YBI_ML_PROJECT_.ipynb

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      "cell_type": "markdown",
      "source": [
        "MACHINE LEARNING PROJECT"
      ],
      "metadata": {
        "id": "OosN9MS_lffv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "MILEAGE PREDICTION REGRESSION ANALYSIS\n",
        "\n"
      ],
      "metadata": {
        "id": "ufrckgcPHJtD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "IMPORT LIBRARY"
      ],
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        "id": "zUPE2UcXnmtE"
      }
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    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "trIwP12tlVy4"
      },
      "outputs": [],
      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np"
      ],
      "metadata": {
        "id": "NP3qSQgunOSs"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "LATi4P_ynSQa"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n"
      ],
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        "id": "qQI20W_jnS7g"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
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      "source": [
        "IMPORT DATA\n"
      ],
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        "id": "8f4AulILnr65"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('https://github.com/YBI-Foundation/Dataset/raw/main/MPG.csv')"
      ],
      "metadata": {
        "id": "4xEfCUyrnTP8"
      },
      "execution_count": 5,
      "outputs": []
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        "df.head()"
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              "    mpg  cylinders  displacement  horsepower  weight  acceleration  \\\n",
              "0  18.0          8         307.0       130.0    3504          12.0   \n",
              "1  15.0          8         350.0       165.0    3693          11.5   \n",
              "2  18.0          8         318.0       150.0    3436          11.0   \n",
              "3  16.0          8         304.0       150.0    3433          12.0   \n",
              "4  17.0          8         302.0       140.0    3449          10.5   \n",
              "\n",
              "   model_year origin                       name  \n",
              "0          70    usa  chevrolet chevelle malibu  \n",
              "1          70    usa          buick skylark 320  \n",
              "2          70    usa         plymouth satellite  \n",
              "3          70    usa              amc rebel sst  \n",
              "4          70    usa                ford torino  "
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              "      <th>4</th>\n",
              "      <td>17.0</td>\n",
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              "mpg             129\n",
              "cylinders         5\n",
              "displacement     82\n",
              "horsepower       93\n",
              "weight          351\n",
              "acceleration     95\n",
              "model_year       13\n",
              "origin            3\n",
              "name            305\n",
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 398 entries, 0 to 397\n",
            "Data columns (total 9 columns):\n",
            " #   Column        Non-Null Count  Dtype  \n",
            "---  ------        --------------  -----  \n",
            " 0   mpg           398 non-null    float64\n",
            " 1   cylinders     398 non-null    int64  \n",
            " 2   displacement  398 non-null    float64\n",
            " 3   horsepower    392 non-null    float64\n",
            " 4   weight        398 non-null    int64  \n",
            " 5   acceleration  398 non-null    float64\n",
            " 6   model_year    398 non-null    int64  \n",
            " 7   origin        398 non-null    object \n",
            " 8   name          398 non-null    object \n",
            "dtypes: float64(4), int64(3), object(2)\n",
            "memory usage: 28.1+ KB\n"
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              "              mpg   cylinders  displacement  horsepower       weight  \\\n",
              "count  398.000000  398.000000    398.000000  392.000000   398.000000   \n",
              "mean    23.514573    5.454774    193.425879  104.469388  2970.424623   \n",
              "std      7.815984    1.701004    104.269838   38.491160   846.841774   \n",
              "min      9.000000    3.000000     68.000000   46.000000  1613.000000   \n",
              "25%     17.500000    4.000000    104.250000   75.000000  2223.750000   \n",
              "50%     23.000000    4.000000    148.500000   93.500000  2803.500000   \n",
              "75%     29.000000    8.000000    262.000000  126.000000  3608.000000   \n",
              "max     46.600000    8.000000    455.000000  230.000000  5140.000000   \n",
              "\n",
              "       acceleration  model_year  \n",
              "count    398.000000  398.000000  \n",
              "mean      15.568090   76.010050  \n",
              "std        2.757689    3.697627  \n",
              "min        8.000000   70.000000  \n",
              "25%       13.825000   73.000000  \n",
              "50%       15.500000   76.000000  \n",
              "75%       17.175000   79.000000  \n",
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              "      <td>398.000000</td>\n",
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              "      <td>392.000000</td>\n",
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              "      <th>mean</th>\n",
              "      <td>23.514573</td>\n",
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              "      <th>std</th>\n",
              "      <td>7.815984</td>\n",
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              "      <th>min</th>\n",
              "      <td>9.000000</td>\n",
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              "      <th>25%</th>\n",
              "      <td>17.500000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>104.250000</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>2223.750000</td>\n",
              "      <td>13.825000</td>\n",
              "      <td>73.000000</td>\n",
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              "      <th>50%</th>\n",
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              "      <td>4.000000</td>\n",
              "      <td>148.500000</td>\n",
              "      <td>93.500000</td>\n",
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              "      <th>75%</th>\n",
              "      <td>29.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>262.000000</td>\n",
              "      <td>126.000000</td>\n",
              "      <td>3608.000000</td>\n",
              "      <td>17.175000</td>\n",
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              "      <td>8.000000</td>\n",
              "      <td>455.000000</td>\n",
              "      <td>230.000000</td>\n",
              "      <td>5140.000000</td>\n",
              "      <td>24.800000</td>\n",
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              "\n",
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          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "df.corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        },
        "id": "p4n7sMjqpKZ6",
        "outputId": "512df98a-7a73-4cc8-f3f8-5b6b632f13b2"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-11-2f6f6606aa2c>:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  df.corr()\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   mpg  cylinders  displacement  horsepower    weight  \\\n",
              "mpg           1.000000  -0.775396     -0.804203   -0.778427 -0.831741   \n",
              "cylinders    -0.775396   1.000000      0.950721    0.842983  0.896017   \n",
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              "weight       -0.831741   0.896017      0.932824    0.864538  1.000000   \n",
              "acceleration  0.420289  -0.505419     -0.543684   -0.689196 -0.417457   \n",
              "model_year    0.579267  -0.348746     -0.370164   -0.416361 -0.306564   \n",
              "\n",
              "              acceleration  model_year  \n",
              "mpg               0.420289    0.579267  \n",
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              "displacement     -0.543684   -0.370164  \n",
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              "  "
            ]
          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Remove Missing Values"
      ],
      "metadata": {
        "id": "optfegrFpRbs"
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    {
      "cell_type": "code",
      "source": [
        "df = df.dropna()"
      ],
      "metadata": {
        "id": "-AeHPrkGpNBe"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        " df.info()"
      ],
      "metadata": {
        "colab": {
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        },
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        "outputId": "15fe7731-24f5-4816-8ab5-233257fc0b12"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 392 entries, 0 to 397\n",
            "Data columns (total 9 columns):\n",
            " #   Column        Non-Null Count  Dtype  \n",
            "---  ------        --------------  -----  \n",
            " 0   mpg           392 non-null    float64\n",
            " 1   cylinders     392 non-null    int64  \n",
            " 2   displacement  392 non-null    float64\n",
            " 3   horsepower    392 non-null    float64\n",
            " 4   weight        392 non-null    int64  \n",
            " 5   acceleration  392 non-null    float64\n",
            " 6   model_year    392 non-null    int64  \n",
            " 7   origin        392 non-null    object \n",
            " 8   name          392 non-null    object \n",
            "dtypes: float64(4), int64(3), object(2)\n",
            "memory usage: 30.6+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "DATA VISUALIZATION"
      ],
      "metadata": {
        "id": "f5fbINzNpoXI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        " sns.pairplot(df,x_vars = ['displacement','horsepower','weight','acceleration','mpg'],y_vars=['mpg']);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "dfz_KznZpktv",
        "outputId": "19627a1c-b795-47e2-ec78-5db06cdb625e"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1250x250 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.regplot(x= 'displacement',y = 'mpg', data = df);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "Caum3kYTqRV-",
