ml0101en_reg_simple_linear_regression_co2_ipynb —_kopija.ipynb

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        "<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-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork1047-2023-01-01\">\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",
        "\n",
        "# Simple Linear Regression\n",
        "\n",
        "\n",
        "Estimated time needed: **15** minutes\n",
        "    \n",
        "\n",
        "## Objectives\n",
        "\n",
        "After 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"
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      "source": [
        "### Importing Needed packages\n"
      ]
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      "source": [
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import pylab as pl\n",
        "import numpy as np\n",
        "%matplotlib inline"
      ]
    },
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      },
      "source": [
        "### Downloading Data\n",
        "To download the data, we will use !wget to download it from IBM Object Storage.\n"
      ]
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            "--2023-07-14 09:07:24--  https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%202/data/FuelConsumptionCo2.csv\n",
            "Resolving cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)... 169.45.118.108\n",
            "Connecting to cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)|169.45.118.108|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 72629 (71K) [text/csv]\n",
            "Saving to: ‘FuelConsumption.csv’\n",
            "\n",
            "FuelConsumption.csv 100%[===================>]  70.93K  --.-KB/s    in 0.08s   \n",
            "\n",
            "2023-07-14 09:07:24 (858 KB/s) - ‘FuelConsumption.csv’ saved [72629/72629]\n",
            "\n"
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      "source": [
        "!wget -O FuelConsumption.csv https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%202/data/FuelConsumptionCo2.csv"
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      "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"
      ]
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        "\n",
        "## Understanding the Data\n",
        "\n",
        "### `FuelConsumption.csv`:\n",
        "We 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-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork1047-2023-01-01)\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"
      ]
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      "source": [
        "## Reading the data in\n"
      ]
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              "   MODELYEAR   MAKE       MODEL VEHICLECLASS  ENGINESIZE  CYLINDERS  \\\n",
              "0       2014  ACURA         ILX      COMPACT         2.0          4   \n",
              "1       2014  ACURA         ILX      COMPACT         2.4          4   \n",
              "2       2014  ACURA  ILX HYBRID      COMPACT         1.5          4   \n",
              "3       2014  ACURA     MDX 4WD  SUV - SMALL         3.5          6   \n",
              "4       2014  ACURA     RDX AWD  SUV - SMALL         3.5          6   \n",
              "\n",
              "  TRANSMISSION FUELTYPE  FUELCONSUMPTION_CITY  FUELCONSUMPTION_HWY  \\\n",
              "0          AS5        Z                   9.9                  6.7   \n",
              "1           M6        Z                  11.2                  7.7   \n",
              "2          AV7        Z                   6.0                  5.8   \n",
              "3          AS6        Z                  12.7                  9.1   \n",
              "4          AS6        Z                  12.1                  8.7   \n",
              "\n",
              "   FUELCONSUMPTION_COMB  FUELCONSUMPTION_COMB_MPG  CO2EMISSIONS  \n",
              "0                   8.5                        33           196  \n",
              "1                   9.6                        29           221  \n",
              "2                   5.9                        48           136  \n",
              "3                  11.1                        25           255  \n",
              "4                  10.6                        27           244  "
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MODELYEAR</th>\n",
              "      <th>MAKE</th>\n",
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              "      <th>0</th>\n",
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              "      <td>2014</td>\n",
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              "      <td>1.5</td>\n",
              "      <td>4</td>\n",
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              "      <td>MDX 4WD</td>\n",
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              "      <td>255</td>\n",
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              "      <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",
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              "      <td>8.7</td>\n",
              "      <td>10.6</td>\n",
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      "source": [
        "df = pd.read_csv(\"FuelConsumption.csv\")\n",
        "\n",
        "# take a look at the dataset\n",
        "df.head()\n",
        "\n"
      ]
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      "source": [
        "### Data Exploration\n",
        "Let's first have a descriptive exploration on our data.\n"
      ]
    },
    {
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              "       MODELYEAR   ENGINESIZE    CYLINDERS  FUELCONSUMPTION_CITY  \\\n",
              "count     1067.0  1067.000000  1067.000000           1067.000000   \n",
              "mean      2014.0     3.346298     5.794752             13.296532   \n",
              "std          0.0     1.415895     1.797447              4.101253   \n",
              "min       2014.0     1.000000     3.000000              4.600000   \n",
              "25%       2014.0     2.000000     4.000000             10.250000   \n",
              "50%       2014.0     3.400000     6.000000             12.600000   \n",
              "75%       2014.0     4.300000     8.000000             15.550000   \n",
              "max       2014.0     8.400000    12.000000             30.200000   \n",
              "\n",
              "       FUELCONSUMPTION_HWY  FUELCONSUMPTION_COMB  FUELCONSUMPTION_COMB_MPG  \\\n",
              "count          1067.000000           1067.000000               1067.000000   \n",
              "mean              9.474602             11.580881                 26.441425   \n",
              "std               2.794510              3.485595                  7.468702   \n",
              "min               4.900000              4.700000                 11.000000   \n",
