Starter_Code.ipynb

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  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.6"
    },
    "colab": {
      "name": "Copy of Copy of Copy of Starter_Code.ipynb",
      "provenance": [],
      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Karla-Flores/Neural-Network--Charity-Donation/blob/main/Starter_Code.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lwo5h_UH1jIy"
      },
      "source": [
        "## Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "id": "MKEyB0hE4f7q",
        "outputId": "5b2e3cde-9d4a-4da7-e462-ccf8b43a5a96"
      },
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-0658f984-11b7-4108-a654-f33764566333\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-0658f984-11b7-4108-a654-f33764566333\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving charity_data.csv to charity_data (1).csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "21kRfveu5cM1",
        "outputId": "7184a51e-b668-4fff-e92d-a4b007dda7b2"
      },
      "source": [
        "# Import our dependencies\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "import pandas as pd\n",
        "import tensorflow as tf\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "#  Import and read the charity_data.csv.\n",
        "import pandas as pd \n",
        "# application_df = pd.read_csv(\"../Resources/charity_data.csv\")\n",
        "application_df = pd.read_csv('charity_data.csv')\n",
        "application_df.head()"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>EIN</th>\n",
              "      <th>NAME</th>\n",
              "      <th>APPLICATION_TYPE</th>\n",
              "      <th>AFFILIATION</th>\n",
              "      <th>CLASSIFICATION</th>\n",
              "      <th>USE_CASE</th>\n",
              "      <th>ORGANIZATION</th>\n",
              "      <th>STATUS</th>\n",
              "      <th>INCOME_AMT</th>\n",
              "      <th>SPECIAL_CONSIDERATIONS</th>\n",
              "      <th>ASK_AMT</th>\n",
              "      <th>IS_SUCCESSFUL</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>10520599</td>\n",
              "      <td>BLUE KNIGHTS MOTORCYCLE CLUB</td>\n",
              "      <td>T10</td>\n",
              "      <td>Independent</td>\n",
              "      <td>C1000</td>\n",
              "      <td>ProductDev</td>\n",
              "      <td>Association</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>N</td>\n",
              "      <td>5000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10531628</td>\n",
              "      <td>AMERICAN CHESAPEAKE CLUB CHARITABLE TR</td>\n",
              "      <td>T3</td>\n",
              "      <td>Independent</td>\n",
              "      <td>C2000</td>\n",
              "      <td>Preservation</td>\n",
              "      <td>Co-operative</td>\n",
              "      <td>1</td>\n",
              "      <td>1-9999</td>\n",
              "      <td>N</td>\n",
              "      <td>108590</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>10547893</td>\n",
              "      <td>ST CLOUD PROFESSIONAL FIREFIGHTERS</td>\n",
              "      <td>T5</td>\n",
              "      <td>CompanySponsored</td>\n",
              "      <td>C3000</td>\n",
              "      <td>ProductDev</td>\n",
              "      <td>Association</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>N</td>\n",
              "      <td>5000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>10553066</td>\n",
              "      <td>SOUTHSIDE ATHLETIC ASSOCIATION</td>\n",
              "      <td>T3</td>\n",
              "      <td>CompanySponsored</td>\n",
              "      <td>C2000</td>\n",
              "      <td>Preservation</td>\n",
              "      <td>Trust</td>\n",
              "      <td>1</td>\n",
              "      <td>10000-24999</td>\n",
              "      <td>N</td>\n",
              "      <td>6692</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>10556103</td>\n",
              "      <td>GENETIC RESEARCH INSTITUTE OF THE DESERT</td>\n",
              "      <td>T3</td>\n",
              "      <td>Independent</td>\n",
              "      <td>C1000</td>\n",
              "      <td>Heathcare</td>\n",
              "      <td>Trust</td>\n",
              "      <td>1</td>\n",
              "      <td>100000-499999</td>\n",
              "      <td>N</td>\n",
              "      <td>142590</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        EIN                                      NAME  ... ASK_AMT IS_SUCCESSFUL\n",
              "0  10520599              BLUE KNIGHTS MOTORCYCLE CLUB  ...    5000             1\n",
              "1  10531628    AMERICAN CHESAPEAKE CLUB CHARITABLE TR  ...  108590             1\n",
              "2  10547893        ST CLOUD PROFESSIONAL FIREFIGHTERS  ...    5000             0\n",
              "3  10553066            SOUTHSIDE ATHLETIC ASSOCIATION  ...    6692             1\n",
              "4  10556103  GENETIC RESEARCH INSTITUTE OF THE DESERT  ...  142590             1\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5g3IfC8C56Rz",
        "outputId": "359b171f-2620-4260-c21c-2ba7b6b47252"
      },
      "source": [
        "application_df.shape"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(34299, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d7V6M_0P1jI3",
        "outputId": "df2cb040-51fa-418a-ac7d-79c76610c4a9"
      },
      "source": [
        "# Drop the non-beneficial ID columns, 'EIN' and 'NAME'.\n",
        "application_df = application_df.drop(columns=['EIN','NAME'],axis=1)\n",
        "application_df.shape"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(34299, 10)"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eeZqEnyq1jI4",
        "outputId": "03a21b7d-36ea-42b0-c485-7c49e6914fdd"
      },
      "source": [
        "# Determine the number of unique values in each column.\n",
        "application_df.nunique()"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "APPLICATION_TYPE            17\n",
              "AFFILIATION                  6\n",
              "CLASSIFICATION              71\n",
              "USE_CASE                     5\n",
              "ORGANIZATION                 4\n",
              "STATUS                       2\n",
              "INCOME_AMT                   9\n",
              "SPECIAL_CONSIDERATIONS       2\n",
              "ASK_AMT                   8747\n",
              "IS_SUCCESSFUL                2\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4YpoUx021jI4",
        "outputId": "d671dbc6-06b1-45fa-c952-0e7465055382"
      },
      "source": [
        "# Look at APPLICATION_TYPE value counts for binning\n",
        "application_value = application_df.loc[:, 'APPLICATION_TYPE'].value_counts()\n",
        "application_value"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "T3     27037\n",
              "T4      1542\n",
              "T6      1216\n",
              "T5      1173\n",
              "T19     1065\n",
              "T8       737\n",
              "T7       725\n",
              "T10      528\n",
              "T9       156\n",
              "T13       66\n",
              "T12       27\n",
              "T2        16\n",
              "T14        3\n",
              "T25        3\n",
              "T15        2\n",
              "T29        2\n",
              "T17        1\n",
              "Name: APPLICATION_TYPE, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oGkzfpso1jI5",
        "outputId": "93678b2f-7d5b-4591-8591-7afe444f4e63"
      },
      "source": [
        "# Look at CLASSIFICATION value counts for binning\n",
        "classification_value = application_df.loc[:, 'CLASSIFICATION'].value_counts()\n",
        "classification_value"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "C1000    17326\n",
              "C2000     6074\n",
              "C1200     4837\n",
              "C3000     1918\n",
              "C2100     1883\n",
              "         ...  \n",
              "C1732        1\n",
              "C4120        1\n",
              "C1370        1\n",
              "C2561        1\n",
              "C2500        1\n",
              "Name: CLASSIFICATION, Length: 71, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kQvgVZ771jI5",
        "outputId": "0a2df460-6db0-4b57-bcbf-20b283517988"
      },
      "source": [
        "# Choose a cutoff value and create a list of application types to be replaced\n",
        "# use the variable name `application_types_to_replace`\n",
        "application_types_to_replace = list(application_value[application_value<200].index)\n",
        "\n",
        "# Replace in dataframe\n",
        "for app in application_types_to_replace:\n",
        "    application_df['APPLICATION_TYPE'] = application_df['APPLICATION_TYPE'].replace(app,\"Other\")\n",
        "\n",
        "# Check to make sure binning was successful\n",
        "application_df['APPLICATION_TYPE'].value_counts()"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "T3       27037\n",
              "T4        1542\n",
              "T6        1216\n",
              "T5        1173\n",