        "outputId": "7c780753-45be-4472-f434-329817888ab0"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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exPYUXZnCVE9dEARBEGYFEshMIqN1uHY9v4IpaOnlAMZTipBlsKAhjGXoWLpGYyQwTUcgCIIgCDMbCWQmkdE6XCfCJitbYkSDJosbIixsCLOkMcpJzRGiAYOejM2yuXWsnj8zvHAEQRAEYaYhYt8poMJHxlNYer97L1Asz3apj1gEDZ2869GTKRAwdD5w/mJczy/JXtgQ4aq1rZjm2OJPaYEgHI/I51oQjm9qvX9LIDNFjPRLd3Cg43kKVykKtkvW9ihdIF2DeNji5kuXccPFy2rar7RAEI5H5HMtCMc/EsgUmSmBzGiUAp0ndh/hu0+9QW+mQMauJhIGQ9e49e0rRg1mhjPj687Y1AUNvnz1GvmlL8w65HMtCCcGUn49y9B1jdXz4zy55yh52yHn9AcxGv7SUgnXU9z52B4cZ2igU2KwGV/IMtB1jZBl0BIPks673L1pD553XMexwnGGfK4FQRiMBDIziO1tSfZ0pPEUDPw9rMBvXVB8bOiQyto8tPXQqGM1RAIVHjXg+9bURyz2dKTFo0aYVcjnWhCEwUggM4PoyhToy7skc07V11XxRyv+ebAnM+JYw5nxAQQNXTxqhFmHfK4FQRiMBDIziPqwRdZ2GU21VApmFtRHht1mJDM+gLzriUeNMOuQz7UgCIORQGaG4HmK3R1pfH/fkXE8iIUtrlrbOuw2o5nxiUeNMBuRz7UgCIORQGYG8OTuTq69dzNf+tkOHLc2keI7T28Z0U9mNDO+uqDBTZcsE98NYVYhn2tBEAYjgcw08+TuTm57cCvbDvSg6xq6rjGa313I1NnXnR21MmP98ma+fPUaVrXGyOQdOtJ5MnmHVa2xMZWoep5i24FeNr1yhG0HeqUiRJhWJupzLQjC8YE53RM4kfE8xR2/2Elbb6742MFTjLi0pAEN0UC5MmPNwsSI+1i/vJl1S5vG7YAqxmPCTORYP9eCIBw/SCAzjWzYvI8dh1KgFKahYxgarqewR8h46BocTecJB8yaKzN0XRs14KnGcMZjOw+luH3jNvn2K0wr4/1cC4JwfCFLS9OE5ym+v3kfnqcwDQ1d09A0zQ9oqnyp1DUImDqWoeN6imzBpT5sTer8xHhMEARBmOlIIDNNbG9L0pHM49thVEYuxqBIxjI0AqaOoWnlTbVJzqCL8ZggCIIwG5ClpWmitCwUNA3yjodmgFaKUlTJ8s5HLwYSnlK4nkLX/MxIT9ae1PkNNh5TSpGzPRzPQ9d8Lw8xHhMEQRCmEwlkpomSsVd9JMCRVB7HVRi6n2kZ6CQTNHU8pVCe/1rIMkiELZRSk2r6NdB4LKQbpPMOR1I58o5XNuzTNY39XcO7CwuCIAjCZCNLS9NEydgr73jMr/c1KJ5SOK7C81SxyzUsnxNlXjxEU12AefEQSxrD5B2vbPo1WaXRA43HUjmbg91ZsrafiTF0PzujUNzzu708ubtzQvYpCIIgCGNFMjJThOepIaWiN12yrFgV5NKSCOJ5kLNdMrZL0NQpOB6vdKT9oEH5GZnDSY2maICbLlnG03uPctdju3m5PYXtKCxTY2VLjI9furyimqi0786+PD19Ng0Ri6a64IjlqiXjsdse3MrBniyuqzAM8Dx/iUvXNBbUh8ui33VLm2oqfa12HqRkVhAEQRgvEshMASN5sXz56jX9r3kKS9dYsyDBxac0869PvAZ5AK1C3KuA7W29/OsTr9HVV+jvzVSAP7zWxasdL/CP73sT65c3l/e9o62XZM7B8xS6rhEPWZxWDKaGK6Fev7yZGy5exhf+azse4Ln+3jXAMrUhot/RSmHFk0YQBEGYaDQ1uGHJcUYymSSRSNDb20s8PvX9V4bzYunO2NQFDb589Zohxl6rWmJc951n2HkoybxYkLyjcDwPU9cJmhqHU741e7bgomlg6nq5I7bj+RqW1fPj3Pr2lXzuJy/R1VfwrdyLmRTPU2iaRjRo0BAJjOgHc/dje/jaIy+j4Vcr6RqggeuBoWm0JkJkbJevv/cMLjl1zjGdBwlmBEEQhBK13r9FIzOJ1OrFArBmYYJLTp3DmoUJdranyqXPuq4TDhjEQhbhgIGu64RMnb68C4CplyqaKD/WgF3tKb72yC5SORvXU3gKLF3H1HWsYg8Ex1Wk886wfjCep3hkezsAhq5hGTqGrmNoOpau4SpFRyqPqTGi8Fg8aQRBEITJQgKZSWS8XizVSp8H4hRv+Bpgu4qC41FwPQqOh+362RbbU7ze2UckYFJwPT/AKc5BQ8PQ/YqksGUM6wfje93kCJpGsXVCf6ChFUW/ecdlXiI8Yrdh8aQRBEEQJgsJZCaR0QKSoKFje2qIF8vA0udqlAIZV/VnYkrhgaeKrytwi0tJSg223CuWeRcFxNXmMHD+c+NBdE3zK6qK1UqeUriur5e5YvW8EQW74z0PgiAIgjAaEshMIqMFJHnXw9K1IcsyA0ufB0uYlFLYTv94Wul/xZ+B4USppNv3pqmkFMQoRdU5DJx/wNBZ0BCuLBFXioBpUB8JcNHy4bUxx3IeBEEQBGE0JJCZREYLSHoydtkPZiCl0ue6oEF70hf2ep4ia7u0J/OEAwal5IYq/8//s/RXQ/fLozMFl4Ch43iqPAeF7xAcMHSytlt1DoPnHw0YnNQcYUljlIUNYRY3RIgGDU6bHx9xWelYzoMgCIIgjIYEMpPIcAFJpuBwoDuLocEVq1uqvnf98ma+fPUaVrXGyOQdOtJ5MnmHVa0xrl1/EnUBqyz0VfiZlVKIYOoa0aDJ209vJRYyMXQdXQPb89sLlDI6pqFRFzS56ZJlVZeGBs8/Z3sETR3T0OnNOSO+t5bzUArM6oJGTeMIgiAIwmCk/HqCGMnobaB/Sl+hv2w6ZBlEA8aIXirVxt3eluTG7z2LpkFP1iZveyjli3yDlk4ibIGCb3/wHFI5e9w+MiUG+78AzI0H+X/OW8z7z1tccwBSMU7RM0d8ZARBEIRq1Hr/ntZA5u677+buu+/m9ddfB2D16tV8/vOf58orrwTg0ksvZdOmTRXvufHGG/nWt75V8z6mIpCpxejN8xQbNu/j//vtq+Rtj+ZYgKBhjMtLxfMU1967mZ2HUsyLBar4zBRY1RrjO9edh65r43L2rbbPDZv38cDmfbQnc6AgYOpjDkTE2VcQBEGohVnhI7Nw4UK+8pWv8Nxzz/Hss8/y1re+lXe9611s3769vM0NN9zAoUOHyj//8A//MI0zHkrJ6G3noSTRoMncWJBo0GTnoRS3b9xW0Yfoke3tuJ5iYUOYsGWO20tl4FLN4VQBNIgGTNDgcKow7FKNrmksn1vHxUW/mrEEEE/vPcq//m4v7ckcDRG/79Nwxzna3Ad65kgQIwiCIBwL09qi4Kqrrqp4/KUvfYm7776bp59+mtWrVwMQiURoaamuI5luBhu9lTxSQrpBS1ynPZkv9yEai5fKaFb/0K+hKWWCeotLNataYxUZkoloCzCW45TARBAEQZhKZkyvJdd1+dGPfkRfXx8XXHBB+fn777+f//iP/6ClpYWrrrqKv/mbvyESiQw7Tj6fJ5/Plx8nk5NnsjaW4KQWL5XeMXqprF/ePKS9wWBtTqktQNgyMA3fU2bnoSS3b9xW81LWRAdhgiAIgjBRTHsgs23bNi644AJyuRx1dXVs3LiR0047DYD3v//9LFmyhPnz57N161ZuvfVWdu3axYMPPjjseHfccQdf+MIXpmTuYwlOBnqphHRjyLYT7aVSyqJ0Zwo4rqI3a5e9YwKGju16NWdRJiMIEwRBEISJYNoDmRUrVvDCCy/Q29vLf/7nf3LttdeyadMmTjvtND760Y+Wt1uzZg2tra1cdtll7Nmzh2XLllUd77bbbuOWW24pP04mkyxatGhS5j6W4KTkpbLzUIqWuF6R2Sh5qaxqjY3JS+WJV4/w9V++wv6uDJ5ShK3+CqhYyGJHW7Lck0nTQC8a4+Udj7wDO9qSNWVRpjoIEwRBEIRamXYfmUAgwPLlyzn77LO54447OOOMM/inf/qnqtuef/75AOzevXvY8YLBIPF4vOJnshiL0VstXipXrG7hd7s72Xagtyz69TzFtgO9bHrlSMXz9zy+h49891m2HuihJ1MglbPpzthsPdDD7Ru38btXj5DM2XieKrvxFtx+V17PUyRzNkfT+SHHdSzHKQiCIAhTybRnZAbjeV6FxmUgL7zwAgCtra1TOKPhKQUnt2/cRnsyT33EImjo5F2PnmJJ9cDqoeEEuq2JIAB3Pbq7QpB78SnNPP5q5xCh7puXN/GNX/tl3Jahoeu+9sV2PRwXoMBPXmjDcVXZ+VejvyVBKRZRruJoX2HUkuixHqcgCIIgTBXT6iNz2223ceWVV7J48WJSqRQbNmzgq1/9Ko888ghLly5lw4YNvOMd76CpqYmtW7fy6U9/moULFw7xlhmJKfeRqcHobWDgsL8rwz2P76Gv4NIQCRAwdAqux+FknkzBIRo0mBsLlZ/vzhRI5mxsR2GZGobWn1RT+BkXy9AJWzpdGbv8WimIGcySxgjxsMnRdGHUqiYxtBMEQRCmillhiHf99dfzm9/8hkOHDpFIJFi7di233norb3vb29i/fz9/9md/xksvvURfXx+LFi3i6quv5nOf+9yYApKZ4Ow70nt8Y7tkRVmzUorXOvvIFFwiAYOT50TRiu0gM3mHvZ19KCBo+p0iSyLeUrDieh4B0yBbcIc0ixxMKVPTmghRHw6MatAnhnaCIAjCVDArApmpYKoCmfGw7UAvN37vWaJBk5DVL6LNFlze6OorP17SGCUc8F9P5Wz2Hc1Q6iOt4Qt4SwGJqeu4nkckaJItOHiqsg/TYDRA1yFsGZzUFEXTNJRStCfzFe7AgiAIgjCVzApn3xOdUlmzZWhkCy6pnE224GJ7HkoVq4wUOJ5Xfo+p6xVBSSmIUYCnoOB6eApaE2HiIRNjlCDE0DVMXSfveORsfz+DvWFmIsOJoAVBEIQTixkn9j2RaIwE8JTitc4MTjF4KWVVSoFJ6XGJkKUP0btUu4V/9sqV/OvvX+PF/b1klYM9zI3e9VR5PD9g8jM/M9kbZiLcigVBEITjA8nITCO92QJ9BYec7aIBpqGhaxoFxy/NdoomdKFA/2XKFtyqot3B7O/JctMly2iImBUl04PzMwpwXDUkYJqp3jBj6W0lCIIgHP9IIDNNeJ7i24/vJWDoWIaGW9SyaIBhaOUsi2lo5Gyv7DnTmc6jaWDpQ4MSDSjqf3lg8z7WLW3ihosry6IV/pJVwNApPa3wg5iQ5X8cZqo3zOCeTyHLGHfjTUEQBOH4QJaWpolS/6J58RCOpziSypF3+peXwpaBZcDJTVFe78pgOx6WqXNyc5TXOjP0FWwCZinwqKxawlO0J3Nsb0uyqDFCImzhFYMTRTHzg+8P47n+TT8S0FEKco47Y71hpOeTIAiCMBgJZKaJgf2LQpZGNBAlZ3s4noep6wQMjTe6M7zSkSJX8PAAveCy10sTCZgkc2DoCl3rT80opXA8RagY4JR8apI5B9fzUPiBju0odM2vVgqYOp7nj9ORzlftoD1TSq6l55MgCIIwGAlkpoBqgcDg/kWaphVLrH2xbVtPttwnyTI0LM0X/6ZyLumcC5qvbTENVa5acjyFoWkkIhYoymZ7nlIo5Y/j+8z4mpjmuiAFx2NVa4y/umIlPVm7agftmSKslZ5PgiAIwmAkkJlkhgsEbrx46bBNJD3Po6vPzyoEDDCKIlxdA13zyDuqf2moGKT4y1E6zXW+VmRlSx0Pv9ROX8FlQX2Ytp4cjqcwdQ3T8IOgznSeBfVhPn7pcs5YVF917rdv3EY671S4DpeEtdUM8yaTyWi8WWKmZJ0EQRCEsSGBzCQyUiDwuZ+8xAfOX8z+rsyQ/kWHe3MowNDBGJR50DUdy/BwXUU8ZOJ6EA4YhC0DTYPerENd0ODtp7dy16O7aYgECFkGCxq0QTocDQ2Nj7x56bBtFAYKa0tBQ0g3aInrtCfz3L1pD+uWNk3ZDX+yej7NpKyTIAiCMDakammSqKXC5vFXO/niH5/OqtYYmbxDRzpPJu/QGPWXRsxhbsh6sTLpspXzWLMwgQak8g7Zgsuq1hhfvnoNixojFXqSuqDJSU1RljRGWdgQZnGxx9KixkjVfYxFWDuVlBpvDj5npeMea+Ah5dyCIAizG8nITBK1BgKJcIDvXHdexbLGK4dT/PV/vohXdPcdjFcs0z5/aRPL59bxwr4elAZnLqpnzYIEuq6x7UDvUD2J5i/B5B0P2/VdfOvDVtX5z2Rh7frlzaxb2nTMS0EzMeskCIIgjA0JZCaJsQQCuq5VlAuvaonx9z/bQW/GRtc8vzKpiKc8HFcRCRo8uOUAr3X2VV0OGawn6Su4HOrNkre9skeNBnx24zZuf8eqIZmMmS6sHXzOxoOUcwuCIMx+ZGlpkhgYCFRjpEDANHVuvnQZhq5RcBSO5/kBjOdRcPyKo4Chs+twikjQIBby49FtB3u5rbgcUtKT1AUN9ndneONoH7kBQQz4lU4vtSW5ecOWIUsopUCoO2MzuK/oTDXMGyu1BJu2lHMLgiDMaCSQmQCqNTCsJRBYOqcOTyke3dXBxi0HeezljvL7b7h4Gbe+fQXxsInrKgqOwnUVsZDBkqYIhq5RFzRp782zvztDRypPOmfT1pPljl/sxPMU65c388U/Pt2f4yCz26LMBoDujM2Xf76zwhF3YCDUnsyTtd2yu3B7Mj9uYW2tzR6noinksQSbgiAIwsxAlpaOkZEqXkaqsDF1v9fSn9+3mWTO8U3pdI14yOK0+XFuvHgp4YDJgvow+70srlIETJ2TGiPs784QskzaenJ4SmHoGpoOSmk4rseOQyk2bN7Hn61bQiIcwBoQbGiD/qIpPzOz63CKbQd7K8qwS8La0vH1eqqqYd5EnKuBY01VFdFklnMLgiAIU4OmBqcLjjOSySSJRILe3l7i8Ym9IQ1XXt1dLAX+8tVrAPpvysVAoKkuQEcqT7bgkrVdXM932vU83w8maGrYnm9cp5Rfhh00DeojAdI5m1TeIWDovi+M4ZdRl/CUR8FVrG6N81+fuIjf7e7k5vufI10019MGJ1CKgYwG/O1Vq7n2wpOGHOdEeKzUcq7WL2+uebuJon9/btVy7qn2yhEEQRB8ar1/y9LSOKm1geG6pU1857rz+PYHz+Hr7z2Du//sbBJhC8f1cD2F6/mRhOuBB7gKMrbCdhWegoCpYeg6ecfjSCpPJGgUeyJ56BoVQYyPhqFp5V5LjZEAxpDopQoaqGE203Wt7EbclSmwvS05pqWeWs+V43hT3hRyosu5BUEQhKlFlpbGyVgrXkpVL9sO9LL3SB+RgElPNjtEuzKYUgm2aWg4riKVc7BMXwRc/q/YORsUrlKETL/KqCtT4M3LmzmpOcqLB3qB/gaT/gPK4l9L1zizirsvHPtST63n6qGth6alimiiyrkFQRCEqUcyMuNkvBUvpfdp+FmYEprGkNwKgO0qCo5HwfHQNMg7HvGg7/3iuJC3PfKOR8H1l5Q8z++cHTB0GiMBdF3jr65YgWUM1H8Ufwbse0VLjDULhgYHE2EYV+u5OtiTmbYqolI59yWnzmHNwoQEMYIgCLMECWTGSWMkgK5BpuDgVZEZDVfxUqqUyTv9UUwtKz+e8vsjeZ7fsdrQNb+b9YBtSpVIXZkCTXWBskj1olPm8NeDgpnS9oauMTcW5LYrVw25eQ9cEpoXD6IU9BUclIJ58UDNSz21VgctqI9IFZEgCIIwJiSQGSer58c5qTlKT9am4LjYrlcOaEbyWSlVymQKbn8GZjTJh+YHOwpwlSJTcAlb/Y0kLV0jYGhYhl+9VI0bLl7GvR8+lzMWJoiHTKJBg6aoxQVLG/nH972p6hJRaUkoaBq8cTTLG119HOj2/3zjaJagqdfUpqBWT5qr1rYe9941giAIwsQiGplxousa160/ib//2Q460wWCpl7OrBQcj1jI4saLl5Z1F/GQyd4jfRzqzXL6ggSvHk6RzjtAsWpIDR/PDE74mIYvAE6ELXqzNnnH89sWaP6yUjxscTRdGKIlueiUOaxf1jxECwK+dmewPqQrU6Cv4NKXs1GArmnouj/hnO2St10iQXPUpZ5amz2apj4pTSEFQRCE4xcJZI6Bc09u5Kq187l/8z56k/lyGXM0ZHLGwgR3Prqb1zv7SOYcMgW3/LquQcjSCVp6uWXAWOpwVrfG2XEoRX04QEMkQM72XX9NXSdk6SgFHel8OcAYqXx6JCFvfdgik3dwipNzlUIrlogbuobnKXK2O2y/poHU6kkz0d41giAIwvGNBDLjRCnFM6918dDWNgwN5saD6JqGp/zKov/eeohwwMDS/T5H5ffhBwKZgi/ejYVMNCBoGegaHEn5AVE12Ymh+c9vP5REKeX3QbIMwgED6O+HlHPcspZkpEAFqOrZUhLyvmXFHOxBE1EUhcJFwfJYXIhqrQ6SKiJBEAShViSQGSef/+l2frbtELbj0pIIlf1cFIrerI2rwHFdMnl3yHtL/jAFxy+XftPC+nLzR9PQcT2PgKFju15xOUdD03yxb6SojXEVdPUVaE2EhnWk7c0W+NxPXqoaqNz24FbiYWvYzs+HenP88NkDFfMeULVdziCFLJ2erF3zeau12WPJu6YUzGxvS0owIwiCIAxBAplx0JMp8INn91MoVh7ZXVnqIxaxoEneUdiuh6lDwfFN7gbjFcufTUMjV3B591kLOXVejK5Mgf1dGf75t6/Skcpj6JpfnVSsWNI1jbnxEIau0d1XIGDqw2pJbrx4Kd9+fO+wgcqB7iztyTyLGsNVPVvCAYPuTAFD84OKkstwaXkM/CWygKlPShXRVLUpEARBEGY3EsiMg588f7AcxIDvstuezHNELxAJGHgeGMbIuhffmM537z3Um+WasxeWX0vlbL7xq1dQSuG4/lJUyDKYEwtSFzTLfZk+uG4xv9x+mL2dfXjKb22wqjXOxy9dTixkjWguFw4YJHM2XvVKZwzNL++2DA23WPJd8p/xh1MUXEVLPDThVUTDtSkoLXmJ464gCIJQQsqvx8GfrVvCbVeuHOLL4nq+PsbDN6sbkaLeRQOiAZPsAB3NRcvnMKcuSGsizMKGMEsao5zUHKEu6MededfD8xQPbjnIzvYUqbxDX8EhmXPoyfjLPKOZ0IUtX1OTs6tP1FUKHYiHLXTNdxUG/Kol+jNEf3re4gld7qm1ncFkdMMWBEEQZh8SyIwD09C54c1LOWtxA7GQWRbsDmSkSqSSmsbx/Aqni5Y3c6g3y8GeLH15p+y7krU96oIm4YDRr8FRiiOpHKmczZ4jfThFkzjL8KuIdhxK8ukfvsD+rsyI5nKaBqauk7Hdqp4t2YJLLGyhFMyv9wMKTykcV+F6HrqucVprjPeft/gYzuRQxtL6QRAEQRAkkBknJR+ZeMjE1DXm14dpjFjUkpzQNbBdvwrpA+ctxihmdvK2y+FkjoM9Wa69YAnRgK+BydounqfI2i7tyRx5x/MzJhpYpo6h6xiajmX4XjZdfQV+sa2NpXOGN5frzTqsaKkjEbaq7CNPLGRy86XLqAuapPMuLYkgixoizI0FqQtZLKgPV3UDPlbG2/pBEARBODERjcwxcO7JjdzytlPZsHk/+4/2oTRoilhEQxZ9eZcj6XzV97kKopbBBy9YwvvOXTTk9bzj4rjw9tNb+f3uTo6m8/Qq38F3YUOE14qZGMPQ8TyFp/xSaE33tS2egl2H03zq8lM50J0ZVhB825Wr8JTi6798hf1dGTylCFtGhWfL6vmJftFt0dNlzYLEuEW3I3naQGU7g5BuoFDkCv0+OWhK2hQIgiAIZaY1kLn77ru5++67ef311wFYvXo1n//857nyyisByOVyfOYzn+GBBx4gn89zxRVXcNdddzFv3rxpnHUlZy5u4IxF9ew+3EdvrkAiFGD5vCgasO1gL999ah/P7+sesszUZ7tseuUIJ8+Jcs6ShvIyyvP7utmweT/7OtPkHIWGorEuyDVrW7l8VQvd2QKfeuAFPA9cz6sct7iK5Gd8FIsaIyOaywF8+/G9HO7N4np+hmduPMSNFy+tMKibCE8Xz1Ns2LyP72/eR0fSD/CqVSKVltV2HkpRF/ToTBfIO265u7emaaxsiUmbAkEQBAEATQ1ed5hCHnroIQzD4JRTTkEpxXe+8x2+9rWv8fzzz7N69Wpuuukmfvazn3HfffeRSCT4xCc+ga7r/P73v695H8lkkkQiQW9vL/H4xN78jqbz9I7iofL8vm7+4ZFd9GTsYj+modssaYxw9VkLmBsLcueju+nJ2LiewnE9PPxKIcvUuP7Ck7lgWTOf2LCFZM4Zcb/xkMn9H1nHmoWJqlmQp/cerVoZ1F3M1kxkZdCTuzu54xc72XEohecpDB2CpkF9JEDe8Ybs78ndnXz6hy9wJJUvN7ZEA9dVKGBOLDhsfyhBEATh+KDW+/e0BjLVaGxs5Gtf+xrvec97mDNnDhs2bOA973kPAC+//DKrVq3iqaeeYt26dTWNN5mBTDJn05UuVO1+DeApxa0/3sbeI2ma6wIoRbmyqJoI19A1dHwNi1d8rOGb7Nmub6L391edzt//bAfpwshlUdGAzoufvwLTHKo18TzFtfduZuehZIXHDMV9tyfzrGqN8Z3rzjtmDcyTuzu57cGttPXm8DyFafg9uv0MkMb8+hDpvFuxP89TvOvOJ3i5PVWcky9ODpo6zXXBIdsLgiAIxx+13r9njNjXdV0eeOAB+vr6uOCCC3juueewbZvLL7+8vM3KlStZvHgxTz311LDj5PN5kslkxc9kEQ9ZLG6M0FQXJFAlYNh9uI/9R/uIhyw0NHRNoz5ssaQxzIL6ECGr8j2up7A9haMqrf91TcfSwXYUdz++F6WGMX8ZQM5RbD9U/dinqjKoVEpdylqZho6u6eiahmn47Rw60wXqI2bF/ra3JTmaLrCkMcKSxmh/CXpTlFjIksolQRAEocy0BzLbtm2jrq6OYDDIxz72MTZu3Mhpp51Ge3s7gUCA+vr6iu3nzZtHe3v7sOPdcccdJBKJ8s+iRUPFtBOJrmskwhYLGyLMrw9TFzLLwUFvruALZI2hwUI0YLKwPkw8bLJ+WdOQQEjh61xsV+EWDWc04Egqh+ONnoXwlOL5/T1VX5uqyqBSwBQJmH5WZcBrGr5rcd5x8Twq9leaX9D0+0jFQpZfgl48r1K5JAiCIJSY9kBmxYoVvPDCC/zhD3/gpptu4tprr2XHjh3jHu+2226jt7e3/LN///4JnO3IhCyDubEQSxojNEWDNEWDWLqG7VZfeiq4ipCh86F1J/HVq9cSqpLVUYDj+UtLfsNGhVdDRkYp0IZZNBxYGVSNfNGb5lgrg0oBScgy0LShvjqa5s8zZ7sV+5uq+QmCIAizn2kPZAKBAMuXL+fss8/mjjvu4IwzzuCf/umfaGlpoVAo0NPTU7H94cOHaWlpGXa8YDBIPB6v+JlqdF0jEbF468q5LJ8XI5VzUINu4wpFKmezqCnK8nlR1iyKc1JzFABDL7UBYNB7IGN7DBMXDeHkpghOlWCgVBk0nMdMT8Zm2dy6Y64MKgUkelHf4niqYn+lSqSM7Vbsb6rmJwiCIMx+pj2QGYzneeTzec4++2wsy+I3v/lN+bVdu3axb98+Lrjggmmcoa/92Hagl02vHGHbgd5h7fJ1XeMTb1lOPGzS1WdjFxsv5hy/rDgSMHj/ef7S1+7DfVy0vAlL93sbmRqYOkMcg4GqlU9D9q1B2nbZ352lI5UjP6Bngq5r3HTJMuqCRlUzvLqgwU2XLDtmIW0pIOnJOjTXBTE0P8vieB6O62IX+1XVh62K/U3V/ARBEITZz7T6yNx2221ceeWVLF68mFQqxYYNG3jsscd45JFHSCQSXH/99dxyyy00NjYSj8f55Cc/yQUXXFBzxdJkMNauzOuXN1d4uaQ9haHBqfNivO+cRYBf2bT/aB+2pwiYOk7BxR599WhEgqZOKudnNNI5h3TOIRwwSIQtIgFzyLwGe8xMRGlzKSDxy7xdYiGD7oxXsdQWsQw+cP7iIfubivkJgiAIs59pLb++/vrr+c1vfsOhQ4dIJBKsXbuWW2+9lbe97W1AvyHe97///QpDvJGWlgYzkeXXw3VlrsV7pZqXy5O7O7mtOF48ZGEZvp7mUG+G4aqr/XLs0akLmnz9PWdwakvdkNcsQ6c+YlEX9EW4x2p2NxqDfWR0DUxDI17s5VQXNIc9d6M5AU82071/QRCEE5VZ6yMz0UxUIDPR3iuDx1PK7zjteB57OvrKwYqp+1VOA/1kasHQ4O/+5+mcv6wRvZrgBr9pZCJsEQuZk3pz9jzFh/59My8d7CURNrEMg1BAR0ObcN+aiWSs2TdBEARh4ph1PjIznYn2Xhk8nq5rWIZONu9WZFw0zfef8f/Ua75groLP/vQlPnzvM/zk+YNkq6R4HM/jaF+efV0ZuvoKfpn3JLC9LcneI2nmxILEw4GKbt4ztaN1Kfu281CSaNBkbixINGiy81CK2zdu48ndndM9RUEQBAEJZGpmor1XhhvPGRxMHGNscaA7yzd/u5v3/ctT3P3YHg71Zods4ylFT6bAvq4Mnek89jBlz+NlMnxrahVcj4eSkV8679ASDxGyDHRdI2QZtMR9Z+G7N+2Z0H0KgiAI40O6X9fI4K7Mgxmrt8lw41kDbvYavmC2lP8Zy23T1MH1+t/Tl3f50XMH+PGWA1ywrIn3nLWQtQsTQ5bIklmbVM4hGvSFwUFz6LGOlYk+d5O95DOW7NuahYlj3p8gCIIwfiQjUyMT7W0y3HiJiMlAmYimFTUymq+SqTVXsnZBgu/8+blcc9YCIoH+4MFT8PvdR/n0D1/ko997jl+81E7BqRy1VOl0sDvLod5s1WWpsTCR524qlnymyvlYEARBOHYkkKmRifY2GW68vKMIF3swKfz+S57yvVcKjsLUNS5bOWfECxcPmXxg3RIWNkS4+S3L+eGN6/jEW5azoD5csd2eI3187ZFd/Om/PM29v3+No+n8kLGyBZdDvVkOdGdI50fuuD3WYx187oARl4umaslHnIUFQRBmD1K1NEYqljWK3ibHsqwx3HiLG8L8bFs7yZxd7v4cD1nc/JZl3HDxMu55fA//99ev0jcoW7KgPsQtbzuVMxc3DNmX43n89PlD/HJHO692pIe8bmgaZy9p4EPrlmAaOr25AolQgOXzfMfh3Yf7SBccFtSHOe+kBoxhMhZjPdYbL17K60czfH/zPjqSfjBVbblo24Febvzes0SDJkFTJ2f7AZ6p64QsnZzj0Zez+eu3r6KxLjDucun+irIULfHgpHYHFwRBEKpT6/1bNDJjZP3yZtYtbZowb5Hhxnt671FeP9rH9rYkBUcRMDVWz4+xen6/JqNaDOpPY+hcnt/XzYbN+8vGe41hE93Q6cnYZYGxqxSbX+9i8+tdGDqETIOIpdMQ9TMPPRm7HIAsbory0Tcv5fLT5tV87NWOtTdb4KsPv1z2mDF0CJoG9ZFAebmo5DFTWvIpOB6HerPkHa8c5AVNnUjApDtT4Is/24GuaePWzgw08mtP5qmPWAQNnbzr0VP0DBJnYUEQhJmBZGRmIKMZ7124rIkHntk/bM+l+rDJP7znDFa1xrFdj+f3dfONX71CpuBWGO91pgtkCw6moeEqqpZf61p/S4S5sSDxkIntKpI5m0jA4DNvW8ElK+aQCFuY48jQ3PbgVtp6c3iewjQ0QMP1FLqmMb8+RDrvlrMf29uSfPjeP5DMOXgKTF0rGwTarkexSTgLG8LEQ1bNZoUjzW8is2+CIAhC7UhGZpYyWAdSWtYI6QYtcZ1DvVl+8OyBchAzMAFTCkl7sw7fefJ1vnf9+WQLLj94dj+ZgktzXaDs3xIwwVMeHmDoGovrQ+zryg3RhQyMbY6mC4QsnZBp0FwXoDNd4P7N+1i7KEEy51AXNEmELQJVungPd5y9WRsA09DLxn2aAU4x0GpJBMsVQqtaYrjKfy1gav1Gf0qVj13TIBG20DStfM7ak3nu3rSHdUubxpRFmejsmyAIgjDxSCAzwxit9NfU9fJS0OBVJE3zgxlVHKdkMHegK0tTNICp63jKX5LK2wrb9TB1P5uRyrt4ysMy/AEdrz84KOEqxb6uLBHLKLc42H+0j92H+zi1pY5UziaVs4kWA5qQNaBaapDVv6cUezrSRAIm6bxb0e1bQ8PQIe+4eB7lCqGd7SkMTcMoNtZEV2iaP6/SVDVNI2d7hIuVWsdaLq3rmpRYC4IgzGDGFcg0NDQMucmCf9MIhUIsX76cD3/4w1x33XXHPMETjdFKf70aVwILrlcuD7ZdRdD0K3yUUnhKwys4KAW6Dp4HjuuVH2toWDoUhlm7ytgumV4XU9ewTI3DqWxFT6e+vENf3iFk+V40L+7vGeL70hAN0Jd3aaoL+AEYlcoeTQPlQc52yxVCXZkCuqaxoCHM0XSBvOOiPL91g4a/1KTwRc3QH0QFDZ1eKZcWBEE4LhlXIPP5z3+eL33pS1x55ZWcd955AGzevJmHH36Ym2++mddee42bbroJx3G44YYbJnTCxzujmccN1zdpMAFDL5cHDxxP0zQMDUKWWc7gaJq/tDPwscLv1wT+44HmeiUcT+EUFHf8/GVeXNvL1WcuqCjxztkuT+3p5Bu/eoWs7dJY1Puk8g6vH+0jW3CJBDSCpk7W9jB1P5zxs0oKlCJju6xZkGD1/Djb25JYhkbA0DmpOUKu4FctOa7icCrnzxW/h9RApFxaEATh+GVcgcwTTzzBF7/4RT72sY9VPP/tb3+bX/7yl/z4xz9m7dq1fPOb35RAZoyUzOP80l99SOmvX26s+Us/gDYglaEG6GZWz4+XDeaqjReydIKmQabgErb8oCeVc8jbLugK14Og6YuAC87IWaCc4/HgloNs3HKQdUubuObsBZy5qB4FbNjcr8/J2v3VRqDhKWhPFpgTC5CzPfKOYmC4pOFXI5UqhCrPTbC4fGSglKI7UyBTcIkE/IaUA89ZT8ZmVWusZrNCQRAEYfYwLkO8Rx55hMsvv3zI85dddhmPPPIIAO94xzvYu3fvsc3uBGQ087hYyOJPzllYzpYoirqYAbFGLGRw0SnNZY1MtfFyjoeh6xi6hmlopPK+WFcpsF2/WikcMLFHCGIG54YU8NTeo/zlj7byke8+x32/f4N9nWniIYus7XG4N0fW9tCLWSGjmPnpSFVvWDkosVJxbtp6shxO5jjcm6MjlUeD8rHkbO+YzAoFQRCE2cO4ApnGxkYeeuihIc8/9NBDNDY2AtDX10csFju22Z2grF/ezJevXsOq1hiZvENHOk8m77CqNcaXr17Dl969lpObo1XfqwOWYfDvT7zOjd97lmvv3QxQdbwzFiX4k3MWous6h3qyHEnnQfO1JiFTJ5m1R+zvZJkaCxtC1AUNWuKhitde6+zjP/7wBp19Nr1Zm85UDlf5zsSa33cBwxiQbRp4DBo0RS1WzovhelS49a5f3syFy5pI5hw6Unk60nk6UnlyjsclpzazdmF91XMm5dKCIAjHJ+NaWvqbv/kbbrrpJh599NGyRuaZZ57h5z//Od/61rcA+NWvfsUll1wycTM9wRip9Pf6+zaz+0hf1fd5QNZ2ObkpSsH1KkzlSl4sA83oPveTlzB1jfn14bJGJmv7bsG67hEwdbr7Cn4GSPP1K755iyoGFxqWqfMXly9HQ+PBLQfY/Hp3eT4K6CmWWJe0NyiFpml4g8TEhuZnXTylSOVc4mFvSMXRPY/v8cvPPYWpU/aRcT3Fplc6+asrVvDXV6ycsHLpwdVWUn4tCIIwsxhXIHPDDTdw2mmn8c///M88+OCDAKxYsYJNmzaxfv16AD7zmc9M3CxPUKqV/uZyDr95+ciI78sU2xb4PYgqfVRK45Vs+Af71QAklGJ/V4as7dIUDdCbtf3GlQDFYERp4LmKgu1iaRoN4SCnttRx3smN7Dua4cHnD/LL7e3kBjSk9Jet/AojXVNDDP10zfeG0QHb8ziSyrG4IVKuOHIcjzsf24Pr+T4yJWGwroGhKwqO701z/YUnY9bgZTMak91lWxCOV+QLgDCVjNtH5sILL+TCCy+cyLkINfD3P99Z03aHenMsaAgP66Myml9NPGyRzjvYrleuKrKKy0Ia/S7AWcfj1Hmxcj8mgMVNET51+Slcf9FJ3PO71/j5tkMVxnoKKgz9VPl55bvzFpe3crZLb84uVxw9tPUQqayNofsBkSr6xxSTRRg6pLI2D209xNVnLRjzuR3IcO7Kg9smCIJQiXwBEKaacQcyruuyceNGdu70b6ynnXYa73rXuzBN8dibTF7vqr6kNJi8299MspqPymh+NbGQia5rJHMuzXVB2npy2MXlHFB+GwFdozFiceOblxI0DexBrsCxkMUtbzuVi5Y38bcP7SBnD+0mPTAp4ydvVFG462dwejMOqxfEWTGvjk2vdBQN/fztSwEMyncgLrUoONiTqekcDcdo7srjdQoWhOMd+QIgTAfjyr9v376dU089lWuvvZaNGzeyceNGrr32Wk455RReeumliZ6jMICTGquLfAcTNPo9aKr5qAz0q6lGwVXEQxbRoEE679IcCxAydVxP+UZ5msbKlhh3vHstf3R6C4saI7QkQkQCQwPZ805u4u/ftZo5dQHCpk7YGvlj55va+VmbcEDnfecs5GBvjmjQHODgS3/ZlEbZGVgBrYnwkDHHwmjZqoEZLkEQfAZ/AQhZvgmnv8QdJJ13K4T7gjBRjCuQ+chHPsLq1as5cOAAW7ZsYcuWLezfv5+1a9fy0Y9+dKLnKAzgb96xqqbtWhN+FVHJR2XZ3LoKH5WSJ0t3xh7SRbv0ntPmx/nae9ayqjUGCsIBvzXB6tY4X/ifq/npzRdVfLuKBExaEiEWN0aojwQqjOnOXtLI/75yJactSBALmiRCtWXubn7rcs5c3IBSijmx4IA5Vm43MEuzdE5twd5wjJatChp6uW2CIAg+8gVAmC7GtQ70wgsv8Oyzz9LQ0FB+rqGhgS996Uuce+65Eza544nxit+qve+ylXNGFPxGin2GsrZLT7H7c8lHZeB4V6xuYV9XhvZknvqIRdDQybsePZkCAUNn/bJmEuEA9157LjvbUxxN5znaV6A3Z+O6im0He1mzIDHkOExDpzEaoCFikSm4pHIOmYLDmYsbOGNRPbsP9/FSWw93PrpnxPJugDt+/jLPn97Du89cQF/eIWzqZIsC4mrdGsKWzosHeknmnHGLDEdzVxanYEEYSi1fAKRViDAZjCuQOfXUUzl8+DCrV6+ueL6jo4Ply5dPyMSOJ8YrfhvufRef0symV47gVF8VwtQ1OtJ5LF1jVWusvJ9q4zXVBYiHTI6mC/R6fkm1qxRKufz7E6/xvadeL+/zv15sY1d7utjLCCxD59R5ddx25aqqx6FpGtGgSTRo4rgeqZxDOu9waksdLx3qBfySa08NbX9QIu94/PSFNn76Qhur58cJmDpB09fuDDx8HYgGdfIO3Pnb3cX5jU9kOJq7sjgFC8JQ5AuAMF1oavC6Qg38/Oc/56//+q/527/9W9atWwfA008/zd/93d/xla98hYsuuqi8bTw+vb/sk8kkiUSC3t7eaZnLcOK37mKmZDjx23DvO5zMk8zZo+73T89dyAfOP6mckRhpHtGAzg0XLyOVs/nuU29QcFwao8HyNh2pHOmcg1cqdS5a8rrFNglzYkH+8X1vqjlYyBQc/u3x1/jGr1/xvWA0regvQ9mjxvVgmDgNXYPGiIWuaUWTPR1dU7QnCxg6zIuHMHSt7IlTFzTHLDLsP1/uoGzVyNdNEE5USpYOpRYig78AtCfzrGqN8Z3rzhORvFATtd6/xxXI6AO0D6UPa2mYgY81TcMdUD0zHUxnINP/Dzs5xKtlpH/Yw71PKcXuw0lyNZzSeMhgy+f+CNPUa5rHypYYoHi5PVW5TxSvHUnTV/DDiqChla+/QmE7Hrqucf7JTXz3z2v/BfXi/h7e+60ncVyFaWpoAxoeKBSOo9B1eN85i/n9nk7eODq0EknTIBGyCFs6R9J5HA8CBuXKJk2DoGlgGhprF9aP+RdoRQbLU1i6lJEKwkjIFwBhIqn1/j2upaVHH3103BM7kRiL+G2g8d1w78vZHvka48Jkzi37qdQyj13tKRRq6D4LpWaO5Tf0/xUN09BxPY9d7akhxzESaxYkWNESY3tbEsdRmIY/tKfAdf1Mz9LmKH9+0Ulcf9FJPPdGNw8+f5Cn93aVx1DKdw3uyRZLsQHH9TNGerF8O2e7YMOOtuSY5gcjuysLgjCUUnuV0heA3uIXgIFL3IIw0YwrkLnkkkvI5XJs3bqVjo4OPK9yEeB//s//OSGTm+2MV/w23PsczxtVHDuQkp9KLfMouB4oqu9zwE49T4Hen/EAhVJQcL0xifh0XeO2K1fx6R++QFef3zSytB9dh/pIgBsvWYZeDJzOOamRc05q5EB3ho3Pt/HwS+3lVgr+LPpRRT8ZXdPQDHAcRW+2wIHuDKcviA8J5kab51iCH0E40ZEvAMJUM65A5uGHH+ZDH/oQnZ2dQ16bCctJM4Xxit+Ge5+p6xVOuKOxoD5S8zwCho5CVd/ngJ06Ree5kpwF/IAmYOhjFvGtX97MRy46mTsf3UMy5zeo1DSoC1p89M0n8/bTW0nmbLKF/s/TwoYIn3zrcq678CQefqmdjc8f5FBvrvy6Ks0R31TP798Engf7ujLs78qSCFtlw78SE2GpLrbsguAjXwCEqWRcgcwnP/lJ3vve9/L5z3+eefPmTfScjhvGW/0y3PtClk7QoGaNzFVrW2ueR79GJl25z4BfJeQU/OCgIvNRHgPmxYNjruJ5cncn9/9hH4YOC+rD6JrfMDJTcNmweT+nL6hn/fJmCo5HMmcXBcf+XuuCJu85eyFXn7mA7z71Bv/x9BtDAjzXU5ROlaFBPGzheB5H+/J0ZwrEwxbxkMnm17qO2VJdbNkFQRCmh3EZ4h0+fJhbbrlFgphR0HWNmy5ZRl3QoD2ZJ2u7eJ4ia7u0J/MV/i61vC/neAQsq6Z9f/Ktp5QbJ9Yyj49fuoyPX7p86D5tD2PQ/Aaa6oL/IRrDag0w1AU0aBplce5gF9CAqdNcF2RxY4SmuiDWgOUvQ9e4cFkzibDpd+geBlfBv/3uNTa9cgTXU3hK0ZMp8NMX2vjrH29lR1sv0aDJ3FiQaNAsW6o/uXto1nEwJYHjzkPJcY8hCIIgjI9xBTLvec97eOyxxyZ4KscnJfHbqtYYmbxDRzpPJu+wqjU2ooJ//fJmvvjHp7OwIUxXOs/B3ix9OZszFiX47DtW0hCpHtCYOnz2HSu54eJleJ5i24FeNr1yhFjI4ot/fHr/PFJ5ejM28+JBPvLmpZx3UiOxkMWfnLuYBfUhejIFDvRk6ckUWNpcRzxkFjtOF7tf4wczYUuntT7M0XRhTI6dJQFy0NR5/WiG1zr72Nfl//n60QwBUxviAqrrGomwxaLGCK2JMNGgiaZpLJ8XZdncGCHLJGzpGFploFVi95E0X3hoBx/41z/wwOZ99GQKbNi8j768Q2M0UA7YxmKp7nmKux7bTU+mQDRglrVDYssuCIIwNYxraemf//mfee9738vvfvc71qxZgzUoS/C//tf/qmmcO+64gwcffJCXX36ZcDjM+vXr+epXv8qKFSvK21x66aVs2rSp4n033ngj3/rWt8Yz9WlhPOK3J3d38u3H99KRzKEAQ9OYlwhz48VLueiUOVx/0VLue3Iv//TrV0nlXDQNogGdNQvrWT0/MexSx40XL+X1oxke2LyP9mSOw705/vFXu/j6L3dhaBqOq8jaDgpf9xIwTGzXN8mzdB1P9/CUQkMjYOq0JIJELJOOdH5MYt+uTIG+vEs6b5c7Yfso+gouOdulLmQNO2Y4YBAOGDiuRzrv8MF1i/naI7voy7vEw5Z/LJ5HOudiex72gJ10pPL8y+9e474n30DXoCEaQMN3PfZQaJ6GoWskwmbVqrKBbNi8j82vdeMpRTqfLWaVdObEQtQFzWEr0wRBEISJYVyBzPe//31++ctfEgqFeOyxxyo0F5qm1RzIbNq0iZtvvplzzz0Xx3G4/fbb+aM/+iN27NhBNNrfL+eGG27g7/7u78qPI5HIeKY9rYxF/DbYvK4hEqDgehzozvK5n7zEl69eA8B3n9qHZRosmxsum9ftOtzHp3/4AuBrRAZ3oP3Mj16seK3geLT1ZnFchab5lT4lT6CC8qiP6LzR1Udf3kUDAqaOqflmc7brcbA7x5xYcMyOnfVhi3RhcBDTj6sgnbepD4+8lGYaOvWRAH985kLiIYtvPb6H14/0kVUelqaxan6c95+3iJBl8OMtB8tLS0C5Yeah3hy9AYP6sEW02N7BcRU6GnnXozOdH/Y6/X+/fRXb9bBMDR3f2C9rexzszrKgIUzEMsSWXRAEYRIZVyDz2c9+li984Qv87//9vyvM8cbKww8/XPH4vvvuY+7cuTz33HNcfPHF5ecjkQgtLS3j3s9sYrB2pCy61Q1a4jrtyTx3PbYHUFW3mRfXeOVwGoBT59aVr0+11zRN41BvFk+BZUDBBU8pgqaGVszO9GYL5WWR0v81/EomzQDb8Z1/zz+5aUxiX9f1GKbxdv82nr9drbx11TwuXTGXF/f3cLA3S8DQWTonWi7h/tw749x48VL+68U2HnqxjWTOKb83U3DJFFwsQ6M+bJEIWRQ8hQEUHD+YSYStsj6ndJ3ytoeh+546mubb+lk62J7iSCrHvHhIbNkFQRAmkXEFMoVCgT/5kz85piCmGr29fv+dxsbGiufvv/9+/uM//oOWlhauuuoq/uZv/mbYrEw+nyef7/8GnUzOrk6rYzWvA8gWXBzPw9R1lFLFjIpG3lGEB9w/83bla6DIOx6mXpLvloIG32nX0H0TPvBLmV3Pz1SYhqooA/cUvP30ljGVGj+y43DN2511UuOQ54crddZ1jTOXNHAmfsfsdN4hmXPIFz1n5sSCXH/RyfzZ+Yv59c7D3PXY3go/GttVHEkXONpXwDJ0Tplbx7K5UZJZm2TWJho0SYQtXj2cZk9HmuZYAMdT5GwXzegPaEzd7xN1NF1gzcKE9GUSBEGYJMYVyFx77bX84Ac/4Pbbb5+wiXiex6c+9SkuvPBCTj/99PLz73//+1myZAnz589n69at3HrrrezatYsHH3yw6jh33HEHX/jCFyZsXlPNWMzrCo7Hod4seccri0wNXcPzfO8Uv7ljvydMydxO0yg3fiw9HqhFLcY6aFp/h2nT8JecLFMvm9eVRK2mobOocWzLfX0FZ/SNhtmu1lJnTdOIhSxiIYuc7ZLM2fTlXZRSBC2Dd66dT2sixFd+8TK9OadCR+MpPxDZ3pbk//2v7Vxz1kLOWJigL+/Ql3d4tSNF3vGoD1vMiQU52O0vzxl66bwpHE8RNPWqlWmCIAjCxDCuQMZ1Xf7hH/6BRx55hLVr1w4R+37jG98Y85g333wzL730Ek888UTF8x/96EfLf1+zZg2tra1cdtll7Nmzh2XLlg0Z57bbbuOWW24pP04mkyxatGjM85kuajWvyzsubcVlIVPX/Jsn/lKPB1BspjiQsrld8e9A+X0Dkz+lv/e79/o3dkPXWJAI+8tOpQwQimzBHfPSyZmLGviPp/f173PAa2rQdgMp6YdSOZtIwMQydDyl2NGW5PaN24atBAtZBiHLwI0q0jmHZM7Gdj3OWtLIbe9YxYbN+3n9SJp0waXg9DsoK+D3u4/y+91HWTYnyrvPWshlK+cSsfxy74ztEgmYzK8P0ZkukHdclH8BCBg6n7zsFPGREQRBmETGFchs27aNM888E4CXXnqp4rWx2L+X+MQnPsF///d/8/jjj7Nw4cIRtz3//PMB2L17d9VAJhgMEgwGxzyHmUIt5nUr5tX5PYpcRcDUyhoQDT9z4jp+xiQwKA4KWlp5vJIOJmjqZG0PQ6u0ulP4guCQpaOUryGJFCuF/DGMisaXY106edcZ8/n8f71EX7F5VDXNbzRocNWaVrYd6KUrU6A+bHHXY7vLLQ2SuVw52AoYOrbr61bWLW0aNgNi6BqJiEUiYpEtuKRyNmctaeSMRfXsPtxHb65A0DB49UiKnzzfxsGebPm9e4708bVHdnHP43t559pW5iXCHOjOYBl+BdfChhC2o3A8j96cw+nzE7z/vMVjOi+CIAjC2JjWppFKKT75yU+yceNGHnvsMU4++eRR3/PCCy8A0NraOiFzmGmUzOtu37iN9mSOsGWUl3iytktd0