAwOML93Fo8R5ldkm55LxEvCSKOmUljbq6OhwcHODu7o7Bg7/dysXd3R0DBw6U2qZdu3a4cOECMjMzoa2tDQAIDAyEiooKqlUr+qKh0mAxcswitLGxwcGDB9GtWzfo6Ojg7du3qFWrFvz9/dGmTRukpMh+gytVdUuZ2/yX5ERX3jvHloWRDgvKO4QKb1quZnmHUKHxWaU+q1+lXeDk/rzSf9ypiMtl9llZG8Yqbd1aq07JVP/cuXMYN24cDhw4gDZt2uDQoUP4888/8fHjR1hZWWH58uWIiooS3XQ4MzMTDRo0QOvWrbF27VokJiZiypQp6NSpE/78Uzk3f5QrsxMVFQUbGxuJcqFQKPGcLEIIIYQoWAW69HzkyJFISkrCunXrEBMTg8aNG8PNzQ1WVoWnYmNiYsTuuaOtrQ13d3fMnTsXzZs3h6GhIUaMGIENG5SXkZRrsNOoUSN4eHiIOvLVhQsXYG9vr5DACCGEEFI5zJo1C7NmSX+U0PHjxyXK6tevD3d3dyVH9Y1cg53Vq1dj3LhxiIqKglAoxOXLlxEQEICTJ0/ixo0bio6REEIIId+jp57LRK6T1P3798e5c+fg5uYGFosFV1dX+Pn54fr16+jRQ/rzcAghhBBCyoNcmR0A6NWrF3r16qXIWAghhBBSEhVozk5lQJcfEEIIIaRKK3FmR19fv8Q3/vv6FHRCCCGEKIES77NTFZV4sLNr1y4lhkEIIYSQEqPTWDIp8WBnwoQJyoyDEEIIIUQpSjzYSU9Ph66uruj/i/O1HiGEEEIUT5lPPa+KZJqzExMTAxMTE+jp6Umdv/P1KacCgUChQRJCCCGEyKvEg5379+/DwMAAAPDgwQOlBUQIIYSQn6A5OzIp8WCnU6dOYv+fm5uLd+/eIT4+XuxJ4YQQQgghFYlcNxW8desWxo8fj8TERIn36DQWIYQQomSU2ZGJXDcVnDNnDoYPH46YmBgIhUKxhQY6hBBCCKlI5MrsxMfHw9nZGaampoqOhxBCCCE/QzcVlIlcmZ1hw4bh4cOHCg6FEEIIISUiZJS3VEFyZXb27NmD4cOHw8PDA7a2tlBTUxN7f968eQoJjhBCCCGktOQa7Jw5cwa3b9+GhoYGHj58KHbPHRaLRYMdQgghRImYKpqBURa5BjurVq3CunXrsGzZMqio0IPTCSGEEFJxyTXYycvLw8iRI2mgQwghhJQHyuzIRK7RyoQJE3Du3DlFx0IIIYQQonByZXYEAgG2bduG27dvw87OTmKC8o4dOxQSHCGEEEKkoCcXyESuwc779+9hb28PAPjw4YPYe9IeEEoIIYQQUl7kGuzQg0AJIYSQckRzdmQi12CHEEIIIeWIBjsyocupCCGEEFKlUWaHEEIIqWQYhjI7sqDMDiGEEEKqNMrsEEIIIZUNzdmRCWV2CCGEEFKlUWaHEEIIqWwosyMTyuwQQgghpEqrMJmdmE425R1ChTbSYUF5h1ChnfPeVd4hVHg16/Qv7xAqtCU69uUdQoXmmK9Z3iGQ7zCU2ZFJhRnsEEIIIaSEaLAjEzqNRQghhJAqjTI7hBBCSGVDDz2XCWV2CCGEEFKlUWaHEEIIqWRogrJsKLNDCCGEkCqNMjuEEEJIZUOZHZlQZocQQgghVRpldgghhJDKhq7GkglldgghhBBSpVFmhxBCCKlk6Gos2dBghxBCCKls6DSWTOg0FiGEEEKqNMrsEEIIIZUMncaSDWV2CCGEEFKl0WCHEEIIqWyESlzksG/fPlhbW4PL5cLBwQEeHh4lavf06VOoqqqiadOm8n1wCdFghxBCCCFyO3fuHBYsWICVK1fCx8cHHTp0gKOjIyIjI4ttl5aWhvHjx6Nbt25Kj5EGO4QQQkglwwiVt8hqx44dcHJywpQpU9CgQQPs2rUL1atXx/79+4ttN336dIwePRpt2rSRcyuUHA12CCGEECKXvLw8eHt7o2fPnmLlPXv2xLNnz4psd+zYMYSEhGD16tXKDhEAXY1FCCGEVD5KvM8On88Hn88XK+NwOOBwOBJ1ExMTIRAIYGpqKlZuamqK2NhYqesPCgrCsmXL4OHhAVXVshmGUGaHEEIIqWSUeRpr8+bN4PF4YsvmzZuLjYfFYonHxzASZQAgEAgwevRorF27FnXr1lXoNikOZXYIIYQQIrJ8+XI4OzuLlUnL6gCAkZER2Gy2RBYnPj5eItsDABkZGfDy8oKPjw/mzJkDABAKhWAYBqqqqrhz5w66du2qoJ58Q4MdQgghpLJR4mmsok5ZSaOurg4HBwe4u7tj8ODBonJ3d3cMHDhQor6uri7ev38vVrZv3z7cv38fFy9ehLW1demCLwINdgghhBAiN2dnZ4wbNw7NmzdHmzZtcOjQIURGRmLGjBkACjNFUVFROHnyJFRUVNC4cWOx9iYmJuByuRLlikSDHUIIIaSSkecScWUZOXIkkpKSsG7dOsTExKBx48Zwc3ODlZUVACAmJuan99xRNhbDMBXiARsJPTqVdwgV2vRAXnmHUKGd895V3iFUeDXr9C/vECq0JTr25R1ChWZUUN4RVHxjok+V2Wcp899MY/dHSlt3eaHMDiGEEFLJVKTMTmVAl54TQgghpEqjzA4hhBBSyVBmRzYyZ3aOHDkitbygoADLly8vdUCEEEII+QmGpbylCpJ5sLNo0SIMHToUycnJojJ/f3+0bNkS58+fV2hwhBBCCCGlJfNgx8fHB3FxcbC1tYW7uzv27t2LZs2aoXHjxnjz5o0SQiSEEELI9yrSU88rA5nn7FhbW+Px48dYuHAhevfuDTabjZMnT+KXX35RRnwKwe0/CJrDf4GKoQEKwsORtX8P8j+8k1pXtZEttKdOB7t6DbA4XAjiYpH773XkXL4gVk9j8DBw+w8E28QUwrQ08D0eIuvIn0B+Xhn0SDlGLhiFHqN7QounjSCfQPzpcgCfgj4VWb/7Lz3ReWgX1KhXeC+FkPfBOL3tLwS/DRJb58iFo8TapcSnwKnFBOV0opx5vXmPY2cuwtc/GAlJydi92QXdOrYt77DKjPPSWRgzYTh4errw8X6Hlb9uQKB/SInaDhjiiP1HtuPWv/fgNHaeqJzNZmPRstkYPLwvjE2MEB+XgPNnrmH39gOoIHfOkKrVwiFoPLoLuDwtxPqE4IHLcSQHRhXbxsaxBVovHgZeDROkRcbDc9sFhNz2Er1v0bIeHGb0hYmtNbRN9XF9yk6E3vEWW8f8SOmXP3tsPIvXB/8tfccUoM6E7mg4sw80TPSQGhgFb9dTSHgZUGR9k9b10WzNGOjVtUR2XCr89t1A0F/3Re/XGtEBbXZNl2h31noShPx8AICqFhdNlgxDdcfm4BjqIuVjOLxcTiH5bajiO0gqHLkmKN+4cQNnz55F27ZtERAQgD///BMdO3aEhYWFouMrNU6nLtCeOQeZf+xE/scP4PbtD96mrUh2mgBhQrxkg9xc5Fy7goLQEDC5uVBrbAud+YvA5OYi1+164Tq7dofWlGnI2L4N+b4fwK5WDTq/Fs5Xyjqwtyy7pzCDZwxB/ykD8cfi3YgJjcKwuSOw+vQ6zOkyC7lZOVLbNG7TGE/+eQx/b3/k8/MwaMZQrP5rLeb3mIPkuG+nOSMDIrBmjIvotVBQRX86AMjJyUU9m1oY1KcnFq7cUN7hlKlZ850wbdYELJy9EqEh4Zi/eDrOXj6Mji37Iiszu9i2ltXN4bpuMZ4/85J4b/YCJ4ybNAILZq1AgF8wmtg3xo49G5CRnoEjB8vuviaycJjZD/ZTHOG+6CBSQ2PRYt5ADD69DCc7/4r8rFypbcya2cBx7xx4/u8iQm55oXbv5nDcNwcXhq5H3JvCAaOaJgeJvpHwPf8Y/Q4tkLqePx1mi72u2bkJuv82BcE3Xyq0j/KyGtAKDmvH4tWK40h4GYg647qiy+lfcaPzUmRHJUnU16pujC6nFiP49EM8m7Mfxi3rosWmichNysAnt1eiennp2bje4Vextl8HOgDQ+n9TwKtXDc/m7kd2XCqsh7ZDt3PLcKPzUuTEpiivw0rCCKvm3Bplkfk01vTp0zFixAgsWbIEjx8/xrt378DhcGBra1sh5+xoDB2B3FtuyL35LwSREcjavweChARo9Jd8ZgcAFIQEgf/gHgQR4RDGxYJ/zx153q+gZmsnqqPWsBHyP34A/8FdCONike/tBf6De1CrW7+suqVw/ZwG4NKe83hxyxORgZH4fdEucLgcdBzYscg2u+bvwK2/biLcNwxRIVHYv3QPWCoqsGvXRKyeoECA1IRU0ZKenK7s7pSbDm1aYN60CejRuV15h1LmpswYh993HMLNG3cR4BeMBTNXQEOTi8HD+hbbTkVFBXsObcP2LXsRGf5Z4n2HFk1w2+0+7t15jM+fovHvP3fw6MEzNLFvpKyulJq9U2+82nMNIbe8kBT4Ge7OB6HGVUe9QUVn+eydeiPS4wO89l5HSkgMvPZex6envrB36i2qE/HwHTy3Fw6GipKdkCa21OrZDJ89/ZAemaDQPsqr/jRHhJx9iJAzD5EeHA3v1aeQHZ2EuuO7Sa1fZ3xXZEUlwXv1KaQHRyPkzEOE/v0IDWb0Ea/IMMhNSBNbvmJz1VC9Twv4bPgb8S8CkBkeh/f/u4zMTwlFfi6pWmQe7Dx9+hQvXrzA4sWLwWKxYGZmBjc3N6xbtw6TJ09WRozyU1WFat26yPN+JVac5/0Kqo1K9gwO1dp1Cgc3796IyvI/vIdqnbpQrVc4uFExM4d6y9bgv/BUWOhlybS6KfRNDPDG442orCCvAB9ffEQ9hwYlXo+6BgdsNTYyUjPEys2tLXD45THsf/InnP9YDNPqkk/CJZVbDatqMDUzxqP7T0VleXn5eP7UC81bFn9n4oVLZiIpMRl/n7os9f2Xz33QvlNr1KpdeLq0YeN6aNnaHvfcPRTXAQXSrWEMLRM9RD7+9rBDQV4BPr/wh7lDnSLbmTezEWsDAJGP3hXb5mc0jXRRs2tTfPz7odzrUCQVNTYM7KwR8+iDWHnMow8wai69n8YOdSTqRz98D8Mm1mCpskVlqlpcDHq5C4O9fkfnE4ug39hK9B6LzYaKKhuC7zI9ACDIyYNxy3ql7Va5oDk7spH5NJa3t7fUp6HOnj0b3bt3V0hQiqLC44HFVoUwJVmsnElJgYq+QbFtDc5cgApPD2Czkf3XceTe/Haum//wPlg8Pejt3AOwWGCpqiLnn6vIOXdGGd1QOj0TfQBAakKqWHlqYiqMLY1LvJ5xy8YjOTYZ756+FZUFvgnA7847ER0aDT0jPQybOwKbLm/D/B5zkPnDoIhUXiamRgCAxATx0xAJ8UmoVr3o09vNW9lj1Ngh6NFxaJF19u46DB1dbTx6eQMCgQBsNhtbN+zGtUtuiglewbSM9QAA2YlpYuXZiWnQtTQqsp2msZ7UNprG8j8qpsGwDsjPykVwMZmgssQx0IGKKhu5P/QzNyENGiZ6UttwjXliWRoAyE1Mg4qaKjgGOsiNT0VacDQ8FxxCqv8nqGlroP6UXuh5zRVu3VcgIywOBVm5SPAKhO2CQUgPikJuQhqsBrWFUbPayAiLU1Z3SQUi82CnuMe+16tXshEyn88Hn88XLxMKwVFR0g2df5zDyALwk4mNqc5zweJqQq1BQ2hNmQZBdBT4D+4BANTsmkJr9NjCeUB+fmBbWkJ71lxoJich+/RJ5fRBgToO6oTpm2aJXm+ctO7L/4lvExZLoqhIg6YPQfsBHeE6ciXyv/v15PPwtej/IwMiEPDaH/seH0KXYV1x/fA1ebtAytng4X2xdcca0evxI2cCgMSEYRaLVeQkYi1tTfxxcAt+XbAaKcmpRX7WgCGOGDqiH2ZPXYJA/2A0sq2PtZuWIS4mARf+Lv99qN6gtui6+VtW+5+J2wFIfsUUbovi1yXxPov10++q4jQc0Qn+V55JZDTKncTGkdx3iqvPYomXJ70OQdLrbxPhE14FwvHOBtSd3BPeLn8BAJ7NPYDWO6ZiiM8eCAsESH4fjvArnjCwrVna3pQLporeD0dZ5JqgfPHiRZw/fx6RkZHIyxO/+uj169dFtPpm8+bNWLt2rVjZYusaWFK7pjzhFEmYlgZGUAAVA/EsDktPH8LU4iekCWNjAQCC8FCo6OtDc9xE0WBHa6ITcu/eEWV7BOGhyOJyobNgMbLP/FWqL6ey8NL9JQJ9AkWv1dQLdwM9Y32kxH/bLjxDPaQmpv50fQOnDcLQ2cOwZowrIvzDi63Lz+EjMiAC5jUr3mR2UnJ3bj6Aj9e3Uy7qHDUA+HK1VKKo3MjYQCLb81XNmjVQw6oajp/9Nqlf5csPnoiEt+jYoh8iwj/BZd0i7Nl1BP9cvgkA8PcNQrVqFpizcEqFGOyEur9GrM+3f2jZnMLjScuYh+z4VFG5hqGuRObme9kJqdD6IYujaaiL7ET55rhZtKwHAxsL3Jy9R672ysBPzoCwQADul+zXV1wjyezNV7kJaeD+kPXhGPIgzC8APyVT+gcxDJLfhELX2kxUlBkRj7tDN4KtwYGajgZy41PR/sAcZFaQuUyyqqqnm5RF5lTK77//jkmTJsHExAQ+Pj5o2bIlDA0NERoaCkdHxxKtY/ny5UhLSxNb5lvXkDn4nyooQEFgINSbNRcrVm/WHAUfPxTRSAoWCyw1tW+vORzJAY1QWPhzg1XxR9u5WTmIjYgRLZ+CPiElPhlN2jcV1VFVU0WjVo0Q4O1X7LoGTh+MYXNHYv2EtQh5H/zTz1ZVV0U1m2pIiU/+aV1ScWVlZiM8LFK0BPqHIC42AR27fJuAq6amhtbtmsPrpY/UdQQHhaJr24Ho2XGoaLlz8wGeebxEz45DER1V+INDQ0MDjFD8m10gFIgGRuUtPysXaRFxoiU5MApZ8amo0eHbvEAVNTaqtaqPGO+gItcT8zpYrA0A1OhoW2yb4jQa2Qlx70KR6BcpV3tlEOYLkPwuDOYdxftp3rExEr2k9zPBO0iyfqfGSHobBqZAUORn6TeyQs53g82vBDl85ManQp2nCfNOtvh821uyMalyZM7s7Nu3D4cOHcKoUaNw4sQJLFmyBLVq1YKrq6vYXZWLw+FwJE6H8ZX0xZVz6Tx0lq5EQWAA8v0+gtunH9gmJsi58Q8AQGvyVKgYGSNj2yYAAHfAIAjj4yH4FAEAUG1sB43hI5Fz9dvkybznz6AxdAQKgoOQ7+8LtkU1aE2YDL7n08JBTyV048g/GDp7GGLCoxETFo0hc4aDn8vH42uPRXXm7ViApNhknN5WeKpu0PQhGLVoDHbO3474z3HQ+/JrLTcrF7nZhZfXTlg5Ca/uvkRidCJ4hjwMmzsCGtqaeHjpvkQMVUF2dg4iP0eLXkdFx8E/MAQ8XR2Ym5mUY2TKd/jAX5jrPBVhIREIC43AXOdpyMnOxZWL3+a77d6/CTEx8diybhf4/DwE+IkPkNPTCudxfV/ufush5jlPQ9TnGAT4BaOxXQNMmzUBf5++UjYdk4PPkVtoMXsAUsPikBoWixZzBiA/Nw8BV5+J6vTcOR2ZsSl4trXwKtY3R29j2IVVcJjZD6F3vFGrpwOqt2+EC0PXi9qoaXLAq/ltgj+vujGMGtYAPzULGdHfMmjq2hqo07clPDZUvHmE/oduos3vM5H0LhSJXsGwGdsFmpaGCDpZmDlvunwENMz04Tn/IAAg6OR91JvUA81Wj0Hw6Qcwam6D2qM64+msbxlBW+fBSPQORkZYLNR0NFDPqRf0G9XAqxXHRXXMO9kCLBbSQ2KgY20Ke5dRSA+JQci5x6iM6NJz2cg82ImMjETbtoW/3jQ0NJCRUfjlNG7cOLRu3Rp79lSclCkA8B89AEuXB82x46FiYIiC8DCkrVwKYXzhpDQVQ0OomHz3jxBLBVqTp4JtZg5GKIAgOhpZhw8h999/RFWyTxeeqtKa6AQVI2MI01KR9/wZso4eLuvuKcyVA5ehzuVg2oYZ0NLVRtCbQKwbu1rsHjtGFsYQCr9ltHqPc4QaRw1LDog/E+3czrM4t+ssAMDQzBDOfyyGjr4u0pPTEegTgGWDf0VCVOVMHf/MB/8gTJ67VPR62x+HAAADHbtj46pF5RVWmdi3+wi4XA42bXcR3VRw9NCpYvfYsahmLrYPlcSqpRuxZMU8bNruAkMjA8TFxuPU8QvYuW2/orugMN77b0CVq44uGyeCo6uJ2DchuDpmq9g9dnQsjMB8ty1ivINwc84etFk8HG0WDUNaRBxuzt4juscOAJjY1cKw8ytFrzuuHgsA8L3wGO6LDonK6w5oDbBYCLhW8a4QjfjnBdT1dWC7cHDhTQUDPuPh2N+Q9eUeO1wTPWh9N5E761MCHozdDoe1Y1F3YnfkxKXA2+Wk2D121HQ10eo3J3CNecjPyEbyhwi4D9mApDehYnWaLh8BTXMD5KVmIdLtJd5uuVBsdohUHSxGxluQ1qpVCxcvXkSzZs3QokULTJkyBdOnT8edO3fwyy+/lDi786OEHp3kavdfMT1Q/isy/gvOee8q7xAqvJp1+pd3CBXaEp3iL5H/rzMqKO8IKr4x0WV3k8vI5sq7P1ANr3tKW3d5kfncUdeuXXH9euGdhJ2cnLBw4UL06NEDI0eOxODBgxUeICGEEEJIach8GmvlypWwtLQEAMyYMQMGBgZ48uQJ+vfvX+IJyoQQQgiRH83ZkY3Mgx0bGxvExMTA5Ms8lxEjRmDEiBFISkqCiYkJBAI6/0kIIYSQikPmwU5RU3wyMzPB5XJLHRAhhBBCikeZHdmUeLDj7OwMoPAuoK6urtDU1BS9JxAI8OLFCzRt2lThARJCCCFEXAW/d22FU+LBjo9P4Y3BGIbB+/fvoa6uLnpPXV0dTZo0weLFixUfISGEEEJIKZR4sPPgwQMAwKRJk7B7927o6uoqLShCCCGEFI1OY8lG5jk7x44dU0YchBBCCCFKIdeDQAkhhBBSfuip57KpGE/SI4QQQghREsrsEEIIIZUMUzmfOV1uKLNDCCGEkCqNMjuEEEJIJSOkOTsyocEOIYQQUsnQBGXZ0GksQgghhFRplNkhhBBCKhm6qaBsKLNDCCGEkCqNMjuEEEJIJUMPApUNZXYIIYQQUqVRZocQQgipZGjOjmwos0MIIYSQKo0yO4QQQkglQzcVlA0NdgghhJBKhm4qKBs6jUUIIYSQKo0yO4QQQkglQ5eey4YyO4QQQgip0iizQwghhFQyNEFZNpTZIYQQQkiVRpkdQgghpJKhq7FkQ5kdQgghhFRplNkhhBBCKhm6Gks2lNkhhBBCKhkhw1LaIo99+/bB2toaXC4XDg4O8PDwKLLu5cuX0aNHDxgbG0NXVxdt2rTB7du35d0UJUKDHUIIIYTI7dy5c1iwYAFWrlwJHx8fdOjQAY6OjoiMjJRa//Hjx+jRowfc3Nzg7e2NLl26oH///vDx8VFajCyGqRjJsJk1R5R3CBXawBwalxbHKe99eYdQ4YUHXS/vECq03c1cyzuECq2A5sP+1LKIU2X2Wa8sBytt3S2irshUv1WrVmjWrBn2798vKmvQoAEGDRqEzZs3l2gdjRo1wsiRI+HqqpzjkP4FJYQQQogIn89Henq62MLn86XWzcvLg7e3N3r27ClW3rNnTzx79qxEnycUCpGRkQEDA4NSx14UGuwQQgghlYwy5+xs3rwZPB5PbCkqQ5OYmAiBQABTU1OxclNTU8TGxpaoL//73/+QlZWFESOUd4aHrsYihBBCiMjy5cvh7OwsVsbhcIptw2KJn+dkGEaiTJqzZ89izZo1uHbtGkxMTGQPtoRosEMIIYRUMsqcbMvhcH46uPnKyMgIbDZbIosTHx8vke350blz5+Dk5IQLFy6ge/fucsdbEnQaixBCCCFyUVdXh4ODA9zd3cXK3d3d0bZt2yLbnT17FhMnTsSZM2fQt29fZYdJmR1CCCGksqlIDwJ1dnbGuHHj0Lx5c7Rp0waHDh1CZGQkZsyYAaDwtFhUVBROnjwJoHCgM378eOzevRutW7cWZYU0NDTA4/GUEiMNdgghhJBKpiI9G2vkyJFISkrCunXrEBMTg8aNG8PNzQ1WVlYAgJiYGLF77hw8eBAFBQWYPXs2Zs+eLSqfMGECjh8/rpQYabBDCCGEkFKZNWsWZs2aJfW9HwcwDx8+VH5AP6DBDiGEEFLJCMs7gEqGJigTQgghpEqjzA4hhBBSyTCoOHN2KgPK7BBCCCGkSqPMDiGEEFLJCCvEI7wrD8rsEEIIIaRKo8wOIYQQUskIac6OTCizQwghhJAqjTI7hBBCSCVDV2PJhgY7hBBCSCVDNxWUDZ3GIoQQQkiVRpkdQgghpJKh01iyocwOIYQQQqo0yuwQQgghlQzN2ZENZXYIIYQQUqVRZocQQgipZCizIxvK7BBCCCGkSqPMDiGEEFLJ0NVYspFrsKOvrw8WS3JDs1gscLlc2NjYYOLEiZg0aVKpAySEEEKIOCGNdWQi12DH1dUVGzduhKOjI1q2bAmGYfDq1SvcunULs2fPRlhYGGbOnImCggJMnTpV0TETQgghhJSYXIOdJ0+eYMOGDZgxY4ZY+cGDB3Hnzh1cunQJdnZ2+P3332mwQwghhCgYPfVcNnJNUL59+za6d+8uUd6tWzfcvn0bANCnTx+EhoaWLjpCCCGEkFKSa7BjYGCA69evS5Rfv34dBgYGAICsrCzo6OiULjpCCCGESGCUuFRFcp3GcnFxwcyZM/HgwQO0bNkSLBYLL1++hJubGw4cOAAAcHd3R6dOnRQaLCGEEEKIrOQa7EydOhUNGzbEnj17cPnyZTAMg/r16+PRo0do27YtAGDRokUKDVQR+i4YjvajukGTp43wN0H42+UIYoI+F1nfvE419HceiRq21jCsZoIL647j/lE3iXo8U30MXjYWjTo3hTpXHXFhMTi1ZD8iP4QpszsKVX1iD1jP7g+OiR4yAz7D3+UkUl74S63LMdFDvbXjwLOzhmYtM0QcvgV/l5NidUz7tECt+YOgaW0Glhob2aGxCN//L6IvepRFd5TCeeksjJkwHDw9Xfh4v8PKXzcg0D+kRG0HDHHE/iPbcevfe3AaO09UzmazsWjZbAwe3hfGJkaIj0vA+TPXsHv7ATBM1fuN5fXmPY6duQhf/2AkJCVj92YXdOvYtrzDUoq2C4fAbnQXcHhaiPUJwV2X40gKjCq2TR3HFmi/eBh4NUyQFhkPj20XEHzbS/R+k7Hd0HRcN+hWMwYAJAV+hufuKwh7+E7sc+v1bw1dCwMI8gWIex8Gj20XEPumZPtqWWq/YAiajO4CLk8LMT4huONyHIlBxW+jeo4t0GHRMOjVMEFqZDwe/3YBgd9to++1ntUfnZeOxKsjt3Bv3Smx9wxtLNB52S+o3qo+WCosJAZG4drsP5AenaSw/ikT3VRQNnLfZ6ddu3Zo166dImNRqp4zBqKbU1+cXLwP8WExcJw7BPNOrcKargvAz8qV2kZdg4PEyDi8dvPEMJcJUuto6mrh10vrEeD5EXsmbkJGUjqMa5giOz1bmd1RKLOBbdBg/QT4LjuClJcBqD6+OxzOLsOTDouQGyV54Ktw1JCXlI6QXVdQc3ofqevMT81CyK6ryAqOgjBPAJOezdB49wzkJaYh8bsv5spi1nwnTJs1AQtnr0RoSDjmL56Os5cPo2PLvsjKLP5vbVndHK7rFuP5M8kv5NkLnDBu0ggsmLUCAX7BaGLfGDv2bEBGegaOHDwlZW2VW05OLurZ1MKgPj2xcOWG8g5HaVrO7AeHKY64teggUkJj0XreQAw/vQxHOv+K/CK+b8yb2aD/3jl48r+LCL7lBZvezdF/3xycHbpeNFDJiE3G4y3nkBoeBwBoNKwDBh12xsk+K0UDqeTQGNxzPYG0yHioctXh4OSI4aeW4nDHRchJziibDVACrWb0Q4spjvh38UEkh8ai7dyBGHl6Gf7s8ivyithGFs1sMHDPHDz+30UE3vZC3V7NMXDvHJwath4xPwzmzOxqoenoLoj3jZBYj14NE4y96IK35x7hyc5LyE3PhlEdSxTw85XSV1L+5L6DskAgwMWLF7F+/Xps2LABly5dQkFBgSJjU6iuk/vg1t4reHP7JaIDP+HEor1Q1+CgxcD2RbaJeBeCy5tPwev6MxTkST8Ies4ciJToJPz1635EvA1B8ucEBDz7gMTIOGV1ReFqzuiLz2ce4PPpB8gKioa/y0nkRiWhxsQeUuvnfEqA/6oTiL7ggYL0HKl1kp/5Iv7mK2QFRSMnIg4Rf95Ehm8k9FrVV2ZXlGbKjHH4fcch3LxxFwF+wVgwcwU0NLkYPKxvse1UVFSw59A2bN+yF5HhkllEhxZNcNvtPu7deYzPn6Lx7z938OjBMzSxb6SsrpSrDm1aYN60CejRufL8UJJHM6feeLHnGoJueSEx8DNuOh+EKlcdDQYVncVycOqNCI8PeLn3OpJDYvBy73VEPvWFg1NvUZ3Quz4Ie/AWKWGxSAmLxZPfLiAvOxfm9jaiOv7XPBH55CPSIhOQFBiFh+tPg6OrCeMGNZTaZ1m1cOqNZ3uuIfDLNvp30UGocdXRcGDR26jF5N4Ie/IBz/cVbqPn+64j4qkvWkzuLVZPTZODAbtn4ubSI8hNk/wx0vHX4Qh58BYPN/+NuI8RSPuUgJD7b5CdlK7wfiqLkMVS2lIVyTXY+fDhA+rWrYsJEybgypUruHz5MiZMmIA6derg/fv3io6x1Iyqm4Bnog9fj7eisoK8AgS98EVth3qlWrdd9+aIeB+KKXsXYpvXn1jx71a0+6VbaUMuMyw1NnTtrCWyLYmP3kGveV2FfY5Bh8bQsjFHiqefwtZZVmpYVYOpmTEe3X8qKsvLy8fzp15o3tK+2LYLl8xEUmIy/j51Wer7L5/7oH2n1qhV2woA0LBxPbRsbY977pX3dN9/Ha+GMbRN9BD++Nt3oSCvAJ9f+MPSoU6R7Sya2Yi1AYDwR++KbMNSYaFe/9ZQ0+Ag5nWQ1DoqamzYje6C3LQsJEjJcJQXXvUv28hDfBt9kmMbhT2W3EY9109EyP03iHj6UXIlLBZqd22K5LBYjDi5BHO992L81TWo09OhdJ0qYzRBWTZyncaaMmUKGjVqBC8vL+jr6wMAUlJSMHHiREybNg2enp4KDbK0dI31AAAZCWli5ekJaTCsZlSqdRvVMEHHsT1w7/C/uLXvCmo2scGINZNQkJePF5cfl2rdZUHdQBcqqmzk/bBt8hLSwDHRK9W6VXU00Pntfqioq4IRCOG77CiSHle8wfDPmJgW7iOJCeKn9BLik1CtukWR7Zq3sseosUPQo+PQIuvs3XUYOrraePTyBgQCAdhsNrZu2I1rlyTnhpHKQevL901WovgxlZWYBl3Lor9vtIz1pLbRNOaJlRnVq4bRV9dAlaOGvKxcXJu2C0lB0WJ1anVrin575kBNQx2Z8am4OGYrclIyS9ErxdL+8t2SlSDbNtIuYhtpfbeNGvRvDdPGNXFigKvUdWgZ6YKjrYHWM/vBY/tFPNzyN2p1aoIhB+fjzC+b8KmIuYqkcpNrsPP27VuxgQ5Q+AiJjRs3okWLFj9tz+fzwefzxcoEjABsFluecCS0GNgeozdNE73eN3kzAEhM+GSxWCjtHFAWSwUR70Nw7bezAIDPH8NhUac6Oo7tWSkGO9/8sCFYkttLVgWZuXjWdSnYWlwYdmiM+mvHISciHsnPfEu1XmUbPLwvtu5YI3o9fuRMAEXtP9K3kZa2Jv44uAW/LliNlOTUIj9rwBBHDB3RD7OnLkGgfzAa2dbH2k3LEBeTgAt/Xyt1X4jyNRjUFj02Txa9vjxxe+H//HhIsVg//9kstY14YXJoDE72XgkOTxN1HVvAccd0nBuxQWzA8+mZH072XgkNA23YjeqC/vvm4PTANeV2mqbhoLbovenbNrowqXAbSWyOEmwjiUPuu22kY26A7qvH4dy4rRAUMf/m66OOgtxf49WRWwCAeN9IWDrUgf2YbpVmsEMTlGUj12CnXr16iIuLQ6NG4vMK4uPjYWNjU0SrbzZv3oy1a9eKlTnwGqKFnmLmKby764XwN9/SuqrqagAAXRM9pCekisp1jHSR8cOvBFmlxacg9ocrumJDPsPesVWp1ltW8pLTISwQQP3Lr9Gv1I14EtkemTEMsr9MpMz4GAGtupaoNW9ghR/s3Ln5AD5e3zJQ6pzC/afwaqlEUbmRsYFEtuermjVroIZVNRw/u1dUpqJSeNY4IuEtOrboh4jwT3BZtwh7dh3BP5dvAgD8fYNQrZoF5iycQoOdSiLY/TVifL5NjmVzCr9WtYx5yIpPFZVrGuoiu5jvm6yEVLEMxbc24gMUYb4AqRGFx1XcuzCYNamFZpN7w335UVGd/Bw+UiPikBoRhxifEDg92o7Gv3TCy72S90crC8Hur3H0u22kql64jbR/2EZahroSmZvvZSakQvuHbVTYpnAbmdlaQ8uYh4k31oveV1Flo3qrenCY0AO/1ZmI7JQMCPILkPTDVV9JwVGo1qJ00xpIxSXXYGfTpk2YN28e1qxZg9atWwMAnj9/jnXr1mHr1q1IT/92cOrq6kq0X758OZydncXKFtsq7qGh/KxcJPwwmz8tPgUN2tvh88dwAABbjY06rRriypbTpfqsUO8AmNYSP5VhYm2BpKiEUq23rDD5AqS/C4NRJ1vE33wlKjfqaIv4Ii7nlBeLxYLKl4FnRZaVmY2szEixsrjYBHTs0hYf3xf+6lNTU0Prds2xac0OqesIDgpF17YDxcqWrJwHbW0tuC7fjOioWACAhoYGGKH4bzSBUCAaGJGKLz8rF6k/fN9kxqfCqkNjxH8snCejosZGtVb18XjLuSLXE/06GFYdGsP7S7YBAGp2tEWUt/T5OCIsFtjqP/kqZ7FEP/rKQ15WrsQVVpnxqajZvjHivttG1VvVx8OfbKOaHRqLMjKA+DaKePoRh3ssE2vTd/s0JIVE4/n+G2CEDBihADHvQmFQy1ysnoG1OdKiElFZ0INAZSPXYKdfv34AgBEjRohSgl/T+f379xe9ZrFYEAgEEu05HA44HI5YmaJOYRXl/lE39J49GPHhMUgIi0Xv2YORl8PHq2tPRHUm/G82UuOScW1b4Skpthob5nWqffl/VeiZGqBaQ6vCwdSXX1b3jvyLXy+tR+9Zg+H97zPUbGKD9qO64fTyQ0rtjyKFH/gXdntmI+1tKFK9AlF9XHdwqxkh8sRdAEDdlb+AY2aA93P3idroNCqcUMvW4kDdUBc6jawgzC9A1pfLX2vNG4i0N6HIjoiDipoqjLs1hcXwDvBdeqTsO6gAhw/8hbnOUxEWEoGw0AjMdZ6GnOxcXLn4r6jO7v2bEBMTjy3rdoHPz0OAX7DYOtLTCi/7/b7c/dZDzHOehqjPMQjwC0ZjuwaYNmsC/j59pWw6Vsays3MQ+fnb6Zao6Dj4B4aAp6sDczOTcoxMsV4fuYVWswcgJSwOqWGxaDVnAApy8+B39ZmojuPO6ciMTYHH1vOFbY7exi8XVqHlzH4IvuMNm54OqNG+Ec4O/ZalaL9kBMIevkVGdBLUtbioP6ANqrdugEvjtwEA1DQ4aDV3IELcvZEVnwquvg6ajusOHTN9BPz7omw3wk+8OnILbWYPQEp4HJLDYtFmzgDk5+bB99q3bdRvx3RkxKbg0bbCbeR17DbGnF+FVjP6IcjdG3V6OKBmu0Y4NaxwG+Vl5SIxUDzTnp/NR05Kplj5y4NuGLhnDj698EeEpx9qdbaDTXd7nBm5sQx6TsqDXIOdBw8eKDoOpbtz4BrUuOoYtX4KNHlaCHsTjD/GbRS7x46BpZHYHAyeqQFWuv0met1j+gD0mD4Agc8/YucvhafhIt6F4MD07Ri0ZDT6zB+KxE/xuLDuhNggqqKLveYJNX1t2DgPBcdUDxn+n+A9egtyPxf+yuGY6EPjh0mD7e5vFf0/r2ltWAxtj5zIBDxqMRcAwNbkoOHWyeCaG0KQm4es4Gi8m70Xsdcq1uT1ktq3+wi4XA42bXcR3VRw9NCpYvfYsahmDqFQtnlOq5ZuxJIV87BpuwsMjQwQFxuPU8cvYOe2/YruQoXwwT8Ik+cuFb3e9kfhj4KBjt2xcVXFuxGpvF7uvwFVrjq6b5wIrq4mYt6E4OKYrWL32NG1MALz3f4S7R2EG3P2oN3i4Wi3aBhSI+JwY/YesZsBahnpos/OGdAy0UNeRjYS/D/h0vhtiPD4AAAQCoUwqG2ORsPmQ0NfB7mpmYh9G4q/h2346Q0Ny9qLAzegxlVHzw2F2yj6TQjOjd0qlgH6cRtFeQfh2tw96LhoODouGoaUyDhcm7NH4h47PxN42wu3Vx5F61kD0H3teCSHxODKjN347BWosP4pGz0IVDYsRs5ZqLm5uXj37h3i4+Mh/CENP2DAAJnXN7PmCHnC+M8YmEOnNYrjlFf5rvIqa+FB5TNfo7LY3Uz61TukUAH92/pTyyLK7kagpy3GKm3dY6Kr3g1N5crs3Lp1C+PHj0diouT5zaJOXRFCCCFEMarq/XCURa50wZw5czB8+HDExMRAKBSKLTTQIYQQQpRLyFLeUhXJNdiJj4+Hs7MzTE1NFR0PIYQQQohCyTXYGTZsGB4+fKjgUAghhBBSEkIlLlWRXHN29uzZg+HDh8PDwwO2trZQUxO/f8O8efMUEhwhhBBCSGnJNdg5c+YMbt++DQ0NDTx8+FB0rx2gcIIyDXYIIYQQ5aEJyrKRa7CzatUqrFu3DsuWLaM7vRJCCCGkQpNrsJOXl4eRI0fSQIcQQggpB1X1qillkWu0MmHCBJw7V/TzSwghhBBCKgq5MjsCgQDbtm3D7du3YWdnJzFBeccO6Q9HJIQQQkjpVdWrppRFrsHO+/fvYW9vDwD48OGD2HvfT1YmhBBCiOLRYEc2/5kHgRJCCCHkv4lmGBNCCCGVDMNS3iKPffv2wdraGlwuFw4ODvDw8Ci2/qNHj+Dg4AAul4tatWrhwIED8n1wCdFghxBCCCFyO3fuHBYsWICVK1fCx8cHHTp0gKOjIyIjI6XWDwsLQ58+fdChQwf4+PhgxYoVmDdvHi5duqS0GGmwQwghhFQyFelxETt27ICTkxOmTJmCBg0aYNeuXahevTr2798vtf6BAwdQo0YN7Nq1Cw0aNMCUKVMwefJkbN++XY5PLxka7BBCCCFEhM/nIz09XWzh8/lS6+bl5cHb2xs9e/YUK+/ZsyeePXsmtY2np6dE/V69esHLywv5+fmK6cQPaLBDCCGEVDLKzOxs3rwZPB5PbNm8ebPUOBITEyEQCGBqaipWbmpqitjYWKltYmNjpdYvKChAYmKijFuiZOS6GosQQgghVdPy5cvh7OwsVsbhcIpt8+NtZxiGKfZWNNLqSytXFBrsEEIIIZWMMh8EyuFwfjq4+crIyAhsNlsiixMfHy+RvfnKzMxMan1VVVUYGhrKF/RP0GksQgghpJIRspS3yEJdXR0ODg5wd3cXK3d3d0fbtm2ltmnTpo1E/Tt37qB58+YST2RQFBrsEEIIIURuzs7OOHz4MI4ePQo/Pz8sXLgQkZGRmDFjBoDC02Ljx48X1Z8xYwYiIiLg7OwMPz8/HD16FEeOHMHixYuVFiOdxiKEEEIqmYr0uIiRI0ciKSkJ69atQ0xMDBo3bgw3NzdYWVkBAGJiYsTuuWNtbQ03NzcsXLgQe/fuhYWFBX7//XcMHTpUaTHSYIcQQgghpTJr1izMmjVL6nvHjx+XKOvUqRNev36t5Ki+ocEOIYQQUslUpMxOZUBzdgghhBBSpVFmhxBCCKlklHnpeVVEmR1CCCGEVGmU2SGEEEIqGVnvh/NfR4MdQgghpJKhCcqyodNYhBBCCKnSKLNDCCGEVDI0QVk2lNkhhBBCSJVGmR1CCCGkkhFSbkcmFWawU1eoXt4hVGh8Fk1HK84SHfvyDqHC293MtbxDqNDmv15X3iFUaHl/ri3vEAiRW4UZ7BBCCCGkZOjnr2xozg4hhBBCqrRSD3Zyc3MVEQchhBBCSohR4lIVyTXYEQqFWL9+PSwtLaGtrY3Q0FAAgIuLC44cOaLQAAkhhBAiTqjEpSqSa7CzYcMGHD9+HNu2bYO6+reJxba2tjh8+LDCgiOEEEIIKS25BjsnT57EoUOHMGbMGLDZbFG5nZ0d/P39FRYcIYQQQiQJWcpbqiK5BjtRUVGwsbGRKBcKhcjPzy91UIQQQgghiiLXYKdRo0bw8PCQKL9w4QLs7el+J4QQQogyCcEobamK5LrPzurVqzFu3DhERUVBKBTi8uXLCAgIwMmTJ3Hjxg1Fx0gIIYQQIje5Mjv9+/fHuXPn4ObmBhaLBVdXV/j5+eH69evo0aOHomMkhBBCyHfo0nPZyH0H5V69eqFXr16KjIUQQgghROHkyuxMnjwZJ06ckChPT0/H5MmTSx0UIYQQQopG99mRjVyDnePHj2PWrFmYN28ehMJvmyYnJ0fqIIgQQgghpLzI/biIf//9Fzdv3kSvXr2QkpKiyJgIIYQQUgy6Gks2cg92GjZsiOfPnyM/Px8tWrSAn5+fIuMihBBCSBFogrJs5BrssFiFt1g0NDTE3bt30blzZ7Ru3Rr//POPQoMjhBBCCCktua7GYphvYz9VVVUcPnwYDRs2xKxZsxQWGCGEEEKkq6oTiZVFrsHOgwcPYGBgIFbm7OwMOzs7PH36VCGBEUIIIYQoglyDnU6dOkkt7969O7p3716qgAghhBBSvKo6kVhZSjzYcXZ2xvr166GlpQVnZ+di6+7YsaPUgRFCCCGEKEKJBzs+Pj6iJ5r7+PgUWe/r5GVCCCGEKAfldWRT4sHOgwcPpP4/IYQQQkhFJvezsb4XERGBrKws1K9fHyoqct+6hxBCCCElQFdjyUamkcmJEyewa9cusbJp06ahVq1asLW1RePGjfHp0ydFxkcIIYSQHzBK/K8qkmmwc+DAAfB4PNHrW7du4dixYzh58iRevXoFPT09rF27VuFBEkIIIYTIS6bTWIGBgWjevLno9bVr1zBgwACMGTMGALBp0yZMmjRJsRESQgghRAydxpKNTJmdnJwc6Orqil4/e/YMHTt2FL2uVasWYmNjFRcdIYQQQkgpyTTYsbKygre3NwAgMTERHz9+RPv27UXvx8bGip3mIoQQQoji0VPPZSPTaazx48dj9uzZ+PjxI+7fv4/69evDwcFB9P6zZ8/QuHFjhQdJCCGEECIvmQY7S5cuRXZ2Ni5fvgwzMzNcuHBB7P2nT59i1KhRCg2QEEIIIeKqZv5FeWQa7KioqGD9+vVYv3691Pd/HPwQQgghhJQ3ue8AmJqaisOHD2P58uVITk4GALx+/RpRUVEKC44QQgghkmjOjmzkuoPyu3fv0K1bN+jp6SE8PBxTp06FgYEBrly5goiICJw8eVLRccqs9cIhsB3dBVyeFmJ8QvDA5TiSAosfiNk4tkDbxcPAq2GCtMh4PN12ASG3vUTvt5jdHza9W8CgtjkKcvMQ7R2EJ5vPISU05ts6ejeH7ZiuMLW1hoaBDk71XoEE30il9VNRak7sDptZ/cA10UNGQBTeu55E8osAqXU5JnpovGYM9OysoVXLDKGHb+OD619idXTqWaL+r8Oh18QamtWN8d7lJEL/vFUWXVGYVguHoPGXfSj2yz6UXIJ9qPV3+5DnD/uQRct6cJjRFya21tA21cf1KTsResdbbB3zI09JXbfHxrN4ffDf0ndMQdouHAK70V3A+bJ97pbgGKvj2ALtv9s+HtsuIPi77dNkbDc0HdcNutWMAQBJgZ/hufsKwh6+E/vcev1bQ9fCAIJ8AeLeh8Fj2wXEvglRTkfLkNeb9zh25iJ8/YORkJSM3Ztd0K1j2/IOq1yoNusKtVZ9wNLmQZgQjby7pyH8HFh0A7Yq1NoNhGrjtmBp8cBkpCD/2T8oeOdRdkGXIbr0XDZyZXacnZ0xadIkBAUFgcvlisodHR3x+PFjhQUnr+Yz+6HZFEc8cDmBM/1ckZ2QiiGnl0FNi1tkG/NmNui7dw78Lj/Bqd4r4Hf5CfrumwOzprVFdaq1aoC3J9zx96A1uDRmK1RU2RhyailUNTiiOmqaHER7BeLJlnNK7aMiWQxsDdt14xG46yoe9liBpBf+aHNmKTQsDaXWV+Gogp+UgcDd15D2UfpAjq3BQVZkPHw3/I3cuBRlhq8UDjP7wX6KIx66nMDf/VyRlZCKwT/Zh8ya2cBx7xz4X36CM71XwP/yEzjumwPT7/YhNU0OEn0j8dDlRJHr+dNhttjivugQGKEQwTdfKrSPpdFyZj84THHEPZcTOP1l+wwvwTHWf+8cfLz8BCd7r8DHy0/Q/4djLCM2GY+3nMOpfi441c8Fkc98MeiwMwzrWorqJIfG4J7rCRzvuRxnh65D2qdEDD+1FBoGOkrtc1nIyclFPZtaWOE8q7xDKVfsBi2h3n0M8p9dR85RVwg/B4A7chFYugZFtuEMmg12zYbgux1BzqFl4F/bD2FSTJH1yX+LXIOdV69eYfr06RLllpaWFeI+O82ceuPlnmsIvuWFpMDPuO18EKpcddQfVPQvJHun3ojw+IBXe68jJSQGr/Zex6envrB36i2qc2X8Nvhe9EBSYBQS/SJxZ9Eh6FYzgqltTVEdv8tP8WL3VUQ++aDMLiqUzfQ+iDj7EJFnHiIzKBofXP9CTlQSak7oLrV+zqdEfHA5iU8XPFCQkS21TuqbUPiuO4Ooa54Q5hUoM3ylsHfqjVd7riHkyz7k7nwQalx11PvJPhTp8QFeX/YhLyn7UMTDd/DcfhEht7yKXE92QprYUqtnM3z29EN6ZIJC+1gazZx648Weawi65YXEwM+4+eUYa1DM9nH4coy93HsdySExeLn3OiKf+sLhu+0TetcHYQ/eIiUsFilhsXjy2wXkZefC3N5GVMf/micin3xEWmQCkgKj8HD9aXB0NWHcoIZS+1wWOrRpgXnTJqBH53blHUq5UmvZGwVvH6Pg7SMwSTHIu3sGTHoyVO27Sa3PrmULdo16yD2/A8JwXzBpiRDGhEIYFVzGkZcdelyEbOQa7HC5XKSnp0uUBwQEwNjYuNRBlQavhjG0TPQQ8fi9qEyQV4CoF/6wcKhTZDvzZjZibQAg/NG7Ytuo62gCAHJTs0oZdflhqbHBs7NGwnenCQAg/tF7GLSoW05RlS/dL/tQ5A/70OcX/jD/yT4U+cM+FPnoXbFtfkbTSBc1uzbFx78fyr0ORePVMIa2iR7CpWwfy2L6atHMRqwNUHiMFdWGpcJCvf6toabBQczrIKl1VNTYsBvdBblpWUjwjZCjN6TCUWFDxawmBGHiPxgFYR/ArmYjtQm7jj2EMeFQa90HGnN2QWP6Vqh3/QVQVSuLiEklINdgZ+DAgVi3bh3y8/MBACwWC5GRkVi2bBmGDh2q0ABlpWmsBwDITkwTK89OTIOmcdE3PNQy1pO5TSfXMYh6GYCkwM/yB1zOOAY6UFFlIzdBvO/8hDRwi+l7VaZVzD6kVcw20ZRjH/qZBsM6ID8rF8HFZILK2tftk/VDX7NKcIyVpI1RvWqY53cYC4OPo8emSbg2bReSgqLF6tTq1rSwTtAxOEzpjYtjtiInJbMUvSIVBUtTBywVNpgs8X2FyUoDS0v6/sXSM4ZK9TpQMa4G/qXfkXf3NNj1mkO95/iyCLlcCJW4KEtKSgrGjRsHHo8HHo+HcePGITU1tcj6+fn5WLp0KWxtbaGlpQULCwuMHz8e0dHRRbYpilyDne3btyMhIQEmJibIyclBp06dYGNjAx0dHWzcuPGn7fl8PtLT08WWAkYgTyioP6gtZvsdFi0qquzCN37MxLFYP78xwQ/vs1gsgJHeqMv6CTCqXx1uc/bKFXeFI9H3Irte5dQb1BYz/Q6Llq/70I/9Z7FYP90mEu+XckM2HNEJ/leeQcDPl3sdpdVgUFvM8zssWoo6xlgKOsaSQ2NwsvdKnB60Bm9P3YPjjukwrGMhVufTMz+c7L0SZwavRfjDd+i/bw40DXVBqhIpX0pF7GAslgrAAPx/DkAYEwpByDvk3TsLVbv2lN2pQEaPHo03b97g1q1buHXrFt68eYNx48YVWT87OxuvX7+Gi4sLXr9+jcuXLyMwMBADBgyQ+bPluhpLV1cXT548wf379/H69WsIhUI0a9YM3btLn+Pxo82bN0s8Hb2nri168+xkjiXE/TVifL5dhaHKKeySpjEPWfGponJNQ12JX93fy0pIlfiFqWGoi+xEydN1ndeOR+0ezXB++AZkxibLHHNFwk/OgLBAAK6JeN/VjXjgF7O9qpJQ99eI/W4fYn/Zh7SMecj+bh/S+Mk+lJ2QKpH50SxiHyoJi5b1YGBjgZuz98jVXlGCfzjGvt8+sh5jJdk+wnwBUiPiAABx78Jg1qQWmk3uDfflR0V18nP4SI2IQ2pEHGJ8QuD0aDsa/9IJL/del7ufpGJgsjPACAVgaemJlbM0dcFkST+WhJmpYGWmAPycb2VJ0WCxVMDSMQCTEqfMkMtFZZtb4+fnh1u3buH58+do1aoVAODPP/9EmzZtEBAQgHr16km04fF4cHd3Fyv7448/0LJlS0RGRqJGjZLP05P7PjsA0LVrVyxevBhLliwp8UAHAJYvX460tDSxpbtuI7liyM/KRVpEnGhJCoxCVnwqrDp8e2yFihoblq3qI9pb+nl/AIh5HSzWBgCsOtpKtOmybjzqODbHxV82If1TxZkwKi8mX4C0d2Ew7mQrVm7SqTGSXxVzmWcV8uM+lPxlH6rxwz5UrVV9xPxkH6rxwz5Uo6NtsW2K02hkJ8S9C0WiX/neuiA/K1c0sEj9coxlSjnGqrWqj6hi+hot5Rir2dG22DYAABYLbPWf/C5jsaCqTr/gqwShAMLYcLCtxf9NYFs3guCz9AnHws9BYGnrAWrfroxVMTADIxSCyajcP0jLg7SzL3w+v1Tr9PT0BI/HEw10AKB169bg8Xh49uxZideTlpYGFosFPT09mT5frswOANy7dw/37t1DfHw8hELxs3xHjx4tolUhDocDDocjVqbKYssbioTXR26hxewBSAmLQ2pYLFrOGYCC3Dz4X/22QXvtnI7M2BQ83XoeAOBz9DZGXFiF5jP7IeSON2r3dECN9o1wfui3u0V33TAR9Qa2wT9TdiIvK1eUCeKnZ4tOM3B4WtC1NISWqT4AQL+2OQAg68uVNRVR8EE3OPwxC6lvQ5HsFYSaY7tCw9II4SfvAQAarBgJDXMDvJ67X9RGt5EVAEBViwt1Q13oNrICk1+AjC/3WWGpsaFTtxoAQEVNFVxzA+g2soIgKxdZ4RX/V5bPl30o9cs+1GLOAOTn5iHgu32o55d96NmXfejN0dsYdmEVHGb2Q+gdb/y/vfsOa+r6/wD+vkBIwkiQJWhRwIG4B6jgAFtcdWAdrYpYHNQF7lpta8VK9adW1GqrFVutq9VWrV+torVInYCiiIshS0WQIUNGgCTn9wc1GtkhTD+vPnmecu65955zvIFPzor1kF6w6N8Jv7/2DPF0+BBbNlf8LLYwgXHHVijMysOLpxmKdG09IdqN6I1LvofqoLbVd/OnAPR57T3W57/32IPX2mf4f+1z6b/2ufnzWUz8/Uv0njMSD8+Foe1/77FfX2uf/ss+RHzQbbx4mgFtXQE6jHaARV9bHJ26AQDAE/LRx9sVsX+HIS81C4Jm+uju7gJ9s2aI+iukbhuhFuTnF+DRk1fzEZKePkNkdCzEIn2Ym5nWY8nqVnFoAPijZkGeHA9Z0kPwug8CJzKC9FYgAIDnNAGcfjMUndoFAJDeuwZev9Hgj5iJokvHwenoQ/vdiZBGXASk9TcEXJtqc25NWaMvq1atgo+Pj8rXTElJgalp6WfY1NS0yqu4JRIJli9fjsmTJ0Mkqt6wtUrBzurVq/H111/Dzs4O5ubmJePuDciNHaegJdDGe994gC/SQUp4LI65rUdxnkSRR7+FMZj8VTdgclgMTntth+PSCXBcMh5Zic9wet52pY3Kuk0t6b368Pcvle53dvGPuP9HycZVbQb3xFC/V8vyR3zvDQC4tvkYgjcfU39l1eDpiWBoN9ODzeKx4Jsa4EXkEwS7bUDBk3QAgKC5Qak9dwb9s07x/wbdrGExrh/yH6fhb/sFJeeYNVPK027uSLSbOxLpV+/jyljfOqhVzYT99wwNeu0Z+rMKz9AZr+1wWDoBDkvGIzvxGc7M245nrz1Dpl2tMf7IF4qfB66aAgC4//tF/L1klyK9/ei+AMch6sS12qymykL/ax+XbzwgEOkgOTwWf7zRPqI32udpWAxOeW1Hv6UT0O+/99ipN95jusYivL95NnRNDVD0Ih9pkY9xdOoGJF4qWZkjl8th2MYcncYvgLCZPiRZuUi5HYffxvtWuqFhY3A3MgbTvT9T/LxhW8kz4TrcBd98uaS+ilXnZA9CUSTUA6+fK7T1DCBPS4LkiB9YTskHAk5PDI3X99wpLoTk143gD5kC4TQfsILckmtcPFpPNah98lqcVLlixQosXrxYKe3NDoqXfHx8SgVGb7p+/ToAlBkrMMaqFEMUFxdj4sSJkMvl+OGHHyrN/yaOseq3mLm5OTZs2FDhxKLq2txqitqu1RRZF9F+mRVJ0K7RiOxboWl+vlWfBTe/ru8iNGhF/hX/QSOA7oryNwtVN/fWY2vt2vsTq/7BPD09Henp6RXmsbS0xKFDh7B48eJSq68MDAywefNmTJs2rdzzi4uL8eGHHyIuLg6BgYEwMip7w9uKqNSzU1RUBEfHt3MLc0IIIaS+NZTpycbGxjA2Nq40n4ODA7KzsxEaGorevXsDAEJCQpCdnV1hPPEy0ImJicGFCxdUCnQAFScoz5w5E4cONcy5BIQQQghpWGxtbTFs2DB4enoiODgYwcHB8PT0xMiRI5VWYnXo0AHHjx8HAEilUowfPx43btzAwYMHIZPJkJKSgpSUFBQVFVXr/ir17EgkEuzatQvnz59H165dweMpr4Lw8/NT5bKEEEIIqYLG+O3kBw8exPz58zFkyBAAwOjRo7F9u/LWGlFRUcjOLlnM8+TJE/zvf/8DAHTv3l0p34ULF+Ds7Fzle6v8recvb3z3rvKW3g1tsjIhhBBC6p+hoSEOHDhQYZ7XpxFbWlpChWnFZVIp2Llw4YJabk4IIYSQ6mtsmwrWN1rCQgghhJAmrco9O2PHjsXevXshEokwdmzFS96OHWuY+8kQQgghTQFtRlI9VQ52xGKxYj6OWPx2fhs2IYQQ0hA0xgnK9anKwc6ePXvK/H9CCCGEkIZM5e/GIoQQQkj9oAnK1aNSsNOjR48yl5hzHAeBQIC2bdvCw8MDgwYNqnEBCSGEEEJqQqXVWMOGDUNcXBx0dXUxaNAgODs7Q09PD7GxsbC3t0dycjJcXFxw4sQJdZeXEEIIeevJa/HVFKnUs5Oeno4lS5Zg5cqVSum+vr5ITEzEuXPnsGrVKqxZswaurq5qKSghhBBCiCpU6tk5cuQIJk2aVCp94sSJOHLkCABg0qRJiIqKqlnpCCGEEFIKY6zWXk2RSsGOQCDA1atXS6VfvXoVAoEAACCXy8Hn82tWOkIIIYSQGlJpGMvb2xuzZ89GWFgY7O3twXEcQkNDsXv3bnz++ecAgLNnz6JHjx5qLSwhhBBCaJ+d6lIp2Pnyyy9hZWWF7du3Y//+/QAAGxsb+Pv7Y/LkyQCA2bNnY86cOeorKSGEEEIANN2JxLVF5X123Nzc4ObmVu5xoVCo6qUJIYQQQtSmRpsKFhUVITU1FXK5cozZqlWrGhWKEEIIIeWjTQWrR6VgJyYmBtOnTy81SZkxBo7jIJPJ1FI4QgghhJCaUinY8fDwgJaWFk6dOgVzc/Myd1MmhBBCSO2gCcrVo1KwEx4ejrCwMHTo0EHd5SGEEEIIUSuVgp2OHTsiPT1d3WUhhBBCSBU01c3/aotKmwquX78ey5YtQ1BQEDIyMpCTk6P0IoQQQghpKFTq2XFxcQEAvPvuu0rzdWiCMiGEEFL7aJ+d6lEp2Llw4YK6y0EIIYSQKqKl59Wj0jCWk5MTNDQ04O/vj+XLl6Nt27ZwcnLCo0ePoKmpqe4yEkIIIYSoTKVg5+jRoxg6dCiEQiFu3bqFwsJCAMCLFy+wdu1atRaQEEIIIcrkYLX2aopUCnZ8fX2xc+dO+Pv7g8fjKdIdHR1x8+ZNtRWOEEIIIaSmVJqzExUVhYEDB5ZKF4lEyMrKqmmZCCGEEFIBWnpePSr17Jibm+Phw4el0i9fvgxra+saF4oQQgghRF1UCnZmzZqFBQsWICQkBBzH4enTpzh48CCWLl2KuXPnqruMhBBCCHkNzdmpHpWGsZYtW4bs7GwMGjQIEokEAwcOBJ/Px9KlS+Hl5aXuMhJCCCGEqIxjNRj4y8/Px/379yGXy9GxY0fo6empXJDH9u+pfO7bYEWquL6L0KANL9ap7yI0eI95led5m3nPpm0zKqLtuaq+i9Dg8YzrbhqH8zsutXbtoCfna+3a9UWlnp2XdHR0YGdnp66yEEIIIaQK5DRBuVpUmrNDCCGEENJY1KhnhxBCCCF1j/p1qod6dgghhBDSpFHPDiGEENLINNUl4rWFenYIIYQQ0qRRzw4hhBDSyFDPTvVQzw4hhBBCmjTq2SGEEEIaGfoi0Oqhnh1CCCGENGnUs0MIIYQ0MjRnp3oo2CGEEEIaGUbBTrXQMBYhhBBCmjTq2SGEEEIaGZqgXD3Us0MIIYSQJo16dgghhJBGhiYoVw/17BBCCCGkSaNghxBCCGlkGGO19qotmZmZcHd3h1gshlgshru7O7Kysqp8/qxZs8BxHLZs2VLte1OwQwghhJBaN3nyZISHhyMgIAABAQEIDw+Hu7t7lc79888/ERISghYtWqh0b5qzQwghhDQyjW3OzoMHDxAQEIDg4GD06dMHAODv7w8HBwdERUXBxsam3HOTkpLg5eWFs2fPYsSIESrdn3p2CCGEkEaG1eJ/teHatWsQi8WKQAcA+vbtC7FYjKtXr5Z7nlwuh7u7Oz799FN06tRJ5ftTzw4hhBBCFAoLC1FYWKiUxufzwefzVb5mSkoKTE1NS6WbmpoiJSWl3PPWr18PLS0tzJ8/X+V7A9SzQwghhDQ6csZq7bVu3TrFJOKXr3Xr1pVZDh8fH3AcV+Hrxo0bAACO40qdzxgrMx0AwsLCsHXrVuzdu7fcPFVFPTuEEEIIUVixYgUWL16slFZer46XlxcmTpxY4fUsLS0RERGBZ8+elTqWlpaG5s2bl3nepUuXkJqailatWinSZDIZlixZgi1btiAhIaGSmryicrCzf/9+7Ny5E/Hx8bh27Rpat26NLVu2wMrKCq6urqpelhBCCCGVqM0vAq3OkJWxsTGMjY0rzefg4IDs7GyEhoaid+/eAICQkBBkZ2fD0dGxzHPc3d3h4uKilDZ06FC4u7tj2rRpVSrfSyoNY+3YsQOLFy/G+++/j6ysLMhkMgCAgYGBSuvfCSGEENJ02draYtiwYfD09ERwcDCCg4Ph6emJkSNHKq3E6tChA44fPw4AMDIyQufOnZVePB4PZmZmFa7eKotKwc62bdvg7++PL774Apqamop0Ozs73LlzR5VLEkIIIaSKanPOTm05ePAgunTpgiFDhmDIkCHo2rUr9u/fr5QnKioK2dnZar+3SsNY8fHx6NGjR6l0Pp+PvLy8GheKEEIIIU2LoaEhDhw4UGGeynZwrs48ndep1LNjZWWF8PDwUulnzpxBx44dVSoIIYQQQqqmse2zU99U6tn59NNPMW/ePEgkEjDGEBoail9//RXr1q3D7t271V1GQgghhLymNoebmiKVgp1p06ZBKpVi2bJlyM/Px+TJk9GyZUts3bq10iVohBBCCCF1SeWl556envD09ER6ejrkcnmZOyMSQgghRP2a6nBTbVF5grJUKkW7du2U1tfHxMSAx+PB0tJSXeVTC73xo6E/5UNoGhuhOC4BmX4/oCi87FVj2t06w8DbE1qtW4ET8CFLeYbcY6eQ++tRRR7hoP4QeUyGlkVLQEsT0sdJeHHgd+SfOV9XVaoVYxd+hEGTB0NXrIvYWzHYu9IfSTGPy83vPNEFA8Y54x2bkg2f4u/E4siGg4i7/VCRx6Z3R4yY5QqrLm3QrLkhNnv+H8LOhdZ6XWqi3ccu6DjnfQhNDZAVnYSwrw4gLTSq3PymfTugp48bDNq3RP6zLDz44RRi9gcqjlt/OAAOW2aVOu9Xq2mQFxYDALR0Bei2bDwshtuBbyRC5r0E3Fh5AM9vx6m/gmrSf+FYdJs8CAKxLpJvxeLcyr1Ij0mq8Byb4fYYsGQ8DFqZIutRKi5u/B3RZ2+Umbfv3FFw/uwjXP8pAP98rTyp0ahtCzgvnwiLPh3AaXBIj07CiXnbkPM0Q231q21aPd8Fr8/74PTEkKc9RdH5g5A/iS7/BE0t8Pq5QquzIzhdMdiLTBRf/R+kEZfqrtANwI3wO9hz6A/cj3yItIzn2LpuJd4bWPY+LYS8pFKw4+HhgenTp6Ndu3ZK6SEhIdi9ezeCgoLUUTa1EA52hsHiuchc/x0Kb9+F3tiRMNm6DikfTofsWWqp/KxAgtwjf6LoYRxYgQT87l3QbMVCMIkEecf/AgDIs18gZ89BFCc8BisuhnCAAwy/WgZ5ZhYkwWX/4m7oRs7+AMNnjsKPS7chJS4Zrt7jsfzgKnw6yAuSPEmZ59g6dMa1/11GdFgkiguLMXL2GHy2fxWWD16AzGfPAQB8HT4ePUjAxd8DsfDHz+qySippPboPeq2eguuf70VaaDTaub+LQQc/xSnnz5CfVPoPqa6FCQYdWIqHB4Nw1WsHTHq3h/1aD0gyXuDx6euKfEU5+Tg54FOlc18GOgDQd9NMiG3ewVXvHch/lgWrcf3w3uHlOOX8GQpSMmuvwirqM3sk7GcOx19Lf8TzuBQ4ervio4PL4T/oUxSV87y06NkWrtu9cHHTH4g+ewPth9rB9XsvHBi/BsnhsUp5zbpao/vkQUi9n1jqOgatTDHlj5W4ffhfXN58FJKcfBi3awnpa+3Z0Gna9oa2ixuKzu6D7Ek0eD0GQfDREhT4rwDLeV7mOfwx88DpilB4+iewzFRwOiJA4+37xp+CAgls2lpjzPtDsOgL3