Regression_Intro.ipynb

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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/tawfiqam/MI564/blob/main/Regression_Intro.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TIB1uKXC4amn"
      },
      "source": [
        "#What is the main difference between regression and classification?\n",
        "If you look at the classifier examples shared earlier, you will find that regression models predict a continuous variable as opposed to a class. So, in classifiers, we \"classify\" things so that they are in different classes. We first used a logsitic regression to [classify students](https://github.com/tawfiqam/MI564/blob/main/Logistic_Regression_Classifier_Intro.ipynb) accepted (class 1) to a program and those who are not (class 2). We used a random forest classifier to [differentiate different types of wine](https://github.com/tawfiqam/MI564/blob/main/Random_Forest_Intro.ipynb). We also used a Naive Bayes classifier to [classify posts to two different online communities](https://github.com/tawfiqam/MI564/blob/main/Naive_Bayes_Intro.ipynb).\n",
        "\n",
        "With regression, we will predict a value. As I laid out before, this value represents a continous variable. A continous variable can take on an uncountable set of values. So while classifiers used discrete variables (basically integers - [0,1,2,3]), regression gets us a predicted continous variable - usually including a fraction [0,0.345,100.45]."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D01LhoVy4arI"
      },
      "source": [
        "##Regression Evaluation Metrics\n",
        "\n",
        "Remember that we used evaluation metrics for classifiers including accuracy, precision, recall, F1 score, and ROC curves when working with classifiers. But these will not work here because, again, we are not classifying into discrete groups, but predicting specific numbers. \n",
        "\n",
        "There are two metrics used to evaluate regression models:\n",
        "\n",
        "**R-squared**: R-squared values range between 0 and 1. They are commonly stated as percentages. It is a statistical measure that represents the proportion of the variance for a target variable that is explained by the independent variables. The higher the R-squared the better the model.\n",
        "\n",
        "**Root Mean Squared Error (RMSE)**: measures the average magnitude of the residuals or error. What is the error? This is the difference between our prediction and the actual value. \n",
        "\n",
        "Ideally, lower RMSE and higher R-squared values are indicative of a good model. \n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
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        "id": "kHQhlJFG8ks_",
        "outputId": "af1be2a0-483e-4d2a-a07c-ea8024d61437"
      },
      "source": [
        "# load and summarize the housing dataset\n",
        "from pandas import read_csv\n",
        "from matplotlib import pyplot\n",
        "# load dataset\n",
        "url = 'https://raw.githubusercontent.com/jbrownlee/Datasets/master/housing.csv'\n",
        "dataframe = read_csv(url, header=None)\n",
        "# summarize shape\n",
        "print(dataframe.shape)\n",
        "# summarize first few lines\n",
        "print(dataframe.head())"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
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            "(506, 14)\n",
            "        0     1     2   3      4      5   ...  8      9     10      11    12    13\n",
            "0  0.00632  18.0  2.31   0  0.538  6.575  ...   1  296.0  15.3  396.90  4.98  24.0\n",
            "1  0.02731   0.0  7.07   0  0.469  6.421  ...   2  242.0  17.8  396.90  9.14  21.6\n",
            "2  0.02729   0.0  7.07   0  0.469  7.185  ...   2  242.0  17.8  392.83  4.03  34.7\n",
            "3  0.03237   0.0  2.18   0  0.458  6.998  ...   3  222.0  18.7  394.63  2.94  33.4\n",
            "4  0.06905   0.0  2.18   0  0.458  7.147  ...   3  222.0  18.7  396.90  5.33  36.2\n",
            "\n",
            "[5 rows x 14 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ac4V7O1F-egz"
      },
      "source": [
        "from sklearn.linear_model import Lasso\n",
        "model = Lasso(alpha=1.0)"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gqVRhYOILPmO"
      },
      "source": [
        "We are going to use the Boston Housing Data in our analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6oNun9NaKwNv"
      },
      "source": [
        "1. Title: Boston Housing Data\n",
        "\n",
        "2. Sources:\n",
        "   (a) Origin:  This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\n",
        "   (b) Creator:  Harrison, D. and Rubinfeld, D.L. 'Hedonic prices and the \n",
        "demand for clean air', J. Environ. Economics & Management,\n",
        "vol.5, 81-102, 1978.\n",
        "   (c) Date: July 7, 1993\n",
        "\n",
        "3. Past Usage:\n",
        "   -   Used in Belsley, Kuh & Welsch, 'Regression diagnostics ...', Wiley, \n",
        "       1980.   N.B. Various transformations are used in the table on\n",
        "       pages 244-261.\n",
        "    -  Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning.\n",
        "       In Proceedings on the Tenth International Conference of Machine \n",
        "       Learning, 236-243, University of Massachusetts, Amherst. Morgan\n",
        "       Kaufmann.\n",
        "\n",
        "4. Relevant Information:\n",
        "\n",
        "   Concerns housing values in suburbs of Boston.\n",
        "\n",
        "5. Number of Instances: 506\n",
        "\n",
        "6. Number of Attributes: 13 continuous attributes (including \"class\" attribute \"MEDV\"), 1 binary-valued attribute.\n",
        "\n",
        "7. Attribute Information:\n",
        "    0. CRIM      per capita crime rate by town\n",
        "    1. ZN        proportion of residential land zoned for lots over 25,000 sq.ft.\n",
        "    2. INDUS     proportion of non-retail business acres per town\n",
        "    3. CHAS      Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\n",
        "    4. NOX       nitric oxides concentration (parts per 10 million)\n",
        "    5. RM        average number of rooms per dwelling\n",
        "    6. AGE       proportion of owner-occupied units built prior to 1940\n",
        "    7. DIS       weighted distances to five Boston employment centres\n",
        "    8. RAD       index of accessibility to radial highways\n",
        "    9. TAX      full-value property-tax rate per $10,000\n",
        "    10. PTRATIO  pupil-teacher ratio by town\n",
        "    11. B        1000(Bk - 0.63)^2 where Bk is the proportion of blacks \n",
        "by town\n",
        "    12. LSTAT    % lower status of the population\n",
        "    13. MEDV     Median value of owner-occupied homes in $1000's\n",
        "\n",
        "8. Missing Attribute Values:  None."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YP2R3_7oiPuG"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "#import the R^2 metric\n",
        "from sklearn.metrics import r2_score\n",
        "#import the mean_square_error metric\n",
        "from sklearn.metrics import mean_squared_error\n",
        "\n",
        "#import the SVM regressor\n",
        "from sklearn.svm import SVR\n",
        "#import the Ridge regressor\n",
        "from sklearn.linear_model import Ridge\n",
        "#import the Lasso regressor\n",
        "from sklearn.linear_model import Lasso\n",
        "#import the Decision Tree regressor\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "#import the linear regressor\n",
        "from sklearn.linear_model import LinearRegression\n",
        "#import the Random Forest regressor\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "\n",