OTKNa283J7C0DVfMKur8jau57vYKqCtN09zLDigA22BeMjC8xQHe3I01QVoigZp681ScIumQhoUHIWGP2Y4YFCw/Q7XrlIc6M6Ul5NyxfnUsnRSTdPyqctO4Y5fvEw1ixVdgz8+Yz7Xf+/Z8hISQE+mgFd0sRmYico5HrrmsaOtt+ZS54El3KmcgzVfL1c0rV2U4OozF7D5tS5+/NwBntvXU35fT9bm/j/sQy+WWrf35miIBgkYGranSOdc4iGTj196bEtK0vJAEARhdMYVyEwUN998Mxs2bOCnP/0psViM9vZ2ABKJBOFwmD179rBhwwbe8Y530NTUxNatW/n0pz/NxRdfzNq1a6dz6pPK+uXNfOD8xdz52B7aerJl87lY2OID5y9mUWMEXdNY0OAb0ZWWM0qalaa6AD0Z2zfT6yvQ6yk8z/eCMTRf0puzXfZ3ZQkHDCxdw3ZVcUnKn4Mq/u9ougDKf49bdPol6+tW6oIGN168dNSlk5E0Lf/PuYt44Nn9FRVMhg6XnjqH3+85Wi5BDxg63ZkCdjHQsHQqMlF+pZBHMufQ2Ve9XHo4TEOnIRqgPmKRzjv0Zm0Kjoeuaaxb2sS6pU281tnHxucP8qsdh8k7/mQ95ZdaA2TtLEFLJ2LqnDynjveft4iTmqP05R2iwbH/M5OWB8MjAZ4gCAPRVMk0ZDp2Pswy1L333suHP/xh9u/fz5/92Z/x0ksv0dfXx6JFi7j66qv53Oc+Rzxe21JGMpkkkUjQ29tb83umm8E6kIE9iGIhk4+8eSl3PbqbaNAkaOnkCl5ZsxIK6ORsj0ze4e4/Oxtd03hi9xG++9QbFByXxmjQ19i4Lp3pAo7rkSm4VUW/tTAvHuQf3/emYW+ugz1xSp423Rmbkp7ZdRVGUUzsNx1VpAsOAUNncWOk/DnpzhQ40O0v9ega5WorrShMdosB29ff+yauPmvBeE8/4Ad6yaxNX8Fl4D+R3qzNz7cd4ifPt3Gkir9Mfdji3Wcv4Kq180kUPXAsQy/3dapl6XWkc1YXNEZ0hD7ekQBPEE4car1/T2sgMxVMdiAz0d8OPU9x7b2b2XkoWeERA5Q1KStb6gCNl9tTtMSDVbdZ1RrjO9edBzDseK7nsvNQuujiC4439kDG1GHd0ia+++fnDznukY7F8zxe6Sh62syrQ9f6hcmZvMNrR/sImQZL50TL70vlbN44mhmip9Hor7AyDY17PngOl66cO7YDGYbSslMq55QrvcAPmn73aic/3nKgamuGgKnztlXzePdZCzi52Td3NHS/iioeMjGHqUqr5fqXru2JloWQAE8QTixqvX9P69LSbGcyvh3W4iOz90gfH3/Lcg50Z2hP5qmPWAN0MHZFM8ptB3qHHS+ZdQdU5/Q7+g4sux4Nz4OX21NVdSkjHUu+KEgGRd6u9Ltxle9TY7seOdsjXFQtl6quBs6tNGopAAtbBk11Eyf2Hrjs1FcUB2cLLoaucemKOVy6Yg672lP8eMsBHtt1BKfkGux4/GzbIX627RBnLa7n3WctYN3SJnoyBXqzNtGgQSJsETQrFdm1XP8TseVBLUaRowm9BUE4PplYR7sTiMnqeFyLj4ztKRY1RmpqRjnSePYAYYpS1SuHRkPhm8hVs+Afad8DfWwGZjqgP2DxqHwtZOlDbu6lOZcs/XRdY1VLbBxHUp1S483HX+3ktSN9zIuFWNgQIR62yhqdFS0xbn/HKr5/w/l8aN2SIQ09t+zr4XM/2c6H/n0zP95ygHTOJp1zONid5VBvlswAr5xar/+J1vJgLAGeIAgnFpKRGQeT+e2wFh+ZkuX9moWJUZtRjjSeNeBmqRU7Ro81mNEAy6huwT/Svgf62Az2uwkFdCzDIO+4GANuWjnbq9CrmMXWAGh+awVd0zA02NmempBsxWgZt8ZIgFTeIZn1PWma6oJ8+MKTeP/5i3l0Vwc/3nKQ3cXlM4C2nhx3PrqHe3//Om9f3cLVZy5gQUOYbMHFMnQSEYuGsFXz9T+RqCXAk55WgnBiIhmZcTCZ3w5LPjLdGZvB8qWSj8yyuXVl35ZSM8pLTp3DmoWJIYHTSOPFw0Z5aaZUbu3vp/b5ahqsbKnuIzPSvn0fG/98Ba1BwZ6CgOl7s/RkbbK2i+cpMgXH18HoWjk7Uxo1bBnMT4TRNZ0tb3Sz6ZUjbDvQW+4TNVZqybjpukYibLGoMUJrIkw06It5A6bOFatb+PafncX//ZMzePMpzQy8LJmCy4PPH+RD/76Z2zduY8sb3RQcl85UnljIZHFThK6+Qk3X/0RhYFBcjRM1wBMEQTIy42Iyvx1W+siMrH+ZiPHqI1Yxo+C3NRhrSqapLsDHL11edT6j7bsx6t90DicLQ15riAT4wPmLefzVTvZ0pOn1/LUvy9CZE/M7gufsAdValk53xiaZs/n/Hn0VDW3cmqXxZNwGe9KUxMFrF9azdmE97b05fvLCQX627VCFCeDTe7t4em8XJzdHufrMBVy+ai7vPXsh3/jVK7T15GiIWoRMY9zX/3ihFqPI8RgzCoIw+5GqpXGw7UAvN37vWaJBk5A1NP2ftV0yeYdvf/CccS9xVCxreApLH3pTHkvF1EjjbW/r5c7H9pDK2gxoN4RlaH5J8zCfkNZEiK+9Zy0XnTJn3McCcOeju9l+KInteFimzurWODe/ZTnrlzdXHGN92OJrj7zMy+3pIdVaqZzNvq4Mhq6xpDFC0DTGXdEyEddXKUVfwS/hzg1oSpktuPxyRzsPbjnI/u7skPfFQybvWNNKNGiw6ZUjHE0X0NAImBqnzIud0GXG/VVLbtUAX6qWZg7i9SNMBFJ+XWQyApn+EtnRy58ny9l1PBVTI43nOB7f+NUr/GzbIXqzNuCXC9dHLPryDoeT+SHJmlhQ502LG2u6uQ6373se38M/P7qbVNbBw1/rjIVNPvGW5dxw8dAWFNVvZi5vHM3georFjRFioX6x7Xiux6ZXjvCXP3yRubFg1e09T9GRzvP1957BJaeOHMQB5B2XZNYhnXfKy0WeUjz7ejcPbjnA5te7q77PMjRCps68eJD/ccYCrjlzIYmoRSxYmx/N8UgtAb4wvYjXjzBRSCBTZLJ8ZKbz2+Fk+GkMN2ZbT4a+QnVdAkDEMpgbD45rn/c8voevPrwL11OYhoZeNOTzu0hr3Pr2FcMGMwNvZijfqG5OLEBjdGjp9VgzZJOVcXO9yoaVJfYdzbDx+YP84qVDFNyh/xwNXSMeMvnfV67g3JOaMHSNuqBJPGxVCLZPFOTb/sxFvH6EiaTW+/eJ91twgli/vLmm8ueJZrB+I2QZ6LpGyDJoiQdJ513u3rRnTCLX4cYMmFrZgr9EyUW3RM7xl0/Guk/H8bjzsT24nt/40tR1dE3H1HUCpr+kdedje3CcoUHU+uXNfOe68/j2B8/h6+89g5vfupx4yKQ+XF3oOdaS5bEKrmul1LByUWOElkSISMCXqC1uivDJy5azan6csKUXHYv7cT1Fd8bmcz/Zznefep2j6Ty9WZv9XRk6krmKpasTgdEE7sL0MBm/mwShFkTsewysX948avnzRDMZhmnDjdmbcSr0MQP3VjKm85RvGjfWfT609RCprF3MxFTG07qmYxoeqazNQ1sPVW03ULqZgV/REjD1CStZnmjBdTUiAZNIwMR2PZJZm2df7+ZQd5a5sRABQyOdd+nJFioCSdtV3PfkG9z/h31ctnIe15y1gGVz60jnHUKWb7A3nr5OY0UyIkI1xMxRmC4kkDlGBt5Qp4LJqJgabkx7mFLXISjGbNJ2sCeDB5jD3P90DdzidqMxGRUtpYxbaQmrt6jHWNU6sYJby9BpqgsSsHRcBQHDNwOMhUxiIZOc7dKTtUnm+k3zbFfx8PZ2Ht7ezhkLE7z7rIWsX9ZEzvb9aOIhi7qQiTHBwYXnKTZs3scDm/fRnswVy+R10T8IgHj9CNOHBDKzjLEY5h3rmDXrLzTGvM8F9RF0/IyOhkJBuXGlVn7e3240JiuDMpUZt+ZokKDv8Idl6LjKb4AZsgxaLINYyKUnU8DQtYqg5sUDvbx4oJeWeIg/PnM+7zi9Fdv16MoUqAuaJMIWAfPYV5Cf3N3JHb/YyY5DKTylMDSNoKkTtPSyt47oH05sJuN3kyDUgmhkZhmTod8YbsxExKwwchu4t9JmuuY3VhzrPq9a20osbGG7irzjUXA8Cq7/Z97xsF1FLGxx1drWmsabLM3SVOkxBl4DrRjMBEwdQ/dN/zIFh5WtcX5w4zpuu3Ilp86rq3h/ezLHtzbt5X3/8hT/9JtXeeNoH6mc7ffj6s2RLYxfR/Pk7k5u27iNl9tToBQBQ8PQNXKOx5FUgbqgIfoHYdK0ZYIwGpKRmWUca/ZhOH1D9TEVYUuvqFoaXOMWMnXiYWvMGQ/T1Hnn6S3cv3l/ZYA04O/vPL2l3PiylozIdGiWJorKa5AjbBllHVKm4BALmnxo3RKCpsHbTpvH5avmsr0tyY+3HOR3rx4pa5lytsdPX2jjpy+0cd7JjVxz1gLOWdJApuAQMHUSYYu6Ecq3B38+VrXEuHvTHnqLNyfT0MutLHT8XlhHUjlaEuFj0j+I7mb2MxXaMkGohpRfz1LG46cxmr/DcGMubgjz4+cPkrOHamZCls5n3nZq1TLpkSh58TzzWhe5KpVJIVPnlHl1JMJ+t+8TxY/insf3lM0JFf7yWixscfOly7jh4mXkbJdkzqYv75a/9XYkc/zkhTZ+tu0QqQHLTiUWN0Z491kLeNtp8whbRrGc2yIWMjEHLB9W+3zMjYfY35UhaOp0pPJoml9FNbDJqAa0xEM4StXsrTMQ8R05vhCvH2GiEB+ZIsdrIANjd/atxd+h2pj/9sRe7vjFy8M6/DZELO58/1lj+iW17UAvH753M71ZG4Xymz8WUQDK183EQyZzYqETwo+idI1SOZtIwETXNDylyBRcYiGz4phdT5HK2SSzTrlDeM52+fXOw/x4y0HeODpUJF0XNHnnmhb++MwFzCu2XogGDeIhiy1vdFf9fBxO5unL28yJBzmSyjOc/tvQNerDJvddd/6YMjLiO3J8Ihk2YSKo9f4tS0uzmForpsbaO2jgmI7jceeje8pBjFb+X/8yU0/G5s5Hd4+p2/fRdJ5kzl+usEx9UCCjyNn+4ImwVTalm4ju4jOVgdeoNRGuWPpJhNWQY/ZdlwMkwhZ9Bbfsxvw/1s7nnWta2bKvhx9vOcAf9naVMyfpvMMPnj3Aj547wEWnNHPNmQs5fUGcZNbm//76FVI5u2LfId1gTl2AdN6mp69Qse5Xnl4xM+N5ClfBqpbYuI55orvIC9PLVFdzCic2EshMIdP1LeVY/B0e2nqIZM4uP1bl/1Hx3Pa25Jj0Ed0ZG89T6LpWEcRApQ5ncAbgePWjGO810jTf5bcuaFYsO529pIGzlzRwsDtbdA1uJ1s0zvMUPP5KJ4+/0skpc+u4cFkzrx1JUxf0xdeG7ou4NU0jHDAImgY5260wQix9BkrLX4YOhqaxsz11zP5Fox2zIAjCQCSQmSKmUwdwLP4OB3syQwS+1SgUS35rpT5qoev+0olSquJGNrDypdqUj0c/ionw4AhZBiHLwI32LzstaAjzibcu57oLT+Lh7X6zykO9ufJ7Xu1I82pHGg2oV2BooAy/DNzQNHRdY24syL6uTMXSoqrYr87ceIhMwZ0Q/6KxHLMgCIKUX08BJR3AzkNJokGTubEg0aBZ9t94cnfnpO5/oL9DNUbyd1hQHxnSLLIaAUMfkz9EczRIPOTrQGxPlQMar/gDflbAMk4MP4pjuUaDKS07LW6KMC8eIhwwiAZNrjlrId/98/P4+3et5szF9RXvUfhZsr1HMxxK5sgVXFxPYTseOccd9jNg6BotiRCWoR+Tf9GxHrMgCCcuEshMMjOh/8ix+Dtcedq8mvaxqmXs3jWnzU8QtkxCpo6nFE4xoAlZhr9UoWsErcHLTsenH8VkeXBEgyatiTALGsLEQn6TyQuXN/N/3nsG93zobN5xeguWUXmOUzmHfd1Z9nVnSOZsOlN5DA3Clo6O78YcNHVCll+K3ZHM05MpTJh/0bEesyAIJxYSyEwyY9EBTBYlf4e6oEF7Mk/WdvE8RdZ2aU/mR/R3+MWOw9Si4nnTksYR9T6ep9h2oJdNrxxh24FeAG66ZBmNUV/MOy8eYkF9mHnFYK+5LkhjNMDhZGFM852tHMs1qoWgaTAnFmRxY4TGaABT11k2p46/vGIFP/zoBVx5eguDh87ZHu3JPK7yl60aIgEMXcPDDzSUp9A0vxt4wNDHPL/JPmZBEE4MRCMzycwUHcB4ewcd7Mn4egnAHSZppAHRwNAloBIj6YO+fPUa7npsNy+3p8qvrWqN8fFLlwOMab6zveRz4DXafThFp+3iKZhTF+TaC09i3dKmY96HXqXaKRGBv7piBW9ZMYe7N+1lf1cGZ1CGsK/gkim4fhsFpXCLr2v4S4AfvOCkcWm9pqqnlSAIxy8SyEwyM6n/yHicbxfUR9AYPogBvxR3uJ5Iw/mElPRBHzh/MVCqXCr5yWhjnu/xYqq2fnkznlL8v/+1nSPpPI7nL/X83UM7+OEz+7j9HadNyPEMqXbK2pxzUiP3LGlg9+E+trX1sHlvF8/t6y6LfBVUmBeauu9NY+kap87zu3BHA8awrsEjHfNsdWQWBGH6EUO8SabkYOt3Zg4O6czcnsyzqjXGd647b0b+4n78lQ4+fO8zw5rhgf+N/L7rzuXiU+dWPN9/7MkKnxDwj31/d4a84xENGDRGg+M2QzueTNWe3N3Jp3/4Ah3JfFWB7XjMB2vFcT16szbpvFPOuHQkc3z8/i10Zexh3zenLsC/f/hcokETU9eJh01iIWvCu28LgnBiUev9WzQyk8xs1gF4nuKe371GaJTuySFT557fvTZEsDySPggNCo6i4HjUhwPjFkHPBDH1ROF5irse20NnunoQA35l0Zd/vnNSjsc0dBoiAXozNrsOp3jtSB9N0SDNdX62cLhP6JF0gT/5l6e567Hd7O/uo6uvwL6uDEdSeQpV2k8IgiBMJLK0NAXMVh1AKRBpqguS68lWzcroGjTWBasal42kD8oVPGzXRdM03EFJwbGYoR1Ppmrb25K8fChZYQJYxYOOXYdTbDvYyxmL6id0/0/u7uSux/awqz1FwfUIGDqtCb81QUPYpCc7tI9TiUzB5T+fO8iDWw5ywbImrjlrIWcsTJDK2YQDBomwRSQgv24EQZh45DfLFDEbdQClQMQ0NHRNw9T9rkhK+boYTSk8NAxNI+8NNcSrD1soFN2ZApGA6ZfrFoMNx/NQyg+ETH1ooFOrCHqmiKkngq5MYUgDzWp5F8dTPL+/Z0IDmdKSVldfwa9IKl7jrkyeYiunqnPRBj3vKfj97qP8fvdRls2J8u4zF3DZqnlkCy6WoZebVc7kz70gCLMLCWSmkNnWf6QkVC4HLpqOMdCBVyk0pXCVGiJYLn27T+UcCq6HqWsETZ05sRB1QRND01CAZfh+JIOpVQQ9k8TUx0pjJFBxfodFgTaBK0uep7jjFzs5ksqj4S8xaZrfKsJ2vBENERX++vQ15yzkyd1HOdiTLb+250gfX/vlK/zL717jf6xt5V1vmo/tenRnCtSFTOIhi8Aoy5bHK7O9wk4QZhISyAjDUjIs23koScDQyTsemuHXGKmigV3I1MkWXE6bHy8blw0U3zbXBelI5nxdUMHlYHeG5liQguMRMHXMKpmUkhnaqtbYqGZo/XNM0RLXhwiKax1nJrB6fpyTmiO8WPTZGRYNzpjAgHjbwV5eOey3KRjYwFPTwDT1UXUuHtAaC/GdPz+Xza918eMtB3nuje7y671Zm/v/sI8HntnPJafO4ZqzFrCq1W9WGQ743bejwRPnV9HxUmEnCDOFE/Pr0HHCYJO5YxWADmdaVxc0MYvur7bj4Xgetueha2DoOrGQWRYsDxTfJkIWlqExJx4kHDDRNLBdRWeqwMqWGJ9526k0Ri3akzl6MgV6swV6MgXak7maRdCDxdSZgkNf3qEznedAd5ZoYOxGbdOFrmtcc/bCUbfTAG0Cj+eFfT3YrleuMvKKPjGlthG18N0/vMGL+3tZt7SJr71nLf/+4XO4am2r7ztTxPUUv325g5s3PM8nNmzh0Zc7SGVtDidz7O/K0FtsJForE/35nwqmu12JIByPTOvXoDvuuIMHH3yQl19+mXA4zPr16/nqV7/KihUrytvkcjk+85nP8MADD5DP57niiiu46667mDevNuv845WJ/lY3mmnd3Zv2sKMtSTLX37U6HjI5bX6iYp/b25LsaEuSKZqtlZalgqbfWBDlBzN/dcXKssbjzsf20NaTLXdSjoUtPnD+4pqPoySmvuMXO9nVnsYpijosQ2dRY3jM52I6SWVH1/J4Cjr78hO2T6X1j+s4vnapdC1qJWe7bHz+AG9ZMYd03uWkpiifftupXH/Ryfx82yF+8kIbHan+Oe84lGLHz3bSXBfgj9+0gHeuba1YdkqE/XYK1fA8xYbN+3hg8z7akzlQEDD1GZ/VGFxhV8oehnSDlrhOezLP3Zv2sG5p06wIvAVhpjCtPjJvf/vb+dM//VPOPfdcHMfh9ttv56WXXmLHjh1Eo1EAbrrpJn72s59x3333kUgk+MQnPoGu6/z+97+vaR/T7SMzGUy0b0ot45WEykfTebozNvVRi+ZocMja/t2P7ebrj+wCKrUWrqfQNY3Wer9L8tffewaWrnH7xm2kcjaRgN9A0lOKTMElFjLHdBxP7u7kto3b6M3YRAJGsQwbejLOrPGSeXJ3Jx+//7kRq4NK3HL5qfyvy0+ZkP2+uL+H99z9JPYAt96SirfWXw6GBvWRAPdddx6nL4iTzjskcw552wX86/+7VzvZ+PwBth0c2o4jYOpcvmou15y1kJOb/X/7kYAf0IQHuEY/ubuTO36xkx2HUnhKYWi+9qo+YpF31Iy+1tsO9HLj954lGjQJWUP1XFnbJZN3+PYHz5lVWjpBmCxqvX9Pa0bm4Ycfrnh83333MXfuXJ577jkuvvhient7+bd/+zc2bNjAW9/6VgDuvfdeVq1axdNPP826deumY9rTykR/qxvLeKP9cvU8xSPbD6OgXOkERaGwAY6r6EjmaYhY1Ictvv7LXaTzDq2JcIW2JRFWYzqO0jH05R0WNlSOFYobs+KbbukY8jX6rozRPHdEVrfGCVg6dt4POlT5f7VRqlxK5myOpvNomkYsZBELWWXX4L6Cy6Ur5nDpijm8cjjFj7cc5NGXO8qtEAqOx8+3tfPzbe2cubiea85awLqlTWQKDgFTJx622La/h9t/8hJtPVlQioDhu0DnHI8jqQLz60Nl36CZeK2Ppwo7QZhJzCiNTG+vr8tobGwE4LnnnsO2bS6//PLyNitXrmTx4sU89dRTVcfI5/Mkk8mKn+OJiW5COZHjbW9Lcrg3S8g0cD0q9BUaGroGecf1l5hgQvc73Y05j5XSMcRD1qjbasDChuotIcbDzvYUIVNnmPvrkGaSg7FM3W8m6Sm6BzkAhyyDufEQixrC1BebTp46L8ZtV67k+zecz4fWLaEhUnnMz+/r4XM/2c6H/n0zP95ygO6+Ah3JHP/461fp7ivgeQrT0NE1HV3TsHTfi6gznScRNmfstR5YYVeN2VRhJwgziRkTyHiex6c+9SkuvPBCTj/9dADa29sJBALU19dXbDtv3jza29urjnPHHXeQSCTKP4sWLZrsqU8ptXyrs8fwrW4ix+vKFHA8mBMLYmga9gDBqFcs0wa4YnULPVl7Qvc7kedkOigdQ0N49CSpoWu88/SWCd23ruksrI8QDeiYesnfB6IBnUUNESJVSuQ1wDI0NPzrq+sa9dHqgZhp6DRGAyxujDAnFiRoGTTVBfnwhSfx/RvWcevbV7B8bl3Fe9p6ctz56B7+5F+e5ks/e5m9R9IDXKb7hciapmHqGvmivmemXutShV13xh4ioi5V2C2bWzfmCrvZKHoWhIlkxtQ83nzzzbz00ks88cQTxzTObbfdxi233FJ+nEwmj6tgZqJ9UyZyvNJYAVNnQUOYI6lc+eaiaRAwDCIBnYuK+oWJ3u9s9pIpHUOf7VeDjXQvigQMdnWkJ0xHMfC6ndxcR872K9NM3ff4yTke0aCFaTik825FE0m72E3UKIq/m6PBEfdVddlJc7lidQt/dNo8th3s5cEtB3lid2d5P5mCy6O7OgAIWToKX3Nj6L4NgAZQNPHL2u6MvdalCrvbN26jPZmnPmIRNHTyrkdPUY821go7KeUWhBmSkfnEJz7Bf//3f/Poo4+ycGF/+WlLSwuFQoGenp6K7Q8fPkxLS/VvpMFgkHg8XvFzPDHR3+omcryBY0UDBic1RVnSGGVhQ5jFDREiAYPT5idYXfScmYz9TuQ33amkdAy9WQdd04Zd5pkbDxC0jAnNOAw8fwDhgEEs1C+y7cnYrGiJsaghwnClAZ6nmF8fHtM5HrzsZBo6axfW87f/czX/cf35vO+chdQN8pfJ2Z5fXeWV3KEVipKkR5HJOyydE8VTakZmKEoVdqtaY2TyDh3pPJm8w8qWOj7y5qXYxexKLXOWUm5B8JnWjIxSik9+8pNs3LiRxx57jJNPPrni9bPPPhvLsvjNb37DNddcA8CuXbvYt28fF1xwwXRMeQhT7dA50d/qah0P/OqWLfu6ae/JMa8+xNmLG1izIFHeV9WxTB1c/2Y40G8GmLDjGLjfQ705wgEDo9jDKVusgJrpXjKlY/jMj14knbcxdA3TAFcpXA8MHRY2hDENg0zemdCMw8Dz19aTAbRyxgMU8XCAi09p4pu/3T1EA1w6o8ciPjYNnXjQ5LGXO3itM019JMjFpzTzsUuWce36k/jl9sNsfP4A+7qyFe9zPXBR6JqfjTF0MAyNI6k8N37vWRxXzciy7MHtSvZ3ZXj4pXbuenR3zVkVKeUWhH6mtfz64x//OBs2bOCnP/1phXdMIpEgHPb9P2666SZ+/vOfc9999xGPx/nkJz8JwJNPPlnTPiaz/Ho607oV+y42oZwwH5lB4wHc8YudvNyeKi8lAJjFxpe3XbmqYp9