/ouTr2hOTvVo9I75datW+jXr1+p9L59+5a5Sqs+6U8ej7wTZ5B34jSkCY+Q5fcDZM9SoTd+VJn5i6MfIv/cBUjjEiFLfob8M+chCb4BfvcuijyFN2+jIOgKpAmPIEtKRu5vx1D8MA7a3TvXVbXUbtiMkTix/ShuBITgSfQj/LjkO2gL+HB0HVjuOTsWbMH5/QF4dD8BybFJ2P3ZDmhocOjUr6siT0TQLfzx7a+4ERBSF9WosQ6fDEfsr0GIPRSEnIdPEbbqAPKfZqD91PfKzN9u6rvIS8pA2KoDyHn4FLGHghD327+wnf2+ckbGIEnLVnq9pCngweJ9e9zy/Q2pIVHITXiGO5uOIfdxWrn3rW/2M4bh6vYTiA64gfToJ/hryY/gCbTR0bX8T9j204ch/vJdBP9wEs9jkxH8w0kkXrkP++nDlPLxdPgYvXUOznz2EyTZ+aWuM/DTCYi9cBtB637Ds3uJyH6chtjAcORn5Ki9nrWF13sYpLcvQnr7X7CMZBSdPwSW8xxaPcr+99a07gLNVjaQHPGDPOE+WHY65MlxkCc9LDN/UzbAwR7zP/kYg51L/w0ipDwqBTscx+HFixel0rOzsxW7KTcIWlrQ7tAekhDl3hZJSBi0u1btq+J57duC37UTCm9GlJuHb98DWq3fQeHNxrmhoolFcxiYNsOdS+GKNGmRFJEh99CuV9V3qeQLtaHJ00RuVulnozHQ4GnCsKsVkv+9q5Se/O9dGNu1K/Mck17tSuV/GnQHRt2swGm92nBTS1eAMaFb8MGN7+D8yxI069xacYzT1ISGliZkb/RMyAqKYNK7eruE1gWxhQn0TA2QcOnV8y4rkuJxSCRa9iq7nYCSnp2Ei8rvkfiLEaXOGbLGA7GB4Ui8cq/0RTgObd7tjufxKfhw3zJ4h32PqX/6oN2QXjWrVF3S0ISGmSVk8crPjSz+LjTfaVvmKZrtekCenABe3/ch9NoC4az10H53IqDFq4sSkwaIlp5Xj0rBzoABA7Bu3TqlwEYmk2HdunXo37+/2gpXUxoGYnBampA9Vx4GkGVkQtPIsMJzzU/9hneunEHzfT8g9/cTyDtxWuk4p6uLlv+ewjvXzsJk81pkbdyOwtAwtdehLhiYGgAAstOylNKz07MgNjGo8nU+Wu6OzJTnuHel/MCwIeMb6kNDSxOSdOXdOyVp2RD+10ZvEpiIlXppAECSng0Nnhb4hvoAgOyHT3Ft4S4Eefjh8tzvISssxpATX0HfquTL76R5EqTdiEaXhWMgbG4AToOD5dh+MO7ZBsLmZd+3Pun91xZ5b9Q7Lz0buibi8s8zMUBeesXn2I7qi+adLRG04UiZ19A1FoGvJ0TfOSMR/28EDruvR/TZMIz9cQEs+nRQsUZ1i9PRB6ehCZan3BYsLxucbtntxxmYQMOiHTRM3kHh0e9QdP4gNG3soD1kal0UmZBGT6U5Oxs2bMDAgQNhY2ODAQMGACj5dtKcnBwEBgZWcjZQWFiIwsJC5TS5HPzaGn9+M1DlAFQy3pn6yUJoCIXQ7mIL8TxPSB8nIf/chVeXzM/HM7dPwOkIIbDvCYNFcyBNSkbhzdvqL7+aOY4ZiOlrX02Y/XbaN2Xm4ziudNuVY8SsMXAY3R/ffPQVihvR3IkyvflscJXs6vnGMY5TTs+4GYuMm6/mpKRdj8bwc75oP30IwlaWbJV+1Xsn+vp5Yuyt7ZBLZXh+JwEJx6/BsItlTWtTYx3HOGLY2umKn3+f9i2AMh6NKjwvpZqR4xSJ+uaGcFnljsPu60v1cr3KXtK4MX/fxPWfAgAAqfcfoWWvdujh9h4eh0RWqU4Nw5vPGVc6TXFIA2BA4f92AoUFAICif34Ff6wXis7tA6SN/D1Hqo0xeX0XoVFRKdjp2LEjIiIisH37dty+fRtCoRBTp06Fl5cXDA0r7jEBgHXr1mH16tVKaQvNLbG4pbUqxSmXPCsbTCqDplEzpXRNw2alenveJHuaAhmA4th4aBo2g+iTj5WCHTAG6ZOnAIDi6FhoWbaCvsekRhHs3Pw7FLG3Xq360NIu6QoXmxggK/VVu4iMxMhOz6r0eu9/4orR88bh/9x88Diy9ITSxqLw+QvIpTII3ujNEhiX7r15SZKWDcEbvT58IzHkxVIUZuaWfSPG8Dw8DiIrM0VSbmIqzo/7BppCPnj6QkhSs9B/pxdyH6XVpEpq8fDvm/j51qtgTUu75NeGnokYealZinRdI1GpnpvX5aZlQe+Nnp+Sc0rm2ph1sYKuiRgep9YojmtoacKijw16fTwYG9t5ID/zBWTFUmS8seor42ES3rFveEN+ZWH5L8DkMnC6BkrpnI4ILK/seUfy3CxwuZmKQAcA5BlPwXEa4PQNwTKf1WaRSQMkb6LDTbVF5X12WrRogbVr16p07ooVK7B48WKltNRBrqoWpXxSKYoioyHo0wsFQVcUyYLevVBw8UoFJ76B48DxKhkb5zhw2o1j/FySJ4EkL0UpLSs1E537d0PivXgAgCZPCx36dMLh/9tf1iUURsxyhavXeKyfugbxd2IrzNvQyYtleB4RD/OBnfEk4NU8L/OBnfHkbNlDlGlhMXhncE+lNHOnzsi4HQ8mLX/+WrNOrZEVWXpZv6ygELKCQmiLdWDu1AW3fH9TsTbqU5QnKbXCKjc1C5b9O+PZvZLgVoOnCYs+HRD0f4fLvc7Tmw9hOaCzokcGACwHdkFSWAwAIPHKPewevFzpnBHffoKM2KcI3nEKTM7A5DIkR8TB0NpcKZ+hlTmyk9JrVM86I5dBnpIATatOkEW/eq40rTpBGn2r7FOexECrgz3A4wPFJb3iGoZmYHI52IuyV28RQl6pcrATERGBzp07Q0NDAxERFc/J6Nq1a4XH+Xw++Hy+Ulp2LQ1hvTj0B4xWL0fR/WgU3rkPvQ9GQNPMFLlHTwIAxPNmQNPEGM991gMA9Ca4QpqSCmnCo5Kydu8M/SkT8OLwn4pr6ntMQtH9aEiTnoLT0oKgXx/ojhiMzP/bWit1qAsBP53C6Hnj8CwhGSnxyRjtNRZFkkJcPXFRkWeW33xkpmTgyIaDAEqGrsYvmYQfFmxG+pNUxfweSZ4Ehfklfxz5OgI0t3zVg2FiYYpWHS2Rl5WLjKcN749T5K4zcPhuDjIi4pB+4yHaThkEnZZGiNn3DwCg+4oPITRrhmsLfgQAxOwLhM20wei5yg0PD16AsV1btJnkjCtzv1dcs8viD5Ae9hAv4lPA0xfCZsZQNOvUCtc/36vIY+7UBeA45MQmQ9+qOXqsnISc2GTEHr6Ihuj6TwFwmDcamQnP8Dw+BQ5eo1EsKcL9E1cVeUb6zcKLlEz8+9/8mxt7zsLtyJfoM3skYv4OQ7vBvWDZrxMOjC/pySnKkyA9+onSfYrzC1GQmauUHvrjabhu98LjkEgkXnsAa+euaOvSA4c+Kns4tiEqDg0Af9QsyJPjIUt6CF73QeBERpDeKpkGwHOaAE6/GYpO7QIASO9dA6/faPBHzETRpePgdPSh/e5ESCMuvnVDWPn5BXj0X686ACQ9fYbI6FiIRfowN3t7Nret7AszibIqBzvdu3dHSkoKTE1N0b17d3AcV2ZjcxzXoFZkFfwdhCyxCKKZ7tA0NkRxbALSF66ALKVkjx1NYyNovv4G4TgYzJsBzRZmgEwG6ZNkZG3fjbxjp15lEQjQ7LP50DQ1ASsshDTxMTK+WoeCv4PquHbqc2rncWgLtOHh+wl0RLqIDY/B+ilfK+2xY9zCGEz+apzYxX0YeHweFuxcpnStY5sP49iWkk/41l3b4IvDr4YlpnxVMvfj4u+B2LV0e21WSSWJ/wuBdjN9dFn0QcmmglFPEDRlI/L+22NHYGoA3ZavNtLMe5yGC1O+Ra/VU9DewwUFzzIRtnKf0h47PJEO+mycAYGJGMUv8vH8biL+HuuLjPA4pTzdV3wIHXNDFGXl4dHpUNz+v98r7B2qTyE7T4En0MYQXw8IRDp4Gh6Lw1PWK/UAiVoYg8lf/Y5ICovBCe/tGLhkAgYuGY/MR89wwmt7qT12KhN99gbOfvEz+s4dDZfVU/E8NhnHZ2/FkxsVbMjXwMgehKJIqAdeP1do6xlAnpYEyRE/sJyS54zTE0ND9NqUgOJCSH7dCP6QKRBO8wEryC25xsWj5dyh6bobGYPp3q/27NqwrSQgdB3ugm++XFJfxSINHMeqGB4mJiaiVatW4DgOiYkVz8to3bp1hcfL8ti+Ye4n0lCsSC1/lQsBhhfr1HcRGrzHjWOUtd54z9asPNNbTNtzVX0XocHjGat33mlF3jGsvX3dnjy/W3mmRqbKPTsvA5ji4mL4+Phg5cqVsLauu39YQgghhBBVVHuiDI/Hw/Hjx2ujLIQQQgipAsZYrb2aIpVmBX/wwQf4888/1VwUQgghhBD1U2npedu2bbFmzRpcvXoVvXr1gq6urtLx+fPnq6VwhBBCCCmNvgi0elQKdnbv3g0DAwOEhYUhLEx5/xGO4yjYIYQQQmpRU/0Oq9qiUrATHx+v+P+X43ucYo98QgghhJCGQ+Wd/H766Sd07twZAoEAAoEAnTt3xu7du9VZNkIIIYSUgSYoV49KPTsrV67E5s2b4e3tDQcHBwDAtWvXsGjRIiQkJMDX11ethSSEEEIIUZVKwc6OHTvg7++PSZMmKdJGjx6Nrl27wtvbm4IdQgghpBbRF4FWj0rDWDKZDHZ2dqXSe/XqBalUWuNCEUIIIYSoi0rBzpQpU7Bjx45S6bt27YKbm1uNC0UIIYSQ8tGcnepRaRgLKJmgfO7cOfTt2xcAEBwcjMePH2Pq1KlYvHixIp+fn1/NS0kIIYQQoiKVgp27d++iZ8+eAIDY2JJvLDYxMYGJiQnu3n31BWK0HJ0QQghRP9pUsHpUCnYuXLig7nIQQgghpIqa6nBTbVF5nx1CCCGEkMZA5Tk7hBBCCKkftPS8eqhnhxBCCCFNGvXsEEIIIY0MzdmpHurZIYQQQkiTRj07hBBCSCNDS8+rh3p2CCGEENKkUc8OIYQQ0sgwWo1VLRTsEEIIIY0MDWNVDw1jEUIIIaRJo54dQgghpJGhpefVQz07hBBCCGnSqGeHEEIIaWRognL1UM8OIYQQQpo06tkhhBBCGhmas1M91LNDCCGEkCaNenYIIYSQRoZ6dqqHgh1CCCGkkaFQp3poGIsQQgghTRrHqC+slMLCQqxbtw4rVqwAn8+v7+I0ONQ+laM2qhi1T8WofSpHbUSqg4KdMuTk5EAsFiM7Oxsikai+i9PgUPtUjtqoYtQ+FaP2qRy1EakOGsYihBBCSJNGwQ4hhBBCmjQKdgghhBDSpFGwUwY+n49Vq1bRpLdyUPtUjtqoYtQ+FaP2qRy1EakOmqBMCCGEkCaNenYIIYQQ0qRRsEMIIYSQJo2CHUIIIYQ0aRTsvOWCgoLAcRyysrIAAHv37oWBgUG9lomQpsrDwwNjxoyp8/s6Oztj4cKFdX5fdUtISADHcQgPD6/vopBGhr4I9C3n6OiI5ORkiMXi+i5Ko+Hs7Izu3btjy5Yt9V0U0shs3bpV7d9WLZPJsGHDBvzyyy9ITEyEUChE+/btMWvWLEybNg0AcOzYMfB4PLXetz5YWFggOTkZxsbG9V0U0shQsKNmMpkMHMdBQ6NxdJppa2vDzMysvotByFuhNj5U+Pj4YNeuXdi+fTvs7OyQk5ODGzduIDMzU5HH0NBQ7feta0VFRfT7iqiscfxFVtG+fftgZGSEwsJCpfRx48Zh6tSpAICTJ0+iV69eEAgEsLa2xurVqyGVShV5/fz80KVLF+jq6sLCwgJz585Fbm6u4vjLYZ9Tp06hY8eO4PP5SExMrJsKlsHZ2Rne3t5YuHAhmjVrhubNm2PXrl3Iy8vDtGnToK+vjzZt2uDMmTMASg9jlaWmbQQA/v7+sLCwgI6ODj744AP4+fmVGi6r7D4NgYeHB/79919s3boVHMeB4zjExsZixowZsLKyglAohI2NDbZu3ao4RyKRoFOnTvjkk08UafHx8RCLxfD396+PaqiVqs/cX3/9hW7dukEgEKBPnz64c+eO0nWr8syoE2MMGzZsgLW1NYRCIbp164Y//vhDqcz//PMP7OzsoKOjA0dHR0RFRSldw9fXF6amptDX18fMmTOxfPlydO/eXXH8zWEsZ2dnzJ8/H8uWLYOhoSHMzMzg4+OjdM3s7Gx88sknMDU1hUgkwrvvvovbt28rjp88eRJz587FhAkTYGVlhW7dumHGjBlYvHix0n1eDmO9rMubLw8PD6Vr1vZ70dnZGV5eXvDy8oKBgQGMjIzw5ZdfKnq+LC0t4evrCw8PD4jFYnh6epY5jHXv3j2MGDECIpEI+vr6GDBgAGJjYxXH9+zZA1tbWwgEAnTo0AE//PCDWutBGgnWhOXn5zOxWMyOHDmiSEtLS2Pa2tosMDCQBQQEMJFIxPbu3ctiY2PZuXPnmKWlJfPx8VHk37x5MwsMDGRxcXHsn3/+YTY2NmzOnDmK43v27GE8Ho85OjqyK1eusMjISJabm1un9Xydk5MT09fXZ2vWrGHR0dFszZo1TENDgw0fPpzt2rWLRUdHszlz5jAjIyOWl5fHLly4wACwzMxMRX3EYrHieupoo8uXLzMNDQ22ceNGFhUVxb7//ntmaGhY7fs0BFlZWczBwYF5enqy5ORklpyczCQSCfvqq69YaGgoi4uLYwcOHGA6Ojrs8OHDivNu3brFtLW12fHjx5lUKmX9+vVjrq6u9VcRNVL1mbO1tWXnzp1jERERbOTIkczS0pIVFRUxxqr2zKjb559/zjp06MACAgJYbGws27NnD+Pz+SwoKEhR5j59+rCgoCB27949NmDAAObo6Kg4/8CBA0wgELCff/6ZRUVFsdWrVzORSMS6deumyPPxxx8r/bs7OTkxkUjEfHx8WHR0NPvll18Yx3Hs3LlzjDHG5HI569evHxs1ahS7fv06i46OZkuWLGFGRkYsIyODMcbY0KFD2cCBA1lqamq5dXNycmILFixgjDFWWFioeHaTk5NZYGAgEwgE7KeffmKM1d170cnJienp6bEFCxawyMhIxftm165djDHGWrduzUQiEdu4cSOLiYlhMTExLD4+ngFgt27dYowx9uTJE2ZoaMjGjh3Lrl+/zqKiotjPP//MIiMjGWOM7dq1i5mbm7OjR4+yuLg4dvToUWZoaMj27t2r1rqQhq9JBzuMMTZnzhw2fPhwxc9btmxh1tbWTC6XswEDBrC1a9cq5d+/fz8zNzcv93pHjhxhRkZGip/37NnDALDw8HD1F14FTk5OrH///oqfpVIp09XVZe7u7oq05ORkBoBdu3at0mBHHW300UcfsREjRijlcXNzq/F96svrfzjKM3fuXDZu3DiltA0bNjBjY2Pm7e3NzMzMWFpaWi2Wsu6o+sz99ttviuMZGRlMKBQqAsSqPDPqlJubywQCAbt69apS+owZM9ikSZMUZT5//rzi2F9//cUAsIKCAsYYY3369GHz5s1TOr9fv36VBjuvtx1jjNnb27PPPvuMMcbYP//8w0QiEZNIJEp52rRpw3788UfGGGP37t1jtra2TENDg3Xp0oXNmjWLnT59Wil/ec9seno6a9OmDZs7d64ira7ei05OTszW1pbJ5XJF2meffcZsbW0ZYyXBzpgxY5TOeTPYWbFiBbOyslIEyW+ysLBghw4dUkpbs2YNc3BwUGNNSGPQ5OfseHp6wt7eHklJSWjZsiX27NkDDw8PcByHsLAwXL9+Hd98840iv0wmg0QiQX5+PnR0dHDhwgWsXbsW9+/fR05ODqRSKSQSCfLy8qCrqwugZN5L165d66uKpbxeFk1NTRgZGaFLly6KtObNmwMAUlNTIRKJKryWOtooKioKH3zwgdJ1e/fujVOnTlXrPg3Zzp07sXv3biQmJqKgoABFRUVKwxcAsGTJEpw4cQLbtm3DmTNnmtQkS1WeOQcHB8VxQ0ND2NjY4MGDBwBQpWdGne7fvw+JRILBgwcrpRcVFaFHjx6Kn1+vp7m5OYCSOrVq1QpRUVGYO3duqTIHBgZWeO83f3eYm5sjNTUVQMn7Ijc3F0ZGRkp5CgoKFEM1HTt2xN27dxEWFobLly/j4sWLGDVqFDw8PLB79+5y71tcXIxx48ahVatWSsOudfle7Nu3LziOU/zs4OCATZs2QSaTAQDs7OwqPD88PBwDBgwoc/J1WloaHj9+jBkzZsDT01ORLpVKaUHGW6jJBzs9evRAt27dsG/fPgwdOhR37tzByZMnAQByuRyrV6/G2LFjS50nEAiQmJiI999/H7Nnz8aaNWtgaGiIy5cvY8aMGSguLlbkFQqFSm/Y+vbmG5/jOKW0l2WVy+WVXksdbcQYK9U+7I0VKZXdpyE7cuQIFi1ahE2bNsHBwQH6+vrYuHEjQkJClPKlpqYiKioKmpqaiImJwbBhw+qpxOqnrmfuZb6qPDPq9LJcf/31F1q2bKl0jM/nKwKLyuqkSpnLaruX15TL5TA3N0dQUFCp816fv6ShoQF7e3vY29tj0aJFOHDgANzd3fHFF1/AysqqzPvOmTMHjx49wvXr16Gl9epPQUN6L778QFkeoVBY7rGXbejv748+ffooHdPU1Kx54Uij0uSDHQCYOXMmNm/ejKSkJLi4uMDCwgIA0LNnT0RFRaFt27Zlnnfjxg1IpVJs2rRJsbrqyJEjdVbuhkAdbdShQweEhoaWOq8692lItLW1FZ88AeDSpUtwdHRU+lT/+gTJl6ZPn47OnTvD09MTM2bMwHvvvYeOHTvWSZkbouDgYLRq1QoAkJmZiejoaHTo0AFA1Z4ZdXq5uODRo0dwcnIqdbysf8832djYIDQ0FO7u7oq0mpa5Z8+eSElJgZaWFiwtLat83svnKi8vr8zjfn5+OHz4MK5du1aq16gu34vBwcGlfm7Xrl2Vg5GuXbvil19+QXFxcamgsXnz5mjZsiXi4uLg5uamtjKTxumtCHbc3NywdOlS+Pv7Y9++fYr0r776CiNHjoSFhQUmTJgADQ0NRERE4M6dO/D19UWbNm0glUqxbds2jBo1CleuXMHOnTvrsSZ1Tx1t5O3tjYEDB8LPzw+jRo1CYGAgzpw5o/QpuLL7NCSWlpYICQlBQkIC9PT00LZtW+zbtw9nz56FlZUV9u/fj+vXryt9ov7+++9x7do1REREwMLCAmfOnIGbmxtCQkKgra1dj7WpP19//TWMjIzQvHlzfPHFFzA2NlasVKrKM6NO+vr6WLp0KRYtWgS5XI7+/fsjJycHV69ehZ6eHlq3bl3pNby9veHp6Qk7Ozs4Ojri8OHDiIiIgLW1tcrlcnFxgYODA8aMGYP169fDxsYGT58+xenTpzFmzBjY2dlh/Pjx6NevHxwdHWFmZob4+HisWLEC7du3VwSPrzt//jyWLVuG77//HsbGxkhJSQFQ0ksiFovr9L34+PFjLF68GLNmzcLNmzexbds2bNq0qcrne3l5Ydu2bZg4cSJWrFgBsViM4OBg9O7dGzY2NvDx8cH8+fMhEokwfPhwFBYWKpblv75ajbwF6nXGUB1yd3dnhoaGpSb6BQQEMEdHRyYUCplIJGK9e/dWrAZgjDE/Pz9mbm7OhEIhGzp0KNu3b1+FE3rrW1kTEVu3bs02b96slAaAHT9+vNIJyozVvI0YK1kV0bJlSyYUCtmYMWOYr68vMzMzq9Z9GoqoqCjWt29fJhQKGQAWGRnJPDw8mFgsZgYGBmzOnDls+fLliompDx48YEKhUGmiZHZ2NrO0tGTLli2rp1qoj6rP3MmTJ1mnTp2YtrY2s7e3LzXJvyrPjDrJ5XK2detWZmNjw3g8HjMxMWFDhw5l//77b6n3CWMlK+wAsPj4eEXa119/zYyNjZmenh6bPn06mz9/Puvbt6/ieFkTlN9sO1dXV/bxxx8rfs7JyWHe3t6sRYsWjMfjMQsLC+bm5sYePXrEGCtpp0GDBjETExOmra3NWrVqxTw8PFhCQkKZ91m1ahUDUOr1+j3r4r3o5OTE5s6dy2bPns1EIhFr1qwZW758uWLCclnP0JsTlBlj7Pbt22zIkCFMR0eH6evrswEDBrDY2FjF8YMHD7Lu3bszbW1t1qxZMzZw4EB27NgxtdaFNHwcY7U4EN6ADB48GLa2tvjuu+/quygEJRPHIyMjcenSpfouCqljQUFBGDRoEDIzM6u1b05jfGYGDx4MMzMz7N+/v76L0uDQTuSkLjX5Yaznz5/j3LlzCAwMxPbt2+u7OG+tb7/9FoMHD4auri7OnDmDX375hTb3IhVqbM9Mfn4+du7ciaFDh0JTUxO//vorzp8/j7///ru+i0bIW6/JBzs9e/ZEZmamYryb1I/Q0FBs2LABL168gLW1Nb777jvMnDmzvotFGrDG9sxwHIfTp0/D19cXhYWFsLGxwdGjR+Hi4lLfRSPkrffWDGMRQggh5O3UpL8bixBCCCGEgh1CCCGENGkU7BBCCCGkSaNghxBCCCFNGgU7hBBCCGnSKNghhBBCSJNGwQ4hhBBCmjQKdgghhBDSpFGwQwghhJAm7f8BFk8acv6EoL8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 700x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# generate correlation matrix: numeric columns\n",
