c09-machine-learning-with-python/res/NB1-Simple-Linear-Regression-Co2.ipynb

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      "language": "python",
      "name": "python"
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    "language_info": {
      "codemirror_mode": {
        "name": "python",
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      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
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  "cells": [
    {
      "cell_type": "markdown",
      "source": "<p style=\"text-align:center\">\n    <a href=\"https://skills.network/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork20718538-2022-01-01\" target=\"_blank\">\n    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"200\" alt=\"Skills Network Logo\"  />\n    </a>\n</p>\n\n# Simple Linear Regression\n\nEstimated time needed: **15** minutes\n\n## Objectives\n\nAfter completing this lab you will be able to:\n\n*   Use scikit-learn to implement simple Linear Regression\n*   Create a model, train it, test it and use the model\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": "### Importing Needed packages\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "import piplite\nawait piplite.install(['pandas'])\nawait piplite.install(['matplotlib'])\nawait piplite.install(['numpy'])\nawait piplite.install(['scikit-learn'])\n\n",
      "metadata": {
        "trusted": true
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "import matplotlib.pyplot as plt\nimport pandas as pd\nimport pylab as pl\nimport numpy as np\n%matplotlib inline",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "### Downloading Data\n\nTo download the data, we will use !wget to download it from IBM Object Storage.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "path= \"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%202/data/FuelConsumptionCo2.csv\"",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "from pyodide.http import pyfetch\n\nasync def download(url, filename):\n    response = await pyfetch(url)\n    if response.status == 200:\n        with open(filename, \"wb\") as f:\n            f.write(await response.bytes())\n\n",
      "metadata": {
        "trusted": true
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "**Did you know?** When it comes to Machine Learning, you will likely be working with large datasets. As a business, where can you host your data? IBM is offering a unique opportunity for businesses, with 10 Tb of IBM Cloud Object Storage: [Sign up now for free](http://cocl.us/ML0101EN-IBM-Offer-CC)\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "## Understanding the Data\n\n### `FuelConsumption.csv`:\n\nWe have downloaded a fuel consumption dataset, **`FuelConsumption.csv`**, which contains model-specific fuel consumption ratings and estimated carbon dioxide emissions for new light-duty vehicles for retail sale in Canada. [Dataset source](http://open.canada.ca/data/en/dataset/98f1a129-f628-4ce4-b24d-6f16bf24dd64?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork20718538-2022-01-01)\n\n*   **MODELYEAR** e.g. 2014\n*   **MAKE** e.g. Acura\n*   **MODEL** e.g. ILX\n*   **VEHICLE CLASS** e.g. SUV\n*   **ENGINE SIZE** e.g. 4.7\n*   **CYLINDERS** e.g 6\n*   **TRANSMISSION** e.g. A6\n*   **FUEL CONSUMPTION in CITY(L/100 km)** e.g. 9.9\n*   **FUEL CONSUMPTION in HWY (L/100 km)** e.g. 8.9\n*   **FUEL CONSUMPTION COMB (L/100 km)** e.g. 9.2\n*   **CO2 EMISSIONS (g/km)** e.g. 182   --> low --> 0\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": "## Reading the data in\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "",
      "metadata": {},
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "await download(path, \"FuelConsumption.csv\")\npath=\"FuelConsumption.csv\"",
      "metadata": {
        "trusted": true
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "df = pd.read_csv(\"FuelConsumption.csv\")\n\n# take a look at the dataset\ndf.head()\n\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 6,
      "outputs": [
        {
          "execution_count": 6,
          "output_type": "execute_result",
          "data": {
            "text/plain": "   MODELYEAR   MAKE       MODEL VEHICLECLASS  ENGINESIZE  CYLINDERS  \\\n0       2014  ACURA         ILX      COMPACT         2.0          4   \n1       2014  ACURA         ILX      COMPACT         2.4          4   \n2       2014  ACURA  ILX HYBRID      COMPACT         1.5          4   \n3       2014  ACURA     MDX 4WD  SUV - SMALL         3.5          6   \n4       2014  ACURA     RDX AWD  SUV - SMALL         3.5          6   \n\n  TRANSMISSION FUELTYPE  FUELCONSUMPTION_CITY  FUELCONSUMPTION_HWY  \\\n0          AS5        Z                   9.9                  6.7   \n1           M6        Z                  11.2                  7.7   \n2          AV7        Z                   6.0                  5.8   \n3          AS6        Z                  12.7                  9.1   \n4          AS6        Z                  12.1                  8.7   \n\n   FUELCONSUMPTION_COMB  FUELCONSUMPTION_COMB_MPG  CO2EMISSIONS  \n0                   8.5                        33           196  \n1                   9.6                        29           221  \n2                   5.9                        48           136  \n3                  11.1                        25           255  \n4                  10.6                        27           244  ",
            "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>MODELYEAR</th>\n      <th>MAKE</th>\n      <th>MODEL</th>\n      <th>VEHICLECLASS</th>\n      <th>ENGINESIZE</th>\n      <th>CYLINDERS</th>\n      <th>TRANSMISSION</th>\n      <th>FUELTYPE</th>\n      <th>FUELCONSUMPTION_CITY</th>\n      <th>FUELCONSUMPTION_HWY</th>\n      <th>FUELCONSUMPTION_COMB</th>\n      <th>FUELCONSUMPTION_COMB_MPG</th>\n      <th>CO2EMISSIONS</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2014</td>\n      <td>ACURA</td>\n      <td>ILX</td>\n      <td>COMPACT</td>\n      <td>2.0</td>\n      <td>4</td>\n      <td>AS5</td>\n      <td>Z</td>\n      <td>9.9</td>\n      <td>6.7</td>\n      <td>8.5</td>\n      <td>33</td>\n      <td>196</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2014</td>\n      <td>ACURA</td>\n      <td>ILX</td>\n      <td>COMPACT</td>\n      <td>2.4</td>\n      <td>4</td>\n      <td>M6</td>\n      <td>Z</td>\n      <td>11.2</td>\n      <td>7.7</td>\n      <td>9.6</td>\n      <td>29</td>\n      <td>221</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2014</td>\n      <td>ACURA</td>\n      <td>ILX HYBRID</td>\n      <td>COMPACT</td>\n      <td>1.5</td>\n      <td>4</td>\n      <td>AV7</td>\n      <td>Z</td>\n      <td>6.0</td>\n      <td>5.8</td>\n      <td>5.9</td>\n      <td>48</td>\n      <td>136</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2014</td>\n      <td>ACURA</td>\n      <td>MDX 4WD</td>\n      <td>SUV - SMALL</td>\n      <td>3.5</td>\n      <td>6</td>\n      <td>AS6</td>\n      <td>Z</td>\n      <td>12.7</td>\n      <td>9.1</td>\n      <td>11.1</td>\n      <td>25</td>\n      <td>255</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2014</td>\n      <td>ACURA</td>\n      <td>RDX AWD</td>\n      <td>SUV - SMALL</td>\n      <td>3.5</td>\n      <td>6</td>\n      <td>AS6</td>\n      <td>Z</td>\n      <td>12.1</td>\n      <td>8.7</td>\n      <td>10.6</td>\n      <td>27</td>\n      <td>244</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "### Data Exploration\n\nLet's first have a descriptive exploration on our data.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "# summarize the data\ndf.describe()",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 7,
      "outputs": [
        {
          "execution_count": 7,
          "output_type": "execute_result",
          "data": {
            "text/plain": "       MODELYEAR   ENGINESIZE    CYLINDERS  FUELCONSUMPTION_CITY  \\\ncount     1067.0  1067.000000  1067.000000           1067.000000   \nmean      2014.0     3.346298     5.794752             13.296532   \nstd          0.0     1.415895     1.797447              4.101253   \nmin       2014.0     1.000000     3.000000              4.600000   \n25%       2014.0     2.000000     4.000000             10.250000   \n50%       2014.0     3.400000     6.000000             12.600000   \n75%       2014.0     4.300000     8.000000             15.550000   \nmax       2014.0     8.400000    12.000000             30.200000   \n\n       FUELCONSUMPTION_HWY  FUELCONSUMPTION_COMB  FUELCONSUMPTION_COMB_MPG  \\\ncount          1067.000000           1067.000000               1067.000000   \nmean              9.474602             11.580881                 26.441425   \nstd               2.794510              3.485595                  7.468702   \nmin               4.900000              4.700000                 11.000000   \n25%               7.500000              9.000000                 21.000000   \n50%               8.800000             10.900000                 26.000000   \n75%              10.850000             13.350000                 31.000000   \nmax              20.500000             25.800000                 60.000000   \n\n       CO2EMISSIONS  \ncount   1067.000000  \nmean     256.228679  \nstd       63.372304  \nmin      108.000000  \n25%      207.000000  \n50%      251.000000  \n75%      294.000000  \nmax      488.000000  ",