jmdtEHsc9j+/hzsf2kMraePjpxljY4uZLl3HDxcvGfE6mgydePcInvv88yayNhh9kBE2DObEg0YBfgbWqNcZ3rjtvwm9Q19+3md+8fGTI82cuStCezHM4mSt7zAwmEjCoD1vjKh2udt3qQiYfumAJV5/pZ2g9pfjBM/v57lNvDFvZFbZ0rGK1XCLs2wi4StGbtcdczj9VjNdKQUq5hROBWu/f0xrIDK4wKXHvvffy4Q9/GOg3xPv+979fYYg33NLSYCYrkJloL5fxMNHZoGrjPb33KJ/+4Qt0JPPDNg2cGw/yj+97U8XxjmVuE3EcA69H2DLK/jVZ26UuODNvYsPxxKtH+Kv/3Eom7xAP+00WC64qZ6om41g+++BW7t+8f9jXLV3D8dSIVdkhS+fbHzyHS06dU/N+73l8D199eBeuV2pOWjTmc/2M0F/+0am855xFpHMOnlI8v6+bf//96+w9kiZrVw9ogqZOQ8QiFjTRNA2F4mjab1XxvevPnzEZCs9TXHvvZnYeSlZkVcDPVo8UtG565Qh/+cMXmRsLVj0ez1N0pPN8/b1njOl6CMJMYlb4yNQSQ4VCIe68807uvPPOKZhRbcyUtO5EN6EcPJ7nKe56bI/fEbn4XOl3benSKeBoOs9dj+2uON6xzO1Yj2O46wGQUGPzpJkJXHTKHP7Pe88oZ6qOpAtYxezXZGT7CgWXHzx7oPx44PeL0nW2a9Bs5GyPWGBodmA4HMfjzsf24HqKgKmha74wSNdA1zwKjuJbj+/lIxctpTESIJVzOO/kJs5YVM/uw320J7NsO5jkid1HOJzsdw3OOx7tyTxH9AL1YYtE2KQuZPLq4RSP7urgvJMbqSsGOdPJWGwDBv/7OB4E7oIwUcyYqqXZxLH8AppNbG9Lsqs9VRYeDjzSUsYD/D9fbk9N2/Eej9djsI5iMvVX33p8b9mYzq8A6n9NY0zeePzu1U7OOqmxpm0f2nqIVNYuZmIq1c26pmMaHqmszUNbD3H1WQtIRCwSEYu+vEM0aHJqoY6LT53Dxy5Zynefep3vb95fPg7wK5uO9hXo6vPbHgB0pvMcSeXp6isUq6fMYVshTDbHYpB3PDVLFYRjRQKZcXCiOHR2ZQoUXK+/UmWYe2ipDHe6jvd4vR4TnXEbjje6+sp/P9Z15n3dmZq3PdiTwQNMzfeR8qBcqq/jZ2bc4nYDiQZNokGTvOP38+rLu1y4bA6PvNSOoev0FRxSOad8LApI5fy2D//6u9fI2R7rlzXRk/GblEYCJvGwSSQwtb8OjyWrMhml3NPJVBdNCMcXEsiMgxMlrdtY1P5opa/lw3QSLBmjTdfxnijXY7JY0hidlrEW1EfQ8YPgaitXuuZ/thbUV3cxDpoGc2MGTsQjFjRZ3BRlz5E08+JBmusC9GYderI27oDBX+1I8//+13Za4iGuPnM+V57eCkCm4GAZOvGQRV3ILHcCn0yONatSKuUuLUH2FsXyk7UEOVmIF45wrMwIH5nZxvHgW1ILq+fHWdESK38zGnikAw9b02Bly/SlsU+U6zFZfPSik0ffqAZMXeNjFy+tefur1rZimfqw5n+e8tsfXLW2deT9GjrNsSCfvvwU6oImnWkbx4OGiMX8RIhIwMAyKgOT9mSOuzft5X3/8hT/9JtX2deVwXY9jvbl2deVoSOVI1dseDlZlLIqdUG/Gi1ru3ieImu7tCfzNWVV1i9v5t5rz+WvrljJtRcs4a+uWMm91547awIA8cIRJgIJZMbBRPwCmg3ousbHL11GYzRQTsQoNSiIAZrqgnz80uXTdrwnyvWYLF7t7KvagmAgQVOnaZj2AyX+5JyFBMYg9h0ro1nxX3jKHL56zVpOXxCn4Lh0ZWzytsuq1jh3XH063/zTN/GWFXMqekflbI+fvtDGh+99hv/94Daeeb0Lz/NI5xzaerIc7MmSyg0NkCfqWGxP8ZE3L2VlS12FQd6q1lhN1WlP7u7kuu88w9ceeZnvPPkGX3vkZa77zjOzIgAYLNL3O9ZrhCyDlniw3AB0JhkaCjOTaS2/ngom20fmrsd2l/1VLENjZUuMj1+6fNZ8I6qFJ3d3jslHZrqYKE+ambBeP5VzKJXyOq5Hd9Ye8npD2MI0db52zVoeeGYfv9xxuCKLYujwp+cs4kvvXjum/W7ccpC//NELKKq3ZCgtLX39vW9iXjzIXY/tYVd7ioLrETB0VrTE+PilQ6/twHNXFzBZUB8i63jlYKQjmeOnL7bxs62HSBa1MwNZ3Bjh6jMX8Eer5xEuerQYut9aIR4yMY9RHFxtKWXpnDrefnoLC+vDdGds6qMWzdHgiNd9tlsOiBeOMBqzovz6+EBDK9Z2+H8ef9/61y9v5ta3r+Trj+xi75E0hQG/fP/qihUz5pflRFT6zIT1+qmeQ0ljlHeqf6dxlSKsazTVBWmKBoYoglV1O5dRKYl9h1MYq6Im66k9nWx69YhvA1DsqaRp8IfXCrzakRriYVRNJO24Hr1Zm1TOYW48xA1vXsoH1y3h1zs7eHDLAV4/2i8o3teV4Z9+8yr/9sRrvGNNC3985gJa4iF6MgV6s377jXjYqnrzHY3h/Kdebk/xakeKubEgR9OFUa97KZvRnSnguL7pX39PMx3b9Wa85cDxKtIXph7JyIyTmWCIN1WcKMc6E45zOubgeYr1X/kN7QO8WAbTEg/yllOb+f6zB4fd5gPnjS0rs3HLQT79wxdG3W5hQ5i2niwavh6mlHlwXA+Fr5H66c0X1XTD9jxFKu+QzNrYrh+BKaXYsq+HH285wB/2dg2Jq3QNLlrezLvPWsCaBYmyKDdoGcRDZs2eNCMZ4KVyNvu6Mhi6xpLGCEHTGPG6bzvQy4fv3UxvMYNm6Fr5vJTEzYmwxX3XnTdjsxmSkRFGo9b7t2hkxsGJtLZ7ohzrTDjO6ZqD43h0pIYPYgAOJ/MjBjEAP3j2AIVC7QLZt62ozXG2I5nzK+NMHV3zM6C6pmGZOhrwyuE02w721jSWrmskwhaLGiO0JEKEAwaapnH2kga+fPUavvPn5/LuMxeUl5TAX/Z6/NVOPvWDF7nxP7bwy+3tFByPvO1yJOWLg7v6CjjuyKmpgX5HANmCSypnkyk4HEnlB+RytVGv+9F0nmRRu2MW2zKUzotp+F3Bkzmbo+mRr+t0IiJ9YaKQQGYcjMWAbbZzohzrTDjO6ZrDtx7fO2zlUIlaQifH8514a+WOR3bVtF3BVeVyaE8pXE/hFW98hq5hux4v7Oupeb8lIgGT1kSYBQ3hcpfthQ0RPvHW5fzgxnV8/NJltCZCFe/Z3ZHmKw/v4v+552nue/J1uvoKuJ6iJ1Ngf3eWw8kc2WGCudJSSsHxeP1oH2909XGgO8sbRzP0FdxyNqUnU8Dz/KBouOvenbHxPFXOQg0+L7qu4Xmq3NF8JiIifWGiEI3MODiR1nZPlGOdCcc5XXMYaIg3lWO9PoZtPeVnjkpNKzWKxnnFgE8dw70uaBrMjRs0DNDR1AVN3nP2Qq4+cwFP7z3Kj7cc5IX9PeX3dGdsvvvUG3x/8z4uXTGXa85awKnzYvTlHfryRU+asN/vqXQjbowE8JRHW28BT/lieU2DQjGTU9ILdfYV6M4UmBMLMScWrHrd66MWuu53KXeKAvyBNk+a5gd59aNUmk03x4sXjjC9SCAzDk4kA7YT5VhnwnFO1xwWN1Q3nBsPYzHEO6kxyu85Oup2GlS0UCjdrD3lZyIsXePMRfVjnutgLEOnuS5IQyRAKmeTzDqAx4XLm7lweTN7jqTZuOUgv365g0KxA7ftKn614zC/2nGYNQvivPushVy0vBnwOJrO091XIBYyiYctVrXEcIsNMf3eUn42qVo2zFW+1w34ncAHX/fmaJCwpZPMVWZ/ym7GCuosneZo8JjPy2Qzle04hOMTWVoaByfS2u6Jcqwz4Tinaw5vPmVivvWO1RDvs29fWdN24QEeN2rAT4mApbO6deLOiaFr1EcCLGoMM7eoVQJYNqeOv7xiBT+4YR0fuehkmusqA8ptB5N84aEdfOBf/8ADm/eRzNp4yq8o2t+V4YndnejF8V3PD8KcUdb0OlI5utL5Idd9VUsMXffPy8DgbuDfdV1nVUvs2E/IFFCqNLvk1DmsWZiQIEYYExLIjIMTaW33eDvW4UzVZsJxTtccUgWX0CiGeBpQFxy53Hishnh7ujIEzZH3GzA0LENnuEPW8Zfcdranat5vrWiaRl3QZH59mPn14XJ1UiJi8f7zF7PhI+fzuXeu4rTWymChI5XnX373Gn/6L0/zj796hdeP+ktoh1M5FDAvHiRo6uWszkh4qv9zMfC672xPYWgapq6ha2AaGpahFYW/flBpaNqknBdBmGnI0tI4OZHWdo+XYx3Nn2UmHOd0zKExEqAxEiCdd6oaxBmar99wXIWuVTeve/+5C8dsiNeVKRAPWaRyNrkqN/WQqRO0DFCwqDFCZ6oY3A10ltY1UnmHJ3Z3TmqJbsgyCFkGjuuRzPnl26ah89aVc3nryrnsPJTkwS0HeeyVI+Xy55zj8dDWQzy09RDnLGng/JObMDV/OUopVXODzresmDvkundlCuiaxoKGMEfTBfKOW/aRCQdMmuoCZArutGnXZoKppHDiID4yx8iJ9A92Nh/rWPxZZsJxTuUc+v1NUsyLBehMF8i7HkFDp7kuwOvFqhrwm4PqgO35jR51Df7qj1Zw01uWj3m/A71QBt/YfTGvRjRoYhkaDZEAtudxsCuLq/wqplJQ5XqKefEQ/+e9Z0xZUO15ilTOIZnr96MB6Ezn+ekLbfz31kNlj5eBWIZW4Y49Gjrwf973Jq4+a0HF8wM9WIKWTq7g4Xgepq4TCujkbG/aPFhmgqmkcHxQ6/1bAhnhuGckIzKlFO3JPKtaY3znuvNmTWA20fQHei71EYugoZN3PXoyBdJ5Bx0/sCi4XoWDrKHrnLEoMa5z5zge53z51/Rm7KL4tX+ZyVMeBUeRCFusnh/n5fYUOdsl5/iCZ03TUPgVO0FTJxwwWNUan5ZrmM479Gb9vk4l8rbLb1/u4MfPH2TvkfFXhZm6xo6/vWLIkt3A4LMlHpwxn+mZYCopHD+IIZ4gFKnVn+WnL7QN25DweKe0pLWqNVbRvHBhQ4RowGBBQ4STm6MsaYyysCHMksYoJzdHmRsPjtvbxtd5+PoOpyh+VUoVRbD+84YOV65pJWDqZG3X18poRaGsq9A1jbnxEPWRwLT5GdUFTRYUdTSRgL9aH7QMrlzTyj0fPJtvvO8MLlzeNK7mJWFL5+XDQ3UuM0HTNZiBho7z4kE85ZeM9+Ud4iGDdN45LswzhZmHaGSE457R/Fls1+NIOs/f//d2DF0/YVPh1cpgO/vy/PWPthIwdDRNIxwwgP7swLF42/g6D535iTBH+/Lknf5sT9jSaYoGydguixojfOiCJXzjV6+glMJx/W1ClsGcWJC6oInnqWn3MwpZBi0Jg4Lj+9Gk877e6E2L6nnTonoefqmdf/rNqxQcryZ9TCxoYJp+ILOwMTKkYeVM0HQNpPSFIWga7D3SR87uP04NCJgaO9qSbG9LSssBYUKRQEY47hnJnyWddzjYncVTimjQJB6yKLgeOw+luH3jthMuFT644eK2A72T5m1Tui4BU+ekpig5e4DOw9L9ZSTXozES4KLlc/jeU29g6BqGrpW1IFoxzzGT/IwCps6cWJCGiEUy55DK2bieYmlzHYmQCRp0pgsjuilr+IGRBsSD1rANK2eSB0tXpkBfwSWdsynJgEqzUEDeUdhugSd2H5FARphQZGlJOO4Z6M/ieV5Fj5vDvVlcTxG2DBJh67jsI3UsTKa3zcCxAcIBg1jIKmZ9qBi7tG3W9qgLmn6PpOJtcqb6GZmGTmM0wOLGCE11QU6bH2NRU5SCo0YtO1fAkXQBxwPH87U3SinSeYe2niwHujPlXkszxYOlPmyRs93KIKa4FFha0fUUPPLS4RP635Qw8UggIxz3lPQEhg6vdKR5/ajf4+b1zgwZ20PXYe4gEfDx1EfqWJhMLcZYxp6JmpBa0TS/UeXipig3X+ofg67pmDqj6ma6MgU+8f0X+MSGLfz25Y5yY8qC49FZbFh5NJ2vqJyaTryBwe6Agxv49IHuzAn9b0qYeGRpSTgBUeWePUD5m/1gjpc+UmOhWtn3ZGoxSmP/829fYevBZLlcd+2COJ9466kVY69b2sRH3ryU72/eR0fS7+psGbPLz+itq+YRsgz++bevsu1gb7lZZAlD81sluEpVlGnvOJRix8920lwX4F1vms//WDOfRMTC9RRd6QL/+dwBuvsKnNxcxzVnLcAcJeMzGfRkbQK6ju2WMkiVr5dch+3j6N9UoeDyrcf38kZXH0sao3zs4qVjMoUUJgYJZITjnlI1hespTp1XR95WOJ6H4ynae7IopTiSyhENRCuyMjNJdzEVjOb/MVlajJ9tbWPz6z1lu/68A5tf7+FnW9vKwcnAuRUcDzRoiYf40/MW8/7zFs/ITMxI6LqOruto+Dd9XxOjs6A+zP84Yz5Xnt7Ck3uO8uCWA2w72J+96EwX+LcnXud7T+/j8pVziQZNHt7eTl/OwcNPsX/p5zu44c0n84m3njKkSm8yaYwECAcNso5bVf+j6C/bPx7+TX32wa384NkDFW0mvvnbV/mTc8ZuDikcGxLICMc9A8uvdU0nHAAwUPjaimzBIe945GyvrM8o6S5WtcZmlO5ishjO/2Ow6HmiRZqffXAr92/eP+R5x1Pl59+5dn7F3Boi/tzak3n+9Xd7WdocnRXZGOg/z92ZAn35fpNB11PYrkd7b54fPbufRQ1hLjl1DpecOodXDqf48ZaDPPpyR/mmWXA8fv5Se3lcXQOzGLMkcw7/99evki24XP/mpcTDFtYwFXsTyer5cVa2xHhqb9fQdEwRT/ktGmb7v6laPrcSzEwdopERjnuGK7/W0JgTC6JrGo6n6Cs4s0Z3MZEM9P9oKTZJnArRc6Hg8oNnD5Qfa1r/T4kHnt3PP//21Smf22Qw8Dw7xWUjy9QxdZ2AoaPQ8JRHpuDywDP7y3qTU+fFuO3KlTzw0XV86IIlNESsoWMrcDxQaJi63z37e3/YR1e6wP6uDO29OTKFoe0nJhJd1/jYJcuG7YsFfsA1hUmiSaGWz+0Pnj1AoeBWebcwGUggIxz3DCy/Hkxd0GRuPETA0LFdr2wEt6o1dsKUXtdqGDjRAs1vPb63nGEYfHMrPXY92NqWnPK5TQal8xy2DAquh6FroHyBbKndQ8H1iAZNDnZn6Urbfq+pIo3RAB9efxLfv2Ed71zTWnUfruebCWpAOufw210dAGQKDu29OfZ3ZejN2JMW+CXCAeIhi4AxNFoJmhotiRBH04VZcb2GY+DnFvzkU+mnhOMpvvX43mmY3YmJLC0Jxz2l0l3fzl0fYueedzzOPamRv7piBT1Ze9b1kTpWRjMMnCzR8xtdtVn324435XObDErn2TQ0lAIPhetVCs+BsmFe1nFZUB8mZ7v0ZOxyRiVg6syLBwHfmrDa9/7SeP/x9Bs0RgOctbgeTdOwXY+jfXm6MwWiQZNE2CIwgcLgrkwBx/MdlwceVal3lqXrZOzpa2Y5EdT6ua11O+HYkUBGOO4ple7evnEb7cn8oF5Cfg+Yj1+6jDMW1U/3VKeFkQwDYfJEz0saozVtZ5n6lM9tMiidZz9w8R2Kq3G0r0B92CwfU8kxOO+49GZt+vIu8+IhNKoHMQPZ353lr/5zKyc1RXj3WQu5fNVcQpaBpxSpnE0qZxMOGMRDFtHgsd8O9ndlSOXsIWJfBeRsjwM92Ypjm43U+rmtdTvh2JGlJeGEYLheQifSEtJwTKbp3Uh87OKlmHrJ1K7ytdJjQ4e18+NTPrfJoHSeMwWn4ng1rd9yRcPX0rgKVrXEKt4fNA3mxkIsagjzrrXzR9SiDOb1oxm+8atX+NN/eZp7freXjmSu/Fq24HI4mWPf0Qw9mQLuOJedPE/xi21tQ49NG7hUqHC9occ2m/joRSdP6HbCsSMZGeGEYSbZuc8kaslYTYboORAw+JNzFparPKoVuvzpOYvKVUuHenOEAwaGpuEqRbbgEguZs0aQXTrPn/nRi3jKLj8/+MavaxqGprGzPVW1Ssw0dI70FYgEDFL54XMyQVNn+dy6Cj1KMufw/c37+cEz+7n4lDm8+6wFrJ4fR9M0HM+jq69Ad8YmGvSzNCGrdk+U7W1Jdh1Oo2uU3X1Lxzbw6igY9tgmm2o+SYM/O6Nt82pnH5GAQWYEMW8kYPBqZ9+UHWMtx3U8I4GMcEIxuJeQ4DNdDQi/9O61/OG1LnYfGaonWD4nWi5h/cD5i7nzsT0c6smW/VJiYYsPnL94VmXT1i9v5pJTm/nBMweqNo7UNI2maAAPRtSRdGUKhCwT21XknKEi9oChEQkafOiCJcyLh9j4/EEe2d5Ozva39RQ89soRHnvlCCvmxbjm7AVccuocLEP3WyHkHNI5h4CpEwtZxILmqDfGrkwB21HDeshA/xLAdGhkRvNJqnWbrkyBaMBE1yBdJZCsCxqEA+aUHWMtcz7emdalpccff5yrrrqK+fPno2kaP/nJType//CHP4ymaRU/b3/726dnsoJwnLN+eTPfue48vv3Bc/j6e8/g2x88h+9cd96k/jL87INbqwYxALuP9PHZB7fy5O5O7v/DPkxdY359mEUNYebXhzF1jfv/sI8nd///7d15eFRVui7wd+/aNaZSlXkkA2EIiATCIAZFQWgJelDA7gbkKg5NO+BVu8F2ejxo2+egzVG7vcemFc8Fr203DkwqoAIaUAhzgAAaSRhCJgIJqaqkUuNe949KbVJJVVIhSU18v6fztKm9q7JWVkF97L3Wuy71W/v62p7yS9j5s+/2MsbQ0GKDKLIu55HEaRRosTm8FjEAYHO6rljpVa69np6aOgSf/LYAj96agxSdyuPcsgsm/OeWn3Dvqn34sPgcLrf7ALY5RDQ0u7ZCuGiywuprYk9bm6wOZ5c7ezMEJxDPnd/zY60RUUoBSdFKRCkFKSdpT/klv84BXP0UGYPFLkLGwbWRKce1/b9rLlB34xfIfl0LgnpFpqWlBaNGjcJDDz2EOXPmeD2nsLAQq1evlr5XKpWBah4h15xAXrHylsfh5r4lsfbAeZxtMEs5Mu1XnOkZQ53RipU7K3BjTnzIX0p358jYHSI4ztVH98aKrN2tGAdjcDLW5TySIQlRXd7aAFwfqFmxaul7rUrAr8dl4J4xA6TU4KNVBul4Q4sNq/ecxT/2ncNtw5Jwz5gBGJykdbW93eRgpVwGnUqAVil4jMeQhCifhZUbAzAkwHOaOuYkudus4mVI0fGoM1rxt6IKAKzLc9zvs+Ep0XAy14ozhcCB565cDxCZCJuj+/ELVL/C5c9GbwW1kJkxYwZmzJjR5TlKpRIpKSl+v6bVaoXVapW+NxrDN6+AkEjWXY4MY665FseqDEjRq7rNkQn1W4ZSjoxCBq6VA8eYK7a/raBhbV8C79pzqat5JO/9cMavn/nVyQu4b2IWTBaHNFlaxnOYNCQBk4YkoLy+GesOV+Hbn+qlvZ3sToavT1zA1ycuYNQAPeaMGYCJg+JduTcArHYnLtqdaGyxuW47qQTIZbzfbUqLUQf0g9WfnKSyOhMYmN95RTIOEGQcHCIg8EwaP4foery78QtUv8Llz0ZvhfyqpaKiIiQlJSE3NxePPfYYGhoaujx/+fLl0Ov10ldGRkaAWkoI6Ql/czZszq5zZMJlE0J3jox7lZJcxksrj65sYArEaZTgeb7LPvn7u6tqMiNBq0RmnAaxGoVUjLgNTtLi2UJXavCDN2UjPsrzdsjRKgOWfX4C9/3Pfnxy8DyaLVfSgZ0iQ5P5SnJwxUWTX21iXd586nv+5CTZnCLsjq7Pcb/PGs028ByPNL0aajkPkTE4RAaRMajlrse7G7++4E+/wuXPRm+F9GTfwsJCzJkzBwMHDkRFRQVeeOEFzJgxA8XFxZDJvM+mf/755/H73/9e+t5oNFIxQ0gI8jdnw73vUyTlyLiXJCsE/koybNsHvELOdzvHoqdZJjKeQ2yUAjEaOYwWB4ytdtjbJV3HahS478YszBufgZ0/X8S6w9Uoq7tSmNQZLfj7ztNYs+cspo9Iwez8dGTGaaTjZpsDcRr/bvv72m2+v/iTk+TaIoL5/T6TyzgoBB7Z8VGw2EU4RBECz0Ml52FxiJA7xX5/TwYr/ykUhfQVmXnz5uGuu+7CyJEjMWvWLHz55Zc4cOAAioqKfD5HqVRCp9N5fBFCQo9fOTIckDdAH1E5Mq12JxQyXspr4TkOPOdaSaSQ8Wi1ObvtU/vfHeB9vx+B5/DoLTkez+M4Dnq1HBlxGiTpVB5bIACuq0TThifjb/fm4//MH40puYkeeTUWu4hNR2rwwOoDeG7dMRw42yiNy7zxGX59oNQaWgO6N5Y/OUm5KdEYlhLt1/us/esBgFohQ7RKLm04G6j3ZLDyn0JRSBcyHeXk5CAhIQHl5eXBbgohpJfcOTJu3vasmTc+A4unDIZWKUOd0YpWuzNsN/Z058holQKEtr2I7A7Xv+btogieA2Q871c2jj+/u7njBkCh8J0Do1UKSI9xrQDrmOrLcRxGpOnx0r9dh3/+ZgLm35ABncrznP1nL+PZdaV4cM1BbDpSAwcYbhoc3+XvQKcScOZSS0D3Wrrye/f9Hnp88iA8Ptm/95k/rxeI92SotCMUcKxjKRckHMdhw4YNmDVrls9zqqqqkJmZiY0bN+Kuu+7y63WNRiP0ej0MBgNdnSEkBL24/hg+PljlsRGfwHOYO26AlCPjkZXRlnETrlkZe8ov4Z3vynG0yiCl/HIcoJbzGJ0Rg8VThvjdpxfXH8O/Dpz3yG7hOWD++Azpd+cvu1OEodWOZotD2nnbTWQMJ6tNKDpVj70VDagxWDo9X6sUMCYzBsWnL8HuZUGVTiUgVadCg9mGN349GrcOTexR+3rLn/dQT95nrnE8hRM1RticDAoZhxFpuh6NX6D65U1vQ/REkeFoVRO2Hq9Dq9WJ0ZkxuHtUGoQ+3LvL38/voM6RaW5u9ri6cubMGRw5cgRxcXGIi4vDK6+8gnvuuQcpKSmoqKjAH/7wBwwePBjTp08PYqsJIX3pP+bkYdm/jcDfd53GucYWZMVF4dFbcjyuJkRSKvOJGgOO1xjQbL0ycZYxwGwTcaLGhBM1Br8/COuMlk4BdCJzPd5TchmPBK0ScRoFjBY7jK0OOEQRJZWX8c/953G+oQV2kUHgXEuo5TIOP9aapKm7zVYHdp3ynVtitDjgFFsRpZCBiQxmmwMaReA+gvx5D/XkfXaixoATNSYYLa5i1MKhx+MXqH511NsQvT3ll/DixlKcvWSWxv/Dfeew7PPjeGrqECy6ZVAf9c4/Qb0iU1RUhClTpnR6fOHChVi5ciVmzZqFkpISNDU1IS0tDbfffjteffVVJCcn+/0z6IoMISRUrNpVgde/KvO4+tSRwHN4tjC32w+Dh9fsx46fLvo8PnVYIv7ngRuuuq2MMez48QJe/vwEWmxO6FRyyGUc7E4Go8UOjcKVHFx2oRlfHa/rNtfGLSFKjrWPFIDnOMhlPHQqObQqodOKqlDmHken6NrN3D3HyeFkkPk5fsHiDtFrtjoQq1FIk+kvt21H0t3ec3vKL2HxPw9Lc4Q6knHAczOG9Un/w+KKzOTJkztNUmrv66+/DmBrCCGk/zgcIt4pquiyiAFcy5rfKarAgxMH+rxMb7E4uixiAGDHTxdhsTigUl3dX/OMAR8Un4PFISJVp4II1+0EpcAhQavApWYbvjlZj9fvGYkHJ2Zj6/E6vP/9adicXfevocUOp4OBl3OwO0U0tFhdsf9Xsb9TMLjHsWMgHs8BPOcKxOtu/IKltyF6osjwznen0NSuiOkYZOlkwDvfBbb/ofVbJoSQCPXFsVqYWu2QdXPhgecAU6sdXxyr9XnOq1t+9Otn+nueN+0D12QyHnIZD7nAg+dd28VEq+Q439CC8gstiFIKyEuP6bZvgCsz57+/81yw4d7fqaapFdVNrTBZOq/ECRXucXRdifH8COU5HoKM63b8gqUnIXq+nn+ixngl96hjkGXb/xstge1/SOfIEEJIpKhuMksbXnaHtZ3vy1k/A/H8Pc8bb4FrPMeBl3FgjIFjQLMVMFhcgWsGiw3d7FAg+aK0FmcaWjBnzABMGpLgcVupfXKwVilAp5ZD7iP0LRjc4yj4KNp4DnCi6/ELFn9C9AxdhOg1mm2wObooMNsijhkLbP+pkCGEkABIj9H4fQmcazvfl+y4KOxG1ynn7vOuVleBaxzHwQlAKeOQGRsFgeehV7nOt/uZEXO8xojjNSeRFK3E3aPTcOfIVOjUcum4U2QwtNphaLVDoxCgUwsBnRzsi3scRQZ4m9Yjsu7HL1h6G6IXp1FAIXDwMT1GiqjmuMD2P3TKXEIIiWAz81IRrZajmykkEBkQrZZjZl6qz3NeumO4Xz/T3/O88SdwbXByNCbkxCEjTo2CQXEYlqLt8c+pN1mx6vszmPveXry57Wecbeh8Fclsc6DOYMH5RjMMZrsUJhgM7nF0OBlE5nkJSmQiHE7W7fgFS29D9NyBgJz0HM/j7m91qsD2nwoZQggJAEHgsXjyII9EXm94nkPhiBT8WGfymYCrUgmYOqzrHJapwxKveqKvux3+Bq5xHAe9RoGl04cjStH1x8rMkSl45958TB2W5HlLySHiy2O1eGjNQSz99Cj2nm7olGfjnhxc2WhGvckCi7fAmn7mHkcZz8HqYLDYndKX1eFatbR48qCQm+gLeI5prcGCy2YbjK12XDbbUGuwdBuix/McFk8ZghjNlStnHcMYZRyweEpg+x8ygXj9hZZfE0JCyex3fkDJeYPXYwLPQaPgoRQEv7I9pr1RhPKLna9gDE6MwvYlk/ukvT0NXJu4fIfXwDwAKMiJxX/MvhLUd6nZite2/oSSyiavW0kOiFVjdn46po9I9nlbSSHw0Knl0CqEgOYKBeJ3319W7arAO0UVMLXapXlb0Wo5Fk8e5NeyaW85MgCgVcr6NEfG389vKmQIISRAXlx/DB/tP+/zuJwHshO0fmV7tM8y6aivs0z8TYH19eEutYsD/vzLPORnxgIAPjlwHqu+Pw1n23wTX3eMohQyzBiZglmj05EWo/Z6Ds9x0KoERKsEKIX+XcLd3xk+/al9joxaLgPHua6mtNqd0CqFbnNk3CjZlxBCrjE2mxMfH6ySvu+YvwEAdtFVzPA812W2h68sE8A1T6Ovs0x4nsPIAfouz2lusXVZxACujJEP95zFqIwYMBH4aH8lnAyQy1xLlxljYAxwiMzjX/otNic+O1SNdYeqMXFQPOaMScfojBiPJcQiYzC22mFstUMllyFaJUCrFDotM+6tQGT49BdfOTIAoGes2xyZ9nieQ35mrFSUBlPo3cQjhJAI9Pddp6UwPI/P1g5XIS41tw8b857tEYpZJos+OuzXeSdqjWhotmHXqUtosTgg8JD6wHGuTRkVAi9l0ijbFWIMwO6KBiz59BgWfXgIW0trYfOy5ttid+KiyTWXprHFBrvTz3XhfghEhk9/6W2OTKiiQoYQQgLgnJ+ZLrYOH7pKGQ97h2wPKZOmiyyT7rJo+pq/P8vJAItDRKvd0XUfeNcy5l+PHYD/fdtgDIj1vKV0+mILVnzzM+a+txf/88MZXGq2dv5ZIkOT2YbzjWbUGSww2xydzumpQGT49Bd/cmQ6vtfCQWhd9yKEkAiV5WemS8cPGW/ZHqGYZZIeo0FlY2u35wltfUmP0UDGAQyc9C/q9henWFsfBsRp8IvrknH36DTsP9OIdYercejcZek8Q6sdH+2rxNoD53Hr0ETcMyYdw1M7z6cw2xww2xyQy3hEqwREq+RXtb9TIDJ8+ktvc2RCFV2RIYSQAHj0lhxp6bXHEosOn6UJ2vZLW71ne4RilsmqBWP8Om9MRgxGpOk8+sDAwHEcOLSFwzIRDhGIUgm4LTcJgGsy74058Vjxyzz83wfGYWZeqsdtJ6fI8O1P9Vj8zxI88c/D+Paneji83FKyO0U0tthcS7iNFrT6udmlWyAyfPpLb3NkQhUVMoQQEgAKhQxzxw2Qvu+YvwG4Lu3bRPjMa3Frn2ViczA4RNFVwIiuib7ByDLRRikwOLHrqxACDzw5bSh4nvPaBwYGJ2OwOV0rnO67MRMyLxs4ZcdH4Xe/GIqPf3sjfjtpIJKilR7HT9aa8KfNP+Le9/fho33nYGiLohUZw891zThwthFltSYYLXbUGlp7FLQXiAyfnhJFhtIqA3b+fBGlVQaf+UM9yQbqy5/b32j5NSGEBNCL64/h44NVHrtgCzyHW4cmwOZkfue1AJ55IAyuqxk9yQPpD2Nf/QYNLZ0z7AUe+H8PTfC6jNxXH34zKQfNVgeazPYuJ+w6RYYfyi9h/eEqlFZ3nqiqEHjkZ8TAZHHgkski/X4z4qNw7w0Z0sobjuMQpZBBp+5+F25feUD5GXpsWHxzl8/tSx45P07mV/5QT7OB+urn9hTlyLShQoYQEmpsNif+vus0zjW2ICsuCo/ekgOFQuZ3Xkt7DoeIL47VorrJjPQYDWbmpQYtVdadbePw8i9zoYtsG3/6YLa5Cpru0nx/vmDCusPV+O6neq/tUAk8YjWujShNVgc0Chl+/4uhnZYRKwQe0So5opWdg/bcWSwmiwN2p+tKmMDzUMh4aFX+Z7H0VvtMmFiNwq/8Iberea/1xc/tCSpk2lAhQwgh/c/hEDHuP7fDYLb7zLbRa+Q4+MK0XhVaFrsThlY7Wqxdr0BqbLHh86M1+PxoDZq87HIo5znoNXI4nCIGJUXj9XtGgveSOeMO2tOp5FAIPESRYeHq/fix1tgpi4W1ZbEMT43GBw/e0K9Jw8FqRyB/rr+f3zRHhhBCSK8FKttGJZchWafCgFgNolVyn4F3cVEKPDAxG6/edT2ilTIoOsy1sYsMl5ptMLQ6cLLGgN2nvK9EcgftVV02o9bQigNnG1F+wRT0LJZgZcKEYhYNFTKEEEJ6LdDZNgqBR2K0EplxGsRqFD6XUpvtDggyHhmxagyIUUOr9Jz7wgC02kW8/MUJvLChFIfOXe60oset1ebE2YYWWB3M1R8v5wUqiyVYmTChmEVDOTKEEEJ6LVjZNjKeQ2yUAjEaOYwWB4ytnhOD9SoF5DwHhwhoFDJoFGrYnSKaWu0wtNql/Z0YgL2nG7H3dCOy4zWYMyYd04Ynd5r0q1cpIPCAxS6CwXXrScZz0m2pQGWxBCsTJhSzaOiKDCGEkF4LdrYNx3HQq+UYEKtGkk4FRds8nMHJUciIj4LRYod7Bye5jEeiVomBCRpo5DxUHebsnG0w481tpzDvvb14b9dp1Buv7Obt8XqMQRQZ7A4RNocIh1PE5RZbQLJYgpUJE4pZNFTIEEII6bVQybbhOA5apYABsRqk6FXQKATce0MGNAoZLjXbYHGIEBmDxSGiscWO2CgFXp01Aq/fMxI3DIzzeC2jxYG1B87j3vf34ZUvTuJ4tQEc4PX1XFksFqjkPOaPz+i01URf6+tMmFD/uV2hVUuEEBLGerOMtj+s2lWB//6uHKZWR7tcGAFPTBkctGwbi92JHScv4P/uOYvzDS2wMwY51zlHBgAqG83YUFKNr0/UwWLvXIzkJkdjzph0xGgEfHKgGqcvXclRyUnQ4t4JV15PLuOhU8mhVQlXtR2CP/aUX8LfiipQVmeCzSlCIeORmxKNxyf3XZ6Lr5/b2yya7vj7+U1zZAghJEwFIpSsp0ak6XF9mh4nao2wO0TIBR4jUnUYkaYPSnsA10qnO0elYerwZOw/04haowV6lRyDk6M6LbnOjNPgqalD8PBNA7H1eC02lNSgrt2tpbILJizf+pNrvyalAJGxdptEeV4XsDtFNLRY0Wi2IUopg07VfdDe1XFt8+D6H+vUjv4wcXACbsyJD4kimq7IEEJIGApUKFm4t8kbh1OUJgaL3XwEOkWGPRUNWH+4CkerOif5AkC0UoBWKcDicPoM2HOTy3jo1N6D9noqXH7fV4tyZAghJEKJIsPKnRVotjqQolNBJZeB5zmo5DKk6JRotjqxcmdFQPe+CcU2+SLIeMRFKZAZp0F8lBIC7/ujUMZzmDQkAW/NHY337huL6SOSO+7zCZPVgVqjBRa7E01m127cvgoku1NEQ7MV5xrNqDdZuk0q9iWcft/9jQoZQggJM6EYShaKbeoO35bumxGnRmK0EnIf2Shug5O0mD16AOI1cujV8k7zXlrtIlpsThw534SVRafRbPGdPswYQ7PFgZqmVlQ3tcJk6bwKqCvh+PvuLzRHhhBCwow/oWSGAIeShWKb/MVxnGtfJZW82z2dDBYbnAAStQokaRUwWR24bLbD6rgyMVhkwLrDVdhcWoPp16Vg9ph0ZMb5zs+x2p24aHeiscWGaJUcOpUAoZuiKpx/332NChlCCAkzoRhKFoptuhoahQCNQvC5p5M7YM/uZFAKrlVJ0UoBFoeIJrMdpnbnW+wiNh2twaajNbghOxb3jB2AcVmxPrdVcIoMTWYbDK32bnfhjpTfd1+gW0uEEBJmQjGULBTb1Bu+9nTyFrDHcRzUchlS9EroVQJSdCpEqzyvE+w/exnPrivFQ2sO4vOjNWjtYm4MYwzNVtdtp6rLZlcCcYe5LpH2++4NKmQIISREiCJDaZUBO3++iNIqg8+JmqEYStabNvnb72Bw7+mUEatGjEYBged9BuxdarZBqxLwzPSh+OS3N2LJL4YiO97zltK5RjP+sv0U5r67F3/fWeGxtNsbm8P75OBQfA8ECy2/JoSQEHA1mTCBCCXrqZ62KRSzcLoiigwmiwPf/nQB/9hX2W3AHmMMJZVNWHe4GntPN3RKeOE54KbBCZgzJh156Xqft53aUwiuJdxahYC9pxtC7j3QV/z9/A5qIbNr1y6sWLEChw4dQm1tLTZs2IBZs2ZJxxljWLZsGVatWoWmpibcdNNNWLlyJYYMGeL3z6BChhAS6nqTBxJqyb49aVM456AwxmBstePg2cu41GKFXqXwGrDXXvXlVmw4Uo2vjtfBbOt8a2lwkhZz8tNx27Akaa+orvAchyilAK1ShvL6lpB6D/SFsChktm7dit27d2Ps2LGYM2dOp0Lm9ddfx/Lly/HBBx9g4MCBeOmll1BaWoqTJ09CpVL59TOokCGEhDJRZFi4ej9+rDUiRafy+Bc5Ywx1RiuGp0bjgwdviIgPJ7dI6rfZ5oCh1Y5WL8WJNy1WB746UYcNJdWoaep8aylWI8fMvDTcNToNcVH+TdZVymVS2rA/V3XCQVgUMu1xHOdRyDDGkJaWhiVLlmDp0qUAAIPBgOTkZKxZswbz5s3z+jpWqxVWq1X63mg0IiMjgwoZQkhIKq0y4JEPDyJKKXhdodJqd8JsdeDd+8Zh5IDgxfz3tUjst9XhXunk9CsTxiky7D3dgPUl1SipbOp0XOA5TM5NxC/HDsDQ5Gi/2sBzHLQqAdEqAUqhP7ZDCJywT/Y9c+YM6urqMG3aNOkxvV6PCRMmoLi42Ofzli9fDr1eL31lZGQEormEEHJV/MkDsUdgHkgk9lspyJAUrUJGrBp6tbzL20yAKzX4psEJeONXo/D+/WNxx8gUj1tKDpFh+4/1ePQfh/Hkv0pQVHYRzm4mQottt7yqL7ei5iqC9sJRyBYydXV1AIDk5GSPx5OTk6Vj3jz//PMwGAzS1/nz5/u1nYQQ0hvt80C8idQ8kEjutyDjEa9VIjNOg7goRZdbILjlJGqx9PZcfLzoRvzm5oFI0Hr2+3iNEX/88iQWvL8P/9pfCWOrvdvXtNiduGiyorLRjIZmK2wO77/rcBdxgXhKpRJKpTLYzSCEEL+480B+rDUhRcd3mivSZLZjeGp0xOWBXAv95nkOMRoF9Go5TFYHDGY77D4KNze9Ro57J2Ti1+MG4PtTl7DucDVO1l7ZZqDeZMWq78/gw+Jz+MWIZMzJT0dWfFSXr+kUGQytdhha7VDJXUF7UQpZxMylCdkrMikpKQCACxcueDx+4cIF6RghhIS7azUP5FrqN8dx0KnkyIjTIEmngtJHWm97gozHlGFJ+O978/G3BfmYNjzJY28ni0PEF0dr8eCag/jDZ8ew93RDtzt5A66rNPVGCyobzWhssXVbWIWDkC1kBg4ciJSUFOzYsUN6zGg0Yt++fSgoKAhiywghpG+4g+DsIsNvJuVgWIoWZqsD9c1WmK0ODE+NDuklyL01cXAC/nP2SAxPjb5m+q1VCkiPUSNVr4Za4d9k3GEpOrxwx3D8a9EE/K8bM6FXyz2OHzx3GS9sOI4HVh/A+sPVMNt8b1bp5t4O4XyjGReMFr+eE6qCumqpubkZ5eXlAID8/Hy8+eabmDJlCuLi4pCZmYnXX38dr732msfy62PHjtHya0JI2PMWBJeTqEXh9SnIiNNEVB5Id0IxCydQfO3p1BWbQ8SOn+qx/nAVKi62dDoepZBhxsgUzBqdjrQYtd+vK5e59o7SqoROO3sHQ1gsvy4qKsKUKVM6Pb5w4UKsWbNGCsR777330NTUhJtvvhl/+9vfMHToUL9/BhUyhJBQE85BcKR/2BwiDK12NFsdfq8yYozhaJUB6w5XYU9559RgDsDEQfGYMyYdozNi/J4Tw3EcopQy6FS+N60MhLAoZAKBChlCSCiJpCA40vccTldBY7I4/Jrz4lZraMXGkhpsKa1Fi5dgvpzEKMzJT8fUYUl+zdFxUwg8ott2+A70+5EKmTZUyBBCQkkkBsGRvucUXXkwRou92+yY9sw2B74+cQEbSqpRdbm103G9Wo5/y0vF3aPTkKD1f4WvezsEnTpwQXv+fn5H3PJrQggJZf4EwRnCLAiO9D0ZzyE2qm3ptsW1BYJD7H6FkUYhYHZ+Ou4enYb9Zxqx/nA1Dp67LB03tNrx0b5KrD1wHrcOTcQ9Y9IxPLX7f+SLjMFkscNkCb0l3FTIEEJIALUPglPxnf9lG85BcKTv8TwHvUYOnVpAs9VV0PgTbMdzHG7MiceNOfE419CC9SXV2HbiAixtz3WKDN/+VI9vf6rH8NRozMkfgFuHJkDwUWC3Z7E7YbE7IeM5RKvk0KvlQZ0cTLeWCCEkgK7MkTEhRaekOTKkx8w2B5rMdljs/m1S6Way2LG5tA4bS6pRb7J2Oh6vVeDuUWmYmZcGvUbu5RW8S4xWIlrl//n+ojkybaiQIYSEmiurlpyI0cihlPGwOkU00aol0gMWuxPGtpVOPeEUGXaXu1KDS6sNnY4rBB7ThiVhzph05CRqu309KmT6GRUyhJBQ5JEjIzLIeQ6DkrR47NZBVMSQHrG3FcE9Wbrt9vMFE9YfrsZ3ZfWwOzs/d3RGDO4Zk44bc+J93j6iQqafUSFDCAlV13IQHOl77j2VTD1c6QQAjS02fH60Bl8crcFlc+cNKVP1KszOT0fh9SnQKj2n11Ih08+okCGEEHItEUXWo5VO7dkcIorK6rHucDVO1Td3Oq6Wy1B4fQpm56dhQKwGABUy/Y4KGUIIIdcixliPVjp1fO7xaiPWlVThh1OX0PECDwdgQk4c5rRdpdGp+36VHRUybaiQIYQQcq272pVOAFBntGBTSTU2l9Z5nVg8KDEKD9+cg9n56X5vhOkPKmTaUCFDCCGEuFjsTjSZ7Ve123Wr3YltJy9gw+FqnGs0dzr+xRM392kaNSX7EkIIIcSDSi5Dil4Gq8O967bT75VOarkMd41Kw8y8VBw8dxnrD1dj35lGAMD47NigbalBhQwhhBByjVEKMiRFy2DXXNmk0t+ChuM4jM+Ow/jsOFQ2mvHV8TpMHZ7czy32jQoZQggh5Boll/FI0CoRq1Fc1dLtzDgNXrxzeL+sWvIXFTKEEELINU7Gc4iLUiCmh5tUhgIqZAghhBACwHOTSpPVAYPZDrsztAsaKmQIIYQQ4oHjOOhUcuhUcrS0ZdFczdLtQKBChhBCCCE+RSkFRCkFWOzulU49X7rdn6iQIYQQQki3VHIZVHIZbA7XSqer2aSyP1AhQwghhBC/KQQeidFKxGrkMFocPnfFDhQqZAghhBDSY4KMR1xU3++x1FN8sBtACCGEEHK1qJAhhBBCSNiiQoYQQgghYYsKGUIIIYSELSpkCCGEEBK2qJAhhBBCSNiiQoYQQgghYYsKGUIIIYSELSpkCCGEEBK2qJAhhBBCSNgK6ULm5ZdfBsdxHl/Dhg0LdrMIIYQQEiJCfq+lESNGYPv27dL3ghDyTSaEEEJIgIR8VSAIAlJSUvw+32q1wmq1St8bjcb+aBYhhBBCQkBI31oCgFOnTiEtLQ05OTlYsGABKisruzx/+fLl0Ov10ldGRkaAWkoIIYSQQOMYYyzYjfBl69ataG5uRm5uLmpra/HKK6+guroax48fR3R0tNfneLsik5GRAYPBAJ1OF6imE0IIIaQXjEYj9Hp9t5/fIV3IdNTU1ISsrCy8+eabePjhh/16jsFgQExMDM6fP0+FDCGEEBIm3BcimpqaoNfrfZ4X8nNk2ouJicHQoUNRXl7u93NMJhMA0C0mQgghJAyZTKbIKWSam5tRUVGB++67z+/npKWl4eTJk7juuuuuiasy7go20vtK/Yw810pfqZ+R5VrpJxD4vjLGYDKZkJaW1uV5IV3ILF26FDNnzkRWVhZqamqwbNkyyGQyzJ8/3+/X4Hke6enpAACdThfxbzS3a6Wv1M/Ic630lfoZWa6VfgKB7WtXV2LcQrqQqaqqwvz589HQ0IDExETcfPPN2Lt3LxITE4PdNEIIIYSEgJAuZNauXRvsJhBCCCEkhIV8jkxfUCqVWLZsGZRKZbCb0u+ulb5SPyPPtdJX6mdkuVb6CYRuX8Nq+TUhhBBCSHvXxBUZQgghhEQmKmQIIYQQEraokCGEEEJI2KJChhBCCCFhK6IKmZdffhkcx3l8DRs2TDpusViwePFixMfHQ6vV4p577sGFCxeC2GL/7Nq1CzNnzkRaWho4jsPGjRs9jjPG8O///u9ITU2FWq3GtGnTcOrUKY9zGhsbsWDBAuh0OsTExODhhx9Gc3NzAHvRve76+cADD3Qa38LCQo9zwqGfy5cvx/jx4xEdHY2kpCTMmjULZWVlHuf4816trKzEnXfeCY1Gg6SkJDzzzDNwOByB7EqX/Onn5MmTO43po48+6nFOqPcTAFauXIm8vDwpKKygoABbt26VjkfCeALd9zNSxrOj1157DRzH4emnn5Yei5Qxbc9bP8NiTFkEWbZsGRsxYgSrra2Vvi5evCgdf/TRR1lGRgbbsWMHO3jwILvxxhvZxIkTg9hi/2zZsoW9+OKLbP369QwA27Bhg8fx1157jen1erZx40Z29OhRdtddd7GBAwey1tZW6ZzCwkI2atQotnfvXvb999+zwYMHs/nz5we4J13rrp8LFy5khYWFHuPb2NjocU449HP69Ols9erV7Pjx4+zIkSPsjjvuYJmZmay5uVk6p7v3qsPhYNdffz2bNm0aKykpYVu2bGEJCQns+eefD0aXvPKnn7feeitbtGiRx5gaDAbpeDj0kzHGPv/8c7Z582b2888/s7KyMvbCCy8wuVzOjh8/zhiLjPFkrPt+Rsp4trd//36WnZ3N8vLy2FNPPSU9Hilj6uarn+EwphFXyIwaNcrrsaamJiaXy9mnn34qPfbjjz8yAKy4uDhALey9jh/woiiylJQUtmLFCumxpqYmplQq2b/+9S/GGGMnT55kANiBAwekc7Zu3co4jmPV1dUBa3tP+Cpk7r77bp/PCcd+MsZYfX09A8B27tzJGPPvvbplyxbG8zyrq6uTzlm5ciXT6XTMarUGtgN+6thPxlx/Sbb/S7OjcOynW2xsLHv//fcjdjzd3P1kLPLG02QysSFDhrBt27Z59C3SxtRXPxkLjzGNqFtLAHDq1CmkpaUhJycHCxYsQGVlJQDg0KFDsNvtmDZtmnTusGHDkJmZieLi4mA1t9fOnDmDuro6j37p9XpMmDBB6ldxcTFiYmIwbtw46Zxp06aB53ns27cv4G3ujaKiIiQlJSE3NxePPfYYGhoapGPh2k+DwQAAiIuLA+Dfe7W4uBgjR45EcnKydM706dNhNBpx4sSJALbefx376fbRRx8hISEB119/