              "25%               7.500000              9.000000                 21.000000   \n",
              "50%               8.800000             10.900000                 26.000000   \n",
              "75%              10.850000             13.350000                 31.000000   \n",
              "max              20.500000             25.800000                 60.000000   \n",
              "\n",
              "       CO2EMISSIONS  \n",
              "count   1067.000000  \n",
              "mean     256.228679  \n",
              "std       63.372304  \n",
              "min      108.000000  \n",
              "25%      207.000000  \n",
              "50%      251.000000  \n",
              "75%      294.000000  \n",
              "max      488.000000  "
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              "      <td>9.474602</td>\n",
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              "      <td>4.700000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>108.000000</td>\n",
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              "      <td>21.000000</td>\n",
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              "      <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",
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              "      <td>25.800000</td>\n",
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              "\n",
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              "\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "# summarize the data\n",
        "df.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6gxTBDW9p4Fk"
      },
      "source": [
        "Let's select some features to explore more.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "id": "y3UdEb43p4Fk",
        "outputId": "f0aee5ae-058e-4165-bdb8-67efbcdf7427"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   ENGINESIZE  CYLINDERS  FUELCONSUMPTION_COMB  CO2EMISSIONS\n",
              "0         2.0          4                   8.5           196\n",
              "1         2.4          4                   9.6           221\n",
              "2         1.5          4                   5.9           136\n",
              "3         3.5          6                  11.1           255\n",
              "4         3.5          6                  10.6           244\n",
              "5         3.5          6                  10.0           230\n",
              "6         3.5          6                  10.1           232\n",
              "7         3.7          6                  11.1           255\n",
              "8         3.7          6                  11.6           267"
            ],
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              "\n",
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              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-51ff25a8-f985-4876-9c16-bfbbb8806e5c button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9035cd72-c26d-4964-87da-a72d1a26686d button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9035cd72-c26d-4964-87da-a72d1a26686d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "cdf = df[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_COMB','CO2EMISSIONS']]\n",
        "cdf.head(9)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M6CH06yMp4Fk"
      },
      "source": [
        "We can plot each of these features:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "mCik0Iyfp4Fk",
        "outputId": "e846abb9-cd00-4c75-9a09-9eb17746008b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "viz = cdf[['CYLINDERS','ENGINESIZE','CO2EMISSIONS','FUELCONSUMPTION_COMB']]\n",
        "viz.hist()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z42dGocUp4Fk"
      },
      "source": [
        "Now, let's plot each of these features against the Emission, to see how linear their relationship is:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "hWnuSNDvp4Fk",
        "outputId": "ad7acf3f-533a-4969-aed4-ba8a8317b00b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.scatter(cdf.FUELCONSUMPTION_COMB, cdf.CO2EMISSIONS,  color='blue')\n",
        "plt.xlabel(\"FUELCONSUMPTION_COMB\")\n",
        "plt.ylabel(\"Emission\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "BXUPEVPXp4Fk",
        "outputId": "0ba9d3c1-0a7c-4d31-b632-19b8485951fa"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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v3jw8+uijoX4JROSioeZ+lJYCH3xQ/36bTdxvVFPMv/5V5OwsWCBaNlgVTFiZX0SkiWKx48ePK506dVKWL1+uXHvttcrEiRMVRVGUY8eOKU2aNFHee+8959j//ve/CgBlw4YNiqIoykcffaQ0atRIKS8vd46ZO3eukpSUpNTU1EjPobKyUgGgVFZWGvOiiEhRFEUpKlIUu11RxMeeuNnt4v5oc/asomRmur9W15vNpihZWWKct+euXq0oJSXi69mzirJoUf3jZWWJ987z/sxMMT7USkp8v17XW0lJ6OdGDYPs57flKzuFhYXIy8tD//793e7fvHkzzpw543Z/586d0bZtW2zYsAEAsGHDBmRnZyM9Pd05ZuDAgaiqqsKOHTt8nrOmpgZVVVVuNyIynpbcj0ind5XDV3IvAHz/vWiiWVIivs6eDfzlL+GTHxMO+UVEMiztjfXOO+9gy5Yt2LhxY73HysvLERMTg+bNm7vdn56ejvLycucY10BHfVx9zJeZM2di+vTpQc6eiGSouR/RTs8uKjW51zPnRQ1eXIvyqfV3fOXH2Gzifc7PD119GzW/6MAB7/NSawKxXQRZzbKVnf3792PixImYP38+mjZtGtJzT5s2DZWVlc7b/v37Q3p+Ioo+Wlc5tCb3hmN+TLi0i6iuBm6+GejRQ3ytrjb3fBR5LAt2Nm/ejIqKClx22WVo3LgxGjdujLVr1+Jvf/sbGjdujPT0dNTW1uLYsWNuzzt8+DAyfm3MkpGRUW93lvp9hp8mL7GxsUhKSnK7EREFQ+suKq3Bi9X1d3yxul3Eb34DJCYCixcD27eLr4mJ4n4ilWXBTr9+/bB9+3Zs3brVebviiiswcuRI57+bNGmClStXOp+za9cu7Nu3D7m5uQCA3NxcbN++HRUVFc4xy5cvR1JSErp27Rry10REDZfWVQ6twUs458cUFNTPLyorC02g4yULAoC4nwEPqSzL2UlMTET37t3d7ktISEBqaqrz/rFjx2LKlClISUlBUlISJkyYgNzcXFx55ZUAgAEDBqBr1664/fbbUVxcjPLycjzyyCMoLCxEbGxsyF8TETVs6irHxInuqzaZmSLQcf3w1xq8hHt+jN0O9OkTuvNVV/sOdFQbN4pxzZqFZk5Unxl96PSwfDeWP3PmzMGNN96IYcOGoXfv3sjIyECpy3YDu92OpUuXwm63Izc3F6NGjcLo0aPxxBNPWDhrImrIZFc5tF72Cpf8mHBx++3GjiPjTZ0qGstOniya5E6eLL6fOjX0c2HXc7DrORFZQ92NBbiv1qjBi7ecl9LS+itHWVn1V46iXY8eIkcnkOxsYNs28+dD7qZOBZ55xvfjvvqsaSX7+c1gBwx2iCKdwyESeQ8dEpd9evWKnBUOPcFLJL9eo9x8s0hGDmToUOD9982eDbmqrRUrOP4qe9vtouZWsJe0GOxowGCHKHJ5CxYyM8Uln0hZ6WDwol11tdh1Fcjx48zZCbVnnxWXrAKZMyf4Glyyn9+WFhUkIgqGlqJ84czI5N6GEjg1awbk5PhPUs7JYaBjhb17jR1nhLBOUCYi8oUdt+vz1Xoi1G0kQuXrr0VA401OjnicQq9jR2PHGYGXscDLWESRaM0a8WEeyOrVod0SrYcRqzG+Vrn8JTsbqbpa7HxStxi/9VboVlWsPDfVF445O7yMRUQRKVwrCvuzbx/QrZv4n3x8PLBjB7Bpk/acI8/g6Kqr/K9y6embdeqU2DGzezfQqZPYWRMX532sZ3G/7dtFPk2oVleaNWMScjiJiQGmTPG/G2vKlNDW2+HKDriyQxSJIm1lp0kT4OxZubFat563bAn8/HPg48q+F0OHAkuW1L8/P7/+Dih/VYwBXk5qyKZOBWbPdl/hsdtFoGPEtnOAu7E0YbBDFHnULuCBKgqXlVmfoKsl0FF5m7+vS1WySkqAESP8j/EV6KhcAx7uiKJAzK6gLPv5zQRlIopIkVJReN8+7YEOUL8RqL+EbFlpaf4fP3XKf6ADiMdPnRL/HjVK7ryy4yj6xMSIS6jPPy++WtEqAmCwQ0QRzOqO2zK6dQvu+WrOUaAu6UYoKtI2TrYyMSsYk9WYoExEEa2gQFxaCdfaMidPBvd8tRGoEYnWFRX+H9+9W+446rjUVHGZLZDUVLnjEpmFKztEFPHUonwjRoiv4RLoAGLXlR6ejUBlu6T7E+gYnTrJHUcd96c/yY2XHUdkFgY7RBTxHA6xO2vBAvE1nAoJ7tih/Tneco4CdUkPdDzXwMkXf1uFvY3bs0duvOw4IrMw2CGiiBbuVYPbtgUaa0wY8JZzJJOQ7YuiyCVrx8WJS4L+5Oefq7cjcwlLyzgiszDYIaKwomWVRt2K7Zm4q/bGCpeA58wZ3wFP48Zit9bq1WJr+OrVIjjwllztLyFbNrk4kMWLfQc8nnV2wrEtAJE3rLMD1tkhChdaOpirdXZ87