    "fig = plt.figure(figsize=(7, 6))\n",
    "sns.heatmap(data=data[['year', 'mileage', 'tax', 'mpg','engineSize','price']].corr(method='kendall'), annot=True)\n",
    "plt.title('Figure 11: Correlation between numeric variables')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "402f4c91-6220-47b5-ad8e-71b737a6fa19",
   "metadata": {},
   "source": [
    "# 4. Model Development\n",
    "\n",
    "## 4.1 Type of Problem\n",
    "\n",
    "To recap, the business goal is to predict car prices. Because we have labeled data, this is a supervised machine learning task. Furthermore, because the target variable ('price') is continuous, this is a regression problem.\n",
    "\n",
    "## 4.2 Data Preprocessing\n",
    "\n",
    "Before modeling, the data had to be preprocessed. First, the dataset was split into a feature set of predictor variables and a target set of the target variable 'price'. The feature set and target set were then split into train and test sets. Data splitting was performed such that 80% and 20% of the rows were assigned to the train and test sets, respectively. The train sets were used for model training and tuning, while the test sets were used for final model evaluation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "f3551605-598c-469b-a9d2-e639f185088d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dimensions:\n",
      "X_train: (5359, 8)\n",
      "X_test: (1340, 8)\n",
      "y_train: (5359, 1)\n",
      "y_test: (1340, 1)\n"
     ]
    }
   ],
   "source": [
    "# split data into X (features) and y (target)\n",
    "X = data.drop('price', axis=1)\n",
    "y = data[['price']]\n",
    "\n",
    "# split features and target into train and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, shuffle=True, test_size=0.20, random_state=1)\n",
    "\n",
    "# confirm 80%/20% train/test splits\n",
    "print(f'Dimensions:\\nX_train: {X_train.shape}\\nX_test: {X_test.shape}\\ny_train: {y_train.shape}\\ny_test: {y_test.shape}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b9c5403-4184-4c03-b045-888369d38156",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "Following data splitting, the predictor variables (features) were transformed. Since the numeric and categorical features required different transforms, ColumnTransformer was used to apply the appropriate transform(s) to each feature. Missing values in the numeric features ('year', 'mileage', 'tax', 'mpg','engineSize') were first imputed using SimpleImputer. The median was used since this is the appropriate statistic for non-normally distributed variables. Then, the numeric features were scaled to a range of 0 to 1 using MinMaxScaler. The categorical features ('model', 'transmission', 'fuelType') were one-hot encoded using OneHotEncoder. Data leakage did not occur since the scaling and one-hot encoding transforms were fit using the training feature set only."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "efe117c0-2475-4cd9-bae0-75e3d6478b26",
   "metadata": {
    "editable": true,
    "executionCancelledAt": null,
    "executionTime": 51,
    "lastExecutedAt": 1711728542948,
    "lastScheduledRunId": null,
    "lastSuccessfullyExecutedCode": "# split data into X (features) and y (target)\nX = data.drop('price', axis=1)\ny = data[['price']]\n\n# split features and target into train and test sets\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=1)\n\n# create list of transforms to apply (MinMaxScaler to numeric features and OneHotEncoder to categorical features)\ntransformers = [\n    ('min_max_scale', MinMaxScaler(feature_range=(0,1)),['year', 'mileage', 'tax', 'mpg','engineSize']),\n    ('one_hot_encode', OneHotEncoder(drop='first', handle_unknown='ignore'), ['model', 'transmission', 'fuelType'])\n]\n\n# instantiate a ColumnTransformer object with list of transforms\ncol_transformer = ColumnTransformer(transformers=transformers)\n\n# fit column transformer to and training features and transform them\nX_train_transformed = col_transformer.fit_transform(X_train)\n\n# transform testing features\nX_test_transformed = col_transformer.transform(X_test)",
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# create list of transforms to apply (SimpleImputer, MinMaxScaler to numeric features and OneHotEncoder to categorical features)\n",
    "transformers = [\n",
    "    ('i_s', make_pipeline(SimpleImputer(strategy='median'), MinMaxScaler(feature_range=(0,1))), ['year', 'mileage', 'tax', 'mpg','engineSize']),\n",
    "    ('ohe', OneHotEncoder(drop='first', handle_unknown='ignore'), ['model', 'transmission', 'fuelType'])\n",
    "]\n",
    "\n",
    "# instantiate a ColumnTransformer object with list of transforms\n",
    "col_transformer = ColumnTransformer(transformers=transformers, verbose_feature_names_out=False)\n",
    "\n",
    "# fit column transformer to training features and transform them\n",
    "X_train_transformed = col_transformer.fit_transform(X_train)\n",
    "\n",
    "# transform testing features\n",
    "X_test_transformed = col_transformer.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03117050-b312-43aa-b8b7-15d84c51f7b1",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "The transformed training features were examined in terms of their relationships. This is important because some machine learning algorithms such as linear regression assume there is no multicollinearity (i.e., linear relationship between two or more predictor variables). The variance inflation factor (VIF) was calculated for each feature in the training set. Generally, VIF values of 5 or greater indicate that a feature is highly collinear with other features. Numerous features had VIF values above 5 (Figure 12), suggesting the presence of strong multicollinearity. Therefore, causal inference was avoided, i.e., trained machine learning models were used for prediction only."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "2b0239f9-5d9e-42b9-9142-226188e47448",
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert X_train_transformed to a regular numpy array\n",
    "X_train_transformed = X_train_transformed.A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "2f270181-ee9c-4f76-bb29-54352fad2b32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['year', 'mileage', 'tax', 'mpg', 'engineSize', 'model_ Avensis',\n",
       "       'model_ Aygo', 'model_ C-HR', 'model_ Camry', 'model_ Corolla',\n",
       "       'model_ GT86', 'model_ Hilux', 'model_ IQ', 'model_ Land Cruiser',\n",
       "       'model_ PROACE VERSO', 'model_ Prius', 'model_ RAV4',\n",
       "       'model_ Supra', 'model_ Urban Cruiser', 'model_ Verso',\n",
       "       'model_ Verso-S', 'model_ Yaris', 'transmission_Manual',\n",
       "       'transmission_Other', 'transmission_Semi-Auto', 'fuelType_Hybrid',\n",
       "       'fuelType_Other', 'fuelType_Petrol'], dtype=object)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# obtain feature names from ColumnTransformer\n",
    "col_transformer.get_feature_names_out()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ffd485df-46c5-46bf-993d-cb08ea4c9367",
   "metadata": {},
   "outputs": [],
   "source": [
    "features = ['year', 'mileage', 'tax', 'mpg', 'engineSize', 'model_Avensis',\n",
    "       'model_Aygo', 'model_CHR', 'model_Camry', 'model_Corolla',\n",
    "       'model_GT86', 'model_Hilux', 'model_IQ', 'model_LandCruiser',\n",
    "       'model_PROACEVERSO', 'model_Prius', 'model_RAV4',\n",
    "       'model_Supra', 'model_UrbanCruiser', 'model_Verso',\n",
    "       'model_VersoS', 'model_Yaris', 'transmission_Manual',\n",
    "       'transmission_Other', 'transmission_SemiAuto', 'fuelType_Hybrid',\n",
    "       'fuelType_Other', 'fuelType_Petrol']\n",
    "\n",
    "# create dataframe from X_train_transformed\n",
    "X_train_transformed_df = pd.DataFrame(X_train_transformed, columns=features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "76aa4105-089e-40ea-8a05-b4d3ff9bf014",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create VIF dataframe with a row for each feature\n",
    "vif_df = pd.DataFrame() \n",
    "vif_df['feature'] = X_train_transformed_df.columns\n",
    "\n",
    "# calculate VIF for each feature\n",
    "vif_df['VIF'] = [variance_inflation_factor(X_train_transformed_df.values, i) for i in range(len(X_train_transformed_df.columns))] \n",
    "\n",
    "# generate bar plot of VIF values\n",
    "plt.barh(vif_df['feature'], vif_df['VIF'])\n",
    "plt.title('Figure 12: Variance Inflation Factors', pad=10.0)\n",
    "plt.xlabel('VIF', labelpad=8.0)\n",
    "plt.ylabel('Feature', labelpad=8.0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "381a1b7d-14da-4642-ac76-594541686830",
