ml_regression.ipynb

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  "metadata": {
    "colab": {
      "name": "ml_regression.ipynb",
      "version": "0.3.2",
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      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/noahgift/regression-concepts/blob/master/ml_regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "4362wTmsMkE2",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# ML Regression"
      ]
    },
    {
      "metadata": {
        "id": "xcpLFdExMo9x",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Ingest"
      ]
    },
    {
      "metadata": {
        "id": "tb1teb6sNLny",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Source:  http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights"
      ]
    },
    {
      "metadata": {
        "id": "Yy9RNaZKMicJ",
        "colab_type": "code",
        "outputId": "ba2e2874-3ced-4d7d-e5ce-5d628eafb4fb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        }
      },
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "df = pd.read_csv(\"https://raw.githubusercontent.com/noahgift/functional_intro_to_python/master/data/mlb_weight_ht.csv\")\n",
        "df.head()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "    .dataframe thead th {\n",
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              "    }\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>Team</th>\n",
              "      <th>Position</th>\n",
              "      <th>Height(inches)</th>\n",
              "      <th>Weight(pounds)</th>\n",
              "      <th>Age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Adam_Donachie</td>\n",
              "      <td>BAL</td>\n",
              "      <td>Catcher</td>\n",
              "      <td>74</td>\n",
              "      <td>180.0</td>\n",
              "      <td>22.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Paul_Bako</td>\n",
              "      <td>BAL</td>\n",
              "      <td>Catcher</td>\n",
              "      <td>74</td>\n",
              "      <td>215.0</td>\n",
              "      <td>34.69</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Ramon_Hernandez</td>\n",
              "      <td>BAL</td>\n",
              "      <td>Catcher</td>\n",
              "      <td>72</td>\n",
              "      <td>210.0</td>\n",
              "      <td>30.78</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Kevin_Millar</td>\n",
              "      <td>BAL</td>\n",
              "      <td>First_Baseman</td>\n",
              "      <td>72</td>\n",
              "      <td>210.0</td>\n",
              "      <td>35.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Chris_Gomez</td>\n",
              "      <td>BAL</td>\n",
              "      <td>First_Baseman</td>\n",
              "      <td>73</td>\n",
              "      <td>188.0</td>\n",
              "      <td>35.71</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              Name Team       Position  Height(inches)  Weight(pounds)    Age\n",
              "0    Adam_Donachie  BAL        Catcher              74           180.0  22.99\n",
              "1        Paul_Bako  BAL        Catcher              74           215.0  34.69\n",
              "2  Ramon_Hernandez  BAL        Catcher              72           210.0  30.78\n",
              "3     Kevin_Millar  BAL  First_Baseman              72           210.0  35.43\n",
              "4      Chris_Gomez  BAL  First_Baseman              73           188.0  35.71"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1
        }
      ]
    },
    {
      "metadata": {
        "id": "94wUpB9ZXux_",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Find N/A"
      ]
    },
    {
      "metadata": {
        "id": "EUEKUcSGX411",
        "colab_type": "code",
        "outputId": "1639dc16-6495-4c09-faa7-88a17d0e8fce",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        }
      },
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1034, 6)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "metadata": {
        "id": "N2lBngydXmTP",
        "colab_type": "code",
        "outputId": "ef949122-db05-426d-b3ed-d6eeafcd4d7a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        }
      },
      "cell_type": "code",
      "source": [
        "df.isnull().values.any()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "metadata": {
        "id": "V1g0_zidXw8N",
        "colab_type": "code",
        "outputId": "6b0e13f4-7cd4-46e8-fa75-24fed98d154a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        }
      },
      "cell_type": "code",
      "source": [
        "df = df.dropna()\n",
        "df.isnull().values.any()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "metadata": {
        "id": "zRAL01iuX6dc",
        "colab_type": "code",
        "outputId": "80ded013-09b8-463a-e728-8aa31439ece7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        }
      },
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1033, 6)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "metadata": {
        "id": "hinonO2OOmYi",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Clean"
      ]
    },
    {
      "metadata": {
        "id": "xGnhpcC9OohW",
        "colab_type": "code",
        "outputId": "fbf3b946-dcc3-493c-dba5-da94b3051316",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        }
