exercise_scaling_and_normalization.ipynb

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        "<a href=\"https://colab.research.google.com/github/karthikheyaa17/AI---Workshop-2022/blob/main/exercise_scaling_and_normalization.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "In this exercise, you'll apply what you learned in the **Scaling and normalization** tutorial.\n",
        "\n",
        "\n"
      ],
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      "source": [
        "\n",
        "\n",
        "The next code cell loads in the libraries and dataset we'll be using. "
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      "source": [
        "# modules we'll use\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "# for Box-Cox Transformation\n",
        "from scipy import stats\n",
        "\n",
        "# for min_max scaling\n",
        "from mlxtend.preprocessing import minmax_scaling\n",
        "\n",
        "# plotting modules\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# read in all our data\n",
        "kickstarters_2017 = pd.read_csv(\"ks-projects-201801.csv\")\n",
        "\n",
        "# set seed for reproducibility\n",
        "np.random.seed(0)\n",
        "kickstarters_2017.head()"
      ],
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              "           ID                                               name  \\\n",
              "0  1000002330                    The Songs of Adelaide & Abullah   \n",
              "1  1000003930      Greeting From Earth: ZGAC Arts Capsule For ET   \n",
              "2  1000004038                                     Where is Hank?   \n",
              "3  1000007540  ToshiCapital Rekordz Needs Help to Complete Album   \n",
              "4  1000011046  Community Film Project: The Art of Neighborhoo...   \n",
              "\n",
              "         category main_category currency    deadline     goal  \\\n",
              "0          Poetry    Publishing      GBP  2015-10-09   1000.0   \n",
              "1  Narrative Film  Film & Video      USD  2017-11-01  30000.0   \n",
              "2  Narrative Film  Film & Video      USD  2013-02-26  45000.0   \n",
              "3           Music         Music      USD  2012-04-16   5000.0   \n",
              "4    Film & Video  Film & Video      USD  2015-08-29  19500.0   \n",
              "\n",
              "              launched  pledged     state  backers country  usd pledged  \\\n",
              "0  2015-08-11 12:12:28      0.0    failed        0      GB          0.0   \n",
              "1  2017-09-02 04:43:57   2421.0    failed       15      US        100.0   \n",
              "2  2013-01-12 00:20:50    220.0    failed        3      US        220.0   \n",
              "3  2012-03-17 03:24:11      1.0    failed        1      US          1.0   \n",
              "4  2015-07-04 08:35:03   1283.0  canceled       14      US       1283.0   \n",
              "\n",
              "   usd_pledged_real  usd_goal_real  \n",
              "0               0.0        1533.95  \n",
              "1            2421.0       30000.00  \n",
              "2             220.0       45000.00  \n",
              "3               1.0        5000.00  \n",
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              "      <th>ID</th>\n",
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              "      <th>0</th>\n",
              "      <td>1000002330</td>\n",
              "      <td>The Songs of Adelaide &amp; Abullah</td>\n",
              "      <td>Poetry</td>\n",
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              "      <th>1</th>\n",
              "      <td>1000003930</td>\n",
              "      <td>Greeting From Earth: ZGAC Arts Capsule For ET</td>\n",
              "      <td>Narrative Film</td>\n",
              "      <td>Film &amp; Video</td>\n",
              "      <td>USD</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1000004038</td>\n",
              "      <td>Where is Hank?</td>\n",
              "      <td>Narrative Film</td>\n",
              "      <td>Film &amp; Video</td>\n",
              "      <td>USD</td>\n",
              "      <td>2013-02-26</td>\n",
              "      <td>45000.0</td>\n",
              "      <td>2013-01-12 00:20:50</td>\n",
              "      <td>220.0</td>\n",
              "      <td>failed</td>\n",
              "      <td>3</td>\n",
              "      <td>US</td>\n",
              "      <td>220.0</td>\n",
              "      <td>220.0</td>\n",
              "      <td>45000.00</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1000007540</td>\n",
              "      <td>ToshiCapital Rekordz Needs Help to Complete Album</td>\n",
              "      <td>Music</td>\n",
              "      <td>Music</td>\n",
              "      <td>USD</td>\n",
              "      <td>2012-04-16</td>\n",
              "      <td>5000.0</td>\n",
              "      <td>2012-03-17 03:24:11</td>\n",
              "      <td>1.0</td>\n",
              "      <td>failed</td>\n",
              "      <td>1</td>\n",
              "      <td>US</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5000.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1000011046</td>\n",
              "      <td>Community Film Project: The Art of Neighborhoo...</td>\n",
              "      <td>Film &amp; Video</td>\n",
              "      <td>Film &amp; Video</td>\n",
              "      <td>USD</td>\n",
              "      <td>2015-08-29</td>\n",
              "      <td>19500.0</td>\n",
              "      <td>2015-07-04 08:35:03</td>\n",
              "      <td>1283.0</td>\n",
              "      <td>canceled</td>\n",
              "      <td>14</td>\n",
              "      <td>US</td>\n",
              "      <td>1283.0</td>\n",
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          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's start by scaling the goals of each campaign, which is how much money they were asking for.  After scaling, all values lie between 0 and 1."