VAKpzo7Km8VlNu21X4cbxWUO3Y09r2QqaAcyrYARN6wqKAGDHaIrKe1t1OkVVD2J9jCa1a+F1On+s/1KSoyplqu2cXpKDKxqCARRQw9HcwjsTeWN1OnitWRyZOBF14QX+Pjxf2yrHwviotFQOO5YmS3GxfoGPEeUcPGOjtEZLlABfNcqwmrKxOyW7GN2LJtFM/Vif37Re8gTw7HudUSmWDB6veiuBiYPl2+cagWvlaOtL5H1LDxMhZ4GYvIagsWiJ1Ugbj2doq0fBFvTREDkZ2/1X3CtORaaRFpP2MKPV7GIqKIoWdlYv36wIGDwyHGWU1dndBa/8fhEB2iA+1Ms7JPmN4dcTK77l56Se5n/NJLemZODQmDHSKyXKCCed6K4kVKzk5trfdLVbJefFGufpAVfcL05FoB8rWR9u6Vm4fsOGq4mLNDRJZTVyZuuUUENq4fnr5WJqzOU/HHdWv4F18YV9FZXS3xFbyEuk+YbK7V888D6eliPj//DPzud/UDJG+vjXV8yCjM2QFzdojChbfcj6wsEej4qrNjVZ6KL95eg5HCqX6QbK6VK7vdd/Dn+dqYs0OBMGeHiCJOQQHw/fdy1YRl8lT++lex+hCqnlm+8leM5LpaYsTrOnUKGD8eGDhQfD11Sv65elbN/M3VddcdIAKYKVP8H2/KFAY6FBhXdsCVHaJI5ms1aPhwEQwYvUPIl0AVnc2k93UNHQosWVL/fs+2EL6cOqW/q7s/rrvugODnSdGLKztE1CB4Ww2aPRv4y19C2zMrUP6KmfS8Ll8BBCDuHzo08DFeeUX+fFq4rhiVlgIffFB/jM0m7g+X/mcU3riyA67sEEUTq3pm6clfMZL6uvbsEdvt/SUoy67I/PnP4rKSr/YMEyaIisZGvwb1ZxOJ/c8otGQ/v7kbi4iiip5qzIHI9GWyulKz+rrOO0/seFJ5u8Ql2yH9wQfP/fuBB0R+jGu1YiN3QXnbdWfGz5IaJl7GIqKoYnT9Hdm+TGqtIKu5BjqACBaGDXO/3LN7t/bjqu0ZXF/3uHH6V1Q8n+etHlCk1FKi8Mdgh4iiipH1d3xVPvb2wW+3A5dfLj/PULv77nOvo1Mn/ceZPVusdAFideuyy7Q932YTtwULAu+6C+daShRZmLMD5uwQRRPZfJSTJ/03qtRa40VmPAAMGSISqjt2BH77W2DUKHF/KP5PvGIF0K9f8Luo5swRlZFlX7MrX3WTvAnXWkoUPrgbi4gaDNc+S7L5KIF2EmntyyQzHgCuuw7Ytg14/32R0OytxYNZ1qwRX+PixLZtvdT2DLKvubAwcN0kb6zs+UXRxdJgZ+7cuejRoweSkpKQlJSE3NxcfPzxx87H+/TpA5vN5na755573I6xb98+5OXlIT4+HmlpaSgqKsLZs2dD/VKIyCKefZZefFHueYHyVrT2ZdLbx8lz6/ycOXLHcZWYqP05ixfrD3jUxGTZ3B9FEXVz+vTRHphY0fOLoo+lu7EyMzMxa9YsdOrUCYqi4I033kB+fj7+85//oFu3bgCAu+66C0888YTzOfEua68OhwN5eXnIyMjA+vXrcejQIYwePRpNmjTB008/HfLXQ0ShpVYs1nMJyFfTUZXWvkzB9HGy28/tJnI4ROVnf5duzjsPmDcPqKgQ+Sq1taICciCeO5YWLxaXtIqKRODSoQPw978DdXW+j2G3i8RkdS4yZMf5EuqeXxSFlDDTokUL5bXXXlMURVGuvfZaZeLEiT7HfvTRR0qjRo2U8vJy531z585VkpKSlJqaGulzVlZWKgCUyspK3fMmIm1qahRlzhxFGT9efNXwn6yiKIpy9qyiZGYqiggJtN/mzQs8P7vd/zHs9nPz1jre33vxr38pis0mbq7PV+9btKj+e5Ga6v/cqaliXCBFRf6PU1R0buybb8q912++Gfi8RHrIfn6HTc6Ow+HAO++8gxMnTiA3N9d5//z589GyZUt0794d06ZNw8mTJ52PbdiwAdnZ2UhPT3feN3DgQFRVVWHHjh0+z1VTU4Oqqiq3GxGFjux2bn+CrVj8yy/+H9fal0lvHydv78VttwE33eT70k1+/rkcJTUP59VX/Z/71VflVkKKi8VKj+dYu13c71pnJysr8PG0jCMyi+VFBbdv347c3FycPn0azZo1w/vvv4+uXbsCAG677Ta0a9cObdq0wbZt2/Dggw9i165dKP21YER5eblboAPA+X15ebnPc86cORPTp0836RURkT/qdm5P6nZuwP0D1Zdga6u0ahXc871R5z17tnvirt0uAp2HHgKys4GDB4E2bcRlJW8ViB0O0bLh/vuBG290v3SzZEn9qsJq4cBFi4D77hOXwDwf05LbUlwMzJgRuJBir15Aaipw5IjvY6WminFEVrJ863ltbS327duHyspKLFy4EK+99hrWrl3rDHhcrVq1Cv369cOePXvQsWNH3H333fjhhx/wySefOMecPHkSCQkJ+OijjzB48GCv56ypqUFNTY3z+6qqKmRlZXHrOZHJtG7n9mflSqB/f/1zUbdh+1JbK3Yt+ctfadRI5Lx4ztVbxeW2bYHDh7XN0fO98JWjpObEqKs+ocptcTiA9PTAwc7hw8yvIXNEzNbzmJgYXHDBBbj88ssxc+ZMXHzxxXhO3WvooWfPngCAPXv2AAAyMjJw2OP/Hur3GRkZPs8ZGxvr3AGm3ojIfFq3c1vp+ef9BzqAePz55+vfHxMj6tA8/7z4qifQAdzfC4dDdHf39uepet+kSeJrnz76dz9psW6d/0AHEI+vW2feHIhkWB7seKqrq3NbdXG1detWAEDrX8tl5ubmYvv27aioqHCOWb58OZKSkryuDBGRtfRuz/bG5T97XQI9//PP5Y4TaNzRo/oCHZX6XmjpExUqbOdAkcLSnJ1p06Zh8ODBaNu2LY4fP46SkhKsWbMGn3zyCfbu3YuSkhLccMMNSE1NxbZt2zB58mT07t0bPXr0AAAMGDAAXbt2xe23347i4mKUl5fjkUceQWFhIWJjY618aUTkRTDbsz0F2yIg0PMTEuSOE2jctdfKHccX9b2QDRj27xeF9vzl2hiF7RwoYoRkb5gPd955p9KuXTslJiZGadWqldKvXz/l008/VRRFUfbt26f07t1bSUlJUWJjY5ULLrhAKSoqqre97Pvvv1cGDx6sxMXFKS1btlTuv/9+5cyZM5rmwa3nRKERzPZsT+rWc8/t2YFuNpuiZGUF3ob99NNyx3v6af/HSUnRvz3e9b1YvVruOY0a1T+G63ZxI8ls/5d5r4n0kv38tnRl5x//+IfPx7KysrB27dqAx2jXrh0++ugjI6dFRCZRt2d7242lGj8euPXWcysTb70FNGtWf5zaSuCWW0SCrsxWCy0tBvxUr9A0rk0bcSlLD9et6mpXdV/FBlWeeUZad7lpYbcDgRbRY2KYnEzWC7ucHSKKLrW1IriYMEF8nTHDdx2X9HQRwCxeDGzfLr4mJgK/+Y33Y/tqJZCVBeTk1B+vKKJ+jcw27OrqwGNkxkn8zVaPt5o2/vpEyXDtVm6U6urA+VV798q/l0RmYbBDRKbxVTwQEFuq58wRKzlz5gCXXOI7kXfjRv8Bj2tvqdWrgd/9TjzHmyVL5IoXGpWPkpIigjh/0tLc34uTJ72vwvgK7pKTA8/TjF1ut99u7Dgis1heZyccyO7TJ/LkcER+vx6zXoOv4oEq15WL6mq5ZpbHj3u/pOXKqFo+b7wB3HFH4DnNmweMGRN4XEaG92AuPR3wUwPVK8+f2XvvyQUy48d73yqvV/fucpf7unUDvv3WuPMSqSKmzg5RpPLstt23r/j+1wLfEcGs11BbKy6b+ON6WcXIFQKjavn89JPcnGTHjR6t7X5/1Mahai2dTp3knie7G05WqBqBEgWLwQ6RDmolW8+6JwcOiPsjIeAx8zVoDTiMrL9j1LF+LesVkMw4f6tczzyjrSeYN+PGBV6Nc+1WbpSrrjJ2HJFZGOwQaSRbyTbQh72VzH4NWgMOI+vvGHUsoxKUta5y6aG3CWmwunQxdhyRWRjsEGkUjpVstTL7NWgNON56S268zDijVjnatJGbU6BxoWqRUVws+mJ5k59v/LZzwLoVJSKtGOwQaRQNJfLNfg1aPwSbNfO+VdxVTo7v5GSHA1izBliwAFi//lyPKF9kVjlyc/0/LjvOyEt0/pSWAh984P2xDz4w59KqVStKRFox2CHSKBpK5AfzGlwDizVrvK9a6PkQ/Ppr3wFPTo543BtvSdb/+pdYzWjk8X+4Ro3q16/xxaiVHSMv0fni77KkSutlSZmfMyDeS38/NzNWlIg0C0k95zDHdhGkRaA2BbLtCKyk9zUsWlS/PUBmprjfm6Ki+u0hArUvOH5cUYYOVZTsbPH1+HHfYxct8v4a1Ps8WzWcd57vuXpasUKuPcOKFf6PY2SLDF9kW0msXi13PC0/56Ii/+c0q1UFkaLIf34z2FEY7JB26oes5wetep/sB6qVtL4Gf4GFv9dcU6Moc+Yoyvjx4mswH+quZPoyaZ2rq5ISuWOWlAQ+ltkBgZFz1fJzDkUgR+QPgx0NGOyQHosWiZUC2VWOcOTtL/isrPqvIVBgYeRq1tmzYgWipER89XVM2dUMvXM1erVEzyqXLKPmqvXnPGeO3HnnzAn+NRJ5I/v5zZwdoiBEerE0b60Wysrq944K1Q40LUUO9SZPy85Vbbzp62dss4keXL16yZ23uLh+iwxfbSG0Uufqj8xctf6cQ5V8TRQs3V3Pjx07hq+//hoVFRWo82izO1pPSVCiCKIW5FMU9/vVgnwLF8o1m4wUodiBpvU9DTYBPNBc/XVV19I93VVMTOCdYnrY7aKasr/2HMOHB56r1p9zKJKviYygqzfWhx9+iJEjR6K6uhpJSUmwufzpY7PZcPToUUMnaTb2xiItHA6x2uDrL2CbTfyVXVYW/n2ySkvFLh7X15KZKT7kXQOLNWvEKksgq1eL9gVa6XlP1eccOFA/QJIxZ47oSxWoH5jse2SlQO8fIFZ2Av1Oav05B9PTLBr6ypH1TO2Ndf/99+POO+9EdXU1jh07hl9++cV5i7RAh0iraCgqCGhrF9GrV+AGnM2ayV/S8aTnPVVXXvQEOna76MAu2w/M8xx6zmmmQO8fYM6lu9dek5uf57ho6CtHkUVXsHPgwAHcd999iI+PN3o+RGEvGooKam0X4XAEbotQXa2/vUSo31PPefrqB6YGhAcOyI23ilHvnxpAAvUDHm+X7nbtkjuv67ho6CtHkUdXsDNw4EBs2rTJ6LkQRYRwKSooW/TNG60rKeoHYCCy4zylpWkfpwZs/nheFvF1mcRXgOevUJ+iaC/UV14OZGQATZuKr+Xl8s/1x8jfyYICkR913nnu92dm1s+bkp2/Oi4a+spRZNKVoJyXl4eioiLs3LkT2dnZaNKkidvjN910kyGTIwpH6lK/r1wRNb9E7yUdGcHmkWhdCViyRG78kiWiQnEoyFy6cTjO5eYcPiwuXfniGuD16aPt0pBMnlJCgth9pTp8WAQf8fHAiROBn++P0b+TBQWiAnWgnBqtQZaWIFtP7heRL7qCnbvuugsA8MQTT9R7zGazwcGwnKKYGbt0tDBiJ1i4rE6pKiq0j5MN2NLTxU6lBQvkxqvH9bx05YvMOM9Ax9XJk+LxYAIeM34n7fbAAceFF8odSx0XDZeAKTLpuoxVV1fn88ZAhxoCLUv9RjLqMoDWRFRf3bQ9yY7z1Ly59nFaAzat43/6SW58oHHl5b4DHdXJk8Ff0rLid1Jrw1c9P2ciI7CoIJFOsgX5jCR7GeD55/3n8mhNRA2UG6OSHefplVe0j9MasPXqBaSm+j9+auq58a1ayc0p0LhLLpE7juw4fwoKRAE/18KFe/aY9zupteGrr67snmTHEcnSHeysXbsWQ4YMwQUXXIALLrgAN910E9aF+15bIoOpS/0jRoivZtcJkV3el9lWrXUlIFC16GCqSX/3nfZxWgM2rYxahTh2TO44suP8KS0VBfwmTwZeeEF87djR3B1OxcUiT8tbMrhnh3k9P2ciI+gKdt5++230798f8fHxuO+++3DfffchLi4O/fr1Q0lJidFzJKJf6cmh8belV3Z16qWXAteWURQxTg+9lXi1BGzr1gFHjvg//pEj53agvfqq3JwCjQsmaKqtFQHbhAnia22t7+dbuaVbthVGp05yx5MdRyRNT+Otzp07K7Nnz653/1//+lelc+fOeg5pKTYCpUihNmr01pXaiOaXvowfL3ee8eP1Hf/4cbnjHz/u+30J1DxUa2fw7Gy58dnZ/l/boUNyxzl0yP15WhqHhrJRazBOnpR7L06etHaeFDlMbQT63XffYciQIfXuv+mmm1BWVhZk+EVEvrheutFCzeWZOFFulcBTMD2QZOoBNWsG5OT4P3ZOju8qzjKXE7UmKBvV9ykjQ2wv9yc+XoxTTZ0q+lx5vlcOh7h/6lT3+yOlqndcXOAk9vx8MY7IUHoiqY4dOyovv/xyvfvnzp2rXHDBBXoOaSmu7FCkyc/XtrLj7eZrlcAbvX+RL1pUf8UhM1Pc701Ojvfj5uQE9XYpihJ4Vcxz9ePIEbnXfOSI3Pnj470/Pz7efVxNTf0VHW8/u5qac8/RumplNV+/v/n5Vs+MIo2pKzv3338/7rvvPtx7771466238NZbb+Gee+7BpEmT8MADDxgbjRGRm6lT5Yv8+eNrlcCbr76SO6brOD05JF9/LZpGDh0KZGeLr8ePi/uDpTWh+c035Y4rO+7ECZFgnp4OxMaKr4cO1a+v89JLgUsHOBzu+VHhVjcpkMWLRU5PYSEwYID4evKkuJ/IFHqjqdLSUuXqq69WUlJSlJSUFOXqq69WFi9erPdwluLKDkUKmb/69azwuK4SeKN15SBQDglgXQ6Jt9WmrKz6q01m5yn5Mm6c3HnHjTv3HObCUEMl+/mtq4IyANx88824+eabjYu6iCKQwxG4pL6RZP7q10pdJZg0yfcYo9sCAKFpC+Dt5yPbCsGonB2tZLfwu46TrVNUVARcfXVofleJwgmLChLpVFoqatj07Ru4po1R9u615rhXXSVXKfeqq8S/jWy1oJe/n49MQvPw4XLnkR0nq2dP7eNkfy9efDF0v6tE4UQ62ElJScHPP/8MAGjRogVSUlJ83oiina98lB9/NLemidGrCLLHXb9eLo9k/Xrxb6NaLehlRM2Z66+XO5fsOFlZWdrH6fm9CEX9HaJwYVMURZEZ+MYbb2D48OGIjY3FvHnzYPOz1jpmzBjDJhgKVVVVSE5ORmVlJZKSkqyeDoU5h0P8VezvMk1WlijOZ/RlgtpasU3ZyEtZdrtIDlVL+nuzYIFYEQikpESslsyfD4waFXj8228DI0fKz1VGoJ+P2gE80M8nNRU4ejTw+VJSAhcr1ELmZ+z5M9P7eyH7XhCFK9nPb+mcHdcA5o477ghqckSRzMp8FLUX0TPPGHdM195FvmjN2fGsaOyL7DgttPQPS0/3nb+SlCQX7Gj5+6i2VuRH7d0rVmPGjav/3mtZRVN/v2JigBtv1L5LT30vzM6dIrKarpydLVu2YPv27c7vlyxZgqFDh+Lhhx9GrYZKZXPnzkWPHj2QlJSEpKQk5Obm4uOPP3Y+fvr0aRQWFiI1NRXNmjXDsGHDcPjwYbdj7Nu3D3l5eYiPj0daWhqKiopw9uxZPS+LSIrV+SjFxfq7i7vy1rvIF615JGqTTn9cm3Qayaj+YQ89JHcc2XFTp4rVF9e+VfHx9bf+798vdzzXcQ4HsHmz3PO8kX3PiCKVrmDnD3/4A/73v/8BENWUb731VsTHx+O9997DVJmiHb/KzMzErFmzsHnzZmzatAnXXXcd8vPzsWPHDgDA5MmT8eGHH+K9997D2rVrcfDgQRS4NLtxOBzIy8tDbW0t1q9fjzfeeAPz5s3Do48+qudlEUkJh3wUvV2hCwvr9y6SqXCstSu5WtPGX0fyYJp0enLtIfXFF9qf7y3X6tQpuefKjNNSEVlPTSOZ1UZ/wqX+DpFp9OxrT0pKUvbs2aMoiqLMmjVLGTBggKIoivL5558rmZmZeg7p1KJFC+W1115Tjh07pjRp0kR57733nI/997//VQAoGzZsUBRFUT766COlUaNGSnl5uXPM3LlzlaSkJKXGT+GQ06dPK5WVlc7b/v37WWeHpL39tlxNk7ffNv7cMvVrtFTQla1wrLfmjGxNm2B46yGl9+Za+8eon7PWish66uzI1kHyvIVLzywivUytoKwoCurq6gAAK1aswA033AAAyMrKcu7Y0srhcOCdd97BiRMnkJubi82bN+PMmTPo37+/c0znzp3Rtm1bbNiwAQCwYcMGZGdnIz093Tlm4MCBqKqqcq4OeTNz5kwkJyc7b1my2x+IEFw+iswqij/B/gV/+PC5cy9cKL9jKZiu5DJd1fXytWKil2v/KKPyjrRWRJbt+G2znftZpqXJPcfz+YBxK2zB/m4TmUpPJNW3b19l9OjRyptvvqk0adJE2b17t6IoirJmzRqlXbt2mo61bds2JSEhQbHb7UpycrLy73//W1EURZk/f74SExNTb3xOTo4ydepURVEU5a677nKuKqlOnDihAFA++ugjn+fkyg4FQ291YK19orzR+xe8r9UE2b/4KyrkjllRYdz7HIgZ1aRdV2qMqgKtdVVMtgO85+9Raqq2n7eRK2xG/G4T6WHqys6zzz6LLVu2YPz48fjjH/+ICy64AACwcOFCXKVWFZN00UUXYevWrfjqq69w7733YsyYMdi5c6eeaUmLjY11JkWrNyJZdrvYXu3P8OHufy0bUfcFMDa3wt9f3orivsohWyzdiKLqP/0EdOggOpx36OA790m2mnRhoVhVKiyUPz+g7+fsjdZVsddekxvv6sCBwNvfL73UnBU2q2pOEWliZIR16tQppba2Nqhj9OvXT7n77ruVlStXKgCUX375xe3xtm3bKrNnz1YURVH+9Kc/KRdffLHb4999950CQNmyZYv0Odkbi7TQ+hd/oPFa8ib0/NUfzE3N8cnKkhuflRXce5uc7P24ycn1x2pdMXnzTbnxb76p7+fsyw8/yJ33hx+0vS49N6N7Y4VzDzRqGExd2dm/fz9+dAnjv/76a0yaNAlvvvkmmjRpElTwVVdXh5qaGlx++eVo0qQJVq5c6Xxs165d2LdvH3JzcwEAubm52L59OyoqKpxjli9fjqSkJHTt2jWoeRD5oqXOjsx4z1UUf2S3ORtFXUlq21ZuvOw4wH0H1bPPino1lZXex1ZWAs2bu9+ndcVEtvCfOk7rz9mX3/xG7rzqOLOqZAOi1ICRjHqPiEynJ5K65pprlDd//fPn0KFDSlJSkpKbm6u0bNlSmT59uvRxHnroIWXt2rVKWVmZsm3bNuWhhx5SbDab8umnnyqKoij33HOP0rZtW2XVqlXKpk2blNzcXCU3N9f5/LNnzyrdu3dXBgwYoGzdulVZtmyZ0qpVK2XatGmaXg9XdkiLefPk/oqeN0+M19ox3J/rrw/Nio7natORI3LPO3JE7j3Uu4PKNSdI6y4nrSs7Ru3Gio2VO05srBgv28Fcz80jxTFoVu5MJFIUk1d2vv32W/zm1z9D3n33XXTv3h3r16/H/PnzMW/ePOnjVFRUYPTo0bjooovQr18/bNy4EZ988gmu/7XZzJw5c3DjjTdi2LBh6N27NzIyMlDqcgHYbrdj6dKlsNvtyM3NxahRozB69Gg88cQTel4WBcnK3RieqwQaaltqtnixtnFaqw/7k5Agd6xgeNuls22b3HNlxgWzg8p1lUStJu2Pa3VorSs7RtVTkl3sVsfJ1tnRQ3anlyyra04RSdMTSSUkJChlZWWKoijKkCFDlFmzZimKoig//PCD0rRpUz2HtBRXdoJn5W4Mb6sEdru43wyyqyvXXy/Gq3kNNpvcKoo/sqtKwdy87dIJZc0Zf7eEhPrHlP35a30N//yn3Ph//tP/ax49Wu44o0eL8UbuuPO8GZ2zw5UdspqpKzvdunXDyy+/jHXr1mH58uUYNGgQAODgwYNITU01MBSjSGDUTiM9tFSmNcqFF2obp1YTBupXFNZa66RdO7lzazVnjv9dOkb9BS+7g8qXVq3q31dcLKpBz5lTvzq0K611c2SrVAcad+mlcsdRx+nZceenL7NTfj4QF6f92P5Y2QONSBM9kdTq1auV5s2bK40aNVJ+//vfO++fNm2acvPNN+s5pKW4sqOfkTuNtNKas2EU2ZwKz7+ijagmbFZtGTVPxRet+S6+BLvTKJg6Plp3DmldwfNF6+9poJVAdby336P8fO/j8/P1v29GvqdERpP9/Jbueu6qT58++Pnnn1FVVYUWLVo477/77rsRHx9vUBhGkUDLTiOjuyprqUw7aZJx542LAxo3Bvz1m23cuP5f0QUF4q/rdetE40Vf3bb9kemIrcdXXwG33+77ca35Lr4Es9MoOdn7yg4g3pNA76u6wnbLLeL30pNnv64LLwSWLw88r0ArfTKd6l1zi1znabO5z1VdwSkpAQ4erN89vaBA9OoqKgJ27xY5Os88Y/yKjsp1roD3uRrZA41ItxAFX2GNKzv6GbnTSCu9/ZqCZWU1YdkcCa031z5LwZw3UG6G3jpB3ursqLTmi8musOldwfNFa26Zr3kWFYVfteJQ9EAj8sbwlZ3LLrsMK1euRIsWLXDppZfC5uci8ZYtWwwIwygSGLnTSCu9/ZqCpaVuSlmZsec2a1dLoF06RuVmyFYHbtpUrJwlJgLbt/s+rpov5rlSo+aLLVxYP/9IdoUtLk6MW7LE9zy15MEUFwMzZoiVRs8VGW+8zfOnn4Bbb9X2ekPBiFVLIjPZFMXbgm5906dPR1FREeLj4zF9+nS/Yx977DFDJhcqVVVVSE5ORmVlJVtHaFRbC8TH+7+0YreLhFFf/1PX69Qpce5ATp40dhk/IUEcM5D4eODECePOCwDz5wOjRhl7TEBcfkpJ8f24wwG0b+//kmVWlgju/H3ATZgAvPCCtrnZ7eIyj2fCcaA52WxAZmbgOQUydKj3gCc/X74MgRFC9XqJIons57f0yo5rABNpwQyZRyaHxOEQ44zO2ZGtR/LVV8aeOzZWLtiJjTXunCqzdrWMHQu8/77vx+124PLL/Qc7l11mXJ8oV+rOOsA94AlVvtiFF3rPnZHdlWcUK/PjiCKdrq3nrqqrq1FVVeV2o4bj0CFjx0XCuW+80dhxWvTqJf56N9ru3f4fr60Fli71P2bp0sDFHMeN07/qMHu2+/Flf64rV+ovdKmWNvBc/1YUfaUNgil+aeV/a0SRTlewU1ZWhry8PCQkJCA5ORktWrRAixYt0Lx5c7fdWRT9rMzZserc331n7DgtXGv2GCnQ5TYtO9/8kal6LHt82Z/rjBnAbbcBffuKOkWydZ9qa0WA5Y9nAObP1Kni0ubkyeJS3uTJ4nvZgMnK/9ZkWFlBnSgQXVvPR40aBUVR8M9//hPp6el+k5UpuqkrDQcO+N7Om5kpxkXLuWXzf8za7vvll8Yf8/zz/T++d6/ccWTGqZeiZs/W/oHoevxAP39vDhwAhg0DFi0KnMhrZGkDdYXI2/O9XaLzxsr/1gIpLQUmTnS/zJaZKQJzKxKmierRs9UrISFB+b//+z89Tw1L3HoenEWLRAE0zyJo6n1mbj/Ve+6zZxVl9WqxJX71am1Fz+68U25L8p13GvEK3dXU+C82p/fWvr2iZGcrytChYnu4pzlz5I4zZ4621zJnjigNMHSovuP7+vkHuqWmBv6ZG1XawMjil1b+txZoTp6vyco5UcMh+/mtK9jp06ePsnz5cl0TC0cMdoIX6v5UrrTW+Ai2j9eVV8p9CF55pXGvUVVcbHyg4+2Wk+N+XrOrVQdzfG8/T5nbihX+52RUgGd0oBhONW2srKBOpCgmBzt79uxR+vfvr8ybN0/ZtGmT8s0337jdIg2DneD4+stO/Z9dKP4nLLtSY8RfobIfrJmZRr5C4eqrQxPseAt4ior8jw82sA3m+K4//1Gj5F7fI4/4n49RAZ4ZxS+DWZk00urVcq9t9Wpr5kfRz9R2ET/99BP27t2L3//+9877bDYbFEWBzWaDg5lpDYbDIa7VK4rvMZMmiZokZtb+sNsDb7f1N1dFETkPMnOVLcWkpWSTTLuDUNu4EaiuBpo1E9/7yrXxVQcnkNpa9wJ7M2boP77rz3/nTm3z8EVrmwdfzCh+KfP7HgrcIUYRQ08k1aVLF6WgoED58ssvlbKyMuX77793u0UaruzoF0l/2Rk11ylT5I4zZYrcvLRcVrv77tCt7AAil8aTa67NnDn6Ll35u+wZ7PFXrJB7bYEuY8nMVYZVDWtDIZL++6foZOrKzg8//IAPPvgAF1xwgbGRF0WcSPrLzqi5yva6lRmntd3BNdcAr74qd34jeNtdFRMTXGNVI3Ym+dOnD5Ca6r8paWqq/MqI1jYPnoxaIQpH4bxDjMiVrjo71113Hb755huj50IRKNxrf7hKSzNm3FVXyR3H2zjXWiQrV/q/rAaIoML1kk7LlnLnNorRfcWMrl3jjd0eOCB89VVtlwnVAO/558XXSAxMzOBa98mzAgm7nlM40bWyM2TIEEyePBnbt29HdnY2mjRp4vb4TTfdZMjkKPypf9kF6pkUTX/ZyeaE7NwJDB587ntvtUj8UZT65f8/+EDTVIP21lvGHs/I2jX+BKpF9OWXoav/IhvgzZgRmUFUQYFYgfRWZ+fZZ1lnh8KDrmDnnnvuAQA88cQT9R5jgnLDYlTPpFCoqDBm3Pffyx3HdZyvy1UyXC+ryRb3M0JOzrnkZKPs2mXsOG9qa4G//tX/mL/+NXTBRagCPCux6zmFO13BTl1dndHzoAilpWeS1X+1GnXJrV07ueOo42R2rPnjelmtaVN9x9AqJwf4+mvjj1tebuw4b55/Hgj0v6i6OjHu/vvljhnMbjkjq0+Hs3DZIUbkjaacnRtuuAGVlZXO72fNmoVjx445vz9y5Ai6du1q2OQo/BnVMykU1Etuvrqb2GzmXHIL1K1aiy5djDmOp2bNgOxsYOhQ4PhxcwIdIDQ5Xp9/buy40lKgfXvRW0vtsdW+vXyPLTO2nhORNpqCnU8++QQ1NTXO759++mkcPXrU+f3Zs2exK5j1Z4o4kfRXq1HJlD/8IHc+dVywO9FcL6s11rUWG1ibNsC2bcD77xt/6crVhRcaO84b2fnLjFMvP3oGqz/+KO6XCXhkOr3b7WIcEZlDU7CjeKzDe35PDU/79saOM5uaTHneee73Z2bW3+btS1aW3LnUcbK7wHxxXeUwKxAJ1apCKD74b7vNmHGBLj8qSv3dct7IdHqP1K3nRJHCpL8TqaHIzjZ2XCC+cie05FQEm0xZVmbsOH9SU90vq61aFfwxvTl9Wn5sMPkroag5o6X+jT8ylx89d8v5YnT1aSLSRlOwY7PZYPNY//f8nhqWn34ydpw/3rZuZ2YCI0YA8+cDBw+eu79NG5GA6mulJphkyj17tI2T3QUmwyVFzlA//ihq/wQKXnz9DJ57Tn6Lsdkf/EYlQR84IHcc2XHBFicMd+HY8oRIpSnYURQFd9xxB2JjYwEAp0+fxj333IOEhAQAcMvnoYYhVMGOr63bP/7ofZXg4EFg2DBg0SLj63ycOqVtXDDJtkeOuK8c5OQAmzbpP54vu3efu6zjK3jRWu3ZHzM/+I36nTTjdzvY6tPhyoggmMhMmnJ2xowZg7S0NCQnJyM5ORmjRo1CmzZtnN+npaVh9OjRZs2VwlCrVsaO8yaYrdtjxgTOqdCqUydt4666Cmikq1a54JrgrDbLNJMavLgm3wZqogrI5a+4MqsqsVG/k6H43Y4GvpK4vf0eEVlF08rO66+/btY8KEJ5JvoGO86bYLZuV1eLtgwDBug/v6f/+z9t49atC1z3xR/XBOeHH9Z/HFneOsAH+hl4q/ZsFaN+J0Pxux3pAgXBnr9HRFYJ4u9NonO1a/wJtnZNsFu3jW55oDVnZ80a4869erVxx/LHNXgBIqvhq1G/k6H43Y50WoJgIisx2KGgqLVrbDbvtWtstuAbAQbbRLS6Orjne5K9VGPU5TO1ceiaNYBHGzrTqcFLJDV8Nep3MhS/25EukoJgatgY7FDQjKhd48+llwb3/GuuqX+fa/fxNWu0BSZau54He1lnxoxzlXtDXZxRvYRmVfVpvYz6nTT7dzvSRVIQTA2bTWFlQFRVVSE5ORmVlZVISkqyejoRy6ytpzffDCxerO+5jRqJXVGuya/B7hz585+Bhx4KPG7WLODBB8X70ry58StMobBiBdCvn/i3mogKuOdoqAFQOH74G/U7yW3V3jkcomDogQPe83ZsNvHfVlkZ3y8yh+znN4sKkmHMagQomyPjzf331w90gt0+rafreWxsZAY7rrWL1FUOb4His8+GX6ADGPc7ySaX3qmX+m65RQQ23oLghn6pj8KDpZexZs6ciZycHCQmJiItLQ1Dhw6t11urT58+zmKG6u2ee+5xG7Nv3z7k5eUhPj4eaWlpKCoqwtmzZ0P5UgjBXRryR89im90OFBW5F6gzavv01q1yc1DHrVsn6uVEog0b3L8vKBBB3OrVQEmJ+FpWFp6BDoUGL/VRJLB0ZWft2rUoLCxETk4Ozp49i4cffhgDBgzAzp07nYUKAeCuu+7CE0884fw+Pj7e+W+Hw4G8vDxkZGRg/fr1OHToEEaPHo0mTZrg6aefDunracjMLCp29dXA+vWBx/XtC3Tr5rtAnVHbp2UDF3VcJCdnuq7sqLjKQZ6CbcFCZDZLg51ly5a5fT9v3jykpaVh8+bN6N27t/P++Ph4ZGRkeD3Gp59+ip07d2LFihVIT0/HJZdcgieffBIPPvggHn/8ccR4qVRWU1PjVu25qqrKoFfUMPmrbqy1sq43vxbsDujqq4Enn/T9uFE7R2Tno46L5ORMprBZK5JyhRgEUzgLq91YlZWVAICUlBS3++fPn4+WLVuie/fumDZtGk6ePOl8bMOGDcjOzkZ6errzvoEDB6Kqqgo7duzwep6ZM2c6qz4nJycjS7aNNdVjVGdof2T/BxponFE7R0aNkjuOOu6qq8L3AyqQESOsnkHDVVoqkn/79j23G699e1YkJtIjbIKduro6TJo0CVdffTW6d+/uvP+2227D22+/jdWrV2PatGl46623MMrl06a8vNwt0AHg/L7cR6e/adOmobKy0nnbv3+/Ca+oYdDSGVqvPn2Apk39j2naNHCwY9T26cmT/T/uOW79euNbVoTKf/9r9QwaJrZgIDJW2OzGKiwsxLfffovPP//c7f67777b+e/s7Gy0bt0a/fr1w969e9GxY0dd54qNjXU2M6Xg/PCDseO8cTiA2lr/Y2prxTh/KyhG7RzRWlQwknN2Ql3Xh9iCgcgMYbGyM378eCxduhSrV69GZoD67D179gQA7Pl1P3JGRgYOHz7sNkb93leeDxlHtv6N3jo5gOiMHai3VF2dGBeIETtHiooCj3EdF8k5O75Wwcg8bMFAZDxLgx1FUTB+/Hi8//77WLVqFTp06BDwOVt/3c/b+tdPkNzcXGzfvh0VFRXOMcuXL0dSUhK6du1qyrzpnBMnjB3nzf/+Z+y4YLdPa51Pr15Aaqr/sSkpooBfSQlwxRVyxw+FnByrZ9DwsAUDkfEsvYxVWFiIkpISLFmyBImJic4cm+TkZMTFxWHv3r0oKSnBDTfcgNTUVGzbtg2TJ09G79690aNHDwDAgAED0LVrV9x+++0oLi5GeXk5HnnkERQWFvJSVQi0b2/sOG9k/6e+bh0wYYLvreeugtk54lIVwZBxgFhB6dPnXIfxTZt0Tc1wv/xi9QwaHrZgIDKepSs7c+fORWVlJfr06YPWrVs7b//6178AADExMVixYgUGDBiAzp074/7778ewYcPw4YcfOo9ht9uxdOlS2O125ObmYtSoURg9erRbXR4yj2yzkWCakshejfz2W+CFF0RicHw8MHWq/nP6M3SotnEyRQWPHDl3WeKmm/TOzHitWlk9g4Yn0vqQEUUCS1d2ArXlysrKwtq1awMep127dvjoo4+MmhZpsG+fseO8uegi7c9xOIBnnhH/dq2i7Pq43vol7dppG6f1skQ4raZ45jaR+diCgch4YZGgTJHr/PONHefNuHH6/8c+e3b9nVzB1i9R//L2x/Uvb62XJQLl94QKVw+swxYMRMZisENBkb3kEsylmZgY4LLL9D3X4XDfpeWrfola7Vkm4LHbAwcwGRnnAjStlyW2bw88B7PZbFw9sBr7kBEZh8EOBeXnn40d501tLbBli/7nq7VijKr2fOoUsHGj/zEbN4pxwLnLEv7O6xpYfPed/2MbqWPH+qtUWVlcPQgXaiL9iBHnEtiJSDsGOw2IGV3Jv/rK2HHevPRScHNVa08aVe1Za50drUJZ22bvXuDWW7l6QETRjcFOA2FWnx2XNmWGjPNm1y79z7XbRc4PIAIZGYHGaa2zo64o+aJWxFUDul/rZobMs8+K/l1cPSCiaMVgpwEws8/O6tVy4z76SP+KUjA5LJdddq7ejlGrUFrr7GitiBvqvrSeeU1ERNGGwU6UC9RnBwiuK3mgnlWqw4f1rygFUxtyy5ZzczSqJtANN8gdRx2ndet5qFd2APbAIqLoxmAnypndZ0dPkKR1RammRvs5VK6rFp06yT0n0DjZkk7qOK1bz195RW68kXT21CUiiggMdqKc2X12ZAMIV1pXlIKp0QOcW7X4wx/kxgcaV1Uldxx1nNat56FeZXHNayIiikYMdqKc2X12du/W9zwtK0pLl+o7h0pdtTAqZ8el56zUOHXrOVA/4PFWETfUqyxTpvjvI0ZEFOkY7EQ5rdV+Q01mRen06eDO8f/+n/hq1G6sli3ljuM6TktF3GAqRmtht4vt8d7aaRARRRMGO1HObhdbiv0ZPlz/h+uJE/qep5JZUWraNLhzvPaa+GrUyo5sDpHnONmKuMFUjA6kUydg/HhgzhxRDoCBDhE1BJY2AiXzORxiy7c/77wDzJypL+BJSACqq7U/z2YTqxoyK0p5ecDbb2s/h2rHDvHVqN1Ysr2rvI1TK+L6E2zFaH+eew4YPNicYxMRhSuu7EQ5o6oG+6KnDYTWzs2ff679HK6+/FJ8NWo3VlKS3HFkx3kKtmK0L02bAgMGGH9cIqJwx2Anyh04YOw4T410/AZp7dx85oz2c7g6e1Z8lcmFkdmZlJgod17ZcZ7M2o01cCCrIxNRw8RgJ8r99JOx4zw1ayY/Tm/vpfbtdU3NSV3ZiokRO4/8kdmZJBsw6A0szNqNtXSpfBFIIqJowmAnygWTXyJj2jS5cY8+qr/30gcfaJ6Wm8YumWnFxWIHkucctOxM6tBB7ryy4zyZtRuLbSGIqKFisBPljhzRP662VuTVTJggvnpbFZD98AzmQ3bbNv3PBeq3myguFjuR5swJz51JMitQerEtBBE1RNyNFeVatdI3bupUYPZs90TZBx4QH8KuQYHWasJ66K3urMrLq39fTIyo4KzHDz8YO84b9T32/BkEi20hiKgh4spOlMvI0D5u6lTgmWfqf8g6HOL+qVPP3Wd2hWYAaN5c/3MBoHPn4J7vSTZgCDaw8LYCNWSI/uOxLQQRNVQMdqKc7KqAOq62Vqwm+DN79rlLWkOHyh1fdpw3772n/7lA8Lu5PBm1q0uGugL1/PPia0mJ/mOxLQQRNVQMdiKYwwGsWSOKBq5Z4z2wka2fo46TqfHimug6f77c8WXHebNokf7nAt6LKsrkI/kSEwPceKP/MTfeaE5g0ayZ9hUjtoUgooaOOTsRqrQUmDjRvWBgZqaokOu6rbuuTu546jjZBFZ13MmTcuNlx3kTbM6KZyAjm4/kbz6BCh1+/rkYZ/SuKocjcLuKzExg8mSxxb9jR7HCxBUdImrIuLITgUpLgVtuqV8Z+cABcX9p6bn7UlLkjqmO05qPEopgJztb/3MB4Lrrzv1bSz6SL2vWBN7lduSIGGc0mYrYP/4oemupl74Y6BBRQ8dgJ8I4HGJFx1v/JvW+SZPOfZhrTVAOZT6KrGDr7Kg5SFrzkXxZtUruvLLjtJDdmRbsDjYiomjCYCfCBPrLXlHce12dd57ccdVxWqsMq60YApEd500w+T75+UBcnPi31nwkX/btkzu37DgtQrH7jYgo2jDYiTBa/7Lv1StwdeTUVPfu48XFIkjwJj/fPa9F62UyPfQWwktOBhYv1n6cQOPatpU7juw4LfT8PImIGjoGOxFGz1/2v/zif6zn46Wl3i8d2WziftecIL1FC7Vo00bf8yorgd/85tz3RtXHcc0BMmIcERGZy6Yo3rI/GpaqqiokJyejsrISSUlJVk/Hr9paID7e/+UYu10kBMfEAEuWyNW4WbxYrNo4HKLxpr9LZVlZYqeP3S4CIFl6f9Nuugn48EN9zwWA48fFlm2t750vDgeQnu4/STk1FTh82PjdWGvWAH37Bh63erXoQ0ZEFM1kP7+5shNh1q+XyztZv178u6hI7rjqOJndPq45QaHw/ffBPf/228VXI7uev/qq/zGvvmpOM08mKBMRacdgJ8Jo/bA7dkxuvDruwAG58bLjjBBs24Xdu8/924iu54CoZbRokahp4yozU9zvWuvISExQJiLSjkUFI4zWD7tWrYCffgo8Xs2pkRnrOq5jR7nE32AClrfeAhIT9T//xAn374uLgRkzxK6rvXv1F94rKBCX/tatE8Fl69YiMdiMFR1Vr14ioAp0mZEJykRE5zDYiTDqh92BA95zYGw28bj6YTdlCvD//l/g46qXd7QmHE+bJnf8adPkjutNs2YiR+bwYX3P79Ch/n3BdD13ZbeHNjfGbgdGjBAFEH0ZPtzcgIuIKNJYehlr5syZyMnJQWJiItLS0jB06FDs2rXLbczp06dRWFiI1NRUNGvWDMOGDcNhj0+9ffv2IS8vD/Hx8UhLS0NRURHOBlPYJYzZ7aIlhK9kX0URvZ7UD7v27eWOq47TWoQwLU1uvOw4b2prgZ9/1v98o7ueW8nh8N7ry9U77wTfYoOIKJpYGuysXbsWhYWF+PLLL7F8+XKcOXMGAwYMwAmX6w6TJ0/Ghx9+iPfeew9r167FwYMHUeCSEOFwOJCXl4fa2lqsX78eb7zxBubNm4dHH33UipfU4MyaZew4b2SKAfpz0036nxtuwjGBnIgo3Fl6GWvZsmVu38+bNw9paWnYvHkzevfujcrKSvzjH/9ASUkJrvu1aMnrr7+OLl264Msvv8SVV16JTz/9FDt37sSKFSuQnp6OSy65BE8++SQefPBBPP7444iJssZAarsIX2w28XhyMlBRAWzdKndc9QP04EG58eo42Z1Sweyo+u9/9T8XCFxnKJJwNxYRkXZhtRursrISAJDya7ndzZs348yZM+jfv79zTOfOndG2bVts2LABALBhwwZkZ2cjPT3dOWbgwIGoqqrCjh07vJ6npqYGVVVVbrdIIdMu4scfgf79gdtuk99dpBYK/OILufHqOK27vfTYtk3/c4HgkpvDDXdjERFpFzbBTl1dHSZNmoSrr74a3bt3BwCUl5cjJiYGzZs3dxubnp6O8vJy5xjXQEd9XH3Mm5kzZyI5Odl5y8rKMvjVmMesv9jVreTffis3Xh13+rTc+JMnRa7JmjXaL0kF6jAeSKCaOJFETVD3VczRZuNuLCIiT2ET7BQWFuLbb7/FO++8Y/q5pk2bhsrKSudt//79pp/TKMEk+vrTrJn4KlvlWB1XVyd/jttuE9V/27d3bzkRSLBXIsvKgnt+OFET1IH6AY/6vWuCOhERhUmwM378eCxduhSrV69GpkuVtoyMDNTW1uKYxzWQw4cPI+PX7UAZGRn1dmep32f42FoUGxuLpKQkt1tDV1EhvgZqMqmSHefNgQPALbfIBzxXXaX/XIDIX4omBQXAwoX1O9pnZor7zSpoSEQUqSwNdhRFwfjx4/H+++9j1apV6OBREOXyyy9HkyZNsHLlSud9u3btwr59+5CbmwsAyM3Nxfbt21GhfloDWL58OZKSktC1a9fQvJAQcnmZhjp+XHz1/AD1RXacN+qq0KRJcpe0gr0kM3ZscM/3x+EQl+b0XqLTq6BAFEScMwcYP1583bOHgQ4RkTeW7sYqLCxESUkJlixZgsTERGeOTXJyMuLi4pCcnIyxY8diypQpSElJQVJSEiZMmIDc3FxceeWVAIABAwaga9euuP3221FcXIzy8nI88sgjKCwsRGxsrJUvzxTBrKj4066d+HrRRXLj1XGxsUBNjfbzKcq5LdKBivIFm1LlraigEUpLxc4314TxzExxmcnsoKO0FJgwwX333DPPAM8/z4CHiKgexUIAvN5ef/1155hTp04p48aNU1q0aKHEx8crN998s3Lo0CG343z//ffK4MGDlbi4OKVly5bK/fffr5w5c0Z6HpWVlQoApbKy0qiXZpq//EVRRKhg7O3YMXH8mhq58TU1YnyTJsGdt6Qk8Gs+e1ZRMjP1HT81VTzfaIsWKYrNVv98Npu4LVpk/Dldz+3vNZt5biKicCL7+W1TFNmU1Ogl2yI+HIwbB8yda/xxDx0SVZFPnQLi4wOPP3kSiIsDGjWST2r2ZvVquXYLQ4cCS5ZoP36zZmLbu5EJuw6HSLL2VQJAbdlRVmZ8orDDIV6Tv11wTZsC1dVMUiai6Cf7+R0WCcokz6yt55dcIr6OHy83Xh3XWOeFUC1bpGtrgaVL9Z2nulrk0hhJptaRWVWMly8PvN3/9GkxjoiIBAY7EUa2d5VW6oa3Dz6QG6+Ok83xcaV1i3Sw7SKMDnasrGI8e7ax44iIGgIGOxFGT3AhQ63bKFs3Rx13/vnaz6V1i/TevdrPYSYrqxjLtr6IphYZRETBYrATYf7wB3OO+/XX4uuvO/oDUsf95