      },
      "cell_type": "code",
      "source": [
        "df.rename(index=str, \n",
        "             columns={\"Height(inches)\": \"Height\", \"Weight(pounds)\": \"Weight\"},\n",
        "             inplace=True)\n",
        "df.head()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>Team</th>\n",
              "      <th>Position</th>\n",
              "      <th>Height</th>\n",
              "      <th>Weight</th>\n",
              "      <th>Age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Adam_Donachie</td>\n",
              "      <td>BAL</td>\n",
              "      <td>Catcher</td>\n",
              "      <td>74</td>\n",
              "      <td>180.0</td>\n",
              "      <td>22.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Paul_Bako</td>\n",
              "      <td>BAL</td>\n",
              "      <td>Catcher</td>\n",
              "      <td>74</td>\n",
              "      <td>215.0</td>\n",
              "      <td>34.69</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Ramon_Hernandez</td>\n",
              "      <td>BAL</td>\n",
              "      <td>Catcher</td>\n",
              "      <td>72</td>\n",
              "      <td>210.0</td>\n",
              "      <td>30.78</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Kevin_Millar</td>\n",
              "      <td>BAL</td>\n",
              "      <td>First_Baseman</td>\n",
              "      <td>72</td>\n",
              "      <td>210.0</td>\n",
              "      <td>35.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Chris_Gomez</td>\n",
              "      <td>BAL</td>\n",
              "      <td>First_Baseman</td>\n",
              "      <td>73</td>\n",
              "      <td>188.0</td>\n",
              "      <td>35.71</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              Name Team       Position  Height  Weight    Age\n",
              "0    Adam_Donachie  BAL        Catcher      74   180.0  22.99\n",
              "1        Paul_Bako  BAL        Catcher      74   215.0  34.69\n",
              "2  Ramon_Hernandez  BAL        Catcher      72   210.0  30.78\n",
              "3     Kevin_Millar  BAL  First_Baseman      72   210.0  35.43\n",
              "4      Chris_Gomez  BAL  First_Baseman      73   188.0  35.71"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "metadata": {
        "id": "ZdOS02NgNBk1",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## EDA"
      ]
    },
    {
      "metadata": {
        "id": "nZE0KT42NC3x",
        "colab_type": "code",
        "outputId": "89faad45-da05-4030-98a0-9c9b7371265f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        }
      },
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Height</th>\n",
              "      <th>Weight</th>\n",
              "      <th>Age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1033.000000</td>\n",
              "      <td>1033.000000</td>\n",
              "      <td>1033.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>73.698935</td>\n",
              "      <td>201.689255</td>\n",
              "      <td>28.737648</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.306330</td>\n",
              "      <td>20.991491</td>\n",
              "      <td>4.322298</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>67.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>20.900000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>72.000000</td>\n",
              "      <td>187.000000</td>\n",
              "      <td>25.440000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>74.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>27.930000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>75.000000</td>\n",
              "      <td>215.000000</td>\n",
              "      <td>31.240000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>83.000000</td>\n",
              "      <td>290.000000</td>\n",
              "      <td>48.520000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Height       Weight          Age\n",
              "count  1033.000000  1033.000000  1033.000000\n",
              "mean     73.698935   201.689255    28.737648\n",
              "std       2.306330    20.991491     4.322298\n",
              "min      67.000000   150.000000    20.900000\n",
              "25%      72.000000   187.000000    25.440000\n",
              "50%      74.000000   200.000000    27.930000\n",
              "75%      75.000000   215.000000    31.240000\n",
              "max      83.000000   290.000000    48.520000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "metadata": {
        "id": "AgY0aQiBNgrg",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Model"
      ]
    },
    {
      "metadata": {
        "id": "8lzc98GwNh_e",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from sklearn import linear_model\n",
        "from sklearn.model_selection import train_test_split\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "7u5EFF1FQfbs",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Create Features\n"
      ]
    },
    {
      "metadata": {
        "id": "9xBm4wektxRh",
        "colab_type": "code",
        "outputId": "0a065ab8-64fd-42a9-ada5-64b9df33f78c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "var = df['Weight'].values\n",