      ],
      "metadata": {
        "id": "9GYh4ncr2mg7"
      }
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      "source": [
        "# select the usd_goal_real column\n",
        "original_data = pd.DataFrame(kickstarters_2017.usd_goal_real)\n",
        "\n",
        "# scale the goals from 0 to 1\n",
        "scaled_data = minmax_scaling(original_data, columns=['usd_goal_real'])\n",
        "\n",
        "print('Original data\\nPreview:\\n', original_data.head())\n",
        "print('Minimum value:', float(original_data.min()),\n",
        "      '\\nMaximum value:', float(original_data.max()))\n",
        "print('_'*30)\n",
        "\n",
        "print('\\nScaled data\\nPreview:\\n', scaled_data.head())\n",
        "print('Minimum value:', float(scaled_data.min()),\n",
        "      '\\nMaximum value:', float(scaled_data.max()))"
      ],
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      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original data\n",
            "Preview:\n",
            "    usd_goal_real\n",
            "0        1533.95\n",
            "1       30000.00\n",
            "2       45000.00\n",
            "3        5000.00\n",
            "4       19500.00\n",
            "Minimum value: 0.01 \n",
            "Maximum value: 166361390.71\n",
            "______________________________\n",
            "\n",
            "Scaled data\n",
            "Preview:\n",
            "    usd_goal_real\n",
            "0       0.000009\n",
            "1       0.000180\n",
            "2       0.000270\n",
            "3       0.000030\n",
            "4       0.000117\n",
            "Minimum value: 0.0 \n",
            "Maximum value: 1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1) Practice scaling\n",
        "\n",
        "We just scaled the \"usd_goal_real\" column. What about the \"goal\" column?\n",
        "\n",
        "Begin by running the code cell below to create a DataFrame `original_goal_data` containing the \"goal\" column."
      ],
      "metadata": {
        "id": "H_PMrdhz2mg8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# select the usd_goal_real column\n",
        "original_goal_data = pd.DataFrame(kickstarters_2017.goal)"
      ],
      "metadata": {
        "id": "wVQ_xuff2mg8"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Use `original_goal_data` to create a new DataFrame `scaled_goal_data` with values scaled between 0 and 1. You must use the `minmax_scaling()` function."
      ],
      "metadata": {
        "id": "7-40Svm_2mg9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2) Practice normalization\n",
        "\n",
        "Now you'll practice normalization. We begin by normalizing the amount of money pledged to each campaign."
      ],
      "metadata": {
        "id": "c4pbX2NR2mg-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# get the index of all positive pledges (Box-Cox only takes positive values)\n",
        "index_of_positive_pledges = kickstarters_2017.usd_pledged_real > 0\n",
        "\n",
        "# get only positive pledges (using their indexes)\n",
        "positive_pledges = kickstarters_2017.usd_pledged_real.loc[index_of_positive_pledges]\n",
        "\n",
        "# normalize the pledges (w/ Box-Cox)\n",
        "normalized_pledges = pd.Series(stats.boxcox(positive_pledges)[0], \n",
        "                               name='usd_pledged_real', index=positive_pledges.index)\n",
        "\n",
        "print('Original data\\nPreview:\\n', positive_pledges.head())\n",
        "print('Minimum value:', float(positive_pledges.min()),\n",
        "      '\\nMaximum value:', float(positive_pledges.max()))\n",
        "print('_'*30)\n",
        "\n",
        "print('\\nNormalized data\\nPreview:\\n', normalized_pledges.head())\n",
        "print('Minimum value:', float(normalized_pledges.min()),\n",
        "      '\\nMaximum value:', float(normalized_pledges.max()))"
      ],
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        "outputId": "4436cf23-85f4-475f-ddde-bc4b009d4f36"
