31_Intro_to_Regression_Bike&boston.ipynb

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        "<a href=\"https://colab.research.google.com/github/jsstar522/facebook_MLClass/blob/master/31_Intro_to_Regression_Bike&boston.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "metadata": {
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      "source": [
        "# Intro to Regression 01\n",
        "\n",
        "## Regression Problem\n",
        "* 자전거 대여량 예측\n",
        "* 데이터 : 날짜, 시간, 회원가입(reg), 대여수(cnt)\n",
        "\n",
        "\n",
        "### Your name :     Park Jong Seok\n",
        "\n",
        "### Kaggle Link :\n",
        "* [Bike-sharing Demand](https://www.kaggle.com/c/bike-sharing-demand)\n",
        "* [boston-housing](https://www.kaggle.com/c/boston-housing)\n",
        "\n",
        "#### 실습목표 I<br>\n",
        "1. Linear Regression 을 이해하고 사용해본다.\n",
        "2. KNN Regression 을 이해하고 사용해본다.\n",
        "3. Artificial Neural Network Regression을 이해하고 사용해본다.\n",
        "\n",
        "#### 실습목표 II<br>\n",
        "1. Pandas 라이브러리로 csv 파일을 불러오고, 전처리할 수 있다.\n",
        "3. Scikit-learn을 통해 간단히 머신러닝을 사용해볼 수 있다.\n",
        "\n",
        "---------------\n",
        "Rayleigh Kim @ D:plus\n",
        "\n",
        "---------------\n",
        "\n",
        "\n",
        "#### 학습 4단계\n",
        "\n",
        "1. 데이터 불러오기\n",
        "2. 데이터 확인 (문제상황체크 + 목표정의) \n",
        "3. 데이터 전처리\n",
        "    * null, 이상치 확인\n",
        "    * dummy (Feature Engineering)\n",
        "    * scaling / normalization\n",
        "    * traing / validation / test data set\n",
        "4. 데이터 선언하기\n",
        "5. 데이터 밀어넣어서 학습\n",
        "6. Predict\n",
        "\n"
      ]
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      "cell_type": "code",
      "source": [
        "'''\n",
        "matplolib inline 명령어를 통해서\n",
        "matplot으로 그리는 플롯들을 주피터 노트북 내에서 볼 수 있게 해준다.\n",
        "포맷을 retina로 바꾸면 그래프의 화질이 훨씬 좋아진다.\n",
        "'''\n",
        "%matplotlib inline\n",
        "%config InlineBackend.figure_format = 'retina'\n",
        "\n",
        "'''\n",
        "라이브러리들을 불러오자.\n",
        "'''\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "'''\n",
        "사이킷런의 라이브러리들을 미리 불러두는 것도 좋지만,\n",
        "세부적인 메쏘드가 너무 많아서 필요한것만 불러오도록 할 것이다.\n",
        "'''\n"
      ],
      "execution_count": 1,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "'\\n사이킷런의 라이브러리들을 미리 불러두는 것도 좋지만,\\n세부적인 메쏘드가 너무 많아서 필요한것만 불러오도록 할 것이다.\\n'"
            ]
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      "cell_type": "markdown",
      "source": [
        "## 다같이 실습"
      ]
    },
    {
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      "cell_type": "markdown",
      "source": [
        "### Data Loading"
      ]
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      "cell_type": "code",
      "source": [
        "data_path = 'https://raw.githubusercontent.com/RayleighKim/Example_datasets/master/Bike-Sharing-Dataset/hour.csv'\n",
        "rides = pd.read_csv(data_path)"
      ],
      "execution_count": 0,
      "outputs": []
    },
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      "source": [
        "다음 셀을 실행하고,  모델링 계획/데이터 전처리 계획을 세워야 한다.\n",
        "\n",
        "* dteday에 날짜가 깔끔하게 들어가있다.\n",
        "* 보통 이렇게 깔끔하게 들어가있지 않으므로 전처리가 필요하다."
      ]
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      "cell_type": "code",
      "source": [
        "rides.head()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<div>\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>instant</th>\n",
              "      <th>dteday</th>\n",
              "      <th>season</th>\n",
              "      <th>yr</th>\n",
              "      <th>mnth</th>\n",
              "      <th>hr</th>\n",
              "      <th>holiday</th>\n",
              "      <th>weekday</th>\n",
              "      <th>workingday</th>\n",
              "      <th>weathersit</th>\n",
              "      <th>temp</th>\n",
              "      <th>atemp</th>\n",
              "      <th>hum</th>\n",
              "      <th>windspeed</th>\n",
              "      <th>casual</th>\n",
              "      <th>registered</th>\n",
              "      <th>cnt</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2011-01-01</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.24</td>\n",
              "      <td>0.2879</td>\n",
              "      <td>0.81</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3</td>\n",
              "      <td>13</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>2011-01-01</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.22</td>\n",
              "      <td>0.2727</td>\n",
              "      <td>0.80</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8</td>\n",
              "      <td>32</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>2011-01-01</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.22</td>\n",
              "      <td>0.2727</td>\n",
              "      <td>0.80</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5</td>\n",
              "      <td>27</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>2011-01-01</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.24</td>\n",
              "      <td>0.2879</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3</td>\n",
              "      <td>10</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>2011-01-01</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.24</td>\n",
              "      <td>0.2879</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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            "text/plain": [
              "   instant      dteday  season  yr  mnth  hr  holiday  weekday  workingday  \\\n",
              "0        1  2011-01-01       1   0     1   0        0        6           0   \n",
              "1        2  2011-01-01       1   0     1   1        0        6           0   \n",
              "2        3  2011-01-01       1   0     1   2        0        6           0   \n",
              "3        4  2011-01-01       1   0     1   3        0        6           0   \n",
              "4        5  2011-01-01       1   0     1   4        0        6           0   \n",
              "\n",
              "   weathersit  temp   atemp   hum  windspeed  casual  registered  cnt  \n",
              "0           1  0.24  0.2879  0.81        0.0       3          13   16  \n",
              "1           1  0.22  0.2727  0.80        0.0       8          32   40  \n",
              "2           1  0.22  0.2727  0.80        0.0       5          27   32  \n",
              "3           1  0.24  0.2879  0.75        0.0       3          10   13  \n",
              "4           1  0.24  0.2879  0.75        0.0       0           1    1  "
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      "cell_type": "code",
      "source": [
        "type(rides)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "metadata": {
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        "colab": {
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      "cell_type": "code",
      "source": [
        "rides.shape"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(17379, 17)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "metadata": {
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        "colab": {
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      },
      "cell_type": "code",
      "source": [
        "## pandas slicing은 numpy slicing과 다르다\n",
        "\n",
        "rides[:24][:].shape\n"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(24, 17)"
            ]
          },
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            "tags": []
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          "execution_count": 6
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      "source": [
        "rides[:24*10].plot(x='dteday', y='cnt')"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8b5717c550>"
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          "execution_count": 7
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          "output_type": "display_data",
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edBbphBBiIVhO+ogIxrIkgcRQ0rejKukZvws4cRQoz2eapvnX1VWMU0knsbB9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\ndcRiGkb6m+NFOzUyKnWk69Cd0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QKwCdIXV4rc767ZPQhNIqtLne+9cw/uvmGSe+zYuUXT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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f8b6e062b38>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 372,
              "height": 253
            }
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "axzIVFKR8uwL",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### 참고\n",
        "\n",
        "시간별로 집계한 자전거 sharing 데이터는 지금, rides라는 변수안에 Pandas Dataframe 형태로 저장되어 있다. 간단하게 Pandas를 사용해보자"
      ]
    },
    {
      "metadata": {
        "id": "JhMSSSsl8uwL",
        "colab_type": "code",
        "outputId": "7a14c56b-675e-4a84-c2c1-de9a8e15000f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1071
        }
      },
      "cell_type": "code",
      "source": [
        "rides['instant']"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0            1\n",
              "1            2\n",
              "2            3\n",
              "3            4\n",
              "4            5\n",
              "5            6\n",
              "6            7\n",
              "7            8\n",
              "8            9\n",
              "9           10\n",
              "10          11\n",
              "11          12\n",
              "12          13\n",
              "13          14\n",
              "14          15\n",
              "15          16\n",
              "16          17\n",
              "17          18\n",
              "18          19\n",
              "19          20\n",
              "20          21\n",
              "21          22\n",
              "22          23\n",
              "23          24\n",
              "24          25\n",
              "25          26\n",
              "26          27\n",
              "27          28\n",
              "28          29\n",
              "29          30\n",
              "         ...  \n",
              "17349    17350\n",
              "17350    17351\n",
              "17351    17352\n",
              "17352    17353\n",
              "17353    17354\n",
              "17354    17355\n",
              "17355    17356\n",
              "17356    17357\n",
              "17357    17358\n",
              "17358    17359\n",
              "17359    17360\n",
              "17360    17361\n",
              "17361    17362\n",
              "17362    17363\n",
              "17363    17364\n",
              "17364    17365\n",
              "17365    17366\n",
              "17366    17367\n",
              "17367    17368\n",
              "17368    17369\n",
              "17369    17370\n",
              "17370    17371\n",
              "17371    17372\n",
              "17372    17373\n",
              "17373    17374\n",
              "17374    17375\n",
              "17375    17376\n",
              "17376    17377\n",
              "17377    17378\n",
              "17378    17379\n",
              "Name: instant, Length: 17379, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "metadata": {
        "id": "DPodsvZ48uwP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1969
        },
        "outputId": "abd50a3d-8ac3-4c0b-d274-aed11de21666"
      },
      "cell_type": "code",
      "source": [
        "rides[['instant', 'dteday']]"
      ],
      "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>instant</th>\n",
              "      <th>dteday</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2011-01-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>2011-01-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>2011-01-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>2011-01-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>2011-01-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>2011-01-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>2011-01-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>2011-01-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>2011-01-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>2011-01-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>11</td>\n",
              "      <td>2011-01-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>12</td>\n",
              "      <td>2011-01-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>13</td>\n",
              "      <td>2011-01-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>14</td>\n",
              "      <td>2011-01-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>15</td>\n",
              "      <td>2011-01-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>16</td>\n",
              "      <td>2011-01-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>17</td>\n",
              "      <td>2011-01-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>18</td>\n",
              "      <td>2011-01-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>19</td>\n",
              "      <td>2011-01-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>20</td>\n",
              "      <td>2011-01-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>21</td>\n",
              "      <td>2011-01-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>22</td>\n",
              "      <td>2011-01-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>23</td>\n",
              "      <td>2011-01-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>24</td>\n",
              "      <td>2011-01-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>25</td>\n",
              "      <td>2011-01-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>26</td>\n",
              "      <td>2011-01-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>27</td>\n",
              "      <td>2011-01-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>28</td>\n",
              "      <td>2011-01-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>29</td>\n",
              "      <td>2011-01-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>30</td>\n",
              "      <td>2011-01-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17349</th>\n",
              "      <td>17350</td>\n",
              "      <td>2012-12-30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17350</th>\n",
              "      <td>17351</td>\n",
              "      <td>2012-12-30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17351</th>\n",
              "      <td>17352</td>\n",
              "      <td>2012-12-30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17352</th>\n",
              "      <td>17353</td>\n",
              "      <td>2012-12-30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17353</th>\n",
              "      <td>17354</td>\n",
              "      <td>2012-12-30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17354</th>\n",
              "      <td>17355</td>\n",
              "      <td>2012-12-30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17355</th>\n",
              "      <td>17356</td>\n",
              "      <td>2012-12-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17356</th>\n",
              "      <td>17357</td>\n",
              "      <td>2012-12-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17357</th>\n",
              "      <td>17358</td>\n",
              "      <td>2012-12-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17358</th>\n",
              "      <td>17359</td>\n",
              "      <td>2012-12-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17359</th>\n",
              "      <td>17360</td>\n",
              "      <td>2012-12-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17360</th>\n",
              "      <td>17361</td>\n",
              "      <td>2012-12-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17361</th>\n",
              "      <td>17362</td>\n",
              "      <td>2012-12-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17362</th>\n",
              "      <td>17363</td>\n",
              "      <td>2012-12-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17363</th>\n",
              "      <td>17364</td>\n",
              "      <td>2012-12-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17364</th>\n",
              "      <td>17365</td>\n",
              "      <td>2012-12-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17365</th>\n",
              "      <td>17366</td>\n",
              "      <td>2012-12-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17366</th>\n",
              "      <td>17367</td>\n",
              "      <td>2012-12-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17367</th>\n",
              "      <td>17368</td>\n",
              "      <td>2012-12-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17368</th>\n",
              "      <td>17369</td>\n",
              "      <td>2012-12-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17369</th>\n",
              "      <td>17370</td>\n",
              "      <td>2012-12-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17370</th>\n",
              "      <td>17371</td>\n",
              "      <td>2012-12-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17371</th>\n",
              "      <td>17372</td>\n",
              "      <td>2012-12-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17372</th>\n",
              "      <td>17373</td>\n",
              "      <td>2012-12-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17373</th>\n",
              "      <td>17374</td>\n",
              "      <td>2012-12-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17374</th>\n",
              "      <td>17375</td>\n",
              "      <td>2012-12-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17375</th>\n",
              "      <td>17376</td>\n",
              "      <td>2012-12-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17376</th>\n",
              "      <td>17377</td>\n",
              "      <td>2012-12-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17377</th>\n",
              "      <td>17378</td>\n",
              "      <td>2012-12-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17378</th>\n",
              "      <td>17379</td>\n",
              "      <td>2012-12-31</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>17379 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       instant      dteday\n",
              "0            1  2011-01-01\n",
              "1            2  2011-01-01\n",
              "2            3  2011-01-01\n",
              "3            4  2011-01-01\n",
              "4            5  2011-01-01\n",
              "5            6  2011-01-01\n",
              "6            7  2011-01-01\n",
              "7            8  2011-01-01\n",
              "8            9  2011-01-01\n",
              "9           10  2011-01-01\n",
              "10          11  2011-01-01\n",
              "11          12  2011-01-01\n",
              "12          13  2011-01-01\n",
              "13          14  2011-01-01\n",
              "14          15  2011-01-01\n",
              "15          16  2011-01-01\n",
              "16          17  2011-01-01\n",
              "17          18  2011-01-01\n",
              "18          19  2011-01-01\n",
              "19          20  2011-01-01\n",
              "20          21  2011-01-01\n",
              "21          22  2011-01-01\n",
              "22          23  2011-01-01\n",
              "23          24  2011-01-01\n",
              "24          25  2011-01-02\n",
              "25          26  2011-01-02\n",
              "26          27  2011-01-02\n",
              "27          28  2011-01-02\n",
              "28          29  2011-01-02\n",
              "29          30  2011-01-02\n",
              "...        ...         ...\n",
              "17349    17350  2012-12-30\n",
              "17350    17351  2012-12-30\n",
              "17351    17352  2012-12-30\n",
              "17352    17353  2012-12-30\n",
              "17353    17354  2012-12-30\n",
              "17354    17355  2012-12-30\n",
              "17355    17356  2012-12-31\n",
              "17356    17357  2012-12-31\n",
              "17357    17358  2012-12-31\n",
              "17358    17359  2012-12-31\n",
              "17359    17360  2012-12-31\n",
              "17360    17361  2012-12-31\n",
              "17361    17362  2012-12-31\n",
              "17362    17363  2012-12-31\n",
              "17363    17364  2012-12-31\n",
              "17364    17365  2012-12-31\n",
              "17365    17366  2012-12-31\n",
              "17366    17367  2012-12-31\n",
              "17367    17368  2012-12-31\n",
              "17368    17369  2012-12-31\n",
              "17369    17370  2012-12-31\n",
              "17370    17371  2012-12-31\n",
              "17371    17372  2012-12-31\n",
              "17372    17373  2012-12-31\n",
              "17373    17374  2012-12-31\n",
              "17374    17375  2012-12-31\n",
              "17375    17376  2012-12-31\n",
              "17376    17377  2012-12-31\n",
              "17377    17378  2012-12-31\n",
              "17378    17379  2012-12-31\n",
              "\n",
              "[17379 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "metadata": {
        "id": "YNBs1w3A8uwV",
        "colab_type": "code",
        "outputId": "580a4d58-5686-4cb4-b76a-b0261f87f1e2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1969
        }
      },
      "cell_type": "code",
      "source": [
        "columns = ['instant', 'dteday']\n",
        "rides[columns]"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <td>2011-01-01</td>\n",
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              "      <th>13</th>\n",
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              "      <td>2011-01-01</td>\n",
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              "      <td>2011-01-01</td>\n",
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              "      <th>18</th>\n",
              "      <td>19</td>\n",
              "      <td>2011-01-01</td>\n",
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              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>20</td>\n",
              "      <td>2011-01-01</td>\n",
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              "    <tr>\n",
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              "      <td>2011-01-01</td>\n",
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              "      <th>21</th>\n",
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              "      <th>22</th>\n",
              "      <td>23</td>\n",
              "      <td>2011-01-01</td>\n",
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              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>24</td>\n",
              "      <td>2011-01-01</td>\n",
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              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>25</td>\n",
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              "      <td>26</td>\n",
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              "      <th>26</th>\n",
              "      <td>27</td>\n",
              "      <td>2011-01-02</td>\n",
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              "      <th>27</th>\n",
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              "      <th>28</th>\n",
              "      <td>29</td>\n",
              "      <td>2011-01-02</td>\n",
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              "      <th>29</th>\n",
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              "      <td>2011-01-02</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <th>17350</th>\n",
              "      <td>17351</td>\n",
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              "      <th>17352</th>\n",
              "      <td>17353</td>\n",
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              "      <td>17354</td>\n",
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              "    <tr>\n",
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              "      <td>17355</td>\n",
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              "      <th>17355</th>\n",
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              "      <th>17360</th>\n",
              "      <td>17361</td>\n",
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              "    <tr>\n",
              "      <th>17361</th>\n",
              "      <td>17362</td>\n",
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              "    <tr>\n",
              "      <th>17362</th>\n",
              "      <td>17363</td>\n",
              "      <td>2012-12-31</td>\n",
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              "    <tr>\n",
              "      <th>17363</th>\n",
              "      <td>17364</td>\n",
              "      <td>2012-12-31</td>\n",
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              "      <th>17364</th>\n",
              "      <td>17365</td>\n",
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              "      <td>17366</td>\n",
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              "      <td>17372</td>\n",
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              "    <tr>\n",
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              "      <td>17374</td>\n",
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              "      <td>17376</td>\n",
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              "    <tr>\n",
              "      <th>17376</th>\n",
              "      <td>17377</td>\n",
              "      <td>2012-12-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17377</th>\n",
              "      <td>17378</td>\n",
              "      <td>2012-12-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17378</th>\n",
              "      <td>17379</td>\n",
              "      <td>2012-12-31</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>17379 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       instant      dteday\n",
              "0            1  2011-01-01\n",
              "1            2  2011-01-01\n",
              "2            3  2011-01-01\n",
              "3            4  2011-01-01\n",
              "4            5  2011-01-01\n",
              "5            6  2011-01-01\n",
              "6            7  2011-01-01\n",
              "7            8  2011-01-01\n",
              "8            9  2011-01-01\n",
              "9           10  2011-01-01\n",
              "10          11  2011-01-01\n",
              "11          12  2011-01-01\n",
              "12          13  2011-01-01\n",
              "13          14  2011-01-01\n",
              "14          15  2011-01-01\n",
              "15          16  2011-01-01\n",
              "16          17  2011-01-01\n",
              "17          18  2011-01-01\n",
              "18          19  2011-01-01\n",
              "19          20  2011-01-01\n",
              "20          21  2011-01-01\n",
              "21          22  2011-01-01\n",
              "22          23  2011-01-01\n",
              "23          24  2011-01-01\n",
              "24          25  2011-01-02\n",
              "25          26  2011-01-02\n",
              "26          27  2011-01-02\n",
              "27          28  2011-01-02\n",
              "28          29  2011-01-02\n",
              "29          30  2011-01-02\n",
              "...        ...         ...\n",
              "17349    17350  2012-12-30\n",
              "17350    17351  2012-12-30\n",