        "var.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1033,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "metadata": {
        "id": "SEUpMhHvQhkc",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "y = df['Weight'].values #Target\n",
        "y = y.reshape(-1, 1)\n",
        "X = df['Height'].values #Feature(s)\n",
        "X = X.reshape(-1,1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "gUR6C0NYxGXJ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#X = df[['Height', 'Age']].values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "eGW5TKEyt64F",
        "colab_type": "code",
        "outputId": "497e6b78-e267-4826-ac39-8bba0c3df8c1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "y.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1033, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "metadata": {
        "id": "a1PeC6cbQHar",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Split data"
      ]
    },
    {
      "metadata": {
        "id": "Pb552OpRON4G",
        "colab_type": "code",
        "outputId": "df34a038-0c36-40db-a104-03d5e45801b9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
        "print(X_train.shape, y_train.shape)\n",
        "print(X_test.shape, y_test.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(826, 1) (826, 1)\n",
            "(207, 1) (207, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "XSX1G3QoQFdy",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Fit the model"
      ]
    },
    {
      "metadata": {
        "id": "KxnwLKlrPorE",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "lm = linear_model.LinearRegression()\n",
        "model = lm.fit(X_train, y_train)\n",
        "predictions = lm.predict(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "lJTO3X5lweum",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "lm.predict?"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "WipFTmTtV29v",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Returns Numpy Array"
      ]
    },
    {
      "metadata": {
        "id": "PE3iilyCUxFX",
        "colab_type": "code",
        "outputId": "6ee7239a-35a8-4078-fe03-0408aa8c2499",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "type(predictions)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "metadata": {
        "id": "wXZj9Jq_TKjr",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Plot Predictions"
      ]
    },
    {
      "metadata": {
        "id": "guGaARR3T-s9",
        "colab_type": "code",
        "outputId": "4e17546c-e6ab-4cd8-c843-16be29c398ba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 400
        }
      },
      "cell_type": "code",
      "source": [
        "from matplotlib import pyplot as plt\n",
        "plt.scatter(y_test, predictions)\n",
        "plt.xlabel(\"Actual Weight\")\n",
        "plt.ylabel(\"Predicted Weight\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Predicted Weight')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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8r6mpCUVFRdDpei/kl5SUoKGhAWVlZWe6TURERCkjYiF/5plnsH79erz++usA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\nAwBg2AcvnEIzo4G4JgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "HyNJLkXvV84d",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Print Accuracy of Linear Regression Model"
      ]
    },
    {
      "metadata": {
        "id": "nwq5XCBjTNaa",
        "colab_type": "code",
        "outputId": "4e608563-dfbf-48f5-8f96-72ee7920de2a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        }
      },
      "cell_type": "code",
      "source": [
        "model.score(X_test, y_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.33282208398739177"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "metadata": {
        "id": "GV4AuHc3VoNm",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Visualize Regression Lasso (Regression) Model Accuracy with Yellowbrick"
      ]
    },
    {
      "metadata": {
        "id": "GcDqXio8XqR6",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Note, uses Lasso Regression"
      ]
    },
    {
      "metadata": {
        "id": "AKUixevWWBqs",
        "colab_type": "code",
        "outputId": "0a62b834-7868-4ae7-acdf-f635c3c6df73",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 378
        }
      },
      "cell_type": "code",
      "source": [
        "from yellowbrick.regressor import PredictionError\n",
        "from sklearn.linear_model import Lasso\n",
        "\n",
        "lasso = Lasso()\n",
        "visualizer = PredictionError(lasso)\n",
        "visualizer.fit(X_train, y_train) # Fit the training data to the visualizer\n",
        "visualizer.score(X_test, y_test) # Evaluate the model on the test data\n",
        "g = visualizer.poof()            # Draw/show/poof the data"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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04HdTIYPJiq9Oq5oKvRV/olbXmxKyLHPhwgX1dWRkpBBqL0SItcDtFBrNJFzI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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "uPc_ziXrYf4K",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Use Cross-Validation"
      ]
    },
    {
      "metadata": {
        "id": "6gOCsSV1YtXp",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_score, cross_val_predict\n",
        "from sklearn import metrics"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "a7oU-REkYwSW",
        "colab_type": "code",