PZ5//nmYzWbpWDj20+l0Yu3atWhpaUFBQUHEjmfHfrpF0nguXrwYd955p8fYAZH3Z9RXP91CfUxDeouCnpowYQLWrFmD3Nxc1NbW4pVXXsGkSZNw/Phx1NXVQaFQICYmxuM5ycnJqKurC06D+4C77e3fRO7v3cfq6uqQlJTkcVwQBMTFxYVV3wsLCzFnzhwMHDgQFRUVeOGFFzBjxgwUFxdDJpOFZT9FUcTTTz+Nm266Cddffz0A+PVeraur8zrm7mOhxls/AeDee+9FVlYW0tLScOzYMTz77LMoKyvD+vXrAYRXP0tLS1FQUACLxQKtVosNGzbguuuuw5EjRyJqPH31E4is8Vy7di0OHz6MAwcOdDoWSX9Gu+onEB5jGlGFzIwZM6T/zsvLw4QJE5CVlYVPPvkEarU6iC0jfWHevHnSf48cORJ5eXkYNGgQioqKMHXq1CC27OotXrwYx48fxw8//BDspvQrX/387W9/K/33yJEjkZqaiqlTp6KiogKDBg0KdDN7JTc3F0eOHIHBYMBnn32GhQsXYufOncFuVp/z1c/rrrsuYsbz/PnzeOqpp7Bt2zaoVKpgN6ff+NPPcBjTiLu11F5MTAyGDh2K8vJypKSkwGazoampyeOcCxcuICUlJTgN7APutnecLd++XykpKaivr/c47nA40NjYGNZ9z8nJQUJCAsrLywGEXz+feOIJfPnll/juu+8wYMAA6XF/3qspKSlex9x9LJT46qc3EyZMAACPMQ2XfioUCgwePBhjx47F8uXLMWrUKPz1r3+NuPH01U9vwnU8Dx06hPr6eowZMwaCIEAQBOzcuRNvv/02BEFAcnJyRIxpd/10Op2dnhOKYxrRhUxzczMqKiqQmpqKsWPHQi6XY8eOHdLxsrIyVFZWetzfDTcDBw5ESkqKR7+MRiP27dsn9augoABNTU04dOiQdM63334LURSlN2U4qqqqQkNDA1JTUwGETz8ZY3jiiSewYcMGfPvttxg4cKDHcX/eqwUFBSgtLfUo3LZt2wadTidd5g+27vrpzZEjRwDAY0xDvZ++iKIIq9UaMePpi7uf3oTreE6dOhWlpaU4cuSI9DVu3DgsWLBA+u9IGNPu+imTyTo9JyTHNCBTigNkyZIlrKioiJ05c4bt3r2bTZs2jSUkJLD6+nrGmGu5XGZmJvv222/ZwYMHWUFBASsoKAhyq7tnMplYSUkJKykpYQDYm2++yUpKSti5c+cYY67l1zExMWzTpk3s2LFj7O677/a6/Do/P5/t27eP/fDDD2zIkCEhtyy5q36aTCa2dOlSVlxczM6cOcO2b9/OxowZw4YMGcIsFov0GuHQz8cee4zp9XpWVFTksaTRbDZL53T3XnUvebz99tvZkSNH2FdffcUSExNDamlnd/0sLy9nf/zjH9nBgwfZmTNn2KZNm1hOTg675ZZbpNcIh34yxthzzz3Hdu7cyc6cOcOOHTvGnnvuOcZxHPvmm28YY5Exnox13c9IGk9vOq7eiZQx7ah9P8NlTCOqkJk7dy5LTU1lCoWCpaens7lz57Ly8nLpeGtrK3v88cdZbGws02g0bPbs2ay2tjaILfbPd999xwB0+lq4cCFjzLUE+6WXXmLJyclMqVSyqVOnsrKyMo/XaGhoYPPnz2darZbpdDr24IMPMpPJFITe+NZVP81mM7v99ttZYmIik8vlLCsriy1atMhjyR9j4dFPb30EwFavXi2d48979ezZs2zGjBlMrVazhIQEtmTJEma32wPcG9+662dlZSW75ZZbWFxcHFMqlWzw4MHsmWee8cioYCz0+8kYYw899BDLyspiCoWCJSYmsqlTp0pFDGORMZ6Mdd3PSBpPbzoWMpEyph2172e4jCnHGGOBufZDCCGEENK3InqODCGEEEIiGxUyhBBCCAlbVMgQQgghJGxRIUMIIYSQsEWFDCGEEELCFhUyhBBCCAlbVMgQQgghJGxRIUMIIYSQsEWFDCGkS5MnT8bTTz8NAMjOzsZf/vKXPnttjuOwcePGPns9Qsi1Rwh2Awgh4ePAgQOIiooKdjPC0gMPPICmpiYq3AjpY1TIEEL8lpiYGOwmEEKIB7q1RAiRtLS04P7774dWq0VqaireeOMNj+Ptby0xxvDyyy8jMzMTSqUSaWlpePLJJz3OffXVVzF//nxERUUhPT0d77zzTpc//9lnn8XQoUOh0WiQk5ODl156CXa73eOcL774AuPHj4dKpUJCQgJmz54tHbNarVi6dCnS09MRFRWFCRMmoKioSDq+Zs0axMTE4Msvv0Rubi40Gg1++ctfwmw244MPPkB2djZiY2Px5JNPwul09vh1v/76awwfPhxarRaFhYWora0FALz88sv44IMPsGnTJnAcB47jPJ5PCLl6VMgQQiTPPPMMdu7ciU2bNuGbb75BUVERDh8+7PXcdevW4a233sK7776LU6dOYePGjRg5cqTHOStWrMCoUaNQUlKC5557Dk899RS2bdvm8+dHR0djzZo1OHnyJP76179i1apVeOutt6TjmzdvxuzZs3HHHXegpKQEO3bswA033CAdf+KJJ1BcXIy1a9fi2LFj+NWvfoXCwkKcOnVKOsdsNuPtt9/G2rVr8dVXX6GoqAizZ8/Gli1bsGXLFnz44Yd499138dlnn/X4df/rv/4LH374IXbt2oXKykosXboUALB06VL8+te/loqb2tpaTJw40c9RIYR0KWD7bBNCQprJZGIKhYJ98skn0mMNDQ1MrVazp556ijHGWFZWFnvrrbcYY4y98cYbbOjQocxms3l9vaysLFZYWOjx2Ny5c9mMGTOk7wGwDRs2+GzTihUr2NixY6XvCwoK2IIFC7yee+7cOSaTyVh1dbXH41OnTmXPP/88Y4yx1atXMwCsvLxcOv7II48wjUbDTCaT9Nj06dPZI4880qvXfeedd1hycrL0/cKFC9ndd9/ts6+EkKtDc2QIIQCAiooK2Gw2TJgwQXosLi4Oubm5Xs//1a9+hb/85S/IyclBYWEh7rjjDsycOROCcOWvlYKCAo/nFBQUdLnq6eOPP8bbb7+NiooKNDc3w+FwQKfTScePHDmCRYsWeX1uaWkpnE4nhg4d6vG41WpFfHy89L1Go8GgQYOk75OTk5GdnQ2tVuvxWH19fa9eNzU1VXoNQkj/oUKGEHJVMjIyUFZWhu3bt2Pbtm14/PHHsWLFCuzcuRNyubzHr1dcXIwFCxbglVdewfTp06HX67F27VqPeTpqtdrn85ubmyGTyXDo0CHIZDKPY+2LlI5t4zjO62OiKPb6dRljXXWZENIHqJAhhAAABg0aBLlcjn379iEzMxMAcPnyZfz888+49dZbvT5HrVZj5syZmDlzJhYvXoxhw4ahtLQUY8aMAQDs3bvX4/y9e/di+PDhXl9rz549yMrKwosvvig9du7cOY9z8vLysGPHDjz44IOdnp+fnw+n04n6+npMmjTJ/453o69eV6FQeEwgJoT0DSpkCCEAXFcXHn74YTzzzDOIj49HUlISXnzxRfC89zUBa9asgdPpxIQJE6DRaPCPf/wDarUaWVlZ0jm7d+/Gn//8Z8yaNQvbtm3Dp59+is2bN3t9vSFDhqCyshJr167F+PHjsXnzZmzYsMHjnGXLlmHq1KkYNGgQ5s2bB4fDgS1btkirnRYsWID7778fb7zxBvLz83Hx4kXs2LEDeXl5uPPOO6/q99JXr5udnY2vv/4aZWVliI+Ph16vv6orV4QQT7RqiRAiWbFiBSZNmoSZM2di2rRpuPnmmzF27Fiv58bExGDVqlW46aabkJeXh+3bt+OLL77wmDeyZMkSHDx4EPn5+fjTn/6EN998E9OnT/f6enfddRd+97vf4YknnsDo0aOxZ88evPTSSx7nTJ48GZ9++ik+//xzjB49Grfddhv2798vHV+9ejXuv/9+LFmyBLm5uZg1axYOHDggXWG6Wn3xuosWLUJubi7GjRuHxMRE7N69u1dtIoS4cIxu4hJC+kF2djaefvppaXsDQgjpD3RFhhBCCCFhiwoZQgghhIQturVECCGEkLBFV2QIIYQQEraokCGEEEJI2KJChhBCCCFhiwoZQgghhIQtKmQIIYQQEraokCGEEEJI2KJChhBCCCFhiwoZQgghhISt/w8hKj41s9YQlgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "DEFINE TARGET VARIABLE Y AND FEATURE X\n"
      ],
      "metadata": {
        "id": "wEPmrzncqymV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7M9d6jgHqusU",
        "outputId": "a97bb1c4-2732-4bf4-8ed7-5e0629c97891"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['mpg', 'cylinders', 'displacement', 'horsepower', 'weight',\n",
              "       'acceleration', 'model_year', 'origin', 'name'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y = df['mpg']"
      ],
      "metadata": {
        "id": "LEFpitgkq9wK"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WF5ioOiTra4k",
        "outputId": "6b000d01-4000-4705-86de-0df93b7085d6"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(392,)"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = df[['displacement','horsepower','weight','acceleration']]"
      ],
      "metadata": {
        "id": "kkXmtpN8rc-I"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wyit--eOrtHC",
        "outputId": "e12611d6-98e5-471c-dba9-3ebbaa690889"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(392, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "4pHjW__JrvlY",
        "outputId": "6417bf60-83d3-40e1-c41d-1ab98e3f5b66"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     displacement  horsepower  weight  acceleration\n",
              "0           307.0       130.0    3504          12.0\n",
              "1           350.0       165.0    3693          11.5\n",
              "2           318.0       150.0    3436          11.0\n",
              "3           304.0       150.0    3433          12.0\n",
              "4           302.0       140.0    3449          10.5\n",
              "..            ...         ...     ...           ...\n",
              "393         140.0        86.0    2790          15.6\n",
              "394          97.0        52.0    2130          24.6\n",
              "395         135.0        84.0    2295          11.6\n",
              "396         120.0        79.0    2625          18.6\n",
              "397         119.0        82.0    2720          19.4\n",
              "\n",
              "[392 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-58741ca1-5330-4c09-95cc-6bd12292cf48\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
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              "\n",
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              "        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>displacement</th>\n",
              "      <th>horsepower</th>\n",
              "      <th>weight</th>\n",
              "      <th>acceleration</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>307.0</td>\n",
              "      <td>130.0</td>\n",
              "      <td>3504</td>\n",
              "      <td>12.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>350.0</td>\n",
              "      <td>165.0</td>\n",
              "      <td>3693</td>\n",
              "      <td>11.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>318.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>3436</td>\n",
              "      <td>11.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>304.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>3433</td>\n",
              "      <td>12.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>302.0</td>\n",
              "      <td>140.0</td>\n",
              "      <td>3449</td>\n",
              "      <td>10.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>393</th>\n",
              "      <td>140.0</td>\n",
              "      <td>86.0</td>\n",
              "      <td>2790</td>\n",
              "      <td>15.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>394</th>\n",
              "      <td>97.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>2130</td>\n",
              "      <td>24.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>395</th>\n",
              "      <td>135.0</td>\n",
              "      <td>84.0</td>\n",
              "      <td>2295</td>\n",
              "      <td>11.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>396</th>\n",
              "      <td>120.0</td>\n",
              "      <td>79.0</td>\n",
              "      <td>2625</td>\n",
              "      <td>18.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>397</th>\n",
              "      <td>119.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>2720</td>\n",
              "      <td>19.4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>392 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-58741ca1-5330-4c09-95cc-6bd12292cf48')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-58741ca1-5330-4c09-95cc-6bd12292cf48 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-58741ca1-5330-4c09-95cc-6bd12292cf48');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "SCALING DATA\n"
      ],
      "metadata": {
        "id": "bhwWJA5Br2LZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler"
      ],
      "metadata": {
        "id": "svDkWqQ1r09V"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ss = StandardScaler()"
      ],
      "metadata": {
        "id": "IHeBuI3ssNEM"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = ss.fit_transform(x)"
      ],
      "metadata": {
        "id": "_WOt34OIsTHV"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "etHQRAQ8sZuR",
        "outputId": "6faa5246-7169-4a09-f824-ee5c77ffc2c6"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1.07728956,  0.66413273,  0.62054034, -1.285258  ],\n",
              "       [ 1.48873169,  1.57459447,  0.84333403, -1.46672362],\n",
              "       [ 1.1825422 ,  1.18439658,  0.54038176, -1.64818924],\n",
              "       ...,\n",
              "       [-0.56847897, -0.53247413, -0.80463202, -1.4304305 ],\n",
              "       [-0.7120053 , -0.66254009, -0.41562716,  1.11008813],\n",
              "       [-0.72157372, -0.58450051, -0.30364091,  1.40043312]])"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame(x).describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "Du2smzzSsbMA",
        "outputId": "48910bdf-6594-4e0f-aae2-b327de960f9a"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  0             1             2             3\n",
              "count  3.920000e+02  3.920000e+02  3.920000e+02  3.920000e+02\n",
              "mean  -7.250436e-17 -1.812609e-16 -1.812609e-17  4.350262e-16\n",
              "std    1.001278e+00  1.001278e+00  1.001278e+00  1.001278e+00\n",
              "min   -1.209563e+00 -1.520975e+00 -1.608575e+00 -2.736983e+00\n",
              "25%   -8.555316e-01 -7.665929e-01 -8.868535e-01 -6.410551e-01\n",
              "50%   -4.153842e-01 -2.853488e-01 -2.052109e-01 -1.499869e-02\n",
              "75%    7.782764e-01  5.600800e-01  7.510927e-01  5.384714e-01\n",