jdy4++4AygpETk6ZWXadgwFu5uqd+/gnu/JyirGOTnGjiMiaggY7ESYr74y57hqgT/ZAtbqONn5HDkCjBghkpG1Js5mZ2sb76mRwb/lVlYx/utfjR1HRNQQMNiJMGZ1tlC7o8u2ZlDHyV5iCuZS1M8/638uAPhokRYUq6oYx8UB+fn+x+Tni3FERCQw2Ikwn31mznFLSsTXZ5+VG6+Ok23SGUwzz2D7gf30U3DP96WgAPj+e3FpTu8lOj0WL/Yd8OTni8eJiOgcSysok3YrVphz3LNnxde33pIb/9ZbwNSpQEoK4NGazKuUFP1zC1arVuYd226XqxNktMWLRU2koiJg926gUydRQZkrOkRE9THYiTB6WjPISEgQX2tr5car42TzUoLJXwm2H1gwfbzCWVwc8MILVs+CiCj88TJWhAk2WdcXtdeq7CqFOk4tRhiI7DhvgtnCbdauKCIiihwMdiKMWQnKN90kvt58s9x4dZya2ByI7DhvAm319mf48Ohtm1BbK3KnJkwQX2VX5YiIGhoGOxFGJj9GD7UIndaEY61FCPXwt9U7kHfeCa76criaOlX0MJs8WVzKmjxZfD91qtUzIyIKPwx2IoxZfUrVgEB255I6TraGTbC1bnxt9Q7ErB5VVpo6VSQjewZxDoe4nwEPEZE7BjsR5uGHzTmuulsqNVVuvDruqqvkxsuO88dzq/dDD8k9z6xLf1aorQ3c92r2bF7SIiJyxWAnwujpRSXj1CnxVetlrC5d5MbLjgtE3eo9YgRw/Ljcc8yqOm0FmaaoDocYR0REAoOdCGNW/ol6eUy2Jo06bty4wAnAdrsYZzRFMXZcJNi929hxREQNAYOdCGNW/onabFM2J0YdFxMD3Hij/7E33ijfhkKLTp2MHRcJZBO09excIyKKVgx2CADw73+Lr1ddJbdSo+bgOBzA5s3+x2/ZYs6KlGwHeLM6xVuhZ09jxxERNQQMdiKMGa0J0tOB5GTx7/Xr5XJC1q8X/163DvjxR//jzdoRJZuLE005O1lZxo4jImoIGOxEGCN2NXmKiTkX4Bw4IPccddyhQ3LjZcdp8cMPxo6LBGqBRX9YNZqIyB2DnQjzyivGH9N15UVrnR3ZVg7BtHzwRba7dzR1AVcLLPrKybHZRDXlaK0aTUSkB4OdCGPWLht15UVrnZ0rrpAbLztOixMnjB0XKdQCi54rPFlZ4v6CAmvmRUQUrhjsRBizdtmoKy9a6+wUFcmNlx2nxYUXGjsukngWWFy9GigrY6BDRORNY6snQNrk5Bh/TNccD60rO6tWyY2XHafFM88AL74oNy4aqQUWiYjIP67shAnZDtZqw04juXYG17qyI9uWwIz2BXFxQH6+/zH5+WIcERE1XAx2woCWDtayFY61cO0M3qKF3HPUce3ayY2XHafV4sW+A578/OhKTiYiIn0Y7FhMawdrrV2/Zbjuxtq4Ue456rjGkhdCZcfpsXgxcPIkUFgIDBggvp48yUCHiIgE5uxYSLaD9YwZ59otqHVWAhXy00rdjSVb6Vgdl5AgN152nF5xcWJVjIiIyBNXdiykp4O13S46fhtN3Y0lW59FHdemjdx42XFERERGY7Bjob17tY9zOIAFC4ybg83mvhtLdreXOk62orMZlZ+JiIhk8DKWSRwOkQdz6JBYNenVq/6qSceOcsdyHSfTi0or14q7sru91HHs1UREROGOKzsmKC0F2rcH+vYFbrtNfG3fXtzvatw4uQ7j48ad+97IHlN2O/DAA+6F6GR3e6nj2KuJiIjCHYMdg5WWArfcUn/15cABcb9rwBMTA0yZ4v94U6acS04GjO0xVVcH/OUv7nOS3e2ljmOvJiIiCneWBjufffYZhgwZgjZt2sBms2Gxx17hO+64Azabze02aNAgtzFHjx7FyJEjkZSUhObNm2Ps2LGorq4O4as4x+EAJk4EFKX+Y+p9kya5JyUXF4tWCp7BgN0u7i8udr9fXUnx1zbC27G88TYnPSs17NVEREThzNJg58SJE7j44ovxop+a/4MGDcKhQ4ectwUe2bkjR47Ejh07sHz5cixduhSfffYZ7r77brOn7lWgfBpFca9poyouBo4fd68Tc/x4/UAHOLeSAtQPeGw2cVuw4FzPpDlz/O/48pyT3pUa9moiIqKwpYQJAMr777/vdt+YMWOU/Px8n8/ZuXOnAkDZuHGj876PP/5YsdlsyoEDB6TPXVlZqQBQKisrtU7bTUmJoojwwf+tpMT9eYsWKUpmpvuYzExxvy/enpOVVf85Rs7J2/GJiIisIvv5Hfa7sdasWYO0tDS0aNEC1113HWbMmIHUX7tQbtiwAc2bN8cVV1zhHN+/f380atQIX331FW6++Wavx6ypqUFNTY3z+6qqKkPmKptP4zpOzfHxvPSl5vj4ugxUUCDaIXjb8eW6E+zwYX1z93d8X2R2oAUrFOcgIqLoEtbBzqBBg1BQUIAOHTpg7969ePjhhzF48GBs2LABdrsd5eXlSEtLc3tO48aNkZKSgvLycp/HnTlzJqZPn274fHv21DYuUI6PzSbyafLzvX+ge+t6XVoqjul6OU0NgLyx2USujbfdUlq6ans7b2amuCRm1KWsUJyDiIiiT1jvxho+fDhuuukmZGdnY+jQoVi6dCk2btyINWvWBHXcadOmobKy0nnbv3+/IfN95RVt42RzfJ5/XuThrFnjP//G104wf4EOEPxuKS070ML5HEREFJ3COtjxdP7556Nly5bYs2cPACAjIwMVFRVuY86ePYujR48iIyPD53FiY2ORlJTkdjOC1orIsjVzJk/2X68H8L9KpPIMaDIzg98tpWcHmtHnUJTgz0FERNErooKdH3/8EUeOHEHrXxNMcnNzcezYMWzevNk5ZtWqVairq0NP2WtKBtJaEVlPzRxfKxkylZUdDrE7y8jdUnp3oBl5DiD4cxARUfSyNNiprq7G1q1bsXXrVgBAWVkZtm7din379qG6uhpFRUX48ssv8f3332PlypXIz8/HBRdcgIEDBwIAunTpgkGDBuGuu+7C119/jS+++ALjx4/H8OHD0caCzpNaKyLL1Mzx5Gu1RHaVKD1dNBLt08eYxF7Z8wZT+fnAAWPHERFRw2JpsLNp0yZceumluPTSSwEAU6ZMwaWXXopHH30Udrsd27Ztw0033YQLL7wQY8eOxeWXX45169YhNjbWeYz58+ejc+fO6NevH2644QZcc801ePXVVy15PVorIvurmeOPt9USPTvBjBCK8/70k9y4hQuBCRNEDlJtrf7zERFRdLEpir8sj4ahqqoKycnJqKysNCR/Z+pUYPZs95UXu10EOt4KBXobL6OkRKzSAOK57duL1Q1vP1F111VZmbFbtdXz+rvMlJUV3HnnzwdGjdL2HH/vNxERRQfZz++IytmJFMXFwMmTIj9m/Hjx9eRJ7x+8paWiP5We5FrX1ZJAlZUBc3pU2e3nAi5fhg8P7ryy/bpcORzAM8+IQJKIiBo2ruzA+JUdWTKrIt74W6XRuqoUrFCs7Oh9nwBxzpMn3ZupEhFRdODKTgSQ2WXkyd8qja9VIoejfndzo4Rip1Sgfl3+OBzASy/pPzcREUU+BjsW0rNDyVdtHJk6O2bUognFbizAd2d1GbL1j4iIKDqFdbuIaCe7Q2nOHLFl3F8vKC31bmRbQMgI5S4wz35dX3wBvPhi4OfJ1j8iIqLoxJwdWJ+zY8QOqgULRJXlQFx3cBnBql1ggNheHh/vf7WKOTtERNGLOTsRwMgdVGassDgcoh+Xv75cVu0CA7TXNSIiooaJwY7F1FwUz+3VWvtWBarGbLOJXVHeupt7U1oqVmz69g3cl8uo16DHlVcG9zgREUU/XsaCdZexXDkc53JR/OXm+KN2BgfcLympAZBs4KEex/M3I9BxjHgNWgTakm7mJTQiIrIeL2NFGLtdJA4H07fKiBWWUHQxN0oompASEVHk426sKOO5Y0nrCoveXV2lpSJIcn1uZqbI5zHrMlaotr0TEVFkY7ATwXxdNlJXifTQE0D4uux14IC436y8HauanxIRUWThZawIpSWBWIu0NG3jrLzsZXRSNhERRScGOxFIXUnxvNykrqSY0RbCFyvzZqzc9k5ERJGDwU6EMXslpaJC2zir82as3PZORESRgTk7EcbsthBa82DCIW8m2KRsIiKKbgx2IozZKylqHkyg9g9qHozW8WYJJimbiIiiGy9jRRizV1K05sEwb4aIiMIdg50IE4odSFrzYJg3Q0RE4YztIhAe7SK0MKotRCBa2z+Eul0EERE1bLKf3wx2EHnBDuC9YnFWlrhkxJUUIiJqCGQ/v5mgHKG4A4mIiEgOg50Ixh1IREREgTHYIZ+Ys0NERNGAwQ55pbWLuRVdz4mIiGRw63kUcjiANWuABQvEV62tI7T23gqnXl1ERESeuBsLkbkby5dgV1gcDtE93VdLCrUiclmZuESldTwREZFRZD+/ubITRYxYYdHaxdzKrudEREQyGOxECaO6oWvtvWV113MiIqJAGOxECaNWWCKx6zkREZE/DHaihFErLFp7b4WiVxcREVEwGOxECaNWWNj1nIiIog2DnShh5AoLu54TEVE0sTTY+eyzzzBkyBC0adMGNpsNixcvdntcURQ8+uijaN26NeLi4tC/f3/s3r3bbczRo0cxcuRIJCUloXnz5hg7diyqq6tD+CrCg9ErLAUFwPffA6tXAyUl4mtZme/ARet4IiKiULE02Dlx4gQuvvhivPjii14fLy4uxt/+9je8/PLL+Oqrr5CQkICBAwfi9OnTzjEjR47Ejh07sHz5cixduhSfffYZ7r777lC9hLBi9AqL2ntrxAjxNVCgpHU8ERFRKIRNUUGbzYb3338fQ4cOBSBWddq0aYP7778fDzzwAACgsrIS6enpmDdvHoYPH47//ve/6Nq1KzZu3IgrrrgCALBs2TLccMMN+PHHH9GmTRupc0dTUUGAPaqIiKhhiPiigmVlZSgvL0f//v2d9yUnJ6Nnz57YsGEDAGDDhg1o3ry5M9ABgP79+6NRo0b46quvfB67pqYGVVVVbrdowhUWIiKic8I22CkvLwcApKenu92fnp7ufKy8vBxpaWlujzdu3BgpKSnOMd7MnDkTycnJzltWVpbBsyciIqJwEbbBjpmmTZuGyspK523//v1WT4mIiIhMErbBTkZGBgDg8OHDbvcfPnzY+VhGRgYqKircHj979iyOHj3qHONNbGwskpKS3G5EREQUncI22OnQoQMyMjKwcuVK531VVVX46quvkJubCwDIzc3FsWPHsHnzZueYVatWoa6uDj179gz5nImIiCj8NLby5NXV1dizZ4/z+7KyMmzduhUpKSlo27YtJk2ahBkzZqBTp07o0KED/vSnP6FNmzbOHVtdunTBoEGDcNddd+Hll1/GmTNnMH78eAwfPlx6JxYRERFFN0uDnU2bNqFv377O76dMmQIAGDNmDObNm4epU6fixIkTuPvuu3Hs2DFcc801WLZsGZo2bep8zvz58zF+/Hj069cPjRo1wrBhw/C3v/0t5K+FiIiIwlPY1NmxUrTV2SEiImoIIr7ODhEREZERGOwQERFRVLM0ZydcqFfyoq2SMhERUTRTP7cDZeQw2AFw/PhxAGAlZSIiogh0/PhxJCcn+3ycCcoA6urqcPDgQSQmJsJmsxl23KqqKmRlZWH//v0NJvGZr5mvOVrxNfM1R6tIfs2KouD48eNo06YNGjXynZnDlR0AjRo1QmZmpmnHb4hVmvmaGwa+5oaBr7lhiNTX7G9FR8UEZSIiIopqDHaIiIgoqjHYMVFsbCwee+wxxMbGWj2VkOFrbhj4mhsGvuaGoSG8ZiYoExERUVTjyg4RERFFNQY7REREFNUY7BAREVFUY7BDREREUY3Bjgk+++wzDBkyBG3atIHNZsPixYutnpLpZs6ciZycHCQmJiItLQ1Dhw7Frl27rJ6WqebOnYsePXo4C3Hl5ubi448/tnpaITNr1izYbDZMmjTJ6qmY6vHHH4fNZnO7de7c2eppmerAgQMYNWoUUlNTERcXh+zsbGzatMnqaZmqffv29X7ONpsNhYWFVk/NFA6HA3/605/QoUMHxMXFoWPHjnjyyScD9piKVKygbIITJ07g4osvxp133omCggKrpxMSa9euRWFhIXJycnD27Fk8/PDDGDBgAHbu3ImEhASrp2eKzMxMzJo1C506dYKiKHjjjTeQn5+P//znP+jWrZvV0zPVxo0b8corr6BHjx5WTyUkunXrhhUrVji/b9w4ev/X+csvv+Dqq69G37598fHHH6NVq1bYvXs3WrRoYfXUTLVx40Y4HA7n999++y2uv/56/Pa3v7VwVub585//jLlz5+KNN95At27dsGnTJvz+979HcnIy7rvvPqunZ7jo/S/WQoMHD8bgwYOtnkZILVu2zO37efPmIS0tDZs3b0bv3r0tmpW5hgwZ4vb9U089hblz5+LLL7+M6mCnuroaI0eOxN///nfMmDHD6umEROPGjZGRkWH1NELiz3/+M7KysvD666877+vQoYOFMwqNVq1auX0/a9YsdOzYEddee61FMzLX+vXrkZ+fj7y8PABiZWvBggX4+uuvLZ6ZOXgZi0xRWVkJAEhJSbF4JqHhcDjwzjvv4MSJE8jNzbV6OqYqLCxEXl4e+vfvb/VUQmb37t1o06YNzj//fIwcORL79u2zekqm+eCDD3DFFVfgt7/9LdLS0nDppZfi73//u9XTCqna2lq8/fbbuPPOOw1tDh1OrrrqKqxcuRL/+9//AADffPMNPv/886j9Q50rO2S4uro6TJo0CVdffTW6d+9u9XRMtX37duTm5uL06dNo1qwZ3n//fXTt2tXqaZnmnXfewZYtW7Bx40arpxIyPXv2xLx583DRRRfh0KFDmD59Onr16oVvv/0WiYmJVk/PcN999x3mzp2LKVOm4OGHH8bGjRtx3333