        "outputId": "f6ff4f23-cf0e-4225-e127-56e84a5931c4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "cell_type": "code",
      "source": [
        "scores = cross_val_score(model, X, y, cv=6)\n",
        "scores"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.29670427, 0.22459508, 0.29543549, 0.30012566, 0.19191046,\n",
              "       0.34579806])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "metadata": {
        "id": "VB5VEe4mZPdY",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Plot Cross-validation Predictions "
      ]
    },
    {
      "metadata": {
        "id": "8O2bc1HxZIcb",
        "colab_type": "code",
        "outputId": "06cbefc6-b9a0-4ed2-fbf8-109adfe038a7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        }
      },
      "cell_type": "code",
      "source": [
        "predictions = cross_val_predict(model, X, y, cv=6)\n",
        "plt.scatter(y, predictions)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f3b14c67b38>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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P4N0P27D7SKzObmGeXlAtSqiO+OV6/p1m2XUbrz1ifPflnYJl5TtaPhOt+eiW\nkG1k9QWvP4B3PmzDZwL1tuC6Cbj39qkAgB++sU8wn3yTHm4BEYloO/n1w7oe6xocYnU7ubJQsL0a\n5lRj+c1X4uGf7sSogEqEXqfBa/92i2g5kglYkWg/SEUwFq8/AJ3RgIDPr/oQlGrfh0wjZAUgRxZO\nLCqQlOwZS+g9GYRGDNNrytHaLl6W2+dOFnzQAiFN2jf/+wsc+aof/U7hKVox6cbO3ot1FF2ukkID\nHC5hCUZWOs6eA23C9jS32UTrliUzKaZYKOXUost78XoXyy2mMfzZkR60nOhFMAh4/cIFEHLGwMU+\npNdpIvIxG7XM60VeI7auvP4A9h+LfXkAINpHPj3Ujb9+0SPojAFgNBBEt92FieWRcaeTHd2y7zHh\nfiAnT6lz5Mx4EMqCWk0BJCoLB2RP9kxIjHxHS5eoM3U4RyTlF3e29oimZxHExTqKLpeYM5ZKx9kz\nMCxmj1e0bqXsTBR+eTn45WYx4huT5Tyj4fpQdD7xXK9vKLauBl1eDAyzZR+jGfEFMORmp2nt6Iv5\nTahtm/Z3Yuv2Dln5Drq8ovUrZJvcPKXOiTgejL/cRO5BDlkBlBSyv3uxjmdD9ow1YmDJFZZa0vN9\nDwDyzTpmucTwjwYSSgcAZ88LT62l005+X0i03PFQajEjz6RPKh+tBsgzRU7WBcSmCpKkproo4n+p\nGSQ5i7N0Ehqb0cfl5Cl1jtPtS7rcRO5BDlkBDLjYo0LW8WzInrFG5WIj/Vm15Tjdk77VsF02N7Nc\nYhxotyeUDgB2HeoW/D2ddvL7QqLljodZteXweEeTymcsCHiipsRPdqfnO6F/NLIDpmIGqdfhieu4\nnDylzunsdWVl5otIL+SQFUB1ZaGolq0GbAF0AFi1pAYNc6pRVmSGVgOUFZlFRdpTAWtUXlZkwi2z\nJgmWZUYNW5tYjLIiE26sm8A8Z+qlJcxyibGwbkJC6QBgSX2V4O+J2ikFJ/nHkWi55VBWZAq3W7L5\nWC2mmJmaqZeWMNMsrLsk3IesFhPMRukXS40m9l5JxQxSvPennDylzqmuLMz4zBeRfmhRlwKw5BtR\nLbKyVI7Mm06rxeqGWiy/+cqMSDdyo3KhVaezaiuwuqFWcOXoxPJC6HWaiO0qHBqEvpFGw60QNhl0\nONhhF1wsVZinD0v+iZVLjJrq0oTSAcD0GuGZCZadchBb8VxVEdkXWO0gh4oSM2wDIzG/8+scCK0M\nTyaf+qkVMf2xrDhPdNV8YZ4ea/7HtRF96D8/+VIy/+qK2HuF3VflzSDFe3/KzZN1jiXfmHS5idxD\nt379+vXZylzpQtNA5gSzkxF4t6QKAAAgAElEQVRA59DrtCjIM0Cvi39iJF47pcTIxcpyy+wq/Hnf\n2Yipbb1Og//z8EIEAsGY691zWy0M+tDDZ3H9JOw8eA6+0YvfHwvz9HjhO/Nh0OlEyzVnajnOnI99\nmL700ALkGfVMe/7+f07DjuaumLTP/+M8FJoNovUjZCcQGsWZjTrodVqMBoLhv0P7dkN5fndlHQ5/\n2S+rL/DLHT0tzEcon4f+9jqM+ALMOhfKZ8Q7Gr5eQOwbBUKzIyyBeqn25Peh6Px1WoDb0Cl1r0j1\nVTnEe3/KyVPqnFSUW2lk6nmbTgoKxGcvaB9ykmR6X1wyAujJkKp9yHLptrvQ2tGHGTVlEdtU5Fyv\nb9CDtjMDmHppSXhkLKdcHZ0ONHf0Y3aNNTwylmvPoQ4btrd0YUl9lejIWMpOa3FexB5eob/5ecbT\nF/jlPvKlHZ8c7sbNdRNx3ZXlkvnE04bR+5C5v8+eH8Luwz1YWDcBky8piqtPyGlPofx9/gCcvjFY\njFpZ90oy+5A54r0/aR9yfKh9HzI55CRRQweRA9mpLshOdUF2KgeWQ5Y1d7lhwwasWrUKy5cvx0cf\nfQQAePvtt3HttddieHg4fN62bduwfPlyrFy5Er/73e+SLDZBEARBjB8kF3Xt2bMH7e3t2Lp1KxwO\nB5YtWwa3242+vj5UVlaGz3O73di4cSMaGxthMBiwYsUKLF26FCUl7NWSBEEQBEHIcMhz587F9OnT\nAQBFRUXweDy49dZbYbFY8P7774fPa21tRV1dHSyW0HC8vr4eLS0tWLJkSZqKThAEQRDqQdIh63Q6\n5OfnAwAaGxuxaNGisNPlY7fbYbVaw/9brVbYbOzoPaWl+dDrlb8IgfVNQE2QneqC7FQXZKfykb0P\nuampCY2NjdiyZYus8+WsFXM4pAPm5zpqWGQgB7JTXZCd6oLsVA5JL+ratWsXNm3ahM2bNwuOjgGg\nsrISdrs9/H9vb2/EN2aCIAiCIMSRdMhOpxMbNmzAq6++ylygNWPGDBw+fBhDQ0MYHh5GS0sL5syZ\nk9LCZgKvP4Beh1uVwdn7Bj347HA3+gbZsXejcbp9aG23wZnAhnyn24djp/qZaYXq/FCHDT/77UEc\n6rBFHJfTPqd7BtG4owOnewYly8e/XvPx83jiP3ah+fj5+IxEaD/xn/acRrddOk51dJ4v/Lo5oTzF\n6laojvjnitkpp275dgqdL3YNsb6Xq/ebnH5LpIdc7ROZQHIf8tatW/GLX/wCl19+efi3G264AXv3\n7sXBgwdRV1eHmTNn4nvf+x7+9Kc/4Ve/+hU0Gg3uuece3HnnnczMc2nqIVFNVCUEBvH4/Hj8l59H\nhCHkIh7lGcWjSflGR/Hc2y0RIQEnX4g+ZNSzv3ZwabtsIelIrSYU2pGfVqjOr55SjN2HewWvaTZq\nAWjg9QUE22fQ7cWjP98dEWJTA+An312I4vzI6DhydIKffeAGVFkLmHa6Rnx49Be7I8Jg6nUa/OTh\nhSg0G8NBHXRaDbr73djf1oujX/VL5skKBiHULhPL87Fy8RU42G7H0ZMOnmZzGdo7B3HONiwq7LH+\n29fj04PnRPt+36AHrV/a8euP2gXDl5YVmTDjqnJoABxst0dc484bL8OTr+6J6XvPPzgP2z49Jet+\nk+rzYnXVN+jBuQEvJpWYJAOKRNctv99OLC/AP37jGpQX5+ds4I1MP4dSEUQlGjnPYLVPWVNgkAu8\n13RCMC5sw5xqrG6oFU2XqQ4ix8GJ8d2Xd4rGBP75Py8STff063vQZY/9zl9Vno9nvz2PmefTv9qL\nLttwzO9VFQV4ds0NAMTrPB747XP/j7eLnrfl+5Gr/eXmHZ0uGlaeDXOqmQ5fKp3YQ+mJTZ/i/ED6\nR26LZk5ES5tNsO/IRasRVvjSAhASWOS3p1SfF3uAi70ESL2AAsD/3rIXnb2x/RYIvXjIeUnPBpl6\nDiU6cJGDnGew2h1ybvWqLJEKTdR0w42IuIfbWBA42+vCc2+3MNP1DXpEH6guz6jo9LXT7RN0xgDQ\nZXczp/Kcbp+gMwaALtswnG5fyrR6ufaRmp7mH48nb9ZUstT0NCceHy8sUXqn25cRZwwAOw92J+WM\nAXG5TTG1Y/79JtXnt27vEKyrR3+xO6bcLs8oHv/l58yyhkbiwv0WiG2L8YhYnSdbJ0p4BmcCcshI\njSZqOgk5OOGHf5fNxXSObWcGmNcWO35CIh3r+NGT/cy0R0/2p0yrl2ufP+87yzyPfzyevBs/Fn/Q\nNO07I6+QKYB7KO0+dC5jeWYDrj2l+nzfoEf0AS6mosV6AQWAk91Dsso4nhwEn3Q6zVx/BmcKcshI\njSZqOunkjRKiGQuGjoshpSsrelxKLIZx3D3iZyZ1j/hTptXLtY+UuA3/eDx5jzHUilwjyY0e44F7\nKHV0sV+UlA7XnlJ9vu3MQEIvdKwX1MI8ebtAx5OD4JNOp5nrz+BMQQ4ZF/VJhcgFbdHqykJoRRxO\ntCh9NJyurBB8neBoaiezHTnr+IyacmbaGTXlzDqPB659GuZMZp7HPx5P3ssWXSl6bOlcdp6phHso\n3To7c3lmA649pfr81EtLEnqhY72gVlVYIEeZdDw5CD7pdJq5/gzOFOSQL7BqSQ0a5lSjrMgMrSak\n1dowpxqrltRku2iw5BtRVSHsdKNF6YV44TvzY5wyt8iFlWd1hfAK4+qKAmaeZcV5yDcJd618kzb8\nEiBU5yzMRh3MRp1g+0yZUMxMG32cnzeLeddNFD0mJtGYDriH0rTLymSnKSsy45b6SaiuLBB1bizk\nBtErKzJjyewq3Dq7Kub+EbuGXqdh3m9Sfb6sOE/0