   "metadata": {},
   "source": [
    "## 4.3 Model Fitting: Spot-Check Multiple Algorithms\n",
    "\n",
    "To identify two top-performing regression algorithms, the training data and 10-fold cross validation were used to train and evaluate five models that used linear regression (LR), k-nearest neighbors (KNN), support vector machines (SVM), random forest (RF), and adaboost (AB). Each model was trained using default hyperparameters. The two models with the lowest root-mean-squared error (RMSE) were KNN and random forest (Figure 13). Therefore, these models were chosen for further evaluation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "3862587b-7b92-4032-b941-496c8ad5b570",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Median RMSE (IQR):\n",
      "LR: 1756.41 (185.79)\n",
      "KNN: 1486.63 (212.75)\n",
      "SVM: 6275.32 (387.76)\n",
      "RF: 1210.69 (256.44)\n",
      "AB: 2768.23 (61.71)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# prepare models to spot-check\n",
    "models = []\n",
    "models.append(('LR', LinearRegression()))\n",
    "models.append(('KNN', KNeighborsRegressor()))\n",
    "models.append(('SVM', SVR()))\n",
    "models.append(('RF', RandomForestRegressor(random_state=1)))\n",
    "models.append(('AB', AdaBoostRegressor(random_state=1)))\n",
    "\n",
    "# empty lists to store cross-validation results and model names\n",
    "results = []\n",
    "names = []\n",
    "\n",
    "print('Median RMSE (IQR):')\n",
    "\n",
    "# evaluate one model at a time\n",
    "for name, model in models:\n",
    "    # create a pipeline with col_transformer and model\n",
    "    model = make_pipeline(col_transformer, model)\n",
    "    # define the cross-validation with 10 splits\n",
    "    kfold = KFold(n_splits=10, shuffle=True, random_state=1)\n",
    "    # run 10-fold cross validation.\n",
    "    # for each of the 10 runs, apply the transforms to the training folds before model training.\n",
    "    # then, train the model and quantify RMSE on the test fold.\n",
    "    cv_results = cross_val_score(model, X_train, y_train, cv=kfold, scoring='neg_root_mean_squared_error')\n",
    "    # append RMSE to results list\n",
    "    results.append(np.abs(cv_results))\n",
    "    # append model name to names list\n",
    "    names.append(name)\n",
    "    # display model name: median RMSE (IQR)\n",
    "    print('{}: {} ({})'.format(name, np.round(np.abs(np.median(cv_results)),2), np.round(iqr(cv_results),2)))\n",
    "    \n",
    "# generate boxplot of RMSEs for each model\n",
    "plt.boxplot(results, labels=names)\n",
    "plt.title('Figure 13: Algorithm Comparison', pad=10.0)\n",
    "plt.xlabel('Algorithm', labelpad=8.0)\n",
    "plt.ylabel('RMSE (GBP)', labelpad=8.0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6251f5a1-fcb4-4379-90df-8354ae48ca03",
   "metadata": {},
   "source": [
    "## 4.4 Model Fitting: Baseline and Comparison Models\n",
    "\n",
    "Based on the results of the spot-check assessment, KNN and random forest were chosen to constitute the baseline and comparison models, respectively. KNN is a popular and relatively simple algorithm that assigns a prediction to a data point by interpolating the values of its closest n neighbors in the feature space. Therefore, the number of neighbors ('n_neighbors') hyperparameter is important to optimize. Another important hyperparameter is 'metric': the metric used to identify the closest neighbors.\n",
    "\n",
    "Random forest is another popular algorithm that uses an ensemble of decision tree base estimators, with each decision tree trained on a different bootstrap sample of the data and, optionally, a random subset of features. With this approach, random forest is known as a robust algorithm. Compared to KNN, random forest is more complex and slower. Key hyperparameters to optimize include the number of decision trees in the forest ('n_estimators'), as well as the maximum number of features that each tree uses ('max_features').\n",
    "\n",
    "Hyperparameter tuning was performed on the baseline (KNN) and comparison (random forest) models using grid search with 10-fold cross-validation. For KNN, 5 neighbors and the 'manhattan' distance metric were the most optimal of the tested values. For random forest, 100 estimators and all features were the most optimal of the tested values. These values and the entire training dataset were used to train final models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "68317a23-bc0c-4f55-85a0-040b51c16381",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 15 candidates, totalling 150 fits\n",
      "{'model__metric': 'manhattan', 'model__n_neighbors': 5}\n"
     ]
    }
   ],
   "source": [
    "# create a knn model object\n",
    "model = KNeighborsRegressor()\n",
    "\n",
    "# create a pipeline with col_transformer and the model\n",
    "pipeline = Pipeline(steps=[('ct', col_transformer), ('model', model)])\n",
    "\n",
    "# define parameter grid of values to test\n",
    "param_grid = {'model__n_neighbors': [1,5,10,15,20],\n",
    "              'model__metric':['euclidean', 'manhattan', 'minkowski']}\n",
    "\n",
    "# create a cross-validation object with 10 folds\n",
    "kfold = KFold(n_splits=10, shuffle=True, random_state=1)\n",
    "\n",
    "# create a GridSearchCV object\n",
    "grid = GridSearchCV(estimator=pipeline, param_grid=param_grid, cv=kfold, scoring='neg_root_mean_squared_error', verbose=1)\n",
    "\n",
    "# fit GridSearchCV object to training data.\n",
    "# there will be 10 runs per combination of parameter values.\n",
    "# for each of the 10 runs, apply the transforms to the training folds before model training.\n",
    "# then, train the model and quantify RMSE on the test fold.\n",
    "grid.fit(X_train, y_train)\n",
    "\n",
    "# display parameter values associated with the highest mean negative RMSE (i.e., lowest mean RMSE)\n",
    "print(grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "f843f3d4-1bbe-415b-9848-9674a3afb2ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 4 candidates, totalling 40 fits\n",
      "{'model__max_features': None, 'model__n_estimators': 100}\n"
     ]
    }
   ],
   "source": [
    "# create a random forest model object\n",
    "model = RandomForestRegressor(random_state=1)\n",
    "\n",
    "# create a pipeline with col_transformer and the model\n",
    "pipeline = Pipeline(steps=[('ct', col_transformer), ('model', model)])\n",
    "\n",
    "# define parameter grid of values to test\n",
    "param_grid = {'model__n_estimators': [50,100],\n",
    "              'model__max_features':['sqrt', None]}\n",
    "\n",
    "# create a cross-validation object\n",
    "kfold = KFold(n_splits=10, shuffle=True, random_state=1)\n",
    "\n",
    "# create a GridSearchCV object\n",
    "grid = GridSearchCV(estimator=pipeline, param_grid=param_grid, cv=kfold, scoring='neg_root_mean_squared_error', verbose=1)\n",
    "\n",
    "# fit GridSearchCV object to training data.\n",
    "# there will be 10 runs per combination of parameter values.\n",
    "# for each of the 10 runs, apply the transforms to the training folds before model training.\n",
    "# then, train the model and quantify RMSE on the test fold.\n",
    "grid.fit(X_train, y_train)\n",
    "\n",
    "# display parameter values associated with the highest mean negative RMSE (i.e., lowest mean RMSE)\n",
    "print(grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "e95916a1-e97b-4cfc-90bc-8b74d49f5ad4",
   "metadata": {
    "executionCancelledAt": null,
    "executionTime": 52,
    "lastExecutedAt": 1711728543001,
    "lastScheduledRunId": null,
    "lastSuccessfullyExecutedCode": "# define linear regression model\nlr = LinearRegression()\n\n# fit model to training data\nlr.fit(X_train_transformed, y_train)",
    "outputsMetadata": {
     "0": {
      "height": 37,
      "type": "stream"
     }
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsRegressor(metric=&#x27;manhattan&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KNeighborsRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.neighbors.KNeighborsRegressor.html\">?<span>Documentation for KNeighborsRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsRegressor(metric=&#x27;manhattan&#x27;)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsRegressor(metric='manhattan')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# define linear regression model\n",
    "knn = KNeighborsRegressor(n_neighbors=5, metric='manhattan')\n",
    "\n",
    "# fit model to training data\n",
    "knn.fit(X_train_transformed, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "60f33d9f-725c-49da-8623-bc629204ba92",
   "metadata": {
    "executionCancelledAt": null,
    "executionTime": 4550,
    "lastExecutedAt": 1711728547551,
    "lastScheduledRunId": null,
    "lastSuccessfullyExecutedCode": "# define random forest model\nrf = RandomForestRegressor(random_state=1)\n\n# fit model to training data\nrf.fit(X_train_transformed, y_train)",
    "outputsMetadata": {
     "0": {
      "height": 37,
      "type": "stream"
     }
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(max_features=None, random_state=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.RandomForestRegressor.html\">?<span>Documentation for RandomForestRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestRegressor(max_features=None, random_state=1)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor(max_features=None, random_state=1)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# define random forest model\n",