            "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>MODELYEAR</th>\n      <th>ENGINESIZE</th>\n      <th>CYLINDERS</th>\n      <th>FUELCONSUMPTION_CITY</th>\n      <th>FUELCONSUMPTION_HWY</th>\n      <th>FUELCONSUMPTION_COMB</th>\n      <th>FUELCONSUMPTION_COMB_MPG</th>\n      <th>CO2EMISSIONS</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>1067.0</td>\n      <td>1067.000000</td>\n      <td>1067.000000</td>\n      <td>1067.000000</td>\n      <td>1067.000000</td>\n      <td>1067.000000</td>\n      <td>1067.000000</td>\n      <td>1067.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>2014.0</td>\n      <td>3.346298</td>\n      <td>5.794752</td>\n      <td>13.296532</td>\n      <td>9.474602</td>\n      <td>11.580881</td>\n      <td>26.441425</td>\n      <td>256.228679</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>0.0</td>\n      <td>1.415895</td>\n      <td>1.797447</td>\n      <td>4.101253</td>\n      <td>2.794510</td>\n      <td>3.485595</td>\n      <td>7.468702</td>\n      <td>63.372304</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>2014.0</td>\n      <td>1.000000</td>\n      <td>3.000000</td>\n      <td>4.600000</td>\n      <td>4.900000</td>\n      <td>4.700000</td>\n      <td>11.000000</td>\n      <td>108.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>2014.0</td>\n      <td>2.000000</td>\n      <td>4.000000</td>\n      <td>10.250000</td>\n      <td>7.500000</td>\n      <td>9.000000</td>\n      <td>21.000000</td>\n      <td>207.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>2014.0</td>\n      <td>3.400000</td>\n      <td>6.000000</td>\n      <td>12.600000</td>\n      <td>8.800000</td>\n      <td>10.900000</td>\n      <td>26.000000</td>\n      <td>251.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>2014.0</td>\n      <td>4.300000</td>\n      <td>8.000000</td>\n      <td>15.550000</td>\n      <td>10.850000</td>\n      <td>13.350000</td>\n      <td>31.000000</td>\n      <td>294.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>2014.0</td>\n      <td>8.400000</td>\n      <td>12.000000</td>\n      <td>30.200000</td>\n      <td>20.500000</td>\n      <td>25.800000</td>\n      <td>60.000000</td>\n      <td>488.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "Let's select some features to explore more.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "cdf = df[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_COMB','CO2EMISSIONS']]\ncdf.head(9)",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 8,
      "outputs": [
        {
          "execution_count": 8,
          "output_type": "execute_result",
          "data": {
            "text/plain": "   ENGINESIZE  CYLINDERS  FUELCONSUMPTION_COMB  CO2EMISSIONS\n0         2.0          4                   8.5           196\n1         2.4          4                   9.6           221\n2         1.5          4                   5.9           136\n3         3.5          6                  11.1           255\n4         3.5          6                  10.6           244\n5         3.5          6                  10.0           230\n6         3.5          6                  10.1           232\n7         3.7          6                  11.1           255\n8         3.7          6                  11.6           267",
            "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>ENGINESIZE</th>\n      <th>CYLINDERS</th>\n      <th>FUELCONSUMPTION_COMB</th>\n      <th>CO2EMISSIONS</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2.0</td>\n      <td>4</td>\n      <td>8.5</td>\n      <td>196</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2.4</td>\n      <td>4</td>\n      <td>9.6</td>\n      <td>221</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1.5</td>\n      <td>4</td>\n      <td>5.9</td>\n      <td>136</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3.5</td>\n      <td>6</td>\n      <td>11.1</td>\n      <td>255</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>3.5</td>\n      <td>6</td>\n      <td>10.6</td>\n      <td>244</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>3.5</td>\n      <td>6</td>\n      <td>10.0</td>\n      <td>230</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>3.5</td>\n      <td>6</td>\n      <td>10.1</td>\n      <td>232</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>3.7</td>\n      <td>6</td>\n      <td>11.1</td>\n      <td>255</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>3.7</td>\n      <td>6</td>\n      <td>11.6</td>\n      <td>267</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "We can plot each of these features:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "viz = cdf[['CYLINDERS','ENGINESIZE','CO2EMISSIONS','FUELCONSUMPTION_COMB']]\nviz.hist()\nplt.show()",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<pyolite.display.Image at 0x447d228>",
            "image/png": 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yy8bPooG1mUAq9ztCCPHHH38Ib29vqzOBWHskJCQIIe7M9rN8+XIxZMgQERgYKJo0aSKaNm0qhg0bJrZv327xGBnaV90xq7wvQ99k7TFp0qQqXyNqXBRC1OH8NxERERFJDq8BJCIiIpIZJoBEREREMsMEkIiIiEhmmAASERERyQwTQCIiIiKZYQJIREREJDNMAImIiIhkhjOB1EFFRQWuXLkCLy8vKBQKR4dD1KgIIXDjxg0EBQXByUl+/6uyfyG6d+TevwBMAOvkypUraNmypaPDIGrULl++LMsJ7tm/EN17cu1fACaAdeLl5QXgzhtIrVbX+/71ej0OHjyIiIgIuLi41Pv+a0JKsQLSildKsQK2x1tcXIyWLVsaP2dy4+j+xRKpvddswTZJg73bJPf+BWACWCeGr2XUarXDEkB3d3eo1eoG/yGXUqyAtOKVUqxAzeOV69efju5fLJHae80WbJM03Ks2ybV/AXgTCBEREZHsMAEkIiIikhkmgEREREQywwSQiIiISGZ4E4gEtZ63FwCgdBZI7gt0XXwAuvLaX8h6cfkoe4VGRI2AoY+prDZ9DvsXooaJZwCJiIiIZIYJIBEREZHMSD4BXL58ORQKBWJjY41lQgjEx8cjMDAQbm5uCA8PR35+vsl6ZWVliI6ORrNmzeDp6YmoqChotdp6jp6IiIio/kk6ATx58iTefvttdO/e3aQ8OTkZa9euxfr165GTkwMPDw9ERkairKzMWGf27NnYvXs30tPTkZWVhStXrmDcuHH13QQiIiKieifZBPDmzZuYOHEiNmzYgKZNmxrLhRBYvXo1Fi5ciDFjxqB79+7YsmULrly5gp07dwIAioqKsHHjRrz55psYNmwYevXqhU2bNuHzzz/HiRMnHNQiIiIiovoh2QQwOjoao0aNQnh4uEl5QUEBNBqNSbm3tzf69euH7OxsAEBubi70er1JndDQUAQHBxvrEBERETVWkhwGZtu2bTh9+jROnjxptkyj0QAA/P39Tcr9/f2NyzQaDVxdXeHj42O1jiU6nQ46nc74vLi4GMCdOQr1en2t2lIbSmdx56eT6c/aqo/YDfuoz+NUF1KKV0qxArbHK5X2EBFJkeQSwMuXL2PWrFnIyMiASqWq130nJSVhyZIlZuUHDx6Eu7t7vcWR3Nf0+dLeFXXa3r59++q0fk1kZGTU277sQUrxSilWoPp4S0tL6ykSIiL5kVwCmJubi2vXruGBBx4wlpWXl+Po0aN46623kJeXBwDQarUIDAw01tFqtejZsycAICAgALdu3UJhYaHJWUCtVouAgACr+54/fz7i4uKMz4uLi9GyZUtERERArVbbqYXV67r4AIA7Z/6W9q7AolNO0FXUfiDoc4sj7RWaVXq9HhkZGRgxYgRcXFzu+f7qSkrxSilWwPZ4DWfYie5mbZDq2spfGmHX7RFJheQSwOHDh+Prr782KZsyZQpCQ0Mxd+5ctGnTBgEBAcjMzDQmfMXFxcjJycGMGTMAAL169YKLiwsyMzMRFRUFAMjLy8OlS5cQFhZmdd9KpRJKpdKs3MXFpV7/8FYegV9XoajTTCD1GXt9H6u6klK8UooVqD5eKbWFiEhqJJcAenl5oWvXriZlHh4eaNasmbE8NjYWy5YtQ/v27RESEoJFixYhKCgIY8eOBXDnppBp06YhLi4Ovr6+UKvViImJQVhYGPr371/fTSIiIiKqV5JLAG0xZ84clJSUYPr06SgsLMTAgQOxf/9+k2sGV61aBScnJ0RFRUGn0yEyMhLr1q1zYNRERERE9aNRJIBHjhwxea5QKJCYmIjExESr66hUKqSkpCAlJeUeR0dERETUsEh2HEAiIiIiqh0mgEREREQywwSQiIiISGaYABIRERHJDBNAIiIiIplhAkhEREQkM0wAiYiIiGSGCSARERGRzDABJCIiIpIZJoBEREREMtMopoKjhqX1vL1mZUpngeS+QNfFB6ArV9i8rYvLR9kzNCIiIgLPABIRERHJDhNAIiIiIplhAkhEREQkM0wAiYiIiGSGCSARERGRzDABJCK6S1JSEvr06QMvLy/4+flh7NixyMvLM6kjhEB8fDwCAwPh5uaG8PBw5Ofnm9QpKytDdHQ0mjVrBk9PT0RFRUGr1dZnU4iIrGICSER0l6ysLERHR+PEiRPIyMiAXq9HREQESkpKjHWSk5Oxdu1arF+/Hjk5OfDw8EBkZCTKysqMdWbPno3du3cjPT0dWVlZuHLlCsaNG+eIJhERmeE4gEREd9m/f7/J882bN8PPzw+5ubkYPHgwhBBYvXo1Fi5ciDFjxgAAtmzZAn9/f+zcuRPjx49HUVERNm7ciLS0NAwbNgwAsGnTJnTq1AknTpxA//79671dRER34xlAIqIqFBUVAQB8fX0BAAUFBdBoNAgPDzfW8fb2Rr9+/ZCdnQ0AyM3NhV6vN6kTGhqK4OBgYx0iIkfiGUAiIisqKioQGxuLAQMGoGvXrgAAjUYDAPD39zep6+/vb1ym0Wjg6uoKHx8fq3Uq0+l00Ol0xufFxcUAAL1eD71eb5f22ErpLCyXOwmTn7awd+zWYqstQ3z1fYzvJbbJ9u3JGRNAIiIroqOjce7cORw7duye7yspKQlLliwxKz948CDc3d3v+f7vlty36uVLe1fYvK19+/bVMRpT1cVWUxkZGSY/GxO2ybrS0lK7bEfKmAASEVkwc+ZM7NmzB0ePHkWLFi2M5QEBAQAArVaLwMBAY7lWq0XPnj2NdW7duoXCwkKTs4Barda4fmXz589HXFyc8XlxcTFatmyJiIgIqNVqO7asel0XH7BYrnQSWNq7AotOOUFXYduc3ucWR9ozNKux1daZBcOQkZGBESNGwMXFxa7bdhS9Xs82VcNwhl3OmAASEd1FCIGYmBjs2LEDR44cQUhIiMnykJAQBAQEIDMz05jwFRcXIycnBzNmzAAA9OrVCy4uLsjMzERUVBQAIC8vD5cuXUJYWJjF/SqVSiiVSrNyFxeXev8jriuvOrnTVSiqrWNg79ht3a+tDPE54jjfa2xT1duROyaARER3iY6ORlpaGnbt2gUvLy/jNXve3t5wc3ODQqFAbGwsli1bhvbt2yMkJASLFi1CUFAQxo4da6w7bdo0xMXFwdfXF2q1GjExMQgLC+MdwETUIEjyLuDU1FR0794darUaarUaYWFh+OSTT4zLOUgrEdVWamoqioqKMHToUAQGBhof//nPf4x15syZg5iYGEyfPh19+vTBzZs3sX//fqhUKmOdVatW4dFHH0VUVBQGDx6MgIAAfPzxx45oEhGRGUkmgC1atMDy5cuRm5uLU6dOYdiwYRgzZgy++eYbAByklYhqTwhh8TF58mRjHYVCgcTERGg0GpSVleHTTz9Fhw4dTLajUqmQkpKC69evo6SkBB9//LHV6/+IiOqbJL8CHj16tMnz1157DampqThx4gQ6d+7MQVqJiIiIqiDJM4B3Ky8vx7Zt21BSUoKwsDAO0kpERERUDUmeAQSAr7/+GmFhYSgrK4Onpyd27NiBzp074/PPPwdg/0FagYYzUKthINTaDMpqSX0M1FrbWB01WKeUBlKVUqyA7fFKpT1ERFIk2QSwY8eOOHv2LIqKirB9+3ZMmjQJWVlZ93SfDWWg1soDodZkUFZL6nOg1prGau/YakpKA6lKKVag+ng5UCsR0b0j2QTQ1dUV7dq1A3BnzK2TJ09izZo1mDt3LgD7D9IKNJyBWg0DodZmUFZL6mOg1trGau/YbCWlgVSlFCtge7wcqJWI6N6RbAJYWUVFBXQ63T0bpBVoOAO1Vh4ItSaDslpSnwO11jRWRyc0UhpIVUqxAtXHK6W2EBFJjSQTwPnz52PkyJEIDg7GjRs3kJaWhiNHjuDAgQMcpJWIiIioGpJMAK9du4bnnnsOV69ehbe3N7p3744DBw5gxIgRAO4M0lpSUoLp06ejsLAQAwcOtDhIq5OTE6KioqDT6RAZGYl169Y5qklERERE9UaSCeDGjRurXG4YpDUxMdFqHcMgrSkpKfYOj4iIiKhBk/w4gERERERUM0wAiYiIiGSGCSARERGRzDABJCIiIpIZJoBEREREMsMEkIiIiEhmmAASERERyQwTQCIiIiKZYQJIREREJDNMAImIiIhkhgkgERERkcxIci5gotpqPW+vTfWUzgLJfYGuiw9AV66wWOfi8lH2DI2IiKje8AwgERERkcwwASQiIiKSGX4FTERE94ytl10QUf3iGUAiIiIimWECSERERCQzTACJiIiIZIYJIBEREZHMMAEkIiIikhkmgEREREQywwSQiIiISGaYABIRERHJDBNAIiIiIplhAkhEREQkM0wAiYiIiGRGkglgUlIS+vTpAy8vL/j5+WHs2LHIy8szqSOEQHx8PAIDA+Hm5obw8HDk5+eb1CkrK0N0dDSaNWsGT09PREVFQavV1mdTiIiIiOpdE0cHUBtZWVmIjo5Gnz59cPv2bfzjH/9AREQEvv32W3h4eAAAkpOTsXbtWrz//vsICQnBokWLEBkZiW+//RYqlQoAMHv2bOzduxfp6enw9vbGzJkzMW7cOBw/ftyRzSMiIglqPW+vXbd3cfkou26P6G6STAD3799v8nzz5s3w8/NDbm4uBg8eDCEEVq9ejYULF2LMmDEAgC1btsDf3x87d+7E+PHjUVRUhI0bNyItLQ3Dhg0DAGzatAmdOnXCiRMn0L9//3pvFxEREVF9kGQCWFlRUREAwNfXFwBQUFAAjUaD8PBwYx1vb2/069cP2dnZGD9+PHJzc6HX603qhIaGIjg4GNnZ2RYTQJ1OB51OZ3xeXFwMANDr9dDr9fekbZYoncWdn06mP2vL3rEb4jMpq2Ws9RGbxXo2xFufr3lVDHE0lHiqY2u8UmkPEZEUST4BrKioQGxsLAYMGICuXbsCADQaDQDA39/fpK6/v79xmUajgaurK3x8fKzWqSwpKQlLliwxKz948CDc3d3r2hSbJfc1fb60d0Wdtrdv3746rV9Z5fjuVtNY6zM2S6qK196x1VVGRoajQ6iR6uItLS2tp0iIiORH8glgdHQ0zp07h2PHjt3zfc2fPx9xcXHG58XFxWjZsiUiIiKgVqvv+f4Nui4+AODO2amlvSuw6JQTdBWKWm/v3OJIe4UG4H/x3a22sdZHbJbYEq+9Y6stvV6PjIwMjBgxAi4uLo4Op1q2xms4w05ERPYn6QRw5syZ2LNnD44ePYoWLVoYywMCAgAAWq0WgYGBxnKtVouePXsa69y6dQuFhYUmZwG1Wq1x/cqUSiWUSqVZuYuLS73+4dWVmyYkugqFWVlN2Dv2qmKpaaz1GZvF+lXE29CSrfp+H9ZVdfFKqS1ERFIjyWFghBCYOXMmduzYgUOHDiEkJMRkeUhICAICApCZmWksKy4uRk5ODsLCwgAAvXr1gouLi0mdvLw8XLp0yViHiIiIqDGS5BnA6OhopKWlYdeuXfDy8jJes+ft7Q03NzcoFArExsZi2bJlaN++vXEYmKCgIIwdO9ZYd9q0aYiLi4Ovry/UajViYmIQFhbGO4CJiIioUZNkApiamgoAGDp0qEn5pk2bMHnyZADAnDlzUFJSgunTp6OwsBADBw7E/v37jWMAAsCqVavg5OSEqKgo6HQ6REZGYt26dfXVDCIiu7D3+HNE1PhJMgEUovqhPBQKBRITE5GYmGi1jkqlQkpKClJSUuwZHhEREVGDJslrAImIiIio9pgAEhEREckME0AiokqOHj2K0aNHIygoCAqFAjt37jRZLoRAfHw8AgMD4ebmhvDwcOTn55vUKSsrQ3R0NJo1awZPT09ERUVBq9XWYyuIiKxjAkhEVElJSQl69Ohh9frg5ORkrF27FuvXr0dOTg48PDwQGRmJsrIyY53Zs2dj9+7dSE9PR1ZWFq5cuYJx48bVVxOIiKokyZtAiIjupZEjR2LkyJEWlwkhsHr1aixcuBBjxowBAGzZsgX+/v7YuXMnxo8fj6KiImzcuBFpaWkYNmwYgDujFHTq1AknTpzgUFNE5HA8A0hEVAMFBQXQaDQIDw83lnl7e6Nfv37Izs4GAOTm5kKv15vUCQ0NRXBwsLEOEZEj8QwgEVENGAae9/f3Nyn39/c3LtNoNHB1dTWZZrJyncp0Oh10Op3xuWEuZL1eD71eX2VMSufqh8ayB6WTMPnZGBiObXXH2Bb2fh1qG5M929RQ2LtNjenY1BYTQCKiBiApKQlLliwxKz948CDc3d2rXDe5772KyrKlvSvqd4f3UEZGhsnPurD367Bv3746rW+PNjU09mpTaWmpXbYjZUwAiYhqICAgAACg1WoRGBhoLNdqtejZs6exzq1bt1BYWGhyFlCr1RrXr2z+/PmIi4szPi8uLkbLli0REREBtVpdZUxdFx+oZWtqRukksLR3BRadcoKuQlEv+7zXziwYhoyMDIwYMQIuLi512pa9X4dziyNrtZ5er7dbmxoKe7fJcIZdzpgAEhHVQEhICAICApCZmWlM+IqLi5GTk4MZM2YAAHr16gUXFxdkZmYiKioKAJCXl4dLly4hLCzM4naVSiWUSqVZuYuLS7V/8HTl9ZuM6SoU9b7Pe8VwbG05ztWx9zGpazz2aFNDY682NbbjUhtMAImIKrl58ybOnz9vfF5QUICzZ8/C19cXwcHBiI2NxbJly9C+fXuEhIRg0aJFCAoKwtixYwHcuSlk2rRpiIuLg6+vL9RqNWJiYhAWFsY7gImoQWACSERUyalTp/DQQw8Znxu+mp00aRI2b96MOXPmoKSkBNOnT0dhYSEGDhyI/fv3Q6VSGddZtWoVnJycEBUVBZ1Oh8jISKxbt67e20JEZAkTQCKiSoYOHQohrN/RqVAokJiYiMTERKt1VCoVUlJSrA4mTUTkSBwHkIiIiEhmmAASERERyQwTQCIiIiKZYQJIREREJDNMAImIiIhkhgkgERERkcwwASQiIiKSGSaARERERDLDBJCIiIhIZpgAEhEREckME0AiIiIimWECSERERCQzkkwAjx49itGjRyMoKAgKhQI7d+40WS6EQHx8PAIDA+Hm5obw8HDk5+eb1CkrK0N0dDSaNWsGT09PREVFQavV1mMriIiIiByjiaMDqI2SkhL06NEDU6dOxbhx48yWJycnY+3atXj//fcREhKCRYsWITIyEt9++y1UKhUAYPbs2di7dy/S09Ph7e2NmTNnYty4cTh+/Hh9N4eIiByk6+IDSO5756euXOHocIjqjSQTwJEjR2LkyJEWlwkhsHr1aixcuBBjxowBAGzZsgX+/v7YuXMnxo8fj6KiImzcuBFpaWkYNmwYAGDTpk3o1KkTTpw4gf79+9dbW4iIiIjqmyS/Aq5KQUEBNBoNwsPDjWXe3t7o168fsrOzAQC5ubnQ6/UmdUJDQxEcHGysQ0RERNRYSfIMYFU0Gg0AwN/f36Tc39/fuEyj0cDV1RU+Pj5W61ii0+mg0+mMz4uLiwEAer0eer3eHuHbROks7vx0Mv1ZW/aO3RCfSVktY62P2CzWsyHe+nzNq2KIo6HEUx1b45VKe4iIpKjRJYD3UlJSEpYsWWJWfvDgQbi7u9dbHMl9TZ8v7V1Rp+3t27evTutXVjm+u9U01vqMzZKq4rV3bHWVkZHh6BBqpLp4S0tL6ykSIiL5aXQJYEBAAABAq9UiMDDQWK7VatGzZ09jnVu3bqGwsNDkLKBWqzWub8n8+fMRFxdnfF5cXIyWLVsiIiICarXavg2pQtfFBwDcOTu1tHcFFp1ygq6i9hcvn1scaa/QAPwvvrvVNtb6iM0SW+K1d2y1pdfrkZGRgREjRsDFxcXR4VTL1ngNZ9iJiMj+Gl0CGBISgoCAAGRmZhoTvuLiYuTk5GDGjBkAgF69esHFxQWZmZmIiooCAOTl5eHSpUsICwuzum2lUgmlUmlW7uLiUq9/eCvfqaarUNTp7jV7x15VLDWNtT5js1i/ingbWrJV3+/DuqouXim1hYhIaiSZAN68eRPnz583Pi8oKMDZs2fh6+uL4OBgxMbGYtmyZWjfvr1xGJigoCCMHTsWwJ2bQqZNm4a4uDj4+vpCrVYjJiYGYWFhvAOYiIiIGj1JJoCnTp3CQw89ZHxu+Fp20qRJ2Lx5M+bMmYOSkhJMnz4dhYWFGDhwIPbv328cAxAAVq1aBScnJ0RFRUGn0yEyMhLr1q2r97YQERFZ0nre3lqtp3QWZmMbXlw+yp6hUSMgyQRw6NChEML63ZkKhQKJiYlITEy0WkelUiElJQUpKSn3IkQiIiKiBqvRjQNIRERERFVjAkhEREQkM5L8CpiIiIhsV9vrCa3hNYXSxzOARERERDLDBJCIiIhIZpgAEhEREckME0AiIiIimWECSERERCQzTACJiIiIZIYJIBEREZHMMAEkIiIikhkmgEREREQywwSQiIiISGaYABIRERHJDBNAIiIiIplhAkhEREQkM0wAiYiIiGSGCSARERGRzDABJCIiIpIZJoBEREREMsMEkIiIiEhmmAASERERyQwTQCIiIiKZYQJIREREJDNNHB0AETV8refttdu2lM4CyX3ttjkiIqoFngEkIiIikhnZJ4ApKSlo3bo1VCoV+vXrhy+++MLRIRFRI8I+hogaIll/Bfyf//wHcXFxWL9+Pfr164fVq1cjMjISeXl58PPzc3R49caeX+9R7dX2dTB8pdp18QHoyhXG8ovLR9krNKol9jFE1FDJ+gzgm2++iRdeeAFTpkxB586dsX79eri7u+O9995zdGhE1AiwjyGihkq2ZwBv3bqF3NxczJ8/31jm5OSE8PBwZGdnW1xHp9NBp9MZnxcVFQEArl+/Dr1eX+X++iVl2iHqOwwvWpMKgdLSCjTRO6G8QlHlOo5W21h///13+8Zxu8S2ejbE66jYzNazEqs946ttbBa39f/j/f333+Hi4mK13o0bNwAAQgi77bs+1bSPqUv/Ys/Xp8r9SKjPsRXbVDv27v+q+xupdBJYeH8Fei74GLpq2pQzf3i1+5N6/2IXQqZ++eUXAUB8/vnnJuWvvvqq6Nu3r8V1EhISBAA++OCjHh+XL1+ujy7B7mrax7B/4YOP+n9ItX+xB9meAayN+fPnIy4uzvi8oqIC169fR7NmzaBQ1P9/jsXFxWjZsiUuX74MtVpd7/uvCSnFCkgrXinFCtgerxACN27cQFBQUD1G5zgNrX+xRGrvNVuwTdJg7zbJrX+xRLYJYPPmzeHs7AytVmtSrtVqERAQYHEdpVIJpVJpUubj43OvQrSZWq2WzIdcSrEC0opXSrECtsXr7e1dT9HYX037mIbav1gitfeaLdgmabBnm6Tcv9iDbG8CcXV1Ra9evZCZ+b/rDioqKpCZmYmwsDAHRkZEjQH7GCJqyGR7BhAA4uLiMGnSJPTu3Rt9+/bF6tWrUVJSgilTpjg6NCJqBNjHEFFDJesE8Omnn8avv/6K+Ph4aDQa9OzZE/v374e/v7+jQ7OJUqlEQkKC2ddGDZGUYgWkFa+UYgWkF29dSL2PqawxvnZskzQ0xjY5mkIIOd8DTURERCQ/sr0GkIiIiEiumAASERERyQwTQCIiIiKZYQJIREREJDNMACVu+fLlUCgUiI2NdXQoVv3yyy949tln0axZM7i5uaFbt244deqUo8MyU15ejkWLFiEkJARubm5o27Ytli5d2mDmijx69ChGjx6NoKAgKBQK7Ny502S5EALx8fEIDAyEm5sbwsPDkZ+f75hgUXW8er0ec+fORbdu3eDh4YGgoCA899xzuHLlisPiJcuSkpLQp08feHl5wc/PD2PHjkVeXp6jw7IrKfSjtpBKX2urht4nSx0TQAk7efIk3n77bXTv3t3RoVj1xx9/YMCAAXBxccEnn3yCb7/9Fm+88QaaNm3q6NDMrFixAqmpqXjrrbfw3XffYcWKFUhOTsa//vUvR4cGACgpKUGPHj2QkpJicXlycjLWrl2L9evXIycnBx4eHoiMjERZWVk9R3pHVfGWlpbi9OnTWLRoEU6fPo2PP/4YeXl5eOyxxxwQKVUlKysL0dHROHHiBDIyMqDX6xEREYGSkhJHh2YXUuhHbSGlvtZWDb1PljwHzkNMdXDjxg3Rvn17kZGRIYYMGSJmzZrl6JAsmjt3rhg4cKCjw7DJqFGjxNSpU03Kxo0bJyZOnOigiKwDIHbs2GF8XlFRIQICAsTKlSuNZYWFhUKpVIoPP/zQARGaqhyvJV988YUAIH766af6CYpq5dq1awKAyMrKcnQodSaVftQWUuprbSWlPlmKeAZQoqKjozFq1CiEh4c7OpQq/fe//0Xv3r3x5JNPws/PD/fffz82bNjg6LAsevDBB5GZmYkffvgBAPDll1/i2LFjGDlypIMjq15BQQE0Go3J+8Hb2xv9+vVDdna2AyOzXVFRERQKRYOd/5buKCoqAgD4+vo6OJK6k0o/agsp9bW2knKfLAWynglEqrZt24bTp0/j5MmTjg6lWj/++CNSU1MRFxeHf/zjHzh58iT+/ve/w9XVFZMmTXJ0eCbmzZuH4uJihIaGwtnZGeXl5XjttdcwceJER4dWLY1GAwBmM0z4+/sblzVkZWVlmDt3Lp555plGN3l9Y1JRUYHY2FgMGDAAXbt2dXQ4dSKlftQWUuprbSXlPlkKmABKzOXLlzFr1ixkZGRApVI5OpxqVVRUoHfv3vjnP/8JALj//vtx7tw5rF+/vsF1Sh999BG2bt2KtLQ0dOnSBWfPnkVsbCyCgoIaXKyNiV6vx1NPPQUhBFJTUx0dDlUhOjoa586dw7FjxxwdSp1IrR+1hZT6WluxT763mABKTG5uLq5du4YHHnjAWFZeXo6jR4/irbfegk6ng7OzswMjNBUYGIjOnTublHXq1An/93//56CIrHv11Vcxb948jB8/HgDQrVs3/PTTT0hKSmrwnU1AQAAAQKvVIjAw0Fiu1WrRs2dPB0VVPUPy99NPP+HQoUM8+9eAzZw5E3v27MHRo0fRokULR4dTJ1LrR20hpb7WVlLuk6WACaDEDB8+HF9//bVJ2ZQpUxAaGoq5c+c2uE5rwIABZkNG/PDDD2jVqpWDIrKutLQUTk6ml8U6OzujoqLCQRHZLiQkBAEBAcjMzDQmfMXFxcjJycGMGTMcG5wVhuQvPz8fhw8fRrNmzRwdElkghEBMTAx27NiBI0eOICQkxNEh1ZnU+lFbSKmvtZWU+2QpYAIoMV5eXmbX3nh4eKBZs2YN8pqc2bNn48EHH8Q///lPPPXUU/jiiy/wzjvv4J133nF0aGZGjx6N1157DcHBwejSpQvOnDmDN998E1OnTnV0aACAmzdv4vz588bnBQUFOHv2LHx9fREcHIzY2FgsW7YM7du3R0hICBYtWoSgoCCMHTu2wcUbGBiIJ554AqdPn8aePXtQXl5uvFbR19cXrq6uDomZzEVHRyMtLQ27du2Cl5eX8XXy9vaGm5ubg6OrHan1o7aQUl9rq4beJ0ueo29Dprpr6MMX7N69W3Tt2lUolUoRGhoq3nnnHUeHZFFxcbGYNWuWCA4OFiqVSrRp00YsWLBA6HQ6R4cmhBDi8OHDAoDZY9KkSUKIO0PBLFq0SPj7+wulUimGDx8u8vLyGmS8BQUFFpcBEIcPH3ZYzGTO2uu0adMmR4dmVw29H7WFVPpaWzX0PlnqFEJwSG0iIiIiOeE4gEREREQywwSQiIiISGaYABIRERHJDBNAIiIiIplhAkhEREQkM0wAZe7ChQt48cUX0aZNG6hUKqjVagwYMABr1qzBn3/+aayn1+uxdu1a9OnTB15eXvD09ESfPn2wdu1a6PV6k22WlpYiJSUFERERCAwMhJeXF+6//36kpqaivLzcpO6RI0egUCisPrZt22as27p1aygUCqsTt2/YsMG43qlTp4zlixcvhkKhwG+//WZSf/fu3RgyZAj8/Pzg7u6ONm3a4KmnnsL+/ftN6v3666+YNWsWQkND4ebmBj8/P/Tt2xdz587FzZs3jfUmT54MT09Ps7iEEPjggw8wePBg+Pj4wN3dHd26dUNiYiJKSkrM6g8dOhQKhQKjR482W3bx4kUoFAq8/vrrZuVTpkxB27ZtoVKpEBAQgMGDByMhIcHisaKGa/PmzVY/D/PmzQMAKBQKzJw50+L627dvh0KhwJEjR4xlkydPtrrNu6dCM3wet2/fXm2cZWVlWLVqFfr16wdvb2+oVCp06NABM2fOxA8//GBW//jx43j88cfh7+8PpVKJ1q1b48UXX8SlS5fM6ho+s/7+/igtLTVb3rp1azz66KMmZTdv3kRCQgK6du1qHNOvZ8+emDVrFq5cuWJyLCx9Tg08PT0xefJks2OiUCjw73//2+I6AwYMgEKhMBtD0NBnGR5+fn4YNGgQduzYAaDq1/ruR+vWrU2OS+W+DAD27NmDhx9+GM2aNTO+Fq+88gp+//13s7qG90P37t1haSCQqt5f1dmxYwdGjhyJ5s2bw9XVFUFBQXjqqadw6NAhs7qXLl3CSy+9hNatW0OpVMLPzw9jx47F8ePHzera83VQqVRo3749Xn31VVy/fr1W7WwMOBC0jO3duxdPPvkklEolnnvuOXTt2hW3bt3CsWPH8Oqrr+Kbb77BO++8g5KSEowaNQpZWVl49NFHMXnyZDg5OWH//v2YNWsWPv74Y+zduxceHh4A7kxKHhMTg+HDhyMuLg5qtRoHDhzA3/72N5w4cQLvv/++WSx///vf0adPH7PysLAwk+cqlQqHDx+GRqMxTn9msHXrVqhUKpSVlVXb9tdffx2vvvoqhgwZgvnz58Pd3R3nz5/Hp59+im3btuHhhx8GAFy/fh29e/dGcXExpk6ditDQUPz+++/46quvkJqaihkzZlT5x6S8vBwTJkzARx99hEGDBmHx4sVwd3fHZ599hiVLliA9PR2ffvop/P39zdbds2cPcnNz0atXryrbcv78efTp0wdubm6YOnUqWrdujatXr+L06dNYsWIFlixZUu3xoIYnMTHRbNaNugxSrFQq8e6775qV12bWi99++w0PP/wwcnNz8eijj2LChAnw9PREXl4etm3bhnfeeQe3bt0y1v/Xv/6FWbNmoU2bNoiJiUFgYCC+++47vPvuu/jPf/6Dffv24cEHHzTbz7Vr15CamoqXX365ynj0ej0GDx6M77//HpMmTUJMTAxu3ryJb775BmlpaXj88ccRFBRU43beTaVSIS0tDc8++6xJ+cWLF/H5559bnVO4Z8+exvivXLmCt99+G+PGjUNqaioiIiLwwQcfmNR//vnn0bdvX0yfPt1YVlUfAwCvvPIK3njjDfTo0QNz586Fr68vTp8+jbfeegvbtm1DZmYmOnbsaLbe119/jY8//hhRUVE2HYOqCCEwdepUbN68Gffffz/i4uIQEBCAq1evYseOHRg+fDiOHz9ufJ2PHz+ORx55xNjmzp07Q6PRYPPmzRg0aBDWrFmDmJgYs/3Y43UoKytDbm4uVq9ejaysLHzxxRd1br8kOXQUQnKYH3/8UXh6eorQ0FBx5coVs+X5+fli9erVQgghpk+fLgCIf/3rX2b13nrrLQFAvPTSS8ayX3/9VZw7d86s7pQpUwQAkZ+fbywzDBacnp5ebcytWrUSw4cPF2q12hibweXLl4WTk5OIiooSAMTJkyeNyxISEgQA8euvvwohhNDr9UKtVosRI0ZY3I9WqzX+npycLACI48ePm9UrKioSf/75p/H5pEmThIeHh0mdf/7znwKAeOWVV8zW/+9//yucnJzEww8/bFI+ZMgQERwcLJo2bSpGjx5tsswwgPLKlSuNZX/7299EkyZNxMWLF6tsC0nDpk2bzN7DlQEQ0dHRFpelp6ebDaht6b1pia2fx1GjRgknJyexfft2s2VlZWXi5ZdfNj4/duyYcHJyEoMGDRIlJSUmdc+fPy/8/f1FYGCguH79urHc8Jnt2bOn8Pf3F6WlpSbrtWrVSowaNcr4/KOPPhIAxNatW83i+fPPP0VRUZHxeXXHwsPDwzi4uhD/Oybjxo0TTZo0MfYjBq+99prw9/cXAwcOFF26dKkyTiGEuHr1qvDw8BAdOnSwaf93q9yXCSFEWlqaACCefvppcfv2bZP6OTk5wt3dXXTr1k3o9XqTY+Dm5iY6dOggunfvLioqKkzWq+r9Zc3KlSsFABEbG2u2PSGE2LJli8jJyRFCCHH9+nUREBAg/P39xfnz503qlZaWikGDBgknJyeTftfer4MQQrzyyisCgPjhhx9q1NbGgl8By1RycjJu3ryJjRs3IjAw0Gx5u3btMGvWLPz888/YuHEjhg0bZvErgejoaDz00EN499138fPPPwMAmjdvji5dupjVffzxxwEA3333Xa3jVqlUGDduHNLS0kzKP/zwQzRt2hSRkZHVbuO3335DcXExBgwYYHG5n5+f8fcLFy7A2dkZ/fv3N6unVqut/rcJAH/++SdWrlyJDh06ICkpyWz56NGjMWnSJOzfvx8nTpwwWebl5YXZs2dj9+7dOH36dJXtuXDhAlq0aGFxzs+720JkDzk5Odi7dy+mTZtm8cyRUqk0uURh6dKlUCgUeP/99+Hu7m5St23btkhOTsbVq1fx9ttvm20rPj4eWq0WqampVcZ04cIFALD4mTZc2lJXY8aMgVKpRHp6ukl5WloannrqKZvPpAYEBKBTp04oKCioc0wAsGTJEjRt2hTvvPOOWQyGS1W+/vprs6/1nZycsHDhQnz11VfGr6Rr688//0RSUhJCQ0Px+uuvQ6FQmNX561//ir59+wIA3n77bWg0GqxcuRJt27Y1qefm5ob3338fCoUCiYmJZtux1+sAwPgtUpMm8vwylAmgTO3evRtt2rSx+LXL3T755BOUl5fjueees1rnueeew+3bt82unavMMIdo8+bNzZbduHEDv/32m9lDWLg+ZcKECfjiiy+MnT5w58P/xBNPwMXFpcoYgDtJkZubG3bv3l3t9R+tWrVCeXm52dc0tjh27Bj++OMPTJgwwWoHYziue/bsMVs2a9YsNG3aFIsXL642xsuXL1u8xoakq6ioyOzzUFeWPmPFxcU12sZ///tfAHf+oFentLQUmZmZGDRokNnX2QZPP/00lEqlxc/AoEGDMGzYMCQnJ5tck1yZ4Z+fLVu2WOwz7MHd3R1jxozBhx9+aCz78ssv8c0332DChAk2b0ev1+Py5cto1qxZnWPKz89HXl4exowZYzXJraqPmTBhAtq3b4/ExMQ6Hbdjx47h+vXrmDBhgk0J2O7du6FSqfDUU09ZXB4SEoKBAwfi0KFDZq97bV8HvV5vfM///PPP2L17N958800MHjzY6nuzsWMCKEPFxcX45Zdf0K1bt2rrfvvttwCAHj16WK1jWFbVmb1bt25h9erVCAkJsXit39SpU3HfffeZPbRarVndYcOGISAgwNgBfPfddzh79qzNnbCTkxNeffVV5ObmIjg4GI888gj++c9/WjzTZohr8uTJ6NSpE2bMmIEPP/wQRUVF1e6nrsdOrVYjNja22rOAf//73+Hq6orhw4fj/vvvR2xsLHbt2mXx4nmSjvDwcLPPQ12UlJRY/IxZ+yNsjeG9akv/kZ+fj9u3b1f5GVAqlejYsaPV/iMhIQFarRbr16+3uo2xY8eiY8eOiI+PR0hICKZMmYL33nsP165dqzbGmpgwYQKOHTuGy5cvA7hz3XGbNm0sfkNgcHfi8dVXX+G5556DVqvFk08+Wed4bOljWrduDbVabfH4Ojs7Y+HChfjyyy+xc+fOWsdRk/cEcCfujh07QqlUWq3To0cP6PV6nD9/3mxZbV6HgwcPGt/zLVu2xGOPPYaQkBB8/PHHNsXcGDEBlCHDf/xeXl7V1r1x40a1dQ3LqjqTMHPmTHz77bd46623LJ4Ni4+PR0ZGhtnD19fXrK6zszOeeuopYwK4detWtGzZEoMGDaq2PQZLlixBWloa7r//fhw4cAALFixAr1698MADD5h0lP7+/vjyyy/x0ksv4Y8//sD69esxYcIE+Pn5YenSpVX+12yPY2c4C1jVjRxdunTB2bNn8eyzz+LixYtYs2YNxo4dC39/f2zYsKHK40ANV0pKitnnoS5UKpXFz9jy5ctrtB179x+G5dY+A4MHD8ZDDz1U5VlANzc35OTk4NVXXwVw5+7aadOmITAwEDExMdDpdNXGaouIiAj4+vpi27ZtEEJg27ZteOaZZ6pc5+7Eo0ePHkhPT8df//pXrFixos7x2OP4Tpw4sc5nAWvyngDuxG1LzHdv+261eR369etnfM/v2bMHr732Gr755hs89thjVZ5dbszk+cW3zBm+KjB0HlUxfAirqltdJ7Ry5Ups2LABS5cuNd71VVm3bt2sDu9iyYQJE7B27Vp8+eWXSEtLw/jx4y1ed1KVZ555Bs888wyKi4uRk5ODzZs3Iy0tDaNHj8a5c+eM1/cFBgYiNTUV69atQ35+Pg4cOIAVK1YgPj4egYGBeP755y1u3x7HztvbG7GxsUhISMCZM2fQtGlTi/U6dOiADz74AOXl5fj222+xZ88eJCcnY/r06QgJCanRsaWGoW/fvujdu3et16/8eXB2drbL++Du/sPHx6fKurZ8BgzLq0oIFi9ejCFDhmD9+vWYPXu2xTre3t5ITk5GcnIyfvrpJ2RmZuL111/HW2+9BW9vbyxbtqzKGO5mrS9xcXHBk08+ibS0NPTt2xeXL1+u9puHfv36YdmyZVAoFHB3d0enTp2qPW62qsnxtXY9sOEs4KRJk7Bz507jtdo1UZO/KcCduG2J2VC3stq8Ds2bNzd5/48aNQodO3bEE088gXfffdfiHceNHc8AypBarUZQUBDOnTtXbd1OnToBAL766iurdQzLOnfubLZs8+bNmDt3Ll566SUsXLiwlhGb69evH9q2bYvY2FgUFBTU6BqcytRqNUaMGIGtW7di0qRJuHDhAnJycszqKRQKdOjQATExMTh69CicnJywdetWq9ut67EzmDVrFnx8fGwazsXZ2RndunXD/PnzjRd2VxUjSZNSqbR61sLw1X9VNyjVRWhoKIA7Q4hUp127dmjSpEmVnwGdToe8vLwqPwODBw/G0KFDq70W0KBVq1aYOnUqjh8/Dh8fH5PPgEqlgk6ns3i2SwiBsrKyKo/dhAkTcPbsWSxevBg9evSoMm7gf4nH8OHDERYWZrfkD7Ctj/npp59QXFxcZZwTJ05Eu3btan0WsCbvCeBO3Hl5eVWemf3qq6/g4uKC9u3bW1xe09fBkuHDhwMAjh49WuN1GwMmgDL16KOP4sKFC8jOzq6y3siRI+Hs7FzlTRBbtmxBkyZNjGPnGezatQvPP/88xo0bh5SUFLvEfbdnnnkGR44cQadOndCzZ0+7bNNwxuXq1atV1mvTpg2aNm1aZb2BAwfCx8cHaWlpZgNgG2zZsgUAzAa1vZvhLOCuXbtw5syZ6ppgZGtbSHpatWqFvLw8i8sM5ZbuCrcHwwDl1gbjvZuHhwceeughHD16FD/99JPFOh999BF0Ol2VnwHgzllAjUZj8W5ha5o2bYq2bduafAZatWqF27dvm9xEZnD+/HmUl5dXeewGDhyI4OBgHDlypE7/eNpDhw4d0KFDB+zcudPqGTVb+hjDWcCzZ89i165dNY5j4MCBaNq0KT788EOrfd3dHn30UZSVlZndyWtw8eJFfPbZZxg2bBjc3Nys7rOur8Pt27cBwGRAfzlhAihTc+bMgYeHB55//nmLN1pcuHABa9asQcuWLTFlyhR8+umnFodiWL9+PQ4dOoRp06ahRYsWxvKjR49i/PjxGDx4MLZu3QonJ/u/1Z5//nkkJCTgjTfeqNF6paWlVhPfTz75BACMg6bm5ORYnK3jiy++wO+//25xcFUDd3d3vPLKK8jLy8OCBQvMlu/duxebN29GZGRklRcvA0BsbCx8fHwsDovw2Wefmc3GAgD79u0zaQs1Ho888ghOnDiB3Nxck/LCwkJs3boVPXv2NBso3V7CwsLw8MMP491337V448CtW7fwyiuvGJ8vXLgQQghMnjzZ7OxdQUEB5syZg8DAQLz44otV7nfIkCEYOnQoVqxYYTbY+5dffmnxLumffvrJeMOBwciRIwEAb731lll9wz+qhjqWKBQKrF27FgkJCTbdCX2vxcfH448//sBLL71klnzl5uZixYoV6Nq1a7WDPT/77LNo165drQaOd3d3x9y5c/Hdd99h7ty5Fs8i/vvf/zYOuPziiy/Cz88Pr776Kn788UeTemVlZZgyZQqEEIiPj7e6T3u8Drt37wZQ9U00jRmvAZSptm3bIi0tDU8//TQ6depkMhPI559/jvT0dON0SKtWrcL333+Pv/3tb9i/f7/xTN+BAwewa9cuDBkyxCQJ++mnn/DYY49BoVDgiSeeMPsvr3v37ujevbtJ2WeffWZxBg9LdQ1atWpV7RAplpSWluLBBx9E//798fDDD6Nly5YoLCzEzp078dlnn2Hs2LG4//77AQAffPABtm7discffxy9evWCq6srvvvuO7z33ntQqVT4xz/+UeW+5s2bhzNnzmDFihXIzs5GVFQU3NzccOzYMfz73/9Gp06dLM6MUpm3tzdmzZplsXNesWIFcnNzMW7cOOOxOn36NLZs2QJfX1/ExsbW+BhRwzZv3jykp6dj8ODBePHFFxEaGoorV65g8+bNuHr1KjZt2mS2zu3bt62etXv88ceNM/kAwP/93//h+++/N6s3adIktGzZElu2bEFERATGjRuH0aNHY/jw4fDw8EB+fj62bduGq1evGscCHDx4MF5//XXExcWhe/fumDx5MgIDA/H9999jw4YNqKiowL59+6xe33q3hIQEPPTQQ2blGRkZSEhIwGOPPYb+/fvD09MTP/74I9577z3odDqTfqJnz554/vnnsWbNGuTn52PEiBHGbezbtw/PP/98tQnBmDFjMGbMmGrjrQ8TJ07EyZMnsWbNGnz77beYOHEimjZtitOnT+O9995Ds2bNsH379mqHyHJ2dsaCBQswZcqUWsVhmD3qjTfewOHDh/HEE08gICAAGo0GO3fuxBdffIHPP/8cAIwxjRo1Cg888IDZTCDnz5/HmjVrqh2mrCavwy+//GJ8/9+6dQtffvkl3n77bTRv3lyW1/8B4EwgcvfDDz+IF154QbRu3Vq4uroKLy8vMWDAAPGvf/1LlJWVGevpdDqxatUq0atXL+Hh4SHc3d3FAw88IFavXi1u3bplsk3DiO3WHgkJCbWqa20097tZmkXB0kwgGzZsEGPHjhWtWrUSSqVSuLu7i/vvv1+sXLlS6HQ647pfffWVePXVV8UDDzwgfH19RZMmTURgYKB48sknxenTp032bW2GgfLycrFp0yYxYMAAoVarhUqlEl26dBFLliwRN2/eNKs/ZMgQs5HshRDijz/+EN7e3mYzgRw/flxER0eLrl27Cm9vb+Hi4iKCg4PF5MmTxYULF6o8XtTw2DITiBBC/Pzzz+L5558Xf/nLX0STJk2Er6+vePTRR8WJEyfM6k6aNKnKz1lBQYEQovrP42effWbcZmlpqXj99ddFnz59hKenp3B1dRXt27cXMTExZrM7CCHE0aNHxZgxY0Tz5s2N79EXXnjB4gw2lma8MBgyZIgAYNIX/PjjjyI+Pl70799f+Pn5iSZNmoj77rtPjBo1Shw6dMhsG+Xl5WLNmjWiR48eQqVSCZVKJXr06CHWrl0rysvLTeraOjuKpc+tLX1WZTWdCcRg586dYsSIEaJp06ZCqVSKdu3aiZdfftliXWt9lV6vF23btq3VTCAG27dvFxERESb95dNPPy2OHDliVregoEC88MILIjg4WLi4uIjmzZuLxx57zOR9ZlDX1+Hu97GTk5Pw8/MTzzzzjMX3qlwohLhHo2YSERERUYPEawCJiIiIZIbXABIREZFFv/76a5V39rq6ulocsJ8aPn4FTERERBa1bt3a6hA+wJ27s48cOVJ/AZHd8AwgERERWbR169YqB9+25e5taph4BpCIiIhIZngTCBEREZHMMAEkIiIikhleA1gHFRUVuHLlCry8vKBQKBwdDlGjIoTAjRs3EBQUdE+mEmzo2L8Q3Tty718AJoB1cuXKFbRs2dLRYRA1apcvXzaZZ1ou2L8Q3Xty7V8AJoB14uXlBeDOG0itVhvL9Xo9Dh48iIiIiGrnX2zoGlNbgMbVnsbUFsC8PcXFxWjZsqXxc1ZfUlNTkZqaiosXLwIAunTpgvj4eIwcORLAnTMHCQkJ2LBhAwoLCzFgwACkpqaiffv2xm2UlZXh5ZdfxrZt26DT6RAZGYl169bB39/f5jis9S9Vkep7QqpxA9KNXapxA/aJ3VH9S0PCBLAODF/LqNVqswTQ3d0darVach+syhpTW4DG1Z7G1BbAenvq++vPFi1aYPny5Wjfvj2EEHj//fcxZswYnDlzBl26dEFycjLWrl2L999/HyEhIVi0aBEiIyPx7bffQqVSAQBmz56NvXv3Ij09Hd7e3pg5cybGjRuH48eP2xyHtf6lKlJ9T0g1bkC6sUs1bsC+scv58gomgEREdxk9erTJ89deew2pqak4ceIEOnfujNWrV2PhwoUYM2YMAGDLli3w9/fHzp07MX78eBQVFWHjxo1IS0vDsGHDAACbNm1Cp06dcOLECfTv37/e20REVJk8r3wkIrJBeXk5tm3bhpKSEoSFhaGgoAAajQbh4eHGOt7e3ujXrx+ys7MBALm5udDr9SZ1QkNDERwcbKxDRORoPANIRFTJ119/jbCwMJSVlcHT0xM7duxA586d8fnnnwOA2bV8/v7+0Gg0AACNRgNXV1f4+PhYrWOJTqeDTqczPi8uLgZw5+suvV5vU9yGerbWbyikGjcg3dilGjdgn9il2G57YwJIRFRJx44dcfbsWRQVFWH79u2YNGkSsrKy7uk+k5KSsGTJErPygwcPwt3dvUbbysjIsFdY9UqqcQPSjV2qcQN1i720tNSOkUgTE0Cyu9bz9tptWxeXj7Lbtohs5erqinbt2gEAevXqhZMnT2LNmjWYO3cuAECr1SIwMNBYX6vVomfPngCAgIAA3Lp1C4WFhSZnAbVaLQICAqzuc/78+YiLizM+N9ylGBERUaObQDIyMjBixIgGc2F/18UHqq2jdBJY2rsCi045QVdR9UX55xZH2is0u2iIx9wWUo0bsE/shjPscsYEkIioGhUVFdDpdAgJCUFAQAAyMzONCV9xcTFycnIwY8YMAHcSRhcXF2RmZiIqKgoAkJeXh0uXLiEsLMzqPpRKJZRKpVm5i4tLjf/I1Wade0VXbvtdlroKRbX1G0q7KmtIx7wmpBo3ULfYpdpme2ICSER0l/nz52PkyJEIDg7GjRs3kJaWhiNHjuDAgQNQKBSIjY3FsmXL0L59e+MwMEFBQRg7diyAOzeFTJs2DXFxcfD19YVarUZMTAzCwsJ4BzARNRhMAImI7nLt2jU899xzuHr1Kry9vdG9e3ccOHAAI0aMAADMmTMHJSUlmD59OgoLCzFw4EDs37/fOAYgAKxatQpOTk6IiooyGQiaiKihYAJIRHSXjRs3VrlcoVAgMTERiYmJVuuoVCqkpKQgJSXF3uEREdkFxwEkIiIikhkmgEREREQywwSQiIiISGZ4DSBVOW6f0lkgue+dsbxqMpwDERERNVxMAKlBs+eg0gCQvzTCrtsjIiKSIn4FTERERCQzTACJiIiIZIYJIBEREZHMMAEkIiIikhkmgEREREQywwSQiIiISGaYABIRERHJDBNAIiIiIplhAkhEREQkM0wAiYiIiGSGCSARERGRzDABJCIiIpIZJoBEREREMsMEkIiIiEhmmAASERERyYwkE8CkpCT06dMHXl5e8PPzw9ixY5GXl2dSRwiB+Ph4BAYGws3NDeHh4cjPzzepU1ZWhujoaDRr1gyenp6IioqCVqutz6YQERER1TtJJoBZWVmIjo7GiRMnkJGRAb1ej4iICJSUlBjrJCcnY+3atVi/fj1ycnLg4eGByMhIlJWVGevMnj0bu3fvRnp6OrKysnDlyhWMGzfOEU0iIiIiqjdNHB1Abezfv9/k+ebNm+Hn54fc3FwMHjwYQgisXr0aCxcuxJgxYwAAW7Zsgb+/P3bu3Inx48ejqKgIGzduRFpaGoYNGwYA2LRpEzp16oQTJ06gf//+9d4uIiIiovogyQSwsqKiIgCAr68vAKCgoAAajQbh4eHGOt7e3ujXrx+ys7Mxfvx45ObmQq/Xm9QJDQ1FcHAwsrOzLSaAOp0OOp3O+Ly4uBgAoNfrodfrjeWG3+8ua8iUzsL6Midh8lPqpPbaVKUxtQUwb09jaRcRUUMk+QSwoqICsbGxGDBgALp27QoA0Gg0AAB/f3+Tuv7+/sZlGo0Grq6u8PHxsVqnsqSkJCxZssSs/ODBg3B3dzcrz8jIqHF7HCG5b/V1lvauuPeB1APDayKV18YWjaktwP/aU1pa6uBIiIgaL8kngNHR0Th37hyOHTt2z/c1f/58xMXFGZ8XFxejZcuWiIiIgFqtNpbr9XpkZGRgxIgRcHFxuedx1VXXxQesLlM6CSztXYFFp5ygq1DUY1T3xpkFwyT12lRFau+z6lRuj+EMOxER2Z+kE8CZM2diz549OHr0KFq0aGEsDwgIAABotVoEBgYay7VaLXr27Gmsc+vWLRQWFpqcBdRqtcb1K1MqlVAqlWblLi4uFv8AWytvaHTl1Sd2ugqFTfUaOsPrIZXXxhaNqS3A/9rTmNpERNTQSDIBFEIgJiYGO3bswJEjRxASEmKyPCQkBAEBAcjMzDQmfMXFxcjJycGMGTMAAL169YKLiwsyMzMRFRUFAMjLy8OlS5cQFhZWr+0hIiLbtJ63167bu7h8lF23RyQVkkwAo6OjkZaWhl27dsHLy8t4zZ63tzfc3NygUCgQGxuLZcuWoX379ggJCcGiRYsQFBSEsWPHGutOmzYNcXFx8PX1hVqtRkxMDMLCwngHMBERETVqkkwAU1NTAQBDhw41Kd+0aRMmT54MAJgzZw5KSkowffp0FBYWYuDAgdi/fz9UKpWx/qpVq+Dk5ISoqCjodDpERkZi3bp19dUMIiIiIoeQZAIoRPVDkigUCiQmJiIxMdFqHZVKhZSUFKSkpNgzPCIiIqIGTZIzgRARERFR7UnyDCAREd079r7RgogaHp4BJCIiIpIZJoBEREREMsMEkIiIiEhmmAASERERyQwTQCIiIiKZYQJIREREJDNMAImIiIhkhgkgERERkcwwASQiuktSUhL69OkDLy8v+Pn5YezYscjLyzOpI4RAfHw8AgMD4ebmhvDwcOTn55vUKSsrQ3R0NJo1awZPT09ERUVBq9XWZ1OIiKxiAkhEdJesrCxER0fjxIkTyMjIgF6vR0REBEpKSox1kpOTsXbtWqxfvx45OTnw8PBAZGQkysrKjHVmz56N3bt3Iz09HVlZWbhy5QrGjRvniCYREZnhVHBERHfZv3+/yfPNmzfDz88Pubm5GDx4MIQQWL16NRYuXIgxY8YAALZs2QJ/f3/s3LkT48ePR1FRETZu3Ii0tDQMGzYMALBp0yZ06tQJJ06cQP/+/eu9XUREd2MCSERUhaKiIgCAr68vAKCgoAAajQbh4eHGOt7e3ujXrx+ys7Mxfvx45ObmQq/Xm9QJDQ1FcHAwsrOzLSaAOp0OOp3O+Ly4uBgAoNfrodfrbYrVUM/W+tYonUWd1q/x/pyEyc/6VNdjZa9jXt+kGjdgn9il2G57YwJIRGRFRUUFYmNjMWDAAHTt2hUAoNFoAAD+/v4mdf39/Y3LNBoNXF1d4ePjY7VOZUlJSViyZIlZ+cGDB+Hu7l6juDMyMmpUv7LkvnVavdaW9q6o933u27fPLtup6zF3FKnGDdQt9tLSUjtGIk1MAImIrIiOjsa5c+dw7Nixe76v+fPnIy4uzvi8uLgYLVu2REREBNRqtU3b0Ov1yMjIwIgRI+Di4lLrWLouPlDrdWtD6SSwtHcFFp1ygq5CUa/7Prc4sk7r2+uY1zepxg3YJ3bDGXY5YwJIRGTBzJkzsWfPHhw9ehQtWrQwlgcEBAAAtFotAgMDjeVarRY9e/Y01rl16xYKCwtNzgJqtVrj+pUplUoolUqzchcXlxr/kavNOnfTlddvEmbcb4Wi3vdtr+SnrsfcUaQaN1C32KXaZnviXcBERHcRQmDmzJnYsWMHDh06hJCQEJPlISEhCAgIQGZmprGsuLgYOTk5CAsLAwD06tULLi4uJnXy8vJw6dIlYx0iIkfiGUAiortER0cjLS0Nu3btgpeXl/GaPW9vb7i5uUGhUCA2NhbLli1D+/btERISgkWLFiEoKAhjx4411p02bRri4uLg6+sLtVqNmJgYhIWF8Q5gImoQmAASEd0lNTUVADB06FCT8k2bNmHy5MkAgDlz5qCkpATTp09HYWEhBg4ciP3790OlUhnrr1q1Ck5OToiKioJOp0NkZCTWrVtXX80gIqoSE0AiorsIUf1QJAqFAomJiUhMTLRaR6VSISUlBSkpKfYMj4jILngNIBEREZHMMAEkIiIikhkmgEREREQywwSQiIiISGaYABIRERHJDBNAIiIiIplhAkhEREQkM0wAiYiIiGSGCSARERGRzDABJCIiIpIZJoBEREREMsMEkIiIiEhmmAASERERyQwTQCIiIiKZYQJIREREJDNMAImIiIhkhgkgERERkcwwASQiIiKSGSaARERERDLDBJCIiIhIZpgAEhEREckME0AiIiIimWECSERERCQzTACJiIiIZIYJIBEREZHMMAEkIiIikhkmgEREREQywwSQiIiISGaYABIRERHJDBNAIiIiIpmRZAJ49OhRjB49GkFBQVAoFNi5c6fJciEE4uPjERgYCDc3N4SHhyM/P9+kTllZGaKjo9GsWTN4enoiKioKWq22HltBRERE5BiSTABLSkrQo0cPpKSkWFyenJyMtWvXYv369cjJyYGHhwciIyNRVlZmrDN79mzs3r0b6enpyMrKwpUrVzBu3Lj6agIRERGRwzRxdAC1MXLkSIwcOdLiMiEEVq9ejYULF2LMmDEAgC1btsDf3x87d+7E+PHjUVRUhI0bNyItLQ3Dhg0DAGzatAmdOnXCiRMn0L9//3prCxEREVF9k2QCWJWCggJoNBqEh4cby7y9vdGvXz9kZ2dj/PjxyM3NhV6vN6kTGhqK4OBgZGdnW00AdToddDqd8XlxcTEAQK/XQ6/XG8sNv99d1pApnYX1ZU7C5KfUSe21qUpjagtg3p7G0i4iooao0SWAGo0GAODv729S7u/vb1ym0Wjg6uoKHx8fq3UsSUpKwpIlS8zKDx48CHd3d7PyjIyMmobvEMl9q6+ztHfFvQ+kHhheE6m8NrZoTG0B/tee0tJSB0dCRNR4NboE8F6aP38+4uLijM+Li4vRsmVLREREQK1WG8v1ej0yMjIwYsQIuLi4OCLUGum6+IDVZUongaW9K7DolBN0FYp6jOreOLNgmKRem6pI7X1WncrtMZxhJyIi+2t0CWBAQAAAQKvVIjAw0Fiu1WrRs2dPY51bt26hsLDQ5CygVqs1rm+JUqmEUqk0K3dxcbH4B9haeUOjK68+sdNVKGyq19AZXg+pvDa2aExtAf7XnsbUJiKihqbRJYAhISEICAhAZmamMeErLi5GTk4OZsyYAQDo1asXXFxckJmZiaioKABAXl4eLl26hLCwMEeFbrPW8/Y6OgTJ6rr4AJL73vlZ14T24vJRdoqKiBylrv2p0lkY+5S81x61U1RE954kE8CbN2/i/PnzxucFBQU4e/YsfH19ERwcjNjYWCxbtgzt27dHSEgIFi1ahKCgIIwdOxbAnZtCpk2bhri4OPj6+kKtViMmJgZhYWG8A5iIiIgaPUkmgKdOncJDDz1kfG64Lm/SpEnYvHkz5syZg5KSEkyfPh2FhYUYOHAg9u/fD5VKZVxn1apVcHJyQlRUFHQ6HSIjI7Fu3bp6bwsRERFRfZPkQNBDhw6FEMLssXnzZgCAQqFAYmIiNBoNysrK8Omnn6JDhw4m21CpVEhJScH169dRUlKCjz/+uMrr/4hIPjjbEBE1dpJMAImI7iXONkREjZ0kvwImIrqXONsQETV2PANIRFQD1c02BKDa2YaIiByNZwCJasnew/FwWBlpuFezDdk61WRV7DWNXlXTQ94LUp5y8u7YpTR9oZSnXLRH7FJst70xASQiagBqOtVkVeo6PaAt00PeC1KecnJp7wrs27fP0WHUmJSnkqxL7JxqkgkgEVGN3KvZhmydarIq9poesKrpIe8FKU85eXfsufEPOzocm0l5Kkl7xM6pJpkAEhHVyL2abaimU01Wpa5T6Tlq2kcpTzmpq1BILpECpD2VZF1il2qb7YkJIBFRJZxtiGqD1wWTlDABJCKqhLMNEVFjxwSQiKgSw2xD1hhmG0pMTLRaxzDbkLXBpImIHInjABIRERHJDBNAIiIiIplhAkhEREQkM0wAiYiIiGSGCSARERGRzDABJCIiIpIZJoBEREREMsMEkIiIiEhmmAASERERyQwTQCIiIiKZYQJIREREJDNMAImIiIhkhgkgERERkcwwASQiIiKSGSaARERERDLDBJCIiIhIZpgAEhEREckME0AiIiIimWECSERERCQzTRwdABHd0Xre3hrVVzoLJPcFui4+AF25wmz5xeWj7BUaERE1MjwDSERERCQzTACJiIiIZIYJIBEREZHMMAEkIiIikhkmgEREREQywwSQiIiISGY4DAwREVEDVNOhoarCYaGoMp4BJCIiIpIZJoBEREREMsMEkIiIiEhmmAASERERyQxvAiEikjjDzQLVzQ9NRGTAM4BEREREMsMEkIiIiEhm+BVwPbHneE5EREREdcEzgEREREQywzOARI0UZxEgIoPK/UFdbxhinyB9PANIREREJDNMAImIiIhkhgkgERERkczwGkAiIiKqEXuPbMFrCuuf7M8ApqSkoHXr1lCpVOjXrx+++OILR4dERI0I+xgiaohknQD+5z//QVxcHBISEnD69Gn06NEDkZGRuHbtmqNDI6JGgH0METVUsv4K+M0338QLL7yAKVOmAADWr1+PvXv34r333sO8efMcHB0RSR37GCLb1OQr5eqGsOHXybaRbQJ469Yt5ObmYv78+cYyJycnhIeHIzs72+I6Op0OOp3O+LyoqAgAcP36dej1emO5Xq9HaWkpfv/9d7i4uAAAmtwuuRfNuOeaVAiUllagid4J5RXSn1y+MbWnPtvy+++/39PtA+afmxs3bgAAhBD3fN/3Qk37GFv7F0sM/YtU399SjRuQbuxSjRuoPnZb+iup9y/2INsE8LfffkN5eTn8/f1Nyv39/fH9999bXCcpKQlLliwxKw8JCbknMTYUExwdgJ01pvbUV1uav1FPO7Lgxo0b8Pb2dlwAtVTTPsZe/YtU399SjRuQbuxSjRuoOvaa9FdS7V/sQbYJYG3Mnz8fcXFxxucVFRW4fv06mjVrBoXif/+FFBcXo2XLlrh8+TLUarUjQrWbxtQWoHG1pzG1BTBvjxACN27cQFBQkKNDqxe29i9Vkep7QqpxA9KNXapxA/aJXW79iyWyTQCbN28OZ2dnaLVak3KtVouAgACL6yiVSiiVSpMyHx8fq/tQq9WS+2BZ05jaAjSu9jSmtgCm7ZHyf+Y17WNq2r9URarvCanGDUg3dqnGDdQ9din3L/Yg27uAXV1d0atXL2RmZhrLKioqkJmZibCwMAdGRkSNAfsYImrIZHsGEADi4uIwadIk9O7dG3379sXq1atRUlJivGOPiKgu2McQUUMl6wTw6aefxq+//or4+HhoNBr07NkT+/fvN7tou6aUSiUSEhLMvs6RosbUFqBxtacxtQVofO0B7l0fY41Uj6FU4wakG7tU4wakHXtDohByvgeaiIiISIZkew0gERERkVwxASQiIiKSGSaARERERDLDBJCIiIhIZpgA2ujo0aMYPXo0goKCoFAosHPnTpPlQgjEx8cjMDAQbm5uCA8PR35+vkmdsrIyREdHo1mzZvD09ERUVJTZILH1ISkpCX369IGXlxf8/PwwduxY5OXlmdSRUntSU1PRvXt346CgYWFh+OSTT4zLpdSWypYvXw6FQoHY2FhjmZTas3jxYigUCpNHaGiocbmU2tKQVXecGwp79KOOUl3skydPNnsNHn74YccE+//Zq693BFtib4jHXEqYANqopKQEPXr0QEpKisXlycnJWLt2LdavX4+cnBx4eHggMjISZWVlxjqzZ8/G7t27kZ6ejqysLFy5cgXjxo2rryYYZWVlITo6GidOnEBGRgb0ej0iIiJQUlIiyfa0aNECy5cvR25uLk6dOoVhw4ZhzJgx+OabbyTXlrudPHkSb7/9Nrp3725SLrX2dOnSBVevXjU+jh07ZlwmtbY0ZFUd54bCHv2oo1QXOwA8/PDDJq/Bhx9+WI8RmrNXX+8ItsQONLxjLimCagyA2LFjh/F5RUWFCAgIECtXrjSWFRYWCqVSKT788EPjcxcXF5Genm6s89133wkAIjs7u95it+TatWsCgMjKyhJCSL89QgjRtGlT8e6770q2LTdu3BDt27cXGRkZYsiQIWLWrFlCCOm9NgkJCaJHjx4Wl0mtLQ1ZVce5oapNP9pQVI5dCCEmTZokxowZ45B4bFWbvr6hqBy7ENI45g0ZzwDaQUFBATQaDcLDw41l3t7e6NevH7KzswEAubm50Ov1JnVCQ0MRHBxsrOMoRUVFAABfX18A0m5PeXk5tm3bhpKSEoSFhUm2LdHR0Rg1apRJTIA0X5v8/HwEBQWhTZs2mDhxIi5dugRAmm1pyKwdZ6mw5f3Q0B05cgR+fn7o2LEjZsyYgd9//93RIZmoTV/fUFSO3aChH/OGTNYzgdiLRqMBALPR/f39/Y3LNBoNXF1dzSZ3v7uOI1RUVCA2NhYDBgxA165dAUizPV9//TXCwsJQVlYGT09P7NixA507d8bnn39ujMtanA2tLdu2bcPp06dx8uRJs2VSe2369euHzZs3o2PHjrh69SqWLFmCQYMG4dy5c5JrS0NW1XH28vJydHg2seX90JA9/PDDGDduHEJCQnDhwgX84x//wMiRI5GdnQ1nZ2dHh1frvr4hsBQ70PCPeUPHBFDmoqOjce7cuQZ5vVBNdOzYEWfPnkVRURG2b9+OSZMmISsry9Fh1djly5cxa9YsZGRkQKVSOTqcOhs5cqTx9+7du6Nfv35o1aoVPvroI3Tq1MmBkTUuVR3nadOmOTAy+Rg/frzx927duqF79+5o27Ytjhw5guHDhzswsjuk3Ndbi72hH/OGjl8B20FAQAAAmN2ZqNVqjcsCAgJw69YtFBYWWq1T32bOnIk9e/bg8OHDaNGihbFciu1xdXVFu3bt0KtXLyQlJaFHjx5Ys2aN5NqSm5uLa9eu4YEHHkCTJk3QpEkTZGVlYe3atWjSpInxP3WptKcyHx8fdOjQAefPn5fcayMldx9nqbDl/SAlbdq0QfPmzRvEa1CXvt7RrMVuSUM65lLABNAOQkJCEBAQgMzMTGNZcXExcnJyEBYWBgDo1asXXFxcTOrk5eXh0qVLxjr1RQiBmTNnYseOHTh06BBCQkJMlkutPZZUVFRAp9NJri3Dhw/H119/jbNnzxofvXv3xsSJE3H27Fm0adNGUu2p7ObNm7hw4QICAwMl99pIyd3HWSpseT9Iyc8//4zff//doa+BPfp6R6kudksawjGXFAffhCIZN27cEGfOnBFnzpwRAMSbb74pzpw5I3766SchhBDLly8XPj4+YteuXeKrr74SY8aMESEhIeLPP/80buOll14SwcHB4tChQ+LUqVMiLCxMhIWF1XtbZsyYIby9vcWRI0fE1atXjY/S0lJjHSm1Z968eSIrK0sUFBSIr776SsybN08oFApx8OBBybXFkrvvAhZCWu15+eWXxZEjR0RBQYE4fvy4CA8PF82bNxfXrl2TXFsasuqOc0Nhj37UUaqK/caNG+KVV14R2dnZoqCgQHz66afigQceEO3btxdlZWUOi9lefb0jVBd7Qz3mUsIE0EaHDx8WAMwekyZNEkLcuZ1+0aJFwt/fXyiVSjF8+HCRl5dnso0///xT/O1vfxNNmzYV7u7u4vHHHxdXr16t97ZYagcAsWnTJmMdKbVn6tSpolWrVsLV1VXcd999Yvjw4cbkTwhptcWSygmglNrz9NNPi8DAQOHq6ir+8pe/iKefflqcP3/euFxKbWnIqjvODYU9+lFHqSr20tJSERERIe677z7h4uIiWrVqJV544QWh0WgcGrO9+npHqC72hnrMpUQhhBD39hwjERERETUkvAaQiIiISGaYABIRERHJDBNAIiIiIplhAkhEREQkM0wAiYiIiGSGCSARERGRzDABJCIiIpIZJoBEREREMsMEkIiIiEhmmAASERERyQwTQCIiIiKZYQJIREREJDNMAImIiIhkhgkgERERkcwwASQiIiKSGSaARERERDLDBJCIiIhIZpgAEhEREckME0AiIiIimWECSERERCQzTACJiIiIZIYJIBEREZHMMAEkIiIikhkmgEREREQywwSQiIiISGaYABIRERHJDBNAIiIiIplhAkhEREQkM0wAiYiIiGSGCSARERGRzDABJCIiIpKZ/weUWWKcPW6dugAAAABJRU5ErkJggg=="
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 0 Axes>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "Now, let's plot each of these features against the Emission, to see how linear their relationship is:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "plt.scatter(cdf.FUELCONSUMPTION_COMB, cdf.CO2EMISSIONS,  color='blue')\nplt.xlabel(\"FUELCONSUMPTION_COMB\")\nplt.ylabel(\"Emission\")\nplt.show()",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<pyolite.display.Image at 0x47c5378>",
            "image/png": 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661//ql27doXoFQGA57zt+cvLk7Zu9X97AESGiA2AhYWFuvHGGzV+/HiX64cOHVJVVZXLdZvNpjFjxmjnzp2SpD179uj06dMuZYYOHao+ffo4y7SloaFBtbW1LjcACJUtWzzv+Zs8mZW+ACJ0EcjmzZv17rvv6u233251X1VVlSQpLS3N5XpaWprzvqqqKsXHx6tHjx7tlmnLkiVLtHjxYh9bDwC+Ky31vOcvK8sMje7uuQYgekVcD+CRI0c0d+5cPf300+rSpUtQn3vRokVyOBzO25EjR4L6/AAgSU8/ba729XTO36pVhD8ApojrAdyzZ48+++wzXXbZZc5rTU1Nev311/XII49o//79kqTq6mqlp6c7y1RXV+uSSy6RJNntdjU2NqqmpsalF7C6ulp2u73d505ISFBCQoJ/XxAAeGD0aOmddzyrY7VKmzZJrHMD0CziegCvu+467du3T3v37nXeLr/8cs2YMUN79+5V//79ZbfbtW3bNmed2tpa7d69W9nZ2ZKkUaNGqXPnzi5l9u/fr8OHDzvLAEC4ueIKz8OfJG3ebM4VBIBmEdcD2L17dw0fPtzlWrdu3ZSamuq8XlRUpAceeECDBg1Sv379dO+99yojI0OTJ0+WZC4KmTlzpoqLi5WSkqLk5GTNmTNH2dnZuvLKK4P9kgDgnOrqpDamPXeouedvypTAtAlA5Iq4AOiOkpISnTx5Unfeeadqamp09dVX66WXXnKZM7hy5UrFxcWpoKBADQ0NysnJ0dq1a0PYagBorbHRPBlhzRrP627eTPgD0DaLYRhGqBsRqWpra2Wz2eRwOJScnBzq5gCIMiUlbZ+Nei5WK+EP6Aif31HaAwgAka6kRFq2zLu6DPsCOJeIWwQCANGusdHs+fPG737Hgg8A50YABIAw0tQk3Xyz58O+knT55dKMGf5vE4DoQwAEgDBRVialpUkvv+x53dGjPV8lDCB2MQcQAMLA009Lt97qWR27XbrySum3v5WSkgLTLkSnpiZpxw6pslJKT5fGjuWUmFhDAASAEPP2dI9PPpHi4wPTJkSvsjJp7lzp6NGvr2VmSqtXc1pMLGEIGABCaMgQ7073KC4m/MFzZWXmCvGW4U+SPv3UvF5WFpp2IfgIgAAQIhaL9PHHntebOlVautT/7UF0a2oye/7a2v23+VpRkXcLkBB5CIAAEAIWi3f1evc25wsCntqxo3XPX0uGIR05YpZD9CMAAkCQ7d3rfd01a5isD+9UVvq3HCIbARAAguzSSz2vk5oq/f73TNKH99LT/VsOkY0ACABBVFLieZ3//E+puprwB9+MHWuu9m1v+oHFImVlmeUQ/dgGBgACrLFRWrvWXPDx+OOe1b38cumBBwLTLsQWq9Xc6mXKFDPstVwM0hwKV61iikGsoAcQAAKopETq2lWaN0967LG2V2C2Z/BgTveAf+XnS1u2mIuJWsrMNK/Tyxw7LIbhyf+O0FJtba1sNpscDoeSk5ND3RwAYaakRFq2zPv6/N8ZgRLrJ4Hw+c0QMAAERGOjtGKF9/UJfwgkq1UaNy7UrUAoMQQMAAHwox95v6FuQ4N/2wIAZ6MHEAD87OmnpQ0bvKu7cCFHvKFtsT5sC/8iAAKAn5w6JV1wgfT5557XtVrN83054g1tKSszj3FreZJHZqa5qpeFG/AGQ8AA4AeTJ5urfT0JfxaLdNdd0sqV0pdfEv7QtrIyc+uWs49x+/RT83pZWWjahcjGKmAfsIoIgGSGvxde8LzewoWEPnSsqUnq27f9M3wtFrMn8NAhhoM9wec3PYAA4JNTp7wLf1OnEv5wbjt2tB/+JHO1+JEjZjnAEwRAAPDBN77heZ3evc2FIkCzujrp29+WRo40/62rM69XVrpX391yQDMWgQCAl+bOlfbu9bzemjUM1+FrV1zheuLLvn1S9+7S6NHu9xKnpwembYheBEAA8MAXX0jf+pb00Ufe7fP3u9+xahNfGz1aeuedtu97+21znmhmprngo60Z+81zAMeODWw7EX0YAgYAN9ntUmqq9MEH3oW/yy+XZszwf7sQmTZubD/8NXvnHelXvzL/22Jxva/561Wr6FGG5wiAAOAGu12qrva+/ujRrsN8iG1lZe7/MVBaKm3ZYs4dbSkz07xOjzK8wRAwAJzDF194H/6SkswJ+klJ/m0TIldTkzl/1F0VFWbIy8vjJBD4DwEQAM7h7J4Xd1mt0vHjHO0GV+fa2uVsAwaY/1qt0rhxAWkSYhABEAA6cOmlUn29d3WLiwl/sayxUVq71uzBGzBAmjXL/HnwdMuW3/42MO1DbCMAAkA7kpOlEye8q3vPPWz0HMtKSqQVK1wXCy1YYP5RcMMN7j/O6NFMH0BgEAABoA02m/fhb9QoafVq/7YHkaOkRFq2rPX1pibz+pkzHW/t0uzyy6W33gpcOxHbWAUMAC00NZln+9bWele/o33dEN2amqQ//1l66KGOy61a9XWZs7d2afb006waR2BFZAB87LHHNHLkSCUnJys5OVnZ2dn605/+5Lz/9ttvl8Vicbnl5ua6PEZ9fb0KCwuVmpqqpKQkFRQUqNqXPR4ARLyyMqlHD+/O9p082ewxpMcmNpWVSX37Sjk5HffqSWZQrKxse2uXrCzp97+XvvvdgDUVkBShQ8CZmZl68MEHNWjQIBmGoSeffFJ5eXl67733dNFFF0mScnNztX79emedhIQEl8eYN2+e/vjHP6q0tFQ2m02zZ89Wfn6+3nzzzaC+FgDhoaxMmjLl3B/ebTlxgnlascybn52KCqmoiK1dEDoRGQAnTZrk8vUvfvELPfbYY9q1a5czACYkJMhut7dZ3+FwaN26ddq4caOuvfZaSdL69es1bNgw7dq1S1deeWVgXwCAsNLUJM2e7V34Y5J+bGve08/Tnx22dkGoRWQAbKmpqUmlpaU6efKksrOznde3b9+uXr16qWfPnrr22mv1wAMPKDU1VZK0Z88enT59WuPHj3eWHzp0qPr06aOdO3e2GwAbGhrU0NDg/LrW20lCAMKKzSadPOl5vUsuYcg3FrXc3sUwPNvTTzJD36xZgWkb4K6IDYD79u1Tdna26uvrlZSUpOeee04XXnihJHP4Nz8/X/369VNFRYV+8pOfaOLEidq5c6esVquqqqoUHx+vHj16uDxmWlqaqqqq2n3OJUuWaPHixYF8WQCCrEcP78Jf9+7Se+/5vTkIc21t7+Ip9odEOIjYADhkyBDt3btXDodDW7Zs0W233aby8nJdeOGFmjZtmrPciBEjNHLkSA0YMEDbt2/Xdddd5/VzLlq0SMXFxc6va2trlZWV5dPrABA6s2ZJDofn9ZKTvauHyDZnjvTII97Xt1rN8Mf+kAgHEbkKWJLi4+M1cOBAjRo1SkuWLNHFF1+s1e1svNW/f3+dd955OnjwoCTJbrersbFRNTU1LuWqq6vbnTcomfMKm1ceN98ARKaFC6XHHvO8nrehEZGpsdHctiUx0fvwl5Rkbvvy5ZeEP4SPiA2AZztz5ozL/LyWjh49quPHjys9PV2SNGrUKHXu3Fnbtm1zltm/f78OHz7sMo8QQHR65plz79V2trg4MzQ++mhg2oTwU1Iide0qzZvn3XGAFot5e/JJaf58hn0RXiJyCHjRokWaOHGi+vTpoxMnTmjjxo3avn27Xn75ZdXV1Wnx4sUqKCiQ3W5XRUWFSkpKNHDgQOXk5EiSbDabZs6cqeLiYqWkpCg5OVlz5sxRdnY2K4CBKPb559JFF5n/eqJbN+mLL/gAjyXtnebhicxMs/cwP98vTQL8KiID4Geffabvf//7qqyslM1m08iRI/Xyyy/r+uuv16lTp/T+++/rySefVE1NjTIyMjRhwgT9/Oc/d9kLcOXKlYqLi1NBQYEaGhqUk5OjtWvXhvBVAQikHj28H7o9dIjwF0saG82FHt4oLJSuuoo9/RD+LIbhzc5XkMxFIDabTQ6Hg/mAQBhLTvb+XF+bTTprujCi3KpV5rCvp6xWc54ffyyEPz6/I7QHEADc1amT91t2EP5iU0WFd/XY3gWRJGoWgQDA2eLjvQ9/d99N+ItVzad0eGL2bFb4IrIQAAFEpZEjpdOnvau7YIF50gNi06xZns3d69VLevjhwLUHCAQCIICoM2qUtG+fd3U3b/Z99SfCX/P+fnPmmP82Nn59X3y8OZzrjrQ0qbo6EC0EAosACCCq3HST9O67ntez26Xf/16aOtX/bUJ4abm/3yOPmP927Wpeb7Z0qbnvY1s9gV26SMOHS8ePSx2cHgqENVYB+4BVREB4ycuT/vAHz+t162ZuEcOWHdGpqUnasUOqrJReeMHcCLw9Cxe6zuVrbDSnA1RUmHMDZ81ioUc04PObAOgTfoCA8LFpk/Td73per3t3qbbW/+1BeCgrk+bOlY4eda88W7nEBj6/GQIGEAW8DX9WK+EvmpWVSVOmuB/+JLO3kAVAiAXsAwggonk77Nu5s+vEf0SXpiaz58+bMS5v9wEEIgk9gAAi1k03eRf+Rowg/EW7HTs86/lryZt9AIFIQwAEEJEmTZL++EfP6112mfT++/5vD8JLZaV39axWc6EHEO0YAgZiXMsVkpFwgH1dnTRwoHd7r914o/Tii/5vE8JPerp39TjODbEi6AGwpqZGb731lj777DOdOXPG5b7vf//7wW4OENPaWiGZmSmtXi3l54euXe254grp7be9q3vzzeYWIIgNY8eaP8uffurePECr1Qx/HOeGWBHUbWC2bt2qGTNmqK6uTsnJybJYLF83xGLRF198Eaym+AXLyBHJmldInv1/gOZfyy1bwisEXn65tGePd3U3bpSmT/dvexA67u7N1/wzLrn+nDf/jN9+u7kHJPv7xR4+v4McAAcPHqwbbrhBv/zlL9W1a9dgPW3A8AOESNXUJPXt2/4keYvF7D05dCg8hoNvvFH6f//Pu7qEv+hSUiKtWGH+DDfrqPeurV7urCzz+Ldw+gMHwcXnd5ADYLdu3bRv3z71798/WE8ZUPwAIVJt3y5dc825y732mjRuXKBb07GJE6WXXvKuLsO+0WXBAmn58vbvP/sUj2aRNs8Vgcfnd5DnAObk5Oidd96JmgAIRCp3V0h6u5LSX9LSpM8+867ujTcS/qLJM890HP4ks2fwgQdaD+VaraH/QwYIN0ENgDfeeKMWLlyoDz/8UCNGjFDnzp1d7r/55puD2RwgZrm7QtLblZT+YLd7H/5uuknautW/7UFoNDVJv/iFdP/97pVdu1YqKgp4s4CIF9Qh4Li49rcdtFgsamo5qSMC0IWMSNU8B7C9FZKhngP4xRdSaqp3dfPypOef92tzECKenuMrSbNnSw8/HLg2ITrw+R3kHsCzt30BEBpWq7nVy5QpZthra4XkqlWhCX8Oh3fh7/zzpU8+kRIT/d8mBE/zCt8//1n60588r88pHoB7OAkEiFH5+eZWL717u17PzAzdFjADB0o9enheb9Qoc7iY8BfZSkqkrl2lefO8C3+c4gG4L+gBsLy8XJMmTdLAgQM1cOBA3XzzzdqxY0ewmwFAZsj75z/N1b4bN5r/HjoUmvA3YIC5r5unbrhBeucd/7cHwVVSIi1b5rq9i6c4xQNwX1AD4O9+9zuNHz9eXbt21T333KN77rlHiYmJuu6667Rx48ZgNgXAvzWvkJw+3fw3FMO+99wj/c//eF4vN9e784ARXhobzRW8vpg/n1M8AE8EdRHIsGHDdOedd2revHku11esWKFf//rX+uijj4LVFL9gEingu+aeH0/16uXdecAIP6tWmcO+3tq8WZo61W/NQQzg8zvIPYD/8z//o0mTJrW6fvPNN+vQoUPBbAqAMNDYKD30kHd1CX/Rw5uhf8mcr/r73xP+AG8ENQBmZWVp27Ztra6/+uqrysrKCmZTAISBjIy2t6E5l/fe839bEDrerNxdvNicv8pxboB3groNzPz583XPPfdo7969+sY3viFJevPNN7VhwwatXr06mE0BEEKHD5v7EHo7AeWSS/zZGoTarFnmMW/uLADhHF/AP4IaAO+++27Z7XYtX75czz77rCRzXuAzzzyjvLy8YDYFQAhUVfl+ukjwZi0jWOLjzRW8Hc0Fvekmc6EH5/gC/hHURSDRhkmkgPu6dZO+/NL7+u+9R89ftCspMVcDt+wJtFrNcMgKX/gTn98EQJ/wAwS4JzFRqq/3ru6cOdKaNf5tD8JX80kgFRXm3MBZs9jbD/7H53cQAmBKSoo+/vhjnXfeeerZs6cszedMteGLL74IZFP8jh8g4Nz69jWPaPNGRoZ5XjEA+BOf30GYA7hy5Up1797d+d8dBUAA0WXgQO/DnyR9+KH/2gIA+BpDwD7gLwigfQ6Hd+f6NhswQDp40G/NAQAnPr+DvA/gu+++q3379jm/fuGFFzR58mT95Cc/UWNjo9uP89hjj2nkyJFKTk5WcnKysrOz9acWJ4cbhqH77rtP6enpSkxM1Pjx43XgwAGXx6ivr1dhYaFSU1OVlJSkgoICVbOzLOA3PXt6X5fwBwCBFdQA+KMf/Ugff/yxJPNUkKlTp6pr164qLS1VSUmJ24+TmZmpBx98UHv27NE777yja6+9Vnl5efr73/8uSVq6dKnWrFmjxx9/XLt371a3bt2Uk5Oj+haz0OfNm6etW7eqtLRU5eXlOnbsmPLZWArwi65dvd+upaaG8AcAAWcEUXJysnHw4EHDMAzjwQcfNCZMmGAYhmG88cYbRmZmpk+P3bNnT+O///u/jTNnzhh2u91YtmyZ876amhojISHB2LRpk/Przp07G6Wlpc4yH330kSHJ2Llzp9vP