        "#import matplotlib for graphing\n",
        "import matplotlib.pyplot as plt\n",
        "plt.rcParams.update({'font.size':20,'figure.figsize':(15,12)})\n",
        "\n",
        "# load the dataset\n",
        "# this url holds a dataset saved as a csv\n",
        "url = 'https://raw.githubusercontent.com/jbrownlee/Datasets/master/housing.csv'\n",
        "\n",
        "df = pd.read_csv(url, header=None)"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "ouv3OMvLOliD",
        "outputId": "f880d8c4-9adb-437c-cb99-13afb1d15422"
      },
      "source": [
        "#let's take a look at the dataset\n",
        "df.describe()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>0</th>\n",
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              "      <th>9</th>\n",
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              "      <th>12</th>\n",
              "      <th>13</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.613524</td>\n",
              "      <td>11.363636</td>\n",
              "      <td>11.136779</td>\n",
              "      <td>0.069170</td>\n",
              "      <td>0.554695</td>\n",
              "      <td>6.284634</td>\n",
              "      <td>68.574901</td>\n",
              "      <td>3.795043</td>\n",
              "      <td>9.549407</td>\n",
              "      <td>408.237154</td>\n",
              "      <td>18.455534</td>\n",
              "      <td>356.674032</td>\n",
              "      <td>12.653063</td>\n",
              "      <td>22.532806</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>8.601545</td>\n",
              "      <td>23.322453</td>\n",
              "      <td>6.860353</td>\n",
              "      <td>0.253994</td>\n",
              "      <td>0.115878</td>\n",
              "      <td>0.702617</td>\n",
              "      <td>28.148861</td>\n",
              "      <td>2.105710</td>\n",
              "      <td>8.707259</td>\n",
              "      <td>168.537116</td>\n",
              "      <td>2.164946</td>\n",
              "      <td>91.294864</td>\n",
              "      <td>7.141062</td>\n",
              "      <td>9.197104</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.006320</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.460000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.385000</td>\n",
              "      <td>3.561000</td>\n",
              "      <td>2.900000</td>\n",
              "      <td>1.129600</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>187.000000</td>\n",
              "      <td>12.600000</td>\n",
              "      <td>0.320000</td>\n",
              "      <td>1.730000</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.082045</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.190000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.449000</td>\n",
              "      <td>5.885500</td>\n",
              "      <td>45.025000</td>\n",
              "      <td>2.100175</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>279.000000</td>\n",
              "      <td>17.400000</td>\n",
              "      <td>375.377500</td>\n",
              "      <td>6.950000</td>\n",
              "      <td>17.025000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.256510</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>9.690000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.538000</td>\n",
              "      <td>6.208500</td>\n",
              "      <td>77.500000</td>\n",
              "      <td>3.207450</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>330.000000</td>\n",
              "      <td>19.050000</td>\n",
              "      <td>391.440000</td>\n",
              "      <td>11.360000</td>\n",
              "      <td>21.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3.677082</td>\n",
              "      <td>12.500000</td>\n",
              "      <td>18.100000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.624000</td>\n",
              "      <td>6.623500</td>\n",
              "      <td>94.075000</td>\n",
              "      <td>5.188425</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>666.000000</td>\n",
              "      <td>20.200000</td>\n",
              "      <td>396.225000</td>\n",
              "      <td>16.955000</td>\n",
              "      <td>25.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>88.976200</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>27.740000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.871000</td>\n",
              "      <td>8.780000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>12.126500</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>711.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>396.900000</td>\n",
              "      <td>37.970000</td>\n",
              "      <td>50.000000</td>\n",
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            "text/plain": [
              "               0           1           2   ...          11          12          13\n",
              "count  506.000000  506.000000  506.000000  ...  506.000000  506.000000  506.000000\n",
              "mean     3.613524   11.363636   11.136779  ...  356.674032   12.653063   22.532806\n",
              "std      8.601545   23.322453    6.860353  ...   91.294864    7.141062    9.197104\n",
              "min      0.006320    0.000000    0.460000  ...    0.320000    1.730000    5.000000\n",
              "25%      0.082045    0.000000    5.190000  ...  375.377500    6.950000   17.025000\n",
              "50%      0.256510    0.000000    9.690000  ...  391.440000   11.360000   21.200000\n",
              "75%      3.677082   12.500000   18.100000  ...  396.225000   16.955000   25.000000\n",
              "max     88.976200  100.000000   27.740000  ...  396.900000   37.970000   50.000000\n",
              "\n",
              "[8 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0-ULxH6kPLer"
      },
      "source": [
        "#now break it down to data (the features or predictors)\n",
        "data = df[df.columns[0:12]]\n",
        "#and the target, which in this case is 13\n",
        "#MEDV Median value of owner-occupied homes in $1000's\n",
        "target = df[df.columns[13]]"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "IIpSymJ0Pmrc",
        "outputId": "c2abab9b-e09d-43fd-a248-89986f5a6ea5"
      },
      "source": [
        "#Now let's take a look at our features\n",
        "data.describe()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>11</th>\n",
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              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
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              "      <td>506.000000</td>\n",
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              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.613524</td>\n",
              "      <td>11.363636</td>\n",
              "      <td>11.136779</td>\n",
              "      <td>0.069170</td>\n",
              "      <td>0.554695</td>\n",
              "      <td>6.284634</td>\n",
              "      <td>68.574901</td>\n",
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              "      <td>9.549407</td>\n",
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              "      <td>18.455534</td>\n",
              "      <td>356.674032</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>8.601545</td>\n",
              "      <td>23.322453</td>\n",
              "      <td>6.860353</td>\n",
              "      <td>0.253994</td>\n",
              "      <td>0.115878</td>\n",
              "      <td>0.702617</td>\n",
              "      <td>28.148861</td>\n",
              "      <td>2.105710</td>\n",
              "      <td>8.707259</td>\n",
              "      <td>168.537116</td>\n",