ISYmBmPGjLF6eiGxePFiHDt2DHfccYfVUzHNQw89hKqqKnTu3Bl2ux0OhwNPPfUURo4cafXUTMFghwxXWFiIb7/9Fp9//rnVUzHdRRddhK1bt6KyshILFy7EmDFjsHbt2qgMePbv34+JEydi+fLlaNq0qdXTCRnXv3R79OiBnj17ol27dnj33XcxduxYC2dmjrq6OlxxxRV4+umnAQCXXnopvv32W7z88ssNJtj5xz/+gcGDB6NNmzZWT8U07777LubPn4+SkhJ069YNW7duxaRJk9CmTZuo/Dkz2CFDjR8/HkuXLsVnn32GzMxMq6djupiYGFxwwQUAgMsvvxwbN27Ec889h1deecXimRlv8+bNqKiowGWXXea8z+Fw4LPPPsMLL7yAmpoa2O12C2cYGs2bN8eFF16IPXv2WD0VU7Ru3bpesN6lSxcsWrTIohmF1g8//IAVK1agtLTU6qmYqqioCA899BCGDx8OAMjOzsYPP/yAmTNnMtgh8kVRFEyYMAHvv/8+1qxZ0yASGr2pq6tDTU2N1dMwRb9+/bB9+3a3+37/+9+jc+fOePDBBxtEoAOIBO29e/fi9ttvt3oqprj66qvrlY343//+h3bt2lk0o9B6/fXXkZaW5kzcjVYnT55Eo0buabt2ux11dXUWzchcDHZMUF1d7fZXX1lZGbZu3YqUlBS0bdvWwpmZp7CwECUlJViyZAkSExNRXl4OAEhOTkZcXJzFszPHtGnTMHjwYLRt2xbHjx9HSUkJ1qxZg08++cTqqZkiMTGxXg5WQkICUlNTozo364EHHsCQIUPQrl07HDx4EI899hjsdjtGjBhh9dRMMXnyZFx11VV4+umn8bvf/Q5ff/01Xn31Vbz66qtWT810dXV1eP311zFmzJioLi8AiN2kTz31FNq2bYtu3brhP//5D2bPno0777zT6qmZQyHDrV69WgFQ7zZmzBirp2Yab68XgPL6669bPTXT3HnnnUq7du2UmJgYpVWrVkq/fv2UTz/91OpphdS1116rTJw40eppmOrWW29VWrdurcTExCjnnXeecuuttyp79uyxelqm+vDDD5Xu3bsrsbGxSufOnZVXX33V6imFxCeffKIAUHbt2mX1VExXVVWlTJw4UWnbtq3StGlT5fzzz1f++Mc/KjU1NVZPzRQ2RYnScolEREREYJ0dIiIiinIMdoiIiCiqMdghIiKiqMZgh4iIiKIagx0iIiKKagx2iIiIKKox2CEiIqKoxmCHiIiIohqDHSKKKPPmzUPz5s0tnUOfPn0wadIkS+dARPJYQZmIDHHHHXfgjTfeqHf/wIEDsWzZMsPOc+rUKRw/fhxpaWmGHVOro0ePokmTJkhMTLRsDkQkL7o7nRFRSA0aNAivv/66232xsbGGniMuLs7y5rIpKSmWnp+ItOFlLCIyTGxsLDIyMtxuLVq0cD5us9nw2muv4eabb0Z8fDw6deqEDz74wO0YH3zwATp16oSmTZuib9++eOONN2Cz2XDs2DEA9S9jPf7447jkkkvw1ltvoX379khOTsbw4cNx/Phx55i6ujrMnDkTHTp0QFxcHC6++GIsXLjQ72t56aWXnPNIT0/HLbfc4nzM9TLWmjVrYLPZ6t3uuOMO5/glS5bgsssuQ9OmTXH++edj+vTpOHv2rMZ3l4j0YrBDRCE1ffp0/O53v8O2bdtwww03YOTIkTh69CgAoKysDLfccguGDh2Kb775Bn/4wx/wxz/+MeAx9+7di8WLF2Pp0qVYunQp1q5di1mzZjkfnzlzJt588028/PLL2LFjByZPnoxRo0Zh7dq1Xo+3adMm3HfffXjiiSewa9cuLFu2DL179/Y69qqrrsKhQ4ect1WrVqFp06bO8evWrcPo0aMxceJE7Ny5E6+88grmzZuHp556SutbR0R6Wdt0nYiixZgxYxS73a4kJCS43Z566innGADKI4884vy+urpaAaB8/PHHiqIoyoMPPqh0797d7bh//OMfFQDKL7/8oiiKorz++utKcnKy8/HHHntMiY+PV6qqqpz3FRUVKT179lQURVFOnz6txMfHK+vXr3c77tixY5URI0Z4fS2LFi1SkpKS3I7p6tprr1UmTpxY7/6ff/5ZOf/885Vx48Y57+vXr5/y9NNPu4176623lNatW3s9NhEZjzk7RGSYvn37Yu7cuW73eea39OjRw/nvhIQEJCUloaKiAgCwa9cu5OTkuI3/zW9+E/C87du3d0sWbt26tfOYe/bswcmTJ3H99de7Pae2thaXXnqp1+Ndf/31aNeuHc4//3wMGjQIgwYNcl568+XMmTMYNmwY2rVrh+eee855/zfffIMvvvjCbSXH4XDg9OnTOHnypN9jEpExGOwQkWESEhJwwQUX+B3TpEkTt+9tNhvq6uqCOq+/Y1ZXVwMA/v3vf+O8885zG+creToxMRFbtmzBmjVr8Omnn+LRRx/F448/jo0bN/rc9n7vvfdi//79+Prrr9G48bn/tVZXV2P69OkoKCio95ymTZtKv0Yi0o/BDhGFjYsuuggfffSR230bN24M6phdu3ZFbGws9u3bh2uvvVb6eY0bN0b//v3Rv39/PPbYY2jevDlWrVrlNWiZPXs23n33Xaxfvx6pqaluj1122WXYtWtXwCCQiMzDYIeIDFNTU4Py8nK3+xo3boyWLVtKPf8Pf/gDZs+ejQcffBBjx47F1q1bMW/ePABitUaPxMREPPDAA5g8eTLq6upwzTXXoLKyEl988QWSkpIwZsyYes9ZunQpvvvuO/Tu3RstWrTARx99hLq6Olx00UX1xq5YsQJTp07Fiy++iJYtWzpff1xcHJKTk/Hoo4/ixhtvRNu2bXHLLbegUaNG+Oabb/Dtt99ixowZul4TEWnD3VhEZJhly5ahdevWbrdrrrlG+vkdOnTAwoULUVpaih49emDu3LnO3VjB1Ot58skn8ac//QkzZ85Ely5dMGjQIPz73/9Ghw4dvI5v3rw5SktLcd1116FLly54+eWXsWDBAnTr1q3e2M8//xwOhwP33HOP2+ueOHEiAFFUcenSpfj000+Rk5ODK6+8EnPmzEG7du10vx4i0oYVlIkorD311FN4+eWXsX//fqunQkQRipexiCisvPTSS8jJyUFqaiq++OILPPPMMxg/frzV0yKiCMZgh4jCyu7duzFjxgwcPXoUbdu2xf33349p06ZZPS0iimC8jEVERERRjQnKREREFNUY7BAREVFUY7BDREREUY3BDhEREUU1BjtEREQU1RjsEBERUVRjsENERERRjcEOERERRbX/DyIeJfRvoJztAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.scatter(cdf.ENGINESIZE, cdf.CO2EMISSIONS,  color='blue')\n",
        "plt.xlabel(\"Engine size\")\n",
        "plt.ylabel(\"Emission\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zW178e-lp4Fk"
      },
      "source": [
        "## Practice\n",
        "Plot __CYLINDER__ vs the Emission, to see how linear is their relationship is:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "gbM-CixSp4Fl",
        "outputId": "f5fee34e-71d3-4079-9900-68052ccbcee9"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# write your code here\n",
        "plt.scatter(cdf.CYLINDERS, cdf.CO2EMISSIONS,  color='blue')\n",
        "plt.xlabel(\"CYLINDERS\")\n",
        "plt.ylabel(\"Emission\")\n",
        "plt.show()\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0a9KwPlYp4Fl"
      },
      "source": [
        "<details><summary>Click here for the solution</summary>\n",
        "\n",
        "```python    \n",
        "plt.scatter(cdf.CYLINDERS, cdf.CO2EMISSIONS, color='blue')\n",
        "plt.xlabel(\"Cylinders\")\n",
        "plt.ylabel(\"Emission\")\n",
        "plt.show()\n",
        "\n",
        "```\n",
        "\n",
        "</details>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "xADXqgYZp4Fl"
      },
      "source": [
        "#### Creating train and test dataset\n",
        "Train/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.\n",
        "This 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",
        "\n",
        "This 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",
        "\n",
        "Let'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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "mr6Hys3wp4Fl"
      },
      "outputs": [],
      "source": [
        "msk = np.random.rand(len(df)) < 0.8\n",
        "train = cdf[msk]\n",
        "test = cdf[~msk]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "zBPF5vTep4Fl"
      },
      "source": [
        "### Simple Regression Model\n",
        "Linear 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"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "v7rPtLRIp4Fl"
      },
      "source": [
        "#### Train data distribution\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "XayjVujXp4Fl",
        "outputId": "395b5abe-1255-49e3-af74-9b6c1b82fc31"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS,  color='blue')\n",
        "plt.xlabel(\"Engine size\")\n",
        "plt.ylabel(\"Emission\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "4EWrOKuAp4Fl"
      },
      "source": [
        "#### Modeling\n",
        "Using sklearn package to model data.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iQEu-_QEp4Fm",
        "outputId": "d9a36684-92aa-4fe2-cec4-3053ab3c648d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Coefficients:  [[38.97328807]]\n",
            "Intercept:  [125.7536746]\n"
          ]
        }
      ],
      "source": [
        "from sklearn import linear_model\n",
        "regr = linear_model.LinearRegression()\n",
        "train_x = np.asanyarray(train[['ENGINESIZE']])\n",
        "train_y = np.asanyarray(train[['CO2EMISSIONS']])\n",
        "regr.fit(train_x, train_y)\n",
        "# The coefficients\n",
        "print ('Coefficients: ', regr.coef_)\n",
        "print ('Intercept: ',regr.intercept_)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IQ35zbIHp4Fm"
      },
      "source": [
        "As mentioned before, __Coefficient__ and __Intercept__ in the simple linear regression, are the parameters of the fit line.\n",
        "Given 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.\n",