6XA4DEmGw+Hwqe1AtDhwwDDM6Of5rVOnULceQKzg89swgtoDaBiGzpw5I8k8/u2GG26QZB4R97//+79ePWZTU5M2b96skydPKjs7W4cOHVJVVZXGjx/vLGOz2TRmzBjt3LlTkrRnzx6dPn3apczQoUPVp08fZxkAnomLkwYN8q7uJ59Ip0/7tz0AgPYF9SSQyy+/XA888IDGjx+v8vJyPfbYY5KkQ4cOKS0tzaPH2rdvn7Kzs1VfX6+kpCQ999xzuvDCC/XXv/5Vklo9XlpamqqqqiRJVVVVio+PV4+zZqi3LNOWhoYGNTQ0OL+ura31qM1AtIqL827I94ILzPNcAQDBFdQewFWrVundd9/V7Nmz9Z//+Z8aOHCgJGnLli3Os4HdNWTIEO3du1e7d+/W3Xffrdtuu00fBnjPiCVLlshmszlvWVlZAX0+QDJPRdi+Xdq0yfzXnfNSg+ngQe/CX1wc4S8SVFVJdrvUpYv5bwd/IwOIIEHtARw5cqTLKuBmy5Ytk9XDwx3j4+OdAXLUqFF6++23tXr1av34xz+WJFVXVyu9xaGj1dXVuuTf50jZ7XY1NjaqpqbGpRewurpadru93edctGiRiouLnV/X1tYSAhFQZWXS3LnS0aNfX8vMlFavlsJlzZK3w75s8Bz+zj6+r7raPMu5a1fp5MnQtQuA74LaA3jkyBEdbfFJ9tZbb6moqEhPPfWUOnfu7NNjnzlzRg0NDerXr5/sdru2bdvmvK+2tla7d+9Wdna2JDMwdu7c2aXM/v37dfjwYWeZtiQkJDi3nmm+AYFSViZNmeIa/iQzOE2ZYt4fat7u6961q9mbhPDV0dnNX35p3g8gcgW1B/C73/2u7rzzTn3ve99TVVWVrr/+el100UV6+umnVVVVpfvuu8+tx1m0aJEmTpyoPn366MSJE9q4caO2b9+ul19+WRaLRUVFRXrggQc0aNAg9evXT/fee68yMjI0efJkSeaikJkzZ6q4uFgpKSlKTk7WnDlzlJ2drSuvvDKA3wHAPU1NZs9fW0OrhmEGr6IiKS9P8rDz3GcHD0oXXuj9oo3ERHqPwlFTk7Rjh1RZKSUktB/+mn355dfDwwAiT1AD4AcffKArrrhCkvTss89q+PDhevPNN/XnP/9Zd911l9sB8LPPPtP3v/99VVZWymazaeTIkXr55Zd1/fXXS5JKSkp08uRJ3XnnnaqpqdHVV1+tl156SV26dHE+xsqVKxUXF6eCggI1NDQoJydHa9eu9f+LBrywY0frnr+WDEM6csQsN25c0Jrl9WKPZhdfLO3d67fmwE/ammrgjksuYU4gEKmCehRcUlKSPvjgA/Xt21c333yzrrrqKv34xz/W4cOHNWTIEJ06dSpYTfELjpJBoGzaJH33u+cut3GjNH164NsjeT/c22z0aOmtt/zTFvjPs89KU6d6VzchQWqxvz4QMfj8DvIcwIsuukiPP/64duzYoVdeeUW5ubmSpGPHjik1NTWYTQHCWov1S34p56s+fXyrf+IE4S8cLVjgffiTfDvrGUBoBTUA/upXv9ITTzyhcePGafr06br44oslSX/4wx+cQ8NArGq53UtTk9S7d/u9bhaLlJUljR0b+HYNHGgON3vrvfekpCT/tQf+UVIiLV/u22MwnA9ErqAOAUvmyR21tbXq2eKk+H/+85/q2rWrevXqFcym+IwuZPhLW3OwUlOl48fNsNfyt7Q5FG7ZEvitYBwO33t5gvt/GHTk1Clp4UJp/37p1Vd9eyy2gkEk4/M7yItAJMlqtbqEP0nq27dvsJsBhI3m7V7ODkpffGH+m5JiBsFmmZnSqlXB2QfwrF9VjxH+wsfkydILL/jnsQh/QOQLeAC87LLLtG3bNvXs2VOXXnqpLB3MJH/33XcD3RwgrLiz3Utiotlb89ln5py/sWODs/VL586+BTjCX/jwJfxlZEhfffV1b/DevWz9AkSDgAfAvLw8JSQkSJJzHz4g1jU2SmvXSuXl597u5ehRM/AFa7Xvp5+avYy+IPyFj1OnfOv5e/jh8Dl1BoD/BDwA3n///W3+NxCrSkqkFSs8O9O3sjJw7WkpIcEMp97avVtiPVfoNf+BUVFhLsLx1jPPEP6AaBX0OYDN6urqdObMGZdrsToRE7GjpERatszzesHY7sXb8JeVJR0+7P/2wDve/IHRlvnzpVtu8U+bAISfoG4Dc+jQId14443q1q2bbDabevbsqZ49e6pHjx6tFoYA0aax0fxg9kSwtnv59FPvwl9cHOEvnCxYYP6B4Uv4s1rNlcIPPeS/dgEIP0HtAbz11ltlGIZ+85vfKC0trcMFIUC0+dGPPPtgbv71WLUq8Is+vJ3z9+mn/m0HvHPqlLnQ489/9v4x7rxTGjZMmjVLio/3W9MAhKmgBsC//e1v2rNnj4YMGRLMpwVCrqxM2rDBszrB2u4lMdG7el27sho0HPhje5e8POmJJ/zSHAARIqhDwKNHj9YRX44UACJQU5N0zz3ul7/1Vum116RDhwIf/jp39u4s106d2AcuHPgr/D3/vD9aAyCSBLUH8L//+79111136dNPP9Xw4cPVuXNnl/tHjhwZzOYAQfGLX7g/VGq1SuvWBX4IztetXk6f9l9b4B1vt3e56iqpWzdp0CBzvqC3PcAAIltQA+Dnn3+uiooK/eAHP3Bes1gsMgxDFotFTb4uWwPCTFmZ5MnuR8XFgQ9/vm71wh5/odFya5cBA6R//MPzx7Bapb/8hTl+AIIcAO+44w5deuml2rRpE4tAEPWaT/lw19Sp0tKlgWuPZA7devt31vvvSyNG+Lc9cI+/tnYJxh8YACJDUAPgJ598oj/84Q8aOHBgMJ8WCIkdOzo+5aOl3r2lp58ObHt8+XurspIFH6Eyf77n2we19ziB/gMDQOQIagC89tpr9be//Y0AiJjgyekda9YEdqsXX8Ifq31Do65OuuQSc8jXV888w6bOAFwFNQBOmjRJ8+bN0759+zRixIhWi0BuvvnmYDYHCCh3T+9YvDiwq327dfO+bpcurPYNhSuukN5+2/fH6dLF7FnmODcAZ7MYRvCmdMfFtb/rTCQuAqmtrZXNZpPD4eAYO0gy52jt2GH2/vXqJd1+u7nitr3fssxM6Z//DFzvX/fuZk+SNzp1YrVvKIweLb3zju+Pc9VVUnl54DcRByIRn99B7gE8++xfIJqUlZmLPlrO+0tNNcOfxeIaApuHZFevDtwHtNUqefsrFx8vNTT4tz04t40bvQt/Dz4oHTkiHTjA9i4A3BOUjaBvuOEGORwO59cPPvigampqnF8fP35cF154YTCaAgREWZk0ZUrrRR9ffGH+m5Liej0zU9qyJXBDcxaL9+Hv6FHCXzDV1Unf/rZ0wQXSjBme17dapXnzpEcekV5+2fyX8AfgXILSA/jyyy+rocUnyi9/+Uvdcsst6tGjhyTpq6++0v79+4PRFMDvmrd7aWuYt7n3LzFRevVV6bPPzLmBY8cGrufP2wUfVqv01Vf+bQs65o+5fmztAsAbQQmAZ08zDOK0QyDgzrXdi2GY91ut0vTpgW2LL6t9CX/B5a/wx9YuALwR1LOAgWjk7nYvnmwL4w1fwh9/kwVXXZ1/wt/y5f5pD4DYE5QAaLFYWp36wSkgiBbubvfibjlvePvr1LUr4S8Uvvc93+ovXEj4A+CboA0B33777UpISJAk1dfX66677lK3f29Q1sCMc0SwsWPNRR3tbfdisZj3jx0bmOf3NvwlJUknTvi3LWjbqVNmaGtepfvxx949TmamuTE0c/4A+Coo+wD+4Ac/cKvc+vXrA9wS/2IfITRrXgUstb3dSyBW/O7ZI11+uXd14+J8P1cW7pk8WXrhBd8f5+mnpe9+1/fHAcDntxTkjaCjDT9AaKmtfQCzsqRVq/wf/nydQcFvfXD4I/wF6mcIiGV8fgd5I2ggmuXnS3l5X58EEqjtXgh/4a+pydz2x5fw17+/tG5dYLcMAhC7CICAH1mt0rhxgXt8wl94a2yUfvQjqbTUtzOUR4+W3nrLf+0CgLMRAIEIQfgLbyUl5spcT09gueYayWYzF3cMGCD99rfmAh0ACCQCIBABCH/hraTEPH/XGxdeaB7fBgDBRAAEwhzhL3w1NkqrV0sPPeT9Y3gbHAHAFxF5EsiSJUs0evRode/eXb169dLkyZNbnSV8++23Ozegbr7l5ua6lKmvr1dhYaFSU1OVlJSkgoICVVdXB/OlAB0i/IWfU6ek2bOlvn2lhASz98/b73NennlONAAEW0QGwPLychUWFmrXrl165ZVXdPr0aU2YMEEnz5p1nZubq8rKSudt06ZNLvfPmzdPW7duVWlpqcrLy3Xs2DHls9cCwoQv4e/99wl/gTB5snl6yqOPSp984ttj5eVJzz/vj1YBgOcicgj4pZdecvl6w4YN6tWrl/bs2aNvfvObzusJCQmy2+1tPobD4dC6deu0ceNGXXvttZLMjaiHDRumXbt26corrwzcCwDO4cMPva9L8AsMf+zpl5Ag3XGHuViEnj8AoRSRPYBnczgckqSUlBSX69u3b1evXr00ZMgQ3X333Tp+/Ljzvj179uj06dMaP36889rQoUPVp08f7dy5MzgNR1hpapK2b5c2bTL/DdVJGfv3Sxdd5F1dwl9gnDrln9M8Nm6U1q4l/AEIvYjsAWzpzJkzKioq0lVXXaXhw4c7r+fm5io/P1/9+vVTRUWFfvKTn2jixInauXOnrFarqqqqFB8frx49erg8Xlpamqqqqtp8roaGBpdzi2trawPymhB8bZ3ikZlpTvAP5qwAX4Z9CX+Bs3Chb/WTkqQnn+Q0DwDhI+IDYGFhoT744AO98cYbLtenTZvm/O8RI0Zo5MiRGjBggLZv367rrrvOq+dasmSJFi9e7FN7EX6az/E9O0B9+ql5PRDn+LaF8Be+Dhzwrp7FIt1yi3mOL6d5AAgnET0EPHv2bL344ot67bXXlJmZ2WHZ/v3767zzztPBgwclSXa7XY2NjaqpqXEpV11d3e68wUWLFsnhcDhvR44c8cvrQOg0NZk9f20FqOZrRUWBHw4m/IW3QYM8r7N0qVRfL23eTPgDEH4iMgAahqHZs2frueee01/+8hf169fvnHWOHj2q48ePKz09XZI0atQode7cWdu2bXOW2b9/vw4fPqzs7Ow2HyMhIUHJyckuN0S2HTtch33PZhjSkSNmuUDYv5/wFwk83atv4ULzFh8fmPYAgK8icgi4sLBQGzdu1AsvvKDu3bs75+zZbDYlJiaqrq5OixcvVkFBgex2uyoqKlRSUqKBAwcqJyfHWXbmzJkqLi5WSkqKkpOTNWfOHGVnZ7MCOIZUVvq3nCfY4y9yJCaa27acayGI1SoVF5u9fwAQziKyB/Cxxx6Tw+HQuHHjlJ6e7rw988wzkiSr1ar3339fN998swYPHqyZM2dq1KhR2rFjhxISEpyPs3LlSt10000qKCjQN7/5TdntdpWVlYXqZSEE/t0h7Ldy7vI1/AUikKJjzz9vhsC29OsnrVwpffkl4Q9AZLAYBv0I3qqtrZXNZpPD4WA4OEI1NZknOnz6ads9ahaLuRr40CH/zePav18aOtT7+l27SmfteY4gOnXKHN49cMCcG7hsGdu6AJGGz+8IHQIG/MVqNbd6mTLFDHstQ2BzL92qVYS/aNHYaO7DV1EhDRggzZrl+Ty9xETpkUcC0z4ACJaIHAIG/Ck/39zqpXdv1+uZmf7dAsZi8S38VVYS/nxRUmIG6HnzzAA3b575dUlJqFsGAMFHDyAgM+Tl5ZmrfSsrzTl/Y8f6r+ePBR+hVVLS9krepqavrzN3D0AsYQ6gD5hDAHcQ/kKrsdHs6etoL0er1VzAwbYtQGzg85shYCCgfAl/f/874c8f1q4990beTU1mOQCIFQwBAwGyZ4/3dQl+/lNR4d9yABAN6AEEAuTyy72r98kn/m1HrBswwL/lACAaMAfQB8whCF/+2O7DF94O/XbqJJ0+7d+2xILm93v/fmnfPnOrliFDzAUeVitzAAG44vObAOgTfoDCU0mJtGKF6wd+sI7o8mWfP8Kfd9p6v1vKy5MGD+74PN+FC1kFDMQSPr8ZAkaUWbjQ/KA/Oww0b/cRyD3ffNnn75NPCH/eaN7epaPevRdekD7+2PzZOHtbH6uV8AcgNtED6AP+gggvzzwjTZvWcZlADfX5stqX30DvuLO9S0tffmm+/6GcGgAgPPD5zSpgRImysnOHP+nr7T6Kivz33Pv3e1+X8Oc9d7Z3aWnhQvMEEH++9wAQqQiAiHinTkl33OF+eX9v9+HtsO/f/+7fdkS7pibXk1oOHPCsvqflASCaEQAR0UpKpOXLpTNn3K/jr+0+qqrMIOKtCy/0TztiQVmZNHeudPTo19dsNs8eY9Ag/7YJACIZcwB9wByC0GrvfNeO+GMO4N690qWXel9fYujXE88+K02d2vq6xeLZ9/HLL83tYQCAz296ABGhGhvNrT88VVzsW/jz9VxfifDniQULzB7etnjyfczLI/wBQEtsA4OI5OkCAMkME75s90H4C67m4X13xHXwf7K8POn55/3SJACIGvQAIiJ5upBj8+a2hxHdtXev93Ul6Z13pFGjfHuMWOJpD++GDdLx422fBELPHwC0RgBERHJ3IUePHtK6dVJ+vm/P58ucv+3bCX+e8rSHNytL+t73AtceAIg2DAEjIs2a1fpUh7PFxUnHjvke/nzdKPhb3/KtfizypIc3K0saOzZwbQGAaEQARESKjzcXdHRk/nzfh/8sFt+OaGPOn3c82apn1apz/zEAAHBFAETEWro0sOe7+rLoY/t2wp8v3Onhlczj/3zt4QWAWMQ+gD5gH6HgOPsEiLFjXcNBY6N/z3c9dEjq39+7ul27SidPev/c+Nq59nmcP1966KHgtQdA9ODzm0UgCHNtnQCRmSmtXv11z098vP/Od7VaPTtVpKXKSslu90878HUP7ooVrgtCrFZz+N/XHl4AiGX0APqAvyACq6xMmjKl9VBq89Dsli3+Hf4j/IUnf/fwAgCf3wRAn/ADFDhNTVLfvq49fy1ZLGZP4KFD/lkAwLAvAMQOPr9ZBIIwtWNH++FPMnsFjxwxy/mDt+Gvc2fCHwAg8hAAEZYqK/1brj179/q22rex0bfnBwAgFFgEgrCUnu7fcm3x9WxfJk8AACIVPYAIS2PHmnP82gtpFotvJ0AQ/gAAsYwAiLBktZpbvUitw1rz196eAGGzed+u+HjCHwAg8hEAEbby882tXnr3dr2emen9FjDnnSfV1nrXnqNHpYYG7+oCABBOmAOIsJafL+XldXwSiLt8GfbdsaN1EAUAIFIRABH2rFZp3DjfHsPXOX9XX+1bfQAAwklEDgEvWbJEo0ePVvfu3dWrVy9NnjxZ+/fvdyljGIbuu+8+paenKzExUePHj9eBAwdcytTX16uwsFCpqalKSkpSQUGBqqurg/lSEAQs+AAAwFVEBsDy8nIVFhZq165deuWVV3T69GlNmDBBJ1vsyLt06VKtWbNGjz/+uHbv3q1u3bopJydH9fX1zjLz5s3T1q1bVVpaqvLych07dkz5/jxbDCHn67Av4Q8AEI2i4ii4zz//XL169VJ5ebm++c1vyjAMZWRkaP78+VqwYIEkyeFwKC0tTRs2bNC0adPkcDh0/vnna+PGjZoyZYok6R//+IeGDRumnTt36sorrzzn83KUjOeCea6rL+Ev8n8rAADt4fM7QnsAz+ZwOCRJKSkpkqRDhw6pqqpK48ePd5ax2WwaM2aMdu7cKUnas2ePTp8+7VJm6NCh6tOnj7PM2RoaGlRbW+tyg/tKSsxzc+fNkx55xPy3a1fzur95G/6Skwl/TU3S9u3Spk3mv01NoW4RAMDfIj4AnjlzRkVFRbrqqqs0fPhwSVJVVZUkKS0tzaVsWlqa876qqirFx8erR48e7ZY525IlS2Sz2Zy3rKwsP7+a6DV/vrRsWesw0dRkXvdnCPQ2/KWmSv/+WyJmlZVJfftK11wjffe75r99+5rXAQDRI+IDYGFhoT744ANt3rw54M+1aNEiORwO5+3IkSMBf85oMH++tGJFx2VWrPDPubq+DPv+7//6/vyRrKxMmjLF3O+wpU8/Na8TAgEgekR0AJw9e7ZefPFFvfbaa8rMzHRet9vtktRqRW91dbXzPrvdrsbGRtXU1LRb5mwJCQlKTk52uaFj7oQ/yewJXLvWt+dizp/3mpqkuXPb/j40XysqYjgYAKJFRAZAwzA0e/ZsPffcc/rLX/6ifv36udzfr18/2e12bdu2zXmttrZWu3fvVnZ2tiRp1KhR6ty5s0uZ/fv36/Dhw84y8I274a9ZRYV3z7NnD+HPVzt2tO75a8kwpCNHzHIAgMgXkRtBFxYWauPGjXrhhRfUvXt355w9m82mxMREWSwWFRUV6YEHHtCgQYPUr18/3XvvvcrIyNDkyZOdZWfOnKni4mKlpKQoOTlZc+bMUXZ2tlsrgNGxkhLPwp9krgr2FHv8+UdlpX/LAQDCW0QGwMcee0ySNO6s4yHWr1+v22+/XZJUUlKikydP6s4771RNTY2uvvpqvfTSS+rSpYuz/MqVKxUXF6eCggI1NDQoJydHa30dh4QaGz0Pf1aruSWMJwh//pOe7t9yAIDwFhX7AIYK+wi1bdUqc4sXTyxcKC1d6n55wp9/NTWZq30//bTt743FImVmSocOeXcOMwCEEz6/I3QOIMLXqVPSunWe1Sku9iz8xfn4U0v4a81qlVavNv/77HDd/PWqVYQ/AIgWBED4zeTJ5sbOH3zgfp3iYmn5cvfLJyb6FuAIf+3Lz5e2bJF693a9nplpXueURACIHhE5BxDhpalJ+ta3pDff9Kyep+EvIcG3vQIJf+eWny/l5ZmrfSsrzTl/Y8fS8wcA0YYACJ+UlUn33GPOHfOEp+HPlzl/nTpJp097Xz/WWK3SWeurAABRhgAIrzWfHOFJz5rV6vmcP1/C3yefSH36eF8fAIBoRACEV5qazJ4/T8Lf4MHSvn1SfLz7ddjgGQAA/2MRCLwyY4bnw77XX+9Z+OvWzbPHb4nwBwBA+wiA8FhJifTMM57XW7bM/bJVVdKXX3r+HBYL4Q8AgHMhAMIj3pzyIZkrSxMT3St78KB3J0506SKdOeN5PQAAYg1zAOGRtWvN+X+eyMuTnn/evbJxcd714MXHm5tQAwCAc6MHEB6pqHC/bG6uOYzrbvjzZfi2ocG7egAAxCICIDwyYIB75X7wA+lPf3J/2JfVvgAABA8BEB6ZNevcp0LExUmPP+7e4zU1mRs1e4vwBwCA5wiA8Eh8vLmRc0fmz3dvu5eyMik11fM5hc0IfwAAeIdFIPBY8ykeK1a4hjdPTvkoK5MKCrxvw1//6n1dAABincUw6EfxVm1trWw2mxwOh5KTk0PdnKBrbDRXBVdUmHMDZ81yr+evqcmcG+jL+bzR+FPb1CTt2CFVVprb4Iwde+7hdgCA52L981uiBxA+iI+Xioo8q1NVJWVmej/sK0Vn+Csrk+bOlY4e/fpaZqa0erWUnx+6dgEAohNzABE03bqZPVvehr+//jV6w9+UKa7hTzKP2psyxbwfAAB/IgAiKLp29e5ot2aGIWVn+6894aKpyez5ayvYNl8rKvKtxxQAgLMxBIyA69PH+1M6OnXyba5guDl73uRFF7Xu+WvJMKQjR8y5gePGBa2ZAIAoRwBEQA0caAYYb/zjH9KQIf5tTyiVlLReOe3uBtiVlYFpEwAgNhEAETAOh2dHx7WUlxd94W/ZstbX3Z3TmJ7u3/YAAGIbARAB07u3d/Vuusn984PDXVOT9Je/SA895F19i8VcDTx2rH/bBQCIbSwCQUBcdpl08qTn9W66Sdq61f/tCYWyMqlvX2nCBO9WLzcPD69axX6AAAD/ogcQfuVwSOedJ331led18/Kip+eveWsXT4JfUpJUV/f115mZZvhjH0AAgL8RAOE3Awd6N+fPapVOnDBPB4kGHW3t0pHFi82eU04CAQAEGgEQfuFt+MvKkg4f9n97QmnHjo63dmmL1SrNnu3eUXoAAPiKOYDw2eefexf+unaNvvAnebdlS3Ex4Q8AEDwEQPikpETq1cu7utXV/m1LuPBkyxarVVq4UFq6NHDtAQDgbARAeK29ve3cMXq0ueghGo0day7g6GiT56Qkafly83g8wh8AINgIgPBYY6MZ/Lzd2+7SS6W33vJvm8KJ1SqtXm3+99kh0GIxb08+ybAvACB0CIDwSEmJOXevpMS7ve369pXefdfvzQo7+fnSli2tN8POzDSvs7ULACCUWAUMt/ky5CtJAwZIBw/6rz3hLj/f3Ntwxw62dgEAhJeI7AF8/fXXNWnSJGVkZMhisej5s3YPvv3222WxWFxuubm5LmXq6+tVWFio1NRUJSUlqaCgQNXRuirBDxobpRUrvKubkCDV1MRW+GtmtUrjxknTp5v/Ev4AAOEgIgPgyZMndfHFF+vRRx9tt0xubq4qKyudt02bNrncP2/ePG3dulWlpaUqLy/XsWPHlM+4XLvWrjU3OPbUZ59J9fWSzeb/NgEAAO9E5BDwxIkTNXHixA7LJCQkyG63t3mfw+HQunXrtHHjRl177bWSpPXr12vYsGHatWuXrrzySr+3OZKdOiWtW+d5vYULpfPP9397AACAbyKyB9Ad27dvV69evTRkyBDdfffdOn78uPO+PXv26PTp0xo/frzz2tChQ9WnTx/t3Lmz3cdsaGhQbW2tyy3aTZ5sLvr44AP367C3HQAA4S0qA2Bubq6eeuopbdu2Tb/61a9UXl6uiRMnqunfY5hVVVWKj49Xjx49XOqlpaWpqqqq3cddsmSJbDab85aVlRXIlxFykyZJL7zgfnmLxQx97G0HAEB4i8gh4HOZNm2a879HjBihkSNHasCAAdq+fbuuu+46rx930aJFKi4udn5dW1sblSGwsVG65BLpo488q7dggdnzBwAAwltU9gCerX///jrvvPN08N/LUO12uxobG1VTU+NSrrq6ut15g5I5rzA5OdnlFm1KSsxVu56EP4Z8AQCILDERAI8eParjx48r/d+HtI4aNUqdO3fWtm3bnGX279+vw4cPKzs7O1TNDDlv9vkbPJghXwAAIk1EDgHX1dU5e/Mk6dChQ9q7d69SUlKUkpKixYsXq6CgQHa7XRUVFSopKdHAgQOVk5MjSbLZbJo5c6aKi4uVkpKi5ORkzZkzR9nZ2TG7Atjbff6uv57jzAAAiDQRGQDfeecdXXPNNc6vm+fl3XbbbXrsscf0/vvv68knn1RNTY0yMjI0YcIE/fznP1dCQoKzzsqVKxUXF6eCggI1NDQoJydHa9euDfprCRfe7vPny8kgAAAgNCyG4c2JrpDMRSA2m00OhyNi5wPW1Unf+570+uvSF194VjcvTzrrEBYAAMJeNHx++yoiewDhH1dcIb39tnd1b7qJ8AcAQKQiAMao0aOld97xru5NN0lbt/q3PQAAIHhiYhUwXD39tPfhLy+P8AcAQKSjBzDGlJVJt97qeb1LLpH++lcpMdHvTQIAAEFGD2AMaWqS5s51v3xKirRypdTQIL33HuEPAIBoQQ9gjGhqkh5+WDp61P063/ymVFQUsCYBAIAQIQDGgLIys+fPk/AnSb/9bWDaAwAAQosAGOW2bJG+8x3P640eLSUl+b89AAAg9JgDGMVKS6Vp0zyvd/nl0ltv+b89AAAgPBAAo9Qzz0i33OL58W6/+533m0MDAIDIQACMQgsWeN7zl5Ul/f730owZgWkTAAAIH8wBjDILFkjLl3tWZ+VKac4cyWoNTJsAAEB4oQcwijz7rOfhLyuL8AcAQKwhAEaJsjJp6lTP661aRfgDACDWEACjgKcnfEhm6CstlfLzA9MmAAAQvgiAUWDHDs83ed60SZoyJTDtAQAA4Y0AGAUqKz0rv3mzd5tDAwCA6EAAjALp6e6XnT/fu7mCAAAgehAAo8DYsVJmpmSxdFxu/nzpoYeC0yYAABC+CIBRwGqVVq82/7u9EPjMM4Q/AABgIgBGifx8acsWqXdv1+vNJ3zcckto2gUAAMIPJ4FEkfx8KS/PXBVcWWnODRw7ln3+AACAKwJglLFapXHjQt0KAAAQzhgCBgAAiDH0AIaxxkZp7VqpokIaMECaNUuKjw91qwAAQKQjAIahpiZpxgzp2Wclw/j6+oIFUnGxtHRp6NoGAAAiH0PAYaasTOrRw9y2pWX4k8xguGyZVFISkqYBAIAoQQAMI2Vl5vm8dXUdl1uxwhweBgAA8AYBMEw0NUlz57bu9Wuv7Nq1gW8TAACITgTAMLFjh3T0qPvlKyoC1xYAABDdCIBhorLSs/IDBgSmHQAAIPoRAMNEerr7Za1Wc0sYAAAAbxAAw8TYsVJmpmSxnLtscTH7AQIAAO9FZAB8/fXXNWnSJGVkZMhisej55593ud8wDN13331KT09XYmKixo8frwMHDriUqa+vV2FhoVJTU5WUlKSCggJVV1cH8VW4slql1avN/24vBMbFSQsXsg8gAADwTUQGwJMnT+riiy/Wo48+2ub9S5cu1Zo1a/T4449r9+7d6tatm3JyclRfX+8sM2/ePG3dulWlpaUqLy/XsWPHlJ+fH6yX0Kb8fGnLFql3b9frSUnSD34gnTpF+AMAAL6zGIY7G4+EL4vFoueee06TJ0+WZPb+ZWRkaP78+VqwYIEkyeFwKC0tTRs2bNC0adPkcDh0/vnna+PGjZoyZYok6R//+IeGDRumnTt36sorr3TruWtra2Wz2eRwOJScnOy319TUZK4Krqw05waOHWv2EAIAAN8F6vM7kkRkD2BHDh06pKqqKo0fP955zWazacyYMdq5c6ckac+ePTp9+rRLmaFDh6pPnz7OMm1paGhQbW2tyy0QrFZp3Dhp+nTzX8IfAADwp6gLgFVVVZKktLQ0l+tpaWnO+6qqqhQfH68ePXq0W6YtS5Yskc1mc96ysrL823gAAIAgiLoAGEiLFi2Sw+Fw3o4cORLqJgEAAHgs6gKg3W6XpFYrequrq5332e12NTY2qqampt0ybUlISFBycrLLDQAAINJEXQDs16+f7Ha7tm3b5rxWW1ur3bt3Kzs7W5I0atQode7c2aXM/v37dfjwYWcZAACAaNUp1A3wRl1dnQ4ePOj8+tChQ9q7d69SUlLUp08fFRUV6YEHHtCgQYPUr18/3XvvvcrIyHCuFLbZbJo5c6aKi4uVkpKi5ORkzZkzR9nZ2W6vAAYAAIhUERkA33nnHV1zzTXOr4uLiyVJt912mzZs2KCSkhKdPHlSd955p2pqanT11VfrpZdeUpcuXZx1Vq5cqbi4OBUUFKihoUE5OTlau3Zt0F8LAABAsEX8PoChxD5CAABEHj6/o3AOIAAAADpGAAQAAIgxETkHMFw0j54H6kQQAADgf82f27E8C44A6IMTJ05IEieCAAAQgU6cOCGbzRbqZoQEi0B8cObMGR07dkzdu3eXxWIJynPW1tYqKytLR44cidmJq5GC9ypy8F5FDt6ryBDu75NhGDpx4oQyMjIUFxebs+HoAfRBXFycMjMzQ/LcnEQSOXivIgfvVeTgvYoM4fw+xWrPX7PYjL0AAAAxjAAIAAAQYwiAESYhIUH333+/EhISQt0UnAPvVeTgvYocvFeRgfcp/LEIBAAAIMbQAwgAABBjCIAAAAAxhgAIAAAQYwiAAAAAMYYAGCF+9rOfyWKxuNyGDh0a6mbFvNdff12TJk1SRkaGLBaLnn/+eZf7DcPQfffdp/T0dCUmJmr8+PE6cOBAaBob4871Xt1+++2tfsdyc3ND09gYt2TJEo0ePVrdu3dXr169NHnyZO3fv9+lDL9boefO+8TvVfgiAEaQiy66SJWVlc7bG2+8EeomxbyTJ0/q4osv1qOPPtrm/UuXLtWaNWv0+OOPa/fu3erWrZtycnJUX18f5JbiXO+VJOXm5rr8jm3atCmILUSz8vJyFRYWateuXXrllVd0+vRpTZgwQSdPnnSW4Xcr9Nx5nyR+r8KWgYhw//33GxdffHGom4EOSDKee+4559dnzpwx7Ha7sWzZMue1mpoaIyEhwdi0aVMIWohmZ79XhmEYt912m5GXlxeS9qBjn332mSHJKC8vNwyD361wdfb7ZBj8XoUzegAjyIEDB5SRkaH+/ftrxowZOnz4cKibhA4cOnRIVVVVGj9+vPOazWbTmDFjtHPnzhC2DO3Zvn27evXqpSFDhujuu+/W8ePHQ90kSHI4HJKklJQUSfxuhauz36dm/F6Fp06hbgDcM2bMGG3YsEFDhgxRZWWlFi9erLFjx+qDDz5Q9+7dQ908tKGqqkqSlJaW5nI9LS3NeR/CR25urvLz89WvXz9VVFToJz/5iSZOnKidO3fKarWGunkx68yZMyoqKtJVV12l4cOHS+J3Kxy19T5J/F6FMwJghJg4caLzv0eOHKkxY8boggsu0LPPPquZM2eGsGVAdJg2bZrzv0eMGKGRI0dqwIAB2r59u6677roQtiy2FRYW6oMPPmDOc5hr733i9yp8MQQcoXr06KHBgwfr4MGDoW4K2mG32yVJ1dXVLterq6ud9yF89e/fX+eddx6/YyE0e/Zsvfjii3rttdeUmZnpvM7vVnhp731qC79X4YMAGKHq6upUUVGh9PT0UDcF7ejXr5/sdru2bdvmvFZbW6vdu3crOzs7hC2DO44eParjx4/zOxYChmFo9uzZeu655/SXv/xF/fr1c7mf363wcK73qS38XoUPhoAjxIIFCzRp0iRdcMEFOnbsmO6//35ZrVZNnz491E2LaXV1dS5/yR46dEh79+5VSkqK+vTpo6KiIj3wwAMaNGiQ+vXrp3vvvVcZGRmaPHly6Bodozp6r1JSUrR48WIVFBTIbreroqJCJSUlGjhwoHJyckLY6thUWFiojRs36oUXXlD37t2d8/psNpsSExNlsVj43QoD53qf6urq+L0KZ6Fehgz3TJ061UhPTzfi4+ON3r17G1OnTjUOHjwY6mbFvNdee82Q1Op22223GYZhbldx7733GmlpaUZCQoJx3XXXGfv37w9to2NUR+/Vl19+aUyYMME4//zzjc6dOxsXXHCB8cMf/tCoqqoKdbNjUlvvkyRj/fr1zjL8boXeud4nfq/Cm8UwDCOYgRMAAAChxRxAAACAGEMABAAAiDEEQAAAgBhDAAQAAIgxBEAAAIAYQwAEAACIMQRAAACAGEMABAAAiDEEQCBC3H777bJYLK1uBw8e1Lhx41RUVNSqzoYNG9SjRw/n1z/72c/afIyhQ4c6y7T3WC299tpruuGGG5SamqquXbvqwgsv1Pz58/Xpp586yzQ1NWnlypUaMWKEunTpop49e2rixIl68803W7XRYrEoNzfX5XpNTY0sFou2b9/uvFZeXq5rr71WKSkp6tq1qwYNGqTbbrtNjY2Nbb7eliwWi55//nlJ0j//+U9ZLBZZrVaXNktSZWWlOnXqJIvFon/+858u5ZtvqampmjBhgt57771W97V127Bhg7Zv3y6LxaKampqAfo/OJRDv3bBhw1o9T2lpqSwWi/r27duqfPMtKSlJo0aNUllZmdvtB+AfBEAgguTm5qqystLl5s4B7C1ddNFFrR7jjTfecLv+E088ofHjx8tut+v3v/+9PvzwQz3++ONyOBxavny5JPOQ+GnTpum//uu/NHfuXH300Ufavn27srKyNG7cOGcQa9apUye9+uqreu2119p93g8//FC5ubm6/PLL9frrr2vfvn16+OGHFR8fr6amJo++B8169+6tp556yuXak08+qd69e7dZ/tVXX1VlZaVefvll1dXVaeLEierevbvL93L+/PmtvsdTp05t9ViB+B6dSyDeu27duumzzz7Tzp07Xa6vW7dOffr0adWG5ORk5/flvffeU05Ojm655Rbt37/f69cFwAshPYgOgNtuu+02Iy8vr837vvWtbxlz585tdX39+vWGzWZzfn3//fcbF198cYfP095jGYZhHDlyxIiPjzeKioravP9f//qXYRiGsXnzZkOS8Yc//KFVmfz8fCM1NdWoq6tzaeMPf/hD44orrnB5LEnGa6+9ZhiGYaxcudLo27dvh20/+/W2JMl47rnnDMMwjEOHDhmSjJ/+9KfGoEGDXMoNHjzYuPfeew1JxqFDh1zKv/fee85yb775piHJeOmll1zqt/c9bj6LOJDfo44E8r2bPXu28R//8R8uz5WQkGD8n//zf4wLLrjAeb2t96epqcno3Lmz8eyzz57zNQDwH3oAAbittLRUjY2NKikpafP+5uHXjRs3avDgwZo0aVKrMvPnz9fx48f1yiuvuFz/2c9+pn379mnLli1tPrbdbldlZaVef/11315ECzfffLP+9a9/OXtA33jjDf3rX/9qs91nS0xMlCTn8LOnAvE96kgg37s77rhDzz77rL788ktJ5lBvbm6u0tLSOmxTU1OTnnzySUnSZZdd5ulLAuADAiAQQV588UUlJSU5b9/5znc8fox9+/a5PEZSUpLuuusut+oeOHBAycnJSk9P77Dcxx9/3Oa8MEnO6x9//LHL9YyMDM2dO1f/+Z//qa+++qpVve985zuaPn26vvWtbyk9PV3f/va39cgjj6i2ttattrelc+fOuvXWW/Wb3/xGkvSb3/xGt956qzp37txhvZqaGv385z9XUlKSrrjiCq+eOxDfo44E8r279NJL1b9/f23ZskWGYWjDhg2644472nwMh8Ph/LmLj4/X3Xffrf/7f/+vBgwY4NHrAeAbAiAQQa655hrt3bvXeVuzZo3HjzFkyBCXx9i7d6/+67/+y626hmHIYrG4XdZTP/7xj/X55587A1lLVqtV69ev19GjR7V06VL17t1bv/zlL53z7bx1xx13qLS0VFVVVSotLW03uEjSN77xDSUlJalnz57629/+pmeeeeacvVwd8ff36FzPFcj37o477tD69etVXl6ukydP6oYbbmizXPfu3Z0/d++9955++ctf6q677tLWrVs9fk4A3iMAAhGkW7duGjhwoPPW3JuTnJwsh8PRqnxNTY1sNpvLtfj4eJfHGDhwoHr16uXW8w8ePFgOh+OcgWvw4MH66KOP2ryv+frgwYNb3dejRw8tWrRIixcvdg4nnq1379763ve+p0ceeUR///vfVV9fr8cff1yS+X04efKkzpw541KneeXt2d8LSRoxYoSGDh2q6dOna9iwYRo+fHi7r+uZZ57R3/72N/3rX/9SRUVFuyHHHYH8HrX3fIF872bMmKFdu3bpZz/7mb73ve+pU6dObT5GXFyc8+du5MiRKi4u1rhx4/SrX/3K7dcCwHcEQCAKDBkyRO+++26r6++++26bH9bemjJliuLj47V06dI2728OWtOmTdOBAwfa7NVZvny5UlNTdf3117f5GHPmzFFcXJxWr159zvb07NlT6enpOnnypCTz+/DVV19p7969LuWavzftfS/uuOMObd++vcPeP0nKysrSgAED2t1qxhPB+h41C/R7l5KSoptvvlnl5eXn/D6ezWq16tSpUx7VAeCbtv9EAxBR7r77bj3yyCO655579B//8R9KSEjQH//4R23atKnVB/lXX32lqqoql2sWi8VlKPPzzz9vFaLS09OVlZWllStXavbs2aqtrdX3v/999e3bV0ePHtVTTz2lpKQkLV++XNOmTVNpaaluu+02LVu2TNddd51qa2v16KOP6g9/+INKS0vVrVu3Nl9Lly5dtHjxYhUWFrpcf+KJJ7R37159+9vf1oABA1RfX6+nnnpKf//73/Xwww9LMre4mTBhgu644w4tX75c/fv31/79+1VUVKSpU6e2u73LD3/4Q33nO9/xS7BzVyC+Rx0Jxnu3YcMGrV27Vqmpqe22wzAM58/fqVOn9Morr+jll1/Wfffd5/ZrAeAHIVyBDMADHW0DYxiG8dZbbxnXX3+9cf755xs2m80YM2aMc9uTZvfff78hqdUtISHBWeZb3/pWm2V+/vOfO8u88sorRk5OjtGzZ0+jS5cuxtChQ40FCxYYx44dc5Y5ffq0sWzZMuOiiy4y4uPjjeTkZCMnJ8d44403XNrU1tYgX331lXHhhRe6bHHy7rvvGrfeeqvRr18/IyEhwUhNTTW++c1vttqu5F//+pdxzz33GAMGDDASExONQYMGGSUlJcaJEyecZdra1qWl995775zbwLTH3W1gAvE9ckeg37uWVq5c2WobmLN/7gYPHmz84he/ML766iu3XwMA31kMw4vZvgAAAIhYzAEEAACIMQRAAIgCv/zlL1vt79h8mzhxYqibByDMMAQMAFHgiy++0BdffNHmfYmJie0ugAEQmwiAAAAAMYYhYAAAgBhDAAQAAIgxBEAAAIAYQwAEAACIMQRAAACAGEMABAAAiDEEQAAAgBhDAAQAAIgx/x/0Z8BPuzc+NAAAAABJRU5ErkJggg=="
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 0 Axes>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "plt.scatter(cdf.ENGINESIZE, cdf.CO2EMISSIONS,  color='blue')\nplt.xlabel(\"Engine size\")\nplt.ylabel(\"Emission\")\nplt.show()",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "scrolled": true,
        "trusted": true
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<pyolite.display.Image at 0x4748de0>",
            "image/png": 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"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 0 Axes>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "## Practice\n\nPlot **CYLINDER** vs the Emission, to see how linear is their relationship is:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# write your code here\n\nplt.scatter(cdf.CYLINDERS, cdf.CO2EMISSIONS,  color='blue')\nplt.xlabel(\"Cylinders\")\nplt.ylabel(\"Emission\")\nplt.show()\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<pyolite.display.Image at 0x59f6698>",
            "image/png": 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"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 0 Axes>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<details><summary>Click here for the solution</summary>\n\n```python\nplt.scatter(cdf.CYLINDERS, cdf.CO2EMISSIONS, color='blue')\nplt.xlabel(\"Cylinders\")\nplt.ylabel(\"Emission\")\nplt.show()\n\n```\n\n</details>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "#### Creating train and test dataset\n\nTrain/Test Split involves splitting the dataset into training and testing sets that are mutually exclusive. After which, you train with the training set and test with the testing set.\nThis will provide a more accurate evaluation on out-of-sample accuracy because the testing dataset is not part of the dataset that have been used to train the model. Therefore, it gives us a better understanding of how well our model generalizes on new data.\n\nThis means that we know the outcome of each data point in the testing dataset, making it great to test with! Since this data has not been used to train the model, the model has no knowledge of the outcome of these data points. So, in essence, it is truly an out-of-sample testing.\n\nLet's split our dataset into train and test sets. 80% of the entire dataset will be used for training and 20% for testing. We create a mask to select random rows using **np.random.rand()** function:\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "msk = np.random.rand(len(df)) < 0.8\ntrain = cdf[msk]\ntest = cdf[~msk]",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "len(msk)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 16,