              "      <td>2.164946</td>\n",
              "      <td>91.294864</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.006320</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.460000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.385000</td>\n",
              "      <td>3.561000</td>\n",
              "      <td>2.900000</td>\n",
              "      <td>1.129600</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>187.000000</td>\n",
              "      <td>12.600000</td>\n",
              "      <td>0.320000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.082045</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.190000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.449000</td>\n",
              "      <td>5.885500</td>\n",
              "      <td>45.025000</td>\n",
              "      <td>2.100175</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>279.000000</td>\n",
              "      <td>17.400000</td>\n",
              "      <td>375.377500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.256510</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>9.690000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.538000</td>\n",
              "      <td>6.208500</td>\n",
              "      <td>77.500000</td>\n",
              "      <td>3.207450</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>330.000000</td>\n",
              "      <td>19.050000</td>\n",
              "      <td>391.440000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3.677082</td>\n",
              "      <td>12.500000</td>\n",
              "      <td>18.100000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.624000</td>\n",
              "      <td>6.623500</td>\n",
              "      <td>94.075000</td>\n",
              "      <td>5.188425</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>666.000000</td>\n",
              "      <td>20.200000</td>\n",
              "      <td>396.225000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>88.976200</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>27.740000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.871000</td>\n",
              "      <td>8.780000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>12.126500</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>711.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>396.900000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               0           1           2   ...          9           10          11\n",
              "count  506.000000  506.000000  506.000000  ...  506.000000  506.000000  506.000000\n",
              "mean     3.613524   11.363636   11.136779  ...  408.237154   18.455534  356.674032\n",
              "std      8.601545   23.322453    6.860353  ...  168.537116    2.164946   91.294864\n",
              "min      0.006320    0.000000    0.460000  ...  187.000000   12.600000    0.320000\n",
              "25%      0.082045    0.000000    5.190000  ...  279.000000   17.400000  375.377500\n",
              "50%      0.256510    0.000000    9.690000  ...  330.000000   19.050000  391.440000\n",
              "75%      3.677082   12.500000   18.100000  ...  666.000000   20.200000  396.225000\n",
              "max     88.976200  100.000000   27.740000  ...  711.000000   22.000000  396.900000\n",
              "\n",
              "[8 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SKd6Uky1PojZ",
        "outputId": "078b7350-de27-4668-f546-fc7ab5f6fac7"
      },
      "source": [
        "#and our targets\n",
        "target[0:5]"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    24.0\n",
              "1    21.6\n",
              "2    34.7\n",
              "3    33.4\n",
              "4    36.2\n",
              "Name: 13, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ndQMCIERPCPO"
      },
      "source": [
        "# Separate train and test data\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(data, target, test_size=0.3, random_state=0)"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "aYBC_ENiepyy",
        "outputId": "164a12ce-1317-4b09-e44a-b1f8e3d0aa5f"
      },
      "source": [
        "X_train[0:2]"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>10</th>\n",
              "      <th>11</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>1.62864</td>\n",
              "      <td>0.0</td>\n",
              "      <td>21.89</td>\n",
              "      <td>0</td>\n",
              "      <td>0.624</td>\n",
              "      <td>5.019</td>\n",
              "      <td>100.0</td>\n",
              "      <td>1.4394</td>\n",
              "      <td>4</td>\n",
              "      <td>437.0</td>\n",
              "      <td>21.2</td>\n",
              "      <td>396.90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>272</th>\n",
              "      <td>0.11460</td>\n",
              "      <td>20.0</td>\n",
              "      <td>6.96</td>\n",
              "      <td>0</td>\n",
              "      <td>0.464</td>\n",
              "      <td>6.538</td>\n",
              "      <td>58.7</td>\n",
              "      <td>3.9175</td>\n",
              "      <td>3</td>\n",
              "      <td>223.0</td>\n",
              "      <td>18.6</td>\n",
              "      <td>394.96</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          0     1      2   3      4   ...      7   8      9     10      11\n",
              "141  1.62864   0.0  21.89   0  0.624  ...  1.4394   4  437.0  21.2  396.90\n",
              "272  0.11460  20.0   6.96   0  0.464  ...  3.9175   3  223.0  18.6  394.96\n",
              "\n",
              "[2 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-5SlEoGZi9XD",
        "outputId": "e8cd8b61-04c6-4736-a1dd-0f1182f43a15"
      },
      "source": [
        "y_train[0:5]"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "141    14.4\n",
              "272    24.4\n",
              "135    18.1\n",
              "298    22.5\n",
              "122    20.5\n",
              "Name: 13, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VyfHyav7bR87"
      },
      "source": [
        "#Since the features are of different magnitudes (e.g., crime per capita and average # of rooms in a house)\n",
        "#so, we use the sklearn scaling function\n",
        "#if you want to know more, you can check this forum: https://towardsdatascience.com/preprocessing-with-sklearn-a-complete-and-comprehensive-guide-670cb98fcfb9\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "#note that the training and testing data are scaled separetely so\n",
        "#they don't affect each other. You do not want the training data to leak to the test data\n",
        "X_train = sc.fit_transform(X_train)\n",
        "X_test = sc.fit_transform(X_test)"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PT7O9SSeeHXJ",
        "outputId": "fa6aa563-d681-4e77-de17-5ef8df2b3ade"
      },
      "source": [
        "X_train[0:2]"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.20735619, -0.49997924,  1.54801583, -0.26360274,  0.58821309,\n",
              "        -1.83936729,  1.10740225, -1.1251102 , -0.61816013,  0.20673466,\n",
              "         1.2272573 ,  0.42454294],\n",
              "       [-0.38886492,  0.34677427, -0.58974728, -0.26360274, -0.79782145,\n",
              "         0.32748658, -0.36766106,  0.07509   , -0.73363701, -1.04949303,\n",
              "         0.05696346,  0.40185312]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "31XQ8qj3eznw"
      },
      "source": [
        "# create target scaler object\n",
        "# Now, let's scale our target, the median value of the house in 1000s\n",
        "target_scaler = StandardScaler()\n",
        "#we need to reshape because this is one variable\n",
        "target_scaler.fit(y_train.to_numpy().reshape(-1,1))\n",