              "17351    17352  2012-12-30\n",
              "17352    17353  2012-12-30\n",
              "17353    17354  2012-12-30\n",
              "17354    17355  2012-12-30\n",
              "17355    17356  2012-12-31\n",
              "17356    17357  2012-12-31\n",
              "17357    17358  2012-12-31\n",
              "17358    17359  2012-12-31\n",
              "17359    17360  2012-12-31\n",
              "17360    17361  2012-12-31\n",
              "17361    17362  2012-12-31\n",
              "17362    17363  2012-12-31\n",
              "17363    17364  2012-12-31\n",
              "17364    17365  2012-12-31\n",
              "17365    17366  2012-12-31\n",
              "17366    17367  2012-12-31\n",
              "17367    17368  2012-12-31\n",
              "17368    17369  2012-12-31\n",
              "17369    17370  2012-12-31\n",
              "17370    17371  2012-12-31\n",
              "17371    17372  2012-12-31\n",
              "17372    17373  2012-12-31\n",
              "17373    17374  2012-12-31\n",
              "17374    17375  2012-12-31\n",
              "17375    17376  2012-12-31\n",
              "17376    17377  2012-12-31\n",
              "17377    17378  2012-12-31\n",
              "17378    17379  2012-12-31\n",
              "\n",
              "[17379 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "metadata": {
        "id": "QydhurIh8uwZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1201
        },
        "outputId": "1e391cbe-db3a-4aeb-d1f2-5593270e7576"
      },
      "cell_type": "code",
      "source": [
        "rides[:24]"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>instant</th>\n",
              "      <th>dteday</th>\n",
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              "      <th>yr</th>\n",
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              "      <td>10</td>\n",
              "      <td>13</td>\n",
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              "      <td>2011-01-01</td>\n",
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              "      <td>8</td>\n",
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              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>2011-01-01</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
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              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.32</td>\n",
              "      <td>0.3485</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>8</td>\n",
              "      <td>6</td>\n",
              "      <td>14</td>\n",
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              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>11</td>\n",
              "      <td>2011-01-01</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
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              "      <td>1</td>\n",
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              "      <td>2011-01-01</td>\n",
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              "      <td>1</td>\n",
              "      <td>0.36</td>\n",
              "      <td>0.3333</td>\n",
              "      <td>0.81</td>\n",
              "      <td>0.2836</td>\n",
              "      <td>26</td>\n",
              "      <td>30</td>\n",
              "      <td>56</td>\n",
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              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>13</td>\n",
              "      <td>2011-01-01</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
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              "      <td>6</td>\n",
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              "      <td>0.42</td>\n",
              "      <td>0.4242</td>\n",
              "      <td>0.77</td>\n",
              "      <td>0.2836</td>\n",
              "      <td>29</td>\n",
              "      <td>55</td>\n",
              "      <td>84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>14</td>\n",
              "      <td>2011-01-01</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.46</td>\n",
              "      <td>0.4545</td>\n",
              "      <td>0.72</td>\n",
              "      <td>0.2985</td>\n",
              "      <td>47</td>\n",
              "      <td>47</td>\n",
              "      <td>94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>15</td>\n",
              "      <td>2011-01-01</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>14</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.46</td>\n",
              "      <td>0.4545</td>\n",
              "      <td>0.72</td>\n",
              "      <td>0.2836</td>\n",
              "      <td>35</td>\n",
              "      <td>71</td>\n",
              "      <td>106</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>16</td>\n",
              "      <td>2011-01-01</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.44</td>\n",
              "      <td>0.4394</td>\n",
              "      <td>0.77</td>\n",
              "      <td>0.2985</td>\n",
              "      <td>40</td>\n",
              "      <td>70</td>\n",
              "      <td>110</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>17</td>\n",
              "      <td>2011-01-01</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>16</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.42</td>\n",
              "      <td>0.4242</td>\n",
              "      <td>0.82</td>\n",
              "      <td>0.2985</td>\n",
              "      <td>41</td>\n",
              "      <td>52</td>\n",
              "      <td>93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>18</td>\n",
              "      <td>2011-01-01</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>17</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.44</td>\n",
              "      <td>0.4394</td>\n",
              "      <td>0.82</td>\n",
              "      <td>0.2836</td>\n",
              "      <td>15</td>\n",
              "      <td>52</td>\n",
              "      <td>67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>19</td>\n",
              "      <td>2011-01-01</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>18</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0.42</td>\n",
              "      <td>0.4242</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.2537</td>\n",
              "      <td>9</td>\n",
              "      <td>26</td>\n",
              "      <td>35</td>\n",
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              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>20</td>\n",
              "      <td>2011-01-01</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>19</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0.42</td>\n",
              "      <td>0.4242</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.2537</td>\n",
              "      <td>6</td>\n",
              "      <td>31</td>\n",
              "      <td>37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>21</td>\n",
              "      <td>2011-01-01</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.40</td>\n",
              "      <td>0.4091</td>\n",
              "      <td>0.87</td>\n",
              "      <td>0.2537</td>\n",
              "      <td>11</td>\n",
              "      <td>25</td>\n",
              "      <td>36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>22</td>\n",
              "      <td>2011-01-01</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>21</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.40</td>\n",
              "      <td>0.4091</td>\n",
              "      <td>0.87</td>\n",
              "      <td>0.1940</td>\n",
              "      <td>3</td>\n",
              "      <td>31</td>\n",
              "      <td>34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>23</td>\n",
              "      <td>2011-01-01</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>22</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.40</td>\n",
              "      <td>0.4091</td>\n",
              "      <td>0.94</td>\n",
              "      <td>0.2239</td>\n",
              "      <td>11</td>\n",
              "      <td>17</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>24</td>\n",
              "      <td>2011-01-01</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>23</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.46</td>\n",
              "      <td>0.4545</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.2985</td>\n",
              "      <td>15</td>\n",
              "      <td>24</td>\n",
              "      <td>39</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    instant      dteday  season  yr  mnth  hr  holiday  weekday  workingday  \\\n",
              "0         1  2011-01-01       1   0     1   0        0        6           0   \n",
              "1         2  2011-01-01       1   0     1   1        0        6           0   \n",
              "2         3  2011-01-01       1   0     1   2        0        6           0   \n",
              "3         4  2011-01-01       1   0     1   3        0        6           0   \n",
              "4         5  2011-01-01       1   0     1   4        0        6           0   \n",
              "5         6  2011-01-01       1   0     1   5        0        6           0   \n",
              "6         7  2011-01-01       1   0     1   6        0        6           0   \n",
              "7         8  2011-01-01       1   0     1   7        0        6           0   \n",
              "8         9  2011-01-01       1   0     1   8        0        6           0   \n",
              "9        10  2011-01-01       1   0     1   9        0        6           0   \n",
              "10       11  2011-01-01       1   0     1  10        0        6           0   \n",
              "11       12  2011-01-01       1   0     1  11        0        6           0   \n",
              "12       13  2011-01-01       1   0     1  12        0        6           0   \n",
              "13       14  2011-01-01       1   0     1  13        0        6           0   \n",
              "14       15  2011-01-01       1   0     1  14        0        6           0   \n",
              "15       16  2011-01-01       1   0     1  15        0        6           0   \n",
              "16       17  2011-01-01       1   0     1  16        0        6           0   \n",
              "17       18  2011-01-01       1   0     1  17        0        6           0   \n",
              "18       19  2011-01-01       1   0     1  18        0        6           0   \n",
              "19       20  2011-01-01       1   0     1  19        0        6           0   \n",
              "20       21  2011-01-01       1   0     1  20        0        6           0   \n",
              "21       22  2011-01-01       1   0     1  21        0        6           0   \n",
              "22       23  2011-01-01       1   0     1  22        0        6           0   \n",
              "23       24  2011-01-01       1   0     1  23        0        6           0   \n",
              "\n",
              "    weathersit  temp   atemp   hum  windspeed  casual  registered  cnt  \n",
              "0            1  0.24  0.2879  0.81     0.0000       3          13   16  \n",
              "1            1  0.22  0.2727  0.80     0.0000       8          32   40  \n",
              "2            1  0.22  0.2727  0.80     0.0000       5          27   32  \n",
              "3            1  0.24  0.2879  0.75     0.0000       3          10   13  \n",
              "4            1  0.24  0.2879  0.75     0.0000       0           1    1  \n",
              "5            2  0.24  0.2576  0.75     0.0896       0           1    1  \n",
              "6            1  0.22  0.2727  0.80     0.0000       2           0    2  \n",
              "7            1  0.20  0.2576  0.86     0.0000       1           2    3  \n",
              "8            1  0.24  0.2879  0.75     0.0000       1           7    8  \n",
              "9            1  0.32  0.3485  0.76     0.0000       8           6   14  \n",
              "10           1  0.38  0.3939  0.76     0.2537      12          24   36  \n",
              "11           1  0.36  0.3333  0.81     0.2836      26          30   56  \n",
              "12           1  0.42  0.4242  0.77     0.2836      29          55   84  \n",
              "13           2  0.46  0.4545  0.72     0.2985      47          47   94  \n",
              "14           2  0.46  0.4545  0.72     0.2836      35          71  106  \n",
              "15           2  0.44  0.4394  0.77     0.2985      40          70  110  \n",
              "16           2  0.42  0.4242  0.82     0.2985      41          52   93  \n",
              "17           2  0.44  0.4394  0.82     0.2836      15          52   67  \n",
              "18           3  0.42  0.4242  0.88     0.2537       9          26   35  \n",
              "19           3  0.42  0.4242  0.88     0.2537       6          31   37  \n",
              "20           2  0.40  0.4091  0.87     0.2537      11          25   36  \n",
              "21           2  0.40  0.4091  0.87     0.1940       3          31   34  \n",
              "22           2  0.40  0.4091  0.94     0.2239      11          17   28  \n",
              "23           2  0.46  0.4545  0.88     0.2985      15          24   39  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "metadata": {
        "id": "2F1a8UPM8uwd",
        "colab_type": "code",
        "outputId": "0666e31f-320a-484b-f82b-8e948d962c3f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 793
        }
      },
      "cell_type": "code",
      "source": [
        "rides.iloc[0:24, [2,5]]"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>season</th>\n",
              "      <th>hr</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <th>1</th>\n",
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              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>1</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>1</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>1</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>1</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>1</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>1</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>1</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>1</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>1</td>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>1</td>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    season  hr\n",
              "0        1   0\n",
              "1        1   1\n",
              "2        1   2\n",
              "3        1   3\n",
              "4        1   4\n",
              "5        1   5\n",
              "6        1   6\n",
              "7        1   7\n",
              "8        1   8\n",
              "9        1   9\n",
              "10       1  10\n",
              "11       1  11\n",
              "12       1  12\n",
              "13       1  13\n",
              "14       1  14\n",
              "15       1  15\n",
              "16       1  16\n",
              "17       1  17\n",
              "18       1  18\n",
              "19       1  19\n",
              "20       1  20\n",
              "21       1  21\n",
              "22       1  22\n",
              "23       1  23"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "metadata": {
        "id": "xN4Pa1qp8uwm",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Dummy Variable\n",
        "\n",
        "Season, weathersit, mnth, hr, weekday 등의 Categorical Variable들은 모델에 넣기 전에 Binary dummy variable로 만들 필요가 있다.\n",
        "\n",
        "그리고 사용하지 않을 변수들은 제거하자.\n",
        "\n",
        "\n",
        "익혀야 할 방법들\n",
        "pd.get_dummies, pd.concat, (Pandas Dataframe).drop"
      ]
    },
    {
      "metadata": {
        "id": "8lDydft58uwn",
        "colab_type": "code",
        "outputId": "fbdf4243-0b07-4730-ce7e-72a1a3f0f730",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        }
      },
      "cell_type": "code",
      "source": [
        "dummy_fields = ['season', 'weathersit', 'mnth', 'hr', 'weekday']\n",
        "for each in dummy_fields:\n",
        "    dummies = pd.get_dummies(rides[each], prefix=each, drop_first=False)\n",
        "    rides = pd.concat([rides, dummies], axis=1)\n",
        "\n",
        "fields_to_drop = ['instant', 'dteday', 'season', 'weathersit', \n",
        "                  'weekday', 'atemp', 'mnth', 'workingday', 'hr']\n",
        "data = rides.drop(fields_to_drop, axis=1)\n",
        "data.head()"
      ],
      "execution_count": 13,
      "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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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>yr</th>\n",
              "      <th>holiday</th>\n",
              "      <th>temp</th>\n",
              "      <th>hum</th>\n",
              "      <th>windspeed</th>\n",
              "      <th>casual</th>\n",
              "      <th>registered</th>\n",
              "      <th>cnt</th>\n",
              "      <th>season_1</th>\n",
              "      <th>season_2</th>\n",
              "      <th>...</th>\n",
              "      <th>hr_21</th>\n",
              "      <th>hr_22</th>\n",
              "      <th>hr_23</th>\n",
              "      <th>weekday_0</th>\n",
              "      <th>weekday_1</th>\n",
              "      <th>weekday_2</th>\n",
              "      <th>weekday_3</th>\n",
              "      <th>weekday_4</th>\n",
              "      <th>weekday_5</th>\n",
              "      <th>weekday_6</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.24</td>\n",
              "      <td>0.81</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3</td>\n",
              "      <td>13</td>\n",
              "      <td>16</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.22</td>\n",
              "      <td>0.80</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8</td>\n",
              "      <td>32</td>\n",
              "      <td>40</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.22</td>\n",
              "      <td>0.80</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5</td>\n",
              "      <td>27</td>\n",
              "      <td>32</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.24</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3</td>\n",
              "      <td>10</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.24</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 59 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   yr  holiday  temp   hum  windspeed  casual  registered  cnt  season_1  \\\n",
              "0   0        0  0.24  0.81        0.0       3          13   16         1   \n",
              "1   0        0  0.22  0.80        0.0       8          32   40         1   \n",
              "2   0        0  0.22  0.80        0.0       5          27   32         1   \n",
              "3   0        0  0.24  0.75        0.0       3          10   13         1   \n",
              "4   0        0  0.24  0.75        0.0       0           1    1         1   \n",
              "\n",
              "   season_2    ...      hr_21  hr_22  hr_23  weekday_0  weekday_1  weekday_2  \\\n",
              "0         0    ...          0      0      0          0          0          0   \n",
              "1         0    ...          0      0      0          0          0          0   \n",
              "2         0    ...          0      0      0          0          0          0   \n",
              "3         0    ...          0      0      0          0          0          0   \n",
              "4         0    ...          0      0      0          0          0          0   \n",
              "\n",
              "   weekday_3  weekday_4  weekday_5  weekday_6  \n",
              "0          0          0          0          1  \n",
              "1          0          0          0          1  \n",
              "2          0          0          0          1  \n",
              "3          0          0          0          1  \n",
              "4          0          0          0          1  \n",
              "\n",
              "[5 rows x 59 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "metadata": {
        "id": "bFdWsejU8uwp",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Scaling target variables\n",
        "\n",
        "mean shifting & Scaling : 평균을 0으로, 표준편차를 1로<br>\n",
        "사실 scikit-learn에서 한방에 하는 방법도 있다.<br>\n",
        "[Missing-Value처리](http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.Imputer.html), [StandardScaling](http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler)<br>\n",
        "연습삼아 직접 해보자!\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "9npdTrWj8uwr",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "quant_features = ['casual', 'registered', 'cnt', 'temp', 'hum', 'windspeed']\n",
        "# Store scalings in a dictionary so we can convert back later\n",
        "scaled_features = {}\n",
        "for each in quant_features:\n",
        "    mean, std = data[each].mean(), data[each].std()\n",
        "    scaled_features[each] = [mean, std]\n",
        "    data.loc[:, each] = (data[each] - mean)/std"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "NYgbzL8F8uwt",
        "colab_type": "code",
        "outputId": "03d392e2-d072-4e9d-fb14-101dc53ce2f2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "cell_type": "code",
      "source": [
        "'''\n",
        "위에서 쓰인 Dictionary 잠깐 짚어보기.\n",
        "Dictionary의 각각의 원소는 key : value 쌍으로 되어 있다.\n",
        "'''\n",
        "\n",
        "scaled_features"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'casual': [35.67621842453536, 49.30503038705308],\n",
              " 'cnt': [189.46308763450142, 181.38759909186473],\n",
              " 'hum': [0.6272288394038783, 0.19292983406291508],\n",
              " 'registered': [153.78686920996606, 151.35728591258314],\n",
              " 'temp': [0.4969871684216583, 0.1925561212497219],\n",
              " 'windspeed': [0.1900976063064618, 0.12234022857279049]}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "metadata": {
        "id": "RSOF5WWn8uwy",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Splitting the data into training, testing, and validation sets\n",
        "\n",
        "맨 마지막의 21일을 test_set으로, 그리고 마지막의 81~22 (60일)을 validation set으로, 나머지를 Training set으로 둔다.<br>\n",
        "물론 이것도 [scikit-learn에서 한방](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html)에 할 수 있으나 우리는 직접한다!\n",
        "\n",
        "굳이 Validations set을 왜 사용하는지는, 이론만 달리는 날에 신나고 자세하게 달립시다!\n",
        "\n",
        "* Validation set = 얼마나 모델이 잘 맞는지 판단\n",
        "\n",
        "* Testing set = 테스트용"
      ]
    },
    {
      "metadata": {
        "id": "uHnuBUJu8uwz",
        "colab_type": "code",
        "outputId": "52394e56-7c70-47d1-f433-a514fb400f38",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "cell_type": "code",
      "source": [
        "test_data = data[-21*24:]\n",
        "val_data = data[-81*24:-21*24]\n",
        "train_data = data[:-81*24]\n",
        "\n",
        "target_fields = ['cnt', 'casual', 'registered']\n",
        "test_features, test_targets = test_data.drop(target_fields, axis=1), test_data[target_fields]\n",
        "val_features, val_targets = val_data.drop(target_fields, axis=1), val_data[target_fields]\n",
        "train_features, train_targets = train_data.drop(target_fields, axis=1), train_data[target_fields]\n",
        "\n",
        "train_targets.head()"
      ],
      "execution_count": 16,
      "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>cnt</th>\n",
              "      <th>casual</th>\n",
              "      <th>registered</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-0.956312</td>\n",
              "      <td>-0.662736</td>\n",
              "      <td>-0.930162</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.823998</td>\n",
              "      <td>-0.561326</td>\n",
              "      <td>-0.804632</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.868103</td>\n",
              "      <td>-0.622172</td>\n",
              "      <td>-0.837666</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.972851</td>\n",
              "      <td>-0.662736</td>\n",
              "      <td>-0.949983</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-1.039008</td>\n",
              "      <td>-0.723582</td>\n",
              "      <td>-1.009445</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        cnt    casual  registered\n",
              "0 -0.956312 -0.662736   -0.930162\n",
              "1 -0.823998 -0.561326   -0.804632\n",
              "2 -0.868103 -0.622172   -0.837666\n",
              "3 -0.972851 -0.662736   -0.949983\n",
              "4 -1.039008 -0.723582   -1.009445"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "metadata": {
        "id": "CurBFRz88uw2",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# 우리는 타겟 중 첫번째 컬럼(cnt)만 사용할 것이다.\n",
        "test_cnt = test_targets['cnt']\n",
        "val_cnt = val_targets['cnt']\n",
        "train_cnt = train_targets['cnt']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "BYr1vtpd8uw4",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Model Training : Simple Linear Regression\n",
        "\n",
        "이제 데이터가 다 준비가 되었다!\n",
        "\n",
        "해보자! 모델링!\n"
      ]
    },
    {
      "metadata": {
        "id": "oRB84_HE8uw5",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "'''\n",
        "단순한 multivariate regression을 해보자!\n",
        "'''\n",
        "import time # 학습시간 측정용\n",
        "from sklearn import linear_model\n",
        "from sklearn.metrics import mean_squared_error, r2_score"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "UCEKvUAu8uw7",
        "colab_type": "code",
        "outputId": "465e7b33-9eaf-4950-bc11-a52788e27861",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "# 모델 준비\n",
        "simple_regression = linear_model.LinearRegression()\n",
        "\n",
        "# 모델 training\n",
        "# Test셋의 첫번째 컬럼(cnt)만 사용할 것이다. \n",
        "start_time = time.clock()\n",
        "simple_regression.fit(train_features, train_cnt )\n",
        "end_time = time.clock()\n",
        "\n",
        "print('----  {0:.5f}sec, training complete  ----'.format(end_time-start_time))\n",
        "\n",
        "# Training & Validation set에서의 예측값 준비\n",
        "train_pred, val_pred = simple_regression.predict(train_features), simple_regression.predict(val_features)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "----  0.09239sec, training complete  ----\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "0ZRwQJXY8uw9",
        "colab_type": "code",
        "outputId": "e68da19f-d5c0-4e61-b2e0-d4e0b36c1992",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "cell_type": "code",
      "source": [
        "# Training & Validation set에서의 성능 확인\n",
        "\n",
        "print(\"Mean Squared Error on Training set : {0:.5f}\".format(mean_squared_error(train_cnt, train_pred)  ))\n",
        "print(\"Mean Squared Error on Validation set : {0:.5f}\".format(mean_squared_error(val_cnt, val_pred)  ))\n",
        "\n",
        "print(\"R-squared Score on Training set : {0:.5f}\".format(r2_score(train_cnt, train_pred)))\n",
        "print(\"R-squared Score on Validation set : {0:.5f}\".format(r2_score(val_cnt, val_pred)))"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Squared Error on Training set : 0.30120\n",
            "Mean Squared Error on Validation set : 0.46260\n",
            "R-squared Score on Training set : 0.69078\n",
            "R-squared Score on Validation set : 0.64146\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "9Psj4F_d8uxA",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Check out on Test set\n",
        "\n",
        "Golden rule : Test set을 모델 학습에 사용하지 말지어다."