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      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original data\n",
            "Preview:\n",
            " 1     2421.0\n",
            "2      220.0\n",
            "3        1.0\n",
            "4     1283.0\n",
            "5    52375.0\n",
            "Name: usd_pledged_real, dtype: float64\n",
            "Minimum value: 0.45 \n",
            "Maximum value: 20338986.27\n",
            "______________________________\n",
            "\n",
            "Normalized data\n",
            "Preview:\n",
            " 1    10.165142\n",
            "2     6.468598\n",
            "3     0.000000\n",
            "4     9.129277\n",
            "5    15.836853\n",
            "Name: usd_pledged_real, dtype: float64\n",
            "Minimum value: -0.7779954122762203 \n",
            "Maximum value: 30.69054020451361\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The values have changed significantly with normalization!\n",
        "\n",
        "In the next code cell, you'll take a look at the distribution of the normalized data, where it should now resemble a normal distribution."
      ],
      "metadata": {
        "id": "GzeurIJG2mg-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# plot normalized data\n",
        "ax = sns.histplot(normalized_pledges, kde=True)\n",
        "ax.set_title(\"Normalized data\")\n",
        "plt.show()"
      ],
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      "execution_count": 15,
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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "kickstarters_2017.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dgyPHfGV-fZg",
        "outputId": "33e04335-b0c1-4d0e-cb79-b46e99845ec3"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(378661, 15)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "kickstarters_2017"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 991
        },
        "id": "TRWumU_P_lPa",
        "outputId": "5f91ef06-0998-4093-8f52-f0ea5168d807"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                ID                                               name  \\\n",
              "0       1000002330                    The Songs of Adelaide & Abullah   \n",
              "1       1000003930      Greeting From Earth: ZGAC Arts Capsule For ET   \n",
              "2       1000004038                                     Where is Hank?   \n",
              "3       1000007540  ToshiCapital Rekordz Needs Help to Complete Album   \n",
              "4       1000011046  Community Film Project: The Art of Neighborhoo...   \n",
              "...            ...                                                ...   \n",
              "378656   999976400  ChknTruk Nationwide Charity Drive 2014 (Canceled)   \n",
              "378657   999977640                                          The Tribe   \n",
              "378658   999986353  Walls of Remedy- New lesbian Romantic Comedy f...   \n",
              "378659   999987933                           BioDefense Education Kit   \n",
              "378660   999988282                  Nou Renmen Ayiti!  We Love Haiti!   \n",
              "\n",
              "               category main_category currency    deadline     goal  \\\n",
              "0                Poetry    Publishing      GBP  2015-10-09   1000.0   \n",
              "1        Narrative Film  Film & Video      USD  2017-11-01  30000.0   \n",
              "2        Narrative Film  Film & Video      USD  2013-02-26  45000.0   \n",
              "3                 Music         Music      USD  2012-04-16   5000.0   \n",
              "4          Film & Video  Film & Video      USD  2015-08-29  19500.0   \n",
              "...                 ...           ...      ...         ...      ...   \n",
              "378656      Documentary  Film & Video      USD  2014-10-17  50000.0   \n",
              "378657   Narrative Film  Film & Video      USD  2011-07-19   1500.0   \n",
              "378658   Narrative Film  Film & Video      USD  2010-08-16  15000.0   \n",
              "378659       Technology    Technology      USD  2016-02-13  15000.0   \n",
              "378660  Performance Art           Art      USD  2011-08-16   2000.0   \n",
              "\n",
              "                   launched  pledged     state  backers country  usd pledged  \\\n",
              "0       2015-08-11 12:12:28      0.0    failed        0      GB          0.0   \n",
              "1       2017-09-02 04:43:57   2421.0    failed       15      US        100.0   \n",
              "2       2013-01-12 00:20:50    220.0    failed        3      US        220.0   \n",
              "3       2012-03-17 03:24:11      1.0    failed        1      US          1.0   \n",
              "4       2015-07-04 08:35:03   1283.0  canceled       14      US       1283.0   \n",
              "...                     ...      ...       ...      ...     ...          ...   \n",
              "378656  2014-09-17 02:35:30     25.0  canceled        1      US         25.0   \n",
              "378657  2011-06-22 03:35:14    155.0    failed        5      US        155.0   \n",
              "378658  2010-07-01 19:40:30     20.0    failed        1      US         20.0   \n",
              "378659  2016-01-13 18:13:53    200.0    failed        6      US        200.0   \n",
              "378660  2011-07-19 09:07:47    524.0    failed       17      US        524.0   \n",
              "\n",
              "        usd_pledged_real  usd_goal_real  \n",
              "0                    0.0        1533.95  \n",
              "1                 2421.0       30000.00  \n",
              "2                  220.0       45000.00  \n",
              "3                    1.0        5000.00  \n",
              "4                 1283.0       19500.00  \n",
              "...                  ...            ...  \n",
              "378656              25.0       50000.00  \n",
              "378657             155.0        1500.00  \n",
              "378658              20.0       15000.00  \n",
              "378659             200.0       15000.00  \n",
              "378660             524.0        2000.00  \n",
              "\n",
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              "      <th>0</th>\n",
              "      <td>1000002330</td>\n",
              "      <td>The Songs of Adelaide &amp; Abullah</td>\n",
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              "      <td>1000003930</td>\n",
              "      <td>Greeting From Earth: ZGAC Arts Capsule For ET</td>\n",
              "      <td>Narrative Film</td>\n",
              "      <td>Film &amp; Video</td>\n",
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              "      <td>2421.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1000004038</td>\n",
              "      <td>Where is Hank?</td>\n",
              "      <td>Narrative Film</td>\n",
              "      <td>Film &amp; Video</td>\n",
              "      <td>USD</td>\n",
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              "      <td>45000.0</td>\n",
              "      <td>2013-01-12 00:20:50</td>\n",
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              "      <th>3</th>\n",
              "      <td>1000007540</td>\n",
              "      <td>ToshiCapital Rekordz Needs Help to Complete Album</td>\n",
              "      <td>Music</td>\n",
              "      <td>Music</td>\n",
              "      <td>USD</td>\n",
              "      <td>2012-04-16</td>\n",
              "      <td>5000.0</td>\n",
              "      <td>2012-03-17 03:24:11</td>\n",
              "      <td>1.0</td>\n",
              "      <td>failed</td>\n",
              "      <td>1</td>\n",
              "      <td>US</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5000.00</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1000011046</td>\n",
              "      <td>Community Film Project: The Art of Neighborhoo...</td>\n",
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              "      <td>Film &amp; Video</td>\n",
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              "      <td>2015-08-29</td>\n",
              "      <td>19500.0</td>\n",
              "      <td>2015-07-04 08:35:03</td>\n",
              "      <td>1283.0</td>\n",
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              "      <td>14</td>\n",
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              "      <th>...</th>\n",
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              "      <th>378656</th>\n",
              "      <td>999976400</td>\n",
              "      <td>ChknTruk Nationwide Charity Drive 2014 (Canceled)</td>\n",
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              "      <td>2014-10-17</td>\n",
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              "      <th>378659</th>\n",
              "      <td>999987933</td>\n",
              "      <td>BioDefense Education Kit</td>\n",
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              "      <td>2016-02-13</td>\n",
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          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We used the \"usd_pledged_real\" column. Follow the same process to normalize the \"pledged\" column. "
      ],
      "metadata": {
        "id": "qczxgsXy2mg_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# TODO: Your code here!\n",
        "# get the index of all positive pledges (Box-Cox only takes positive values)\n",
        "index_of_positive_pledges = kickstarters_2017.pledged > 0\n",
        "\n",
        "# get only positive pledges (using their indexes)\n",
        "positive_pledges = kickstarters_2017.pledged.loc[index_of_positive_pledges]\n",
        "\n",
        "# normalize the pledges (w/ Box-Cox)\n",
        "normalized_pledges = pd.Series(stats.boxcox(positive_pledges)[0], \n",
        "                               name='pledged', index=positive_pledges.index)\n",
        "\n",
        "print('Original data\\nPreview:\\n', positive_pledges.head())\n",
        "print('Minimum value:', float(positive_pledges.min()),\n",
        "      '\\nMaximum value:', float(positive_pledges.max()))\n",
        "print('_'*30)\n",
        "\n",
        "print('\\nNormalized data\\nPreview:\\n', normalized_pledges.head())\n",
        "print('Minimum value:', float(normalized_pledges.min()),\n",
        "      '\\nMaximum value:', float(normalized_pledges.max()))"
      ],
      "metadata": {
        "id": "rGRK5oAv2mg_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4a417990-7d4b-41c3-c58c-909a2b3b8b4f"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original data\n",
            "Preview:\n",
            " 1     2421.0\n",
            "2      220.0\n",
            "3        1.0\n",
            "4     1283.0\n",
            "5    52375.0\n",
            "Name: pledged, dtype: float64\n",
            "Minimum value: 1.0 \n",
            "Maximum value: 20338986.27\n",
            "______________________________\n",
            "\n",
            "Normalized data\n",
            "Preview:\n",
            " 1    10.013887\n",
            "2     6.403367\n",
            "3     0.000000\n",
            "4     9.005193\n",
            "5    15.499596\n",
            "Name: pledged, dtype: float64\n",
            "Minimum value: 0.0 \n",
            "Maximum value: 29.63030787418848\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plot normalized data\n",
        "ax = sns.histplot(normalized_pledges, kde=True)\n",
        "ax.set_title(\"Normalized data\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "u8B3Edy59aWd",