Aa7XCRvLegEFQk7hppmTGJaGGE8Ogk+6nWYu\nP4MzBa2yjiLe5fxKWGXNEY+ubLJ5xrPVil/n/kBAcBvRE/fMxqTy0AuCWPvEs+2Jn/eJ0/34aePh\nmGPrv309Li0Xn30AgH7XCP71lc9ifn/hn+bjz389K7jKWqsFxkRWNT31d3Ow90gPDpyww+EcQanF\njFm15RGrWG1DHjz+H8ILlLQaYFJ5AR6481pUlOSF68jp9qHttAO//L9HY+rnxYcW4E97zqClzQaH\n04tSiwn1U4VXKyMq7ZP31aO6whLOJ/r+kbstjLW9S3qVdWRdJbPKmrtm8/FeOFw+mPRaaLQa+PwB\nwbbIFTK/ylq8fyYLq0+ofZU1OeQkUcI+5FTkGY/QO594XwI4uu0utHb0YUZNGSZKOMVoTvcMYt8x\nG+ZOq5AcOfPZc6QbOw6dwy3TJzFHxkJ0dDqw+3APFtZNiBg58/ch9zo8qK4shNGgCz9w9n3Rg4/2\nn8VtcybjxhlVMelYL4bHTvXhk4PncPPMSaiutMjuF6d7BnHk9BCum1IUUT+s/bxcG7o8voTqNpn2\nzOQ+ZKFrAuIvgbmCGvYhy4EcchpResUC6uggciA71QXZqS7ITuVA+5AJgiAIIschh0wQBEEQOQA5\nZIIgCILIAcghEwRBEEQOQA6ZIAiCIHIAcsgEQRAEkQPo1q9fvz5bmbtVIP5dUGDKqB3ddhd2H+pG\ngVmfsn3IXn8A/UMj0Ou10AvEDjzdM4iP9nXCpAdKCtmRrYTSNu3rREGeTjStUP6ftnbh9T9+AYNO\ng0snFMWVJ7+OjAYd0zY+n7Z2YePvW6HXICLPvkEPDrbbkW/WI98sHFiCb2eeySA7z2On+vCfH3+J\nArMeRQWmcLrAWFDyGlLtxodvw7FT/fjVH4/CqNNgEm9PcDzXi7cscuqQfw2+/XLqQux6w94A/P7R\nlNuTa2T6OZQt1GBnQYF4iFHah5wEyQTMiDfAh1TEIzlEb+aX0jZNJPIVh1BaAHiKF9lJKP8rq4vx\n1y96Y6739P1zcXml+P49QLiOOMqKTJh+ZRka5kyGtcgcEVnq+Ff9ePkPsZG6HrjzGvz6oxMYHhGO\n+OT1B9Bpc4pKYJYVmXDt5aWYc/UlmHKJJaKdWdG2zEYtAA28voCg3ixXb/uOncfgsB/FBQbMnXaJ\nYLQkoYhp0az/9vX49OA5WRq30QE+otuwpNCAyycW457ba1FSaJYVsY1/jb4hb9j+EV8AZqMWwSDg\n84/J1t6NKJPTC6tFXjrOCTftP4tDX/alXO83nahhf64c1GAnBQZJMcmElEw07bdf2C4s9K4BXn98\nCTNPMccbDAbxl+aumPM5QfD7f7xd9Jpbvs/Ok5WWE3oPjI1hR4t8OUGpPP9hw3YExIR2o/KfeVU5\nggBa2+1xaxYXmvWYd90EwbCYYkS3M6t+hOCLtL/5py+w82BPzDmLZk7A391xTcRv3315Z8Kaxvw8\nxV4IF9RdIlgWAJhcWYi+QTfc3thGKTDr8YtHFgEQF6YX49bZVfjW0qmix+UI3fOJfiFIJM9sowZH\nJQc12EmBQVKMlHB6qtN229lSdN12cflFQFxUfPdh4QfpgRN2dHQ6mNc83TOY0DHw8t95MD5t309b\nY18eOLrtLlnOmMv/L81d2N7cFbczBkKyi1x9yoXfzsdO9cWdJ6c36/UHRB3gzoM9EZq0fYOehJ0x\nP08AgjMPo4GgaFmAkL1CzhgAhkdC2sQsjV0xdh/uEdXeTUSzl39/JJInQaQKcshxIiWc7mR830g0\n7d4vzjPLxDrOekCN+IQfMA7nCP5zRwczzz988qXosU8OijtOPnIdKMe2z06KHtvV2h3fxbJEl82F\n333MrlshwnrIZ9l6yPzjLO3fePLstrsEPwMkS3ObjamxK8aILwDbgEfwWLyavXJfCFh5EkSqIIcc\nJ1LC6Z294qPVRNOWl7AXUrGOJ/LAK7WYMeTxM8+xD4pfszRNWrGljMUQ0GTty0tcjAWBEW/8o1ZO\nb/ZkzxDzPP5xlvZvPHm2dsQ/opdHkKmxy04q3N7xavbGdX9k7+seMU4ghxwnUsLp1ZXiSjaJpp1R\nI6xBKuc46wFlNgqrtMyqLcfXF1zOzPN/LrhM9NiCOmlN2URYydBFvWl6evJMNVoNcOfCK+JOx+nN\nzhbRo+XgHy8rzovRwU4kzxk18jWY42H21Eqmxq4YZqMOFaX5gsfi1eyV+0LAypMgUgU55DiREk5n\nrZhONK0l34iqC1rAMenKC5h5sh5QC+omiAqCS8kPso6XFeehwCztCKorhW0Sgy9rGM3E8kJkYndK\nYZ4ei2fFJ83Ip6qiMC5px2iR9onlhdDrhN/q9DpNjLThC9+ZL8sps4ThpfJsmFMdd93nm3RhSUS+\nML0GIecn9rIIhPotS/IvHqF7uS8EUnkSRCqgVdYJkI1V1snkKSUqLqZtesbuwvrX/xpzvfXfvh6X\nSmjasrbblBWF8l+x+Ao0fvwVWtpscDi9KLWYMO3yYuw+FLvt6aWHFsAqsQdaeCUw8Pi36vHZkfM4\n1NEXtn/mVWUXVln3oW9oRPSaZUVmTLusBFOrS3D1lFKUFedF1CcrrVYLjF34Th7dXj0Dbjy5aY9g\nOqvFhBk1sVu02Hayt8D1DXrw+ZFu/H7XqZhjXHuyNG6l8nR6/PjxO80473BH9M9H/9cMPL15L3Pb\nE0e0BjG3Bam1oy/cP+qnyt+C5PUHoDMaEPD5mc40+v4wGnQIBoPw+sdgjTPPbKGG1cdyUIOdtO0p\nTWRyH3Ky6YDERcX3HOnGjkPncMv0SXGN7oBIcfvCfKNg/kLl6uh0YPfhHiysm8AcGQsRvVeWlQ//\nt31f9OAvB7tw68wqzL1mgmRd8dOePT8ULu/kS4rCv/v8AWZ7HeqwYXtLF5bUV2HqFGtc7SNmpxTJ\ntKdUnmL9k98PuJGxXBLtt0B8D/DoF4JE88wGanBUclCDneSQ04gaOogcyE51QXaqC7JTOdA+ZIIg\nCILIcWQtwdywYQOam5sxOjqKBx98EHV1dfje976HQCCAiooKvPjiizAajdi2bRveeustaLVa3H33\n3Vi5cmW6y08QBEEQqkDSIe/Zswft7e3YunUrHA4Hli1bhvnz52P16tX42te+hp/85CdobGzEXXfd\nhY0bN6KxsREGgwErVqzA0qVLUVKS3F5IgiAIghgPSE5Zz507Fy+//DIAoKioCB6PB3v37sWtt94K\nALjlllvw+eefo7W1FXV1dbBYLDCbzaivr0dLi3QoSYIgCIIgZDhknU6H/PzQhvjGxkYsWrQIHo8H\nRmNoBWVZWRlsNhvsdjusVms4ndVqhc0WX4xagiAIghivyA7j09TUhMbGRmzZsgW33XZb+HexRdpy\nFm+XluZDr8/9LQViDLq8aG234bKJRTEh+YQY8Y3CMeRFaZEJZmNiEZSSuYZYWqlrnj3vxO93dGDu\nNZegojQvaRvkcPa8E/u+OI+511yCyZdcXJWYijoU48vOAfzxv77ATTMn4crqi59a5OQ56PLiVPcQ\nLptYBJNRJ7uM/HRy+lCq0gLs1Z7pyjOTjPhG0W0fRmlxXlr7aq6QSHsqETXbKauX7tq1C5s2bcLr\nr78Oi8WC/Px8jIyMwGw24/z586isrERlZSXsdns4TW9vL2bOnMm8rsPhTq70WSLeIB1SusNySOYa\nYmm5wBxi14wOBvHGfx0NX7MsTTqxQnnqdRq8uHYB/vjZ6aTqUIxo7eb/3NEODYAXH1qAP+05w8wz\nui8AgE6rwdhYkFnGbASX4RPv9pFU5JkpEtVDVjJq2A4kBzXYyXqh0K1fv349K7HT6cSjjz6KX/3q\nV+Ep6Y6ODng8Hlx99dV44403UF9fj0WLFuFnP/sZ7rrrLoyOjuJnP/sZHnnkEZhM4m/RboYyUi7z\nwzf342yvK/wADwIYGvahtaMPt8yqijn/N39pR9P+Tni8IXUljzeAr84NweMdRd0V8uIEJ3MNsbSt\nHX042G6P+b1vcATXXm7FIz//VFTlhzu3/8K5gbEg+odGoNdrI/5ubbfhnQ/bkGfUYZJI8Ipuuwu7\nD3UDCOLpzX+NUYEaCwIf7j17wV5h+zlxeb1ei1Pdg3h/9ykU5ulRkGcULBf/7+/+bBeErPzor+w8\nnW4fntmyD9397oj03OQQ//xJZfk42G5HvlmPfLNBtA/t/eI85l1zCXz+AL7qGoTZqIsJTCG3//Hr\nhG9v+1kHtm5vh1mvQUWJvPjM8fZ5FmLl0qco9mkq7rdE4duWKnvkUFBgUuzzNB7UYGcBQyRH8tX2\ngw8+gMPhwCOPPBL+7cc//jHWrVuHrVu3YtKkSbjrrrtgMBjw2GOPYc2aNdBoNFi7di0sFvVNLciR\nUORHKJLSZ11+85WSkYC8/gD2HxeWWGw+fp55Da8/gJa22FCUgLi61GdHenD0VL8syb3dR3qwv60X\nGo0GXl8AJqMOQBAjvkiv2nZ2EMBRPPvADaiyhmJYC4VjjJcDJ2wIBMZw6Mu+GD3bT3iSjGZeufh/\nF+VpBZ0xi+bj53H8tAPn7MOi6l18mvZ3oml/Z/j/ArMewyPCik+2gRH8888/Df8fPRJ1un04K9Ju\nZ3tD/S/frI+YERFrk10X9Khf+Kf5qCgSj57FRd8SorM3ts+LET1Twy9XqmZcWP29pc0m635LhFTM\nghGEpENetWoVVq1aFfP7G2+8EfPbHXfcgTvuuCM1JctR5EgoTrvs4uI2OfqslRIqMoMuLwZcwnKI\nDpefeY1Blxf9TuE3SpYvGXTJfwv1+i8+6MU0ljmefm0vtnx/CQBh0ft46RvyYseBc5Ln8cvF/3vI\nE6coM0J17hBpDzmIOWMhxoIhR/vc2y145v7rmfKeQKj/HeiwR7wASLXJ4//xebhNxK4p1kpBxPZ5\nMbZu7xAtV9+QN3xsdUOt5LXEYPX3fqdX1v2WCNG2pcoeYnxBr25xUlnKjsMbfTxefVYhhobZeq2s\n4zoxvccs0nz8fMpE73PQvLTAzb70DXqY553vHxadkWFx7JS45nFJIXv0K3UcYM8U8Tlwwg6vn/0C\nwSLPpGdKnOaZUv+9W2oWLBl7iPEFOeQ4CUjMUUYfj1efVYiD7WyBeNbxXgf7AZ4Nmpo7UyZ6L2fK\nWA1wsy+7j/Qwz9t1uFt0RobFJwfFZxkGJGZLpI4D7JkiPtysUaJ4vKPMGSyPV/7shFzkzIIRhBzI\nIcdJcaEJpYUGwWOlhUbBEW88+qxCzJ3G1mtlHa+uLGSOGBbNnAirJbPbVxpmVyctem+1mHBLfRWs