      ]
    },
    {
      "metadata": {
        "id": "52ZSdvS08uxC",
        "colab_type": "code",
        "outputId": "5c0ad08f-2b1a-49cb-bf85-3465453a13c2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        }
      },
      "cell_type": "code",
      "source": [
        "simple_predictions = simple_regression.predict(test_features)\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(10,5))\n",
        "\n",
        "# 예측값도 Scaling 되어 있으므로 그것을 원래대로 되돌려주는 과정\n",
        "mean, std = scaled_features['cnt']\n",
        "\n",
        "ax.plot((simple_predictions)*std+mean, label = 'Simple_Prediction')\n",
        "ax.plot((test_cnt.values)*std+mean, label = 'Real')\n",
        "ax.set_xlim(right = len(simple_predictions))\n",
        "ax.set_xlim(left = 0)\n",
        "ax.legend()\n",
        "\n",
        "dates = pd.to_datetime(rides.iloc[test_data.index]['dteday'])\n",
        "dates = dates.apply(lambda d: d.strftime('%b %d'))\n",
        "ax.set_xticks(np.arange(len(dates))[12::24])\n",
        "_ = ax.set_xticklabels(dates[12::24], rotation=45)\n",
        "\n",
        "print(\"Mean Squared Error on Test set : {0:.5f}\".format(mean_squared_error(test_cnt, simple_predictions)  ))\n",
        "print(\"R-squared Score on Test set : {0:.5f}\".format(r2_score(test_cnt, simple_predictions)))"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Squared Error on Test set : 0.36277\n",
            "R-squared Score on Test set : 0.45262\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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6vPYRERERUeliYCsCYmNeLYJAGt7hzzJji4i6oWPZnnNF2PiPiYiIiIj0xcBW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\nCmMcH3huGkKNAAETf8RZwkCaOsbWMvHwALDV4b9ZE4K8atmSwWweW8OZx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R0AACAASURB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/9Lszf63lgS0K5J2EFFGO4z/75L/HUzefxSdf+Cw+/tKn5s9vR1sYjpOFHlsA0CX3CZ8geFm3\nga2WistxuDFmZKQzAVtbfDAvKaa0lvU/klIQFU6dsBeoZljKcTTzGykb1urijnSEQAf47jd/B+7b\nvQcP7t2HH3jn9+G9d71rqe+OdMT05ZPUDbAlo0ppc6dFw3rf3YLBs2HzOoj67IakuiOMPDC28lzN\nJyiDbmRopMvanLFlShEn09TwMVu35IQnCjSGwj8ud8jY0kqza06mYrrw2ZITyYgAkHQSxIBjANvL\nMLYapIhRqFlT4S8VsUqEZUxVoBYAf/n46vy6pObxTQxJCUIrx1JEulBBWWjFd/uWIpoeWy6ArVyM\nEdRfsN+hRus5oFLEML/zs1c/xx5fWALY0koz4K88/kMHoL6xUEEWkeqA1XN7fFw/jocMyKyTImpD\nijiTHfnyaKGMLaWLMd0qRYxZQ/Sm0w8an5fPpYj15vHTLGbeIC7HiLIMKWJNA00biL2t9cf2QGvm\nm+Jnn+rvd7TBkPf6puanqUwposvFJL4vPG2Nv/aZx6/gL/+T38Bf/j9+A5/4wsvWz0sbPLYoC3Yd\nY/plSpqPV99nY2wtliK+8Vy9jQZlOfkEttKMxkoUta6UU7Klgjopok/GVoN5fPF6tW91CofD4ZQB\nVKpGijhKxobvng+GSS1jCwB0On89STNMw5voPPwYwrP8GsrjDpLLDyI9OINgfAbf/cgfmr9G+4gc\nOVTI98ELC80AIPnvuIocli7aSl8rWvS1NlIRpbKBnpu2ZPRf/vTz+NjnLhvA1rIeW74kvk2AmZQx\nPy88U8sqgS3DYyu1M7bKup2K+DoqoxEiUsTAMJ0FgoiDQLZV4t6SUsRBKIGt2EmTV+exBQABZUzM\nVvhDwnApB943bt+Fv/Kev4j/8d3fj4dO3b/0dyuloHOCAuduPI7kCj83j+eDWq6J9h3KytRYtVgy\nYmeEY1fAFmNsVTHqTYytTYEtw9h7Bsy4GtgMKaLFY6s0j980EbEsSvGmxx+w39hWrTrq/7ZgbElg\na2cJxlYgzk/arCul2ITRpRRRjhNlw8IAfaDymxDA1jgdY3+WSkdvmk0MSQNccixFTKRnIhv7bObx\nDqWImkoRS8bW5lJsY/GFsGWkbLRIbTXLSERcAtgCgC69l83AOhdAsSlFUq0tlAAAIABJREFUrI7T\nlmRszX5LKUWkbC2gXopoAFuze7mPZCPpsRXo2ZguzOOzLMfxZMJA8fPb2+YHxsU51ScsIQnGTj0z\ntiTTJAxN0+A4KeSIdVLEdYomwroc98pq8jNpYmytD2z5M4+n96dAc69Yee/6mz/xKSRpjizP8c9/\n/ks1nycXEglji5nH+/HYqgPM5NxiNEnY4sG9d/JrKAo1/ug3PIg3iWROWqy58wlsWX6rdRlbpsdW\nnXm8R8ZWA0uQvm5jxpR1OIw5mFPD2BonY9Po36cUUZu/mwqn89/9eBRDE7YtrXy0jXwywPSJ9+L9\nvT+Ku7cr2b9U+OSCBe/j/DPZnPUg0KKi51OdxxbAgXV/sj3qsSXOPbKdB8f2+eUvffp5C2Orvlfs\ntOGxtSRji3mDi+qHg4KksAxjqyVQv6zbHlstlelHRWR7lF2gcuxtdzAinlGDsG+AU8DywEMpoZqv\n44TxXHO+Scl9Mpo74ckS9TKUl3d/Q9AEAAJ0kKG4iJLcFWOLX/CU6iplTyMCokRBtLFnGVDIEcvm\nUHVHGB66kbflUopYmsf3QmBs3+5Nj5H8+5LhNJ6ma0/aaRkgUKjZdQNUjC0XUkSA71Oq+DVEm611\nS04OKGMLtEEWYOSm5vFA0UiVQKpLrxkZnFGFTEgfrGLAkIwtoGBtnerusZtmE0MyMKSIBbDlwlsQ\nMBcqggbJMgB0atIb1ynqsTUHgYYx4iQz2BqrlAS/6WcNugLY6iwG0rpBB2f7pxe+DygWaQ4wA5Bm\nx99F0qOxUEGOzVZPgN0qRxRq7M7Yj3Ea4zcufxrP7POAk6UZW3Mpoh8WkBKMrUh4bE2zBMfjGLnw\nkxlEg2L8IEB5nnRw/lQP95yvGnYJHJfm8WW5lu1JpkkYaGZmD1QsPmpwe2rTtNtuiFuzsdt30qOs\nJhlV06p+U/mUifFgEz1nfgMmSLRMIyv/JmjRY0vK/pu26+pNDuR/23vvxReevoEvfuUG3vrAGXzn\nBx60evPRos2dT4aT7Xxz7bFlsAJb3J86wGSaZLXG1wfDKR+Ha4CtYTJCGChm9O/VPN7G2ArjOVPu\naBRDhXxeuaX2cOuFc8gOq0Xwtz7IFT2yN8wV7yPaCMmQY/dKwBaTIi4HArVhiC/llZTxWddTv/TK\nMT5z6Rp7rlGKGFFgyxcLbTkWcZMSqqcL8DQXjC27FPE2sPW6rJTKrwx2U3XQlcrx/d/5Vvzqjafm\nzw2ivjVZalnGllYa/aCPYVrItFQQ49CJFNHu2wTMmrvy5dngHUbpHNjalA0EACE6iFH4HiS5Iy+q\nBp8Z2bBSEMWVOfQZ0giq7ghH19zLcZiBdzdEPvTjsWWcszNgazRJnEgD5Up4FGjEifi9sgBndnv4\n6ge4Mf+6RVfCEiHFcsLYSuT5RwBI8poSBuvbg8XNXaTlTbmeIeBSkkOBLSpZtrGqgNxgbAGFz9Yj\nZ97EPLaaGVtiMqSLSe9okq7tXUOLr2Rxf0GbFNG3xxYA3Dqa4Pwp04tx2WryF9ySwFYNY4vW3dtv\ntMjz7MWkiKW88sCtvBLgDNztnumxdXa3B6UUsjzDP/38T+HxG182PvNUHbBlEOB9NkZcihgoXZzX\nZPU0TuNChiiup17YxaPqm/El/EcAQHa8g7tO7+CH/tg7GRNDAsfTNObspkniDCie7xOpKNBGypyU\nFAHA7oaMrYFv3zAxptOmmb62yqp+U/n02KINURiojVlVzYwtRd7nHthqSv+S23XlJrc4uOvMAF/3\nVXet9H1tNXe232ptKWJNaqVkNflgpZYlWWMmY6t4venaLRhbxIS9gbGllGJG/z5BIOmxBRQLwOU+\nHY3i+bwZKLxDv+/NfwF/85O/NX+u2wnwpgun2GdIxlaq+dzUCwvNSK+sl4wuKmYe38TYYmBxGx5b\n9YytJo/dzzxR+cT2OgF2GxZjKJDnTYrYyNiqjtOowbs6yGc9nGRsWaWI1Xt8BRfQui1FbKmyGtYC\nwKWI3a7Cwxf2MEyqG2kdY2sV35YBBRnC2InRdQYB1pHVOx5JX7xPkxTBTUGT4juq/U+Ve2Arz3kj\nJBkm45QAW44aVyZF1BmO48WGpYtKshaQV2lTzVLEzcAneXNVQcXYclG2VEQ5qP5P/83X4Z/81W8y\nqPPrFpUijtMJWzFy47FlBxe2ehGbBGUpbyaXkyLWm8cD3D/AZYPHfDqIebyU7iqVATqFrU++MktG\n5B5b9decBJdKdsuRozCGZhm2ea5t6r/HPovsW3FOFPu2qc+WkZxKGktptL4MsPXuO9++9Hd36CLN\nDIjxbYjfMTze8jnr4tee/5gV1AKA3RopYlDH2PLSGAnzeK1xarvDxvJJMmNmC2CrH/bwXe94H9Ln\nHkVy9QLeePx+/LU/9R6D1RqqgIVlxOmUgUBpljudeJsgh8aWGNeOhrEhu93EYwvghvk+gC2DyUC+\nj77mToro0WOLjOWFuf9mPljG/Y5IhUPPkpwmzx+5XVcEY+vO06svILTV3Nmkr+t6lBmpldqeiugT\nqFuWsSX9teg0wgDE6zy2ZnN6BkJ6NI+3MbYKKWLx/PE4Yf6Ig3AL9965w+7Nj9532rjmpVoiA5+b\n+vANM45TdxPGVvXebhNjqwWJG92vTqjZ/JSOh3RhOwwUHrrbPk/4fe+50JieTo9l7Amso+OpnG8v\ny9g6KBWy0mMrM++hvZbYqmXdBrZaKpPdRNLVSCNUTsSHcXUjLRhb/EbaC7pLr4YDwHanArYKjy3H\nUsRc1xriK4t3jgspIpP36MQJbVOCdU3eOT4YWzIZcZgf1Lxz+WIJbsBMOlWcf1s9bsJPa9NjFOgA\nPQJglpRqVz4m5gq/QkwG1VCH+JpH34BBQ1LRqjUQMcp05WXfAbAlV73Lm9xWL2SToKnAZ7b6i5sg\nyWJKcsHkoA2eQ68Z6UdVTsoCIzXTztYCgMvHVwCs4LElwaXZGOQqGbGOhQb4Z2xF0q9rtm83NvTZ\nyhpYxQYI2WkGth4+9QC+/u6vW/q7WSjDrOnYdH8Ak4XWtFChdMHYeuHwJfzcU79o/bxB2K/1S1Pi\nfqy8ShFNj63drS6TBVSMLX7O94Ie7jyzhb/3x74Xf/Xr/xx+5I99o1XKrJRi+yrN4wG3QJAEL6LQ\nZGwVwBafbJ/a2GOLyiv9S3RoY07H+1WSs5rKL2OLAlvVPALgspwmNhT7PMOc3O6x5YOxJb+bvSal\niDeqhebtfrTWnKItKaLNEH+VRLqmzwrnjC1+jrXpGSZTC9MaxtYdZ6qe53AY80TuhlREgF+jXs3j\nLYwtBPH8eEkp4lY4QBRqfP3bC+9KBeBbv+Ye4yPk3H2STVg4kLfFFroNG3hs0fOp0WOLvObrmqIs\n1SjkYD597ZAGmmx38YG3mf6i2/0Iv/+99zV+HwO2fIVmkM+VklEKZlPyhqynnyvuwzIVMbFIETud\ndhlbt6WILVUqksGYeTxpOMuGiZpgD8IBZ1xhdSkflYWpMHbS4Jlmw81Jj6mazufiLoCtbtCt5I5B\nguEkxV4Dur9MSQAykvLKmjKazTXrbJ9L5ibqaGO5h2zuFPQ8mn7Q9WceDxTn3XxwLD22HDUPthV+\nKlWLHIGNtPpR9ZuMkhHObHXwyn6xf04YW4YUsWJs0UnQdFLt+1YvRKAXg9ymxxY/DrRpdXWMjO9h\nUkTpsZVZ/bWAQooIAHFW/cZN15xWmvsKqnKi6ChkQnhsRUEzsOUK+AYs57VOgSzArcPN9o0vVHCw\nTvoL6m414ekGHUxS/t1/5i1/fKWFFwb8aZeMLX4eszAQixT27G4XH3riXyMhf9fR0XxcoZHqsrQc\no30DW4SxFeqgAHgosJXNLAcsjC2gmGQv8ubr6OrYSvN4oGgmT227CeawsVUlY+twNDX8//Y29Nga\ndP0wVcuSwBIdZxOyGEcZJ51IN67qN5X0pHEKbBGgQzK26PE7tlhd2OYxpjm5PRVxXSldUzV9pnzt\n6q1qPn7nmfXk3txA2Z90z87YWleKaJ+PvJoYW+Xr8tq983QfV2aA5CROGfBQJ0UsgS3OrvPB4qz3\n2KJSxGMBbO12Cw/EP/n73ox3X7wDp3e6uOuMqebpC7VEkfYYzD1UvRriz2oTKWLMzOOXS0X0AWxl\nWc4W/Moxr9w+us/U3mdvu4uveeQO/PSvPMmujT/0/7P3psGWZVd54LfPdKd335T5MrMqMytrUNWr\nkkpCE5LQQGkCJEAgSwiwTIMbHDbdDreBAHfQAc1gm+humsYQpj3Q3W4Cug3GBuR2Y+PAEAzCoAlJ\naHqluUpVqpzfcOcz9Y97zz1rr73PuWfY576kI1dERb075D333LPP3nt96/u+9er7V1phUBVIY2Ad\n8w2jbGFabJnkSBEn48X3ZMCW7t+01zT3JXGXsbWmoDdHnCNFjBEjiiMGbHXQY1LEVkF/rfQzGpAi\nRjJY52YCW/NzDwgd1gRoQj3GhB0Y2ZRGHICUmHXZE5KpxHXDZe2ibb82ws2TO5pwdtqOYv63fM2A\nXJQyDVPzeDMbBb5h51JETwMw1A26YZiGM2z20mOYALZ0nR6BhWSUAFvjSfp3EeN4QAWSwmg9jC1u\nHp/ME6p5fLQcIzxOZgOM/BGmBRlbQggZjFkCW81IESnTQN8VsSGPLWB5biYZW7EC6sv3kkWkiHud\ns3hs95H5V4HAd7/g3YVN45OQ5/IFY+t4ijiul9AGIQe20vPQNS/o9AM8dfKl5VPP393Hj7zyB/FV\n93wlXnj2Mbzz4bdlHksF8pqUIjKPLcuadwck3nsxYhwNJ1qPraLBGVsc2DJpIK82AxFajy0KeLqO\nVbsRSZOdHgF1TqfHy2Js1TmntTG2nGzzeJ2Rsu57ZAEn/O9GGFs5vws/Hl3bd/rV9kXr6gymY3lU\nZmwp5v7z6+3YlswAapSxxQATlzO2FioXzthictHrR2R9zJAiThbAVqehIl8SuR5bbipFHEx8wFWB\nLcsSeOzKjhbUAlSG+CycSQDRxG8ewK8lRaQeWzkNcbyGARN1fpIbv9HX6Ryx1fPQaTl45fPPLZ87\ns9nGG15yceUx3YYl2IA8H+QztnKKi/5ijEU2BIGShsFIeSud+4IwkgvDDcRdxtaaQulKlyFFBOaT\nEPWS6boddLkUsaT/kQQwmDKPB02ELOmcJOmKFQEiQhiblSJ23DaQrFX2vANU3cjq4AYglxVjKnHl\ngJ9YnFedjS5nbFGT417byZQibrb6lY+5/HwC1NGuiCZCka7YltSRw2mAscV9w7oEhzQhRVQN8VPJ\nqCDVvckkvfc2ugWBrVPy2FI7CC42xwxoE1aEOIOxBQBXR9cxmA2Wj/mcyMMRDoJFuwphWorIAPCV\njK0GpYjCChGjPsOJA+A0cVFAIDdNEjZbffyNx/8LfOjaR3G+exYPbt1f+thSoWZRTZ/6YW2zfz/K\nBrZ0jMHYO5GeeuPl1+FMZwff8di7Vh5r3V0RJSmisObMJVakOB5PNIyt4owT6g03N49foxTRtuA6\nNjzXWnquDEY+bh2nY2+r59U2r5eApiBCEEaV2VK64B2o+PGSoL9l1Y6IwDzppQb1Jn3QaELkWLL9\nBGVk6ICtqR8q7Issc3IA0n7ytKWIx2Td2Cy43vLwXDlhjaIYllVv7OpC91tVZWzlMeqowfps1lyi\nmo65GN59T+JXn/kQ7L09hNcvS69zYOv8jgz63CCsu1WMLco2MlnkS2J5jbK6IkqMrXTsbXg99f2a\ncC1XYqxPoxnaHinK3uGMLcljqyBjqznJfxquwlLVz3lbi+ZY3/L65+Fk5GM49vHur3mkUNdqlzBu\n/SAy2qAlCZpnZDEggXzGVhwkewOxaE4394Me+SqwpTA8ZxG67eZ4VWsHtvb39/cA/PcA/gqA8wAO\nAfwxgL9/cHDwIfbeDoAfBvDtAK4AOAbwewB+9ODg4En2XgvA9wH4LwE8jDnk8V4AP35wcPD+Js+p\nSChMoAwpIgAMmGF41+kqHebKsmm6khQxwMnYgMwjFwSinR6jWpvrrNjwKFgX4mhQ/5yCPIlRDmPL\nlOzNYwsSrGAuUdiq/pkhS+4oQzBPinhP73z1gyafT6/zYoE2VeXXVXspsGVKHkqDA7KdTrqwDcZ+\n7aRIZS3oGVv0mvU7xUATPs/4zGNLNjWO4QdRoYV4VQRKB8GEscWlYHGmFBGY+2zRatBuO58V5Fg2\nkBzasHm84i8oAVsaxlajUsT5uKgNbIXyOeXKsMk+a8vbRMv28FX3vLzysaUKsx1gDtoI3D6ZoNve\nqPy5PmNsUSNaHWNrah9KT13onUPRWC+wpTK2XMeGZzvUJRLH47GGsVV879AiPpazaKbx2DIoWc5I\nonttFzN/PrZPRjMcknFeV4YIqOyo8TRAv0CX2aKhMrbk6jU9btZ3KhuuYyFcJK8mq/60mu848j5W\nkuVoijy6rmVZ5uT87yCMjSd4RaWIQRhJ16bq2ODeUFM/rH2ddaFjolUFBvMYdS0vBbbW4bFlbdyG\nfeHzuDEF3CvXEd66AIRu2hVxwoEtxtgiwNYqjy2ZsdUguymzK+L8+ePxBGI7/a6KoiMjhBBwbXdJ\nkJiFM1leuQYQiDOBio7BOaMnvf+8XMZWs2Ax97y1bUvL2IrjWFJBJfL8jY6Lv/POF5U6Jj3fGPO5\nyHXMAluSIb5rQyAtkwWSFDHbSzUBtrY3PGy2ehiN5rjFUPNvOHg29c10J8+KtUoR9/f3zwH4EIDv\nAfBri///MwBvAvDH+/v7LyHvFQDeA+BHAPwRgO8G8D8BeD2A/7y/v/8Q+/h/DuBnADwJ4G8C+FEA\n+wD+cH9/v7iLbUPBvZvypCsnhJUAJObx3GOrrBRRnuRPpga67eX6zMgyINvV+3zUia2OPMnfHAwy\n3lk8svyAABUYoJFlKFw2hBCSKf6csVVvYVWkiOQ8uhnm8Z7lrgQOioSOsaXz36gSOpow9dhqWooI\nAO2O/B3qSnyzpIi9jitV96h8dKugebIlLCn55oCnkuAZqlTS8RdHKQikY8xkmccDwGcOPy89Xg1s\nqT5/jTC2YrmDl06KaNQ8XjHdn/++dYEtDuqv6vSYxJZXn9nZoubxAsbAOi5FzCq+AEC7BRwHt9LH\ndgvbreIVhUwpYgMSiVkQSN2MEiZax5P3BSfjqXRPOcJWx09O0HXN1zC2RoaAYiAb1KdyRIWxZcDf\nq0lDfEAFGjhjK5HbUuP6uoCHzGQwyKojSY9jWZnm8bp1UNedMcucHJDnVMC8z1Zep0AJpGPnUpWx\nxSV0TYFBOvP46sAWS+wJaLBuaaVoU8ZVDOHN5wGdFNGyxLLDbRJUimg5xaWIJsH7JJbATVZXxMXr\nJ1M5pykKbAFyMW0WziRwYdZIp0d5rLRb8ngvOgY5AJ7nscWbOJiUxgMaBQU3j1+c09QPJWbsZo2C\nCwfyTDRF46EY4jv6AsVglmNvEcx/+5c+sifleTrGFmfdNTlfAOtnbP0DAJcAvPPg4OA3kif39/ff\nD+C3MGdnfevi6W8H8DUAfvrg4ODvkff+JwAfAPDTAN6xeO6rMAfJfv3g4OBbyXt/A3Og6xcAvLS5\n01odiil5hscWoAG2nA7adhuWsJafU9ZjiwMvw9kUURyrhrclImRmw7Z0TrLH1mbfAr1FTABb2x0Z\n7DMCbEmTb35XRBomGRktu41ptEjo7KB28sCliPQ8Oi1by9i60DtXyvw5K6RunrYPIJba4tYJSRZh\nCwghJI+tJszju2zcui3Vi2OnXz3Z4htJ1ybJXQZji1Pu88KxnGUVL+AeW6yqMp4G2DTAXAiZF1+y\niOoM3vMYWxzY2mlt5x7X5nJowIgEG2D+grEl+ws2LEXkc3kiRTwcTBFGUaFGArrghQpnhbwyic2W\nvq11mVDWMysAIhu3aoN1FFQVEs2fd3p81QvO4bnRp5ePz3fPlWKI8HV8ydiahcbZJrNQvk+SMdH1\nPNBt5XA6hSBFpbLelpT1qpcimtugclDfJYytJAZjuStiM4wts5tuhcmgeM/MK/KmpIjJMRJpvMlO\njzJjS07yVnls6ZKZPEaQa3NgywyDOP28PMZW9rlUZWyty3Bdz9iqKEWMsq/PuoCt5bgS8jGEHSCG\n3jy+13aUuYF6IIkMj61xOEEcxxKrcjwLjM/fCWNL57EF21++PghkAsKGV5y93LK9pfpnGvprZ2x5\njg0hgMQms+gY5EBOHrDFPRiHE7+w52yR4HLluceWKkU8ZuB3nYYqfI6bBRGK7/CLBb1W7oKFltQ/\npLlvooJUaczvh5c9soc/PE6/4VAHbHHGVsOdEddtHv8sgH8J4DfZ8/8B8/Im5ex95+L/P0/fuJAr\n/gmAb9zf399m7/059t5nFsd6yf7+/gtqf/saEVOBgAICrWZsCSGw5aUJxFbJZEJJ8kUodeGpEjxh\ndWm1zZKTyhc+LFe+TQBbXU9mz9weGWChERCIm/znd0U0l7hyI+X6jC3msSUoa8HSAnb39C7UOmYS\nkgRWAHB8IybrgFx1SK6TT8CaRoAtxpzkTMS6PltKV8QEBLIAQaRH1EuHm6TmBU3mAyZF5MmUKdNU\nbrSeCRZbKmOLymFvTm5Jr+2284EtHWPLFFswUuYJwsDVSRGNMrb0UsQ4Bo4G1cdfIDE72Tk1zNhq\nM2ArNZCvZ4ivrFE5DNz7793A1UX3TaCcDHH+6fquiFEcG2ebzAJ5HCfAVq8l/46D6QRwqgNbHpMi\ntlu2dJZGzeOzOsKSROXm0Vi6h40AW2zTbbrqn2ceD6SJ3MigFJHKPMYGWXW+JiFKIpELAnpgSydF\npPeFACQZEf1sfmwTkccgkb1z5N+vX5GxpbAWGkrudL+TMcaWTRlbzXdvAwgTiINAC6ZyMq7o/dNt\nu+i0HIlhRkMLKGG+rs8iX5LRxbF54C7PY0s4fspCY0bcpRhbZM/hRzMJXDDlc0tDx7it0gBiGnCA\nLBum4HK2ujktD92apDsnLr3eqlHcdhvsarv8zJwCBb3nh3mMLQBnNlt45L5tye92pJEirpuxtVZg\n6+Dg4McPDg7efXBwwHd5fczXtWPy3CsAPH1wcPAlqPFnAFykLKxXYO6m8r6M9wLAKyt/cQPBPbbo\nBttiG+wTX2VsAcDXP/Bm2MLGbnuntJ+JwiiyIu3mo0xwFlqWdGV3y8OLH5WBLdNdEQHgaJyHLhcL\nxTeMaJsThokuTDK2Oi75bezAAADJPbbk8SZ1sFyECX8tAEo3T2H7RkzWAXmhTIGt9XpsWQzYqgva\nqVLE+Xjj7CrK2CoFbBGwh5vH8y42piQ5eX5UdCwK5rHVtls4097VfqaAWCkTc7jPHwwytiDP5xIA\nru2K2KTHVvr73q7hMxjlMraygS0zjC0GUFjzcWBUirhCXnk0PZb8LS90ywFb/PMoEG16I6cwtpz5\nvbvRltfE0XQm3VNlC0oeY2xZQkgSE5OyPZ20HJCr8s/dlNf47RoJRBKddrNSRJqYCKFu8pPXTTO2\nkjDJ2KJJj23LBVoglVjpPbbU70GBZ/5ZihTRcIJXVIrIGeamPLZ0QJ+J0AEIJjy2bEtICg/PWxNj\nazFGBAeBFvNa0umYemzNm+2IzMY6WYwtABgHY81eqCFgSwewOT78cC5RnkQySFBOipiO02k4W3v3\nV9VovagUsQxjiymRDO3vktD5Puo8tjj4XYexxeeJJkBIuiapLLT0taGv99i6ZD2KVzx2Dn/7HS+E\nbVlSnleEsdXEOdG4U7oifu/i//8XAOzv7/cB7AI4yHj/U4v/P4i5mfz9AK4dHBzoRjV9b6XY26tf\nkYaIU3e2WOD8uf5SH7wzlCerwJYX0svn97Dd6eOb996Eb3j8iblXTkm5ydlATj6EFcJpufXOzZJZ\naLs73eXnbXRJsi0i8AZmF8+dxd5Gvd/1fCwzNkb+tPa1sqXNlMCZnZ70mY7tSHK3JLb7PTPjBMB2\nbwNfXDTnEnYA2Fatzz605GTGcx3p81qOC77MPXrvA0bO517/jPyE42Mw9o18tkMWvJZnY2+vjwjp\nhNnvpIPO1LXZDOQFK26PQafRoOax2swI/sK5TexstjGYyWxE6rH12PP20PaKTeUtxwUW04vjCem7\nHk3YhqJdc35Igs0T5/Y2lp/r2k4quxWRtNHreV1c3DmHj938pPKRO50tXDifz9hqu+S3XHzuaBKY\nm8+TiAXOnetjbyEJnQbqpuaeszvY2zQzBn3GVKPV59iqPldIuEwsz30TLxu8evCee7DXq3du50J2\nTvZcXjmchrWul+2R+TwWOLObnlNvJt8z1/3r0uNH7r1S6tjPRTzpSMdIr99ejg8T4bQEQKaEM9t9\n7O31cW57Ex8n/vcRQlgE2NrslFunNnvpOQWY3zu9jofxdL7hjYUwNrd2nj6SHp/bm5/T3m52Mnf/\npe3ax/eZxMjxHGPnBAAumZtdx8bOtjwOZn4EP4ikZOPsbrfWd9jZTNf8WVDvHqJB07yNbgvbm/Le\nYnunh07L0XoZtjqe8j08L01MXUeeu3bZ77S13cXemeKJ/ap46mZ2IdQjYyC2rkmv3X95p5K32/kT\neV/f7qq/h4lod24pz8XIv0+zXvNa6fVx2PXZJL9BEJnbY/GwkqSbgUAJOBUuzs0n3mK9znzvsrvZ\n1rOYSTGo39qQvKw6mzbOn5Ulf51ey+j52QkrR8fYEoDVirC51UVkyYWdK/ecx2armBxxo9MBkia/\ndoSzZP0ZTwOcPbthVF7Zbst71/Pn+vBcC0mfMjdnXqXP3x7L2cjemew1a8QZhYbn7ptsHjuz20OX\nMIjFYr8Vf06+5zZ7XuW56uJtmSXlmtqHk4ji9Hfb3GhLwJPj2Mvj+XF6/tGkgzc9/Eq02sA7HnsL\ntjtpYXnv+jbw9PxvP/KxtdOC5xBglVGZBDlGE7FuKaIS+/v7b8W8S+IHAfyTxdPJGWetPEP2vn6J\n955KcOlKniTiaCq3HO956YTk2E5pUAvQSGFEVEu2AsjVtvk5peekzNBiAAAgAElEQVThMmbIiCG/\nXY50VQhuoJ+vBy4W3DyeVwt0bAwAaDnmpEacsTWoyazjjC1+Dp6jVrUub91T65hJ8PbEYiFFzKuW\nFg2aCCT304yAjiblX0m0HA/39lM22xePnpbGyGHNzpxKy/vFefmMoZFIEc9stQuDWoDM2PIZC6zb\n4X4Fhszji3o3WbLHVsdtY6/HgNFFnO3qmVw06MKabGSPhjOp407VUDwT8zoI8u9SMxT2F9mk15nT\nFRm2k3GdWGy36zO2FCbRYhzcOMruylMkOGMr7zp98VAmh1/cLCfHzuqKCJivkM9YcaW9mMO3Nxh4\nZoUQdvreTsl1lzLpZsF8bFEPE5MV8qy5L4t1AQBnturvI3jVv0k5i+dYioeKH4aSj6bonGAgrsoM\nypJB5Zum5nGAVfodeS4H0mq/zkdzlccW/yzldzLM2MpjgFH2Ce22bQlzjK2mWAtNMbb49aH7jqmh\nRjP675AlRZwf83g4vz70vk0kals9PQAZi/S9u22Z+T2ajRWJm/GGEotzypJEHk8GOB7NIBziCwaB\nDbd4cYTO3ZNgKs0JQRgbl8Lq5m9qO1GUccm/F79vaHA/rYFpxhY/J9uS8tzkux6xfX8dxlaPrXd1\n8z9d0Gs1tK4i7qTVMDr3JV1CASCetfHuF70d3/3Sb5NALUDN8wY+Z1bL+7vDmkz8VXGqjK39/f3v\nBPC/AfgCgLcdHByYv4IG4vr1k9VvWhHcY+v2reESLR+yZOTGSTrIXMvF4a0JgHpeI8NjdsNbIZ69\neozrF2q0Ug/IZB8LjIbT5W81I9TdIApw/VBuoz46DDC16v2uw6G82AymE1y9elyr3et4Qq5FLDAc\nTKTrn2WoPhvHRsYJAFgBkVDZAW4ejmt99s3b7N/GQvo8RzkngXjo4vqo/vnMGNYonBmiGPj8U7dq\nd7QaDNPJUWB+n06C9Dma+5m6NgBwsXsRz55cBQB89tYX0e8+iptH8/F+9caw1rEOj+Qf7PBwhPFw\niptj+f5JpIh7W+1SxxNEwjgay2N7zOQj124MjPxuAQHl4ljg+HiM64uvIcm3RCx5bLnw0Ar1G7m+\n3V/53QRhtYkF+BNFMb7w1K1aXWsAFQA/PhwhoMkpNcUHMDj0EQ/NjMHBjC2TBNh69upx5Ws2ndE1\nQmA0TMfH0VgPMPWcrpH1aTRgicPinK7frjf3jSZkA8Xmc95U49rw5vJvR9iwRi1cnxQ/9vER+w0I\nsPWlZ4/Qtc1Vx48G8jwxGYW4fv0EDu9wy3zr7NAp9XuGpNQ6DWe4du1Y8tI8OpkYm1tvH8rndHw0\nhh1FQA7AE/tB7ePzpP/6TTPzXhLHJOmxLYHJWL5/fT/CEPN7z9q6Du+RD+J3br4XeN9VfNNDb6l0\nTEEq8oORb+x8qEwo9ENMmH/X1avHmGy0tMnYjZvq2nhCQSNLXqdHQzn5uXb9BG2Dpfibt7MLoYNB\nupe9eiNl82z2Wrh5s1qTotFAnh+uG1pfeRwequc1nenvk4Q1kfU96PWxhfy+mNw3o0n9+zArhslY\nYuymhLF1c7FGnJDx0uu4uH79RPIBSyNCTD6rZ8s50LM3bsGdyODCs88dY6djLl1e7l01jC0AuHp4\nG08/cwjhpvdR2+7g5s3iPsJxSPZ60wnAGh099cxhrUZHPA6P5T3C0eFI6tx7QvLDJHTj79oN+f4a\nDdR/lwSXLV69bvaeunFL/r0HgwnokDpa7Ceeu55+55Zno90qt87S8Nn68OWrJ7h+wey9lcig7fNf\nwPv9TwH3AvbkcYQ3LmE4nC2/+5h6bIUOxsMJZmN1bo8m8n329HPXEW6ke/s4juHYYgnofvnaSebv\nY4LJdWqMrf39/R8F8EsAPgLgtQcHB18mLydeW1lcvg32vuMS7z2ViIkniyUsiQLKuykNiHl84q9V\nN9Qqv2GPLch+VNzLhyK/ruVqDZbLhurLEpo/J+77kMHYMmlULvmP2QGGNc1fuXk875rWYoytntgy\nRlHu8QqTMz8XEz5bVP+eVIbkrojN4PZXNi8t/x4HE2xspRsqXrkpG1ldEbM8tsr4awEyS4V/ZqfF\nK8qGGFs55vESa0ZEEruk7bQyDeJ32zsrj9siHhMgnhp1WXUAlibJ8weaeYLNb0bN49lcTm9f7vVQ\nJvKYdVnz9WbLDBFaNY+fj73xNKhVLZfZqvI55XkmnuvulV6j8hhbdedwHpzBmYy37R6bb1mnUc5y\nXhW0KUoURwjjkPk3GWQDcY+WhLHV1q+tjm0pXbGqhGPLLCrTfjqyn4mlmqIHEUYL+Y2z9/QyGfyd\nL/6etG8qE/QaBWFkrGU8ZTDYtgWHFRGzuoQBGR5blBHE9iV8Tm3aPJ6eCT0WnVM3N6rP45xZ3ZQv\nld48vhpLWfLYYtdj3V0RFXbTovhxMk66fxKPrQWTR2v0z/y1+qzT4CycaTtEm4xMFtoiBv4Ig7EP\n4aRjr1fCXwuQ9z+z0FdYaKbXJM5usi1ZxVOUsVXGY8tzbWnubvqcHNvCBrELSTxbKUO1bkOTLvcN\nM8wgjuN4eV97Vz61fN578GMA5E6QszjdKzvCy+y4zfM87rMlhJCYrjpGr8k4FWBrf3//HwH4SQD/\nFsATBwcHkoj94OBgAOA6gEuafw4AVxb/T3pzfw7Auf39fd2I4u89laDVe4ttprnp7PEsRTJNSPYA\nNckXVlQrCQI0cpwMY94gCjAhGzQTHREBTbJohTg0KK/k0hUAsDPAEpPm0GpXRNNSRPkcEuPhJLac\n1aBB0eDAbLJQm+iMyDt7xHEsyetMGvrTuH/zsvTY7qeYuW5DXyboRtISYsk+lDvWpR5b50r69thC\nBpxpOLYldREylbTGSpMJPQAuRIQ2GS4dp50JYO2s6IgIAC0KllC5noGxF+eYxwPy7ywgMgHxKsHn\n8laLdPCqQcXPawbC54wktnK8t8pEiwFbNPmoYyDP5fLKdcoAr86X7IgIqAUq6kg0HJtOjDiwNb8+\nuz0231qhxNjq2NXN44FFgkSBLYOb7izzeJ6QJbHT94wVYJoC6wAm53IsJVGbzIKlpNPelf2cPnr9\n45WOyc3nTRnIq23i9VJEHWOLdzsDgIDIwjng17R5PB9v1Aw9JEAQTcJqmUIr170pKaIKYlWVIkrA\nI7s+HqGu+EGEyIDEXxd+cj4KY2thHj/yEcWx1MU5mTO0slFL/t03WVffaThV7p+xYanl/HrEmYyt\noT+cy+qIFJEDcKtCbvwxU+cE05JrJlsVQs4Ji0qJZwEHtvJhCro+mAaBVPN4uSHBzI8w80OpWUZd\nJUqnZUtNGkyDdausOOg8G8SUKJAN2HGcYhiorFGqkjCR/+XF2oGtBVPr7wL4FwDecXBwkMUH/hMA\nl/b39+/TvPY6AGMAHyLvtQC8KuO9APDeyl+6ZnDJA6/scmCLdmYyxdjSddKqq0eWOoPFcocczlig\nmltTwBZnbAkrrM3GyGOXALrkZR4mQRTeMXK0ouXqquCMLV4ZpVUhANhtrfYvKhqWsNAhY1iYZGyx\nSngYhxKA3ERXRAC4tHGvdA+HrVQmWFcPzz1Mls9H7F5NGFvbNRhbsbwJEEJICd7EUDIkzxPZXRFb\nLQGX0OU7Tht9d0PLvMtictGgwDftglTXWxAAIklabinyZ/o7t2xzCTgwv6coIOiRabDOhkGZ+3J8\nIJMwxdhS5/J0bNYCtpSOnOw6ZQCOZTsiAlCvMXlo2vvDj/XFCt4VEY4vyUHKdiPm69osaq67lq46\nDqgeWEnUARl4NNkxjINBPMEcjv1l8hJN5ELF+6/+ufJ5s3CG//iF38fvPvUHik9iEkpibuicaKLn\nOHKHTGAOCgZhpE2adYytXI8tBTQzC5yE7PMoSyfIYmzVYGM0nbAmoffYqsrYosAjY2wxVlNTrK0l\nuMYAqeTxYOxjMg3kxgY5jK2z2/Jz3B9oFs6Uroim9kJJBGEEiBhZ24JxOMZwEkhSxK1OWWCLmHdH\nM4UJZBrAp4Vmd7F3pXuIooxL3i3Uc/KLgpTR2yRYBySMLdXXixa06wJbQohGwbpVACMtNkQiPS+l\n+EiCdkUEgJGmM+LW/1+Brf39/TcA+AkAvwngbxwcHOTNFv/74v/fzz7jCQAvA/CrC2YXMAfJYs17\nHwbwNgC/f3Bw8Nn6Z1AtVgJbOZIHY4wtjeFwbWCrIGMLAAZ+Kq8su7nOCsdyYNEhbAe1ARN+rXjV\nMIuxZRJE4XIRbrxfNpRzsuVrE9kycHa2c7bW8XhINFWDjC05YRAK+GNSHip9ru3iYi81lp44qTfP\ncBLIMrWSwZOg5fOMXYUlY6sssJXN2AJkOaKpKmVRo/VHH9jGLEpBjLbdhhBCy9raaRVhbKnm8QBw\nNDQgRZSk5eruVAKeGmhiQMe2S6akOixcLsOm7L0sAMgUY8uxHHldJEDkrePqwL7CwC3I2LpQibEl\nfxaVzhiXSLC5LhlvfKwJVx7rpYEt9nmz0GcgUFhrvqPhkyRaAMvx18vwtjHpD9Ol817DCZ9OEpSY\nxwuWvH/q1qclBj8A/NZnfxvv+dy/x29+5v/Ff/j872qP2WmbZ2dEUSx103IsC33Wxfdk5GcmYjrg\ng4JLNgedM9hgpoJ/XtvNArbMMLaaTliT0P1OYRhVuk8lKSK3sGAMNB1waSL8LCnigrE1mgQYsN8y\nAXE2NYyt5z8kr1l9VwaMpmuRIkaZbC0AmEbjhRQxHXu73XJrLZ27gyhAuyVfv5HpNUkDgkrAVmHG\nlvw+vU9aGtI9Zdo8XsMi7muALVNzRBL0nMxfp5zrIKJlgSmMQsQivafz9g6rpIiADDIfNSxFXLd5\n/P+8+P/vAnjH/v6+7j2/fXBwMDo4OPh/9vf3fwPA9+3v728C+D3MZYU/COBLAP675B8cHBx8ZH9/\n/2cB/MD+/v5vAvgNAGcB/ADmzK6/U+dLv++TV/HCB89IG7oyEbEFRWVsZd+4XcdMi3BeeRVGPLa4\nz4xeYgQAg1nKQisrh8gLz/YwCeeJjwnGFu9eWbTC7+XQNMtGm/0+Y3+KOI4rsz5CXt1n1+ah7qP4\n/NFfLB+/6MzzKx0nK3pOFzcwB3+ShdoEY8tn1d4Zk+c0BWwBwH2bl/H04FkAwAA35puU2EIYxRhP\nw0z5zKrgspXl87wyvwC2zpeUIuZ5bAFAxzPP2OKyPQqYUDBgGs4kcDJhdu62d3B1dF36zGKMLdqW\nOQIQAbCMMLbiOFoycnQNJeh5NSGJ9SwHCdztuFSKWP3c8gDIphlbQgi07BbGwfysqC9UHT8GzkLj\nhYqs+fx8d6/0sbhfV9u1ly2ZjVdd2b2bzOl8bqcVfwDo5FRddcHHrh/5EvgdxTFmfpTbuapo8Lkv\nWe/Wwdii/kfGk1jGLOZsqsHYx3wrFUvSo/kzMT549SN4w+XXLp/77OEXln9/7uiL2mM2wdhSkjzH\nUjpWDsazzOSSMzH4Zyo+hWzvZR7Y4owtR3nNDyLJc22z5pjrddxlQdl0Ep5EEKgAVoy5BIn/pqsi\nzJGKcmCrOcZWhhRxAQLHAG4eysXfPI+t/St9vP/z6eMNTwW2HNuC51hLkMW0FNEP4kx/LQAI7CGO\nBpNlIRgAtjvl1lo+d7uePC6a7P5aC9hi48hdwdii64N5KeJqxtbJ2JcKils1fPiSmJ/TfEw3La+U\nwpktX5+Eci7dc7Pz9pbdgiWs5R5yFKhkDMp2PRnO5cO6orCJWLcU8aWYpwO/AODXM/6jpdK/CuDH\nMJcT/gvMJYz/DsCrDw4OnmOf/YOYA1jPA/CLAH4UwPsX761mVLCIf/qej+OnfvmDCkBVNCIGLKyS\nItIwJUVU2rVbYX2PLe4z42Qztk6IvNKUFBFg7Ca7AY8tRYqov1bbLTPMBUBFxiPL124KiwZnbLms\n8vbVDz+O6OqDiIZ9tJ57CR6+UK7N/aqgaH5iDm7CPFCWRVgqY6shKSIgG8hHCCE6KSNxUKPC4pNN\nqczYkhe3eCFFLJtQUq8k7tsFNOM1Q+cJAblxBp2XjqdyN5z2EtiSQayW7Uny1qzI8m06NOKxRTwT\nNcDWOhlblpP+voORX5lBk+exZQlLe56mGFuA7C1oEcZWnY1d3jkB2QzcMwWaE/DgBaqWR2RHhpNY\n7pvoLoBHxUuTAVtlGVuuwtjSeLWYkrlpEiNgLhGzNZ2OTQJb9JzGhr2POJNBYWyNfIzGPmCFEJZ6\n737g6oelxzRpmEb6uayJa6QkeZZQgIOTkZ+pBFgpRWTXmPubmjaPD1m3TUmKuHiNn0vdpHVDYmw1\nA2xl/U5celkk8oBHFdgye32SWJ6PwthKx9NVDmzleGxtbcrfu2V7Egg0C+f3FGemmoxVjC3RmuCp\nG7clqWLZIhKX99uOfLxmPbY0UsQKHltCqIAqD9pAZDRtjoUG6IGta7fHEgBcV4oIyOdkeu+QN48K\nd7a8jrTDPAD0Wtl7biGEJEfUMba2yL0YxbHx8UdjrYytg4ODUvDcwcHBDHOT+Z8s8N4YwD9e/Gc8\nnrkxxJdvjnDxbLnOFIDK2OKU3nVIERNflmWCLKJaRsOAbAod53U7A5aVeACFktKiIZnbWmHtrnR5\nrAVAf608yy1kZl00Orxz1aIzYtWqOE+CbFu+7c9sdvCz3/a9+MTnb+F592wofkF1QxrDiceWAdYM\nTYRcWwW2vIa6IgLA/Zuy9Z/VO0I4mif5w7EPlPS+SiJrI6l4qUTVahKyFFFdWHpk0TbBqgNkdhNP\n/uk8cZIJbMkgw057pxB7UddcAqFbe46I41jqeKcHtmSPLdNBQVvLTsdMGMUYTYNMlkteqGxVFdTn\nIPmWQUCf/k62S8C6GuuUnLwW80xs261Ka5Rgn+URZpN5KaKesWVbNhCL5fgUjAFU9rx41X8aztBh\nCdZ4GhiRBXJpeRJCCPTajtKYw6QUsUmPLVmKaMG2LLQ8G9MFgDaY+BAQCgiZxBeOn8LN8S2c6cy9\nL6k1wTTU/xuuLjBxTj5P8hwLnZYDS4jlHndA/MJ46Bg91M+Fd91TpIgaJlKd4Ekr3V8lx+KWCXWT\nVrq+mm4okUQWsy2IIrRQbg8pg7LcPH7dHlucsZX+ftdvy8BWIkXc6nmwLbEEHl7/kovKPrFle/Bs\nD7PF8wlY3G45yz2QqQ7RSQRhpEorSQhvjGeeuQWQGnN583h53xEhgOdayyJ5kx5bdRhbtJDlOfbK\nvR71DjN9T4UaKSJnqX75xlB6bATYIvNEk8w6HsKdLueP46kMTvXb+eqQrtvFycJySOexxf0Jj4Yz\nBSQ0FafSFfEva1T1BVptHt+8FBGQq/zCCjGdhbXaQHPz+CxTaB4mGVu0pa0wwNhSWGh8s6U5r/Pd\nPW1yWzW4FBFWWGty41JE11bP4fL5Pr7uVVe0ngR1g7YpXnZFNMDY4l0R/XA9HlvA3FxaYs30jpZ/\n10nEs4AtBYSKLS2LYVVQYJYDngBwdisdezePxkY6HVF2kwCf+0i1jXnJdTKArSIyREDfXAKoD9jx\n+dzWLKOUGcdZLyZCGnu2/H2qMnHVuS/bED8J3lGqTrQIoO8QYKtOxVJiS8dCasgA6BlbuwWBUx58\nDWi5lLFleHMKPbAFABZNXhWPrZJSRFsnRWzGmJzP5zR6mg1wU8BWk10RkzmbMqqGY38+xp3seekj\n1+et2MMoXFovAMA00IP0/BqZSI7UJM+CJQQ2iAdaLmNLk1CFuVLE9Xls2ZZgDOn5a1zavVXDPB5g\nTIw1mscD1bpKSr+RwtiSHzcFbCWAquAMJ8LYusYYW0nC3Gk5eOurrkAI4NJeD9/yxEMKGOzZnsTu\nXjK2CNDZyJzAzmfTSfc1wptgChkc2GzXA7am4UwqeDUJmCT3ktQVscD9G8cxPv65W8vHRTxk6T01\n9UOj8wT/zjrG1pdvcmCr/n6v2+A8kf4+6t6eAluHQ/m8NlcAWz1CYBiukCICzRrI3wW2SkRVTyo5\nYVCBLCtPimiIsQUwBouVUK2rT24x6wzm5DC2aJgEtjwqNzLiscV8wwowtqq0hs8LnnyIBWOraqhS\nxPp+KGWiJ3VFDABEDZjHW8uKW/pcc8CWbdm4p3d++Vi00k1IHWCLVrVckoTrPLZe+OCZ0p+/irFF\nNxJBGNfqSJeEbLSezdjikUjTFGCrgHE8oGFKLTbCddmCSqFCcw73Eanq/ZuXax1PFxJoyyq/Ve+t\nWPFMXC3DNsvYSuc9YUqKyDv3WqsLFUWBUx58Xfe89LH5lt36rojz75H+rXhs1TaPnylSukY67rGx\np/Ms3DaQQCRBfcOms9AIoJ+ElPAt9hPc8Hg48TMZWwDw5wtgaxzIjRRmGVLEdsuWHN9MJOZqkjc/\nwgYphg3GfiaIu8o8XgHS2WPTUkReRKLm9ck1OxlyKWJNxpbkB9QUsKUfu1U6I06ILJcbqnMFwcyw\nhDeJ5XXK6IoIZDO2AOAdX/0g/vH3fTV+4rtfgW7bWQJXSXiWK+0VphopovmuiKrH1pl2up8TArC6\nx9LrpRlbvKNt6MuAeoOm5EneVJax9fS1gQRSvvSR1V6XvOhhErDj8l3Hnue59F54+roMAO306+e3\nHICsaoOki+U8oGEMzqWI89cPx/J5bXfz1WqUhKNlbHXvAlt3ZFSuhK9kbDXvsQXwZGg+UVcG6+II\nHPGlG5Es7xLAXFdEQE5ehRXgeDirNQlQeaWAUMztdNfqQve88lydUBhbdlBrY8q73+WBCU1El3XM\ngBNgMPIVj4uywTem6+qKmEQiDQEA0UoX40YYW7HK0Hj31zxc+vNdyWNLHVN7TELJK6FlIyIyREBN\n/oswO7nfUVHZr9IhbjHnTf2wVjIerpjPAeDtD3093vWCb8C3v/Cb8Nb731z5WFlBmTS0cw1gaJ3S\nyLC5T2PbbhmVWUpzOTGPr8PYCqX5XGPyr5kLd9u7ynNFgrO8PLdBYEtpCJKeR8elayLzESrZuEWX\nHClsoAY8trhkVCetbcpjCzBrFq2b03vkeIPxvCsil40+upPO758/+iKOpscYBXLCkCVFtIRA27C8\nUuc3A0DqEjYYzTLHutZji0oRLQ5sNc3YkkE1ab1dvMb3xyaliOOpWXZJEpmMrQrHouOGNpYB1mce\nnwJbTIpI1gi+T+GdVDstZzk/c2ArkSImofXYMm0er2FsXejJII7oycBWWcYWX5t5UcJ8p8f8roiz\nAuqgDxzITYJetr8a2NJ1mTUVFEy3hFjatFDWFgdozla0IaFBWWgxzF6r5f0k1Osh3OnyPj6eyPfU\nzkb++KNeyjqPrbuMrTs06oFAaageWzlSRIOMLVfpElY9CVIMimNLYjdldZsCTDO2yM1ihwijuBaw\nQK8V90wB9KDQPYYZW4qEyg5qVSF81i3Qc9bbDJW3ghXODDGqj70kaAXIcYQiRWyiGx0NCrgIbwIs\n2CF1EnGdTwGgemz9zN99A85ulZ8bbKkrorqwnWOL8vWawNYqECiXsbWYJ7ZbW0tPMwGBr9h7vNCx\nVcYW6bRXY1Hl87mj82lyWnjX49+Idzz/rYqcy0RQ0DZisrSqnRFVdhMH9eXzNNURMQlqHh+Tanyd\nZgwRK1TwMMvYkj+L5oGmk9hQA3Snx80eb+WliCw5ivzGzOOz5j5ABbZ6bUfx+KkTirzSYNXf1yR8\nsi+Mj+EkWMr0k3jNxVcu/44R46M3Pq50mwqiQCspB2SwzgSLgUvZknOhnjMniaxSE7oGOBTs4ux4\nDqxXYRzlRchkdo5Wipiei2WJ2p4wvXYz9w6NLGZMbWCrtX5gK4wiLFMNrRRx0cGNsMU8x8rtpDfl\n+0Tb0zO2CCtn0kCnVF50uNiXC+TU3gIANmt6bM2imfE5gYbOI5FeBz+IchvbxHGMD3zq2vLxhd1u\nIU9rfk+Z7CKYxSjVddsE5sWsrNfKhArWmVyPMpoxAIAzgx9E8IMQJxMZnDqzAtiiWAUvwABzMJDW\n/kzY0WTFXWCrRFQ1W1eBLXnSPU3GVtUEjyesnN2UZ4hv1GOLTt5WfakR7+DGQ8vYMgxs2ZYtXys7\nqDWxBYwZpXTIbDhUYGvhs1UDXIjjWNro6szjm5QiAsAZwuwQVgzhzWV7tRhbtCuiQzfa8vW/d7ea\nBGyVFPHMVltafOoCW8rcJ/jclz0Wk3lCCIHvfdFfx7se+Wb84Mv/tiQBzQulKyIBS+pIllUp4vqX\nUdr9TgG2qkoRqReaEAoDic/pJjsiAvJcHlvpPTQcm+n0qCtU6OZzLn0tGvz3cj35eCaTWNk7TD4P\n3hkxiZbtlfaC5KzXWThrzGNLlrLIvyVnYGwb9NcCNJ5UBq9VyCTz/HhLjy0iRbSFjRedfb5k9v/h\nax9TfAiBYgbyZhhbemCLMrbyPLZ0wAcFJPg14MB6FY+ovFAZW6oUke5RNnte7cY6XDZluuMZkMfY\nKjeHRnHMrk++FLGJroj0O3MgSAi5iUsS3RXgI5XvJk21PAnYmu8N2pLvnjnQLo7juZk9Z2xtyOwk\nq52CA67lwXPKsaNXychNgiXAavP4OIbUPZDHszeGeO5Wes4vf3SvkNclL3qMGmJsUeBb5/kIAGc2\n25X8OXlsNHhOydyma14gFp6cw0mAwUxea8728wuZtCviNJwp+YVlCalL6V3G1h0SpqSI5eQ4zZjH\nJ2jt4bBagrdKXukUSFhNhCxfmS8+dZJWmkBZmgo/T+4sYWGvc7by8bKC/kZzxlYdFpA8wejM45sM\nDmzBALClk0XM2ETavBRRToATOeI6zOOzktdVQRkqMWLVq8e2sEs8Aq7drgtsyZ/PQaB8j630e/S9\nDbz+0muUbpR54Vl6KSJQz0Be9Uxc7/0EyKCtHwWS50N1Fi6h3a8wxAfMGscDsnl8SMC6IIy1bI8i\nEVeSIlYDthSPLQbOmExiI5CW6JA7R2XNDVU6PXK24Syad+ele3dTxvh5UkS+0d8xKEMEVFBlYtAv\nyNcAdpRlMBj7ODyZSlLEDbcHx3LwwrOPLZ978vCzuDG+qeKqu1oAACAASURBVHx+kojzoEmsCaCO\nAybu0mNL9o3Kmn+4FDGO41ypmxBcHmgY2CIFP8fijK1EipieS13jeEBNwk2DC0Cex1a534/7SrVP\nQYoofWfO2AIkFnYSqzoCUyA4yR9aWimizNiqWlzhkeVx1HU6EIF+jN2zUb54rna09SWmqHlDfAIU\nJx5bbB7P89n6IJchPlLsnJtkN8nNLQhjKwPY2jVUcFHOyWDzmUzPOqSenMNJgJEv+znubebv97jl\nzFBThNm8C2zdeTGoLEWUJ0TFwDYnuTMqRSQb3iTJOzwxJK+sIDEyEZ6GsVUL2JJYC7rkTj6vvc7Z\nXHZa1ZAkI3ZQa2LzGXixbmCLd/ZMGFt1wAVd9XidXREB1fspAbbMSRGpeXx6DS1hVWYJcbYe9+oB\nZAP5uh5b3EeNg0BZ7EFb2JXBuyR4xZJ2UarF6lwhLV9H0E2rH/kS/b0qxZvOfTpmj8LYMmgcD8gM\nuzkLLf0+VX0zKAipK1To1qnKjC22nXI5sGVywy0BW3zt1c97VdZdS1jSfeiHPiwhJL+Mo5oNW9LP\nzjaP5xXyv0yMLcrCTaWIsiRoPA1kYMubS3BeTGTXURzh4zcPlM/PYmx1G/bYspeMLcK0jLPXDM7o\n8YNIYnBwRhAg30PmzeOJvxfz2EoSWirrruuvBajMw/UytkoCW8xXil8fx5Y7M+s81OqGNOZ0DBNL\nHderpGDUYyspftEimM5jK4Y54C71OFKVFE6kl949uvdQ6eNoGVtsTmisSYaGsQXkA1uf+OLt5d97\n223cd76Y9FIBiw3eU/IckZ7LRkcPQO5umsltFWanScZWck4aoDhhbI0mPsYU2IostNz8+4oTGHRy\nxM1e/X1qkbgLbJUIY4ytglJE13JrJ3fS59kqY+uoIcZWrhTRNtjp0eZGuXE9/5ycDm6AaopvWoaY\nBGWrCDvAUY1JIAzTBTmO1E6PTceGxmMLqIfY802u66hSRM9uVnLJE2DhJYytGrJRjWwFgHRudeYE\nDiTp5IjUQP6GYY8t7keVNfd1nPqU7hZjm1gE2KrKVAU0YN2amzEAMmjrh75E8a7M2Fox93FQ3zyw\nle2JVpUFuapQwaWwjuWg76329dCFwlp2m2NsSdcK8nXJZGxxaW7BkJK+hYyHGrcfGqq8ZrFVAdVH\nxaRxPKAm7abklVEUS4XNZE7nPmWA3MFyw52PwYsb90rveW54Vfl3WYytjmE/Hd2aC8iMLQCZnXSD\nMJIS6TFjxXFwEZCLBk16bLm2JReSwhhxHEtdEU0AW5x52ERnxExgqySIwe8B3fWhPnfTBroipmBJ\nrJVO0WJVEg/cu5X7mRNyv7QW8r6Wo/PY4pJrM+eX3Ef8fBxhowU9K6YKsKWYx0czZR41aYqvk+15\nJYAt6mF95Xy/8P6Ps5tMeodl7cf5nJeEMWCrQWZnkDH+ACzUNBGGk0C6T0S0miTQczhjK99A/i5j\n6w6Jpszjs3wvOCOkbngaj63DiswFnrDy5C5Xiug25LEFAFZYi920SorIpVUXug0BW7S6bge1quKS\n8XgsFHpw09F22rJx8wLYqsXYUoxsxdq7Inq2J7VgFq35RF7PY4vKVqg0Ih13dbzDOJDE2XyAzNga\nToJam29VisgZW3pQqF0xCafBK5atdnpvHxtkbDmn4bHFpIi0St0UsMXneNNSROWaE6p8VVBopbyS\nndNua7u0D1USnLXs2pyxZVCKSO6rosBWVaY0HWuzBSuWSgHrMKRp6NrFJ8Er2DuGGVtKV8QGDPGB\nOTMIADptzTUi5vEJsMUNo29OboPHuhhbIT+XBVsnS5YDQNlB0c5o3JCbAwkA85ls0GOLm8cDcz8g\nibFlQoqoeGyt0Ty+5O9XBHhskc6vjUgRkyKSToYIaOVUD1/Ob/4xJhKpRE3QsmRgK45jtBnYzRls\nVWN5HTSMrZ6lX1MfOfNA6ePwIuYsnCnzTlMg0FKKyIGtHNYgBW+6K+SkNBzbkvzemgCBgHTuBpDZ\nRGJ3c74uhVGID1/7Czx5+zOVjqs0mWjCN0zHgBQA3BlGEx/TMGVs2Vh9Pbi6bKQDtqgUcVTdO3VV\n3AW2SsRgHCiywiKxKhESQmg305