        "y_train = target_scaler.transform(y_train.to_numpy().reshape(-1,1))\n",
        "y_test = target_scaler.transform(y_test.to_numpy().reshape(-1,1))"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "igJmFVI8jImD",
        "outputId": "8d667583-fafe-40f0-bfb5-c7640b8d038f"
      },
      "source": [
        "y_train[0:5]"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.90645092],\n",
              "       [ 0.17970697],\n",
              "       [-0.5045725 ],\n",
              "       [-0.02666303],\n",
              "       [-0.24389461]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6s4RXDgTkZmK"
      },
      "source": [
        "#now let's fit all the numbers into one array using the ravel function\n",
        "y_train = y_train.ravel()\n",
        "y_test = y_test.ravel()"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-B8fjh6Dvff6",
        "outputId": "e152c6de-9aac-4bf8-f12c-4946910c83d9"
      },
      "source": [
        "y_train[0:5]"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-0.90645092,  0.17970697, -0.5045725 , -0.02666303, -0.24389461])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XHidG6INtFaf"
      },
      "source": [
        "##Linear Regression\n",
        "\n",
        "Mean Square Error (MSE) is the most commonly used regression loss function. MSE is the sum of squared distances between our target variable and predicted values.\n",
        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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RyxaFumgwVZS"
      },
      "source": [
        "So, in the figure above, the MSE is the sum of the squared distances (residuals or errors) colored in grey. The lower the number, the lower error, thus the better model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ckrk0f_tPtKb",
        "outputId": "c57a023f-f223-4b78-a6ed-8265bde3691c"
      },
      "source": [
        "# There are three steps to model something with sklearn\n",
        "# 1. Set up the model\n",
        "model = LinearRegression()\n",
        "# 2. Fit the model\n",
        "model.fit(X_train, y_train)\n",
        "# 3. Get the predictions\n",
        "y_pred = model.predict(X_test) \n",
        "# 4. Get the R^2\n",
        "print(r2_score(y_test, y_pred))\n",
        "# 5. Get the RMSE\n",
        "print(np.sqrt(mean_squared_error(y_test,y_pred))) "
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.5744787579615156\n",
            "0.6465262120165035\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 709
        },
        "id": "MC39AO2vOmKz",
        "outputId": "86479cf5-3f7e-49cc-9385-a977b0094334"
      },
      "source": [
        "# plot prediction and actual data\n",
        "plt.plot(y_test, y_pred, '.')\n",
        "\n",
        "# plot a line, a perfit predict would all fall on this line\n",
        "x = np.linspace(0, 6, 6)\n",
        "y = x\n",
        "plt.plot(x, y)\n",
        "plt.show()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H2thp3O2tJ3W"
      },
      "source": [
        "##Ridge Regression\n",
        "\n",
        "Ridge regression is particularly useful to mitigate the problem of multicollinearity in linear regression, which commonly occurs in models with large numbers of parameters. \n",
        "\n",
        "Source:\n",
        "https://en.wikipedia.org/wiki/Tikhonov_regularization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TeIDmKorPBRg",
        "outputId": "77f17cc5-92b3-4976-8174-02218523b996"
      },
      "source": [
        "# There are three steps to model something with sklearn\n",
        "# 1. Set up the model\n",
        "model = Ridge()\n",
        "# 2. Fit the model\n",
        "model.fit(X_train, y_train)\n",
        "# 3. Get the predictions\n",
        "y_pred = model.predict(X_test) \n",
        "# 4. Get the R^2\n",
        "print(r2_score(y_test, y_pred))\n",
        "# 5. Get the RMSE\n",
        "print(np.sqrt(mean_squared_error(y_test,y_pred))) "
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.5740182242513445\n",
            "0.6468759790407583\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 709
        },
        "id": "of9GharUQNPp",
        "outputId": "9693912b-2f1a-4e91-aebf-819cde3e5595"
      },
      "source": [
        "# plot prediction and actual data\n",
        "plt.plot(y_test, y_pred, '.')\n",
        "\n",
        "# plot a line, a perfit predict would all fall on this line\n",
        "x = np.linspace(0, 10, 10)\n",
        "y = x\n",
        "plt.plot(x, y)\n",
        "plt.show()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8RoCOzbatN8H"
      },
      "source": [
        "##Lasso Regression\n",
        "\n",
        "This model solves a regression model where the loss function is the linear least squares function and regularization is given by the l2-norm. \n",
        "\n",
        "The regularization is a penalty applied to the features. Basically, this is a penalty added to make sure that the regression does not overfit. \n",
        "\n",
        "Source:\n",
        "https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Ridge.html"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UA1NSNifQaNp",
        "outputId": "94646fed-31c1-4a58-b631-e708d1b338fe"
      },
      "source": [
        "# There are three steps to model something with sklearn\n",
        "# 1. Set up the model\n",
        "model = Lasso()\n",
        "# 2. Use fit\n",
        "# 2. Fit the model\n",
        "model.fit(X_train, y_train)\n",
        "# 3. Get the predictions\n",
        "y_pred = model.predict(X_test) \n",
        "# 4. Get the R^2\n",
        "print(r2_score(y_test, y_pred))\n",
        "# 5. Get the RMSE\n",
        "print(np.sqrt(mean_squared_error(y_test,y_pred))) "
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "-0.006019731947687124\n",
            "0.994097002178065\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 709
        },
        "id": "-o6aPjq6Qq5O",
        "outputId": "73fa8125-db7a-476a-ec62-28742ba8e3ce"
      },
      "source": [
        "# plot prediction and actual data\n",
        "plt.plot(y_test, y_pred, '.')\n",
        "\n",
        "# plot a line, a perfit predict would all fall on this line\n",
        "x = np.linspace(0, 10, 10)\n",
        "y = x\n",
        "plt.plot(x, y)\n",
        "plt.show()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MUO1C9srtRok"
      },
      "source": [
        "##Decision Tree Regressor"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9i1FC5qmxlvs"
      },
      "source": [
        "Remember when we introduced the [Decision Tree Classifier](https://github.com/tawfiqam/MI564/blob/main/Decision_Tree_Intro.ipynb) last week, we talked about it can be used for classification or regression. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RCYdym2-apPE",
        "outputId": "af9fa6c4-aefc-49f0-ddeb-4d3187fa864f"
      },
      "source": [
        "# There are three steps to model something with sklearn\n",
        "# 1. Set up the model\n",
        "model = DecisionTreeRegressor()\n",
        "# 2. Fit the model\n",
        "model.fit(X_train, y_train)\n",
        "# 3. Get the predictions\n",
        "y_pred = model.predict(X_test) \n",
        "# 4. Get the R^2\n",
        "print(r2_score(y_test, y_pred))\n",
        "# 5. Get the RMSE\n",
        "print(np.sqrt(mean_squared_error(y_test,y_pred))) "
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.3627162636165524\n",
            "0.7912103000216458\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 709
        },
        "id": "P_AuFdhHatsJ",
        "outputId": "eb9fb007-c994-44c5-ba63-324032ad111e"
      },
      "source": [
        "# plot prediction and actual data\n",
        "plt.plot(y_test, y_pred, '.')\n",
        "\n",
        "# plot a line, a perfit predict would all fall on this line\n",