              "T19       1065\n",
              "T8         737\n",
              "T7         725\n",
              "T10        528\n",
              "Other      276\n",
              "Name: APPLICATION_TYPE, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "7OdwpsaX1jI6",
        "outputId": "04a512c7-351b-46ad-dbe6-3125106acbd5"
      },
      "source": [
        "# You may find it helpful to look at CLASSIFICATION value counts >1\n",
        "classification_value.plot(kind='density')"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4fde175c90>"
            ]
          },
          "metadata": {},
          "execution_count": 56
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H7MuvQXo1jI7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "855a370e-f222-454c-c704-5b08694dfbf1"
      },
      "source": [
        "# Choose a cutoff value and create a list of classifications to be replaced\n",
        "# use the variable name `classifications_to_replace`\n",
        "classifications_to_replace = list(classification_value[classification_value<1000].index)\n",
        "\n",
        "# Replace in dataframe\n",
        "for cls in classifications_to_replace:\n",
        "    application_df['CLASSIFICATION'] = application_df['CLASSIFICATION'].replace(cls,\"Other\")\n",
        "    \n",
        "# Check to make sure binning was successful\n",
        "application_df['CLASSIFICATION'].value_counts()"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "C1000    17326\n",
              "C2000     6074\n",
              "C1200     4837\n",
              "Other     2261\n",
              "C3000     1918\n",
              "C2100     1883\n",
              "Name: CLASSIFICATION, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "1hsTTF7DNITV",
        "outputId": "42808f01-a6a0-457a-8583-ebb7f4b6ca0f"
      },
      "source": [
        "application_df"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>APPLICATION_TYPE</th>\n",
              "      <th>AFFILIATION</th>\n",
              "      <th>CLASSIFICATION</th>\n",
              "      <th>USE_CASE</th>\n",
              "      <th>ORGANIZATION</th>\n",
              "      <th>STATUS</th>\n",
              "      <th>INCOME_AMT</th>\n",
              "      <th>SPECIAL_CONSIDERATIONS</th>\n",
              "      <th>ASK_AMT</th>\n",
              "      <th>IS_SUCCESSFUL</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>T10</td>\n",
              "      <td>Independent</td>\n",
              "      <td>C1000</td>\n",
              "      <td>ProductDev</td>\n",
              "      <td>Association</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>N</td>\n",
              "      <td>5000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>T3</td>\n",
              "      <td>Independent</td>\n",
              "      <td>C2000</td>\n",
              "      <td>Preservation</td>\n",
              "      <td>Co-operative</td>\n",
              "      <td>1</td>\n",
              "      <td>1-9999</td>\n",
              "      <td>N</td>\n",
              "      <td>108590</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>T5</td>\n",
              "      <td>CompanySponsored</td>\n",
              "      <td>C3000</td>\n",
              "      <td>ProductDev</td>\n",
              "      <td>Association</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>N</td>\n",
              "      <td>5000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>T3</td>\n",
              "      <td>CompanySponsored</td>\n",
              "      <td>C2000</td>\n",
              "      <td>Preservation</td>\n",
              "      <td>Trust</td>\n",
              "      <td>1</td>\n",
              "      <td>10000-24999</td>\n",
              "      <td>N</td>\n",
              "      <td>6692</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>T3</td>\n",
              "      <td>Independent</td>\n",
              "      <td>C1000</td>\n",
              "      <td>Heathcare</td>\n",
              "      <td>Trust</td>\n",
              "      <td>1</td>\n",
              "      <td>100000-499999</td>\n",
              "      <td>N</td>\n",
              "      <td>142590</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34294</th>\n",
              "      <td>T4</td>\n",
              "      <td>Independent</td>\n",
              "      <td>C1000</td>\n",
              "      <td>ProductDev</td>\n",
              "      <td>Association</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>N</td>\n",
              "      <td>5000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34295</th>\n",
              "      <td>T4</td>\n",
              "      <td>CompanySponsored</td>\n",
              "      <td>C3000</td>\n",
              "      <td>ProductDev</td>\n",
              "      <td>Association</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>N</td>\n",
              "      <td>5000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34296</th>\n",
              "      <td>T3</td>\n",
              "      <td>CompanySponsored</td>\n",
              "      <td>C2000</td>\n",
              "      <td>Preservation</td>\n",
              "      <td>Association</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>N</td>\n",
              "      <td>5000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34297</th>\n",
              "      <td>T5</td>\n",
              "      <td>Independent</td>\n",
              "      <td>C3000</td>\n",
              "      <td>ProductDev</td>\n",
              "      <td>Association</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>N</td>\n",
              "      <td>5000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34298</th>\n",
              "      <td>T3</td>\n",
              "      <td>Independent</td>\n",
              "      <td>C1000</td>\n",
              "      <td>Preservation</td>\n",
              "      <td>Co-operative</td>\n",
              "      <td>1</td>\n",
              "      <td>1M-5M</td>\n",
              "      <td>N</td>\n",
              "      <td>36500179</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>34299 rows × 10 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      APPLICATION_TYPE       AFFILIATION  ...   ASK_AMT IS_SUCCESSFUL\n",
              "0                  T10       Independent  ...      5000             1\n",
              "1                   T3       Independent  ...    108590             1\n",
              "2                   T5  CompanySponsored  ...      5000             0\n",
              "3                   T3  CompanySponsored  ...      6692             1\n",
              "4                   T3       Independent  ...    142590             1\n",
              "...                ...               ...  ...       ...           ...\n",
              "34294               T4       Independent  ...      5000             0\n",
              "34295               T4  CompanySponsored  ...      5000             0\n",
              "34296               T3  CompanySponsored  ...      5000             0\n",
              "34297               T5       Independent  ...      5000             1\n",
              "34298               T3       Independent  ...  36500179             0\n",
              "\n",
              "[34299 rows x 10 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qgHZyWz81jI7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        },
        "outputId": "46f6df3f-3612-4a85-8064-df9768570c28"
      },
      "source": [
        "# Convert categorical data to numeric with `pd.get_dummies`\n",
        "application_df = pd.get_dummies(application_df, dtype=float)\n",
        "application_df"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>STATUS</th>\n",
              "      <th>ASK_AMT</th>\n",
              "      <th>IS_SUCCESSFUL</th>\n",
              "      <th>APPLICATION_TYPE_Other</th>\n",
              "      <th>APPLICATION_TYPE_T10</th>\n",
              "      <th>APPLICATION_TYPE_T19</th>\n",
              "      <th>APPLICATION_TYPE_T3</th>\n",
              "      <th>APPLICATION_TYPE_T4</th>\n",
              "      <th>APPLICATION_TYPE_T5</th>\n",
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              "      <th>APPLICATION_TYPE_T8</th>\n",
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              "      <th>AFFILIATION_Family/Parent</th>\n",
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              "      <th>AFFILIATION_National</th>\n",
              "      <th>AFFILIATION_Other</th>\n",
              "      <th>AFFILIATION_Regional</th>\n",
              "      <th>CLASSIFICATION_C1000</th>\n",
              "      <th>CLASSIFICATION_C1200</th>\n",
              "      <th>CLASSIFICATION_C2000</th>\n",
              "      <th>CLASSIFICATION_C2100</th>\n",
              "      <th>CLASSIFICATION_C3000</th>\n",
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              "      <th>USE_CASE_Preservation</th>\n",
              "      <th>USE_CASE_ProductDev</th>\n",
              "      <th>ORGANIZATION_Association</th>\n",
              "      <th>ORGANIZATION_Co-operative</th>\n",