    "rf = RandomForestRegressor(random_state=1, n_estimators=100, max_features=None)\n",
    "\n",
    "# fit model to training data\n",
    "rf.fit(X_train_transformed, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2040f970-ea45-4538-a18a-1992707ea9f4",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "# 5. Model Evaluation\n",
    "\n",
    "Root-mean-squared error (RMSE) and R<sup>2</sup> were used to assess the performance of the baseline and comparison models. RMSE was chosen because, unlike mean absolute error, it gives a relatively high weight to large errors. It is appropriate to penalize larger errors more than smaller errors in this context. Large overestimation of car prices could lead to a lack of consumer interest and even a perception that Discount Motors is price-gouging, while large underestimations could result in significant revenue loss. The other evaluation metric, R<sup>2</sup> or the coefficient of determination, quantifies the proportion of the target variable's (price) variance that is explained by the model’s independent variables (features). R<sup>2</sup> is a useful statistic for evaluating the overall effectiveness and explanatory power of a regression model.\n",
    "\n",
    "Table 2 shows the RMSE and R<sup>2</sup> for each model. The random forest model had a lower RMSE than the KNN model. Its value of 1210.38 indicates that, on average, the model’s predictions are approximately 1210.38 GBP away from the true car prices when larger errors are weighted more heavily. The R<sup>2</sup> value for the random forest model was higher by only 0.005. An R<sup>2</sup> of 0.96 indicates that the model can explain approximately 96% of the variance in car prices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "cfb65a6a-912e-4769-a35d-d5f6d5cf93d8",
   "metadata": {
    "executionCancelledAt": null,
    "executionTime": 50,
    "lastExecutedAt": 1711728547601,
    "lastScheduledRunId": null,
    "lastSuccessfullyExecutedCode": "# use lr model to predict car prices\ny_pred_lr = lr.predict(X_test_transformed)\n\n# calculate mae of lr model\nmae_lr = mean_absolute_error(y_test, y_pred_lr)\n\n# use rf model to predict car prices\ny_pred_rf = rf.predict(X_test_transformed)\n\n# calculate mae of rf model\nmae_rf = mean_absolute_error(y_test, y_pred_rf)\n\n# print mae of each model\nprint('Logistic Regression MAE: {}'.format(np.round(mae_lr, decimals=2)))\n\nprint('Random Forest MAE: {}'.format(np.round(mae_rf, decimals=2)))",
    "outputsMetadata": {
     "0": {
      "height": 57,
      "type": "stream"
     }
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# use knn model to predict car prices\n",
    "y_pred_knn = knn.predict(X_test_transformed)\n",
    "\n",
    "# calculate rmse of knn model\n",
    "rmse_knn = root_mean_squared_error(y_test, y_pred_knn)\n",
    "\n",
    "# calculate r2 of knn model\n",
    "r2_knn = r2_score(y_test, y_pred_knn)\n",
    "\n",
    "# use rf model to predict car prices\n",
    "y_pred_rf = rf.predict(X_test_transformed)\n",
    "\n",
    "# calculate rmse of rf model\n",
    "rmse_rf = root_mean_squared_error(y_test, y_pred_rf)\n",
    "\n",
    "# calculate r2 of rf model\n",
    "r2_rf = r2_score(y_test, y_pred_rf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7315a818-92de-4594-87b4-48e756048d52",
   "metadata": {},
   "source": [
    "Table 2: Model evaluation results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "40f462f3-0367-4af7-a61b-66cc36e2c337",
   "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>Model</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>R²</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KNN</td>\n",
       "      <td>1298.541</td>\n",
       "      <td>0.957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Random forest</td>\n",
       "      <td>1210.384</td>\n",
       "      <td>0.962</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Model      RMSE     R²\n",
       "0            KNN  1298.541  0.957\n",
       "1  Random forest  1210.384  0.962"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a table to summarize evaluation results\n",
    "results = pd.DataFrame({'Model':['KNN','Random forest'],\n",
    "                        'RMSE':[rmse_knn, rmse_rf],\n",
    "                        'R\\u00b2':[r2_knn, r2_rf]})\n",
    "\n",
    "# display table\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3827b329-410f-473a-be94-c1239f33d749",
   "metadata": {},
   "source": [
    "# 6. Key Performance Indicator\n",
    "\n",
    "Discount Motors needs to know the error of the model in terms of the percentage of the actual car price. The goal is to estimate car prices within 10% of their actual prices. While RMSE is a common metric for evaluating regression models, it does not provide this information and therefore cannot serve as a key performance indicator (KPI). A new metric to monitor needs to be designed.\n",
    "\n",
    "The recommended KPI is called Percent of Predictions within 10 Percent (PP10P). PP10P describes the percent of predicted prices that were within 10% of actual prices. Figure 14 displays the PP10P for each model (74% for KNN and 78% for random forest)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "b029115f-1a2d-41f1-97b5-6d153c3ddabe",
   "metadata": {
    "executionCancelledAt": null,
    "executionTime": 56,
    "lastExecutedAt": 1711728547870,
    "lastScheduledRunId": null,
    "lastSuccessfullyExecutedCode": "# create an independent copy of y_test to serve as a prediction error dataframe\npred_errors = y_test.copy()\n\n# create new columns containing car prices predicted by lr and rf models\npred_errors['pred_lr'] = y_pred_lr\npred_errors['pred_rf'] = y_pred_rf\n\n# calculate the error of each prediction\npred_errors['error_lr'] = pred_errors['pred_lr'] - pred_errors['price']\npred_errors['error_rf'] = pred_errors['pred_rf'] - pred_errors['price']\n\n# calculate the error of each prediction as a percent of the actual price\npred_errors['percent_error_lr'] = np.round((pred_errors['error_lr'] / pred_errors['price'])*100, 2)\npred_errors['percent_error_rf'] = np.round((pred_errors['error_rf'] / pred_errors['price'])*100, 2)\n\n# calculate the percent of predictions within 10% of actual prices\npp10p_lr = ((pred_errors['percent_error_lr'] >= -10) & (pred_errors['percent_error_lr'] <= 10)).sum() / len(pred_errors) * 100\npp10p_rf = ((pred_errors['percent_error_rf'] >= -10) & (pred_errors['percent_error_rf'] <= 10)).sum() / len(pred_errors) * 100\n\n# display pp10p of each model\nprint(pp10p_lr, pp10p_rf)",
    "outputsMetadata": {
     "0": {
      "height": 37,
      "type": "stream"
     }
    },
    "tags": []
   },
   "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",
       "      <th>pred_knn</th>\n",
       "      <th>pred_rf</th>\n",
       "      <th>error_knn</th>\n",
       "      <th>error_rf</th>\n",
       "      <th>percent_error_knn</th>\n",
       "      <th>percent_error_rf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2201</th>\n",
       "      <td>10498</td>\n",
       "      <td>11190.6</td>\n",
       "      <td>10419.53</td>\n",
       "      <td>692.6</td>\n",
       "      <td>-78.47</td>\n",
       "      <td>6.60</td>\n",
       "      <td>-0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4394</th>\n",
       "      <td>7999</td>\n",
       "      <td>8067.6</td>\n",
       "      <td>8133.55</td>\n",
       "      <td>68.6</td>\n",
       "      <td>134.55</td>\n",
       "      <td>0.86</td>\n",
       "      <td>1.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5665</th>\n",
       "      <td>17295</td>\n",
       "      <td>18186.8</td>\n",
       "      <td>17658.53</td>\n",
       "      <td>891.8</td>\n",
       "      <td>363.53</td>\n",
       "      <td>5.16</td>\n",
       "      <td>2.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5601</th>\n",
       "      <td>9795</td>\n",
       "      <td>8703.2</td>\n",
       "      <td>7722.77</td>\n",
       "      <td>-1091.8</td>\n",
       "      <td>-2072.23</td>\n",
       "      <td>-11.15</td>\n",
       "      <td>-21.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>694</th>\n",
       "      <td>9939</td>\n",
       "      <td>10925.8</td>\n",
       "      <td>10358.96</td>\n",
       "      <td>986.8</td>\n",
       "      <td>419.96</td>\n",
       "      <td>9.93</td>\n",
       "      <td>4.23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      price  pred_knn   pred_rf  error_knn  error_rf  percent_error_knn  \\\n",
       "2201  10498   11190.6  10419.53      692.6    -78.47               6.60   \n",
       "4394   7999    8067.6   8133.55       68.6    134.55               0.86   \n",
       "5665  17295   18186.8  17658.53      891.8    363.53               5.16   \n",
       "5601   9795    8703.2   7722.77    -1091.8  -2072.23             -11.15   \n",
       "694    9939   10925.8  10358.96      986.8    419.96               9.93   \n",
       "\n",
       "      percent_error_rf  \n",
       "2201             -0.75  \n",
       "4394              1.68  \n",
       "5665              2.10  \n",
       "5601            -21.16  \n",
       "694               4.23  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN PP10P: 74.40\n",
      "Random forest PP10P: 77.69\n"
     ]
    }
   ],
   "source": [
    "# create an independent copy of y_test to serve as a prediction error dataframe\n",
    "pred_errors = y_test.copy()\n",
    "\n",
    "# create new columns containing car prices predicted by knn and rf models\n",
    "pred_errors['pred_knn'] = y_pred_knn\n",
    "pred_errors['pred_rf'] = y_pred_rf\n",
    "\n",
    "# calculate the error of each prediction\n",