        "x = np.linspace(0, 10, 10)\n",
        "y = x\n",
        "plt.plot(x, y)\n",
        "plt.show()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QpIO3yMDtV91"
      },
      "source": [
        "##Random Forest Regressor\n",
        "Remember when we introduced the [random forest classifier](https://github.com/tawfiqam/MI564/blob/main/Random_Forest_Intro.ipynb) last week, we talked about it can be used for classification or regression. \n",
        "\n",
        "Let's take a look at the regressor. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r-LMyoebQ-e4",
        "outputId": "62a187de-0c3c-4588-879d-27fe16207c64"
      },
      "source": [
        "# There are three steps to model something with sklearn\n",
        "# 1. Set up the model\n",
        "model = RandomForestRegressor()\n",
        "# 2. Fit the model\n",
        "model.fit(X_train, y_train)\n",
        "# 3. Get the predictions\n",
        "y_pred = model.predict(X_test) \n",
        "# 4. Get the R^2\n",
        "print(r2_score(y_test, y_pred))\n",
        "# 5. Get the RMSE\n",
        "print(np.sqrt(mean_squared_error(y_test,y_pred))) "
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.6499070737677758\n",
            "0.586431354499502\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GgZXdO0UtZXh"
      },
      "source": [
        "## SVM Regressor\n",
        "\n",
        "Remember when we introduced the [Support Vector Machines](https://github.com/tawfiqam/MI564/blob/main/SVM_Intro.ipynb) last week, we talked about it can be used for classification or regression. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 709
        },
        "id": "R-5e0Qv_ahhs",
        "outputId": "9cf065b8-86ef-4bd1-fffb-457f86936388"
      },
      "source": [
        "# plot prediction and actual data\n",
        "plt.plot(y_test, y_pred, '.')\n",
        "\n",
        "# plot a line, a perfit predict would all fall on this line\n",
        "x = np.linspace(0, 10, 10)\n",
        "y = x\n",
        "plt.plot(x, y)\n",
        "plt.show()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-pLwE62HamWK",
        "outputId": "700c6be0-17ea-4069-84e8-37bd09c12d89"
      },
      "source": [
        "# There are three steps to model something with sklearn\n",
        "# 1. Set up the model\n",
        "model = SVR(kernel='rbf')\n",
        "# 2. Fit the model\n",
        "model.fit(X_train, y_train)\n",
        "# 3. Get the predictions\n",
        "y_pred = model.predict(X_test) \n",
        "# 4. Get the R^2\n",
        "print(r2_score(y_test, y_pred))\n",
        "# 5. Get the RMSE\n",
        "print(np.sqrt(mean_squared_error(y_test,y_pred))) "
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.6705836286893923\n",
            "0.5688504475153567\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 709
        },
        "id": "0GfJVgZAa4JE",
        "outputId": "91eca1c5-2d68-4734-8b65-17558c689401"
      },
      "source": [
        "# plot prediction and actual data\n",
        "plt.plot(y_test, y_pred, '.')\n",
        "\n",
        "# plot a line, a perfit predict would all fall on this line\n",
        "x = np.linspace(0, 10, 10)\n",
        "y = x\n",
        "plt.plot(x, y)\n",
        "plt.show()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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74butoddjUG/f0Kmk7Uqr3L0PbAV+EEXRPnEcr/ziQhRF/wXUI9mqKUmSJGWGTWvgicHwwXPQahCccSdUrxk6lbRDqZS7OI5XR1F0PXAPMD+Kogkks3cHAV2AZ4GL0sgiSZIkfaN/vgOP94F1S6HzfdBqYOhE0jdK7YYqcRzfF0XRR8AjwIVfuvQB8OjXt2t+WRRFQ4AhAM2aNavKmJIkScp385+E8T+BWnXh/MnQrHXoRNIuSe0ohCiKrgOKgEdJVuzqAMcBi4BRURTdtaPXxnH8YBzHreI4btWwoaN5kiRJqgJlZfDC/4Mx/aHRYTBkhsVOWSWVchdF0cnAncCTcRxfHcfxojiON8Zx/BpwNvAxcE0URQemkUeSJEn6is3roLAPvPTfcEx/GDgF9vpO6FRSuaS1ctd52+P0r1+I43gjMGdblmNSyiNJkiQlViyA4ackN0758d3Q5Q9QvVboVFK5pTVz98Wfjh3tqfzi+a0pZJEkSZIS706BcUOgRm0YMAn2bxM6kVRhaa3czdz2OCSKoqZfvhBFUSfgJGAz8NeU8kiSJCmflZXB9N8kWzH3ORiGvGixU9ZLa+WuCHgOOBV4J4qi8cBy4DCSLZsRcEMcx6tSyiNJkqR8tfkzGH8RLJgC3z8XOt+brNxJWS6tc+7Koij6EXApcA7JTVT2BFYDU4Dfx3H8TBpZJEmSlMdWfpCs1q1aCJ3ugh8MgSgKnUqqFGmec1cM3LftlyRJkpSu96bBE4OhWg04byI0bxs6kVSpUit3kiRJUhBlZTDzbph+OzRuCef8LzT4buhUUqWz3EmSJCl3bVkPEy6GdybBUb3hzPuhxh6hU0lVwnInSZKk3LRqIRT2hZXvwel3wAmXOF+nnGa5kyRJUu55/zl4YhBE1aD/ODjw5NCJpCqX1jl3kiRJUtWLY5h5D4zqAfWbJefXHXhy2ExSSly5kyRJUm7Y+jlMvBT+MR6O7A5dhkLNPUOnklJjuZMkSVL2W/1hMl+34h3oeCu0udz5OuUdy50kSZKy28LpUDQw2ZLZtwgO7hA6kRSEM3eSJEnKTnEMs34PI7tBvSYwZLrFTnnNlTtJkiRln60b4cmfwrwiOLwrdB0GteqGTiUFZbmTJElSdlmzGEb3heXzoMMt8MOrnK+TsNxJkiQpmyyaAWPPh7JS6DsWWnQMnUjKGM7cSZIkKfPFMcweBo+dDXUbJfN1FjvpK1y5kyRJUmYr3gSTroS3CuHQznD2A1CrXuhUUsax3EmSJClzrV0Co/vBsjeh/S+h7TVQ4OYzaXssd5IkScpMH70MYwZA6Vbo8zh8r1PoRFJGs9xJkiQps8QxzBkO034OezdPit0+LUKnkjKe5U6SJEmZo3gzTL4G3hgJh3SCbn+C2vVDp5KyguVOkiRJmWHdxzCmP3w8F9rdAO2ud75OKgfLnSRJksJbPBvGnAfFG6H3KDisc+hEUtax3EmSJCmcOIZXH4Gnr4MG+8OASdDo0NCppKxkuZMkSVIYJVtgys/gtRHQ4jToNhz2aBA6lZS1LHeSJElK32fLkm2YS+dA22uh/Y1QUC10KimrWe4kSZKUriVzYHR/2LIeev0FDu8aOpGUEyx3kiRJSs/cEclRB/WbQv/xsO/hoRNJOcNyJ0mSpKpXshWmXp/cPOWgU6D7w7Dnt0KnknKK5U6SJElVa/2nyXzdklfgpCuhw83O10lVwHInSZKkqrN0LozuB5vXQo9H4MjuoRNJOctyJ0mSpKrx+kh46mqoty9c8Aw0bhk6kZTTLHeSJEmqXKXFMO1GmPMgNG8HPR91vk5KgeVOkiRJlWfDChg7ABbPghMvg1N/DdX8kVNKg3/SJEmSVDk+eR0K+8HGldDtITiqZ+hEUl6x3EmSJGn3vVkIk66AOg1h0DRocnToRFLesdxJkiSp4kpL4Nmb4JVhcEDbZL6uzj6hU0l5yXInSZKkivl8FRSdDx++BK0vhtNug2o1QqeS8pblTpIkSeW37M1kvm7Dp3DWA3B0n9CJpLxnuZMkSVL5vF0EEy9LjjcYNBWaHhs6kSQsd5IkSdpVpSXw/K/gr3+AZm2g1wio2yh0KknbWO4kSZL0zTauhqJBsGg6/GAInH6H83VShrHcSZIkaeeWz4PCc2H9MugyFI7tHzqRpO2w3EmSJGnH5o2DiZdC7fow8GnYr1XoRJJ2wHInSZKk/1RWCi/cBi/fC99tDb0eg3r7hk4laScsd5IkSfqqTWvgicHwwXPQahCccSdUrxk6laRvYLmTJEnSv/3zHXi8D6xbCmfeD8edHzqRpF1kuZMkSVJi/pMw/idQqy6cPxmatQ6dSFI5WO4kSZLyXVkZvHgHvPTf0LQV9B4Je30ndCpJ5WS5kyRJymeb18G4IfDeVDimP/z4bqheK3QqSRVguZMkScpXKxYk59et+Sgpda0ugCgKnUpSBVnuJEmS8tG7U5IVuxq1YcAk2L9N6ESSdpPlTpIkKZ+UlcGMO2HGb6HJMdB7FNRvGjqVpEpguZMkScoXmz+D8RfBginw/XOh873Jyp2knGC5kyRJygcrP4DCPrBqIXS6C34wxPk6KcdY7iRJknLde9PgicFQrQacNxGatw2dSFIVsNxJkiTlqrIymHk3TL8dGreEc/4XGnw3dCpJVcRyJ0mSlIu2rIcJF8M7k+Co3nDm/VBjj9CpJFUhy50kSVKuWbUQCvvCyvfg9DvghEucr5PygOVOkiQpl7z/HDwxCKJq0H8cHHhy6ESSUlIQOoAkSZIqQRzDzHtgVA+o3wyGvGixk/KMK3eSJEnZbuvnMPFS+Md4OLI7dBkKNfcMnUpSyix3kiRJ2Wz1hzC6H/xzPnS8Fdpc7nydlKcsd5IkSdlq4QswdmDycd8iOLhD2DySgnLmTpIkKdvEMcz6PYzsDns1hSHTLXaSXLmTJEnKKls3wpM/hXlFcHhX6DoMatUNnUpSBrDcSZIkZYs1i2F0X1g+DzrcAj+8yvk6Sf9iuZMkScoGi2bA2POhrBT6joUWHUMnkpRhnLmTJEnKZHEMs4fBY2dD3UbJfJ3FTtJ2uHInSZKUqYo3waQr4a1COLQznP0A1KoXOpWkDGW5kyRJykRrlyTn1y17A9r/EtpeAwVuupK0Y5Y7SZKkTPPRyzBmAJRuhT6F8L1OoRNJygKWO0mSpEwRxzBnOEz7OezdHPo8Dvu0CJ1KUpaw3EmSJGWC4s0w+Wp4YxQc0gm6/Qlq1w+dSlIWsdxJkiSFtu7jZL7uk9eg3Q3Q7nrn6ySVm+VOkiQppMWzYcx5ULwReo+CwzqHTiQpS1nuJEmSQohjePURePo6aLA/DJgEjQ4NnUpSFrPcSZIkpa1kC0y5Fl77C7Q4DboNhz0ahE4lKctZ7iRJktL02TIY0x+W/h3aXgvtb4SCaqFTScoBljtJkqS0LJmT3DhlywboOQKOOCt0Ikk5xHInSZKUhrkjYPI1UL8p9J8A+x4eOpGkHGO5kyRJqkolW2Hq9cnNUw46Bbo/DHt+K3QqSTnIcidJklRV1n+aHHOw5BU46UrocLPzdZKqjOVOkiSpKiydm8zXbV4LPR6BI7uHTiQpx1nuJEmSKtvrI+Gpq6BeY7jgGWjcMnQiSXnAcidJklRZSoth2o0w50Fo3g56Pup8naTUWO4kSZIqw4YVMHYALJ4FJ14Gp/4aqvmjlqT0+DeOJEnS7vrkdSjsBxtXQreH4KieoRNJykOWO0mSpN3xxuMw6Qqo2wgGTYMmR4dOJClPWe4kSZIqorQYnrkJ/vZHOKBtMl9XZ5/QqSTlMcudJElSeX2+Kpmv+2gmtL4YTrsNqtUInUpSnitI+w2jKOoQRdH4KIqWR1G0JYqiT6IomhZF0Y/SziJJklRuy96EB0+GJXPgrAeg028tdpIyQqord1EU3QX8DFgKPAmsBBoCxwEnA1PSzCNJklQubxfBxMuS4w0GTYWmx4ZOJEn/klq5i6LoQpJiNwIYEsfx1q9d95+8JElSZiotgedugdlDoVkb6DUiuYGKJGWQVMpdFEW1gNuB/2M7xQ4gjuPiNLJIkiSVy8bVUDQQFr0Ix18Ip98B1WuGTiVJ/yGtlbuOJNsv7wPKoij6MXAksBmYE8fx7JRySJIk7brlb0NhX1i/DLoMhWP7h04kSTuUVrk7ftvjZuB1kmL3L1EUvQT0iON4RUp5JEmSdm7eOJh4KdSuDwOfhv1ahU4kSTuV1t0yv9iU/jMgBtoC9YCjgGeA/wLG7ujFURQNiaLo1SiKXl2xwv4nSZKqUFkpPHtLshWzcUsYMsNiJykrpFXuvnifEqBLHMcvx3G8IY7jt4GzSe6e2S6KohO39+I4jh+M47hVHMetGjZsmFJkSZKUdzatgVE9YdZ9cNxAGPAU1Ns3dCpJ2iVplbu12x5fj+P4oy9fiON4IzBt26c/SCmPJEnSV306Hx5sDx++BJ3vgzPv88YpkrJKWjN3C7Y9rt3B9TXbHvdIIYskSdJXzZ8I4y+GWnXh/MnQrHXoRJJUbmmVu+dJZu0Oj6KoII7jsq9d/+IGKx+mlEeSJAnKymD67TDzd9C0FfQeCXt9J3QqSaqQVLZlxnG8GJgENAOu+PK1KIpOA04nWdWbmkYeSZIkNq2Fx89Jit0x/WDgFIudpKyW1sodwKXAMcA92865ex1oDpwFlAKD4zhel2IeSZKUr1YsgMf7wNrF8KPfwfGDIYpCp5Kk3ZJauYvjeGkURccBNwNdSI4/+IxkRe83cRzPSSuLJEnKY+9OhnEXQY3aMGAS7N8mdCJJqhRprtyx7ZDyn277JUmSlJ6yMphxJ8z4LTQ5Jpmvq79f6FSSVGlSLXeSJElBbP4Mxl8EC6bA98+FzvcmK3eSlEMsd5IkKbetfB8Kz4VVC+GMO6H1Rc7XScpJljtJkpS7FkyFcRdCtRpw3kRo3jZ0IkmqMpY7SZKUe8rKYObdyRl2jVvCOaOgQbPQqSSpSlnuJElSbtmyHsb/BN59Co7qDWfeDzX2CJ1Kkqqc5U6SJOWOVQuT+bqV78Hpd8AJlzhfJylvWO4kSVJueP9ZKLoACgqg3zg4qH3oRJKUKsudJEnKbnEML98Lz98K+x4J54yEvQ8InUqSUme5kyRJ2WvLBph4KcyfAEd0g65DoWad0KkkKQjLnSRJyk6rP4TCvrDiHeh4K7S53Pk6SXnNcidJkrLPB89D0SAghr5j4eBTQyeSpOAKQgeQJEnaZXEMs+6HUT1gryYw5EWLnSRt48qdJEnKDls3wpOXwbwn4PCu0HUY1KobOpUkZQzLnSRJynxrFsPovrB8HnS4GX54tfN1kvQ1ljtJkpTZFr0IYwdCWSmcOwYOOS10IknKSJY7SZKUmeIYXhkGz/wSvt0C+jwO3z4odCpJyliWO0mSlHmKN8GTl8PbY+DQznD2A1CrXuhUkpTRLHeSJCmzrF2SzNctexPa/wLaXgsF3uBbkr6J5U6SJGWOj16GMQOgdCv0KYTvdQqdSJKyhv8MJkmSwotj+NufYEQX2GNvGPy8xU6SysmVO0mSFFbxZnjqKnjzf+GQM6Dbg1C7fuhUkpR1LHeSJCmcdR/D6H7wyWvQ7npod4PzdZJUQZY7SZIUxuLZMKZ/cmfM3qPgsM6hE0lSVrPcSZKkdMUxvPowPH09NGgGA56CRoeGTiVJWc9yJ0mS0lOyBSZfA68/Bgd3hO4PwR4NQqeSpJxguZMkSen4bFmyDXPp36HtNckZdgXVQqeSpJxhuZMkSVVvyZzkxilbNkDPEXDEWaETSVLOsdxJkqSqNfdRmHwt1G8K/SfAvoeHTiRJOclyJ0mSqkbJVnj6Opj7ZzjoFOj+MOz5rdCpJClnWe4kSVLlW/8pjDkPlrwCJ10BHW5xvk6SqpjlTpIkVa6lrybzdZvWJqt1LXuETiRJecFyJ0mSKs/rI+Gpq6BeYxj8LDRuGTqRJOUNy50kSdp9pcUw9efw9+HQvB30fNT5OklKmeVOkiTtng0rYOwAWDwLTrwMTv01VPNHDElKm3/zSpKkivv4tWS+buMq6DYcjuoVOpEk5S3LnSRJqpg3HodJV0DdRjBoGjQ5OnQiScprljtJklQ+pcXwzE3wtz/CAW2T+bo6+4ROJUl5z4/rAKkAACAASURBVHInSZJ23ecrYez58NFMaH0xnHYbVKsROpUkCcudJEnaVcvehMK+sOGfcNYf4ehzQyeSJH2J5U6SJH2zt8bCkz9NjjcY9DQ0PS50IknS11juJEnSjpWWwHO3wOyh0KwN9BqR3EBFkpRxLHeSJGn7Nq6GooGw6EU4/kI4/Q6oXjN0KknSDljuJEnSf1r+djJft34ZdBkKx/YPnUiS9A0sd5Ik6avmjYOJl0Lt+jDwadivVehEkqRdYLmTJEmJslJ4/laYdR98tzX0+gvUaxw6lSRpF1nuJElSMl/3xGBY+DwcNxA63eV8nSRlGcudJEn57tP5UHgurFsKne+DVgNDJ5IkVYDlTpKkfDZ/Ioy/GGrVhfMnQ7PWoRNJkirIcidJUj4qK4Xpd8DM30HTVtB7JOz1ndCpJEm7wXInSVK+2bQWxl0I7z8Dx/SDH98D1WuFTiVJ2k2WO0mS8sk/303m69Yuhh/9Do4fDFEUOpUkqRJY7iRJyhfvPAXjL4Iae8CASbB/m9CJJEmVyHInSVKuKyuDGXfCjN9Ck2OS+br6+4VOJUmqZJY7SZJy2ebPktW6BVPg++dC53uhRu3QqSRJVcByJ0lSrlr5fjJft2ohnHEntL7I+TpJymGWO0mSctGCqckdMavVgPMmQvO2oRNJkqqY5U6SpFxSVgYz74bpt0PjlnDOKGjQLHQqSVIKLHeSJOWKLeth/E/g3aegZS84836ouWfoVJKklFjuJEnKBasWJvN1K9+D026HEy91vk6S8ozlTpKkbPf+s1B0ARQUQL9xcFD70IkkSQFY7iRJylZxDC/fC8/fCvseCeeMhL0PCJ1KkhSI5U6SpGy0ZQNMvBTmT4AjukHXoVCzTuhUkqSALHeSJGWb1R9CYV9Y8Q50vBXaXO58nSTJcidJUlb54HkoGgTE0HcsHHxq6ESSpAxREDqAJEnaBXEMs+6HUT1gryYw5EWLnSTpK1y5kyQp023dCE9eBvOegMO7QtdhUKtu6FSSpAxjuZMkKZOtWQyj+8LyedDhZvjh1c7XSZK2y3InSVKmWvQijB0IZaXJfF2LjqETSZIymDN3kiRlmjiG2f8Dj50NdRrCkOkWO0nSN3LlTpKkTFK8CZ68HN4eA4d2hrMfgFr1QqeSJGUBy50kSZli7ZJkvm7Zm9D+F9D2Wihwk40kaddY7iRJygQfvQxjBkDJFuhTCN/rFDqRJCnL+M+BkiSFFMfwtz/BiC6wx95w4QsWO0lShbhyJ0lSKMWb4amr4M3/hUPOgG4PQu36oVNJkrKU5U6SpBDWfQyj+8Enr0G766HdDc7XSZJ2i+VOkqS0LZ4NY/ond8bsPQoO6xw6kSQpB1juJElKSxzDqw/D09dDg/1hwFPQ6NDQqSRJOcJyJ0lSGkq2wORr4PXH4OCO0P0h2KNB6FSSpBxiuZMkqap9tizZhrn079D2muQMu4JqoVNJknKM5U6SpKq0ZE5y45QtG6DnCDjirNCJJEk5ynInSVJVmfsoTL4W6jeF/hNg38NDJ5Ik5TDLnSRJla1kKzx9Hcz9Mxx0CnR/GPb8VuhUkqQcZ7mTJKkyrf8UxpwHS16Bk66EDjc7XydJSoXlTpKkyrL01WS+btPaZLWuZY/QiSRJecRyJ0lSZXh9JDx1FdRrDIOfhcYtQyeSJOUZy50kSbujtBim/hz+Phyat4OejzpfJ0kKwnInSVJFbVgBYwfA4llw4mVw6q+hmt9aJUlh+B1IkqSK+Pi1ZL5u4yroNhyO6hU6kSQpzxWEfPMoivpFURRv+zU4ZBZJknbZG4/DI2dAVACDplnsJEkZIdjKXRRF3wWGAhuAuqFySJK0y0qL4Zmb4G9/hAPaJvN1dfYJnUqSJCDQyl0URRHwZ2AV8ECIDJIklcvnK+Gxs5Ni1/pi6D/eYidJyiihVu4uB04BTt72KElS5lr2JhT2hQ3/hLMegKP7hE4kSdJ/SH3lLoqiw4DfAvfHcfxS2u8vSVK5vDUWHj4d4jIYNNViJ0nKWKmu3EVRVB14DPg/4MY031uSpHIpLYHnboHZQ6FZG+g1Auo2Cp1KkqQdSntb5s3AMcAP4zjetKsviqJoCDAEoFmzZlUUTZKkbTauhqKBsOhFOP5COP0OqF4zdCpJknYqtXIXRVFrktW6u+M4nl2e18Zx/CDwIECrVq3iKognSVJi+dvJfN36ZdBlKBzbP3QiSZJ2SSrlbtt2zL8A7wE3pfGekiSV27xxMPFSqF0fBj4N+7UKnUiSpF2W1g1V6gKHAIcBm790cHkM3LLta4Zve+6+lDJJkpQoK4Vnb0m2YjZuCUNetNhJkrJOWtsytwAP7+DasSRzeC8DC4BybdmUJGm3bFoDRRfAwufhuIHQ6S7n6yRJWSmVcrft5imDt3ctiqJfkZS7EXEcP5RGHkmSAPh0PhSeC+uWQuf7oNXA0IkkSaqwUIeYS5IU1vyJMP5iqFUXzp8MzVqHTiRJ0m6x3EmS8ktZGUy/HWb+Dpq2gt4jYa/vhE4lSdJuC17u4jj+FfCrwDEkSflg01oYNwTenwbH9IMf3wPVa4VOJUlSpQhe7iRJSsWKBfB4H1i7GH70Ozh+MERR6FSSJFUay50kKfe9OxnGXQQ1asOASbB/m9CJJEmqdJY7SVLuKiuDGXfCjN9Ck2OS+br6+4VOJUlSlbDcSZJy0+bPYPxFsGAKfP9c6HxvsnInSVKOstxJknLPyveT8+tWLYQz7oTWFzlfJ0nKeZY7SVJuWTAVxl0I1WrAeROhedvQiSRJSoXlTpKUG8rKYObdyRl2jVvCOaOgQbPQqSRJSo3lTpKU/bashwkXwzuT4KjecOb9UGOP0KkkSUqV5U6SlN1WLUzm61a+B6ffASdc4nydJCkvWe4kSdnr/efgiUEQFUC/cXBQ+9CJJEkKpiB0AEmSyi2OYeY9MKoH1G8GQ1602EmS8p4rd5Kk7LJlA0y8FOZPgCO6QdehULNO6FSSJAVnuZMkZY/VH0JhX1jxDnS8Fdpc7nydJEnbWO4kSdlh4QswdmDycd8iOLhD2DySJGUYZ+4kSZktjmHW72Fkd9irCQyZbrGTJGk7XLmTJGWurRvhyZ/CvCI4vCt0HQa16oZOJUlSRrLcSZIy05rFMLovLJ8HHW6GH17tfJ0kSTthuZMkZZ5FM2Ds+VBWCn3HQouOoRNJkpTxnLmTJGWOOIbZw+Cxs6FOw2S+zmInSdIuceVOkpQZijfBpCvgrdFwaGc4+wGoVS90KkmSsoblTpIU3tolyXzdsjeh/S+g7bVQ4OYSSZLKw3InSQrro5dhzAAo3Qp9CuF7nUInkiQpK1nuJElhxDHMGQ7Tfg57N4c+j8M+LUKnkiQpa1nuJEnpK94Mk6+GN0bBIZ2g25+gdv3QqSRJymqWO0lSutZ9DKP7wSevQbvrod0NztdJklQJLHeSpPQsng1j+id3xuw9Cg7rHDqRJEk5w3InSap6cQyvPgJPXwcN9ocBT0GjQ0OnkiQpp1juJElVq2QLTLkWXvsLHNwRuj8EezQInUqSpJxjuZMkVZ3PliXbMJf+Hdpek5xhV1AtdCpJknKS5U6SVDWWzElunLJlA/QcAUecFTqRJEk5zXInSap8c0fA5GugflPoPwH2PTx0IkmScp7lTpJUeUq2wtTrk5unHHQKdH8Y9vxW6FSSJOUFy50kqXKs/xTGnAdLXoGTroQONztfJ0lSiix3kqTdt3RuMl+3aU2yWteyR+hEkiTlHcudJGn3vD4SnroK6jWGwc9C45ahE0mSlJcsd5Kkiikthmk3wpwHoXk76Pmo83WSJAVkuZMkld+GFTB2ACyeBSdeBqf+Gqr5LUWSpJD8TixJKp9PXofCfrBxJXQbDkf1Cp1IkiRhuZMklccbj8OkK6BuIxg0DZocHTqRJEnaxnInSfpmpcXwzE3wtz/CAW2T+bo6+4ROJUmSvsRyJ0nauc9XJfN1H82E1hfDabdBtRqhU0mSpK+x3EmSdmzZm8l83YZP4awH4Og+oRNJkqQdsNxJkrbv7SKYeFlyvMGgqdD02NCJJEnSTljuJElfVVoCz90Cs4dCszbQa0RyAxVJkpTRLHeSpH/buBqKBsKiF+H4C+H0O6B6zdCpJEnSLrDcSZISy+dB4bmwfhl0GQrH9g+dSJIklYPlTpIE88bBxEuhdn0Y+DTs1yp0IkmSVE6WO0nKZ2Wl8PytMOs++G5r6PUY1Ns3dCpJklQBljtJyleb1kDRBbDweThuIHS6y/k6SZKymOVOkvLRp/OT+bp1S6HzfdBqYOhEkiRpN1nuJCnfzH8Sxv8EatWF8ydDs9ahE0mSpEpguZOkfFFWBtNvh5m/g6atoPdI2Os7oVNJkqRKYrmTpHywaS2MGwLvT4Nj+sGP74HqtUKnkiRJlchyJ0m5bsUCeLwPrF0MP/odHD8Yoih0KkmSVMksd5KUy96dDOMughq1YcAk2L9N6ESSJKmKWO4kKReVlcGMO2HGb6HJMcl8Xf39QqeSJElVyHInSblm82cw/iJYMAW+fy50vjdZuZMkSTnNcidJuWTl+8n5dasWJoeS/2CI83WSJOUJy50k5YoFU2HchVCtBpw3EZq3DZ1IkiSlyHInSdmurAxm3p2cYde4JZwzCho0C51KkiSlzHInSdlsy3qYcDG8MwmO6g1n3g819gidSpIkBWC5k6RstWphMl+38j04/Q444RLn6yRJymOWO0nKRu8/B08MgqgA+o2Dg9qHTiRJkgIrCB1AklQOcQwz74FRPaB+MxjyosVOkiQBrtxJUvbY+jlMuATmT4AjukHXoVCzTuhUkiQpQ1juJCkbrP4QCvvCineg463Q5nLn6yRJ0ldY7iQp0y18AcYOTD7uWwQHdwibR5IkZSRn7iQpU8UxzPo9jOwOezWBIdMtdpIkaYdcuZOkTLR1Izz5U5hXBId3ha7DoFbd0KkkSVIGs9xJUqZZsxhG94Xl86DDzfDDq52vkyRJ38hyJ0mZZNEMGHs+lJVC37HQomPoRJIkKUs4cydJmSCOYfYweOxsqNMwma+z2EmSpHJw5U6SQiveBJOugLdGw6Gd4ewHoFa90KkkSVKWsdxJUkhrlyTzdcvehPa/gLbXQoGbKiRJUvlZ7iQplI9ehjEDoHQr9CmE73UKnUiSJGUxy50kpS2OYc5wmPZz2Ls59Hkc9mkROpUkScpyljtJSlPxZph8NbwxCg7pBN3+BLXrh04lSZJygOVOktKy7mMY3Q8+eQ3aXQ/tbnC+TpIkVRrLnSSlYfFsGNM/uTNm71FwWOfQiSRJUo6x3ElSVYpjePURePo6aLA/DHgKGh0aOpUkScpBljtJqiolW2DKtfDaX6DFadBtOOzRIPUYcxev4ZVFqzjhwG9z3P57p/7+kiQpHZY7SaoKny1LtmEu/Xtydl37G6GgWuox5i5eQ9+HXmFrSRk1qxcwavAJ31jwLIOSJGUny50kVbYlc2B0f9iyHnqOgCPOChbllUWr2FpSRlkMxSVlvLJo1U4LW0XKoCRJygzepk2SKtPcR+HPP4IatWHws7tU7OYuXsP/TP+AuYvXVHqcEw78NjWrF1AtghrVCzjhwG/v9Ou3VwYlSVJ2cOVOkipDyVaYen1y85SDToHuD8Oe3/rGl1X1Stlx++/NqMEn7PI2yy/KYHFJ2S6VQUmSlDksd5K0u9Z/CmPOgyWvwElXQoebd3m+rrzbJiviuP333uXfs7xlUJIkZQ7LnSTtjqVzk4PJN62BHo/Akd3L9fJMXCkrTxmUJEmZw3InSRX1+kh46iqo1ziZr2vcsty/hStlkiSpsqRS7qIo+jZwNvBjoCXQFNgKvA38GfhzHMdlaWSRpN1WWgxTfw5/Hw7N20HPR3dpvm5HXCmTJEmVIa2Vu57AH4FlwHTg/4B9gW7AQ0CnKIp6xnEcp5RHkipmwwoYOwAWz4ITL4NTfw3V3AQhSZLCS+snkveALsDkL6/QRVF0IzAH6E5S9J5IKY8kld8nr0NhP9i4EroNh6N6hU4kSZL0L6mccxfH8QtxHE/6+tbLOI6XAw9s+/TkNLJIUoW88Tg8fDpEEQyaZrGTJEkZJxP2EhVveywJmkKStqe0GJ65Cf72RzigbTJfV2ef0KkkSZL+Q9ByF0VRdeC8bZ9ODZlFkv7D5yth7Pnw0UxofTGcdhtUqxE6lSRJ0naFXrn7LXAkMCWO42k7+qIoioYAQwCaNWuWUjRJeW3Zm8l83YZP4awH4Og+oRNJkiTtVCozd9sTRdHlwDXAu0D/nX1tHMcPxnHcKo7jVg0bNkwln6TsNnfxGv5n+gfMXbym/C9+uyiZr4tLYdBUi50kScoKQVbuoii6DLgfmA90iON4dYgcknLT3MVr6PvQK2wtKaNm9QJGDT5h186RKy2B526B2UOhWRvoNQLqNqr6wJIkSZUg9ZW7KIquBP4AzAPab7tjpiRVmlcWrWJrSRllMRSXlPHKolXf/KKNq2FU96TYHX8hnDfRYidJkrJKqit3URRdTzJn9wbQMY7jlWm+v6T8cMKB36Zm9QKKS8qoUb2AEw789s5fsHweFJ4L65dBl6Fw7E53ikuSJGWk1MpdFEU3AbcCc4HT3Iqp/9/evcfHXdf5Hn99J2kqRSyxyuFS2qVU0C3uriQrRVax4rLCVlkpFbl4QQu62z2Cl6Oes2sVjj7Oukc9eGHdBVYRqdzaAoIiFykIalaagFrQcqmNRApCGy5aaC7zPX/8ZkKaziQzycz8JjOv5+PRx5CZycxnSJrOO5/v9/uRqqVjfjurVyyma/M2Fi+YM/6SzI3r4LqV8KLZcMaNMLezdoVKkiRVUE3CXQjhPSTBbhi4E/hQCGHs3bbEGC+pRT2SGl/H/PbxQ112GH54Hvz4fDjwCHjHpbDXvrUrUJIkqcJq1bk7KHfZApxT5D53AJfUpBpJze25fljzfnj4h9BxBhz3r9DalnZVkiRJU1KTcBdj/AzwmVo8lySN6/H7k/11T/fB0vOh84y0K5IkSaqItIeYS1Lt3P9duOaDMPPF8N7vwbwj0q5IkiSpYgx3khpfNgvrPwd3fgEO6ISTL4OX7Jd2VZIkSRVluJPU2J57CtadBQ/eBK85Hf72S9A6M+2qJEmSKs5wJ6lxPbEJLj8FnuqF478Af7kCdj+pV5IkqSEY7iQ1pl9/D9Z9AGa8CN5zPcx/XdoVSZIkVZXhTlJjyWbhjs/DHf8C+78m2V83e27aVUmSJFWd4U5S43j+GbjmA7Dp+/Dnp8LSL8GMPdKuSpIkqSYMd5Iaw5MPJvPrtj0Mb/k8HPEB99dNUndvP12bt7F4wRw65renXY4kSSqR4U7S9LfpB7DuTGiZAe++Dg56fSplNEIo6u7t57SLuxgYytLWmmH1isXT9rVIktRsDHeSpq9sFu78YjLDbt9XwztXw97zUimlUUJR1+ZtDAxlyUYYHMrStXnbtHwdkiQ1o0zaBUjSpOx8Fq5+N6z/LLx6ObzvptSCHRQORdPR4gVzaGvN0BJgRmuGxQvmpF2SJEkqkZ07SdPPtoeT/XVPPgDHfg6OXJn6/rp8KBocyk7rUNQxv53VKxZP++WlkiQ1oxBjTLuGsnR2dsYNGzakXYaktDx4K6x9H4QMnPRNOHhJ2hWNaIQ9d5Ikqf6FELpjjJ1jr7dzJ2l6iBHu+n/ww/Pgvx0G77wM2v8k7ap20TG/3VAnSZJSY7iTVP92/gGuWwn3XwuLToQTvgZte6ZdlSRJUl0x3Emqb9t/A1ecBk/8Ct58Lhx1dur76yRJkuqR4U7SlFVtr9nDt8HVZwARTrsaFr65co8tSZLUYAx3kqaku7efUy7qGjkl8vIzKzDfLUb4yVfh1k/Dy1+ZzK976YLKFCxJktSgnHMnaUrW9fQxMJQlAgNDWdb19E3tAQd2wNoVcMun4JVL4f23GOwkSZJKYOdO0pSMHaYypeEq/b1w5Wnw2EZ406fg9R91f50kSVKJ7NxJmpJlh8+lrSUQgLaWwLLD507ugTbfARe+Efp/C6deBW/4mMFOkiSpDHbuJE3Z8s4DiSRBr+z9djFC19fh5n+GOQvhlMthzsFVqVOSJKmRGe4kTVp3bz+nXdzFwFCWttZM2V27noe3sudNH+HQ338/2V/39n+HmXtVqVpJkqTG5rJMSZPWtXkbA0NZshEGh7J0bd5W8uf+4r6NzLz0OA79/ff5cnY53Yu/UlKw6+7t54L1D9Hd2z+V0iVJkhqOnTtJk9Y+q41MCEBkRmuGxQvmlPaJW+5i4bWnM8zzrBj4KOtjB62/6afjT8b//LGdwtUrKjB2Qbup2txCSZJUVYY7SZPS3dvPeTfcx3A20pIJrFq6aOIgECP87CL4wSfJvGQ+y55fyQNxv5KDYaFOoeGjsgzQkiRNX4Y7SZOytqePnYPJfLsYI/07Bsb/hMHn4XsfgXtXwyFv4UUnXshnH8+W1SFavGAOba2ZkYHpJXcKVTIDtCRJ05fhTlLZunv7WdPdNzLTriUTxg9aT/8OrjwdHu2Boz8BR38SMhk65lNWcOiY387qFYtdMlhFBmhJkqYvw52ksnVt3sbQcBaAQDIKoWjQ6v0pXPUuGHwOTr4MXvXWKT13x/x2Q10VGaAlSZq+DHeSyja2u3NioREIMcKGb8CNH4e958F7boB9Xln7YlU2A7QkSdOT4U5qILU65XDC7s7QTvj+x6DnUlj417DsYthj76rVU02eHClJkqYLw53UIGp9ymHR7s4zW5NlmH13w+s/Ckv+CTItVaujmjw5UpIkTScOMZcaxFQGilfMIz+DC4+Gx++H5d+CY1ZN22AHdfL/VJIkqUSGO6lB5PfBtQTSOeWw+xL45vEwYw9YcQss+rvaPn8VpP7/VJIkqQwhxjjxvepIZ2dn3LBhQ9plSHUplf1hQwPwg08kh6cc/CZY9p8w66W1ee4acM+dJEmqNyGE7hhj59jr3XMnNZCan3L47ONw1bvhkS446mw45tPTehlmIZ4cKUmSpgvDnaTJ6etOBpM/15906159UtoVSZIkNTXDnaTy3XMZ3PBhePG+8P6bYb8/S7siSZKkpme4k1S64UH4wf+Euy+Cg94AJ10Ce3rIiCRJUj0w3EkqzR+egKvfA70/hiP/Ed58LrT4I0SSJKle+M5M0sQevQeuOB12PAknXgR/9o6qPp0nVEqSJJXPcCdpfPdeDtefDXu+HN53E+z/F1V9uu7efk67uIuBoSxtrRlWr1hswJMkSSqBQ8wlFTY8CDd+Eq79IMz9Szjr9qoHO4CuzdsYGMqSjTA4lKVr87aqP6ckSVIjsHMnaXd/fBKufi9suROO+CAc+1lomVGTp168YA5trRkGh7LMaM2weIEHtkiSJJXCcCdpV1t/nuyv+8Pj8Hdfh784taZP3zG/ndUrFrvnTpIkqUyGO6lBTepQkl+ugev+EWa9FN53IxzQUd0ii+iY326okyRJKpPhTmpAZR9KMjwEt34afvo1mHckvONSePE+tStYkiRJU+aBKlIDKutQkh3bYfUy+OnX+OX+y+l547cMdpIkSdOQnTspp9Kz1dKc1VbyoSSPbYQrTiX7zFY+lT2Ly3/zRlq/0cNJHXNZdvhcl0ZKkiRNIyHGmHYNZens7IwbNmxIuww1mErPVquHWW0ThsuN6+C6lfCi2axZ+H/4eFcb2dyPgwDMnOGMOUmSpHoUQuiOMXaOvd5lmRKVn61WD7PaOua3s3LJwt3DWXYYbvk0rDkD9n01nHU7B/3FG2lrzRByd4k4Y06SJGm6MdxJvLCMsSVQkdlqlX68inmuH1Yvhx+fDx1nwHtugL32HRk/cOoR82hrCfVXtyRJkibkskwpp5H23BX0+P1wxanwdB8c/6/Q+b6Cd6u7uiVJkrSLYssyDXdSM7j/u3DNB2Hmi5MxB/MWp12RJEmSJqlYuPO0TKmRZbOw/nNw5xfggE44+dvwkv3TrkqSJElVYLiTGtVzT8G6s+DBm+A1p8PffglaZ6ZdlSRJkqrEcCc1oic2weWnwFO9/Hbx/+b6tuNY/LsddMxPwp376iRJkhqP4U6qIxUJXb/+Hqz7AMx4EZv+5juccEOWgaEHaFv/EKtXJHvt0p7BJ0mSpMoz3El1YsqDz7NZHv3uZ9j/3i/zx5f9GXu+63Ju7XmegaFNu83bGzuDz3AnSZI0/TnnTqoTUxp8/vwzPPXN5ex/75dZM/wGXvf4x+h+as+C8/bqdgafJEmSpsTOnVQn8qFrcChbXuh68kG44lRe8uTDnDv0br459De0hEDX5m2sXLKQ1SsW77bUs9B1kiRJmt4Md1IdyO+1W7V0Ef07BkoPXZt+AOvOhJYZPPiWy7j8e4GWsGs47JjfvttjFbpOkiRJ05vhTkrZpPbaZbNw5xeTGXb7vhreuZpD957H6v08BVOSJKlZGe6kFHX39nP+rQ+wczBLpMQDTnY+C9f+Pfzqenj1O+CtX4a2WYAdOUmSpGZmuJPKUO6ogvHuP7pjF0lON5pwr922h+GKU+HJB+DYz8GRKyGEqb0oSZIkNQTDnVSicpdPTnT/0adjZgIctfBlnPPmQ4o/5oO3wtr3QcjA6evg4CWVfomSJEmaxhyFIJWo3FEFE91/9EiCttZM8WAXI9z5JVh9EsyeB2fdbrCTJEnSbuzcSSUqd1TBRPfvmN8+8UiCgT/Ctf8A918Li06EE74GbXtW8mVJkiSpQYQYY9o1lKWzszNu2LAh7TLUBArtl8tf1z6rraSRBeXu0dvF9t/AFafBE7+CYz4NR53t/jpJkiQRQuiOMXaOvd7OnVRAsf1yHfPb2fTYs6y6biPD2cjMGePvvZv06ZUP3wZXnwFEOO1qWPjmqb0gSZIkNTz33EkFFNsv193bz6rrNjKUjURgYHDivXdliRF+/BW4bBm8ZP9kf12BYNfd288F6x+iu7e/cs8tSZKkj47S/wAAHcFJREFUac3OnVRAsf1yXZu3kR21lDmTCRPuvSvZwA747n+HjWvgT0+AE/4NZr54t7tNaui5JEmSGp7hTiqg2GEn+dA3MJQlEwLnnXBYZYJVfy9ceRo8thGOWQV/9ZGi++sKdRUNd5IkSTLcSUUU2i9X0gmX5dp8B1z9XsgOw6lXwSHHjnv3ck/tlCRJUnPwtEwpLTFC19fh5n+GOQvhlMthzsElfeqUTuGUJEnStOZpmdIUVDxMDT4H158Nv7gSXrkU3v7vMHOvkj990qdwSpIkqWEZ7qQJVPwAk6ceSfbXbf05LPkneP3HIOPBtZIkSZoaw500gYoeYLLlLrjqPTC0E065Ag49rrLFSpIkqWnZLpAm0D6rjUwIZKDoASYTzp2LEf7rQrj0BNijHc68zWAnSZKkirJzJ42ju7ef8264j2yMZDKBVUsX7dK16+7tZ21PH2u6+xgaLrJsc/B5+N5H4N7VcMhb4MQL4UWzU3g1kiRJamSGO2kco5dkBiL9OwZGbsvvxds5mCV/5uxuyzaf/h1ceTo82gNHfwKO/qT76yRJklQVhjtpHOPNlMsHv3ywC7ywbLO7t5/ee27lrb/+JDOyz8PJq+FVS1N5DZIkSWoOhjupiPz4g1VLF3Hfo08TgU2PPTsyEmF08GvJBJZ3HsiJh8+FGPnuNz7LP4dL+B0v548nXsOiV7027ZcjSZKkBme4U9Mab3bd6PEHrZkAITCY69JlAiN761YtXcSNG7dy3GH7ceoR8+jZ/BhPrz2HczM3sn74z/nw0D9y5vaXsijF1yJJkqTmYLhTU5podl3X5m0je+kGhyMQR5Zf5kcirOvpY21PHwNDWe7esp09nv89B932QZaEB/na0AmcP7yc1tbWgqdr1vK1SJIkqTl4soOaUqHZdaO1z2obCXMRaG0JI39ZMiHZWxdh5DEOG/41S+5Yziv4LX8/cDZfGjqZIxfuU5OgNdFrkSRJUnOwc6emNN5BKQD9OwbIhKRLlwmwvPNADth7D9pntdG/Y2Dk/ut6+nh79oec2/oNds7cj3f+4X9yfzyAthkZznnzITXpoE30WiRJktQcDHdqSh3z21m9YjHrevpGOnSjjQ1Myw6fu3tQGxrgzj+9npdvWs3T+7+e2adfymeeCDXf+5Z/Le65kyRJam4hxkJvbav0ZCHMBc4D3gLMAbYC1wLnxhj7S3mMzs7OuGHDhuoVqaYx0V61cQ8pefZxuOrd8EgXjx32Ada99P0ccfA+BitJkiRVXQihO8bYOfb6mnXuQggHAz8B9gGuA34NvBY4G3hLCOGoGKObhVQza3v6Xjg0ZezwcZKOWMGw1tcNV55Odsd2rpr3GVbd+0qGhh+ibf3mquyx8yRMSZIklaKWyzL/jSTYfSjG+NX8lSGELwEfBj4HfLCG9ajOlBtiphJ6unv7WdP9wpLMlkwoba/aPZfBDR9m5x778I6Bc/nFAwcSyQJJQPyPOx7mucHhkdEIU+VJmJIkSSpVTcJdrmt3LLAFuGDMzZ8GzgLeFUL4aIzxj7WoSekpFMrKDTFTDT1dm7cxNJyEskByYMq4nz88yO/XfJR9fvUtntnvKK466Dx+uf7xkXAYgJAJ3Hz/4wDc+eCTAFMOeIVOwjTcSZIkqZBajUJYkru8OcaYHX1DjPFZ4MfALGBxjepRSvKh7Is3b+K0i7vo7k22WpZ7nP9kj//v7u3ngvUP0T6rjbbWDC0BZrSEkdsK+sMTPHvh8ezzq29x0dDfcmTfPzBr9j4jn9/WEjj1iHks2u8lu3zajRu3llTTePIHu7Tkxi94EqYkSZKKqdWyzENzlw8Uuf1Bks7eIcAPa1KRUlGsE1Xucf7j3b/Ycs2x3b5VSxex8dGnWdPdx+U/+y1re/p27wA+eg9ccTp7/OEJPjz4D1wz/Fe0hGRUwtgTKr/zX7/l532/HPnU4w7bb8r/vzwJU2lwn6ckSdNTrcLd7Nzl00Vuz1+/d6EbQwhnkSzdZN68qe9jUnqKhbJyQ0yx+4+3XHNssOzfMcABe+/B0PA4yx7vuwZC4JbFl3L97VkyIY7UPfbAlfwSzBs3bq3Ynrv8a/UNtmrFfZ6SJE1f02LOXYzxQuBCSEYhpFyOpmC8EFduiCl0//H2qC1eMIfWTGBwOO5ygEpba4aBoSwhBNpnte36JG9axb3z3suHL9vEcDb5vFVLFxWss7u3n/4dAzUbXi5Vg/s8JUmavmoV7vKdudlFbs9f/1QNalEVlLOMq5KdqLHPO+HyzhCAmLtMalm1dBGrrtvIcDZy3g33cei+e71QX0srP340y8BQMjIhxkj/joGCddjtUCMod4m0JEmqH7UKd5tyl4cUuf0Vuctie/JUx9IKNsWed2xnMB8AH33qOYaGX5hrt66nj4757dz36NMMZ2PReXelvNm126FG4T5PSZKmr1qFu/W5y2NDCJnRJ2aGEPYCjgJ2AF01qkcVVEqwqcYBDcWed3RncHQAbM0EWjKB7HAS5K7e8AiL9p/N1RseeWHeXcvu4a2UN7vFlnxK05H7PCVJmp5qEu5ijA+HEG4mORFzJfDVUTefC+wJ/Icz7qaniTpb1ersFXresSFydAAczkYOO2A2v+h7mkjy8Y0btzKUTaJdAE7qmFuwtpLe7I5Z8ilJkiTVUi0PVPkH4CfAV0IIxwC/Ao4gmYH3APBPNaxFFTRRZ6taSxbHPi/AaRd3sXMwS0smcN4Jh+0WAE/+y3lsevy+kY+PO2w/7t6yfeTjZYfPnVQt+aHoERgedlmmJEmSaq9m4S7XvesEzgPeAhwPbAW+DJwbYywyQVrTwXidrWoe0DD6eS9Y/xA7B5OANZSNrLpuI1d+4MiCwXP0uIJD991r3CWXpSwp9RAKSZIkpa2moxBijI8AZ9TyOZW+Wh3QsHjBHFoyYWSZ5XA2cv6tD3DOmw9h5ZKFQBLUzrvhPgaGsty9ZTuH7rvXuI9Z6pJSD6GQJElS2qbFnDtNf4U6e5U+ZKVjfjvnnXDYyFiDCNz14JPcvWX7SCgbu0R0XU8fa3v6ioa3cpaUegiFJEmS0pRJuwA1p3xH7As3beLk//gp3/mv35b8eResf4ju3sKreE89Yh5XfuBI/uoVLwMgAgODSSiDF5ZPtgSY0ZpJbh8T3kYbe/9aLrec6LVKkiRJo9m5Uyq6Nm/bbX/cLsPDCyhliWS+G7hov5dw54NPApAFfv7IU3