\nlvgUr5SKVhNqy4bZ1czzls6eLDojw+LmmZNEj0n1oepKaaUp1kwRH7mzRqx8ykTyKSsypWWrVipm\nwQgCiGMfMhHCZNBh9tWXRHwv4ph9daXgiFen1WJ1Qy2W33xlQpJuUyYUQwPhb76aC8fFsOQbUVVR\nKLgQqKqiEH93xzR4/QG8+2Gb5OgrVcy++hIAIT3dRKatF143AffcPhUmgw46rUawLdJJYZ5eUOc5\nnVRVhOQMQ3V3VPS8eddNxFc9zrjrZNpl4i9IUn1IzoIubqZIqlxyZ40SyWdWbUVaFnSx80zOHmJ8\nIbntKZ0odfn6NZeVwuMdxaDLB69vFNYiMxbWTcCqJTXQasQ/aup1WhTkGRLaDrG4fhI+2ns24jcN\ngJ98dyHMBvZ71cK6S9Da0QeX24cgLo5qnrqvHjptaHvGjJqyGJuuvdyKc/bhmOs9fk898ow6DLp8\nGPGOwmzUQa8LbWHh/hZztM8+cAOK8kIP8FtmV+HP+87KnnYuu1DP99xWC8OFgDL8tmBNR4bLGAhG\n/F1WZEbtJDPOD8T2xbV3XYfiQmNMOz+8og6Hv+wP16cYWg0wsTwPwTHAN3px8Vhhnh5P3z8XO5q7\nJG2ObisAmHNNpWBarm6j+6dJok1e+Kf5KDAJz/pwSPUhOYiVi2sHOfdQIvnIvT+VlicfNWwHkoMa\n7GRteyI95CTw+gPQGQ0I+PwZews+3TOIfcdsmDutgjkyFkJMPJ6PkBh8077TaDrQhYZZVWiYO0Xw\nXACCfx/50o6m5k40zK4Oj4yj4UTva6qL4B8NorqyEB/uPYmPD3Rj8ayJ+PqNNZIzC/yynD0/hN2H\ne7CwbgImX1IkWUbumu99+AV2HjmPRdddgtW3X8Osk+j6PN8/HM7zEmtBTD33DXrQdmYAUy8tQVnx\nxYV/HZ0OwXQAJNuq+fh5Zt2Ktc9XXQPYc9yGeVdXMEfGQsjpQ1KIlSvV91A27k+xvpJu1BAwQw5q\nsJMVGIQccpKooYPIgexUF2SnuiA7lQPLIdOiLoIgCILIAcghEwRBEEQOQA6ZIAiCIHIAcsgEQRAE\nkQOQQyYIgiCIHEA1DtnrD6DX4c5o3FivP4Bu+3BG83S6fTh2qh/OBPbine4ZROOODpzuGYwrXbfd\nhd/v6EC3nS1swMFvi75BDz473C0ZgzmViPUFqT6SqvaMp42y0W+dbh9a220J9aF4yIZtBKFkFB+p\nKxuyZ9kQQE9GIH7Q7cWjP98dDmLxwd4z4aAixfnim9SF5BH1Og1+8vBCFJpj96Dy6yVaChEIBcR4\n4TvzkWdkB6FIFLG+sGLxFWj8+CvRPpKq9oynjbLRb5PpQ/FAUoQEkRiKj9SVDTHybOSZjED82v+z\nUzCi1Ed7z+KOGy4VFYl/6Kc7Y6I7jQWBP+87i68vvDzmetH1Eo1vdAw7D57D1+ZNifhdSNS92+7C\n7kPdKDDrIwJQON0+fNU1CLNRFxF4wesP4O0/teGTg+di2qW1ow8H2+0xv/cNjuDay6343cdfCrZn\n/4XjYpHV+OVuP+vAs2/uR9+QV7CNFlw3IcJGsT4klKeY6H3foAcH2+3IN+uRb5Z+yYmnD4nlKYds\n3B9iqCGykxzITuXAitSl6BEyW/YsPWLkUlJryeYpFOlHjii9WNSk0z2DTC3bh37yCQwGHby+QMRI\nprffLRpqcTQQRLfdhYnlF0UF5MrruTyj6La7YC3OQ//QCJr2n0Vrhx39Th+sFiOuvdKKzw+fD+f9\n24+/hF6nwY+/Mx8v//ZQzOju+/fMwh92nkRLW6+oDm6XTbjuPjvSg2OnHRj2CKfbfaQHx073Y0ZN\nORrmTIa1yAwA4XIf+rJPcCYgmrO9Ljz52h4MOEOjxek15WhpO8/Ms35qpejI/s4bL8OTr+6JiKct\nNfvARdgSopPXh6RGt1KRqFj9YP/xXnx9wWUJR/hKN9mKspUs3KeWgD+gqHITsSjaIQ+6vKIPxL6h\n9IiRs/McSThP1oNQjii9mED8vmNsJxkIAoELQvF8UXWrhEJNa0dfhENm1Us0P3hjP/Q6YMQ3FvF7\nv9OHXQdjBS5GA0H86yufRfw2Fgw5usd/+bmk0AMrVrbDyS5zv9OHHQfOYceBczAbtQA0GPHF/02U\ny6dvyIsdLewY1v1OH5r2d6LtzEDEixjXPjtaOhGIrDq4PKP4t42f4ZV/uVnwmp28kXE0QVzsQ1u3\nd0SIJHB5BoNBaDQayWlolhThgMuH9Vv2YfbVuTV9rdQp9mx8OiPSi6JbLRti5MWFJojN4Om0SFhq\njXsQclOe3INw6/YOGPTs4PSs49deXhp3WQ6csF9wPuIU5kXWbXGhSTINx2hgLMYZJ0omVZdGfGMJ\nOeNEERvZRztjDrc3ILp4rqSQPSotKTQyR7e7D/eI9k8+UjKLDpdwumzCuvdymYhyB5VTbkIcRTvk\nbIiR+/wB0QdiYCx0PF6kpsH3Hetlpm9ps4seG06gDhzOEXwsoUQkLGifflWb8YRcFSw+e48KT4Xb\nBtir3G0DHuboVuxF5MAJe8Qqak6KUIrodNlC6t7LhTIKodRyE2wU7ZCLC02iAvVWS3rEyOVMH8cL\n60HocI7A65dyquKjTU0CTrLUYsaohu0NvKORZRp0eeHN4OhRKejEpnBkkEhSj88v+HthAXuEXFhg\nlBzdCuFwhj7T8Fm1pAYNc6qZo3KhdNlA6t7LhTIKodRyE2wU7ZBNBh3qp1YKHqufmh4x8urKQuY0\nOSedFw+sB2GpxYxb6quZ6W+aIb7KunZySdzlmVVbjmU3XsE85xsLI48n8jBPBdFT57nED/5uNm6p\nZ6+AZ1FVEX9fWnDdRJndyPoAAB9BSURBVOFrlRcyP7VUlRcyR7dinyNKLeaYF1+dVovVDbV45v7r\nUSryUiyULhtI3Xu5UEYhlFpugo2iHTJw8W28rMgMrSYkYt8wpxqrltSkJT9LvlH0QVlVkZhGLOtB\nOKu2HFMmFDNfAviLq4TLK77IzGzUwWzUxdSdmHYxR/RxuVOVyTCZ9zKk1YT+f+E788PtnwiJppMi\nz6jFlAnFov1zglU8X+6cp+6rF0zLcqxifcFk0GHRzEmCxxbNnBR+eRUr74I6YUc/q7Zc9MXXkm/E\n7KvF+3UurAiWuvdyoYxCKLXcBBvV6CFncstCOgIsXFwxaYfDOYJSixmzasvDKybjDdIhVF5upa0G\nQFVFAR6481pUlOQBEBaJ7+ofxtOv7Y253rMP3IAqawHThr6hEdHymI2hPLy+AExGHYAgRnxjKCsy\n4erLirH7UOw385ceWgBroTm8fae6MvLlx+sPoLPXheffaY5YTawB8OJDC/CnPWfQcsIGx5AXpUUm\nzLiyLLyVacjjw+P/8XlMnv+2aib2t9twqKMP/UMjF8oaWidQajFjek0ZbqybiJ9uPQjXCHsLUnT/\n5NqEv4q6mtcm0Xus+WkT7Qtc+3BbxKwWI+qnVgquyo3OU6p/SuUZb7pUIUc/N9tlTBSlljsZ1K6H\nrBqHnA2cbh+cvjFYjNqU7a2UerHotrvQ2tGHGTVlzJGxWHmFnJkUzcfP45PD3bi5bqLkyDnahp4+\nF/Yds2HutApMKCsM/w5A8G/O5o5OB3Yf7sHCugmoqY5vpfjpnsFwnlMmFAuWS6huj53qw57jNsy7\nugLTLisTTCdUViAUpKPtzACmXlqCsuI82WVNtE2AxPuC1x+AzmhAwOeP++U10RffbO3xjecBruR9\nyIm2p9Igh5xGlF6xgDo6iBzITnVBdqoLslM5sByyOuc1CIIgCEJhkEMmCIIgiBxAlkM+ceIEGhoa\n8O677wIAvvzyS3zrW9/CPffcg3Xr1mH0wp7Ubdu2Yfny5Vi5ciV+97vfpa/UBEEQBKEyJB2y2+3G\ns88+i/nz54d/e+mll/DAAw/g3XffxcSJE/Hf//3fcLvd2LhxI95880288847eOuttzAwMJDWwmeb\nbOghJ4OS9GnllFXsnEQ1mPm6z5mqq/Gi400QhDSSe3SMRiM2b96MzZs3h387ffo0pk+fDgC46aab\n8N5776G8vBx1dXWwWEIfrOvr69HS0oIlS5akqejZQ2lB3ZUUPF9OWcXOSUQFCRDWfQZCW6bSVVfj\nRcebIAj5SDpkvV4PfdTe2traWnzyySe46667sGvXLtjtdtjtdlitFxWHrFYrbDa20lBpaT70euUt\n09/8/x8WVMTJzzPiH+6qy2LJhElVeVmrA1OFnLKKnbPz4Dn4RiPDiLo8o3ji1b349Q+/JprnPz7+\nvqDUJF9oINVtm40+pLR+myoy0W9zAbJT+SQUxeLxxx/H+vXr8fvf/x7XX389hHZOydlN5XC4E8le\nkEztIfT6A9jdKiy8sLv1HL52/eS05h/vvtV4yyumx5zofms5e3jjKavPH8CuA8LnRDtjjqFhH453\n9KIw3xhTFo/XL5pOKP9UtG02+lCq8lTaXt1Mb5NRwn5rJaMGO1kvFAk55IkTJ+LVV18FAOzatQu9\nvb2orKyE3X5Rdai3txczZ85M5PJxkempPzlB3eXoIbMiTgnd0IlGB5NbXqF6nF5ThvbOQXT2DofT\nTK6UztPrD6B/aARN+8+i+XgPhjxjMOoArU4Hry/A1NJlaU2/82Ebjp7sw+CwsIACizf/+zh6+t3o\nH/LCaNBCownJKeYZ5T00+4dGYHO4UV2Z2Ns5v11tDndaNLVZSNUty7ZwezZ34lCHPec/e2QDJX0W\nInKXhBzyz3/+c0yfPh2LFy/G73//e3zjG9/AjBkzsG7dOgwNDUGn06GlpQVPPvlkqssbg5igOgCs\nbqhNeX7FhSaUFBrgcMU6heILijksxBzr9++ZhT/sPCl6Qz/7VjO6bBcd41gQONvrwrNvNePZNTcw\ny2vQa+AbjZ2x0Os04fIK1eOOlliJxbO9Lqzf8lc8/8CCmGP8h1L0w98XABAIhK8t1EZSdffZkR7m\ncRZHTznCf3v9F0fEHpkKVUEALzceivshG10nZqMWrMkjbRKa2iw4vWoxHWoh21jtme77TGlk+jlE\nqBPJp8qRI0dw77334g9/+APefvtt3Hvvvbj55pvxyiuvYPny5aisrMTixYthNpvx2GOPYc2aNfj7\nv/97rF27NrzAK11kQxPUZNDBL/C9EQD8gTHJaSoufjGndcs51sd/+bmoSLrT7Ytwxny6bMNwun3M\nPIWcMf93Vj0K0dM/IpgnXzBdDtFt5JKwI9skIgAfXScjvrGIF4JoxhLU1JaDX6QfAMK2yWlP0t4l\nbWIidUiOkK+77jq88847Mb83NjbG/HbHHXfgjjvuSE3JZJCq6eN4cLp9cI8I6xO7R0bhdPtEv7OG\nHKuwXjJ/ZTCfAyfsuLQyVsiBz9GT/Zh37QTBY10S31u6bE4U5hlF61GMjw904us8CcZ4nToQ20Z/\n2Cnf0WWTAyfsWH7zlZIvX4nUCQCc7B7C9CvLEy2eILYBDwJj0us6ONtCf0uXPV33mZLIxnOIUCeK\n/riRDU3QTt7oNpqxYOh4ImnFcDhHYB8QV04CAPeI+DdVMUfPP56IlnHb6cg95qyHkhjRbXTi7GBc\n6bOFXAH4ROoESJPGs8yQ9ZxtcstO2rukTUykDkU75GxoglbzNHmj0WpCxxNJK0apxYwbrmErLM2o\nER9NXT6xiJn28olFCWkZ33b95Ij/E3Hq0W30N/OmxJU+W8h9yCZSJzotUJWGbR0VpfkwG6Vvd842\nuWUn7V3SJiZSh6IdMiAuqL5qSU1a8rPkG1FVIex0qyrYW5FYacVGRbNqyzGxvBD5JuGmyjdpmXJ/\nlnwjqkWmvKsrC8LlFapHFtNrIh9A8Th1sTZaXD9ZJIU84nzXCZdFLJ3Y73Ifsom86Nw0c1JaHuAm\ngw4L6iZKnsfZJlX2dN9nSiPTzyFCnahGfjGT+/8S3YLESntxlbWw2LjH58fjv/w87ihU8ZaXX4+O\nYS+e3LQn5nrP/+M8TCiJ/SbGF0zvGxKeZn/qvnpUV1hE2+jLniE89+b+mN+fuG829n1xPrwK3WjU\nQgPNhW1Uobr6Hwum4N82fhYR5EOv0+DFtQvwx89Oo6XNBofTi1KLCTOuKkfD7GpYi8zwBwIxkbr4\n6ZIRgOfXSf/QCEwXtll5/QGYDDqMjY3BNxpEaaERs6+uzEikrpa2UKQuk0ELjUYDnz8gaBu/7Jz9\n06+0omHOZFiLzIoY+dE+ZHWhBjtJDzlNJBMwI959yBx9gx60nRnA1EtLmCPjePKU4lCHDZ8ePY8b\nr70kZmQsBN+Gnj4X9h2zYe60CkyZUCw7z49bzuKj/Wdx25zJESNnOYFGuu0utHb0YUZNGSaWFwqm\nFarbbrsLHT3DqJlQEFc6OYiVW8yGdMIXtJeTv9KCgfBRwwNcDmSnciCHnEbU0EHkQHaqC7JTXZCd\nyoHlkBX/DZkgCIIg1AA5ZIIgCILIAcghEwRBEEQOQA4ZocVOx071S4agzAWyUdZsCNp7/QH0OtwJ\n5ZlMWoIgiGyRhpBAyiGZ7UuZJhtlzYagfTKqOaS4QxCEkhnXTykxoYfn3m7JbsEEyEZZ+eICwWBi\n4gpJ5Yn48kwmLUEQRLYZtw6ZJfTQZXPJmhKWM5UrNn0az7RqKsoab56pUrDh5ymVv5w8WfWZTcUd\nJU2TZ+MTBEEQ0uTWvGwGkSMSMe0yq+BxOVO5YtOnKxZfgcaPv4prWjWZsrLKwspTStBeSsGGy3P/\nsR4MDI/CoAN0Ot2FyFomTL+yLCbiE0vQoH9oBO9+2IbjZxyCNmRLcSfZafJMBt1I9ScIJQcMIYhc\nZNw6ZE7oQcjRSYlEyBEjFzvn+BkHOnuHY34PBoP41tKpomWVsoXFb/7Sjr80d8Xk6R4Zxb23TxV8\nmEqJJ0gdf+fPbdh5oDv8vz8A+AOBcP47DpzDjgPnUMZzYHkmPQx6jaB+s0YD7D7SE2MDV2/FhSbR\ntHqdJqa8qXImYnXLak8gO9+75fRbOdC3eoJID+PWIXNCD2cF5BJZIhFSU6NSWrJ8Z8xn9+FurFhc\nk/KRhtcfwO7DPYLHPjvSg+On+1E/NTaGcv+gh3nd/kFPRHjJ6Dz5zpgF5xTazgzAPeIXdKiA8IsT\nAOw+3IMVi0MB/MXS8n9PpTNh1S1XLrH2TJVzjKesUv1Wbt/LdNkJYrwwrl9nn7qvHpN5kohaDTC5\nMrRyWQw5U6OJ6OCO+MZgGxB2gifPsXWCWcdtAx6M+MS/FfY7fYILn/567DwzT9bxU93x6xqf7XWJ\nTpGzGPEFYBvwoEsinB53PJULv1h1y5VLiGx875bTb+WQ7W/1BKFmxu0IGQCMej2euf/6uEQXOJ1Y\nIefB18kVO4eFzz8q+HthAbtMzOMyQ5VHj5IKzGwFKdbxLrvwLEDaCAbhcLIXtjmcPtmzG/Hkm8jx\nbHzvlttvpcjWt3qCGA+M6xEyhyXfiGmXWWUpIMkRI09EBxcAjAbh96Oq8kLoRFpKpw0dF0OuMH30\nKKlUQg+ZdZxVnlRjNupQUZoPq4XtUKwWU8pGiRysuuXKJQTnHIWIxznGg5x+K4dslJ0gxgvkkBNA\njhi50Dm31FfBqBeWvTcZtKgoEZZTNBl0WDRzkuCxRRKC9nKF6aMfprWTS5jns45fNrEIwlamngV1\nE2Ay6DCpgvXSosGkisKUOxNW3XLlEkuXCucYL3L6rRTZKjtBjAfG9ZR1oui0WqxuqMXym68M68pG\nP4j45/BX82o1iFiVy3Hj9InMh9nqhlrotFq0tPWi3+mD1WIML8aS4pu3XgWtRhMWphci+mFqyTei\nuqIAnbbY6efqigLmbILJoMPNsybh4wPnYo5dP60SBXkGHOroC4ve55v1govr+CyaORFGvQ4tbTY4\nnF6UWkyon1oRtt9k0OHmmZOwvSU2z5tnXqzbWbUVEQuSxOyXC79uhcolBnf8wAl7uB5m1ZbH5Rzj\nRU6/lUM2yk4Q4wHSQ06SePU5k90Lmsx2Ha8/gP6hETQ1d0Y4RO5hGp1/MuE6OTubeY5q9tSLdvLt\n0Os0F+rEjv6hEZiMOgTHgvCOjqG00IjZV1cKpou2n8uz5YQNjiEvSotMqBfdHx7rTHRabcJ6q4m2\nS7b28qZCV1YJ+5DVoJ8rB7JTObD0kMkhJ0mmH+CpIp78nW4fnL4xWIxaWd/ZE82Hfy6ApF48pNKK\nnaOGG14OZKe6IDuVA8sh05R1ljAZdFldjRpP/pZ8I66YktiNEE8+0ecmWj9y8sx2/RMEQURDi7oI\ngiAIIgcgh0wQBEEQOYAsh3zixAk0NDTg3XffBQDs27cP3/zmN3HvvffiwQcfxOBgKDLT66+/jhUr\nVmDlypX45JNP0lfqHCEZ1ZxsqwPFq/xE6kAEQRDpRfIbstvtxrPPPov58+eHf/vRj36El156CVdc\ncQU2bdqErVu34mtf+xo++OAD/OY3v4HL5cLq1atx4403QqfLzdWXyZDMSulsB+aPJ/9UqwMRBEEQ\n4kg+VY1GIzZv3ozKysrwb6WlpRgYGAAADA4OorS0FHv37sVNN90Eo9EIq9WKqqoqdHSoUxg+Ih5y\nML54yKmMpZwI8eSfjJ0EQRBEfEg6ZL1eD7M5Mkzik08+ibVr1+L2229Hc3Mzli1bBrvdDqv1oiav\n1WqFzSYcN1jJJBNcP9uB+Z1uH/Yf75WVf6rKmujUfLan9NOJmm0jCCJxEtr29Oyzz+KVV17B7Nmz\n8cILL+C9996LOUfO9ubS0nzo9cqa0u62D4tGu3I4R6AzGlBRXpDytBwjvtFw0AuzUV7zBQJj2PL+\nUXza2oUBl7AIQ3T+yZaVy3PPkW7YBjyoKMnDvOsm4v6vXwudWIzLJNKlghHfKLrtwygtzpNdt/GQ\nTduEYO2HVBNkp7pQs50JPXXa2towe/ZsAMCCBQvw/vvvY968eTh58mT4nPPnz0dMcwvhcLgTyT6r\nBPwBWC3iqjkBn190v25SaSW+/bKCYfz6z22C4Tr5GA26iPwD/gA0AMReq1hlBYD3mk5EhKjsdXiw\nbddXcHt8TM3cRNMlQ6a+lWfaNlafUEOABTmQnepCDXamPDBIeXk5Ojo6UFNTg8OHD2PKlCmYN28e\n3njjDTz88MNwOBzo7e1FTY36YttywfUTiYccb1r+A/U/P/lSUBQ+GAxCo9EwHfXuwz1x2+nzBzAm\n4o3HgqHjrChYUjKHQmkTTZcs3LdyDq5uAaTMUWbStmwvHCQIIjEkHfKRI0fwwgsvoKurC3q9Hh9+\n+CGeeeYZrFu3DgaDAcXFxXj++edRVFSEu+++G/fccw80Gg3Wr18PrUpv/hWLr0DbmYGYGM8rFl8h\nmVZOYH6hB+rwiF/wersP92DEd/FbZLQzsQ14Io6L4fUFIrRsT54bYp5/8twQpteUCx5LVDM3G1q7\nmXKUmbQtEy8YBEGkHkmHfN111+Gdd96J+f03v/lNzG/33nsv7r333tSULIdp/PirCHWisSBwtteF\nxo+/knzgialA8RF6oIoh5mw5ZwKZocqtRZHyg5Z8A/N81nFO5lBsal5M5jDRdMmQKUeZKduyNctA\nEETyqHMIm0ZStfqYi6UsNE0tdv144JxJRWk+zEbpZo6eMg/pCwurGnP6wmIkqpmbDa3dVGski5Ep\n2+S8YBAEkZuQQ46TdD/wWNcXQszZcs7EZNBhQd1E0fRiIvUhfWHhdHx9YTFWLalBw5xqlBWZodWI\n55OqdImSyZeATNiWqRcMgiBSD6k9xUm6px5Z1zcbdcg36THg8oa/PY8Fg9gusIKa70y+eetV0Go0\naOFpE8+4qhwNs6thLTKLOp1vNtRCq9WG9IUvpOP0haWQMzWfynTJIOe7firIhG3JLDokCCK7kB5y\nAkRvX+FomFOdkkUzrOtHP8wvLgCLdSbRK2oT1WD2+gPQGQ0I+PyqfqCrxU45fUIN20fkQHaqCzXY\nydr2RA45AeJxgpm6fqLOVi5quBHkoCY7aR8y2ak21GAnOeQ0ke4RVbqdbDyo4UaQA9mpLshOdaEG\nO1kOmRZ1JYHp/7V3tyFRb3kcwL+T46w7NdpMOnGNLaS69UZre1jswaRakrgUK0sGMoEvWgITSQjR\nkBKkh7EH6AFKpl6UFRW+WIpCQyqSS0SrS2hQYkG1dtGZ0jSfHc6+CKenUcdpxjnn+P28us78rd+X\nc+/53fmfmflFR+GX+Olha5ajvRObiIj0w4ZMREQkATZkIiIiCbAhExERSYANmYiISAJsyAh+YPzA\nkBd/eHq0HzSvcs5g15aIaLJN6W/qCnZM3WTNz400lXNyBCERqWZKN+Rgx9RNlfF2KudUuXYimpqm\n7EuFYKc2hWrak+xUzqly7UQ0dWnTkCd6Vhjs1KZwT3uS5cxT5TF+stcuyxoTkVyUv2Ud7FlhsFOb\nwjXtSbYzz3BPtQonWWuXbY2JSC7K7wIjZ4XvuwYg8OWs8Pq9ljF/L9g5uOGanxtsjnCZzDnBoSZr\n7bKtMRHJRemG/LNnhcEOjA/1oHlZzzxDnXMyyVa7rGtMRPJQ+pZ1IGeFdqt51N8PdmD8178XimlP\nP5sjXEKdczIFu7bhIusaE5E8lH6FPHJW6M9EzgqDnaoUqmlPocoRLuGeahVOskzMkn2NiSjylG7I\nsp4VTpQuOWh0XGMiGo/St6wB+M4E/9vsQUd3P6yWGPz113glzjm/pksOGh3XmIjGYhBCiEj95W53\nd8j+rIEhb0TOChMSLFrkGE+oc8pqMnLKsMZcT70wpzoSEiyjPqf8K+QRI2eFqtMlB42Oa0xE/ih9\nhkxERKQLNmQiIiIJBHTLurm5Gbm5ucjJyYHD4UB+fj46OjoAAJ2dnVi6dCnKyspw/vx5VFdXw2Aw\nIC8vD+np6WEtnoiISBfjNuTe3l6UlZVh1apVvsdOnTrl++fi4mJs27YNb9++xZ07d3Dt2jV8+vQJ\n2dnZWLt2LaKi5HljkkxkeGNPoAaGvPjD0wPvkFf6WomIVDVuQzaZTHC5XHC5XD889+rVK3R3dyMl\nJQVVVVVIS0uDyWSCzWbDnDlz0NLSgkWLFoWlcFWpNGDgm1q7B2CzyFsrEZHqxm3IRqMRRqP/yy5d\nugSHwwEA8Hg8sNlsvudsNhvcbveYDdlqNcNoVP8V11hvY/+e69+NqP3P/3w/jwwYMP/ZhH/9Izkc\n5QVNpVpDaSLrqTLm1Atzqi/ojz0NDg6ivr4epaWlfp8P5OPNHR29wf710pjI5+IGhrz4/Wmr3+d+\nf/oOm//2F2luCatUayjp8DnHQDCnXphTHWP9D0XQ9x2fPHmClJQU3892ux0ej8f3c1tbG+x2e7B/\nvJYCGTAgC5VqJSLSQdANubGxEYsXL/b9nJqaigcPHmBwcBBtbW1ob2/HggX8SsCvqTRgQKVaiYh0\nMO4t66amJjidTrS2tsJoNKKmpganT5+G2+3G3LlzfdclJiYiKysLDocDBoMBpaWlmMY3/nxjZMDA\n1+eyI2QbMKBSrUREOtDmu6wjZaJnGl/eufzjgAHZ3rmsUq2hosMZVSCYUy/MqY4p8V3WqoiaNg3Z\nf/8V/0yfL/3nkL+uNcoUDe/gkLS1EhGpTs+XOQoYGTCgQoP7U3QUfomfrkStRESqYkMmIiKSABsy\nERGRBNiQiYiIJMCGTEREJAE2ZCIiIgmwIRMREUmADZmIiEgCbMhEREQSiOhXZxIREdFnfIVMREQk\nATZkIiIiCbAhExERSYANmYiISAJsyERERBJgQyYiIpKAMdIFqKK5uRm5ubnIycmBw+HA0NAQioqK\n8Pr1a0yfPh2nTp1CXFwcbt68iYsXL2LatGnIysrCtm3bIl36hHyf88mTJzhx4gSMRiPMZjPKy8sR\nFxeH8+fPo7q6GgaDAXl5eUhPT4906QErLy9HfX09hoeHsWvXLiQnJ6OwsBBerxcJCQk4evQoTCaT\n8mvpL2dxcTGGh4dhNBpx9OhRJCQkaJdz06ZNAIC6ujrs3LkTL168AADtcq5fv17LPej7nFarVbs9\naFSCxtXT0yMcDocoKSkRlZWVQgghLl++LMrKyoQQQly7dk3U1taKnp4esWnTJtHV1SX6+vrEb7/9\nJjo6OiJZ+oT4y5mZmSlevnwphBDi7NmzoqKiQrx580ZkZmaKgYEB8f79e5GRkSGGh4cjWXrAHj16\nJHbu3CmEEOLDhw8iPT1dFBUViTt37gghhDh+/Li4cuWK8mvpL2dhYaG4ffu2EOLzv79Op1PLnEII\n0d/fLxwOh1izZo0QQmiZU8c9yF9O3fagsfCWdQBMJhNcLhfsdrvvsfv372Pr1q0AgO3bt2Pjxo14\n+vQpkpOTYbFYEBMTg2XLlqGhoSFSZU+Yv5xWqxWdnZ0AgI8fP8JqteLx48dIS0uDyWSCzWbDnDlz\n0NLSEqmyJ2TlypU4efIkACA2NhZ9fX14/PgxNm7cCABYv349Hj16pPxa+st54MABZGRkAPiyrjrm\n9Hq9OHfuHLKzs2EymQBAy5w67kH+csbFxWm1B42FDTkARqMRMTEx3zzW2tqKhw8fYseOHSgoKEBn\nZyc8Hg9sNpvvGpvNBrfbPdnlBs1fzn379mH37t3IyMhAfX09MjMzlc4ZFRUFs9kMAKiqqsK6devQ\n19fn27hnzZoFt9utdEbAf06z2YyoqCh4vV5cvXoVW7Zs0TLnmzdv8Pz5c2zevNl3nY45ddyD/OUs\nKSnRag8aCxtykIQQSEpKQmVlJRYuXIiKigq/16iurKwMZ86cQU1NDZYvX46rV6/+cI2KOWtra1FV\nVYX9+/d/8/hoWVTMCPyY0+v1orCwEKmpqVi1atUP1+uQ8/DhwyguLh7zeh1y6rwHfZ1T1z3IHzbk\nIMXHx2PlypUAgLVr16KlpQV2ux0ej8d3TXt7+ze3f1X04sULLF++HACwevVqNDU1/ZCzra1NqZx1\ndXU4d+4cXC4XLBYLzGYz+vv7AXzJosNafp8TAIqLizFv3jzk5eUBgHY5e3t78erVK+zduxdZWVlo\nb2+Hw+HQLqfFYtF2D/o+p4570GjYkIO0bt061NXVAQCePXuGpKQkLFmyBI2Njejq6kJPTw8aGhqw\nYsWKCFf6c+Lj431nM42NjZg3bx5SU1Px4MEDDA4Ooq2tDe3t7ViwYEGEKw1Md3c3ysvLUVFRgZkz\nZwL4/B95TU0NAODu3btIS0tTfi395bx58yaio6ORn5/vu063nLNnz0ZtbS1u3LiBGzduwG634/Ll\ny9rlBPTcg/zl1G0PGgunPQWgqakJTqcTra2tMBqNmD17No4dO4aDBw/C7XbDbDbD6XQiPj4e1dXV\nuHDhAgwGAxwOh+9NFyrwl7OgoADl5eWIjo5GXFwcDh06hNjYWFRWVuLWrVswGAzYs2eP39ufMrp+\n/TpOnz6NpKQk32NHjhxBSUkJBgYGkJiYiMOHDyM6OlrptfSX8927d4iNjcWMGTMAAPPnz0dpaal2\nOZ1OJxITEwEAGzZswL179wBAy5xHjhzRag/ylzM/Px/Hjx/XZg8aCxsyERGRBHjLmoiISAJsyERE\nRBJgQyYiIpIAGzIREZEE2JCJiIgkwIZMREQkATZkIiIiCbAhExERSeD/wa98EMgYeaEAAAAASUVO\nRK5CYII=\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "3PuDYQmEZXB5",
        "colab_type": "code",
        "outputId": "85c011d6-0fc2-40f9-eb07-e994c877ea83",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "accuracy = metrics.r2_score(y, predictions)\n",
        "accuracy"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.280770222008195"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "metadata": {
        "id": "xqenN6Q1YQMx",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Visualize cross-validated scores for Linear regression model"
      ]
    },
    {
      "metadata": {
        "id": "M2v8VOVpZMca",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "See this:  http://www.scikit-yb.org/en/latest/api/model_selection/cross_validation.html"
      ]
    },
    {
      "metadata": {
        "id": "ALdm-HA_YY4Z",
        "colab_type": "code",
        "outputId": "f097ab38-d826-4ac0-b286-8a43ecc93e62",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import Ridge\n",
        "from sklearn.model_selection import KFold\n",
        "from yellowbrick.model_selection import CVScores\n",
        "\n",
        "# Create a new figure and axes\n",
        "_, ax = plt.subplots()\n",
        "cv = KFold(12)\n",
        "\n",
        "oz = CVScores(\n",
        "    linear_model.LinearRegression(), ax=ax, cv=cv, scoring='r2'\n",
        ")\n",
        "\n",
        "oz.fit(X, y)\n",
        "oz.poof()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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PPfUU586do169ekRHRwMwduxYZs6cyRtvvEH//v2JiIggNjaW9u3bV2nd4eHhrFy5kv/8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\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "0v6peWR9Zqn4",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Conclusion"
      ]
    },
    {
      "metadata": {
        "id": "zg1XKgnGZsrS",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "* Cross-Validation improved Accuracy\n",
        "* Adding more data or more features could improve the model\n",
        "* Major League Baseball may be a strange set to predict Weight\n",
        "* Bigger Data Set here:  http://socr.ucla.edu/docs/resources/SOCR_Data/SOCR_Data_Dinov_020108_HeightsWeights.html\n"
      ]
    },
    {
      "metadata": {
        "id": "NNiasM4RaCS6",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}