              "max    2.493416e+00  3.265452e+00  2.549061e+00  3.360262e+00"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-51729b97-e6eb-486b-a0fb-3fdb65fea111\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>3.920000e+02</td>\n",
              "      <td>3.920000e+02</td>\n",
              "      <td>3.920000e+02</td>\n",
              "      <td>3.920000e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>-7.250436e-17</td>\n",
              "      <td>-1.812609e-16</td>\n",
              "      <td>-1.812609e-17</td>\n",
              "      <td>4.350262e-16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.001278e+00</td>\n",
              "      <td>1.001278e+00</td>\n",
              "      <td>1.001278e+00</td>\n",
              "      <td>1.001278e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-1.209563e+00</td>\n",
              "      <td>-1.520975e+00</td>\n",
              "      <td>-1.608575e+00</td>\n",
              "      <td>-2.736983e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-8.555316e-01</td>\n",
              "      <td>-7.665929e-01</td>\n",
              "      <td>-8.868535e-01</td>\n",
              "      <td>-6.410551e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>-4.153842e-01</td>\n",
              "      <td>-2.853488e-01</td>\n",
              "      <td>-2.052109e-01</td>\n",
              "      <td>-1.499869e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>7.782764e-01</td>\n",
              "      <td>5.600800e-01</td>\n",
              "      <td>7.510927e-01</td>\n",
              "      <td>5.384714e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2.493416e+00</td>\n",
              "      <td>3.265452e+00</td>\n",
              "      <td>2.549061e+00</td>\n",
              "      <td>3.360262e+00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-51729b97-e6eb-486b-a0fb-3fdb65fea111')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-51729b97-e6eb-486b-a0fb-3fdb65fea111 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-51729b97-e6eb-486b-a0fb-3fdb65fea111');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "AFTER STANDARDIZATION MEAN IS ZERO AND STANDARD DEVIATION IS ONE"
      ],
      "metadata": {
        "id": "W-CWucy4snEC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "TRAIN TEST SPLIT DATA"
      ],
      "metadata": {
        "id": "hK5k9IAryD8L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "ycJ2xwjBslTz"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train, x_test ,y_train , y_test = train_test_split(x,y,train_size = 0.7, random_state = 2529)"
      ],
      "metadata": {
        "id": "e0GE2hDOzOOX"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train.shape, x_test.shape,y_train.shape, y_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b8b3wNCP0Ddk",
        "outputId": "439599c8-d4b2-4f95-b56d-e8e474fcda11"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((274, 4), (118, 4), (274,), (118,))"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "LINEAR REGRESSION MODEL"
      ],
      "metadata": {
        "id": "XOSdvm5n0XGk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression"
      ],
      "metadata": {
        "id": "Xhrsklix0SlD"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lr = LinearRegression()"
      ],
      "metadata": {
        "id": "68UhjkbG0m2Y"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lr.fit(x_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "vBERyBvI0sey",
        "outputId": "a10d9030-558a-449b-f7c2-e4760d076ee3"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lr.intercept_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sfOgIinV0179",
        "outputId": "897f5db8-5a85-441e-9597-f494f79b9c4b"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "23.485738559737584"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lr.coef_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H7BnTBg50-am",
        "outputId": "f3a6bee1-e642-42ec-cdd4-3d2e8adddb3e"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-1.05767743, -1.68734727, -4.10787617, -0.11495177])"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Milege = 23.4 - 1.05Displacement- 1.68Horsepower - 4.10Weight - 0.115Accelertation + error"
      ],
      "metadata": {
        "id": "Pmn5KEYc1Oan"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "PREDICT TEST DATA "
      ],
      "metadata": {
        "id": "nupfAOy41kW4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = lr.predict(x_test) "
      ],
      "metadata": {
        "id": "YxM_TbDA1HY8"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PmOpFNxL1sf0",
        "outputId": "f156715a-c324-4330-dd86-41f04a0a1103"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([18.51865637, 15.09305675, 14.30128789, 23.6753321 , 29.7546115 ,\n",
              "       23.68796629, 26.61066644, 24.56692437, 15.06260986, 11.94312046,\n",
              "       24.08050053, 27.96518468, 31.66130278, 31.01309132, 18.32428976,\n",
              "       19.32795009, 28.08847536, 32.1506879 , 31.15859692, 27.15792144,\n",
              "       18.82433097, 22.54580176, 26.15598115, 32.36393869, 20.74377679,\n",
              "        8.78027518, 22.19699435, 18.20614294, 25.00052718, 15.26421552,\n",
              "       23.13441082, 17.10542257,  9.87180062, 30.00790415, 20.41204655,\n",
              "       29.11860245, 24.4305187 , 21.72601835, 10.51174626, 13.12426391,\n",
              "       21.41938406, 19.96113872,  6.19146626, 17.79025345, 22.5493033 ,\n",
              "       29.34765021, 13.4861847 , 25.88852083, 29.40406946, 22.41841964,\n",
              "       22.07684766, 16.46575802, 24.06290693, 30.12890046, 10.11318121,\n",
              "        9.85011438, 28.07543852, 23.41426617, 20.08501128, 30.68234133,\n",
              "       20.92026393, 26.78370281, 22.9078744 , 14.15936872, 24.6439883 ,\n",
              "       26.95515832, 15.25709393, 24.11272087, 30.80980589, 14.9770217 ,\n",
              "       27.67836372, 24.2372919 , 10.92177228, 30.22858779, 30.88687365,\n",
              "       27.33992044, 31.18447082, 10.8873597 , 27.63510608, 16.49231363,\n",
              "       25.63229888, 29.49776285, 14.90393439, 32.78670687, 30.37325244,\n",
              "       30.9262743 , 14.71702373, 27.09633246, 26.69933806, 29.06424799,\n",
              "       32.45810182, 29.44846898, 31.61239999, 31.57891837, 21.46542321,\n",
              "       31.76739191, 26.28605476, 28.96419915, 31.09628395, 24.80549594,\n",
              "       18.76490961, 23.28043777, 23.04466919, 22.14143162, 15.95854367,\n",
              "       28.62870918, 25.58809869, 11.4040908 , 25.73334842, 30.83500051,\n",
              "       21.94176255, 15.34532941, 30.37399213, 28.7620624 , 29.3639931 ,\n",
              "       29.10476703, 20.44662365, 28.11466839])"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "MODEL ACCURACY"
      ],
      "metadata": {
        "id": "LPT6Y1Ot15Kp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_absolute_error, mean_absolute_percentage_error,r2_score"
      ],
      "metadata": {
        "id": "BCygmdVr1uIk"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        " mean_absolute_error(y_test,y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y3uIMbNV2Lta",
        "outputId": "fa5886ef-8c0c-47d8-80e4-5bd6403e5606"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.3286968643244106"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        " mean_absolute_percentage_error(y_test,y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "73pxPkcO2mc_",
        "outputId": "ca7d8d97-080d-481c-abe4-35702a05aa9a"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.14713035779536746"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "r2_score(y_test,y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FZ0HNgZ42v7_",
        "outputId": "c22ca7cb-ccb6-4690-bc70-281b3548792f"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7031250746717691"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "POLYNOMIAL REGRESSION"
      ],
      "metadata": {
        "id": "_THj4vJ42584"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import PolynomialFeatures"
      ],
      "metadata": {
        "id": "HbKuTmvP22hW"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "poly = PolynomialFeatures(degree=2,interaction_only=True,include_bias=False)"
      ],
      "metadata": {
        "id": "fb8tLwvH9XTm"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train2 = poly.fit_transform(x_train)"
      ],
      "metadata": {
        "id": "LjuLaVKdDOWd"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_test2 = poly.fit_transform(x_test)"
      ],
      "metadata": {
        "id": "AM23qUkoDd1I"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lr.fit(x_train2,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "-T_K21xpDk33",
        "outputId": "05039406-4795-4494-9ccf-9a4a511994ea"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lr.intercept_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6Sz777LuDvR4",
        "outputId": "41f46ba1-e697-49b3-d286-3d6416a50d81"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "21.27336450063766"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lr.coef_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n0StI5g-D4oA",
        "outputId": "4a4bf3f5-03fe-482f-ece6-330cd81e2037"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-2.76070596, -5.00559628, -1.36884133, -0.81225214,  1.24596571,\n",
              "       -0.12475017, -0.90542822,  1.35064048, -0.17337823,  1.41680398])"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_poly = lr.predict(x_test2)"
      ],
      "metadata": {
        "id": "ijvwjKzrFlR_"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "MODEL ACCURACY\n"
      ],
      "metadata": {
        "id": "B0ev8hOkFSlA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_absolute_error, mean_absolute_percentage_error,r2_score"
      ],
      "metadata": {
        "id": "8sCkKlboD7jE"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mean_absolute_error(y_test, y_pred_poly)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bytG7fxFEVZ6",
        "outputId": "84838fad-7b3c-4efa-c045-ef2f3796b75a"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.7887147720295977"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        " mean_absolute_percentage_error(y_test,y_pred_poly)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RlQ7qkp1EbN8",
        "outputId": "1cdda46a-678f-473b-f4d3-6c596dc86697"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.12074018342938687"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "r2_score(y_test,y_pred_poly)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NQVCpeURF4Jy",
        "outputId": "cef62a34-64a5-4be5-9c0b-788254044cc8"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7461731314563803"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "10JDGlVqGASS"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}