        "Notice that all of the data must be available to traverse and calculate the parameters.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "RTtZ73FBp4Fm"
      },
      "source": [
        "#### Plot outputs\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pF7kIYKFp4Fm"
      },
      "source": [
        "We can plot the fit line over the data:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "KqhQxvKBp4Fm",
        "outputId": "e765f055-fa99-4a63-d53f-0d8142eebc6b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Emission')"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS,  color='blue')\n",
        "plt.plot(train_x, regr.coef_[0][0]*train_x + regr.intercept_[0], '-r')\n",
        "plt.xlabel(\"Engine size\")\n",
        "plt.ylabel(\"Emission\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "tnmdZvvop4Fm"
      },
      "source": [
        "#### Evaluation\n",
        "We 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",
        "\n",
        "There are different model evaluation metrics, lets use MSE here to calculate the accuracy of our model based on the test set:\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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wrFDVGWAp4Fm",
        "outputId": "f2edbf4b-f0db-4163-e6f2-488ca0ddb54d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean absolute error: 24.59\n",
            "Residual sum of squares (MSE): 1001.05\n",
            "R2-score: 0.77\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import r2_score\n",
        "\n",
        "test_x = np.asanyarray(test[['ENGINESIZE']])\n",
        "test_y = np.asanyarray(test[['CO2EMISSIONS']])\n",
        "test_y_ = regr.predict(test_x)\n",
        "\n",
        "print(\"Mean absolute error: %.2f\" % np.mean(np.absolute(test_y_ - test_y)))\n",
        "print(\"Residual sum of squares (MSE): %.2f\" % np.mean((test_y_ - test_y) ** 2))\n",
        "print(\"R2-score: %.2f\" % r2_score(test_y , test_y_) )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RnROCQN1p4Fm"
      },
      "source": [
        "## Exercise\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4n-b-oYwp4Fm"
      },
      "source": [
        "Lets see what the evaluation metrics are if we trained a regression model using the `FUELCONSUMPTION_COMB` feature.\n",
        "\n",
        "Start 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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "V0UKhdBzp4Fn"
      },
      "outputs": [],
      "source": [
        "train_x = train[[\"FUELCONSUMPTION_COMB\"]]\n",
        "\n",
        "test_x = test[[\"FUELCONSUMPTION_COMB\"]]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t2ZiYDC6p4Fn"
      },
      "source": [
        "<details><summary>Click here for the solution</summary>\n",
        "\n",
        "```python    \n",
        "train_x = train[[\"FUELCONSUMPTION_COMB\"]]\n",
        "\n",
        "test_x = test[[\"FUELCONSUMPTION_COMB\"]]\n",
        "\n",
        "```\n",
        "\n",
        "</details>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jeJmD99Ep4Fn"
      },
      "source": [
        "Now train a Linear Regression Model using the `train_x` you created and the `train_y` created previously\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "id": "xCZczuehp4Fu",
        "outputId": "4701dd86-53a6-48f8-cc75-d3d6c7a7fd8b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
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            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "regr = linear_model.LinearRegression()\n",
        "\n",
        "regr.fit(train_x, train_y)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jvLtau7Hp4Fu"
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      "source": [
        "<details><summary>Click here for the solution</summary>\n",
        "\n",
        "```python    \n",
        "regr = linear_model.LinearRegression()\n",
        "\n",
        "regr.fit(train_x, train_y)\n",
        "\n",
        "```\n",
        "\n",
        "</details>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ndUmUfpyp4Fu"
      },
      "source": [
        "Find the predictions using the model's `predict` function and the `test_x` data\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iABdBjw8p4Fu"
      },
      "outputs": [],
      "source": [
        "predictions = regr.predict(test_x)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dDjVPSL8p4Fu"
      },
      "source": [
        "<details><summary>Click here for the solution</summary>\n",
        "\n",
        "```python    \n",
        "predictions = regr.predict(test_x)\n",
        "\n",
        "```\n",
        "\n",
        "</details>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OQKehGyIp4Fu"
      },
      "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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VogWqFLDp4Fu",
        "outputId": "8dc73525-4597-449b-deac-03f37836559a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Absolute Error: 21.81\n"
          ]
        }
      ],
      "source": [
        "print(\"Mean Absolute Error: %.2f\" % np.mean(np.absolute(predictions - test_y)))\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EXEF8dcop4Fu"
      },
      "source": [
        "<details><summary>Click here for the solution</summary>\n",
        "\n",
        "```python    \n",
        "print(\"Mean Absolute Error: %.2f\" % np.mean(np.absolute(predictions - test_y)))\n",
        "\n",
        "```\n",
        "\n",
        "</details>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qiY-tSaWp4Fv"
      },
      "source": [
        "We can see that the MAE is much worse when we train using `ENGINESIZE` than `FUELCONSUMPTION_COMB`\n"
      ]
    },
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      "source": [
        "<h2>Want to learn more?</h2>\n",
        "\n",
        "IBM 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-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork1047-2023-01-01\">SPSS Modeler</a>\n",
        "\n",
        "Also, 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-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork1047-2023-01-01\">Watson Studio</a>\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kiTL5JABp4Fv"
      },
      "source": [
        "### Thank you for completing this lab!\n",
        "\n",
        "\n",
        "## Author\n",
        "\n",
        "Saeed Aghabozorgi\n",
        "\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-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork1047-2023-01-01\">Joseph Santarcangelo</a>\n",
        "\n",
        "Azim Hirjani\n",
        "\n",
        "\n",
        "## Change Log\n",
        "\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",
        "\n",
        "## <h3 align=\"center\"> © IBM Corporation 2020. All rights reserved. <h3/>\n"
      ]
    }
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