              "      <th>ORGANIZATION_Corporation</th>\n",
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              "      <th>INCOME_AMT_1-9999</th>\n",
              "      <th>INCOME_AMT_10000-24999</th>\n",
              "      <th>INCOME_AMT_100000-499999</th>\n",
              "      <th>INCOME_AMT_10M-50M</th>\n",
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              "      <th>INCOME_AMT_25000-99999</th>\n",
              "      <th>INCOME_AMT_50M+</th>\n",
              "      <th>INCOME_AMT_5M-10M</th>\n",
              "      <th>SPECIAL_CONSIDERATIONS_N</th>\n",
              "      <th>SPECIAL_CONSIDERATIONS_Y</th>\n",
              "    </tr>\n",
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              "      <th>2</th>\n",
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              "      <td>1.0</td>\n",
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              "    <tr>\n",
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              "    <tr>\n",
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              "    <tr>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
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              "    <tr>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>34297</th>\n",
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              "    <tr>\n",
              "      <th>34298</th>\n",
              "      <td>1</td>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>34299 rows × 44 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       STATUS   ASK_AMT  ...  SPECIAL_CONSIDERATIONS_N  SPECIAL_CONSIDERATIONS_Y\n",
              "0           1      5000  ...                       1.0                       0.0\n",
              "1           1    108590  ...                       1.0                       0.0\n",
              "2           1      5000  ...                       1.0                       0.0\n",
              "3           1      6692  ...                       1.0                       0.0\n",
              "4           1    142590  ...                       1.0                       0.0\n",
              "...       ...       ...  ...                       ...                       ...\n",
              "34294       1      5000  ...                       1.0                       0.0\n",
              "34295       1      5000  ...                       1.0                       0.0\n",
              "34296       1      5000  ...                       1.0                       0.0\n",
              "34297       1      5000  ...                       1.0                       0.0\n",
              "34298       1  36500179  ...                       1.0                       0.0\n",
              "\n",
              "[34299 rows x 44 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hMZgRSa4tf5h",
        "outputId": "298e2afe-592d-4c67-8d00-179c2f0337e0"
      },
      "source": [
        "application_df.dtypes"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "STATUS                            int64\n",
              "ASK_AMT                           int64\n",
              "IS_SUCCESSFUL                     int64\n",
              "APPLICATION_TYPE_Other          float64\n",
              "APPLICATION_TYPE_T10            float64\n",
              "APPLICATION_TYPE_T19            float64\n",
              "APPLICATION_TYPE_T3             float64\n",
              "APPLICATION_TYPE_T4             float64\n",
              "APPLICATION_TYPE_T5             float64\n",
              "APPLICATION_TYPE_T6             float64\n",
              "APPLICATION_TYPE_T7             float64\n",
              "APPLICATION_TYPE_T8             float64\n",
              "AFFILIATION_CompanySponsored    float64\n",
              "AFFILIATION_Family/Parent       float64\n",
              "AFFILIATION_Independent         float64\n",
              "AFFILIATION_National            float64\n",
              "AFFILIATION_Other               float64\n",
              "AFFILIATION_Regional            float64\n",
              "CLASSIFICATION_C1000            float64\n",
              "CLASSIFICATION_C1200            float64\n",
              "CLASSIFICATION_C2000            float64\n",
              "CLASSIFICATION_C2100            float64\n",
              "CLASSIFICATION_C3000            float64\n",
              "CLASSIFICATION_Other            float64\n",
              "USE_CASE_CommunityServ          float64\n",
              "USE_CASE_Heathcare              float64\n",
              "USE_CASE_Other                  float64\n",
              "USE_CASE_Preservation           float64\n",
              "USE_CASE_ProductDev             float64\n",
              "ORGANIZATION_Association        float64\n",
              "ORGANIZATION_Co-operative       float64\n",
              "ORGANIZATION_Corporation        float64\n",
              "ORGANIZATION_Trust              float64\n",
              "INCOME_AMT_0                    float64\n",
              "INCOME_AMT_1-9999               float64\n",
              "INCOME_AMT_10000-24999          float64\n",
              "INCOME_AMT_100000-499999        float64\n",
              "INCOME_AMT_10M-50M              float64\n",
              "INCOME_AMT_1M-5M                float64\n",
              "INCOME_AMT_25000-99999          float64\n",
              "INCOME_AMT_50M+                 float64\n",
              "INCOME_AMT_5M-10M               float64\n",
              "SPECIAL_CONSIDERATIONS_N        float64\n",
              "SPECIAL_CONSIDERATIONS_Y        float64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "saKVSK7u1jI8"
      },
      "source": [
        "# Split our preprocessed data into our features and target arrays\n",
        "y = application_df.IS_SUCCESSFUL.values\n",
        "X = application_df.drop('IS_SUCCESSFUL', axis=1).values\n",
        "\n",
        "# Split the preprocessed data into a training and\n",
        "# testing dataset\n",
        "X_train, X_test, y_train, y_test = train_test_split(X,y,random_state= 42)"
      ],
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CW9XH8z21jI8"
      },
      "source": [
        "# Create a StandardScaler instances\n",
        "scaler = StandardScaler()\n",
        "\n",
        "# Fit the StandardScaler\n",
        "X_scaler = scaler.fit(X_train)\n",
        "\n",
        "# Scale the data\n",
        "X_train_scaled = X_scaler.transform(X_train)\n",
        "X_test_scaled = X_scaler.transform(X_test)"
      ],
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CXt0y8JGe6wG",
        "outputId": "1718369d-cea8-4bcc-8827-fa66b04b55f1"
      },
      "source": [
        "X_train.shape"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(25724, 43)"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BXdoy6oa1jI8"
      },
      "source": [
        "## Compile, Train and Evaluate the Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8HozuEPbC4Eh"
      },
      "source": [
        "# Model Evaluation 1"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Y4n0uD01jI9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1b7f66e3-a537-4961-c3ae-0bfff65f58ff"
      },
      "source": [
        "# Define the model - deep neural net, i.e., the number of input features and hidden nodes for each layer.\n",
        "number_input_features = len( X_train_scaled[0])\n",
        "hidden_nodes_layer1=80\n",
        "hidden_nodes_layer2=30\n",
        "\n",
        "nn_model = tf.keras.models.Sequential()\n",
        "\n",
        "# First hidden layer\n",
        "nn_model.add(tf.keras.layers.Dense(units=hidden_nodes_layer1, input_dim=number_input_features, activation='relu'))\n",
        "\n",
        "# Second hidden layer\n",
        "nn_model.add(tf.keras.layers.Dense(units=hidden_nodes_layer2, activation='relu'))\n",
        "\n",
        "# Output layer\n",
        "nn_model.add(tf.keras.layers.Dense(units=1, activation='sigmoid'))\n",
        "\n",
        "# Check the structure of the model\n",
        "nn_model.summary()"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_3\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_10 (Dense)             (None, 80)                3520      \n",
            "_________________________________________________________________\n",
            "dense_11 (Dense)             (None, 30)                2430      \n",
            "_________________________________________________________________\n",
            "dense_12 (Dense)             (None, 1)                 31        \n",
            "=================================================================\n",
            "Total params: 5,981\n",
            "Trainable params: 5,981\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "biizZk3J1jI9"
      },
      "source": [
        "# Compile the model\n",
        "nn_model.compile(loss = 'binary_crossentropy', optimizer = 'adam', metrics=['accuracy', tf.keras.metrics.Recall()])"
      ],
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0h4pMQLZ1jI9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "788412ba-986b-4a82-a5d0-b3e1eaa25e73"
      },
      "source": [