f69yrP1Qkp0V9MqlUBk1jxmSnG2LKFrSC7dUJX5a8DLEjJnSZh\nvTmWN5ZNMbZodV3YQS3plGweL5QNXNNhCUtKghOT9XoeW/lSRAGxFu8jyUA+8diq5Yeml+NQZpVJ\nxhbvrgbIjC2gns+WOvcVY6ua8OGby6jSOa9FcuE6LBMO1vG5bx1BzytGjF6Xemw1I0XkxQrz5vEy\nWEHbuQ8qblajVfJKNv6qyhAB1TyeG6CbTGJjkQ1smZQiArLPViLT2dpI193DDIZO2fAlKYv829EN\nMTA36TUZ6zDEB9Kqv84HSCdF9GxPYm/TezSJTMZWm8mOaiYRRRlbNC4zORGVI3K5JwcSACbHNy5F\nlGX/fF80l4im95kJ+SsHUNfK2CrJeFMYWxrgkfpsNQJsBdlJOCCvEUk8fF/+HE4lUklzLloEixEj\niALlfE3Jk5fMOc7Yshz0HZVtZkdt7PXOlD6OJSyJzDANZ+i2uCl5Q8DWsitiMcZWHMdS8YoDO6ui\nJ5niNyNFpISALDA/WZd+5SO/iV/82C/j5/78n+NPv/yB0sf1XFuaj4yCdct7Sn+/CneG4SSQiAKO\nWA3qcyniMFDzBloc8IPIqJcljbvAVomI4rjSRMDNhnUsBV2Cd2nDMLBFNqhLj62qXRGZHEc1j9cn\nrJtecYppkdAZRNcDtihjS709ro9vSI/Xxdg6HE4ro9uSQXhsSZWHdcV2K12wEx13HcaWzzZprmNh\nxuR6JsdZVlBWZQJsTWdh5U14FvU5IACUU0NiyecenRTxHAO26nRG5B5eqr+g/lw2Sra2zgoKfLte\nOmbqAMWKvHLNnnUA4LHfrdtJx/qgahdBsQIEUjy2DDO2HJY42gYYW6tYaAaBLUtYEjOVDwujjC3i\nsWWJolLEisCWJEWcnwOVAg4nQa7EpGjkSRHvO7+Bhy/N15ArF/p47IpZNrvryH5BxpJYzix2MqSI\nIoJw0mMmjC1AZW3xKMLYilFfKhZqGrYA2ewFALjvvDxHUB8mztjSyTMbNY9n481h+yLaoQ0Azm7X\nt9CwLCGdZxOMrSwAi+/ZV4UqRVTXOQpsVW3wkRd5fkAAlOTccyxcPpd/v4yClImSFNB4UWWq6f7K\nzfSrRhZjy7Uc7LTUea0fn6u8l6WA3Szym2UCBSoIxIEtytiUnvdlvz0uL1wVtFDQFFgne2zlSxF/\n5zN/sHzulz/5ryrtx6gfXyO+YRn3lHCnGE0CBEjXFddaDWxxL2UtY4uxXpuSI94FtkpGlWr4Ko8t\nYE3AFt3wLqoF01lYqTq5Sl6ZxdjaNJwMtTTJUB1gixoW65gYj+4+LD0+191T3mMiaJIn7BAzvzq6\nTWVsiNbP2AJkPx7hzTcWdcwDVSmiJUku68j1ysSZDmFseVNgkUhXGYNxHDMpIjWPb4axFWikiJyx\nVQfYquovaMq/iQJbjpt+l6regoDKmjiNrogdVh1rddLrGEZx6cR8VTMQQC1WbBpmbPGufZTBYsJj\nS1eo4MBrHWALkFlbMWIpia23LqURxzFisim1C0sRK3psUdlrmABb8gbVhIG8rqtWEkII/NBffQl+\n4YfegJ/9/ieUhKluCCGkRNa0n04SOvP4+QvyWkiBrf4KyW+RrohA/YQv61z63eyk5z4GMlBgi0vd\neNc9gBV3DHtscfN47tv2mWeOpMcmgC2AJaxrZGyVBZ/5flPrsUUkYKakejRy/YCQdkJP4r4LfQmA\n0IUkRVyso9y2QAdsmWZxKh5blqN02gaAs+49lY/lsW6PTXUQjONYW5jgtidZclh+H6zqbMmjOcaW\nnkWcCWz1W5gEU6Vg/OnDz5U+dlNgXZbHWxJzxpaPEOnvyIFfXbSdllTY0wFbfK2oQ27Ii7vAVsmo\n4l/CKyU6TxZdZ62L/eoTmi5Uj635wl5lcClynIIeW8blK8YZW/lSxDdeft3SI+xV97y8crKwKqj8\nYG6iHFf2MpEYWxBwrNNgbBFgK2Fs1ZFXrpAiNu2vlQRNhIWIl6BdlTEYRjJkQgFIU6AdBycCjRSx\n23akhbuOFJGzm9yCXRG3Dc0TdGNnOel3ORn5lRkAHAw5FaCYJbu2J88NZSthRYAtOse7lmNELkqD\nM1OcVnoPVd+s5jO2jqbH0uPddr4/y6qgv1sURyyJNbM5DaNYqraqnUb180PV67XKPB6o7tVJw89g\nqybh2Bbuu7AJ12kGSO5KZutmkiPOcnIzgC3hyMejjNVVxcBZqP+uphNzna8lMGftcJNoYJ5scjCI\nShGLMIIowGm6KyL1DHMsC2e25Pvj008fSo95wadq0IS1jm1BVmSta2FJ83ieTOuAR1mK2ARjK1+K\nCNYV8YEL+fuGKI4wCqjH1vya8kYzs2iGjiePR1NG68txrPHYOr+xq7z/Uu9S5WPJwJavgt2GwDo+\ntpYeW678G2aBq3yslWVsUU8usx5bGV0RNfLrfteF59q4OriuvPbeZ/+s9LG7DYF1y3kvS4roTHE0\nmCG20mMW2TtYwpJ8tlZJEYG7jK07JqowtvyQJUIFGFubXt+4Oa8kRRRYSk+qVFsjxlrgCfO6GFu8\n0lLbY0vkM7Yu9M7jH731x/FTb/5v8R2PvqvycVaFxNgSMWBFlYGggCTicWytrGg1ERJjywkAK8Bw\n7JemxyehepfI5vHemoAt3uAhkSNWoQ5n+bEA62VsAfN2y0mYlSIWM483xdiSuqay6m7VRVUtVKyf\nscV/n9iV5/CyBZhVXW6BNAkAgN32rnGpL2emeB0CbFWU7ND5XFeoOJ6dSI/rM7YYsEU33IYYW2EY\nS4ke7+y4DvN4FdgywNiibNWKa9SN8U2877kP4WQ2KP1v+7QVuaENt8JyWkoR5fWJdkQE5E7Cq/aB\nmYytltkkljOmnBWJ3r17GxLwATDGluKxtUKKaNo8niTjji0U37ZPf0lmbJ0xxtiic4JZltOcOaMH\nsMoztjiwtUqK2IDH1irZFFvT779n9b1Cy4dJIs7ziFk4U8ajKRbnEuy25KKLJSyc2VC//4Pblysf\nixb+Zw16bPGx5WYwtrLGYF3GVlMgUJY8XsfY2u3P54/nNMDWh69/TNslMC82GgLrsoDVZbgzXLs9\nlu6tbsGGb9Q/e+APldcVKWIN1U5e3AW2SkYVxpYCbGk8WTiwZVqGCGhYLDU6I66SImZ5bJliYiSh\nAFtWiOHEr2GUmu8zAwA7nS0878z9jXo4tbkfysJnq0rIHlvCuIyjSGy1ZFNM4U0Ro3oHN13CIHls\nnYIUESAG8pWArezEQQK2GvbYAuTq9DWDUkQ+9zUvRSQJuJDPtSqDwQ9WFyqaDp7sRtZEelwX2NIV\nJl597yvQczuwhIU33/dEqc8vEm27JYEybjs9h+pSRAoAaRhbM87YqgdsqYwtujk1s+EOoggih7GV\n6bHlVEvMaZEgZWzJ625dYCuKY6nqz/2OeJzMBsqY/c/Pvh8/8ac/jV/6xK/iZz/0TzOZTFmx3Uvn\nClMSCZVZPD+vdsuWOgYKRYpIGFsrgS39d+WMrfrAll6KCOgTvYvnNuC58j1HPXaUrnsaRlCj5vEM\nSO13XYl5Rn+vjY6rgHRVoynZFJAv1+RdLVcFBXLang1Lw/RvvCviCnYJmHn8A/fk7xtGvrxOpowt\n7rE1VRiE3BOuauiAhSRf0sl69zarkwGojHwazpR5ZzQ1tCZlzHNFuyJygI0ynYvExho8tuhc5NiW\nAvQmHRGfO1GBrSAK8L7nPlTq2I2BdQsvtGwp4nTuL0jurY1WsYZvSdMTABhoikt9VgC5y9i6Q6IK\nYytQGFvqz84lQRc3zMoQAR2wVb0z4ipT6LV5bNmqFDGO60xu+RX+dQWXONbpjCgxTGIhmeSuK7YZ\nUCHchc9WxYktCFQQ6FSkiC1ZulQH2OLVLCrDoMyqLPlekVC6ImYyttIk+PbxtDKzblVXxKYZW1Ri\nEEKeE2YVGQCzUP6c0zCP77ldCXifCbkaWHadWlWoAIArm5fxT972U/jFb/4f8ep7v7LU5xcJIYTE\n2rI8E1JEysBVr9MbL79OesznqbJBfbwiRHISa4idEQQRY2wVkyI+sHlfpeNxOQswT8AsUtipK0Xk\nSXde8eVff/y38cN//PfxI+/9hziaHiOOY7zns/8ev/KpX1+O46uja3h/yURC6vRoQFoJqOtUwmCw\nmKeX4rHlUfN4Mx5b45oJX1byCugT8ot7G2gx2WiWFNGxLe01l+T4poGtSAa2hBCKHDGJJGk1EU2w\nOJPIA/+CklJEKr3T+WsBa+iKmPgBFTSPP7eTD96PmDyqswS2VI8t27IkYNaUFDEBVCmwkOzL+h0X\n/lP7y+dnn38+dmp046Rztx/NYAkhzQumQCClKJtIEbl5fIZclTOSujUYW34QGWMPBhldygEVzE+M\n4788uKb9rPc/9+eljt1rTF65otOoO8M08KXxudUtBmz1SUFGx9hKCghJ3DbUUZnHXWCrQLQIMmuC\nseVqEiFOnb/Ub4CxxVgsQlTvjLgqYU2otTzMt4jnUsT5OVWWIxLpig6AXFe0uVlfHWCLgpCxdUqe\nQAzY8up1RtTJ9nzWFXEd4dqu5HdUj7GVnTgEhs6Ne9/pPLYAOVGJUZ2Gr4JADADPYmyZ8tgiVPwg\nlq9J1c1Pkfm86bCEJSW8k0jeRJQHtuSNaRZbte220W+Z6Vipiz7xFhJOuukxMZ/rChWvufeVeHDn\nPriWg2975O2ZBZnChyNgT9gUYyuUPbY4WK1jdD668zAu9Kp18NWZx1uWYEBQvQ2qrylU6CKKIvyb\nT/w2YsQ4mp3g1578LXzg6ofxH7/4+8p7f+/pPyrVjYr6f4yngZHkiIMx1AaAJmNcikglHSu7Igbr\nYmwRkNgS0ljv6xhbexuSuTiQ3RVR568FNGweLzVqmR+HyxGTSGRGJoKyUUaToIa6QI1cYKtkIYcC\njzoZIgB4jQNbqmyPBpciWitUFONALgAljC2dFBGQWYSmpIiBjrG1mMPbng1cfwDTT70c00+8AuLW\nldyuo6uCsm2ni7m70zIPbPF5LqsrYhY4rUoRy3ZFbMYUP69TL2cfJXOHzmMLAJ4ZflnJm/OCnlOd\nTus8/FUeW+5UYULu9Irt+WhBJssOgAK1d4GtUwy6aJ9USlgZu6lAInSpAcYWbxFfh7HFF+OinR6b\n9tgSljlg6zS6nSXB/VCEVUOKSCfTWKyUeTQR21yKuPAEqsrY0kkR/VOQIgK8M2LisVV+Yc3z2PIJ\nOFnn3Ph96mdIEU21heayPQ7KZbHPTPkLUuBbAbYqMrZUBu7pzBMU/Bv4A7kAU3L+4yb/OsbWOoJe\n98hOZSNVN6rUT0Unj+97G/gfvvaH8Svf8vP46kuvrnQMGlSKGDOPrdEkQFSSNaGLIJQZWxys1t1T\nb7zvdcpzRUNnHg/IQFDdroh5Mjcag9lQYpl+5PrH8Kdf/oD2vc+NruETt54s/B22uG+YAZlE3pwu\nAVvEPL7rdKR5urrHljzeTUoReddKrRRxbwMthbGhlyLqZIj8OKaliH6oSl/PrpmxNS8aNWN2rbxW\nknVNgUfu15aE7LEVGQXpgNXsko1eupd95xMPrvy8kS8zthKPLR1jCwDrlGoYBNIwtoQQ2Oh4iI7P\nIhrsYnujVcvyhHdFBFi3PVOdHjVdygEoTSWyPbbS7yGQzRDMih6bf0w1/8iSIuqOmSdFBOZyxBvj\nW4WPrZ6TYbAuy7fOnUKwpgy7G8WALcrYGvojLZBHiwR3ga1TDMpeqGQeH/HkbnUidK67V/o4q6JJ\njy1XkwjpEr7mGVvzG7IKsKUYKN9BUsQ6jC2pg2UsToWx1XHaUvUo6R5YXYqospsoSLMu83iAdUY0\nKEV0JSliM+bxWVJEvpGtCizMVrCbdEl4392ozZxJgnpn+Apjq6oU8fQZW4BcJDiaHctmoyXHH+9y\nm+WR2HRQdkpAfMOGY78U+2YZZPOWxUIDYMwvUTaPj7FBwIsYZhKJIJQ9tvg6y9extt3CY7uPVD4e\nBdL9KFh+PjWQryvdUwCgDCni4eRYee7Jw88u/35w636p5fjvP/1Hhb8D9w2rC9YBmnXKSb9bN0OK\nuOH26D9ZWQzM8thyHVta62t3RZS6CMr3i9Y8/mxPYvQA2VLErES2UY8tTRfObCmiQcYWk1mZlCPm\nyTXLMt4o8Kgz9gdkNQsA+IY7I65Kwi+db+PxB3fxxIvvxdd+5WqT9VGg99jiBfIU2ErPz5QUMdQY\n4tM9Hc0368gQATk/SooSzUgR9cCWwtgK9PtNCkR1Ws5K5h0Pfk/VaSBGQ5IisnPhLNXdzTZmoY+b\n49vL5563/YD0nqsjvUxRF1xKbpTxjWyPLTj+UlGTxE6nWKGZdvONEWsN8+8ytu6QoJTDKlLEvKpd\nVuRtwquGIkVcDOwqMgKeCOlYaLySLCAkqYmJsIQlJ/oJY6vKdYp4cneaUkTG2LKDyrI96o8Un5IU\nUQghd0ZcAFtGpYin4LEFyGDtnIkWN2oe79Qxj+dSxAzGlqm20P4KPyodK9KUvxagekxQz6VZxiZr\nVaw6p3UFlcAeT08k1sSgJGOwCAN3HUE9tnxMgIX5exjFmMzKXa8gCkH3x+tgoeWZxwNmNqcB64rI\nwWFaNQWAN9/3+lr7CV4kSAoI230KbNWUIubIsGkcT0+U5yiQ94bLr8WL9x5fPv7krSfx9Mmzhb7D\nVk9OIqsWkmio50UZW6TQQ6SIVM4BqNeTRxZjC5BZW3WT2DzGFvfYssQcDFHM4zO6IhaSIhrsihjH\nsbYLZ6YU0SRjq9OMbArIN4ivI0XsZEgRuaG+aTniqiTcdiP8wLe+GN/1lkfhOqvXrBGTIi49tiy9\nFLFNmIQTQ1LEZE7QeWwBwGNXdrR/Vwm5o+0C2CIgpSmwRJEiLgB8nmtkMbbo3MT3nkWCM0arNqXi\nIYP5nKUqj5kzm23cGN+Unnvh2edLj58bFge2GpNXJtcgS4ooANGV19nt9pb2vTz6rCij89miwNZo\nGijdV03EXWCrQMjAVgXz+Gh1IvSND3zt8u93Pu8bSx+jSGQxtqps4Lh0RcfO4slR39toBLCjyWsd\nKSKvJpyWHAeYM5yksIPKFeRQYWytX4oIMDlibSmialYpe2ytD9iiFXVhxYDtY1Bhw6BUvRphbLGu\niBkeW7wCVjUp8gP536mMrfUBWzFkb6Ky7c+T4FLEIgzcJmKT/E7DYIReNx0vg3E98/jTmiOUwgdJ\n+MtuwgOle2Xz87ngwJbCzjDD2JKr/fIxHt19eOmLdaF7Dm+qIUME1I5h44UBM2U4DSf1PKmypCw8\njjTAVhKWsPDY7sOK7PLffPrfFmL7KYwtA1LEkK1T2VLE9Fg9lhzYlq2wuGhkMbYAoEPGX33GVnou\nvDjL2QsbC6DLtixpLpnSrohS170sxhY1jzcncwsjKlJO19qzW3rzcaMeW00ytnLWtNLm8dRjK4Ox\nxYFL88BWfhI+yQF1dTEmUkQBsVRGOJYjMT21UkTD5vFyV8T0ON/y+ofw177mEXzn1+3jba+5v9ax\nKGCXsG0lxlbDUkQhZIVIESkivz+KBPe7qmIZpItAI1dOYm87nRO6LQdbPQ/XGbD14NaVJSsQKAts\nNSyvzGrIAMDqyszo7U6xPfkG27/pfLY4C7EJ1tZ6HJb/kocsRZxLIspIF1R2iZoIveX+N+H5Fx+C\nbVm4aFfrXrQqFHnWomIwWhilctp4XvAKv+6cuJzFZMJKo2W3UsrjwkyySiVixpLwO8k8XtgBhpMA\nfhAWqkzRiEjLe8RCMrBdZzTJ2HJsCzMK/qzRY4t7oAh3VmmzqkgRMxhbdc6Nszs4SzEJUzRoxWjd\nKsDYMihX1jaXCObHrLoJ5+fklbwfTcU2+51a3XSMlAX2pTkCp+cvqLuXYn++gRyOA5wtVjgEoF7f\ndbDQVMaWeTkBB7Y4g7PvbeB/eeuP4ckbn8W9zmVFZlM2OINoMBtiu7UlSRGB+VxOu6mWCc5WzWK1\nH02yga3nbT2AjtPBA5tX8OjOw/jU7U8DAD59+Dl86NpH8bLzX5H7HfpdD0IAydamLgsNWMHYalFg\nKx0X1Dg+iU2vr61+AyUYWwaliHwPwZmJNNFsuTaCBct1Nks/Y1Kg615THltZjVrW0hWR/VZVimBZ\nkeuxVZaxRRiyRTy2gAaBrYwkPA/U1QXtithx2stCuxACLbuFSTjfl850UkRTIJDGEJ8W+Bzbwpte\ndsnIsVRTfF/a29XtlJpEntrAc6zldcxmbJH5r1MeluCM0SoEFB5xHOeax7/qBRfwoSev45kbQ3zL\n6x+CZQlcH9+Q3rPXOYsLvfP43NEXAMx9H4sG34Obk1eqjEEeCrBVMHfnhclVjC1gDmzdcya7cFMl\n7jK2CgRdpMMoLt0dQ6nwa6REQgi8/OKL8JJ7Hjfm98FDlSKm36sswMC9efSMLfk8twwZQvPQMbaq\nUFGnCmPr9MzjHcuRk8uFd1gVdp3ExoitU2NjSMDWQrJnymOLd0Vcp8eW0vHRnVZjDOYkQU15bBWV\nIlbdAKkdBLnRdbOMLQ5s0TnPFGOrjjS0TnDvHbeV3ktlpYiKDPu0pIhMdpU0mQDKJ4BKR841nBNn\nbHGJhInNqR9Ec2boInTzwbneGbz2yiuMSP+z5AWc4VQHCNI1A9GFToqYxONnHwMw30u965Fvktjh\nv/GZf7cyCbYsgc0uNcQ3bx5Pz0uaY0kXqo6rgit51zHvvLpSB7R6Y4/Oly7bQ3CT6PsupHO41DlP\nYmytliJKHltBVM1nTxNZifjWhgeb+YcJQAFx68QGLxoZYHEmkdsVsYR5fBTFmFKPrQwpIn9+3VLE\naVBuzqHAFmXSAECL5Ec6xpbxTns5nW1NhQJsRTNJWB6cWAAAIABJREFUAj0Losr7IBqqFNHS/p3d\nFZFKEdPv9xc3PoF/9eR78NTxl3KP7zqWNBZNSBHDiM8RzFew4+Lvvful+Ln/5nV43YvuBQBcH6XA\nVttuY8Pt4UI37Ub83PBa4TmMg3VV7HV0oeuK2HPlYoropkwrF+3Ce9wN985gbN0FtgpEn2lpT0rK\nPAJuzHtKjJksKSJQfhPHJUa6c+KJxKZh4/gkJH384pyqMWbuHGBLCCHJEZOKbpVOTRHriljWmNFU\nUCmisGLA8SsztugCaVsCELEx8KdscIBBeFMMx0HpTbjOEB9YVI6kroh1PLbkMR3G+o1oy7WlcVJ1\nU8e7LrqO/N1toZ7LlsHOqQpbhcx5VaVTvBmId2oeWwxQbaUbhPE0KMVw4PP5abE6lXuJSLTKzukq\nA7f562QROYtOimgC2JoG8pxZZz4oEhxUSTarPNmvYyBfWIo40bcQB4DHzzy6/PtC7zxef+k16Xeb\nHuFPnn3fyu+xRcC6ql2IaagFmHR8pGMjgrBJh0BbBbbyOiPOcoCtzZ45g3+a6PH54cqFPs4sWE22\nJfCdX//Y8jUKeiVzbhzLBeJs83jSZRRqslk1stjRlhCKz9bWhmd0z95tSGIEmDOP5743WdeHM7Zm\nJX0QV8Uqxlbe2NeFBGy5MrDlaYzWKdA9nYVGgDsdY2ZtwFboK95NJtakvPnble7/LGCLMLYW3+/Z\nwXP4Zx/9JfzBl96L//Wj/8fKa13XMohHXlEiK6gUca97BkIIXOilwNYknOBopjZA0UW37Ugg+7Ep\n37BAZQyeactebnRsduzibKoNBpAN7gJbd24o+t2SA4zfIN4pVfhVYCv9XmWrrYocp0DSYDJhpSGx\nMuw6Hlt3jhQRAHYoELTotlfFZ4vKjASsxhiBq0JlNk0wHPuSuX3R8JnxK2cerVOKyJmIwp0iiuPS\n1PWszmCKR1+NTZAlLMlLIouxJYQw4sWgslULeGw1KEV0vPQ3npny2LoDuiICMrsJKAdGclD/1HzD\nvGzGVllga8rBunWbx0MjRTQBbIXyZ3gNg/iKb4afBWxV36AWbbCT5bG11zmjdJL++ge+RioOPX3y\nzMrvQc/puAHzeFsnRbQZiOCqcs48YGsazjKLKFRCdzSYVVprk5AZW/L1cWwL3/+tL8Y3fNUVfN+3\nfgUunUu/LwU/ksR2FkSSnUUWI4gfh3uWVY2sLm6AKkc02RERmK/r9De5E83juSolC9jK63ppIlKP\nrSwpYnWPrY7C2ErvleRzN3vy/qGqwoCGv0bGFt//zMKZ0iTDhOQ6S9oLrGZsxXGsNY//2M1PInHC\nO5kN8OzwudzvwC2D6obC6iywf5CArc4ZAJCALaC4z5YlhNRt9tgAWAcQ5iYZf12nqzQsS2LDKc76\nti1bktKfaKSIbc+RmMR3ga1Tirr6XS7zKOuRZCo4oEZlOaWBrYhv2NSJeUQWEaA5xpYsRZxPkFWA\nrekdkrAmsdvZXf6dAFtVqq60g6V1ire8ZB6PObMpRv1Oo44tMIvkz1ineXzH6chMqEUyXtbAMkuK\nyA3evRqgnRBCApOyPLYAuStL1aoyB4FajLGl29A1KUV0XQJsVay+3inzed/dkEDK0JZbmZeZA2fh\nncFWbdtteUwQ8/hByQRQuZ/WLkWMYVuWtIkzUR3nTLSmQfyu05FkfQlja6PrShVls1LEjK6IGR5b\nT1x6jVKw6ThtnCcykNvTo5XfY6tHGVsmqv7Z3mGdxfwqOLClY2zlFAVjxJIMnwatjkdxXEteGbI1\nl8e9Z3t45xMP4QX370rPS1LExZzLiz5ZHk5KVzVDPlsqGyM9H87Y2u2bkyEmQQFvU945AOAH2fKp\nMlJEbpRelLFlXIqoYZdIr8dhZnFOF3lSRJpHJMWDrZ75hhK6c+JMelPBLTmm4QzbfXMS8iQUKSJl\nbK3obDqZhRITM2GyPsUKETfHt3O/A21gYUSKWJKxNfRHuD05XD4+1zkLAJIUESjns0UZgycGxh6Q\nXgPKynJtJ7N4st0utx+nnpw6xhYA7JCCy11g65RiQ6m61vMvOS3AhCf7dGDfLjm5Bcy/RGegPGRo\n7Z3O2OIJw2lKEQGZHiq8MYAYRxXkEbT/D02E1x3cgHBpIF9hs81bj/sKi2F9wJYQAn2yKCQsk/LM\nTr3vh+p/VK+6R+V/WV0RAdkDproUMb9xhtY8vqGuiADgeOlvXJmxxaWIzukwcG3LljYRgZBbmZdh\nB/khB0tOZ+4TQkg+W3aruhRxVUfOJoKbxwNyK3IjwBaf6xoef5awpK58yWbVEkJiNBye1JAi5pgP\n06CMrRfvvRB/64Xfhf/6K74HT1x6tfb9tJhyOD3UvocGlSKeDOsxnAB5nbKEgEWAwCWY4zAQgXdE\nRj5jC8j22TIp+/ClDmHFU4eWS6VIemArq+seTyZNGcjnNWo52zBjC5DnBBNJeBL896HAUxm2G78+\nnQxGXdPAVgKYCJH9uWXkiHlSRJpHJJ+5tWGesaWTVzZViOX7Hz+aYYc3/TDiJah2KU/CJfc/Z4YD\nqndUsu/kDNtbkxXAFgWBStoF6UItjOXnTu999s+kXOtS/yIAYKe9LeUjV0t0RtyUGFuGuyISYotj\nuZn5+ZmCHRGToD5bCbubB12Xbp1MtO+pE3eBrQLBJ2/dzZkX3BfgtJIGW9gSsNFup3/fPq4nRdRt\ndDiLZj2MrRTYKutxVOSc1hln2oSxZcWAO63G2EJ6XuIUb/lNDmwtAKAqFFtameTG8fPn1gdsATpj\n/ArMTr7ZzpAi1vUPK8rYol4go4rAlgKAFzCP5wbidaLFuovajgHG1h0iLQdk2eYMMkO2TMJ0J819\nNIl3vPQcyoJC3Dx+HYUKbh4PQJITNAJsrWGuo8AWlRdQA/laUsQcoIEGZWxttTbxor0X4AVn9iVG\nGQ0q5z+cHK3cE1CZTgzgeGjOcJ2z0HpLxpZ8DC6RAooAW/rffrcvgzJ1gK28DmF5ITO25p8xYV5M\nHS+LsSX/ZmU7+2VFHpCqSBEbYGxRQNgEWJIEX5so06oM241LEbOAx1bD5vHhCikiULwzYhzHkhSx\ny7qPtiTG1vw+4bI9I4wtrcdWQ4wtW2VscXmlESlinseWTYEt9ToOGAjVa7sY+WPcILI+ALg1yS9M\nUMugwah8/seDA8F5c14YhfiDL/3J8vFOewsvXDQzsYSF80QmX1SKCAD9HpVXmpknlgUKCVjNZmyd\n7W2X+vw+KbaezPSdfCm4epexdUpRV0fOOwieVoVfCCElxW2yft84Loearup2pou1MLYWwFYYxcrm\naVUoPjOnLEU805EN/azWuFJ1JY5p1fj0zsm1HClBShhbVTZ2fJOtmJSvkbEFyImHWMinynYxyfIp\nUM6tZiJLGV95NH4jUkTOVnW5ebyum6q5MepZ8iaOAltVuwFxw/3Tms8BWaI0CuXq2LDENeNz32l1\nRQRks3LLS+eGshsghbG1hnNaD2NLPq/WGoAtek2ovIAmfnU8QPI6wiYxC32Mg3SfUgQA326nwNYs\n8jEOxjnvVjs9VmFI06DJEQfrloUDLkXUMLa4gT9f34oytm4ZA7aKM7/p/nkW6BlbWV0R+W/WnBRx\nvYytLcIuMeWdA6i/D/Uuy/Pf4lHcPF6+Pv/ydz+Nn/w/34+nrmZ3Ly0TfiFgq9iY9iNfasLD7zNP\nw9ji/spVPG55rLUroqV6bDm2JTGBmvfYove/DthSGVtfGqh+iGUYW2FU3uOWR9FOvQDw59c+ikMi\ndf+6h5+Qrul54rNVVYpozGNLJ0W0nEy5e1kFBfXkHBRgbJ2MfCOdOWncBbYKhMsG9KwkY0uVrpwi\nuEA2wa1WOvncKglscb1+EdbCqqpj1fAUKeJ8M1k2iVBZC6fsscU7VbTGBqSIp3vL00nS3n0OonNS\nqQrGgS3OYlhnV0RABhiEZ9hjSwG2ajK2REHGFm0VX9U8nn1+m819TTcy4B5btlu/KyL3DTvN+Zwy\ntoaBvIkoM/+p5t2nCNaRdSJ20vnu5lG5NYr7hq2DhaZjbNHOiCaALUV27awX2KItvE0xT4p0oOKb\nZFoZzoptxhJf5bO1ZVim4+ewnDZ7Lja7LgSTIrYLSBF323IVPSu573ddKcm8XUP2weX/RSKKIzhk\nzs3y2CrSFXH+HQx1RczxBHro4tYS3NrouHjBA7JnmInos/umLrskCc4yaRMmXJnfjq/3WVJEx7bA\nl/AvPHeC9/zx5wsfKy9CDbuER1HG1oiB2nldEZPPdGxLKkyYMY/XeGytzTx+vnbQJhl1u6UC+YWJ\nVebxvAC80XYVfy2gCLBVr8kbD4WxlSFFjOMY/+npP1o+dm0Xb37oddJ7LnTPL/8+np3gw9c/Vug7\n0HOa+RGmBrqOLnN3LkXMUFSVJaT0CXlh6I8UYg+gFgtMgKs07gJbBcKxLckkNatlaVbcKVJEQK70\neWTOu30yLeVfUMRji8c6Jm8hYkBUA7Y4YHnaXRGVFqzeuFJXrZhsCuwMyca64kVnn7/8WzgBvEc+\niOvD/AVLF3SBdB2hSBHXIc+hITG2HB8QUXkpIjknASznHPNSxKIeW7IUscrmW/UXXC9gwj0mLLt+\nV0Tuu3Oa8zntyDnwh6DTcBnGIGcFnubcRzsjhtYEWHR1vXUykTqprQqFhbaGsadjbPWNSxEZY2sN\njMF+hm8GBbYGo2odbgGdFFFNIk5mHNgqwtiSAaDDaX6rddNm0XnyPduy8F+9/XHcf1FOsLmpNQD0\n3C7aRFZ9nhkSTwP99xRCSNXxelLEch5bn775efzQH/4YPuL9GqytOUsh2TsXlbqpwJaZyr5iDE2O\n49gWfvS7Xo6/+bbn4ye/5xWZoFud2GTskqqFIx6c+UAZW2V+u0nBrohCCMWqBQCuH5rxzfE1sj0e\nRRlbvJkVv89algpsAfKcYMY8PgIQMyliQ4wttv+ZRvPvv03mhEMDUrCsvSvAgC0dY4vtk7ttR9vB\n9tbkdu4e1DSwVZSx9fTJM3jq5EvLx09ceSU2W/La9PjZR6XH//en/rXE8MqKTda4zgRrS9e8IE+K\nWJaQwrsoD4OR8h6T3o+6uAtsFQyPGmCWZGxFinTl9BIhamLnOOkkEcflUFPOWtCxm9724FuWfz9+\n5lHldVPBfXQSan/ZJIKf02myFoB55bbnpj4AojUuzQICgBhk0TllYOtrr7wRD27dv3xstSY4CP+4\n9OfQRMjReWytWYq4xSZ/4U7Lm8cT3zDHsZZsJg4+OTXPzZY8toqZx4dRXMk/gye6npt/TxVJVsuE\nazmSr6CwKWOrYhLO5vPWinNqMigDMkaMXj/9bmXmP85W9U4RrOvzjZQ7P48gLNfVzedM6TWcE2Ug\nJiBcryNXXasyBZPgIOQ6pIi0ScE0nC0r/zTpq9rhFigmRawEbLFOvKsM5LkUsW4leRXLaf++Hbz2\nZeel53RSREtY+IYHvxYCAue75/Dai6+UXs9L7neIz5Y5KeLqfcQ/+IOfxyScIhQ+nHu+AICYx3Op\nWxYjiPmSmTOPz+4eCMxlTa96wQWJ2WIyOIBqymeL/z5VgS3KqBNQvbRoPHCPyvQwJRkNNbI97qdX\nmbGV47HlR/6yMLFpGtiKomXxPQlHrAfY8jXzthmPLf3eFSgAbLF9Si8D2JqE01wpeZ+BQHU9qRTw\nO4Ox9albn5Yev+Xh1yvvudy/iDdceu3y8dAf4Vc++esri8V9Pk/UPKcoihf7kphJEd1MKWJZb2zK\n2ALUdRvQSeTNGsjfBbYKBgWjyiZEXLbnnqInC5Ui2o58U90qYSCvyCs1ScMbL78Wb3/s6/DGB1+D\nb9v/KyW/afHg7BxqIF8mpkF5FlrTIXVGbI0rJUZUinjajC3PdvG3XvRdcPx0Ej22nynNBuLVYy7P\nOU2PrfkXmFaQwuoTB+NSRIsCVnnm8fJxqhjI08+PY71k+YlLrwEwH5vf84K/VvoYeSGEkBmdFNgq\nWaBI4k5ibPGGDJ2eGWDLOcW5jwMWgsoRS0jmT6MrokW2VBFUjy2gPmtLmQ/WIUVkflaJLNBUgp7X\nVSuJE9ZleaOAxxb3Bzmc5FfJXceWJNi1pYhSAUafGI1maUXbtdxMj8E3Xn4dfu71P4UfeeUP4Fxn\nT3otL7mn5udlmwTRKOOxdXt8hLGf3qv25i0A8wLJ8WhWWIrIPbbMmcfrG7WsK7iBd3PAVjUpIgUe\n2y0bVo5lwHe99VF89VfcK3+Pimsrj1S2l35ejwFSk6DYmOagSJ4UEUhle7QzopGuiEGkSCub8rTk\n7P5knqCA7fHIrw0Y50muywBbAgDsENdGN7THuZljIN9n62wVEgCNImsSADx5+Nnl39utLVzeulf7\nvm966K24p5cWMT5560kc3P5M7nfgjK2Tus1MlkAxU5FlMLYs2NpCS17w/dtAYyDPG3LcZWydUphi\nbMWRyF0kmg6a8NMkDyjnYVIErPNsD+9+0dvxvV/5HYpflMloMYPEZBEsa949mcnvb58iEyOJXdoZ\nsTVfmMskRnEcy9UunD5Yt+H2sOM/b/k4FiEmBenkSchSREvpwHmaHltAwtiqLkWkUhzT/mG0Ophv\nHi9vFKoAWxK7KRZwHXXue9fD34Qff8P342fe8qN4eOeh0sdYFV4WsFWRsUWBrTheT7e9rOBMQa9b\nrYugMp+fqscWA7bc9D4qs0ZNfD6fNw8AaaWIhoEtVTba/Pjj8oKkCmsqQadzn23p90iDCowt13Ik\nUK6I/GPLUKdHoJh8byR1astPImzLhiUsVWKUy9iifjpTRFF5STlQTor4/mc+kvnas9eHktTNdazM\nz+PP+4Y8tlSz6/WmQlw2dVxTNpWEz+4jCipUZWy1MzpWJnFuu4O//tZH8foXpwm9KTPoUCNF5Pf9\nJCy2JnApIk/YuR9VAgJxKWJdP7QgjBUz/KakiNb/x96bR8mSn9WBN5ZcK7PWV/X2pdX9OnuR1Nql\nFpskQGhBIBsDNmYTYsDY+BgYw3CYwYA9nrEBj48H7GGGY/DBY46wjEFsIyNLCK2gVrek7lZ3Vy+v\n31Lv1atXe+Wesc0fmZHxfV8sGZGZlRHS6XtOn35ZlVmZkRHx+/2++7v3forKaj43FH9REAuTEnZh\na9f+4/hWxHJRx63mJtuMp4jK2ar4rIjTy0cEgscIy7bwwoGXJ3d58WWhubF5LYf3Pfg9zEHwyO0v\nRH6Ged84MaXzpAp3kqoHZmyV1LnEObhyw6keECBfKujMwjzJhksQXiK2YmISxZbl0OenR2oBoigW\ng2uS3XCpWiikWAgVdGlFHFOxZQiLRwaILabYyrcBOImOy3b4eUpbseXCXygl66Lj74ooyJ8ZZ2zJ\nSWE8K2KwbaUhlArVwmR2PVoIR2ZsiV30cXJA2DjhqNACFgeKouCBtXtxZv5U4r8fB2zBqngTusxg\nigs2njvpjufUqgwA+YL32ZKFx2enI6y0Iiq58RRbHTGeF/PHPyYEhscfs2LruGwsFLKYDCO2xrXq\nGMJaHgRqadBVnWVORYF2RhwVHg8INcNUC74wYsu7posB+VpBCCvCg0CJLVcxlRSO4/js/1H43MYX\n+ettb5y8udNkiqAwG2LQ+0zNipgysXVcVkQaeK3rKjRCMgQptg4bXewc+O1dlHiMmzGmjwgJHwdG\ngBVRjkVUGRiF0VZEPp64JBAd4wzT9uXDJYVhBSm2jm8Mp2OFuwEsLdf7kxL4IWtXIFyxZVo2nr2+\njx2SxxaWr+Uiitgq5DT2XpOHx4vaKcCKeK1+g22q3ztiY/Zs5TTbvP3i9hO+jWuKadsrh+fJd/3l\nUM6VfM3F4qiiJeIothRFYeTqS4qtFPDxjU9DK3hffFIrGC3ulJQLIVrwW46JOWI3SmTzkFbEgADJ\nWUG2tFXUQcZWgnb3ANAR1pW0uyICwEqJKLZUB0q+k0hiK4ktmU+QFlbKnAjaqkdnn0iYwuJhWDJg\nfbbEls8+lU9ObIXJuSWxJYMpk4KFxyewIjYT3k+AsDo66ahVqbrBITtV3bHD472/oaS8UVEWxJae\n9+6Dr1RiS0ri8yXvOHYSKLbSsJYHKbaO3Yo4g7FOWhFdW+D0rIijbW5HhNiq5iqxd5JpzlYsxRbL\nn5lmV8QQKyJRksS1fcj4hWhii//NcYoIy+YaiqBwfxdNo4Uv31lnP1PUfq4LMCC2qCIogjiRRfK0\niC1paRxlrQzCjfpNfPzGp2NdUxKVco7NHNPIbgLE9aYqPNJAfHfPbRzgh//5R/C+f/bn+PBfX2e/\ni0s8UoyynI0DK6CDYDlXZuvYdlzFlhmt2PJZEW2/YgvAWJ3JKUzL9oXhH6fDIFCxJbLjDurH1/01\nr9M50YFl27AdB//bf3wU/+O/+QQ+99Tt4e/LxRxuN7eGj4taga1Xo4gtRVGYEnLa4fFBduVn919g\njy8vjnYcvP7kq4f/7lhdPLn7dOhzC3mNKZuOJrQiuuS2EqDYUhUVJZWvJ6WVPw7mcmW2Lg5SbAHA\nIu0MO4VQfIpsVLkZxwef/RD2Tn4MbnempN207Awptmh4vGEbrO1mEmKLLq4dJ91CSE5Irswy6SJb\ntlJVM3B7BHVGTGKxtETQdZq2KYpzS7yF9o293USvlzvhaYfH66rOg/5zPXQNKxEJTndU6e4+3fFQ\nFdWXDTHOZx2+Z8zweGDMjK0MjH10x9JRvOvEMOyxbAVZUmzJzk5Kzju+ZseI3UUwjaD1MJT0IjTF\ne/9i2fu+E1kRRUD1LMY+Hh4fTGyN09mWQpLRs7Ei8kBY1xZYzPNd8nEL9FEh64CX6wUAVfF5opCY\n2KpQ61F3IuuRFeO4moaXsRWX2JJ2xCgr4vK8COodw/ZhybyZCIXTkztPi3F/gEFTn1vbDaZ6iVIE\nScJpWoSJLz8noWLryuFV/Ornfx0ffO5D+NXP/1ufPXgUNFVlSs6pZWwJ5UwUsfXJxzfRGax5//Nf\nPI8Xbnn3BiUe4yq2cuy9nEQdbMMwJBdEBzc670nCKgxtQiAXtYJvPvDbe11ia9q2PWemii26Lj0a\nOCN8xNYULddSmZoTG0o9w8a123Vcve13aVSKOvZIg4/l4hKWC15n2yhiCwCqJe8c1tuThsfz6zfI\nbfDc/pXhv5cKizhRWvY9R+JVqy+HTtY3v/Xkf8KvfeE38e++9FvYbfuPj5N107IiBhOrSyXebGWt\nwrsKx4GqqOyakxECLipT7hbNPsNU/9pXMSy9BaXULzCTKrZsO/3izgUt+A3LwAohtpIseAyb3GC2\nlmrQupTluzk6ewmIOgDomvzmyoK6SWaTKYX2V4UV8dLKCfZ48zB6wpIwhOReZmzJHe1ZgCpNXPtU\nMtVM8K5Xkyi2KrnknncJOqlGWRGnkbFFiVXFSefaoxYDm1gR+7uHYxBbjFhI935SFZUVw4rONxzi\nnjNbWMv1FBucKIrCFJD5oncMSTZfesKKOIuxjxJyYcTWxKG2vi6px3+u5M65uwurKAoLuB236KNd\n6sIse9SK6OucGQFKbLXNzsiwaVrImpaD5hjjnos4xzWOYgvg65644fEAsD9GB6ok1r0vbj8Z+HNl\nQGxJxVaUIkh+Z+OM10GIo8YIQ9vs4F8/9hvD/Mj97gFu1DcSfwaqBJq0YHUhN/woMeg4YPlqVzeP\n2Gt/60+fHtrzKfEYpaijkN/hNIL+hxlbCie2iuQ+6ZjJFVulAMuv397bHyf8iq0p2MFmlLEFAEsB\nVuz5uRzoMnKq3V8jwuOB/r0XVpuViznW4GO5uMhqoJHE1nEqtmTen23ihcOrw8eXl8LztSjKuRIe\nPHH/8LEDB8/sP4cv7z6DP7v6Ed/z56eobAoiigFAH9RLyyWu0FoI6ZQ4CjRqRjZ9GT6n9BKxlQm4\nIbZJFVt09yp1K6JQbFFia/ewE3t3ku1Q2VrobuQsICckV7G1m3Bnsisyd7JAbK2IHYCkxJbcOc2K\nYuv8iRX2eLuRTM4v8z5oV0RN0VI5dzRnyyW2kkyuLGdGp1JeTmxNiqCuiE/uPI2f/8z/jv/j0X83\nVIiVCvxaGceKmAVSn+7E2go/hnEC5GkHwbStiADPCnFUvvCJqw6Siq1Cyh1hactoNc/D4+POUdKK\nOIsxgSu2+p8zp6vMTjDpIk52Mp0FsaUoCrMjUhUpVzhNw4oYg9hKkP2xVOC70IcjVFsyf+ZwgqIv\nznHRjK1kxJZHWEUptqpzeZYPM5YV0UdsBY97juPg2bBOXwNiq9kxcXuPqNQiFVuimJyWYsuU66L4\n4/gHn/2Qb8Nwo34r8WdgBevUuiLygH9/+H749bi528IffqofhD2eFZE/bxo5W0aAFVFXddZkIa5i\niz4vSPUu64hhxpYcD6aRuycVW8rxzbdLRPF0MOgqqKkqu/4mJbbC1q5AMOEZZvHWNRX7RLG1WFzE\ncpEqtqIjSyix1ZiQBPIrtvhxXTu6wZwi98awIbqgdkSKq4fXfT/jG0eTWhGDM7ZcXkDGQAQFyscB\nXb8ddYPzkymx1WybU1F4uki/cv8KgqIPiK2kii02Cab7ldOMrZ5tYmXBmyC6hhV7d5Le0I6tzTx8\nk8JnRRwsoI6avUQB0XKHP83ulS4KWp6RGUphQiviMU6gSVDU81As71o87BxFPNuPKCvirG2ILpiK\nYEhsxZ9cw4KGpWJrUrDweNuE7dj43Wd+H3udfbxweBUfuf5xAP3FT5EsascKj2ekfjpjBM3gs8CP\nIWmHW4CHzmfBrkwX6ZbCr7e46iBJlqSpwAX4jp+je4vuJHNUGsQWz9jyvlO+iJuQ2EpBsQVw+x8l\nmaah2BpFADmOw7I64nREdLEoiK1RAfILwqYzSSEbi9jqUStifJt5XMWWqijMejQOsRVXsbXfPQjt\ncOwqtgBO7iYhtqaWsSXOS1wV9CM3v4S/vv2o7+cbjc3En4EW4dPK2JJZdfL7owRlEMH+kUc20OmZ\nY4XH+5Q5U1Rs0aYvuqqzJgtxwuN32rvYbe/BJLtSAAAgAElEQVQNH0sLPxBuRayUcqwWmEZDCZmx\ndbyKLY8Y6lhdtAcEHx0TDifMEoxqkuFTOpl2aE7ZfFVBmyjwlgpcsdUwmpFjHQ1bn1SxJccaeRwv\nHl5jj0cFx1O8fOU+zInmBQCw3d71rcWmakU0wzO2AD+xJbu9xwVdv714dA1//MKHfZsBdE1kOw5T\n8U6K9FflX0kYFKsTKbYyl7HFF3FxM0xMav2ytLHCN6cFX86M7n22vQSLuF4KhVAcrBRJgHyhncg7\nLrtXZkWxBQA5eOetYQbLVcPAdh51hSkIcyl16KSh7n3FljOBYis4Y2suQbZMGDSFh8ffbGzisOcR\ni88deLkBtLnEOFZEG+mPfQXWMEMotsZYfNNGBVkYI+gCyQAfv+Oqg0xbLuLS7QhLiQtT4ccUd47q\nWbNX4PKuiN4OJF3ETW5FFIqtGW1WcHsBIbbmJlds0fE8p/vHibbZZgtjmfkVBdoVERids+WzHk1Q\n9I06LsM22dw1vmIr+jMukXVe0ogGICCTKkShf6txO/DnAIYbjhKlfPhYI7+z4+iKGHRegmA7Nj7w\n+IcCf7fRmEyxNWkR7oLOZzld9WeUkfMYpMA2LRtPX91Hl2zey66uYQgiMCZF/zw5ImMrxxRbYeHx\nm80tfPDZD+GXPvvL+IXP/kvcapKQ8lhWxP49pSoKqnOEhJyYBJptxhZVbAHA/sDqt0SIrZlaESMU\nW+fP8e9hqbDgi2OJsiNSEqhn2r7c5CSQY428l45IJ/e8mvO5a6KQ03J434Pfg0vzF9jPLcfCbmeP\n/UyOE5Mom8KsiB6xxRVakuiKC6le+/C1j+ED63/AfjbtpjoU6a/Kv4LgEiaTKLbSJrZyIjha5i/E\nXfTQTCPF0aCp6V1KmqqxnU5XWQckCxuW6o0sFK0AZ80VvZfQiphOERQHZd0rTgylE5uxt2wbdGzX\nhWIrn5Jia5FaEVUHSrE5FcUW7YpYnbZiyzHxzN5z7Pcb9VtDa2ep4H2XYxFbNGMrpemmoJPMHEls\nJRzLAR7enYUxgiq2DIcvUGNbETPUFRHgVrOO3QJIT7a4nRHTyNiiCj5K6lZK3rw7qWLLpootJ77a\nZFLQc0IVW5QIaraNscgHaS2XqIsA2iRWRKnYGkVsSSviwQRd0OhxBYUPy3yg4tgZW9Gfka7zkmz2\nufAXecH30ibpaCaRzwcXZMVC+Fgjv7PjCI+P6zb4qxuP4cZRsDLrVuO2T5EwCvS+6RrWREW4C7qG\nKhf0UMWWadksR4viL7/ESbrzq/HuteNRbDmA4rA8KF3R2XqfhsK72G3t41c+/2v4+Mancae94/v9\n2eoZ38/CrIgAP1dTsSLOsCsiVWwBGFr96Dg3cfdXM/x+ktdFz7QZOXh6ZQ4/+M778LN/9zVYWOLf\ny5LI2ALAlHcSVLEFTKZwkmS+HIto04+5Mdbl96/ci59+3Y/jJ179o+znW61t9pgek+04Y63DXVgh\nVkR9UDNdnD83/Fley+NkeXWs9/nas2/E2y++lf3s07f+Gi3ynfmIrSmR+8BLxFYiuLk5vYTdtLJQ\n3LnIiVDt+SpfVOzEJra8AYMqQNICtUm4WWhAwrDhjBJbRbIzC9VKNADY0oqYomVUggYTKrkuNndb\nEc/2IBdMOU1FlwQC053sWeLuxbvYY211I5E6wwhYbFu2xbIhxplAJXhXRAtP7z3Lfm85Fm40bvbf\njym2xsjYygCpX9K8YtGCySb1pBlbpmUzxUwWrL1l0oGmY/FFflwSXKpV09yoALhiy3KsYW4iEH9M\np8o6AFBncK6kzddFhSxOJ87YQjoNGahKqmE0h2sguqPsYDz1ySiigSpKgWRWxIKWZ4XwKCtiqaDz\nTo8TFH1R3cIAfz5QkJIkDIkUW1Wuzki66z9KveAiithaXAi+VqOsbqqicDv8BEUdxSgiVcJ2bHzw\nyT/1XqPqeMfFtw0fG7aBOy0/gRIFWYQfTiFAnn4/pWLOd57c8xhFrj9xhXeovngqnmojP2Via9jc\nReYBaTpTNgYptj565VOB90RZL+Hrzz6Mt53/Ot/vaGQBwMli2lAizEYXB2HHdLyKLU7suzlV1IrY\naBsTna+orrZ+wtNiuYWnV+fw9Q+dwb3nF4cZYC6Wi4tYLfM83kfvfCn0c1Sn2KiFHpOmKr5oGhoR\nQrsAJsXJuTX2WBJb82V+TJNYYV3FVpgV8UL1HL773vfiodWX44df/n2JNlooVEXFt9/9TrznZd/C\nfr5L1HYvKbYyAlexlbSbFs110ZCOmsSFzB8qlRQ2+cVVbNHOTFkgtip0B5cotpJ0euz5CqFs3B50\nJ0nRLDQSEAy+1vAZKMRdrJS9CVfRe9jcjWdHlPaxnK6yXI+0iK0L1XM4M3dq+Fg/cROHrfjEatBi\nu2lysi+JBScM9BqwHAsvHLzoe86VQX5AmRBbzTE88JzYSl+xBQBQveNIksEH9IOP6aIgrTw3Ctn6\nnOYgj0tszYIEioIkLnIl7zjiqnANhy8AfU1GjgGSbBh2RixOrwMQ3axQMbvzRFVSpm0Ox1xp3Rtn\n4c2tYf5xYke0QV8Ru/ijQIu7UeHxiqKwY5rEpjMqY0sqtsbuijii0yO1HZmWk1g16LMixlBsnV/g\nqpj5avBr7j4THVBMu/OO08AkCFGZQEF4dOtLuFn3rGxfc+aNeDnpbAYANxPaEefFfVOfQs5Wiym2\n/Nm37uZZVMFPOc/FSp6RolGYtmLLCrFN6SontnpWj+US2baNv7jy2eHjSm4O77z0TfjHr/0H+Jdf\n9wv47trfCLzPNFVjc+k+6c63MKWgf3eNN8uMrcXCAttUdMmjhWNqkpHTosPjDcvGAfkOl6veuaDB\n8QCwUFjAYmEBdy94m8aPbn0Jh93gTN7pKrao2tZP5FPFVnkCYquaq7DrcaspFFtynJiGCi1EMago\nCr7+3JvxI6/4fjy4Uhv7fVzUli6zxzT8v1J+idjKBnLejZ/EwkI7caVNbEmbVt+OyDsjxgHNqskp\n6Rd31KKl5UkRFHd337R9Le/VDHQ8A0RhrpmJFFvSYqRnKGNrrUqJLRM3duJ1RmyLXdtyUWe7c0VJ\nZMwIiqLgzWfe4D3OGdg0rkS8gsMMyP2g+VrAtKyIfBElM3sArztLeZoZWykRxUWNL2JpiHE3oWKr\n0TaYesjXuCIF0N1CBw7K5BKJTWwZ2VKrSmJrnmw6xxnTHcdh9mRgNudKjj2unWWOWBHbXWuirCCm\n2JolsSXOiWsPlAX6OC3JRxFAeyJ3RNpTRoHaEWk7+TDQom/cQlZugAapnNrSiqiNR2x1QnKGXCwK\nciKpCk12EQw6R7Zj4zYhti4vX2K/nwuYuh5+8BTuPb/o/wUBbws/HcWWEaEwCcJnNx8Z/jun6nj7\nxbfgTOU0IwySBshPgxCWoHN0uZALtSJKYvPcavC64tKp+F3RgpQ5k2Bob5OKLWFFBLhq64u3n8Iu\nIcLfdv7r8K0vezvuWrg4cl47UfLUQTttT7nGO1iOn3M0JBZ8XRGPj9jSVA3zZOx2FauLoknGJHbE\nqG6bshFNt2cxEpfm/1EysZqvDAmXb7zgKewsx8InNj4T+DmqgiyZJLtulNqWWxHHJ7YURcEasfz5\nFVtyfp3gmMwwsvh4avhlaYOlxNZLiq1sgFrckoQO21lSbAkrogyQj5u/QLObslDcVZgVMfnufqdn\n+llsLX3CDuAqAEW10TNNFu4ZBanE0FPOzqFYLPJF08398FBICtmdr1zICcVWetfjG069BiD2oF39\n2Yhnc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QnJe4AcE1VszYDYkuon14pNA+TH7f46KktQVZTQzqM031luNsiMLaBPkpwkHQSf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\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gF1eewkx0FqqqYlg+DtGqkQsH6vfCU0T5lmnYZCulRnll+Ai+duSv8RNCUSMKIm5suL4Q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\nsl9CfMgAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f8b5004f2e8>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 603,
              "height": 322
            }
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "UdTSTPwJC--o",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Model Training : KNN Regression\n",
        "\n",
        "[K-Nearest Neighbors Regression](http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsRegressor.html#sklearn.neighbors.KNeighborsRegressor)\n"
      ]
    },
    {
      "metadata": {
        "id": "JqHYp576Djh5",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "'''\n",
        "Nearest-Neighbor Regression\n",
        "'''\n",
        "\n",
        "import time # 학습시간 측정용\n",
        "from sklearn import neighbors"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "2-ZRmsJtHHgj",
        "colab_type": "code",
        "outputId": "326f6ad0-7bcb-4856-95d8-ab6e7c270174",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "# 모델 준비\n",
        "knn_regression = neighbors.KNeighborsRegressor(n_neighbors=5)\n",
        "\n",
        "# 모델 training\n",
        "# Test셋의 첫번째 컬럼(cnt)만 사용할 것이다. \n",
        "start_time = time.clock()\n",
        "knn_regression.fit(train_features, train_cnt)\n",
        "end_time = time.clock()\n",
        "\n",
        "print('----  {0:.5f}sec, training complete  ----'.format(end_time-start_time))\n",
        "\n",
        "# Training & Validation set에서의 예측값 준비\n",
        "train_pred2, val_pred2 = knn_regression.predict(train_features), knn_regression.predict(val_features)\n",
        "\n",
        "# 시간이 조금더 걸림 (Linear R에 비해)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "----  0.04544sec, training complete  ----\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "4Yh5-uWfJmM8",
        "colab_type": "code",
        "outputId": "3b2a9723-9026-4b10-c53a-6823475c1096",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "cell_type": "code",
      "source": [
        "# Training & Validation set에서의 성능 확인\n",
        "\n",
        "print(\"Mean Squared Error on Training set : {0:.5f}\".format(mean_squared_error(train_cnt, train_pred2)  ))\n",
        "print(\"Mean Squared Error on Validation set : {0:.5f}\".format(mean_squared_error(val_cnt, val_pred2)  ))\n",
        "\n",
        "print(\"R-squared Score on Training set : {0:.5f}\".format(r2_score(train_cnt, train_pred2)))\n",
        "print(\"R-squared Score on Validation set : {0:.5f}\".format(r2_score(val_cnt, val_pred2)))\n",
        "\n",
        "# Linear 보다 오차값이 줄어 들었다."
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Squared Error on Training set : 0.20459\n",
            "Mean Squared Error on Validation set : 0.64175\n",
            "R-squared Score on Training set : 0.78996\n",
            "R-squared Score on Validation set : 0.50261\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "892ftJCTJsow",
        "colab_type": "code",
        "outputId": "022c6526-d13e-47fd-f9fd-2debde80871a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        }
      },
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize=(10,5))\n",
        "\n",
        "knn_predictions = knn_regression.predict(test_features)\n",