        "outputId": "ca2c1729-4eee-412e-9334-2c951096fc1b"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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K5fkhBqKpO3dBccmQ5ZGQcfLkfJ7f0caupk5mVham7kVFJpCkahbuPneIHyUKGZHbX9yLO5xYMbr9NLPKcgH4w5rXs/oud5FMSqpmYWYfG6rc3X+V2nBkvKpv7+G+dfXMq8yjKCeFVYok5EbbyA87tz63nfe+afKE6ycSSYVkm6HOjnucD1wCrAaULCQp//34Nvr6BzhjWgH0HnuyGOzMHskcmGbG9JIIu9sH6B9QzULkWCSVLNz9s/HPzawcuDMtEcm4s6eliztW7uRdp1RTmjtAR++xnyvWmb2FWYtOHdFxU4tCvNbSz+a6DqZOOfbXF5mojnUinQ5gbioDkew3eA9J/Ay43dF+vnTPWgA+dV7yw2GPZrjO7KOZWhSbyuy57a0piUFkokm2z+J3xEY/QWwCwcXA3ekKSrJTZ3sLP/nTG7QMFHDFOfPJLSzmnhd388y2Rr77gdOYWpq5O6nzI0ZlQZg1u9szFoNINku2z+K7cY/7gB3uvjsN8UgW273febE+n64+56kHtgKQGw7xzfecwpVnzRhyzYn4O9oPfh9JjynFEV6u3U9f/4BmpxUZoWT7LP5sZlM42NG9NX0hSTZq7urjub1OaZ5xwZxi/vbcmeSEQ8ypzGfalMnDHte1v42fPrYR74vS3d3NyBuYkjelOIdN9T1s3tvOKTVan1tkJJJdKe+DwF+ADwAfBFaamaYoFwB6+gZ4/PX9REJw0aw8ZpbmEGl7g+XPbeBHD65OuChTUWkFhSXp//CeUhz7bvTCdq3bLTJSydbFvwqc7e7L3P1jwDnAv6YvLMkm967bR3NXP+dOMQoidmD6jVBO/pia1qQ4N8zUklxW7WjOdCgiWSfZPouQu++Le97IsY+kknGkq7efm1fWMq0kwvSigQPlgyOW4vskxsLd06fVFLNqexPuPiFn/BU5Vskmi4fN7BHg18HzDwEPpSckySa3r9xBU2eUdy4sBd9/xPbO9hZ++tgbFBQUsuz8OaMf4GFOqynhkc1N7GrqYtYkzRMlkqxEa3DPB6a4+z+b2fuAC4JNzwG3pzs4Gds6e/u44YlXOWdWKVNLcuhoG3q/otIKCgrGxgfzkpmlADz7agOzJs3KcDQi2SNRU9IPiK23jbvf6+5fcPcvAPcF22QCu/W5HTR29HL1+TUpPe9g01Ws+Sq1TVdzKvOZXJLH09uO3ukuIodK1Aw1xd1fPrzQ3V82szlpiUgOfFhWVVVlOhRg6NX99vf08T9PvsZbF1ZzWk0Jz23dl+AsyRtsujqW4bRHu2/D3WlsbOT8eZN4amsDdfv2ETKbsCsWioxEoppF+VG2FaQyEDmooaGB6257NOGQ09EyuLrf9ctXHYjpW7/fSHNnL1/464VJnePgBIDJ1RSOdThtLNFs5IZH1tLd3X3Etu/d/xxvmpJHY0cv195z6DWJyPASJYtVZvZ3hxea2aeAF9MTkgAUlhwtT6eeu1NfX3/IvE7x4lf3+93aN7jzhV38w4XzOH1mcnEebTW7VDtaoiksKePsWbF+i+aBgjE1tFdkLEvUDPWPwH1m9hEOJoclQC7w3nQGJqMrmbXB3Z2719Tx/Sd2csbMcj52esWwyWUoxzIBYDpMLc1jdkU+O1t7OWWKKsgiyThqsnD3OuB8M/sr4JSg+EF3X5HsC5hZGFgF7HH3d5nZXGLTm08iloA+6u69ZpZHbH2Ms4jdx/Ehd98enONfgE8C/cDn3P2REVyjJOlo37Kbu/p4flcHb7Q3ccniyXztkhn88MHVAGNiSOxIvf3ESdz43B729/ZnOhSRrJDssqqPu/uPg5+kE0Xg88CmuOf/CXzf3ecDzcSSAMHv5qD8+8F+mNlJwFXAycBlwE+CBCSjoKOnj+89voP7NrZS39HHovw2/r93zKY4L3JI01S2ecdJkwDY1tiT4UhEskNa78I2sxnAO4GfB88NuBi4J9jlFuA9weMrgucE2y8J9r8CuNPde9z9dWAbselGJM12NXXyvp88y52r61hUlce75hgnVuUSGgcjh2aU5zOlOMK2xp4xcWe5yFiX7ik7fgB8CRicB2IS0OLufcHz3cDgIP0aYBdAsL012P9A+RDHHGBmV5vZKjNbNdRU2DIyr+xt570/eYba1i5+/P5FLJ1dTF44+5NE/D0cCyfl0dozwJOvtmQ6LJExL23JwszeBexz91EZNeXuN7r7EndfMlTnrCRvy75OPvyz5wmHjJ9ftZh5xVHSvdbEaBmcEv2GR9YyvbCfioIw33lsO+3dx74uuMhEkM6axVLg3Wa2nViH9sXAD4FyMxvsWJ8B7Ake7wFmAgTby4h1dB8oH+IYGaFN9d38w92b+Nf717Nt35GrxjV09vHp32wiLxLihisXce8zG7jhkbWjMuR1tAwOrQ2b8ZbZRdTvj/L1BzYwoOYokWGlLVm4+7+4+wx3n0Osg3qFu38EeBwYXAtjGfBA8Hh58Jxg+wqPNSYvB64ys7xgJNUCYmtryAi92tTDszs7qGvv5berd/OuHz/NL595ne5oP+7O1sYeHnqllbyw8ZP3LyC/r52i0vJRWWsiU6qLcrj6/BruW7OH7zy2Q/0XIsNIdtbZVPoycKeZfQtYA9wUlN8E3Gpm24AmYgkGd99gZncDG4kt6XqNu2u84wjVtvXw1Pb9TCmOcNeyU8kpKuOLv1nLN363kR+v2AbuNHVGqc6H00v288uHnqWpvo5Zi0495DyHT6fhTnBndvbOWP/J86YTys3nhide5byZhZw8WfdeiBxuVJKFuz8BPBE8fo0hRjO5ezexlfiGOv7bwLfTF+H4d/+6egYcLppbTG4kRHVpPrd84mz+sPo1Hni5nrxIiOb9XUzP6SSSW8FAtIeurs4jzhO/DGpx5WQGoj3c8NCWI5JKNjEzvvT2Razb0cDz21upKcnJdEgiY072fh2UpPX1D7B8fT0zynIozj14i0pjYyNPrd1K5UALn15SyqKq/KSGxR4+ncZYuTP7eJgZX7t0LpGQ8eyujkyHIzLmKFlMACs276OhI8qiqvwDzUiDbfOxD/7yEU3yN15VFedy6tQCatv72N44fjr0RVJBySLFEk3Ilwl3r9pFVVEOM8tyDsy8Gj/T6mhO8jfWLZyUR8jgvpd1r45IPCWLFBtqOu9Mau7o5YlX6rls8aQDTUxDjW4aD01JqVCQE2J2eS4PbmigO6pxFCKDlCzSINNzJsXXbh56uZa+AeeyEydlLJ5ss6gqj9buPlZsTt2CTiLZLhNDZyXN4qcbf6WriHnVRSycXMjjmxIcKABMK8mhLD/MnzbWcfmp0zIdjsiYoJrFODHYcT3YV1JUWsFAXilrdrdzxek1WjZ0BEJmXHBCOSte2Udf/0DiA0QmACWLcWJwOdHrl6+isbERgDW1XeSGjSvPmnHIvvGT6U30EVCHjw4b9NZ5FbR0Rlm1ozlDkYmMLUoW40h8X0lzVx/bGnu48vQpTC8/9I7k+Mn0JvoIqKFGh7k784uj5ISN5S9uHzOj2kQySclinBhwZ0drH+vruvjDljYee7WdSMj4xLlDt7kfbZ3qiebwv0Nnewvfv/sxKnL6WL5uL5ryXkQd3ONCe08/j+x0WnujQJSVuzspyw/zVycUUV6gqSuORUFxCbML8nlhb5TXm7qZPDnTEYlklpLFOPD8rg46onDBjFxmVRaxdHYhL+7pUqf2caopCfPC3ih/3tbMuSfOynQ4IhmlZqgst2pnGztbo5xUacwqDVOQE+KEyjwlihQozDGqCsM89ao6uUWULLKYu/OjJ3dSnBtiUXmmoxmfZpXnsr62g33t3ZkORSSjlCyy2DPbGtlU18np0woIh1STSIfZZbk4sGKT7uaWiU3JIov9z5OvMqkoh/mVeZkOZdyqKAgzrTSXRzfVjanJIUVGm5LFGNfW3cea2k7+/Ho7z7zWcqB8/Z5WntrawIfOmKJaRRqZGW+dV8HTW+v5+q2PjonJIUUyQclijPvuih2sfqOLHS1RvnD/Fm5fuYPG/T1cc8dqqopzef9pGtOZbm+dV05Pn9Me0n0pMnFp6OwY1tIZ5bEtTSyuzuOk0l4e39HDV+9bz7UPbCBkxq+vPpfSfE2jnW5nziihOC/M7o7+AzWLqqoqjTiTCUXJYgz7/cYGevudE6vzyYlGWTrFWXrmfP6yp4u3LZ7CWbMrdXfxKIiEQyydW8aKVxr47z9tpLiwkC++ewnV1dWZDk1k1ChZjFHuzn3r9nF6TTGVBRE6ohAyuGRhJVct1YfUaHvnyVU8srmJFitlSqkWipKJR30WY9T+KOxs7uEdi6uO+RzDzagqI3fOrDIKQgNsa+7LdCgiGaFkMUY1BPeAnVZTfMznGGpGVTlSMlO2h0PGzIJe9nYM0N6jfiKZeNQMNUY1dENxXpi5kwoS73wUmlk2scEp270vSnHl8KPLZhVE2dKRz5bGngNlg4kG1Okt45uSxRjV2A2nTC8mpA+fUVFUWsFAtOeo+xSEnWnFIbY09NA/EKuBxC9hq05vGc/UDDUG7e/po7UX3jT90Caow5dOlfQ7vIlqfnmEzugAz29vPbBP/KJTIuOVahZj0PraDgDeNO3QZDHYXFJQoKGboyW2XO0beF+U7u5upleGyI8Y979cz3vOXZDp8ERGjZLFGLTujXYATp52ZOd2UWkF+fkFB9rJVcNIv8Emqq6uTsJmLJiUx1OvNvNGSxdaWkomCjVDJcndqa+vH5UmoA17OyjNjXVwDyX2bXcj1y9fRWNjY1pjkSMtrs7HgV89tyPToYiMGiWLJA12ZF6/fFVah6K6Oxv3dpBoIlm1k2dOSV6Yv1pQwa//spOuqIbRysSgZDECo/EBvbu5i5auPirz0/oycpw+fOZUWrui/G697mGRiUHJYoxZtzs2ykZLVIxtb5pezBmzyrn1hdoDw2hFxjMlizEgvj9k3e4WIiGjXMliTDMzPnfJAva297Kt6ej3Z4iMBxoNNQbE39i1rbuIBdWFhK0zw1HJcAbvvbhw0SIWTylibW2XViuUcU81izGiqLSCwpJyNtV1ctLUokyHI0cxOOdWY2Mjf7+0hvbeATYPTuYlMk4pWYwhjV39dPT2c+oQ91fI2DI459b5c8qYXpLDmje6aOvWjLQyfqUtWZjZTDN73Mw2mtkGM/t8UF5pZo+a2dbgd0VQbmb2IzPbZmbrzOzMuHMtC/bfambL0hVzpu1q6cWAN8899sn/3J2mpiaGmz1VUsvMOGdGIT39zi9XvpHpcETSJp01iz7gi+5+EnAecI2ZnQR8BXjM3RcAjwXPAd4BLAh+rgZugFhyAa4FzgXOAa4dTDDjzc7WXk6ZVkRl4bHfF9zZ3sIND62iq6srhZHJ0UwqjLBgUh53ranjpW27NXeXjEtpSxbuXuvuq4PH7cAmoAa4Argl2O0W4D3B4yuAX3nM80C5mU0D3g486u5N7t4MPApclq64M6UzOkBDZz9vmXf8ebCg+OBKbsms1SDH76zpBYQMPnvn2rTfuCmSCaMyGsrM5gBnACuBKe5eG2zaC0wJHtcAu+IO2x2UDVd++GtcTaxGwqxZs44pzk21bXzxrg3MyR39D9Vdrb0AXHBCeUrPm+xaDXJ8inLDvP/kMu5Y28L+kPqcZPxJe7Iws2Lgt8A/untb/OIw7u5mlpJPZne/EbgRYMmSJcd0zqriPF5t6KS7MBURHd3AwAArt+yhtK6D4twQq9/oojQvxPyq41vsaCjJrNUgIxO/6FGsxmZceXI5929sZeXuTjVDybiT1mRhZjnEEsXt7n5vUFxnZtPcvTZoZtoXlO8BZsYdPiMo2wNcdFj5E+mIt7okj3efUs1vX9rH3rYe0jUD+P7efn6/qYVfrmkG9hA2iISMty8o1UprWeLwGltBQSEFOSHOml7IUzs6ePSVJj4yWTU5GT/SORrKgJuATe7+vbhNy4HBEU3LgAfiyj8WjIo6D2gNmqseAS41s4qgY/vSoCwtPnr2NBy4/cW96XoJXq7rprMPzp2ew1tmF3HB7CIuW1hKZcHIcrdGPmVW7N6YQ0euzZ+UR2VBmB8/uYtuTTIo40g6R0MtBT4KXGxmLwU/lwP/Afy1mW0F3hY8B3gIeA3YBvwM+DSAuzcB3wReCMgQW78AABDISURBVH6uC8rSYlppHjVFsGJLel6irbuPLQ3dzC6BeeURFlbl85ULp1BVOPJKnkY+jR2DiTtkcO6MIva29/KLZ17PdFgiKZO2Zih3fxoYrk3lkiH2d+CaYc71C+AXqYvu6CrzYV1jlJbOXsoLc1N67nvX7qNvAE6sSE1zU/zIJ8mcWOLewqxFpzKtpIDzZhby3yu2cuWZM5hcqimEJfvpDu4hlAf5YVNte8rP/eDGBqYVRyjPU9/EeDOYuDvbWyjqbaazd4B///26DEclkhpKFkMYnPF18962lJ53R2MH25u6mV2R2tqKjD1TJpWzuDqPB16uZ1Nta6bDETluShZDyA9DeUGETbWpTRYrNscGfs0sU7KYCBaV9BJigG888HKmQxE5bkoWQzAzFlQXsnlvapuhVmzex5zKfEqHWVtbxpe8sLGotJ/nt7fy7Dbd0S3ZTcliGAuqC3hlb3vKVkHb39PHyteaUn6Htoxtc4r6mFwU4Zu/e5n+/oFMhyNyzJQshjG/qpCevgFeb+hIyfke37yP3v4BJYsJprejjZrcTjbVdXLns1syHY7IMVOyGMbC6ticH6nq5P7t6t1ML8vn9BoNdZ1oFk0tpbIgzE+e3k1vn2oXkp2ULIYxZ1IB4ZCxOQXDZ/e1dfPklnree2YN4ZCGzE40ITPOml7AntYefrZio+aNkqykZDGMvEiIedVFKRkRdd+aPQw4vO/MGSmITLJRpXVQldfPj/68g+176jIdjsiIKVkcxYlTS497RNTOxk5+/vTrnDmrnHnVmrp6ojIzzppeSE8/3LaqNvEBImOMksVRnDithD0tXbR2RUd8rLvzlx2tfOSm54n2D/Dv73tTGiKUbDKpIMTcilxuX7WXurbuTIcjMiJKFkexeFopAJtH2BTVN+A8+Eob19zzCp09/dzyiXNYNFUd2wJLagpxd/7pzhfZt2+f+i8kayhZHMXiqUGyGEFTlLvz7M4O6jr6+OeLZ/PMVy7mtJkaLisxpXlhPnZGBU+91sLnbtfyq5I9lCyOYkppHhWFOSMaPvv4tma2NvZw+tR8Lp4ZJi+iP7Ec6orFZUwuirCq3tmyrzPT4YgkRZ9kR2FmnDi1lI0jGD77mzV1lOSGWFjcw/fuf07fHOUQ7k5rSzMXn1BMbjjE/7lvC3taunB36uvrqa+vV9OUjElKFgmcNrOcDXta2d+TeNWz7Q0drNrVzsKqPEJmR6yiJjK4YFWov4dL55fQGe3n/T95loefW8f1y1/g+uVqmpKxSckigYtPnEzfgLNyR+Jppu98YRdhgwWTtNiNDG9w3YtJhRF+9qHF9PX389nlO2mI5lBUWpHh6ESGpmSRwJmzyikvzOGpV5uP2BbfdBDt6+e3q3ez9IRyinKP/8/q7jQ0NATfMtUsMV7Nry7kpg+fRF7YeXxHLztaejMdksiQlCwSiIRDXLSwmmdeb2XgsLbkhoYGrl++iuuXr+LhNa9T397DO0+uSsnrdu1v46ePbeSGR9Zqje1xavALQW60naWVHZTnGY+92s796/ZlOjSRIyhZJOHixVNo6eqjvqPviG1FpRUUlVbwh00NlORHWDo3dcNki0or1O8xjnW2txz4QuC9XVwyJ4+a0hy+/eh27n5hV6bDEzmEkkUSLlxYTWFOiLV7u4YcqRLtd57Y2szlp0zTUFkZkfgvBJGQ8bZ5JZw3p4wv37uOB17ak+HoRA7SJ1sSygpy+Lvza9jVGmVn65FTf+xo6aUzOsB7zqjJQHQynoQMvry0gjNqSvjCXS/x8HrNIyVjg5JFkq46Ywrl+WGe3dnB3raeA+X9A86a2k5OmFTAuXMrMxihjAed7S3c/OfNTO19g+JQL5+5Yw1PvKI+DMk8JYskRcIhLpxbTN+A879/s5ndzbE7b19p6KatZ4DPvXUmIa1VISlQVFpBaVkZ50/uZ3Z5Dn9/6yoe36xpzSWzlCxGoKowwqXzS2jujHLp95/kS8u38pfdnUwriXD+XHVES2r1dbaxIK+NgpDzyVtWcf2D67SOt2SMksUITSnO4Y6Pncr58yaxelcb8yblceGcYsxUq5DUq6yo4JKZxtT8fn781C7e9+PH2d6wP9NhyQSkZHEMppfl8fNlZ/Ona87iLbOLKcoNH/O53J2mpibAdSOeDCknZFw4p4izJxvr93bytu89yXW/28iups4DN4ZqPilJt0imAxjPBj/8AaqqqoasfcTmCtrCrEWnMhDt4aePvYH3Renu7kYrYMggM2NemVER6oHiydz87Ovc/OzrLJ1bTm/jbr5+xalMrq4e9t+ZyPFSzSIFBpPC4d/uBu/CTjQ53OBcQaAb8eTovKuNgu563jnb+Pg501lfu5+VrcV88I5X+ftfvaD1vSVtlCxSoLO9ZdjpyAfv8BZJlaLSCqorK/jAifnc/P6ZnFHaSX4kzOp65/L/Wc3Xf7uafVq2VVJMzVApotqAjKbYVCGxJssq62TxCZOob+9mY0Mfv3qhlrvW7OXD58xm2flzmFtVlOlwZRxQshgF8X0X6oiUVCkqrWAg2kNXV+yen0n5xoVzi+khl7aefm59fgc3P7udc2aVsuyCebztpCnkRY59MIZMbEoWo2DwW2BBQSHLzp+T6XBknMuJttO1fQtvmzKDHe3Gul3NXHPHGkrzw1x+6nSuOL2Gs+dUEAmrFVqSp2QxSopKKygoKMx0GDJBFBSXMKmigoriHmbU1bI/t4LanlzuX7ObO1/YRUlemHNml/LmOeW89aQZzJtcQlgzEMhRKFmMooP3VOgbnYweMzhhchlzoj20d3RS1+nsao3yzLY+HtvSDH98nbxIiPlVBcyqyGd6aR7Ty3KpKc/nlLnTmFZWoEQiShajKf6eCpFMKCuvoKSohwqvpWr6ZJo7eqhr76G1B3bv3c9r9QV09QMMJofNRELG1JJcppflMb0sj5qyPE6cWcXMyiJmVhRQWZSrezsmgKxJFmZ2GfBDIAz83N3/I8MhHZP4eypEMsnMKM8zKovLGIj20LivluqaSUR7u+kml/bOXnbX1mFFlXT09LKltouXdkGvh4DdB85TmBNielkek4pyKM4LU5wbpjgvTFFumLKCCGX5EcoKIpQX5DBn+mQmFedSkBNWgskyWZEszCwM/Dfw18T+lb5gZsvdfWNmIzsofsSTpuqQbBY2oyQnRJEZOaVOdU0JA9EeQjl5DER7qKvbCwVl7O8ZoMsjdESdpoZ2GloL6Btwoh6iz6Hfh0oGGwDIDRvlBRFK8yOUF0QoyYuQEzZyIyFyQkYkbOSEjUjICJsRDsV+IiEjZBAOQTgUipUbREIhyspKyAmHCFmwX8jo3N9ORVkZkXBs30jYDmwffD54/kgoRPiQ53bE8/gEF3918XlvvCbBrEgWwDnANnd/DcDM7gSuANKSLDrbW464wa6hoYGOtuYDj+PLOttbaaqv43v3t+F9vRRVVON9Ubr2t9PR1oz3RbFITkbKxkIMinV8xRrtaGNSaSkl1odFDO+L0tRTx6QpMw7Zr2FfHSVTZtAb7SdqEXqiA+yr30duSSW9/U60J4fWLmfvvgE8ko+7M0CIAWLrxGAhBjDcOfTTOAsNm1g4ckMy+x765zj0uMvfNI3vffD0Yw11WJYN4/7N7ErgMnf/VPD8o8C57v6ZuH2uBq4Oni4CXjmOl6wChp+fI7voWsau8XQ9upaxayTXM9vdq4fakC01i4Tc/UbgxlScy8xWufuSVJwr03QtY9d4uh5dy9iVquvJljGce4CZcc9nBGUiIjIKsiVZvAAsMLO5ZpYLXAUsz3BMIiITRlY0Q7l7n5l9BniE2NDZX7j7hjS+ZEqas8YIXcvYNZ6uR9cydqWmeT4bOrhFRCSzsqUZSkREMkjJQkREElKyiGNml5nZK2a2zcy+kul4jpeZbTezl83sJTNblel4RsLMfmFm+8xsfVxZpZk9amZbg99ZsQThMNfyb2a2J3hvXjKzyzMZY7LMbKaZPW5mG81sg5l9PijP1vdmuOvJuvfHzPLN7C9mtja4lm8E5XPNbGXwuXZXMEho5OdXn0VMMKXIFuKmFAE+PJamFBkpM9sOLHH3rLvByMzeCuwHfuXupwRl3wGa3P0/gmRe4e5fzmScyRjmWv4N2O/u381kbCNlZtOAae6+2sxKgBeB9wAfJzvfm+Gu54Nk2ftjsXlGitx9v5nlAE8Dnwe+ANzr7nea2U+Bte5+w0jPr5rFQQemFHH3XmBwShHJAHd/Emg6rPgK4Jbg8S3E/lOPecNcS1Zy91p3Xx08bgc2ATVk73sz3PVkHY/ZHzzNCX4cuBi4Jyg/5vdGyeKgGmBX3PPdZOk/mjgO/NHMXgymQ8l2U9y9Nni8F5iSyWBS4DNmti5opsqKZpt4ZjYHOANYyTh4bw67HsjC98fMwmb2ErAPeBR4FWhx975gl2P+XFOyGN8ucPczgXcA1wTNIeOCx9pPs7kN9QZgHnA6UAtcn9lwRsbMioHfAv/o7m3x27LxvRnierLy/XH3fnc/ndgsF+cAJ6bq3EoWB427KUXcfU/wex9wH7F/PNmsLmhjHmxr3pfheI6Zu9cF/7EHgJ+RRe9N0B7+W+B2d783KM7a92ao68nm9wfA3VuAx4E3A+VmNngD9jF/rilZHDSuphQxs6Kgww4zKwIuBdYf/agxbzmwLHi8DHggg7Ecl8EP1sB7yZL3JuhEvQnY5O7fi9uUle/NcNeTje+PmVWbWXnwuIDYYJ1NxJLGlcFux/zeaDRUnGB43A84OKXItzMc0jEzsxOI1SYgNq3LHdl0PWb2a+AiYtMr1wHXAvcDdwOzgB3AB919zHccD3MtFxFr4nBgO/D3cW3+Y5aZXQA8BbwMDATF/y+xdv5sfG+Gu54Pk2Xvj5m9iVgHdphYReBud78u+Cy4E6gE1gB/6+49Iz6/koWIiCSiZigREUlIyUJERBJSshARkYSULEREJCElCxERSUjJQuQYmdkTZrZkBPtfZGa/T9Frf9zM/isV5xJJhpKFiIgkpGQhkoCZzTGzzWZ2u5ltMrN7zKzwsH0uNbPnzGy1mf0mmGtocI2UzWa2Gnhf3P7VwboPG8zs52a2w8yqgm1/G6xL8JKZ/U8wfT5m9gkz22JmfwGWjt5fQETJQiRZi4CfuPtioA349OCG4EP+a8DbgokbVwFfMLN8YvMK/S/gLGBq3PmuBVa4+8nEpo+eFZxrMfAhYGkwIVw/8JFg+olvEEsSFwAnpfFaRY4QSbyLiAC73P2Z4PFtwOfitp1H7MP7mdhUQ+QCzxGb8fN1d98KYGa3AYNTxV9AbM4h3P1hM2sOyi8hllheCM5VQGxSvnOBJ9y9PjjXXcDC1F+myNCULESSc/i8OPHPDXjU3T8cv4OZnX4Mr2PALe7+L4edKysWE5LxS81QIsmZZWZvDh7/DbElKwc9Dyw1s/lwYMbfhcBmYI6ZzQv2i08mzxBbuhMzuxQYXFznMeBKM5scbKs0s9nEJuq70MwmBVNqfyDlVyhyFEoWIsl5hdgCUpuIfbAfWMM4aBr6OPBrM1tH0ATl7t3Emp0eDDq449d4+AZwqZmtJ/bBvxdoD9Z8/xqxFQ7XEVvtbFow4+m/Bed+htjU0yKjRrPOiiQQLLf5e3c/JYXnzAP63b0vqLHcEHRoi4xJ6rMQyYxZwN1mFgJ6gb/LcDwiR6WahYiIJKQ+CxERSUjJQkREElKyEBGRhJQsREQkISULERFJ6P8HfL9vSqIkApAAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "How does the normalized \"usd_pledged_real\" column look different from when we normalized the \"pledged\" column?  Or, do they look mostly the same?\n",
        "\n",
        "Once you have an answer, run the code cell below."
      ],
      "metadata": {
        "id": "Le79cMW02mg_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "*Have questions or comments? Visit the [course discussion forum](https://www.kaggle.com/learn/data-cleaning/discussion) to chat with other learners.*"
      ],
      "metadata": {
        "id": "XvSlAPtK2mhB"
      }
    }
  ]
}