        "fit_model = nn_model.fit(X_train_scaled,y_train,validation_split=0.15, epochs=100)"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5731 - accuracy: 0.7178 - recall_3: 0.7755 - val_loss: 0.5570 - val_accuracy: 0.7339 - val_recall_3: 0.7481\n",
            "Epoch 2/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5583 - accuracy: 0.7275 - recall_3: 0.7804 - val_loss: 0.5475 - val_accuracy: 0.7339 - val_recall_3: 0.7606\n",
            "Epoch 3/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5549 - accuracy: 0.7279 - recall_3: 0.7784 - val_loss: 0.5456 - val_accuracy: 0.7334 - val_recall_3: 0.7670\n",
            "Epoch 4/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5524 - accuracy: 0.7292 - recall_3: 0.7884 - val_loss: 0.5490 - val_accuracy: 0.7341 - val_recall_3: 0.7606\n",
            "Epoch 5/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5506 - accuracy: 0.7305 - recall_3: 0.7885 - val_loss: 0.5470 - val_accuracy: 0.7336 - val_recall_3: 0.7885\n",
            "Epoch 6/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5493 - accuracy: 0.7300 - recall_3: 0.7882 - val_loss: 0.5466 - val_accuracy: 0.7359 - val_recall_3: 0.7392\n",
            "Epoch 7/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5484 - accuracy: 0.7305 - recall_3: 0.7883 - val_loss: 0.5472 - val_accuracy: 0.7339 - val_recall_3: 0.7641\n",
            "Epoch 8/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5475 - accuracy: 0.7323 - recall_3: 0.7917 - val_loss: 0.5467 - val_accuracy: 0.7334 - val_recall_3: 0.7417\n",
            "Epoch 9/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5470 - accuracy: 0.7318 - recall_3: 0.7872 - val_loss: 0.5481 - val_accuracy: 0.7352 - val_recall_3: 0.7636\n",
            "Epoch 10/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5473 - accuracy: 0.7315 - recall_3: 0.7901 - val_loss: 0.5460 - val_accuracy: 0.7349 - val_recall_3: 0.7705\n",
            "Epoch 11/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5463 - accuracy: 0.7324 - recall_3: 0.7907 - val_loss: 0.5478 - val_accuracy: 0.7318 - val_recall_3: 0.7601\n",
            "Epoch 12/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5462 - accuracy: 0.7328 - recall_3: 0.7931 - val_loss: 0.5499 - val_accuracy: 0.7357 - val_recall_3: 0.7571\n",
            "Epoch 13/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5454 - accuracy: 0.7332 - recall_3: 0.7898 - val_loss: 0.5452 - val_accuracy: 0.7318 - val_recall_3: 0.7616\n",
            "Epoch 14/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5452 - accuracy: 0.7340 - recall_3: 0.7932 - val_loss: 0.5447 - val_accuracy: 0.7341 - val_recall_3: 0.7680\n",
            "Epoch 15/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5453 - accuracy: 0.7334 - recall_3: 0.7939 - val_loss: 0.5462 - val_accuracy: 0.7375 - val_recall_3: 0.7745\n",
            "Epoch 16/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5448 - accuracy: 0.7341 - recall_3: 0.7900 - val_loss: 0.5448 - val_accuracy: 0.7339 - val_recall_3: 0.7740\n",
            "Epoch 17/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5443 - accuracy: 0.7340 - recall_3: 0.7952 - val_loss: 0.5453 - val_accuracy: 0.7352 - val_recall_3: 0.7621\n",
            "Epoch 18/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5441 - accuracy: 0.7340 - recall_3: 0.7979 - val_loss: 0.5491 - val_accuracy: 0.7334 - val_recall_3: 0.7486\n",
            "Epoch 19/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5436 - accuracy: 0.7346 - recall_3: 0.7955 - val_loss: 0.5491 - val_accuracy: 0.7346 - val_recall_3: 0.7661\n",
            "Epoch 20/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5434 - accuracy: 0.7356 - recall_3: 0.7958 - val_loss: 0.5448 - val_accuracy: 0.7352 - val_recall_3: 0.7611\n",
            "Epoch 21/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5436 - accuracy: 0.7342 - recall_3: 0.7971 - val_loss: 0.5501 - val_accuracy: 0.7339 - val_recall_3: 0.7666\n",
            "Epoch 22/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5432 - accuracy: 0.7364 - recall_3: 0.7960 - val_loss: 0.5452 - val_accuracy: 0.7365 - val_recall_3: 0.7546\n",
            "Epoch 23/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5428 - accuracy: 0.7354 - recall_3: 0.7957 - val_loss: 0.5489 - val_accuracy: 0.7354 - val_recall_3: 0.7626\n",
            "Epoch 24/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5429 - accuracy: 0.7366 - recall_3: 0.7987 - val_loss: 0.5466 - val_accuracy: 0.7346 - val_recall_3: 0.7730\n",
            "Epoch 25/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5425 - accuracy: 0.7356 - recall_3: 0.7909 - val_loss: 0.5465 - val_accuracy: 0.7354 - val_recall_3: 0.7755\n",
            "Epoch 26/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5420 - accuracy: 0.7366 - recall_3: 0.7945 - val_loss: 0.5469 - val_accuracy: 0.7349 - val_recall_3: 0.7880\n",
            "Epoch 27/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5418 - accuracy: 0.7355 - recall_3: 0.7930 - val_loss: 0.5452 - val_accuracy: 0.7346 - val_recall_3: 0.7775\n",
            "Epoch 28/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5419 - accuracy: 0.7371 - recall_3: 0.7979 - val_loss: 0.5481 - val_accuracy: 0.7331 - val_recall_3: 0.7511\n",
            "Epoch 29/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5411 - accuracy: 0.7364 - recall_3: 0.7943 - val_loss: 0.5459 - val_accuracy: 0.7334 - val_recall_3: 0.7626\n",
            "Epoch 30/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5411 - accuracy: 0.7366 - recall_3: 0.7948 - val_loss: 0.5472 - val_accuracy: 0.7357 - val_recall_3: 0.7675\n",
            "Epoch 31/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5407 - accuracy: 0.7367 - recall_3: 0.7937 - val_loss: 0.5459 - val_accuracy: 0.7346 - val_recall_3: 0.7690\n",
            "Epoch 32/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5403 - accuracy: 0.7369 - recall_3: 0.7943 - val_loss: 0.5475 - val_accuracy: 0.7346 - val_recall_3: 0.7536\n",
            "Epoch 33/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5412 - accuracy: 0.7366 - recall_3: 0.7919 - val_loss: 0.5449 - val_accuracy: 0.7349 - val_recall_3: 0.7621\n",
            "Epoch 34/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5407 - accuracy: 0.7378 - recall_3: 0.7946 - val_loss: 0.5471 - val_accuracy: 0.7326 - val_recall_3: 0.7561\n",
            "Epoch 35/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5406 - accuracy: 0.7373 - recall_3: 0.7927 - val_loss: 0.5459 - val_accuracy: 0.7354 - val_recall_3: 0.7661\n",
            "Epoch 36/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5403 - accuracy: 0.7377 - recall_3: 0.7936 - val_loss: 0.5453 - val_accuracy: 0.7372 - val_recall_3: 0.7745\n",
            "Epoch 37/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5395 - accuracy: 0.7375 - recall_3: 0.7925 - val_loss: 0.5471 - val_accuracy: 0.7354 - val_recall_3: 0.7785\n",
            "Epoch 38/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5403 - accuracy: 0.7368 - recall_3: 0.7959 - val_loss: 0.5472 - val_accuracy: 0.7359 - val_recall_3: 0.7651\n",
            "Epoch 39/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5392 - accuracy: 0.7378 - recall_3: 0.7923 - val_loss: 0.5468 - val_accuracy: 0.7362 - val_recall_3: 0.7835\n",
            "Epoch 40/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5400 - accuracy: 0.7379 - recall_3: 0.7961 - val_loss: 0.5458 - val_accuracy: 0.7359 - val_recall_3: 0.7735\n",
            "Epoch 41/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5392 - accuracy: 0.7383 - recall_3: 0.7982 - val_loss: 0.5457 - val_accuracy: 0.7344 - val_recall_3: 0.7661\n",
            "Epoch 42/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5392 - accuracy: 0.7378 - recall_3: 0.7938 - val_loss: 0.5466 - val_accuracy: 0.7334 - val_recall_3: 0.7641\n",
            "Epoch 43/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5391 - accuracy: 0.7383 - recall_3: 0.7952 - val_loss: 0.5470 - val_accuracy: 0.7326 - val_recall_3: 0.7705\n",
            "Epoch 44/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5393 - accuracy: 0.7375 - recall_3: 0.7931 - val_loss: 0.5478 - val_accuracy: 0.7341 - val_recall_3: 0.7656\n",
            "Epoch 45/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5390 - accuracy: 0.7380 - recall_3: 0.7897 - val_loss: 0.5467 - val_accuracy: 0.7362 - val_recall_3: 0.7750\n",
            "Epoch 46/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5392 - accuracy: 0.7375 - recall_3: 0.7957 - val_loss: 0.5457 - val_accuracy: 0.7349 - val_recall_3: 0.7735\n",
            "Epoch 47/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5379 - accuracy: 0.7394 - recall_3: 0.7972 - val_loss: 0.5490 - val_accuracy: 0.7359 - val_recall_3: 0.7680\n",
            "Epoch 48/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5390 - accuracy: 0.7369 - recall_3: 0.7933 - val_loss: 0.5466 - val_accuracy: 0.7349 - val_recall_3: 0.7611\n",
            "Epoch 49/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5383 - accuracy: 0.7381 - recall_3: 0.7938 - val_loss: 0.5478 - val_accuracy: 0.7336 - val_recall_3: 0.7596\n",