        "\n",
        "# 예측값도 Scaling 되어 있으므로 그것을 원래대로 되돌려주는 과정\n",
        "mean, std = scaled_features['cnt']\n",
        "\n",
        "ax.plot(knn_predictions*std +mean, label = 'KNN_Prediction')\n",
        "ax.plot((test_cnt.values)*std+mean, label = 'Real')\n",
        "ax.set_xlim(right = len(simple_predictions))\n",
        "ax.set_xlim(left = 0)\n",
        "ax.legend()\n",
        "\n",
        "dates = pd.to_datetime(rides.iloc[test_data.index]['dteday'])\n",
        "dates = dates.apply(lambda d: d.strftime('%b %d'))\n",
        "ax.set_xticks(np.arange(len(dates))[12::24])\n",
        "_ = ax.set_xticklabels(dates[12::24], rotation=45)\n",
        "\n",
        "print(\"Mean Squared Error on Test set : {0:.5f}\".format(mean_squared_error(test_cnt, knn_predictions)  ))\n",
        "print(\"R-squared Score on Test set : {0:.5f}\".format(r2_score(test_cnt, knn_predictions)))"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Squared Error on Test set : 0.33837\n",
            "R-squared Score on Test set : 0.48943\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Tm/AV9yL0NKFPC4DRBM99RQno0J8PAAAAeUGgygJT+VgRJjeR0r8ClMCQVQBg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X6/uoOGGSGVW+y4mq6cg1HAXDWv9oQRkgmwfxas6TGqcUAp4rqnoErsn611dR5XJFVVBqcY1B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\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Go6ee6kZSyTS46/K6Enc0bal4Ma4/2IOPbv0AtavjkDUu2uAWgqztT+pJfO6ZP8b/DD+S\nuy/grOYqk6UYGr0NeXdx9ZgH1zTcUFGTMABYTZSyxNJxy4JnTe0gqjluAZ6Pm7uuQxMhhCYMOkts\na2dXid/R8tlQt4aaCL8yfgD/fuQH+NKrX8vkpS1wbceVUOTKaMzC0lvdTX1fAKiAeE3WcFnTllK/\nrYvmFkZYIMv9DAO4puFG3NS7txxvrShURcbduzYiPW5uCE3EJpD2mWUv1WjE1ib+BdLBjgDu2zeA\nbnUDpKSZ16Q2nYLkjOCE82eUMEpyz+AdZc/fAjJO6k/veAA3dl5D3f/9Yw/mSgDnEiEcmjIdexvq\n1y5aIretcRPuX/1W3NK9D7+746O4uXsf+oM9GKjrgVO9NHMon+alHO+SIw6t+RQm0iP41qHvUJ9p\nPDJJfaZNDesrZk5+z+Cd1Eb3SPgC/vKlv8VPTv0caSIKYV1dZZX9kZAb/bpjHs7Vz0JtG8qUdC4g\nQ8ZuDsqDl8rOpm3waeZGApnH7FbduKWbL5E+iyxLuGyNeU1NTpprhtnEHJ6d/ylkl5m5dWXbropt\nsLUUeBeq3g4gDODrzP3/BeAsgF8dHBxcsVXGdR305OMrr/0j5gjb6lXtu6muS5XKQLAXdUxHvJXc\nfSkVAWc1rmnfY7nfqThwU9d1ZXhHgmJQZAV39N1i+xgPmRkrQdAVwANb3o+tDRtR6wri7oHb8waj\n8sKG+jXwa+ZEMpqKUoHP/YEeu5dxzz2Dd+CuvjdB0k2hVw/7sct5B+7cVXjXmkfYPKcsqqziTd3X\n4zfWv6PE72hlcKkuvHf9O2wXl1e2Xs5tMwI7JMm6qfXI2SeRJEr++gM92F2hZc5AZhz/2NYPYG/b\nbiovMsuu5m3wVGDbbIfiwH2r7rLcb6RV7Km+BXetu8bmVXyxbVUDmpKbYKTtRdC7V91aEQK9JEm4\ndmsbPnnfDtw5eKN5v5KGc/2TiEhTufsaPfXorupA0BnAm3tvwob6teV4y7Y4FA239t5IbQYdnz2F\nbx36Lv7ipa/gL178CnWt3VGEwCtLMnY2b8PN3ftK2uX1mvY9eV1ED51+FF878E08c/4F/N3+b1CP\nXdGSvzsqbzgUDb+x/h3UfOjs/AgeOv1o7rYsyZbMyErixq5rKeFQdkWgtdBdxu8auLVgVhqveDQ3\n7h54s+1jN3RevWjTqnJy195e9Ldl1uNppvxvznkq97NsaLix89oSvrPywa1QNTg4WAVgFYCXhoaG\n4uRjQ0NDBoDnANQj46xaEVp8TXk7ONS6gri150bbxyqR9298V87VsrZ2FfedOPKxr3OvxZ1zXcfe\nitm5eaOyq2U7fnXV3dQCp6e6s6Jq4RfD5/Di19bdhz+4/JO4vIV/e65bdeO9G95pEbEBYGPd2rwX\nft5RZAXXdFyJG6rvR/JcL5Jn+3Gl7y7cf82GilissdS6ayyizapgPz694wHc1H0dd7kLS6HBU4d3\nr70v56pscNfhQ5veg7cN3l5x31VPdRc21tkvltfVrsYHNr674l2ybtWNtw68GZ/Y9iFc2Xo5tjRs\nwJaGDbi5ex/u6HtTud/eshkI9lGbYFsaNuAPd38C927j10lFIksS7rlyPRJD26hW9ABQlWrH5paB\nPK/kl70dlyGgEk432Qzy9mlefHDju/Gxbf8Ln9/9KUtDA164redGSuR55sILODZzEmNRs4ucX/OV\nNQB+MWrdNdjakD+f7ZXxA/jmoe9gJEwHV5P5q5VA0BXABzf9OuXMIbm67QouHHvLpcYVxEe3fBC1\nNvM9AOhxrsXe1stL/K5Wjq0NG7GecbwFnNXY27a7TO+oONxOFb/11o0YaA/AiHuhR+zX5r3qZm7L\nz1caya77BA8MDg6uB/AagH8bGhr6FZvH/wKZ0sB9Q0NDDy/jv7D94AfHjuJzj/w5dZ/P4cUnr/wg\n+msra6BdjKnoDM6HxrCqrrdiyw8A4MdHHsE/vvwdAEDQXY2/uulzcGl8u1cEGV449yr+8eXvQpFk\nfGz3e9ERqLxcsV82EqkE/uPQT/DTY48j6K7GvevfjG2tfHW4Wi7HzsxAUSR0t1Seg5TktQuH8KXn\nvgGP6sZb1t2My9u3VZyQU4jp6CwmI9PoCrRBVSpXzJmMTOOPn/gKTs5kMo4UScY1Pbvxri1vg1rB\n19w3AoZhUUzNKAAAIABJREFUYGjiODyau2KvS//84EH81+NHoDccg9p4Gpruxe/v+xD6m8obML5c\nnj/3Kv7kya9S9/kcXnziivdhVX1ldM78u+f/BQ+feDLv47evvgG/suH2Er6jpTMdncUXHv8SxiNT\n2Ne7BzXuAP7h5W/bdjN0qy58eu+HMFBXmY7skdAoPv/oX2MiknHvORUH7l73JtwycE1Fr5uyTEVn\n8JdPfw2HJ44DyJQ3a+EWfPXej6HKU7lCHJC5/j7wkz9ANJmJEfjAjrfjqu7K6EIbi6fwjQcP4men\nfwY0HaEeS8/W4vM3fhirO/jNvWW4qMkpz0LV5QCeAvD1oaGhX7d5/PMAPg3gzqGhof9cxn9h+8EN\nw8AXn/wKXhrJdFG6qmsXfnXjHahyVabj6I2AYRj4n2OP4fTsCG5btQ9Nvoo5eQXIfH+/TIvsXxbE\n98I34vupHJLpTH6iLMm/FIsbQeWQTuvQDQOGYcChVa7oC2TGvC888SW8fD7T4KgS5+fT0Vl86uEv\nYjKS6U7Y7GtAkz8zZ+0OtuOuNTdDU7RCv4IbyGvQaxcO4XsHH8ThieM5wWpn+xa8c9PdqPFUVldx\nlunoLH5w+CEosowb+65CnfeXLxcolU4hmkji9Pl59LXXwKn9clynzsyO4EdHfo7+mi5c23vF4i/g\njJHZCfz2T/8IcT0GxXCgJbUNt625Cnu38NGxtUiEULUcoWp8PJT3gyfSSRyeOoI6dy1afMtvx15f\n78/+X8v+HTzyy/i5xGeqDMRnqgzEZ6ocfhk/l/hMlYH4TJUBT58pkU7i0NQR1Ffw/HwuEcLw3BnU\nu+vQ5F3Z7L1yf1ehxDyOz5xEtbMa3dUdK/I7y/2ZLgXiM1UGPHymmfgshufOoC/QsyL5WqX+TPX1\n/osSqnjeXplb+DdfEaaPed6K4VA0roIYBQKBQCAQCASCNzIORcPGCp+fVzn8WF+3ptxv45Lgd/iw\nqYH/jpICQaUQcFYjUF/ZcRUXA7dh6gBOIlOel6/lQLb93tHSvB2BQCAQCAQCgUAgEAgEAsGlhFuh\namhoKIxMmPqWwcFBKhl7cHBQAXA5gDNDQ0Ony/H+BAKBQCAQCAQCgUAgEAgEKwu3QtUCXwfgAfBe\n5v5fBdAA4Gslf0cCgUAgEAgEAoFAIBAIBIJLAs8ZVQDwVQD3AfjTwcHBTgAvAFgL4AEA+wH8aRnf\nm0AgEAgEAoFAIBAIBAKBYAXh2lE1NDSUBHA9gP8L4C4A/wTgHcg4qa4aGhqKlO/dCQQCgUAgEAgE\nAoFAIBAIVhLeHVUYGhqaQ8ZB9UC534tAIBAIBAKBQCAQCAQCgeDSwbWjSiAQCAQCgUAgEAgEAoFA\n8MZBCFUCgUAgEAgEAoFAIBAIBAIuEEKVQCAQCAQCgUAgEAgEAoGAC4RQJRAIBAKBQCAQCAQCgUAg\n4AIhVAkEAoFAIBAIBAKBQCAQCLhACFUCgUAgEAgEAoFAIBAIBAIuEEKVQCAQCAQCgUAgEAgEAoGA\nC4RQJRAIBAKBQCAQCAQCgUAg4AIhVAkEAoFAIBAIBAKBQCAQCLhACFUCgUAgEAgEAoFAIBAIBAIu\nEEKVQCAQCAQCgUAgEAgEAoGAC4RQJRAIBAKBQCAQCAQCgUAg4AIhVAkEAoFAIBAIBAKBQCAQCLhA\nCFUCgUAgEAgEAoFAIBAIBAIuEEKVQCAQCAQCgUAgEAgEAoGACyTDMMr9HgQCgUAgEAgEAoFAIBAI\nBALhqBIIBAKBQCAQCAQCgUAgEPCBEKoEAoFAIBAIBAKBQCAQCARcIIQqgUAgEAgEAoFAIBAIBAIB\nFwihSiAQCAQCgUAgEAgEAoFAwAVCqBIIBAKBQCAQCAQCgUAgEHCBEKoEAoFAIBAIBAKBQCAQCARc\nIIQqgUAgEAgEAoFAIBAIBAIBFwihSiAQCAQCgUAgEAgEAoFAwAVCqBIIBAKBQCAQCAQCgUAgEHCB\nEKoEAoFAIBAIBAKBQCAQCARcIIQqgUAgEAgEAoFAIBAIBAIBFwihSiAQCAQCgUAgEAgEAoFAwAVC\nqBIIBAKBQCAQCAQCgUAgEHCBEKoEAoFAIBAIBAKBQCAQCARcIIQqgUAgEAgEAoFAIBAIBAIBFwih\nSiAQ2DI4OKiU+z2sNIODg7Xlfg+CxRn8/+2debhlRXmv3x6YQRFwVkDE/eGAIIIoIIMgBJSrqAhR\nUUjgIuLEGJwbIgjSRq44E000cQIVUVFAGpTgEJSAEIQPHJCbCEQEGWTs7pM/vlrdq3efYe/Tp3et\nqv69z9PPPmfvtQ71smrV+u2qWrXMXmFma+cuh5gaM5uVuwwzTY1OQuSktjyhLFEGyhJlUeO1t0an\nUaKOqo5iZtUdGzNbw8zWM7M10+/FO5rZWmb2GDNbJ/1eg9P+AO6+qAafBjP7OvAWM1s9d1lmCjN7\ntJltZGbr5S7LTGFmFwDnAi/NXZaZwszWMbPHm9lj0u/VBBd3HwM5dZWa2vA2yhNlUGOeUJYoA2WJ\n8qjp2ttQk1OONnzW2NjYqP+bYgrMbC13f8DM5rj7otzlmQnM7L3ATsDGwHXAqe5+pZnNdvfFeUs3\nPczsfcAuwKbADcAHktOspmEqDTP7EvDXwEnuPi+9V3w9NLOLgecBrwIuK/X4tDGz04AdgacClwNv\nc/c785ZqxTCzS4C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Sk5xyISc55aRGLzmV4TRdVvmOKliuQmwInAR8AVgX+Djw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\nTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f8b54875eb8>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 597,
              "height": 322
            }
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "JV3NOBIp8uxE",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## 스스로 실습 01\n",
        "\n",
        "이걸로 끝이 아니다.\n",
        "\n",
        "위와 같은 과정을 싸이킷런의\n",
        "\n",
        "[Support Vector Machine Regression](http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVR.html#sklearn.svm.SVR),  [Decision Tree Regression](http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeRegressor.html#sklearn.tree.DecisionTreeRegressor), \n",
        "\n",
        "에도 적용하고 눈으로 확인해보자! 위 링크의 하단에는 각각 Examples가 있으니 적극활용해보자"
      ]
    },
    {
      "metadata": {
        "id": "m1y-iahN8uxE",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "steX3gdU8uxG",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "FdRoI9w58uxI",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "위 방식들의 Regression이 끝이 나면 우리는 단순한 뉴럴네트워크를 이용한 Regression으로 넘어간다. [MLPRegressor](http://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPRegressor.html#sklearn.neural_network.MLPRegressor)와 [Neural Network models (supervised)](http://scikit-learn.org/stable/modules/neural_networks_supervised.html)를 적극 참고하자"
      ]
    },
    {
      "metadata": {
        "id": "fq33DEZn8uxJ",
        "colab_type": "code",
        "outputId": "51ce8749-015b-4c1f-f4fb-f44f11647f38",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "'''\n",
        "사용한 컬럼의 갯수가 56개이다!\n",
        "'''\n",
        "\n",
        "train_features.shape"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(15435, 56)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "metadata": {
        "id": "bFIHcjO-8uxL",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from sklearn.neural_network import MLPRegressor"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "naFuFprx8uxO",
        "colab_type": "code",
        "outputId": "8346107c-12f7-4516-cfc7-e88cd53fbb42",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "# sklearn은 deep learning도 지원한다.\n",
        "# 모델 준비\n",
        "\n",
        "'''\n",
        "히든 레이어의 사이즈를 가지고 놀아보자. 학습하는데 시간을 측정해도 좋다.\n",
        "'''\n",
        "neural_regression = MLPRegressor(hidden_layer_sizes=(16),\n",
        "                                activation = 'relu',\n",
        "                                solver = 'adam',\n",
        "                                learning_rate_init = 0.0001, #0.00001\n",
        "                                max_iter = 500,\n",
        "                                 random_state=2018\n",
        "                                )\n",
        "\n",
        "# 모델 training\n",
        "# Test셋의 첫번째 컬럼(cnt)만 사용할 것이다. \n",
        "start_time = time.clock()\n",
        "neural_regression.fit(train_features, train_cnt )\n",
        "end_time = time.clock()\n",
        "\n",
        "print('----  {0:.5f}sec, training complete  ----'.format(end_time-start_time))\n",
        "\n",
        "\n",
        "# Training & Validation set에서의 예측값 준비\n",
        "train_pred2, val_pred2 = neural_regression.predict(train_features), neural_regression.predict(val_features)\n",
        "\n"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "----  6.80065sec, training complete  ----\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "MtPwP5p68uxQ",
        "colab_type": "code",
        "outputId": "be675530-8607-4be8-9d5d-0df3ccb659eb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "cell_type": "code",
      "source": [
        "# Training & Validation set에서의 성능 확인\n",
        "\n",
        "print(\"Mean Squared Error on Training set : {0:.5f}\".format(mean_squared_error(train_cnt, train_pred2)  ))\n",
        "print(\"Mean Squared Error on Validation set : {0:.5f}\".format(mean_squared_error(val_cnt, val_pred2)  ))\n",
        "\n",
        "print(\"R-squared Score on Training set : {0:.5f}\".format(r2_score(train_cnt, train_pred2)))\n",
        "print(\"R-squared Score on Validation set : {0:.5f}\".format(r2_score(val_cnt, val_pred2)))"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Squared Error on Training set : 0.06477\n",
            "Mean Squared Error on Validation set : 0.17917\n",
            "R-squared Score on Training set : 0.93350\n",
            "R-squared Score on Validation set : 0.86114\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "-e6p790W8uxU",
        "colab_type": "code",
        "outputId": "da45a89b-6b00-484c-a25b-8f0fc379ca33",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        }
      },
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize=(10,5))\n",
        "\n",
        "# 예측값도 Scaling 되어 있으므로 그것을 원래대로 되돌려주는 과정\n",
        "mean, std = scaled_features['cnt']\n",
        "neural_predictions = neural_regression.predict(test_features)\n",
        "\n",
        "ax.plot(neural_predictions*std+mean, label = 'Neural_Prediction')\n",
        "ax.plot((test_cnt.values)*std+mean, label = 'Real')\n",
        "ax.set_xlim(left = 0)\n",
        "ax.set_xlim(right = len(neural_predictions))\n",
        "ax.legend()\n",
        "\n",
        "dates = pd.to_datetime(rides.iloc[test_data.index]['dteday'])\n",
        "dates = dates.apply(lambda d: d.strftime('%b %d'))\n",
        "ax.set_xticks(np.arange(len(dates))[12::24])\n",
        "_ = ax.set_xticklabels(dates[12::24], rotation=45)\n",
        "\n",
        "print(\"Mean Squared Error on Test set : {0:.5f}\".format(mean_squared_error(test_cnt, neural_predictions)  ))\n",
        "print(\"R-squared Score on Test set : {0:.5f}\".format(r2_score(test_cnt, neural_predictions)))"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Squared Error on Test set : 0.21907\n",
            "R-squared Score on Test set : 0.66945\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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6tTDNHfDOOwvxwAOzsdNOu2D69EMAABttVHotH3zwPvT29mKXXXbFTjsFyziPPvp4/OlP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\ntuDxOHlO0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brQUOSRllFlDqsNOQmbco/0IqCNoqpyKi/00NoIKrHKkD1DrX/STdk9F9TP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q2iyrQW1tqcgiuYOnoXQghJbZEJUeULU/dnVClCiUzoALL95Wg0PaNqHtlBHJT4L5Xl45XW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\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3bJIAyDaoeWMSJrqMCX+uoCCZn2UmXGquNjKqAK0VND0BG8z242PvOIR/eP+1kJwGOWBl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\n+EB5ZgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f8b4d725400>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 597,
              "height": 322
            }
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "Dzk5EZ2H8uxZ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## 스스로 실습 02\n",
        "\n",
        "캐글의 입문 컴페티션중 하나인 [Boston House prices](https://www.kaggle.com/c/boston-housing)\n",
        "데이터 셋을 가지고 집값을 예측해보자!\n",
        "* 지나다니는 사람의 인종 등... 다양한 데이터가 들어가 있다.\n",
        "\n",
        "\n",
        "\n",
        "뉴럴넷을 이용한 회귀모델과, 다른 방식을 이용한 회귀모델을 만들어보자"
      ]
    },
    {
      "metadata": {
        "id": "Hl6l7LGO8uxZ",
        "colab_type": "code",