      "outputs": [
        {
          "execution_count": 16,
          "output_type": "execute_result",
          "data": {
            "text/plain": "1067"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "train.shape",
      "metadata": {
        "trusted": true
      },
      "execution_count": 20,
      "outputs": [
        {
          "execution_count": 20,
          "output_type": "execute_result",
          "data": {
            "text/plain": "(858, 4)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "### Simple Regression Model\n\nLinear Regression fits a linear model with coefficients B = (B1, ..., Bn) to minimize the 'residual sum of squares' between the actual value y in the dataset, and the predicted value yhat using linear approximation.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": "#### Train data distribution\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS,  color='blue')\nplt.xlabel(\"Engine size\")\nplt.ylabel(\"Emission\")\nplt.show()",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<pyolite.display.Image at 0x59d6c28>",
            "image/png": 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"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 0 Axes>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "#### Modeling\n\nUsing sklearn package to model data.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "from sklearn import linear_model\nregr = linear_model.LinearRegression()\ntrain_x = np.asanyarray(train[['ENGINESIZE']])\ntrain_y = np.asanyarray(train[['CO2EMISSIONS']])\nregr.fit(train_x, train_y)\n# The coefficients\nprint ('Coefficients: ', regr.coef_)\nprint ('Intercept: ',regr.intercept_)",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 22,
      "outputs": [
        {
          "name": "stdout",
          "text": "Coefficients:  [[39.16753987]]\nIntercept:  [125.35470333]\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "As mentioned before, **Coefficient** and **Intercept** in the simple linear regression, are the parameters of the fit line.\nGiven that it is a simple linear regression, with only 2 parameters, and knowing that the parameters are the intercept and slope of the line, sklearn can estimate them directly from our data.\nNotice that all of the data must be available to traverse and calculate the parameters.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "#### Plot outputs\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": "We can plot the fit line over the data:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS,  color='blue')\nplt.plot(train_x, regr.coef_[0][0]*train_x + regr.intercept_[0], '-r')\nplt.xlabel(\"Engine size\")\nplt.ylabel(\"Emission\")",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 23,
      "outputs": [
        {
          "execution_count": 23,
          "output_type": "execute_result",
          "data": {
            "text/plain": "Text(0, 0.5, 'Emission')"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "#### Evaluation\n\nWe compare the actual values and predicted values to calculate the accuracy of a regression model. Evaluation metrics provide a key role in the development of a model, as it provides insight to areas that require improvement.\n\nThere are different model evaluation metrics, lets use MSE here to calculate the accuracy of our model based on the test set:\n\n*   Mean Absolute Error: It is the mean of the absolute value of the errors. This is the easiest of the metrics to understand since it’s just average error.\n\n*   Mean Squared Error (MSE): Mean Squared Error (MSE) is the mean of the squared error. It’s more popular than Mean Absolute Error because the focus is geared more towards large errors. This is due to the squared term exponentially increasing larger errors in comparison to smaller ones.\n\n*   Root Mean Squared Error (RMSE).\n\n*   R-squared is not an error, but rather a popular metric to measure the performance of your regression model. It represents how close the data points are to the fitted regression line. The higher the R-squared value, the better the model fits your data. The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse).\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "from sklearn.metrics import r2_score\n\ntest_x = np.asanyarray(test[['ENGINESIZE']])\ntest_y = np.asanyarray(test[['CO2EMISSIONS']])\ntest_y_ = regr.predict(test_x)\n\nprint(\"Mean absolute error: %.2f\" % np.mean(np.absolute(test_y_ - test_y)))\nprint(\"Residual sum of squares (MSE): %.2f\" % np.mean((test_y_ - test_y) ** 2))\nprint(\"R2-score: %.2f\" % r2_score(test_y , test_y_) )",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "scrolled": true,
        "trusted": true
      },
      "execution_count": 24,
      "outputs": [
        {
          "name": "stdout",
          "text": "Mean absolute error: 22.96\nResidual sum of squares (MSE): 865.09\nR2-score: 0.77\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "## Exercise\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Lets see what the evaluation metrics are if we trained a regression model using the `FUELCONSUMPTION_COMB` feature.\n\nStart by selecting `FUELCONSUMPTION_COMB` as the train_x data from the `train` dataframe, then select `FUELCONSUMPTION_COMB` as the test_x data from the `test` dataframe\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "\ntrain_x = np.asanyarray(train[['FUELCONSUMPTION_COMB']])\n# train_y = np.asanyarray(train[['CO2EMISSIONS']])\n\ntest_x = np.asanyarray(test[['FUELCONSUMPTION_COMB']])\n# test_y = np.asanyarray(test[['CO2EMISSIONS']])",
      "metadata": {
        "trusted": true
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "<details><summary>Click here for the solution</summary>\n\n```python\ntrain_x = train[[\"FUELCONSUMPTION_COMB\"]]\n\ntest_x = test[[\"FUELCONSUMPTION_COMB\"]]\n\n```\n\n</details>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Now train a Linear Regression Model using the `train_x` you created and the `train_y` created previously\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "regr = linear_model.LinearRegression()\n\n#ADD CODE\nregr.fit(train_x, train_y)\n\nprint ('Coefficients: ', regr.coef_)\nprint ('Intercept: ',regr.intercept_)\n",
      "metadata": {
        "trusted": true
      },
      "execution_count": 26,
      "outputs": [
        {
          "name": "stdout",
          "text": "Coefficients:  [[16.38598565]]\nIntercept:  [66.59648475]\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<details><summary>Click here for the solution</summary>\n\n```python\nregr = linear_model.LinearRegression()\n\nregr.fit(train_x, train_y)\n\n```\n\n</details>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Find the predictions using the model's `predict` function and the `test_x` data\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "predictions = regr.predict(test_x)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "<details><summary>Click here for the solution</summary>\n\n```python\npredictions = regr.predict(test_x)\n\n```\n\n</details>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Finally use the `predictions` and the `test_y` data and find the Mean Absolute Error value using the `np.absolute` and `np.mean` function like done previously\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "#ADD CODE\nprint(\"Mean absolute error: %.2f\" % np.mean(np.absolute(predictions - test_y)))\nprint(\"Residual sum of squares (MSE): %.2f\" % np.mean((predictions - test_y) ** 2))\nprint(\"R2-score: %.2f\" % r2_score(test_y , predictions) )",
      "metadata": {
        "trusted": true
      },
      "execution_count": 28,
      "outputs": [
        {
          "name": "stdout",
          "text": "Mean absolute error: 20.76\nResidual sum of squares (MSE): 859.73\nR2-score: 0.78\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<details><summary>Click here for the solution</summary>\n\n```python\nprint(\"Mean Absolute Error: %.2f\" % np.mean(np.absolute(predictions - test_y)))\n\n```\n\n</details>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "We can see that the MAE is much worse when we train using `ENGINESIZE` than `FUELCONSUMPTION_COMB`.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h2>Want to learn more?</h2>\n\nIBM SPSS Modeler is a comprehensive analytics platform that has many machine learning algorithms. It has been designed to bring predictive intelligence to decisions made by individuals, by groups, by systems – by your enterprise as a whole. A free trial is available through this course, available here: <a href=\"https://www.ibm.com/analytics/spss-statistics-software?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork20718538-2022-01-01\">SPSS Modeler</a>\n\nAlso, you can use Watson Studio to run these notebooks faster with bigger datasets. Watson Studio is IBM's leading cloud solution for data scientists, built by data scientists. With Jupyter notebooks, RStudio, Apache Spark and popular libraries pre-packaged in the cloud, Watson Studio enables data scientists to collaborate on their projects without having to install anything. Join the fast-growing community of Watson Studio users today with a free account at <a href=\"https://www.ibm.com/cloud/watson-studio?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork20718538-2022-01-01\">Watson Studio</a>\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": "### Thank you for completing this lab!\n\n## Author\n\nSaeed Aghabozorgi\n\n### Other Contributors\n\n<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork20718538-2022-01-01\" target=\"_blank\">Joseph Santarcangelo</a>\n\nAzim Hirjani\n\n## Change Log\n\n| Date (YYYY-MM-DD) | Version | Changed By    | Change Description                 |\n| ----------------- | ------- | ------------- | ---------------------------------- |\n| 2020-11-03        | 2.1     | Lakshmi Holla | Changed URL of the csv             |\n| 2020-08-27        | 2.0     | Lavanya       | Moved lab to course repo in GitLab |\n|                   |         |               |                                    |\n|                   |         |               |                                    |\n\n## <h3 align=\"center\"> © IBM Corporation 2020. All rights reserved. <h3/>\n",
      "metadata": {}
    }
  ]
}