T39hc8hGVdT1/RvXJpLbd0KLokSZLKZbhTKsbuj8vGOOEJk2t7+kYCYaElkqMDURgzjuCW+x/nRw8+MRKSRn/eROEtjeWWDkWXJElSuVyWqaqYaElhfn9cayaQCdA2wZLH7t5+1nT3vTBsfMyg8O7efs6/9QF2Dr4w02600YGwUC0rlyysq/CU5nJQSZIkTU927lRxpS4pLGUMQV5+jhwkw8aXdx44cv/RzxeLPgK0tEyfkOTpm5IkSSqX4U4VV+4Jk/nPGf3xWGPnyJ04atj46OfLBHj1AbP51dZnGByOI2EvACd1zJ1WIcnTNyVJklQOw50qrpyB3uXOkVvX08fvn93Jup6+kevHPt+qty5i02PPcuXdv2Xj754mG2FGS2DZqEAoSZIkNRrDXROq9Hy5scpZUljuwSFXb3iEgeGkH3d1dx+Xn7l4l+drn9XG2p4+1nQnJ2COLNMcc8CKJEmS1GgMd02mVkfsl7qksJwuX9fmbQwOv7CrbnQYzD/XaRd3jZyoOdrwsCdOSpIkqbEZ7ppMvR2x3zG/nVVLF3Hjxq0cd9h+BWvJdxrbZ7UxoyWMdO5aW3Y9MTP/2sYGuwCEEGif1TbpOqvd7ZQkSZKmynDXZMrplNVCd28/591wHwNDWe7esn23QeZjO43vO+ogLrpzM9mYhLb8ffLhL//aWjKB5Z0HstfMVi6+6zcMZyPn3XDfhIPSi9XoQHFJkiTVO8Ndk6m3I/Yn6iSOvf2+rc8QSebWDWcja3v6WNfTNxK8Vi1dRP+OgZHXdsH6h8jGWHTweSVqlCRJkuqB4a4J1dMR+/lO4sBgtuDSybGdxuMO24+7t2wf+TjALsGrf8cAK5csLPr5k+lU1lu3U5IkSSrEcKdU5ffcrbpuI9m4+9LJQp3G0YPPAdb29BUNXpXoVI4ewzDekHRJkiQpTYY7pa5/xwDZGIsuexzbaRz78UThrVKdyrW55Z/revrcdydJkqS6Y7hT6qa67LEWy0zddydJkqR6Z7hT6spZOlnKSIJqjC1w350kSZLqneFOdaGU7luxkQSjwxxQlbEFpczjkyRJktJkuFNFVbOzVmhpJOwa5pYdPrcqyycnmscnSZIkpc1wp6K6e/tHTohcdvjcSXfWyr1PMYWWRo4NfBGqsnzSPXeSJEmqd4Y7FdTd288pFyUhDGDNhke4/Kwjxw00pQSgqYSkYnvz2lozDAwlc/IO2382yw6f6547SZIkNZ1M2gWoPnVt3sZgLtgBDA7HkWWQxeQDUEugaAAq5T7FFFrOmd8LlwmB4WwyJw9g5ZKFFe2s5YPlR4491DEIkiRJqkt27lTQ4gVzmJHriAHMaAkTBrFSTr2c7FDx8ZZz5ufkRaq7ZLIWIxckSZKkyTLcqaCO+e1cfubisvbc5T9vovtNJiSNt5zTJZOSJEmS4a7plHpSZf5+J5YY6qpt8YI5tLYkAa6lZdcAN9luoCRJktRIDHdNpNSTKqdyomVVxbjr5ShTXTJZjcHnkiRJUi15oEoTKTYnbrL3q6WuzdsYyib76oazEx/uUo58mP3izZs47eIuunv7K/bYkiRJUq0Y7ppIqSdVTuVEy2qpZk31GGYlSZKkcrkss4mUujet0P0mWrZY7WWN1dxX54EskiRJagQhFti/VM86Ozvjhg0b0i6jqUy0B6/cPXr1uL+tHmuSJEmSCgkhdMcYO8deb+dOuxkbdIqNIcjf73dPPVd0TEGhx67Hw1qcYSdJkqTpznCnXRQKX4WWLY6+X2tLhtZMYDgbJ1zWON68OkmSJEmTZ7jTLgqFr5VLFu623+2C9Q+xczBLBIaGspxyxDz233uPCZc1ur9NkiRJqg7DnXZRLHyNXbbYPquN/G7NLLBo/9mcesS8CR/fgePlcz+gJEmSSmG4awCVfPM/Ufgavc8uEyAbIROgf8dAWc9hSClNve5RlCRJUv0x3E1z1XjzXyx8TXafXSnsThXmHkVJkiSVynBXx0oJPLV88z/6uYaHs7zztaXts5uI3ani3KMoSZKkUhnu6lSpgaeWb/7HPteJh8+tSAizO1WcexSVBjvpkiRNT4a7OlVq4Cn25r8ab86qFTTsTo3PPYqqJTvpkiRNX4a7OpMPZe2z2koOPGPf/FfzzVk1gobdKal+2EmXJGn6MtzVkbGhbNXSRfTvGCg78JT65qyc7l61l2nZnZLqg510SZKmL8NdHRkbyvp3DLByycKyH6eUN2fldPcm0wksNwy6x0eqD3bSJUmavgx3KSkUZir1G/NS3pyVs/Sq3GVa5YZB9/hI9cVOuiRJ05PhLgXFwkypvzEvpcs10ZuzcoJkuaGz3DDoHh9JkiRp6gx3KRgvzEwUyirV5SolSI4OkeUs0yo3DLrHR5IkSZo6w10KphJmKtnlGi9IFgqRpe7/K3fPjnt8JEmSpKkz3KVgKmGmVl2uyeyzG/16yt2z4x4fSZIkaWoMdymZbJipVZernBDpgSiSJElS+gx301AtulzlhEgPRJEkSZLSZ7hTUaWGSA9EkSRJktJnuNOUeSCKJEmSlD7DnSrCA1Fqo5QZh5IkSWpOhjtpmvDgGkmSJI0nk3YBqj/dvf1csP4hunv70y5FoxQ6uEaSJEnKs3OnXdgdql8eXCNJkqTxGO60C8ca1C8PrpEkSdJ4DHc1Vu8HYtgdqm8eXCNJkqRiDHc1VMqSx7TDn90hSZIkaXoy3NVQ1+Zt7BzMEoGBwd2XPNbLfje7Q5IkSdL042mZNdQ+q42Y++9s7uPRPA1RkiRJ0mQZ7mqof8cAmZD8dwDue/TpXW7P73drCZS9383xBZIkSVJzc1lmDS1eMIfWlgwDQ8nSzKs3PMKJh88dWQI52f1u9bKcU5IkSVJ67NzVUMf8dk7qmEuuecdwNu629LJjfjsrlywsK5yNXs65czDLup6+ClYtSZIkaTow3NXYssPnMnPG5JZeFpPvCAIjHUGXZ0qSJEnNxXBXAeXsd8svvfzIsYdWbPlkKR1BSZIkSY3NPXdTNJn9btUYNbDs8Lms6+lz+LgkSZLUpAx3U1RofMFUgttkh5g7fFySJElqboa7KcqPL6hEx2yqp146fFySJElqXoa7Kapkx6zSXUBJkiRJzcNwVwGV6phVsgsoSZIkqbkY7uqI++YkSZIkTZbhrs64b06SJEnSZDjnTpIkSZIagOFOkiRJkhqA4U6SJEmSGoDhTpIkSZIagOFOkiRJkhqA4U6SJEmSGoDhTpIkSZIagOFOkiRJkhqA4U6SJEmSGoDhTpIkSZIagOFOkiRJkhqA4U6SJEmSGoDhTpIkSZIagOFOkiRJkhqA4U6SJEmSGkDVw10I4RUhhE+EEG4LITwSQhgIITweQrguhLCk2s8vSZIkSc2gtQbP8b+Bk4H7ge8D24FDgbcBbwshnB1j/EoN6pAkSZKkhlWLcPcD4PMxxntGXxlCOBq4Bfi/IYSrY4xba1CLJEmSJDWkqi/LjDFeMjbY5a6/A7gdaANeV+06JEmSJKmRpX2gymDucijVKiRJkiRpmkst3IUQ5gPHADuAH6VVhyRJkiQ1glTCXQhhJrAamAl8JsbYP8H9zwohbAghbHjiiSdqUqMkSZIkTSclhbsQwpYQQizjz2XjPFYL8G3gKOBK4AsTPX+M8cIYY2eMsfPlL395qa9NkiRJkppGqadlPgw8X8bjPlroylywuwxYDlwFnB5jjGU8riRJkiSpgJLCXYzxmKk+UQhhBslSzOXAd4B3xxiHp/q4kiRJkqTazLkjhNBG0qk7AbgUOCPGmK3Fc0uSJElSMwjVXhWZOzxlHXA88J/AWVMJdiGEJ4DeCpVXqpcBT9b4OVU//Po3N7/+8nugufn1b25+/VWv3wPzY4y7HUZSi3D3TeC9JP9T/g0o9IS3xxhvr2ohUxBC2BBj7Ey7DqXDr39z8+svvweam1//5ubXX9Pte6AWyzIPyl2+DFg1zv1ur34pkiRJktSYqh7uYoxvrPZzSJIkSVKzS2WI+TR0YdoFKFV+/ZubX3/5PdDc/Po3N7/+mlbfA1XfcydJkiRJqj47d5IkSZLUAAx3kiRJktQADHeSJEmS1AAMd2UIIbwihPCJEMJtIYRHQggDIYTHQwjXhRCWpF2fKieEMDeE8I0QwqMhhJ0hhC0hhPNDCO1p16bqCiHMCSGsCCFcE0J4KITwXAjh6RDCXSGE94cQ/LnZZEIIp4cQYu7PirTrUW2EEI7J/Rx4LPfvwKMhhJtCCMenXZuqK4TwtyGEm0MIfbl/AzaHEK4OIRyZdm2qjBDCSSGEr4YQ7gwhPJP7+X7ZBJ/zuhDC90MI23PfF78IIZwTQmipVd2l8ECVMoQQrgBOBu4H7gK2A4cCbwNagLNjjF9Jr0JVQgjhYOAnwD7AdcCvgdcCS4BNwFExxm3pVahqCiF8EPg6sBVYD/wW+G/AicBsYC2wPPrDsymEEA4EfknyM/7FwJkxxovTrUrVFkL4V+B/AH3AjcCTwMuBDuDWGOPHUyxPVRRC+DzwcWAbcC3J134hyXu9VuDdMcZxQ4DqXwjhXuDPgT+Q/D1/JbA6xnh6kfufQPLv//PAlSQZ4K0kOWBNjHF5LeouheGuDCGE9wI/jzHeM+b6o4FbgAj8SYxxawrlqUJCCDcBxwIfijF+ddT1XwI+DPxHjPGDadWn6gohvAnYE/hejDE76vp9gZ8BBwInxRjXplSiaiSEEEh+th8ErAM+huGu4YUQziQ5+vxbwFkxxoExt8+IMQ6mUpyqKvdz/nfAE8CfxRh/P+q2JcBtwG9ijAtSKlEVkvt69gEPAUeT/DK3YLgLIbwkd7/ZJL/g35C7/kUk3xNHAqfEGK+oUfnjcnlRGWKMl4wNdrnr7wBuB9qA19W6LlVOrmt3LLAFuGDMzZ8G/gi8K4SwZ41LU43EGG+LMV4/Otjlrn8M+Pfch2+seWFKw4eANwFnkPzdV4MLIcwEPkfSsd8t2AEY7BrafJL3xv81OtgBxBjXA8+SdHA1zcUY18cYHyxxFc5JJF/3K/LBLvcYzwP/nPvw76tQ5qQY7ion/8N+KNUqNFX5vZM3F3hz/yzwY2AWsLjWhaku+Pe8SYQQXgX8C/DlGOOP0q5HNfPXJG/i1gHZ3N6rT4QQzna/VVN4EBgAXhtCeNnoG0IIbwD2Am5NozCl6k25yx8UuO1HwA7gdblfDqWuNe0CGkEIYT5wDMkX1zcB09uhucsHitz+IEln7xDghzWpSHUhhNAKvDv3YaEf8GoQua/1t0m6N/8r5XJUW3+Zu3weuAc4bPSNIYQfkSzLfqLWhan6YozbQwifAL4E3B9CuJZk793BJHvubgE+kGKJSkfR94YxxqEQwm+ARcAC4Fe1LKwQw90U5VL6amAm8PEYY3/KJWlqZucuny5ye/76vWtQi+rLv5C80ft+jPGmtItRVa0CXgP8VYzxubSLUU3tk7v8HySHp70euJdk3+UXSH65dzUuzW5YMcbzQwhbgG8AZ4666SHgkrHLNdUUptV7w6Zblpk70j6W8afoiUi5o0+/DRxFcnLOF2r1OiTVTgjhQ8BHSU5OfVfK5aiKQghHkHTrvhhj/Gna9ajm8u+LhoC3xRjvijH+Icb4S+DtJAcwHO0SzcYVQvg4sAa4hKRjtyfJKambgdW5k1SlutWMnbuHSZZblOrRQlfmgt1lwHLgKuB0j0ZvCPnfvswucnv++qdqUIvqQAjhH4Evk/wW/5gY4/aUS1KV5JZjXkqy9OZTKZejdOR/tt8TY9wy+oYY447cacrvJxmPY/hvMCGENwKfB66JMX5k1E09IYS3k/xs+GgI4d9jjJvTqFGpmFbvDZsu3MUYj5nqY4QQZpAsxVwOfIdk5snwVB9XdWFT7vKQIre/IndZbE+eGkgI4Rzg/wEbSYKdy3Ea24t54e/+88kkhN1cFEK4iOSglXNqVplqJf9vQLE3afmtF3vUoBbV3tLc5fqxN+TC/c9IOrivIenkqTlsAjpJ/n3oHn1D7peCB5F0++vie6Lpwt1UhRDaSDp1J5D8hveMsacqalrL/0A/NoSQGTPnbC+SJbg7gK40ilPt5DbV/wvJfpu/jjE+mXJJqr6dwH8Wue1wkjd0d5H8Q2/XpjH9kGRm7Z+O/TcgJ3/Aym9qW5ZqJH/aYbFxB/nrdxuRoYZ2G3Aa8Bbg8jG3vYHkFPUfxRh31rqwQppuz91U5A5PuYYk2P0nBruGE2N8GLgZ+BNg5ZibzyVZe//tGKMzrxpYCOFTJMGum6RjZ7BrAjHG52KMKwr9Ab6bu9u3ctddmWatqo4YYy9wPTAPOHv0bSGEY4G/IenqeWJuY7ozd3lWCOGA0TeEEI4j+QXv88BPal2YUrUGeBJ4ZwihM39lboj5Z3Mffj2NwgoJbhMrXQjhm8B7Sb7A/0by272xbo8x3l7DslRhuUHmPyE5Ne06kmNtjyCZgfcA8LoY47b0KlQ1hRDeQ7KRfhj4KoVPx9oSY7ykhmUpZSGEzwCfBs6MMV6ccjmqohDCXJJ/Aw4k6eTdQ7Ls6u9I/t1/Z4xxbXoVqlpCCBngJuDNJAPLrwEeA15FsmQzAOfEGL+cWpGqiBDC35H8nQbYl+QXN5t5IeA/GWP82Jj7ryEJ91cA20nGYxyau/4d9XL2hssyy3NQ7vJlJEdlF3N79UtRtcQYH879ZuY8khb88cBWkkM1znXcRcPL/z1vAYrtqbqDJABKajAxxr4QQgfJv/NvI1l29QxJR+//xBh/lmZ9qp4YYzaEcDzJyp13kuyvm0XyRv77wFdijDenWKIq5y+A94y5bkHuD0AvMBLuYozXhhCOBv4JWAa8iGQ8xkdIvi/qItiBnTtJkiRJagjuuZMkSZKkBmC4kyRJkqQGYLiTJEmSpAZguJMkSZKkBmC4kyRJkqQGYLiTJEmSpAZguJMkSZKkBmC4kyRJkqQGYLiTJEmSpAbw/wFqRMKdDttYLAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1080x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vY0jIxOXyT5r"
      },
      "source": [
        "##Which is the best regressor of the models introduced in this example? "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hqjSwM8EyaG2"
      },
      "source": [
        "Sources:\n",
        "[1] https://www.analyticsvidhya.com/blog/2020/03/support-vector-regression-tutorial-for-machine-learning/\n",
        "\n",
        "[2] https://www.pluralsight.com/guides/linear-lasso-ridge-regression-scikit-learn\n",
        "\n",
        "[3] https://www.kaggle.com/andyxie/beginner-scikit-learn-linear-regression-tutorial\n",
        "\n",
        "[4] https://machinelearningmastery.com/lasso-regression-with-python/\n",
        "\n",
        "[5] https://www.analyticsvidhya.com/blog/2016/01/ridge-lasso-regression-python-complete-tutorial/"
      ]
    }
  ]
}