        "outputId": "fe3bd5df-95b8-40bb-db80-18688a22b07c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import load_boston\n",
        "boston = load_boston()\n",
        "print(boston.data.shape,  boston.target.shape)\n",
        "'''\n",
        "feature 데이터는 boston.data에\n",
        "target 데이터는 boston.target에 불러와진다.\n",
        "\n",
        "scikit-learn에서는 datasets 안에 미리 연습용 데이터셋을 가지고 있다!\n",
        "'''"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(506, 13) (506,)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'\\nfeature 데이터는 boston.data에\\ntarget 데이터는 boston.target에 불러와진다.\\n\\nscikit-learn에서는 datasets 안에 미리 연습용 데이터셋을 가지고 있다!\\n'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "metadata": {
        "id": "E9FkII3oUjVI",
        "colab_type": "code",
        "outputId": "47f5cc61-bd63-4176-d1c6-379cdec9f35c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "print(boston.keys())"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "dict_keys(['data', 'target', 'feature_names', 'DESCR'])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "BsKK8ucMUkq9",
        "colab_type": "code",
        "outputId": "cf243943-ed88-4aed-92be-de8c86f63181",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "cell_type": "code",
      "source": [
        "print(boston.feature_names)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['CRIM' 'ZN' 'INDUS' 'CHAS' 'NOX' 'RM' 'AGE' 'DIS' 'RAD' 'TAX' 'PTRATIO'\n",
            " 'B' 'LSTAT']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Mz1aa8E2VE4n",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "데이터셋을 이해하는 것으로 부터 출발한다.\n",
        "\n",
        "다음 셀을 실행하거나, [링크](https://www.cs.toronto.edu/~delve/data/boston/bostonDetail.html)를 참고해서 데이터를 이해해보자"
      ]
    },
    {
      "metadata": {
        "id": "o3ejkHGAVHsi",
        "colab_type": "code",
        "outputId": "eb2b65ea-d785-4d3b-c4a1-d6cb911889c9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 955
        }
      },
      "cell_type": "code",
      "source": [
        "print(boston.DESCR)"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Boston House Prices dataset\n",
            "===========================\n",
            "\n",
            "Notes\n",
            "------\n",
            "Data Set Characteristics:  \n",
            "\n",
            "    :Number of Instances: 506 \n",
            "\n",
            "    :Number of Attributes: 13 numeric/categorical predictive\n",
            "    \n",
            "    :Median Value (attribute 14) is usually the target\n",
            "\n",
            "    :Attribute Information (in order):\n",
            "        - CRIM     per capita crime rate by town\n",
            "        - ZN       proportion of residential land zoned for lots over 25,000 sq.ft.\n",
            "        - INDUS    proportion of non-retail business acres per town\n",
            "        - CHAS     Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\n",
            "        - NOX      nitric oxides concentration (parts per 10 million)\n",
            "        - RM       average number of rooms per dwelling\n",
            "        - AGE      proportion of owner-occupied units built prior to 1940\n",
            "        - DIS      weighted distances to five Boston employment centres\n",
            "        - RAD      index of accessibility to radial highways\n",
            "        - TAX      full-value property-tax rate per $10,000\n",
            "        - PTRATIO  pupil-teacher ratio by town\n",
            "        - B        1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\n",
            "        - LSTAT    % lower status of the population\n",
            "        - MEDV     Median value of owner-occupied homes in $1000's\n",
            "\n",
            "    :Missing Attribute Values: None\n",
            "\n",
            "    :Creator: Harrison, D. and Rubinfeld, D.L.\n",
            "\n",
            "This is a copy of UCI ML housing dataset.\n",
            "http://archive.ics.uci.edu/ml/datasets/Housing\n",
            "\n",
            "\n",
            "This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\n",
            "\n",
            "The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\n",
            "prices and the demand for clean air', J. Environ. Economics & Management,\n",
            "vol.5, 81-102, 1978.   Used in Belsley, Kuh & Welsch, 'Regression diagnostics\n",
            "...', Wiley, 1980.   N.B. Various transformations are used in the table on\n",
            "pages 244-261 of the latter.\n",
            "\n",
            "The Boston house-price data has been used in many machine learning papers that address regression\n",
            "problems.   \n",
            "     \n",
            "**References**\n",
            "\n",
            "   - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.\n",
            "   - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.\n",
            "   - many more! (see http://archive.ics.uci.edu/ml/datasets/Housing)\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "SzzG2S2HSCxq",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "bos = pd.DataFrame(boston.data)\n",
        "bos.columns = boston.feature_names\n",
        "bos['PRICE'] = boston.target"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "hWPSSCIsUx07",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "78861e2c-8b87-4cc8-eb5b-5ac61ecab193"
      },
      "cell_type": "code",
      "source": [
        "bos.head()"
      ],
      "execution_count": 36,
      "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>CRIM</th>\n",
              "      <th>ZN</th>\n",
              "      <th>INDUS</th>\n",
              "      <th>CHAS</th>\n",
              "      <th>NOX</th>\n",
              "      <th>RM</th>\n",
              "      <th>AGE</th>\n",
              "      <th>DIS</th>\n",
              "      <th>RAD</th>\n",
              "      <th>TAX</th>\n",
              "      <th>PTRATIO</th>\n",
              "      <th>B</th>\n",
              "      <th>LSTAT</th>\n",
              "      <th>PRICE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.00632</td>\n",
              "      <td>18.0</td>\n",
              "      <td>2.31</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.538</td>\n",
              "      <td>6.575</td>\n",
              "      <td>65.2</td>\n",
              "      <td>4.0900</td>\n",
              "      <td>1.0</td>\n",
              "      <td>296.0</td>\n",
              "      <td>15.3</td>\n",
              "      <td>396.90</td>\n",
              "      <td>4.98</td>\n",
              "      <td>24.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.02731</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.07</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.469</td>\n",
              "      <td>6.421</td>\n",
              "      <td>78.9</td>\n",
              "      <td>4.9671</td>\n",
              "      <td>2.0</td>\n",
              "      <td>242.0</td>\n",
              "      <td>17.8</td>\n",
              "      <td>396.90</td>\n",
              "      <td>9.14</td>\n",
              "      <td>21.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.02729</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.07</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.469</td>\n",
              "      <td>7.185</td>\n",
              "      <td>61.1</td>\n",
              "      <td>4.9671</td>\n",
              "      <td>2.0</td>\n",
              "      <td>242.0</td>\n",
              "      <td>17.8</td>\n",
              "      <td>392.83</td>\n",
              "      <td>4.03</td>\n",
              "      <td>34.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.03237</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.18</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.458</td>\n",
              "      <td>6.998</td>\n",
              "      <td>45.8</td>\n",
              "      <td>6.0622</td>\n",
              "      <td>3.0</td>\n",
              "      <td>222.0</td>\n",
              "      <td>18.7</td>\n",
              "      <td>394.63</td>\n",
              "      <td>2.94</td>\n",
              "      <td>33.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.06905</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.18</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.458</td>\n",
              "      <td>7.147</td>\n",
              "      <td>54.2</td>\n",
              "      <td>6.0622</td>\n",
              "      <td>3.0</td>\n",
              "      <td>222.0</td>\n",
              "      <td>18.7</td>\n",
              "      <td>396.90</td>\n",
              "      <td>5.33</td>\n",
              "      <td>36.2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
              "0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
              "1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
              "2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
              "3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
              "4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
              "\n",
              "   PTRATIO       B  LSTAT  PRICE  \n",
              "0     15.3  396.90   4.98   24.0  \n",
              "1     17.8  396.90   9.14   21.6  \n",
              "2     17.8  392.83   4.03   34.7  \n",
              "3     18.7  394.63   2.94   33.4  \n",
              "4     18.7  396.90   5.33   36.2  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "metadata": {
        "id": "oE7GjUUUU0OI",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "73JiY7NUHCtw",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        ""
      ]
    },
    {
      "metadata": {
        "id": "w2U28ivebZCd",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## 데이터 전처리"
      ]
    },
    {
      "metadata": {
        "id": "uZGZQ_atbmlY",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Q. 위의 데이터는 예측하기 적합한 모델인가? 판단할 필요가 있다.\n",
        "* 시간의존 데이터는 모델링할 때 시간이 하나의 Column에 들어가 있어야 한다\n",
        "* 과거시간의 데이터가 함께 한 Column에 들어가 있어야 과거의 데이터와 비교가 가능하다 (예측이 가능하다)\n",
        "\n",
        "Q. 왜 하나의 Column에 연속적으로 과거 데이터들이 들어가야 하는가?\n",
        "* 시계열(Time Series)에 대해 공부해보자"
      ]
    },
    {
      "metadata": {
        "id": "si2w10NENaA-",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### 1. Scaling - Normalization\n",
        "\n",
        "(참고) 학습할 때 넣는 값은 여러가지 단어들로 쓰인다 = X, Feature, Input, Independent Variable, Explainable Variable\n",
        "\n",
        "(목적) Input 하나의 범위를 조절\n",
        "\n",
        "1. Scaling\n",
        "    \n",
        "    1, 2, 3, 4 ... 100\n",
        "\n",
        "    Min:1, Max: 100\n",
        "    \n",
        "    x - min : 0\n",
        "    \n",
        "    x - max : 99 = Max - Min\n",
        "    \n",
        "    x - min / (Max-Min) = 0\n",
        "    \n",
        "    x - max / (Max-Min) = 1\n",
        "    \n",
        "    => Scaling\n",
        "\n",
        "2. Normalization(표준화)\n",
        "\n",
        "    Z = X-m(평균)/s(표준편차)\n",
        "\n",
        "    Z의 평균 = 0\n",
        "    \n",
        "    Z의 표준편차 = 1\n"
      ]
    },
    {
      "metadata": {
        "id": "yO67c9qnR_D1",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### 2. Time Series\n",
        "\n",
        "* Data에 시간개념이 안들어가있으면 그냥 모델링\n",
        "* Data에 시간개념이 들어가있으면\n",
        "* 시간의 흐름을 고려해서 풀것인가 (데이터가 시간 의존적인가)\n",
        "\n",
        "    (Yes) Feature Engineering 해야한다 (데이터를 다시 구조화 해야한다) => Time-Series Modeling (Autoregression...)\n",
        "    \n",
        "    (No) 그냥 모델링\n",
        "    \n",
        "    multivariable time series anal"
      ]
    },
    {
      "metadata": {
        "id": "0T6S9wA7TdJY",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### 3. R-square, Mean-squared Error\n",
        "1. Squared Error = error를 그냥 더하면 총 합이 0이 나온다. 그래서 error 모두 양수를 바꾸기 위해 제곱을 하면 error값이 왜곡된다.\n",
        "\n",
        "2. RMSE(Root Mean Squared Error) = 왜곡을 방지하기 위해서 루트를 씌운다.\n",
        "\n",
        "3. 왜 절대값이 아닌 제곱을 쓸까?\n",
        "    * 제곱은 매끄러운 곡선으로 나오기 때문에 모든점에서 미분이 가능하다\n"
      ]
    },
    {
      "metadata": {
        "id": "2zI2um1AbNqg",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### 4. Traing / Validation / Test Set\n",
        "\n",
        "* Test Set에서 R-Square 가 음수값이 나오는 경우\n",
        "\n",
        "    https://drive.google.com/file/d/1hAXtdML7hwj8p2rVX_iDlAE4KN7jrnn-/view?usp=sharing\n",
        "    1. Training Set에서는 무조건 0 이상\n",
        "    2. But, Valid / Test 위에서는 음수가 나올 수 있다.\n",
        "    3. MST = MSE + MSR, R^2 = MSR/MST = 1-(MSE/MST)\n",
        "    4. 이런경우는 MSE가 MST보다 높을 때 = 회귀 모델이 그냥 평균을 낸 것보다 성능이 안좋을 때 = 예측값에 대해서 모델의 오차가 심하면 R-Square가 음수가 나올 수 있다.\n",
        "\n",
        "* Test Set은 Real Data처럼 취급하자\n",
        "    1. Overfitting 문제를 해결하기 위해, Test Set의 Error그래프를 보면서 Overftting 되기 직전에 Early Stopping을 수행하면 안된다. (Test Set의 Error 그래프를 본다는 것은 Real Data를 예측하는 일이 아니다!)\n",
        "    2. 그래서 등장한 것이 Validation!\n",
        "    3. Validation을 통해 Overfitting을 막고 좋은 모델을 만들 수 있다.\n",
        "    4. Validation =! Cross Validation, Cross Validation = 예전에 Data가 가벼울 때 Test set과 Training set을 통해 Validation을 구했다. 하지만 지금은 그렇게 하지 않는다\n",
        "    5. 전형적인 Model 제작과정 : Training <-> Validation 의 과정(Tuning) 이후에 Test 진행 // 하지만 이 이후에는 \"모든 Data로 모델링\" 한다. (데이터를 쪼개지 않는다)\n",
        " \n",
        " \n",
        " * Hyper Parameter Tuning\n",
        "    1. Hyper Parameter란 Model Selection, Feature Selection(Feature Engineering), Learning Rate이다. (모두 사람이 직접 조절해줘야 하는 것들)\n",
        "    2. Validation Set을 통해 Hyper Parameter Tuning을 한다.\n",
        "\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "NvecIDKh7kuS",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}