            "Epoch 50/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5378 - accuracy: 0.7375 - recall_3: 0.7959 - val_loss: 0.5481 - val_accuracy: 0.7354 - val_recall_3: 0.7720\n",
            "Epoch 51/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5384 - accuracy: 0.7384 - recall_3: 0.7960 - val_loss: 0.5460 - val_accuracy: 0.7339 - val_recall_3: 0.7606\n",
            "Epoch 52/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5383 - accuracy: 0.7383 - recall_3: 0.7908 - val_loss: 0.5473 - val_accuracy: 0.7346 - val_recall_3: 0.7596\n",
            "Epoch 53/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5382 - accuracy: 0.7385 - recall_3: 0.7941 - val_loss: 0.5441 - val_accuracy: 0.7357 - val_recall_3: 0.7666\n",
            "Epoch 54/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5374 - accuracy: 0.7383 - recall_3: 0.7948 - val_loss: 0.5478 - val_accuracy: 0.7354 - val_recall_3: 0.7611\n",
            "Epoch 55/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5373 - accuracy: 0.7397 - recall_3: 0.7937 - val_loss: 0.5474 - val_accuracy: 0.7349 - val_recall_3: 0.7656\n",
            "Epoch 56/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5378 - accuracy: 0.7386 - recall_3: 0.7961 - val_loss: 0.5457 - val_accuracy: 0.7354 - val_recall_3: 0.7526\n",
            "Epoch 57/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5374 - accuracy: 0.7389 - recall_3: 0.7937 - val_loss: 0.5481 - val_accuracy: 0.7354 - val_recall_3: 0.7506\n",
            "Epoch 58/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5367 - accuracy: 0.7398 - recall_3: 0.7945 - val_loss: 0.5509 - val_accuracy: 0.7380 - val_recall_3: 0.7715\n",
            "Epoch 59/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5378 - accuracy: 0.7389 - recall_3: 0.7947 - val_loss: 0.5471 - val_accuracy: 0.7365 - val_recall_3: 0.7725\n",
            "Epoch 60/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5372 - accuracy: 0.7385 - recall_3: 0.7950 - val_loss: 0.5503 - val_accuracy: 0.7331 - val_recall_3: 0.7621\n",
            "Epoch 61/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5371 - accuracy: 0.7389 - recall_3: 0.7943 - val_loss: 0.5466 - val_accuracy: 0.7344 - val_recall_3: 0.7685\n",
            "Epoch 62/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5370 - accuracy: 0.7391 - recall_3: 0.7973 - val_loss: 0.5469 - val_accuracy: 0.7339 - val_recall_3: 0.7646\n",
            "Epoch 63/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5370 - accuracy: 0.7390 - recall_3: 0.7945 - val_loss: 0.5463 - val_accuracy: 0.7346 - val_recall_3: 0.7710\n",
            "Epoch 64/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5369 - accuracy: 0.7384 - recall_3: 0.7951 - val_loss: 0.5488 - val_accuracy: 0.7362 - val_recall_3: 0.7710\n",
            "Epoch 65/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5367 - accuracy: 0.7391 - recall_3: 0.7954 - val_loss: 0.5479 - val_accuracy: 0.7362 - val_recall_3: 0.7656\n",
            "Epoch 66/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5368 - accuracy: 0.7390 - recall_3: 0.7925 - val_loss: 0.5497 - val_accuracy: 0.7349 - val_recall_3: 0.7646\n",
            "Epoch 67/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5364 - accuracy: 0.7383 - recall_3: 0.7958 - val_loss: 0.5498 - val_accuracy: 0.7354 - val_recall_3: 0.7641\n",
            "Epoch 68/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5366 - accuracy: 0.7390 - recall_3: 0.7908 - val_loss: 0.5504 - val_accuracy: 0.7349 - val_recall_3: 0.7651\n",
            "Epoch 69/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5364 - accuracy: 0.7394 - recall_3: 0.7929 - val_loss: 0.5479 - val_accuracy: 0.7362 - val_recall_3: 0.7661\n",
            "Epoch 70/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5365 - accuracy: 0.7388 - recall_3: 0.7905 - val_loss: 0.5486 - val_accuracy: 0.7367 - val_recall_3: 0.7675\n",
            "Epoch 71/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5365 - accuracy: 0.7396 - recall_3: 0.7943 - val_loss: 0.5494 - val_accuracy: 0.7344 - val_recall_3: 0.7670\n",
            "Epoch 72/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5366 - accuracy: 0.7388 - recall_3: 0.7951 - val_loss: 0.5487 - val_accuracy: 0.7349 - val_recall_3: 0.7511\n",
            "Epoch 73/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5359 - accuracy: 0.7393 - recall_3: 0.7929 - val_loss: 0.5514 - val_accuracy: 0.7346 - val_recall_3: 0.7621\n",
            "Epoch 74/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5361 - accuracy: 0.7389 - recall_3: 0.7927 - val_loss: 0.5512 - val_accuracy: 0.7349 - val_recall_3: 0.7606\n",
            "Epoch 75/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5361 - accuracy: 0.7397 - recall_3: 0.7906 - val_loss: 0.5499 - val_accuracy: 0.7344 - val_recall_3: 0.7675\n",
            "Epoch 76/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5358 - accuracy: 0.7391 - recall_3: 0.7965 - val_loss: 0.5513 - val_accuracy: 0.7334 - val_recall_3: 0.7631\n",
            "Epoch 77/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5359 - accuracy: 0.7399 - recall_3: 0.7947 - val_loss: 0.5497 - val_accuracy: 0.7359 - val_recall_3: 0.7666\n",
            "Epoch 78/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5362 - accuracy: 0.7385 - recall_3: 0.7924 - val_loss: 0.5509 - val_accuracy: 0.7352 - val_recall_3: 0.7695\n",
            "Epoch 79/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5362 - accuracy: 0.7386 - recall_3: 0.7933 - val_loss: 0.5506 - val_accuracy: 0.7344 - val_recall_3: 0.7631\n",
            "Epoch 80/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5355 - accuracy: 0.7396 - recall_3: 0.7953 - val_loss: 0.5521 - val_accuracy: 0.7365 - val_recall_3: 0.7740\n",
            "Epoch 81/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5359 - accuracy: 0.7398 - recall_3: 0.7967 - val_loss: 0.5495 - val_accuracy: 0.7346 - val_recall_3: 0.7516\n",
            "Epoch 82/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5355 - accuracy: 0.7394 - recall_3: 0.7906 - val_loss: 0.5508 - val_accuracy: 0.7362 - val_recall_3: 0.7675\n",
            "Epoch 83/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5356 - accuracy: 0.7395 - recall_3: 0.7965 - val_loss: 0.5528 - val_accuracy: 0.7354 - val_recall_3: 0.7581\n",
            "Epoch 84/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5355 - accuracy: 0.7399 - recall_3: 0.7942 - val_loss: 0.5536 - val_accuracy: 0.7344 - val_recall_3: 0.7551\n",
            "Epoch 85/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5350 - accuracy: 0.7405 - recall_3: 0.7952 - val_loss: 0.5511 - val_accuracy: 0.7334 - val_recall_3: 0.7631\n",
            "Epoch 86/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5355 - accuracy: 0.7395 - recall_3: 0.7944 - val_loss: 0.5498 - val_accuracy: 0.7336 - val_recall_3: 0.7651\n",
            "Epoch 87/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5355 - accuracy: 0.7398 - recall_3: 0.7955 - val_loss: 0.5502 - val_accuracy: 0.7352 - val_recall_3: 0.7646\n",
            "Epoch 88/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5352 - accuracy: 0.7392 - recall_3: 0.7935 - val_loss: 0.5509 - val_accuracy: 0.7359 - val_recall_3: 0.7700\n",
            "Epoch 89/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5355 - accuracy: 0.7400 - recall_3: 0.7959 - val_loss: 0.5501 - val_accuracy: 0.7349 - val_recall_3: 0.7656\n",
            "Epoch 90/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5348 - accuracy: 0.7401 - recall_3: 0.7948 - val_loss: 0.5518 - val_accuracy: 0.7354 - val_recall_3: 0.7700\n",
            "Epoch 91/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5351 - accuracy: 0.7390 - recall_3: 0.7944 - val_loss: 0.5504 - val_accuracy: 0.7344 - val_recall_3: 0.7675\n",
            "Epoch 92/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5354 - accuracy: 0.7398 - recall_3: 0.7967 - val_loss: 0.5497 - val_accuracy: 0.7346 - val_recall_3: 0.7646\n",
            "Epoch 93/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5350 - accuracy: 0.7402 - recall_3: 0.7952 - val_loss: 0.5529 - val_accuracy: 0.7326 - val_recall_3: 0.7586\n",
            "Epoch 94/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5347 - accuracy: 0.7394 - recall_3: 0.7951 - val_loss: 0.5537 - val_accuracy: 0.7341 - val_recall_3: 0.7631\n",
            "Epoch 95/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5351 - accuracy: 0.7397 - recall_3: 0.7955 - val_loss: 0.5501 - val_accuracy: 0.7362 - val_recall_3: 0.7690\n",
            "Epoch 96/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5352 - accuracy: 0.7404 - recall_3: 0.7973 - val_loss: 0.5519 - val_accuracy: 0.7344 - val_recall_3: 0.7661\n",
            "Epoch 97/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5347 - accuracy: 0.7406 - recall_3: 0.7953 - val_loss: 0.5541 - val_accuracy: 0.7336 - val_recall_3: 0.7641\n",
            "Epoch 98/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5347 - accuracy: 0.7399 - recall_3: 0.7959 - val_loss: 0.5527 - val_accuracy: 0.7334 - val_recall_3: 0.7631\n",