    "pred_errors['error_knn'] = pred_errors['pred_knn'] - pred_errors['price']\n",
    "pred_errors['error_rf'] = pred_errors['pred_rf'] - pred_errors['price']\n",
    "\n",
    "# calculate the error of each prediction as a percent of the actual price\n",
    "pred_errors['percent_error_knn'] = np.round((pred_errors['error_knn'] / pred_errors['price'])*100, 2)\n",
    "pred_errors['percent_error_rf'] = np.round((pred_errors['error_rf'] / pred_errors['price'])*100, 2)\n",
    "\n",
    "# calculate the percent of predictions within 10% of actual prices\n",
    "pp10p_knn = round(((pred_errors['percent_error_knn'] >= -10) & (pred_errors['percent_error_knn'] <= 10)).sum() / len(pred_errors) * 100, 2)\n",
    "pp10p_rf = round(((pred_errors['percent_error_rf'] >= -10) & (pred_errors['percent_error_rf'] <= 10)).sum() / len(pred_errors) * 100, 2)\n",
    "\n",
    "# preview pred_errors to confirm calculations\n",
    "display(pred_errors.head())\n",
    "\n",
    "# display pp10p of each model\n",
    "print(f'KNN PP10P: {pp10p_knn:.2f}\\nRandom forest PP10P: {pp10p_rf:.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "0cab4c35-c52f-4793-90ee-691527e58b99",
   "metadata": {
    "editable": true,
    "executionCancelledAt": null,
    "executionTime": 127,
    "lastExecutedAt": 1711728547997,
    "lastScheduledRunId": null,
    "lastSuccessfullyExecutedCode": "# generate bar plot: pp10p of each model\nplt.bar(['Logistic Regression', 'Random Forest'], [pp10p_lr, pp10p_rf])\nplt.title('Figure 14: Percent of Predictions within 10 Percent', pad=10.0)\nplt.xlabel('Model', labelpad=8.0)\nplt.ylabel('Percent of Predictions within 10 Percent', labelpad=8.0)\nplt.yticks(np.arange(0,110,10))\nplt.show()",
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# generate bar plot: pp10p of each model\n",
    "bar = plt.bar(['KNN', 'Random Forest'], [pp10p_knn, pp10p_rf])\n",
    "plt.title('Figure 14: Percent of Predictions within 10 Percent', pad=10.0)\n",
    "plt.xlabel('Model', labelpad=8.0)\n",
    "plt.ylabel('Percent of Predictions within 10 Percent', labelpad=8.0)\n",
    "plt.yticks(np.arange(0,110,10))\n",
    "plt.bar_label(bar)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64212c87-1991-4256-8e02-eadbb723b03d",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "In addition to the PP10P metric, it is important to understand the distribution of errors for each model. Table 2 and Figure 15 show descriptive statistics and a boxplot, respectively, of the percent errors of each model's predictions. Percent error refers to the error of a predicted price as a percentage of the actual price. A negative value represents an underestimate by the model, whereas a positive value denotes an overestimate by the model.\n",
    "\n",
    "Table 3: Descriptive statistics of the percent errors of each model's predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "6b48c6ea-e65b-43b0-aed7-260cf259d50e",
   "metadata": {
    "executionCancelledAt": null,
    "executionTime": 23,
    "lastExecutedAt": 1711730177060,
    "lastScheduledRunId": null,
    "lastSuccessfullyExecutedCode": "# generate descriptive statistics of percent error of each model's predictions\ndisplay(pred_errors[['percent_error_lr', 'percent_error_rf']].describe().round(2))",
    "outputsMetadata": {
     "0": {
      "height": 273,
      "type": "dataFrame"
     },
     "1": {
      "height": 57,
      "type": "stream"
     }
    }
   },
   "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>percent_error_knn</th>\n",
       "      <th>percent_error_rf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1340.00</td>\n",
       "      <td>1340.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.21</td>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13.88</td>\n",
       "      <td>10.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-46.70</td>\n",
       "      <td>-47.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-5.04</td>\n",
       "      <td>-5.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.88</td>\n",
       "      <td>4.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>217.58</td>\n",
       "      <td>114.28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       percent_error_knn  percent_error_rf\n",
       "count            1340.00           1340.00\n",
       "mean                1.21              0.09\n",
       "std                13.88             10.59\n",
       "min               -46.70            -47.03\n",
       "25%                -5.04             -5.14\n",
       "50%                -0.10             -0.26\n",
       "75%                 5.88              4.74\n",
       "max               217.58            114.28"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# generate descriptive statistics of percent error of each model's predictions\n",
    "display(pred_errors[['percent_error_knn', 'percent_error_rf']].describe().round(2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "7942feef-f0da-4746-b49e-b2ff88944a61",
   "metadata": {
    "executionCancelledAt": null,
    "executionTime": 155,
    "lastExecutedAt": 1711728548217,
    "lastScheduledRunId": null,
    "lastSuccessfullyExecutedCode": "# generate boxplot: percent error of each model's predictions\nplt.boxplot([pred_errors['percent_error_lr'], pred_errors['percent_error_rf']], labels=['Linear Regression', 'Random Forest'], showmeans=True)\nplt.title('Figure 15: Percent Error of Predictions', pad=10.0)\nplt.xlabel('Model', labelpad=8.0)\nplt.ylabel('Error (% of Actual Price)', labelpad=8.0)\nplt.show()"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# generate boxplot: percent error of each model's predictions\n",
    "plt.boxplot([pred_errors['percent_error_knn'], pred_errors['percent_error_rf']], labels=['KNN', 'Random Forest'])\n",
    "plt.title('Figure 15: Percent Error of Predictions', pad=10.0)\n",
    "plt.xlabel('Model', labelpad=8.0)\n",
    "plt.ylabel('Error (% of Actual Price)', labelpad=8.0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "166a800a-8e6f-4c0c-8498-b918101773a6",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "Table 3 shows that the KNN model made predictions that underestimated the price of cars by as much as 47% and overestimated prices by as much as 218%. The random forest model's largest underestimation was the same as KNN's (47%), but its largest overestimation was lower (114%) (Table 3). Overall, the range of percent errors was lower for the random forest model, indicating more stability in its predictions than KNN.\n",
    "\n",
    "Based on the PP10P KPI and range of percent errors for each model, the random forest model is recommended for this application and dataset over the KNN model. Discount Motors will need to decide the threshold value for the PP10P metric that indicates acceptable model performance. Once this threshold is set, the random forest model and all future models can be evaluated based on whether they are expected to meet Discount Motors' business goal.\n",
    "\n",
    "# 7. Summary and Recommendations\n",
    "\n",
    "In summary, Discount Motors will soon be losing its car-pricing expert to retirement. An automated system would benefit the organization in terms of efficiency and sales, as manual estimates made by the team are about 30% off. The goals of this analysis were to train and evaluate models that predict car prices. Ideally, the model should estimate car prices within 10% of actual prices.\n",
    "\n",
    "It is recommended that Discount Motors moves forward with additional testing and development of the random forest model. While the k-nearest neighbors (KNN) and random forest models both had R<sup>2</sup> values of 0.96, random forest had a lower RMSE (1210.38 GBP versus 1298.54 GBP). Furthermore, the percent of its predicted prices that were within 10 percent of actual prices (PP10P) was higher (78% versus 74%). The PP10P is the KPI that Discount Motors should continue to monitor. It is recommended that Discount Motors determines the threshold value for the PP10P metric that indicates acceptable model performance.\n",
    "\n",
    "The distribution of percent errors will be important to monitor. Overall, the random forest model had a lower range of percent errors, demonstrating more stability in its predictions than KNN. However, its predictions underestimated and overestimated prices by as much as 47% and 114% percent, respectively. The cars associated with the highest errors should be investigated to determine if there are patterns. For example, perhaps cars of an uncommon model or engine size are associated with high errors. This type of investigation could reveal suitable and unsuitable applications of the predictive model.\n",
    "\n",
    "More cars should be added to the dataset as they become available. Furthermore, data recency is especially critical in this context. Car prices are influenced by economic (e.g., inflation) and regulatory (e.g., zero emissions) conditions. Ongoing data collection and modeling will be necessary. On a regular basis, new data should be collected and incorporated into model training and evaluation.\n",
    "\n",
    "Future work should also include testing of additional regression algorithms. It is possible that another algorithm will perform better than random forest on this application and dataset. Further optimization can be conducted with techniques such as feature engineering, feature selection, and additional hyperparameter tuning. PP10P, as well as the distribution of percent errors, should be monitored throughout the model development process."
   ]
  }
 ],
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