            "Epoch 99/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5347 - accuracy: 0.7398 - recall_3: 0.7946 - val_loss: 0.5523 - val_accuracy: 0.7354 - val_recall_3: 0.7631\n",
            "Epoch 100/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5345 - accuracy: 0.7399 - recall_3: 0.7974 - val_loss: 0.5530 - val_accuracy: 0.7336 - val_recall_3: 0.7646\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k8fYkswE1jI-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5f78dc9f-8d6f-4928-c56c-25dc614676fc"
      },
      "source": [
        "# Evaluate the model using the test data\n",
        "model_loss, model_accuracy, model_Recall= nn_model.evaluate(X_test_scaled,y_test,verbose=2)\n",
        "print(f\"Loss: {model_loss}, Accuracy: {model_accuracy}\")"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "268/268 - 0s - loss: 0.5653 - accuracy: 0.7291 - recall_3: 0.7826\n",
            "Loss: 0.5652697682380676, Accuracy: 0.7290962338447571\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "id": "FmNCIX7iqXi-",
        "outputId": "dd854487-6d30-4adb-a0bf-d404c4bd86c4"
      },
      "source": [
        "# Model Accuracy Graph\n",
        "plt.style.use('seaborn')\n",
        "plt.title('Accuracy \\n',fontsize=20)\n",
        "plt.plot(fit_model.history['accuracy'], 'tomato', label='Training Accuracy')\n",
        "plt.plot(fit_model.history['val_accuracy'], 'turquoise',label='Validation Accuracy')\n",
        "plt.xlabel('Number of Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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27Kjfa2RmZlKnTl1kWSY+Ph6HwwFA48ZNOHjwAABvvfUap0+fIjw8nNDQUJYtW0KfPv2u+/0LglB56Y/swrRlGUGrfkGy5t3o4pRa0Or5hP1vKpr0JPJvG0rac5/ijKqLcc865MzUG108PyLQVyJDhtzFnDmzeOaZv9CmTVvS0tJYvHjRlU8swZ13DmXfvt1MnjyB9PQ0n1p0VlYmJ04cp3v3nt5tderUpW7deiQkHOWxx/7M008/yZQpExk6dAQmk8lvmyRJjBgxmgkTHubNN/9FbGwrvzL07Xs7mzat56mnJmEymYiOjubbb//HU0/9jU8+mc6kSY8REhJK48ZNCo7vT1RUNEFB5ut674IgVGKKC/OyWQBITgf6QztucIFKR85IJmjFHJTQCDInvu5uHdYbye89AsnlwrTh9xtdRD/lOrzuzTffZO/evUiSxIsvvkj79u0BSEpK4m9/+5v3uHPnzvHXv/6VoUOHApCamsodd9zBJ598Qrdu3Thy5AivvvoqALGxsfzrX/8q8XUr8/C6ipaYeIkzZ07TrVsPDhzYx9dff8H06Z/esPKU5j6+8cZU7rxzKB07dq6gUlU9VfXzWNmI+1g2ruU+GnauIfTnT7E3a4f+xH5sMXFkP/JiOZXwClQVAnRtBhK86GtMm5eSPWYyto59Cnc4HUS8NwU5P5e05/+Lag481K0k5TW8rtxq9Nu2bePMmTPMmTOHadOmMW3aNO++WrVq8f333/P999/z7bffUqdOHW6//Xbv/nfffZcGDRp4/z9t2jRefPFFfvrpJ3Jzc1m7dm15FbvaMZuDmTNnFpMmPconn3zIxIl/udFFKpbNZmPChD9hNptFkBeE6sxhx7xiDqpWR87oJ3HUa4r++D6kvIp/6NIknSPi7YmY1i684rFSbhbG7Stx1YjC1uFW351aHZZeQ5EcNkybl5ZTaa9NuSXjbd68mQEDBgDQrFkzsrKyyM3NJTg42Oe4BQsWMHjwYMxms/c8s9lMTEwM4B5OduHCBW9rQL9+/di8eTN9+vRBuLKQkBA++OCTG12MUjEYDHz55YwbXQxBEMqZacsyNJmp5N82FKVGTWztb0V34SSGg1uxdh1w9RdUVTSJZ9Ef34fu+H406YlkP/Q8rqiSM+ClvGzCZr6DJjuDoLW/Yul5B+gMxZd70xIkp4P824aCxj98WrrcTtCqnzFtWkL+bXeDwXT176UclFuNPjU1lfDwcO//IyIiSElJ8Ttu3rx5jB49GnAH9U8//ZRnnnnGuz8jI4PQ0FDv/yMjIwNeRxAEQaj8JGseQavnoxiDyO87EgBb+x4AGPZtvOrr6U4cIPLNJ4j4z98IXjITw7HdaFMvYdy5uuQTnQ5Cf3gPTXoSrohayJZcDPv8E5YLy52PafMyFHMo1s63Bz7IYMLS805kSy6m7auu+r2UlwobXhcoFWD37t00bdrUW8v/8ssvGTNmjE9gL811LhceHoRWq7nicVeruP4P4eqI+1g2xH0sGzf1fdy6Gs4eh9GPl7qPujilvo/zfwFLLox6hJqN6hacHALNWqM/eYgovQPCIkp3LVWF//4AeTnQoz+0ioNmreFfkwg6upOgBycVf96M6XD6MHS+Dc3ox+CFRwndEQ9DhgU+Z+lSsObBiIeJqlez+DINHQ0bFhG8biHBQTpoHQf1GoMnEVpVITsD7DaIquN3enl8Hsst0EdHR5OaWjjMIDk5maioKJ9j1qxZQ48ePbz/37BhA4qiMGvWLM6ePcu+ffv44IMPyMzM9B6TlJREdHR0ia+dkZFfRu+ikEjaKRviPpYNcR8voyjgcpTY7BrITX0fbRYiv/8E2ZpHets+uGr6B53SkGwWataPLtV91J06TNjyX1BCw0nv0B+KnGNq1Y3gE4fIWbMCa887Ck9SVXA5oWC+kaK0Z44SfvYEtjZdyR72Z+/20Ba3YDi4lfSDh3FF1/c7z7RuEcEbl+Oo15TMoRNBNRDasiOGwzvI2LULZ4MWvic47EQs+wXJYCK9fT/UEt+rhKnfPQQvnQXz/geAEhyGs05j5Ox0NOnJSA4bAOlT3sVVt4n3zCqXjHfrrbeybNkyAA4ePEh0dLRf//z+/ftp2bKl9/8//fQTc+fOZe7cufTt25epU6fSsmVLmjZtyo4d7qEXy5cv57bbbiuvYguCUAnoD+9El7C31McHrZhD5LQnkDNFt15R+gNb0B/dHXCfcdda5IKx67pTh67uwnYbhp1rqPH5S9R89SFYMueKp2gvnCD0u7dAUcgZNQn0vg9ltnY9UCUJY5Hmezk9iRqfPE/Evycj5WX7XdO0xR1jLN2H+F6rdRcA9Ie2ByjHScxLf8AVGk72g895y+G5hmnzMr9zjLvXocnJxNptIKrpysN+LX1GkPaPz8keMxlrXG9UWUafsBc5MxVnzTrY2nYjr/9YXDXrXvFaZaHcAn3Hjh1p06YN48eP54033mDq1KnMnz+f+Ph47zEpKSlERl55itMXX3yRDz74gPHjx9OwYUN69ux5xXMqm4kTH/FOXOPx+eef8OOPPwQ8/q67+gPw0Ufvc/HiBZ99J08eZ/LkCcW+Vl5eLtu2uefy//77GRw4sO96ik5KSjK9e3dl3bo113UdQSgVl5PQH6cT+uN0d03uShw2TJuXIdssmLbGX/n4qsLlxLh9JZLNck2nSzmZhP74IaGz3vN/AFIUTBsXoxY01+tOHQ5whQDsVsyLviHyrQmE/vwp2rPHUAwmmP8t+gNbiz1Nk3SOsG+mIdmt5IybgiM2zu8YJTQcR5PW6M4cRc5MRZewl/BP/oHu4ik02emYV8z1fX+5WRj2b8YZVQ9Hs7a+xWzVCVWWMRzc5vc6pvWLkFSVnFGTUMIK44+jeTuckXUw7NuIlJvl3S5nJBO06mdUjRbLrXeX7j4BSlgkto59yBk7hfR/fEHq1O9Im/odmf/3b7Lv/xv5A8b4PeyUl3Ltoy86Vh7wqb0D/Pbbb8We+/bbb3t/bt68ObNnV+110gcOHMyqVfG0bFk4ocyaNav4+OPPSzzvqaf+etWvdfToEbZt20LXrt158ME/XfX5l1uxYjn16zdg5cpl9O7d97qvJwgl0V48heSwITls6E4dvuJCIYb9W7w1U+P2FeTdPhp0+oooarkybo0n5Ldv0CSdI+/uP139+bvWICku98Q0f/xAzr2FSc76IzvRpiVi7dQX/aEd6E6XrkYftPZXgjb/gSukBnk9hmDtfDuSzULEFy8TOvdjMiOicRZpigZ3rTzsm9eR83PIGfVnbO1vLebqYGt/K/qTBwmd8xHaM0dB1pAzYgKmjYsxbl2OpetAXHUaAWDavhLJ5cTSfbBffoFqCsbRpA36E/uRs9K8AV3OTMWwfwvOWg1wxNzi++KyjLX7YIIXz8C4YxWWviMLHlDeQJOdTt6ge1FCw7kmkoRqDLq2c8uAmBmvgvTvP4h16wqzQI8cOUxUVBSqqjJlykSmTJnIpEmPceHCeZ/zJk+ewMmTx0lOTmLixEeYPHkCixYVjvf88ccfmDjxEZ544mG++eZLAD744F1WrYrn11/nM23aq2zcuB6n08mbb/6LyZMnMGHCn7w1/nHjRjB79kz+8pcneOKJh8nP95+GMj5+Kc888xw7dmzDYnHXLnJycvj735/iyScf57nnniY/Pz/gtq+//oJffnE36508eZwHH3wQgPHjR/LKKy/w++8L2b59q/e9vfDCX71T5H744XtMmPAnJk16jJMnj/PKKy+wY4f7Cd1utzN27PAymSZYqFx0p494fzYc8q+RXc643b1EsrVdD+S8HAz7N5db2cqU04H23PHA+1QV0zZ364Rx5xp34tbVUBSM21eianU46jbBuG+TT63dtHExAPm97sbRpBWajJRSdXsYDmxG1erI+Ot/yB90L0pELVx1GsPjz4HTTujMd5CzMwCQLHkYN/1BjS+nosnOIPfOh7B26V/i9W1tu6HKMrrTR1BCapA54TWs3QaSe9fDSKpK8O/fFvTZuzBuXY6qN2Dr2DvwtbzN94Uz7pk2L0VSXFh63R0w+dDaqS+qzoBpazzaswkFZU8n986HyO836or3p7K6KRe1+d6exhbX1c2rLJ+XUJTiM/67a8w8qC++GyI8PIK6detx6NABWrduy6pV8QwcOIS0tFQeeeQJOnbszO+//8r8+fOYMuUZv/N//vkn+vcfxNix9/LDDzM4fvyYd99nn32FLMuMHTuccePu4777HuTkyRMMHz7K22wfH78UvV7PJ598SWpqCpMnT+Snn+YHXNq2aK397NnT5OXl0qVLN+LiOrFhw1oGDhzCjz9+T9euPRgzZjxz5sxix45tHDlyyG9bcS5evMCbb75H06bNWLVqBVOnvkHduvV4/fVX2Lp1MwaDgeTkJL78cgZ79uxi5cp4Bg++k5Ur4+ncuSs7d26je/eeaLU35Ue4XOkPbUd35gh5Qx647kzsa6E74w70qk7v7mMd+mix5dAkn0d/+jD25u3JG3I/hgNbMG1Z5jtjWWWkqoT+9CGGg9vIeugf2Ft18tmtPZeANukcqlaHbM3DuHfDFYNkUbpTB9019rg+WLoPJvy/L2L+/Vsy//KWe7z5yYPYm7fDVbsRjsatMBzaju7UEWxxUcVeU5N0Dm3yBWxtuqJePj48rid5g+8jeOksQme+g6tWfQz7NyM57KiyhrxB92K5beiVb4s5FMttQ9GkXiJn+OOoIe4atCM2DltsRwxHd6E/uA0kCU1WGpZug1CNgfvM7a27wG/fYDi0DWuPwWCzYNy2AiU4DGuHXoFf32TGeksvTNtXUuOLl0FVyLlnUvHD6aoIUaOvQAMHDmHlSvdT+saN6+jbtz8REZHMm/cTf/nLE8ydO9u7hOvlTp8+Rbt27kmD4uIKZ40zGo1MnjyBKVMmkpmZSXa2f8IKwNGjh73L1dasGeWzXOzlS9sWFR+/zLvm/MCBQ1ixwp2ocuzYEdq16wDAuHH307t334DbimM0mmjatBkANWrU4J133mDy5Ans3r2T7Owsn2vdcktHnnhiEt269WD//j04nU7Wr1/LoEF3FHt94doFrZ5P0LpFyFlpFf/iqoru9BFcYZHY2vZAk5WG9sLJYg/31OYtXQegRNTCHhOH7lwC2gsnKqrE18S0cYm3/zhoxRx3LbUI47YVAOQMf8KdoLZlmd8xADhsAbcbtxXeF2fDFljj+qC7eArjjtUEbXDX5j39zY4mrQGu2HxvOOBuBbS16R5wv6X3cPfrXDiBcddalJBwcofcT9o/Pr+q2nDekAfIfuDv3iDv3X7Xw6gaDcFLvsO00T2fvKXHkECXAECpURNHvWboTh50ty7sXINszcPSbXCJXTvea0oS2ff9tcoHebhJa/QP6iN5kKtb57wshuH06dOPmTO/YeDAwTRo0JDQ0FA++WQ63bp1Z8SI0axevYJNmzYEPNe9Spxc8LN7qdjExEvMmTOLb76ZRVBQEA8+OLaEV5d85iBwOBze65W09G18/DJkWWLTpg0oiouLFy+Qk5MTcMnaQNuKLo3ru4Rt4Ufvrbde59///pDGjZvwwQfvFHstrVZLly7d2bFjG6dOnaRt2/YlvF/hmigutElnAdCkJ6HUKGG8cDnQpF5EzsvG2uFWbG26YNy9Fv2hbTjrN/M/2OlwBxRzKPZW7odfS48hGI7uwrh5WaVcLhTctXXz0u+9Q670CXvRH97hroHinlDGuG8TrvBobB37YDiyA8PBbWjPHsPZKNZ7Hc3FU9T436vYY24hZ9xT3nHaUl42hoNbcUbX8x6fN+Q+DAe3YF42G8mWjzOqHvaCPmpnncYoBtMVM+8NB7aiarTYW3UMfIAkkTNqIs66jXHWboSjaZvCseNlwBVVF0vPOwla/xuajBTsTVrjqtWgxHPsbbqgu3AC/ZGdmDYtQdXqsHQfVPLr1GlM1v1/RQmribNB8zIr/40kavQVKCjITLNmLZg581sGDnQ/NWZmupemVVWVDRvWevunL9ewYSOOHHH/Ie7atcN7bnh4OEFBQRw9eoTExMSCAC7hcrl8zm/VqrX3vKSkRGRZJiSk5IkZDh8+SFBQELNn/8KMGbOZOXMOt98+kLVrV9KqVeiHKwUAACAASURBVGt27nQPXVm48Bf++OP3gNvMZrN3PoV9+/YEfJ28vFxq1apNTk4Ou3btxOFw+JT32LEjvP+++wFg8OA7+frrz72tE0LZ0qQlIjns3p8rmqd/3tGoFfYWHVC1OgwBhkgBGA5uQ87Pwdqpr3eMtaNFB5yRtTHu3VDu86brTuxHv38zOAP/zQYi5ecQOns6KArZ454i966HUSWJoJXzvDVzw54NSA6bu6lelr01TM9QMnDP0hY6+wNkaz7GfZsIiv/Ju8+4ay2Sy4m1ywBvl4cSGkFev3uQ83OQXC4st95VGIQ1GpyNYtGmXETKKZyzpChNykW0iWfcv5NimsqBgvne73YnUJZhkPfIv/0elOAwoOTavIetdVcAzEtnubsybrkNteD8ktjbdq82QR5EoK9wAwcOYfv2rfTq5U4gGT58FNOn/5u//vX/6N9/MHv27PImyhU1Zsy9LF68iGefnUxOjvsLrEWLGEymICZNepSVK5czfPgo3n//HWJjW7Jq1XJmz/7ee37//oNQFIUpUyby6qsv8ve/X3mVqPj4pdx1l2+/2l13DWPFiuWMGXMvBw7sY/LkCWzatIE+ffoF3Nanz+1s2LCWp59+ktzc3ICvM2rUGCZNeox3353G/fc/xA8/zKB+/YY0atSEJ598nA8/fI8RI+4BoGXLVmRnZ3sflISypb10xvuzJj2pwl/f0z/vaNIS9EbsLTqgTTqHJvWS37HGgmQ1a+cifdeyjLXbICSn48pToHo47JjW/0bEvycT9sUrBK2c5874LmFon5yVRti3bxI2+wMi35qIeclMNCkXij0eAFUl5OfP0GSmkH/7aBzN2+Gq1QBbu57oLp5Cf3gHqCrGbStQZRlrp37u4jVtizO6nrvPOzcLVJXg+Z+jTUvE0m0QzsjamNcswLB7nfv87StRNVqscb55CpZb78IZWdvdRx3nm8Bm9zbfBx5mpz/oHjpnaxu42b6iqEYz2WOmkN/zDm8LSElc0fVxRtZGk50OuO/Bzahcl6m9UcQytZXX9d7Hs2fP8P777/DRR5+VYamqnvL6PAYtm415zQLAncWec9+zZf4aJYn492Sk/BzSXv4WZBnDjtWE/vIZuXc8gKX3cO9xmtRLRLz/f9ibtiHriVd9riFZcol8ayJKSDjpf/1P8TVLl4uo41txLZiJJisNVacHpwOp4CtRMZjIu+vhgElwwb9+5U76i+2I7nwCckHrgbVDL3LG/V/A5EHjpj8I+e0b7M3akfXoP0F2d5lpks8T/uGzOGs3InfkRMI/ewFb6y7uyVwuOzdv0L0oJjMhv36Fo1EsmU+8iiY9iRqfvYjksJM3cDzBS3/A2uFWcsY/7VcGKS8byeXyGyamPX2E8C9eJr/HHeQNe9TvvBqfPI/20hnSXvoK1RTst78yfz+a//ieoHWLsDdvT9ZjL9/o4pSovGbGuyn76IWqaeHCn1m0aAH//Oe/bnRRqi1PjV6V5Qqv0cvZGWjSk7DFxnmDs71VJ1RJwnBoe2GgVxTvkqKBgrBqCsbaoRemHaswbl2ONUATr5x6ibDv34HkC8haHfm9h5HfewRIoDt5CP3xfRj2biR40dfYm7ZBiaxdeG5mqnup0ohaZD/4d1BVDIe2E7R6Psa9G7C164G9TVef15MsuZjj56AYzWSPm+IN8uCuddra98S4dyMh8z4peF++K7jZOvbBvGw2po2Lkaz5KOYQsu99BjRaXFH1yL7vWcJmvEnwUvcEXNauAwPeY9UcSqCanbN+M1StDv3pQ1w+HklOT0J34aS72T5AkK/srJ37oztxgLxB4290UW4Y0XQvVBkjRozmm29m0axZ9ek7q2y0iWdwhYbjqlkHTVrFBnqtp9m+ceGkUqo5FEfjVmjPHkPKyUCyWQid/T6mHatwRdTC1qZbwGvlDxiLYg4hePF3fhn4kiWXsO/eQpt8AXoNJv1vH5N3x4Oo5hDUoBDsbbuRO+IJckc8geR0ELLwfz6Z7UFrFyK5nOT1u8e9VKlWh619T7LvfQZVkjAvnw2X5cgErV2IbM0jv99Iv2xygPzbx6BKEtqUC7jCIrHHdPDZrxqDsMX1Rs7LRnI5yR4zxXdWtxYdyL3bXRN3RtbxZtKXmlaHo2EMmsSzSBbfLjZDwYx3tnY9Ap1Z6bmi6pI5+R3/+etvIiLQC4IAuBPFNFlpOGs3xhVRG9mah5Rfcc2x3kS8xr4zaNpbd0VSVYLW/0aNz17AcHAb9qZtyJg0rdhhUkpYJNljpiC5nITOno5UMHMeLiehsz9Am3rJvab4n57xCZhF2dr3xB5zS0Ht3j0apmht3nZZP7cruh7WzrejTb6Acdca73Y5Kw3TxiW4wiKLTSBzRdfzzhhn7dzfp8bvYbn1TlSdgbz+YwNOIWvtMZjssf9Hzr1PXdP8B47GrZBUFd3poz7bDQe2oMqydwIaoeoRgV4QBKCw2d5VpxGuiFoAFVqr1505gqrR4qznO5TO1to9dC5o/W9oky+Qf+tdZD368hWzpx2xceT1HYkmPYmQn//rTmL7fQb64/uxtexE3pD7Sy6QJLknbdHpCV78HVJ+DkFrFrhr87ffAxr/YJzffwyqTk/QirnuMe5A0Iq5SE4H+QPGlri6Xt6dD5HfdySWXncG3O+Kqkfq1O/cc6QXwxZ3m9/9Ky1HU//x9HJmKrpzCTiatEE1F798uFC5iUAvCAJQGOiddRrjKuiTLpd+elVFf2QXckbhlKuSzYL24in3ePnLaulKRC0cjWJRdXqyx/6fe973AEE2kPwB47A3aYXh4FbCZryJacsynLUbkjP+qYC15sspEbXI6z8WOTeLkJ8/w7ijoDZ/S+BpV5WwSCw970STnY5p01I0yecx7lyNM7q+Xxa837mh4eQNvq/k4WulfN/XwtEgBlXWoDtxEO3pIwStmEfoTPewVlvbwF0kQtUgkvEEQQBAm3gaAGedRmjSkwHQpF/bWHrt2QSctRuA3ui7w24jZP7nGPduQDEGkTPuKewtO7r74FXVr9neI+tPL4BLQTWXPPeDH42GnPFPE/6fv6M/tgclOIysh/7hP4VrCSy97sK4Zz2Gw+55HfJuH11iwM3vMwLjtniC1ixAf3wvkqqSN/i+cg3SZUJvwFm/Gbqzxwj/wp2drkoS9ubtsd0SeMpYoWoQgV4Qqhg5Kw1qln32s+bSGVStDldkHW8fr3wNTff6A1sJm/UeSnAYebePwdrldtDqkNOTCP3hPXSXTuOs3RBN6iVCZ75N/sBx3klniibiFVViLfcKlNAIsu97FnP8T+Te8SBKePHzuQek0ZIzcgI1Pn+poDZ/W4mHqyYz+f3uIXjJTPTH9+NoFOudua+ys3QfAk4HzoYx2Ju3w9G0TZXMtBd8iUAvCFWIcWs8IQu/hCFjoE+AKY+dDszLf8QecwuO5lcxRbDLiTbpHM7ajUCjwRUejSpJ11Sj90xUI9mshCz6iqANv2Hp0t89f74lF0vXAeQOfRRt0llCv/835uU/oRbUdh1FpngtS46mbcic+Po1n+9sGEPW41NRQiNKVTO3dB+MaeNiNFlp5A65/4YsDnQtbHG3YYsr+UFGqHpEH70gVBHai6cI/u0b93+W/RxwbnLzstkErf+N4IX/A0Xx218cTcpFJJcTZ8Fa32h1KGGRV52MJ+Vloz+2B0edxqQ99yn5Pe9AzkoleNlsJLuFnJETyB05EbQ6nPWakTH5HexN2yC5XDhrNajUtUdH0za4atYp3cE6PdkPPU/2+KdxFtNKIQgVRdToBaEKkKx5hM56353x3X8s5lU/EzLvUzKees/b36w/tJ2gDe5VvbRpieiO78NRsHDJlRRNxPNwRdRCf/KgO3u8hGzxogz7NiMpLmxxvVGDw8gb+iiWW+/CtG0FtjZd/cYyq8FhZD36MsZtKwofMqoJZ90mOOs2udHFEARRoxeESk9VCZ7/BZr0JPL7DHcPrxoyGk1GMuYlMwGQM5IJmfepOzO9YNU205alAS+nSTyLJtl3XnZPIp6rSLAtzLxP9jlWf2g74e/9H5rk837XNu5ZhypJ2Drc6t2mRNQib8j9xU9YotFg7TEYZzGJeKVhVxVSleLnpheEm5kI9IJQyRm3LMO4fzOOxi3JG3ive+OwB3DWboRp2wr0h7YTOns6sjWP3GGPYevUD0f95gVD2HyDtJyRTPhnL1Dj03/47PPW6GsXCfTesfS+/fTGbfFo0y4R/Nu3PjPGyelJ6M4ew9GsrbsvuwLNc2TwlPWcCPaCEIAI9IJQSUm5WZjWLiR48Xfuuc3HP12YCKbTkz12CqpGQ+gP/0Z3/jjWuN7eFc8sPYYgqSqmrcsLL6iqBC/8CslhR7ZbCZn3qbcfX3vpNK4aUaimwuz2gGPpHTb0Jw4AoD++z73iWgHj7vUAWK+QlV4eTip2HKgcUiwV/tqCUNmJQC8I18G0ZgGGPRvK7oKqiu74PkJmf0Dk2xMJXjoLZA3Z457ym6rVVacReQPGIakqzuh65Ix4wpvdbWvXA8UcgnH7KihYX96wfxOGY7uxN2+HrXUX9KcOYdr0B1JOJnJull8fuRKgRq8/eQjJ6cDWqjOqrCF48XfuoXGqimHPOlStDnsx88+Xp2TVPTzvmGKr8NcWhMpOJOMJwjWSM1IIXjYbVdbgiq4XMPFKf2g7mvRkLN0HgVZ3xWsatywnZNFXADhrNcDSdSC2uNuKzUa39B6GEhqBo1lb38lpdHqsnW8naO2vGPZvxt6qM8G/fYuq1ZEz/AlUYxC6M0cwL5vlfTi4PNC7IgsCfZEavf7oLvfr9robV0QtgjYuxrRxMY5m7dCmXsLavieqMeiK77MsuVSVFNXdZJ/gslboawtCVSACvSBcI/2x3QBIiouQuR+TMfkdn2CuS9hL6Kz3kBQF485V5Ix+suR5yFUV0+YlqFodmY9Pxdkw5srjr2UNto6Bp1a1dBuEad0iTJuXojtzFDk3y72eecEQsZwREwmb9R7mJd8Bvhn34J6kRgkKKZw0R1XRH92NYjDhaBSLs04jjHvWEbTqF+wXTwEUOzVseUpTnXgGEp5R7VhVBaMkGisFwUP8NQjCNfLUbm2tOqFNOoc5fo53nyb5AqGzPwBJxtquB9rEs9T47EWClv/onQXuctpzCWhTLrqHoTWKve5JVpTwaOwtO6I7fxzTtnictRq4V2wrYG/bDWtcb6SCfvqiiXgerohaaDKTQXGhSb2IJj0JR4sOoNGimoLJG3gvst2Kcd8mlKAQv+VVK0JSQW1eAyjASdF8Lwg+RKAXAHjbmsjb1mub1/ym5LCjP34AZ1Q9ssc9hSuiFqb1i9CePoyUn0PozLeRrfnkjPozOfc9S+ZjL6OERmJePZ8a//2nd2Wzojwzylk79iuzYlq6Fy6LmjNigl/3Qe7QR3GFRaKYQ7198kW5ImsjuVzIWWnoj7pbMGyxHb37rV1u9z4g2Nr3dK/PXsGSCvrnb9G4uwxEP70g+BKBXgDgkGIRGctXQXfqMJLDhj02Dgwmssf8BYDQeZ8SOut9tGmJ5PcZ4W1WdzRvT8bT72Nt1wPdxVOYNi/zvaDDhmHvRlxhkTiaty2zcjqat8favid5A8YFHKeumsxkPvkmGX9+A2T/rwNvP31akrcFw2cSHllDzqiJOBq0wNLzjjIr99VILhhS10vjzmM4poh+ekEoSgR6gXxVwYrq/qeWftrUm5n+6E4A7C3dtVtn41ZYbhuGJj0J/cmD2Fp3IW/QvT7nqAYTuSOeQDGaCVozH8mS591nOLgd2WbBGte7VMunlposk3PvM+T3H13sIUpohLff/nKuCPcQO+2l0+hOHcJRtwlKaLjPMc4GLch88k1cUfXKrNhXw1OjbyUbiZQ0JLhsqEXG9wvCzU4EeoEMtXCSkUzVdQNLUnXoj+5G0RtxNCqsJecNHIejcUscjVuSPXYKyDIz7KlMs17yHqMGhZDfdySyJY+gtQu92z3N9raOfSvsPZSGp0Zv3L4SyeXCXqTZvrJIVp3okKghaWghG8nC5c3CFwRBBHoByCgS3KtToJczkjFuX3lVi7uUhib1Etq0RBwt2vv2eWt1ZE54jcwJr4HBhKqqrHPmslexkFkk8Fh6DsEVGoFp0xLkrDTkzFR0J9zLmbqi6pZpWa+X4qnRp7inzLXHxt3I4gSUqDiIlrTIkkSM7J6TX/TTC0IhEegF0n1q9NWnJmSOn0vI/M/RH9xaptf19FUHrN1KkjdbPk11kVsw8Ou0Yi88Rmcgf8BYJIedoJXzMO5ei6Sq3lntKhMlpAZqwYI2SlAIzgbNb3CJfOWqLvJQqCW5H7hiZPdcAqKfXhAKiUAvVNsavfbsMQCC1izwmZP9enmyz+1XWBnudJFa5anLapjWjn1xRtfDuGMVxs1LUXV6bO26l1kZy4wk4YqIBgreb1nmD5SB5IIH02jZne3fRDagBRJEjV4QvESgF6plH72Un4M2zd03rrt4Ct3xfVd3vs2CcdsKwr5+zd3872G3ojt5EGedRn5T0l7ulFpYiz9VtEYPoNGQN/h+JFVFk5OJrU03VKOZysgz531lbLZPVtyJeJ4avU6SaCIbOK3YsIvEUkEAxMx4ApBeJLhnVJNArz13HHCP+TYc3UXQmgVktbjyZC7aCycwbluBYc8GZLu7+Vd/fD/acwnkDnsM/YkDSC6nz1jy4nhq9Dokzlwe6AF7q844GsWiO3MUa6e+V/HuKpatbXfk7PRKmYjnmSynllT4VdZCNpCg2Dih2GilMd2ooglCpSECvXBZjb569NHrCgK9tdsgJMWFPmEv2rMJOBv6r4kuWfMx7N2IcVs8uoKpXF01apLXezj2Fu0J/vUrTNtXok08611+tTRB77RiJwwN9WUdBxUrFlXBVHRqVkkie/zT7mFrzdqVwbsuH7a43tjiKn5q29LwDK2LlguTImNkI0vIJqGKBfrjLis/OTL4sz6KmrL4ahbKjvg0CaSrLsIlDfmqUm2a7rXnEgBwNGiOqh+BPmEvQWsXkP3gc95j5MxUglb9jHHvBiS7DVWWsbXugrXrQOwt2nv7ozMnvk7Igi8w7nEvw6qYzDgb+D8wFJWruod4dZBNNJD1HFSsnFHstNQYfY5TatSstEG0KvBMlhNdpEZfFRPybKrCf+zJJKpOdrnyGSSH3ugiCdWICPQ3OVVVyVCdNJIN6HGRSSUJ9IpC2Nev4azdkLyhjwY8JHjR12iSzpH12Cu+s7qpKrpzCbjCo1GDw3CYQ3E0aIHh0HY0yedxRbmT4MyLv0O2WXDViMLatz/WTv28NXYfegM5Y6fgrN8M85KZ2Nr1KFwXvhieLPvGsp4Gsr5gm80v0AvXJ0l1EIbGZxGbSElDuKThWMHEOdJ1rhlQEX5yZJBY0Jp2SQ28FoIgXCsR6G9yuSg4gXBJ481WVlQVuZy/HJ2qyoz0E/Q2RxFjDPPbr714Cv3Jg+jOHCG//xjUoBCf/VJeNsat8UiKC+35Ez5N8pq0RGRLLlZPn7wkkd9nBGE//BvzstlIDjv6hL0oBhM5o/7s7h+/Uja5JGG59S6st/RGNVw5WHv655vIBuoXBHq/hDzhurhUlVTVSbOCsfMekiQRIxvZ6sojRXUSLV15eeAb6YjLyhJnFlGSlhTVySWl8gb6FMXBXEcGD+sjCZYq1wgMoXgi6/4m50m+i5C01JC0KEAO5Z+tfDL9PMtNMvNSjgXcrz+8HQDJ5cKwf4vffsO+zUiKu+yGA5t99nma7Ys2r9tbdcYZXQ/Doe3oE/Zii4kj45npWLv0v6ohY6o5pFTryhet0deTdOiQfIbbCdcvTXXigoCBvEnBw9WFSl47tqsK/7WnADBFH00IcqWu0a925rDWlcsmZ96VDxYqDRHob3KeyXIiCpo7oWIS8lKy3V9u+8PDyXL496XqD+9E1WhQJQlDQd94Uca961ElCVVvwHBgq884eV2R/nkvWSbvjofcq82NfpLsP71wxeFx1+OUYsOARG1Jh0aSaCjrOafacYo52MtMcoCMe4+Igm0ZlTy59CdHBpdUB3dqw2ipMVJH1pGsOirt5+R8wUPIaVU8tFYloun+JucJ9OFFviwzVBf+K5OXrWR7PlADRZbZlZhAvwaFWedyZiq6S6ext+gALif6kweRM5JRwt0Tt8jpSejOHMXevB2KOQzj3g1oL57CWa8p4K7RqxoNzrpNfF7T3rKjdxGa8mRXFS6oDlrIBm8XSGNJzwlsXFDtNJIMxZ57zGVloyuXodoa5ZZ5vcGZiwbooQ0ul+tXFE/GfS3Zv0YfUfDQml6GyaW/OjL9Jj7qrDHT6xrvY4LLymJnFrUlLeN17oWC6kg6jmEjRXVSpxJ2OZwvaKkqrhvKqar85EinnzaEegWtKtXBGmcOwch01lbOuS6uRAT6m5yn6T5c0qDirkVUROZ9cpGkv81KPkUnf9Ufca8MZ2vZCfQG9CcPYtyzgfx+owAw7nbX8K239EY1mjDu3YDhwBZ3oHfY0V46jbN2Y9DdmC+ac4oDBWhUpO+4iWwAVw6nFLvPdg9FVfndmcVsRzoK7mA82RBNXMEa62VFUVW+tKcgAV01ZjRVIFGtOEkBMu49wsu4Rp+hOpnlSPfbvtdloafGfE05LfHObFTgCX0UhoJkwjqyDlzuhLw6VK5A71RVb7fCWcWOS1X9Pj+7XPkscmZxWrHzkjHwiohVjVNV+cKeQrikrbKBXjTd3+Qu76OHign0iTotsuIiJimRfZGRZNvyvfv0hwuWgG3VGVvbbqhaHYbd69zN86qKYc86VJ0ee5uu2FvcgqozoN+/GVQV7aXTSC7XDZ2T/ZTqScQrfNBoXCTz/nK5qot3bUn84EgnTNIwUlsDCwpv2RKZbU/HVYbNuGmqEysqFlROVPGcgWTVd1a8ojzdUGU1AdQxl/tejdTW4AtTQ74wNaS3Jpg8FE5ew31UVZX9ioUQZNrIhcmddQvey8VKmLiZqDq8j+cOVC4GyCXwDGncp1g4Wwnfw7XwvO9U1UlOFR1+LAL9DZDgsrK1kiSzFDbda6hRUX30ikKi2Ux0bh6986wosszOSwVJeXYr+pMHcNZuiBIehWo0Y2vVGW3KBbQXT6E9fwJt6iVsrbqgGoNAb8DWsiPatEQ0iWeL9M+XPM69PHkT8Yo00TeU9UhctrgN7sD/nPUCu5R82ssm3jXW5159BNOM9aglaVnozORftkvkl9F0rueKfDnvUywBjzmp2FjrzCmT1ytPSaoTLYVBvSgzMjqkMgv0CQUBrJ3GRLikJVzS0rGgtWV/MfexJJdUB2mqi7Yak09rQO2CboiKSsg74bKxrpS/a0+zfVRBhSDQQ2vRNQYWO7LKoIT+zip2ljuyUSsoj+F8kb/ZQDNcVgUi0N8A3zjSmG5PwloJ5uLOUF1ogWBkauAJ9OX71OrISiEtJJTaNhuda7r70bdQMN1swj4kp8PdbF/AdsttABh2r8OwZ53PNgB7W/diMIYDWwJm3Fe004oNGWhQpO/YKMnUkXScVuzeLyibqvC+LYk01clYXTgvGmoTVhC0msgG3jHWp7MmiCOKtdRfxldS9EtrvytwgPrclsKn9hSfYyujZMVBtKQL2GwuSRIRksZnZcbrcUyxIQHNi3S7tC2Yda+4+1iSfQXntLts5r7aBTX6RKVikgi/d6TxiT2lVK0SnofE2zTunITL++mdqruVqIGko7akZYMrl6wy/i5RVZVPbcl85UjlaAW1SBV9OL48R6OqEIG+gqmqygXFjgIBm74qWobqJFzSIkkSYZIGifIP9GnpiQDUUiQiajWmZdIl9taMJMeah/7IDsDdbO9hj7kFxRSMYd9GjPs2oQSFYI8pnLfeFhvnbt4/sAXdueMoJjOumjemf1BRVc4odupLevSS759XY1lPPgopBcHnR0c6SaqTu7VhjNaF+wWsIEnmAZ17Ap+yWl/dE7xDkDmmWP0eNi8pDk4XLMaz2VU5Wp0CyVcVclACZtx7hEtaMlUXynXW/DwBrKGk95mYJ1TS0FjSc0SxYrvKh3ZPK0B72TfQGyWZSEnDRbViHrISC76DSlP79nx2bi1IPry8Rn9WtWNHJVZj5E5tGA5Uljuzy7S8hxWrd7GoLa7cMr12cYo+8F7eIldViEBfwTJxYS1IeivPGtM2Z94Vm+AVVSVTdXkzlDWSRCiacg/0KXkZAERrDSBJ9LQ4UWQNuy4ewXBkF0pwGM76RfrYtTps7XugyclEzs3C1r4naIp8wRtM2GNuQZt8Hk16krs2X4rkKLuqsMaZU6ZDmRJVBzZUb598UU0KaoOnFDtHXFb+cGZTR9IxriDjOpA6kg5zQVAuC+dVB1qgrzYEF+4vzqI2F/ny3OIsvy/SXa786/r8JweY4/5y7gTT639wPaPYcaASo/FPomynMeEEjl7F78elqhx0WaklaQOWv46kI011XfXDw9Wyq4p3VMImVy7pV2hFOK/YMSFRv6DGXrR1CtwjRsA9BXFfbQhmZJY5ssp0FcHFTvcDiRbY4sq77oe40jiv2DEiYazCc2GIQF/Bis56db6cZsC6oNh5z57EAkdmicdl4ULBd2hdDUlT7mOPkx3uxLsok3s+7y5R7mFxW+xZ7kAe29F3SlvcGfbenwPMDW9r28P7c2n759e5cvnMnsJSZ9n1JRadKOdynm1HFat3kpQn9VF+Nf+i3LO8GUhWndedO6GqKucVO3UlPbd4+pcva3be4sxDA7SWjZxTHeXyMHpasfG2LZHnrReIv8a+Vk/GfUk1+sKx9NcX6D0PWS1k/xkRPU3v+66i+f6kYiMfxa/Z3sPTT59Yzi1+npYlMzIuYFkJtW+n6k6+qy/rkSSJxrKBXBTSitxbT6tTjGzAKMn014aQjcKGMqp5JyoOdrjyaSYb6KUJJl11+eQElAfP+24g62kk67mgOqrk8sci0FewTrdG+gAAIABJREFUos3158qpeS614A849QqBoTDjvjCZqYakwYJ63bUJ7ekjRE57HO3ZBL99SQUz70WFRAEQGdWAlslJ7K7fgCxTEPZWnfzOcTaKxVmrAc7aDQP2v9tbdUQtqOWXtn/eE8TKsona04fXOMAQOk+Nfokzi0uqg7u0YcSWYu77FgXHJLiu70stTXW3JtWXdcTKBnRIPoE+saDZvr1sor/WPeXwlnJovt9ckIiqoPI/Ryr/sSdjucrPm3fVuhLGmhdm3l/fA5In0McE+J22lI1oubp++uKa7T08mfflPRWuZ4nfwdpQQpCJd2YX+3fvyTyvV1C2xt5pnQs/kwmKFTOyN89giDYMGXe3QFkkzi1xZqECd2nD6F7QfbC5nJvvi77vJrIBBarkaAIR6CtYok+Nvnw+MJ4EmOwr1GQ8TXWX1+iLXuNaBW34HTk3C8Ne/1ntkvTu16ulL/yi8zTfr2/VHnvz9v4XlCT2TZzKvgmvBGyWV41m7K06oer0vjPilcDzRZqg2Egto+Snkmr0oZKGCEmDAldssi/KE2Cut/biebCsL7vzB1rKRs6odm9LgedLs7s2mE4aMzokNpei+d6lqux25ZeqGVVVVba48jAg8Z6xPjGygY2uPP5hvcC5Ev4eUhQHK53Z3n97CwJrrRImFfLU6K930pwExUYIcsAJbIySTKxs5LRq9/t7y1SdAZPc9rssSECbYmr0dSoo8z654PPfQNYzUBtKLgpri/l9e76rPAs0eR5kPfkcGS4HSaqTmCKTRNWUtfTQmDmnOq5pZEJReaqL1c4cIiUN3TVm2ssmzMhsDdB8b1cVdjjLplm/6Pv2DpG9hgpavqpw4BqSNsuKCPQVzFOjry/pSFad5dIP5wnSV+qbLDpZjkeNMhh/LOVle5Pq9CcO+O7MzeFSSChmux1zkY9f51rNAFjatRcY/L8ArarCVDWDl9T0YoNyzj1Pkv7U+34L4BSn6BdpWSX2nFXt1JS0xS740UI2IgGTikySciXNC8653n56z5dW/YKA5Wk6PlDQt+pptu+iCSJIkumgMf0/e3ceH1V5L37885wzS/aVSQj7IgZkU8RWxKVS0bpUbd24Vbz0treL1lt/tUVEK1qL1l7vrbu0danFBYqVam+1uF24bY1SFAFRFpGdkEwgezKZ5ZzfHzNnMklmkplkhmTC9/168dKZzPLkZOZ8z/M83+f7sN/0cbCHC9K1gUbuazvMu3H0/veaXipNH6foWQzXHNzlHMYltnwqTR/3xlhG2GAGuM1zkF97a8L/thit2Ii+ht6SjB59nemn2vQH/24x8j6m6pmYwNaIE7nHNPip5xC3eQ6yLeDpcP92w8NYzUFujM9I2THu0ZcqG1+x52Ej2GuOFiCt0rfWBk1jO/XoPwnVwZjQaYTqYltww6q+JuW97W+kDZOv2PKxKYVNKWbqWRwxA3zW6WLqSW8Nv/RWJWXKIPL3Dl/c9KKD9idfHT9rq2RXH0fleksC/TFWafjIRuOk0MkhFZn3VqDvqVdunQCLOvTo+140x7npH6hAAFPTsFXtRzW25wqYVfuoLCxiaJu3w4lzSNEwTvGabHW5on4Z1vkbacKglWCVqmhDgWZGFkbx0Lja6DdNqkNlRhXJGb73mga1ZqDbeeNvOYq5L2N4QtvVZimNEcrOLqOtT8Vz9nfqlU2LWB522PCx2/QyVcsMX6ScrgergPV0bKwphU/i6LW9Fxq2nxV6bZtSXO8o5gpbAUfMAM/5jnR5ztPeGhow+Iotjx84XOF/P3MO65AF31kyiuZYhXImREnEs0wNDcFH9lqtFRUm8ITXHb6g32Z48Ec8J5oSZUcj9T36qoiExgJlY7aewyHTx0dR/o7hnq0KfnYKlY189PC6civQd57eGK85GabsbAy09no5sd80ed1fjxMVnlKC9hLOkZ/PDwMtrA0F+GRsvBP5e49UDnR6t8TOes7OJCXVJiqlgf7ee+/lmmuuYd68eWzevDl8f1VVFfPnzw//+9KXvsSf//xnjhw5wre//W3mz5/PvHnz2LRpEwDz58/niiuuCD/+448/jvWWA5phmhw2fQzV7OFeVXfDlRa/abIz4Il7KMoK8J4e5tqPxpijh65Fc1pMI+5qXRkf/C8BTWfjV67BQOH4fGv4Z7VVh2izOyg1uvaOLs4NLon7S6fkOMM0ec3fgA2YpGWwyWjlfwN9W1debQbL1JZrTiZrGUkZvreO55BuAn2BsjEuylxvT07UMmjDZF8f8joOmj502nvBY5SDHDQ2Gy3hufjTI0p8ztSzg9nNPZwwwyexHnorpmlSEWjGgepS2vcKeyGjlIO3/I1sDrRXSXzP38S7gWZO1JwssBdzti03/O+EHi6W2ofue/933Wm0Z5LHMk5zkoUWnqf/JNDK6/4Ghis759vyqDR9rPQFV5psibF+PpJNKUqULeU9+mrDTyaK3FAYuNge7H1HW2q3P5R5XhxxrhijOXCbfprMAJ+0NYfqDHQ8TkopTtez8WHyYcTfNRHvB5o5YgY415bbYaRsmpZJFhrvBZowTJNmM8BvvG50oFjpbDJaaO7jtE3k721XihHKwT7Dm/C0gDVt1l/L81IW6NevX8/evXtZuXIlS5cuZenSpeGflZaWsnz5cpYvX84zzzxDWVkZc+bM4dVXX+Wyyy5j+fLl/OhHP+Khhx4KP+e+++4LP2fKlCmpanZKuUPbapYpe7hXFU/m/Rp/A7e3HeKXbVVxlWCM7Ml316uvNbvO0bfvYNfxeb/xuvmJ52CP769X7sF+aDfPfO1abjn1ZN6eejL2XVvCPz9UH+yxldiiLFXSMhmp7FQEmjgSEXQ/DLRQafo4S8/lJkcJmSie9R7pU2C2ektlmp3TQwVA+jp8b2Uxu7oJ9L11Yiio7Qj0rkfQnnFvxxYaSdGUYoqeyREzwOv+enSC9e8tweH7LPaZ3pgXeX7TDJ/E9pnebpPq9oWG7WfoWV164jaluMHpQgOWeWtoNQ0azABPeY9gR3GDw5VwPfkMpZHZx+p40QrldKYrxWQ9gyrTzz7DyxOhvQS+73Ax317EUGXjL/56tgc8bAm0YkcxsZsLBwieIxowaErRUlfTNKkygwWHrJG1MZqTKVoGW4xW9kb0WgOdMu4tVnLpLqONbd5WRig7WVFGWGbZ4hsZiuWdULGoi0LTAJbOw/e/9x7lqBngCnshc215BIANvby4gOi/9xjNQRtmQqMtLRHLGPtr17+UBfqKigrOO+88AMaPH099fT1NTV1PpKtXr+aCCy4gOzubb37zm3z1q18FoLKyktLS0lQ1r19YH45hmj0813Ugjh6aNTf7odHCQs+BHk/28Qb6o2aADBSZEV/OaHP0VrKVD7PHYauMD9ayY+gwXpx0EgAV5VM6zNNXeoKfAVdWfpfnKqW42J5PADosebN6+Bfb8xmi2bjeUUwrwc1ZepvNa/WWypSdL9qyUfQ9w9wd+vumYte5CaGTam8L5xwxA7Rihj93Fmv4vtYMMCVi2N4yq4fh+4OmF+tyy4Ru6+db2fan69E3BhmnObncVkCN6ec53xGe8dZQT4Br7IUM6+VOaEXK1usefSBc6c3R4TsSjTUU/59th8NFkE7UM3Aqje87gqtLHvFWs8f0MlHL6HZJJbQn5B1OUa++AYM2zC7JjNF69Vbm+YhO+RBWctrf/E14TCPq8kOAUcpBmbKzMdCS8PC9YZrsMDwMV/bwssNI1kXEs74j/G+gkTHKweW2gvZppz4M30f7vcdE1MKIV2TS9T6jf7aqTlmgr6mpobCwPau4qKgIt9vd5XGrVq3iyiuvDN92u91cccUVPPHEE9x8883h+x9++GGuvfZa7rzzTjye/pnn6KvI4JKvdHLR4sq832MEE9euthdy1AywpO0Qf/bVxQxy9cTfoy/s1PuMNke/y2ijNVTkp9sPeMCPvvkf3H/5PAylcKL4cOwJqKPVaLXVABwygq9bkhE9Ye5MPYd8dN7yN+IxDfYYbWw1PEzVMhkVOrHM0XOZpmXykdEano9L1KGIHn2+0jlJy2BHHMP3Rww/vhjH3XquKwXbiw4LFc7p7RyfdUE5slPAjJwrnhVlZ66ehu+toUhrqVishEEr296BCteIj+YKeyEjlZ03/Y38I9DMBM3JJbauF4XxKlQ6jRgx/2bd2Ruq9BatUE5n1gVTVSjvI3JFxSQ9kwtteVSHLji6G7a3DAvNhXfO4Tli+LvN06g3A3Gt864yoi9PPEXLokzZ+XugKTx9Z406dr5ItAK9dREY6zgppZilZ+PtxfD9PtNLG2bMqZNpWhaZKHYabejADU4XNqUYpjkYrRxsNlp6vU9EtN/bSkJMpHCO9d1zovDTPxVRj9k2tdGC0saNGxk3bhw5Oe37ObtcLv74xz+ybt06brvtNp5++mmuv/56ysvLGTVqFEuWLOH555/nW9/6Vsz3KizMwmaLntHaFy5XfNncsdQdrQcfnFSUj8uZxdjDmWxpayavOBunFv2aq8UIcHi/j5OdOXx/6ChO9xRxj3svy31HmViQxxlZeR0eb5omDfvaP9hGjh1Xbtd2+0yDhn0G4xyZHX4v0zRx7lc028zw/X+ta4HQ57rSbsQ+Dhsr+N0pM/ncVcrFOUX4TJM3MPm8dCgnVH8GY0dSaQ+eWCYVF+CyRz8xXF7XyrP1VWzI9LE9lOTzjSFDcWW2v+9ifwb/dmg7T/lqyM5xclFOUcys6GiOVFWDH6a4isjQNOY2FrP16EG2Zvq5Mq/rsjfDNHmxoZqn6w5zUU4RtxSP7PKYxppa8EN5cX7M360vJldls97TiL0oGCgS+TzWNrRCG5yUn4cru/15LmDYwSqq/F6+UlJKvt71lHBadR4VrQ14CxwM7/R7VR1tAC9cWVzKZvce9tj8Udv1ubeVQ5U+zs7KZ6Sr+8B9e9sYbji8Ex3FHUPHUGqPP3Gxs7KaTD5u9qAVZeCyRR8ViHUc/97YBh44Nb8AV073x3qIaVJysAp3wMfiktEMz+h40XSTkc1Hlds55PdydnExLmf32w9PbAWqa6jP1HAVhGoatDZwe/U+TnRkscQ1mqERv49pmqxurOGJ2kpOdGTy8NATut2CeFOzH9pgfF5Ol/PDNY0lPHj0IH93tPHNgkKO1rWAFyYX5nf4DhabJhn7D4V76acPKcYV4291odfGy5V1bLS1cZkr/vLUFeG/QX7U8xjAmTUFvNlcy/z8Uk4rGBK+/8t1RTxdf5jtWQHOz0n8YjHa732qkQX7KzlkC8T9/asJfUfOzg62syZbcWo3n6e+xploUhboS0pKqKmpCd+urq7G5XJ1eMzatWuZNau9otn69espLy8nPz+fc845h4ULFwIwd+7c8GPmzJnDa6+91u1719b2fl4mFpcrF7e7bwlgn3uC7cqo9+FWjQwN6GwGNruPRi2wArAtECyYOyyg43Y3MgL4rn0Iv2g7zJa6eiY0d7xAaDID+DFxoPBicqCxGben6wnO6n3m+FWX3ysfHbfPG77/PU8dCnCg2N7aHPM4uD/8B8+dO4chBlwVyGNDoJk3gA/HnsCIjzbQUjCSyoJCNNNEq23DraKPDsw2nbyA4oXaKmpNf7BYRSO4m9rfVwNudpTwUFs1Dxw9wPr6Or7tGNJtFnakfW2tFCudxiPNNAInmcE6/2/VH+Gcto4nqwYzwKNt1eFs5E9amnAbXY/BAU/w56qb360vRgdsrAfec9fwlaFDE/o8bmsLjnzkNQVwt3R83g36EJp0A+/RVrqOucFkv4MKYK27hvPtHS8sP/E0oYAxTcHchK2tzVRXN3S56HrNG9zLfYbf2WO7i4DFzqHYUWTW+XDT+x5QpjcYhHbV1KNHSd7r7nu9sS04fD202cTd2vOx/oHNRZMeoLTRwN3Y9fELbSV8qnkorPfjVt2/Xlbo+7mrqQm3rzG4lbHnAAawzdvCtw9u50aHi5m2bFpMg2Ved3jq6RNvC09XHuBye0HM1//MF3z/7OYAbk/Htswwg6NHq+vdnO/NZLs3+PO8Bn+H7yDAaGVnu9lGjqaTUevFraL/rfJMkzJlp6Klgf3V9XF/Tz+0/gYtdGmn5etGLqPsGl/2ZnX4W04zguHtjbojnNKa+Cjb9rbov3eJsrHd0xL1cx7NTk/w7zLT7+RNYEt9AzNitKevcSbWRULKhu5nz57NmjVrANi6dSslJSUdeu4AW7ZsYeLEieHbb7zxBqtXrwZg+/btlJWVYZomCxYsoKEhuA7z/fffZ8KE/tuZrC8qTR8F6OH5vngy760hosi9za2s7miV76yhemuINtbQ/dHw0rquIx8FSqc+tBmIte53nOZknObkkOmLOs8WaKrjv6dOJqDrfCdzKFlKY0poOHfDCZOw79qC7j7IocJihnh94YSwaAqUjTP1HGpCyYsX2fKjJmKdrGdxf8ZwTtCc/F+gidt6KLpi8ZjB0p3W8Kj1nidpGWw32vgs4KHK8FFl+NgUCOZFfGS0crKWSWkoGzraCJXb9FMYys5NBWvp0o5erMU9YHrRad8drcPr6hndDqeHl+F1WnZlmiZ7DC9lyk6G0jhRc9KIweFOn0tr2N7ew7B9x/fMYlIcQ9w96Uvm/Y5QpbdohXKimahnMDPK9IdlmObgy7a8uIJDkdKxo8LTfb/3HqHWDOYrfM8xBC8mv/RW8Ruvm1s9B3gv0MwkLYMHMkaQj84ffEe7nRZsH7rv2tfLUBpzbXk0YvC3QBMHDR9OFMVRHmt1UCY5srpNlowcvt+YwPD9DqMtXF8/lmLNxgX2/C7nlGGag1HKwaZA1+H7VtPoMb/nQIzfe4zmoBEj7iTPA6aXYqUzKVQPoz/q5acs0M+YMYPJkyczb948fv7zn7NkyRJefvll3nzzzfBj3G43xcXF4ds33HAD7777Ltdeey133HEHd911F0oprr76ahYsWMC1117L4cOHufbaa1PV7JTxmSZu0x9OsoH2uZ/uMu/bK6219/itQH/U6PpBswL7KBVfoO88Rw/BQB8AmjDYZngIEJzLHaM5MIleAvL/9m1mV+kw5tbUheuoF2k2Rig7m0eOJtDSiPp0AzV5+ZSaPZ/orKSgHDTOtuXEfFyJZudnzmFcZMvjoOnjTs+hHhN+rBriQzslIlkJPIvbDnGTZz83efaztO0wtWaAf7EXssg5lNGag1bMDnkQEBzaP2L6u11a11cTelk4J1rGfSJKlQ2XsvFxoLXDsiK36acFIzxXa82jdm7fHtPLQdPHKXpm3D25ZLFWkSRaHa/G8Hep9HYsaUpRpuxUmr7w2vCxysFltgLm2PK4N2MYZcrOW/5Gqkw/l9sKuNNZxijNwXccQ/ADj3vdMefzq00/itj5JF+x5aED/+Or56DpZYTmiHocrA7I5B6mIqB96Wa82feNZoBK08cELaPXf4PTbdn4gQ2h9zRMkz94j7KgdU+3RXyCGffRf+9wQl4cGfQtoU7FiNDOh0M7bVV9rKR0jv7HP/5xh9uRvXeAP//5zx1uFxUV8Zvf/KbL61x00UVcdNFFyW/gMVRl+jChQ+9gRCjod1fzfo/Zhh0Vrn8NkBnaSelIlF5KXbhHb4dA7EAfrSqeJTIhL3LfbGsEYY/hDS/3AlCNteEkmyuGjO/wWlP1TF43fXwyfBSFB7YDF1Fi63nO1TphDVG2HivI2ZRigWMIBvBXfwOfGW3hvcKjiUyKjHSOLZdK09fh6l9HcY4tN1zgJjgK0MIhw0dBxHx2rRkgQPdr6PsqS2kMV3Y+S7BwjpVxP7yXmetKKaZqmbwTaGS34WW83rFCmHXimxBRqvcc2ocQXwtlcJ9rS/7cY0/aN7ZJrEe/JrTS44vd9NBTrUyzsy/g5bG26g6JZgCjNSe/yBjOn311TNQzmBYxUnKaLZszAzn8PdDEX/z1XBplCL/K9AVHDWIE0CLNxiw9J1xdLlaPeraeQ7XNz+W5Q/Ae7b5g0uiI7Ps20+jxe22VfJ7Qi7oTlll6Nn/w1fJeoJlpeiYPt1XzcehC9FPDwwVEn7uvMn34if57tyfkeTk1xgoSS7gaZbh0sIOKQDNu09/tPg3JJpXxjpFDRnuWtyUfnRw0Dsbo0ftNk32Gl1Gao0NPTKngcFK0QG/V2y5SNnLQegz0RTF69BAsmmOt+y3XnO0f8E5Xso43VrBp5GhGeLwUdVo2Z2V1fzhuApV5wZ+5YmTcd3aeLS88OhCPKT1kflvalzl2DHwZSmOBYwg3OEvC/77rdHWoYmeNAnTeWSyVa+gjWYVzdvvi79XHyrhPxNQow/e7O00rjQltlBO5/LPO9POPQBPDlJ1TtPj/lsnSXXW8WL0qj2nwlr+RPDTO1GOPJqWadSHaiMEV9kJGdwp4mUrjakdRhyBv+aajmHx0Vvpqu5Qw9pkmR81At+WDAS6xt3+XY312nEpjnqMoahJnZ1bxnLY4h+/D294mUEWys+Gag5HKzkeBFha2HuRjw8OpehZOVLcjqQci9gHobKyyltj13KM/EN5fInisx/ahjG5fSKA/RsIFWiK+XEopRmoODsfY+tBaozxadf2wFSsbTRhdKt9ZPfoCpZMfmmuPpn3oPvocPQT34d4bse53RLgEZPuH1Lb/M/ZUfY7H4WRKdnGX1zpJz0QDNkyYRGVBEQClfQg43bHKlPY0hx2rRx+P8LKnTicJa7QjlT16aF/C9HFb/OuDO9e4740pEeVyLbs79ehtSjFec7LX9IanT9b4GvATO88i1QrDPfqO34M9RhvXt+7hr01Huzxnnb+RZgzOt+X1uN49laxOgbU2PBG5SuffHUPwYfK0t2NZYXdodDHa/HykcZqTSaHpmM5L63orkeI5yejRB98zBz/QSIDr7UUsdJQyQnNwyPTGHBmL3ACqs8LQ0uhdRluPFfLCFwyqvUcPx36eXgL9MWIFl2Gdij4M1+wxa95bV31jo3zYimLMPVqBPS8U6BsxohZo6G7o3rrv76GdrKzenE0pRoZKQAZME0yTnP95hg/GTQg9rmvPIktpTNCcbC8tY+fQ4UD3e4j3RWFoLnmn4el2DqwyVAq2N73vWDuLWYHelYJiOZGsEZJ/tHQtUxpLd72TeOUrndHKwTbDE74o3Wu2URj6nFkmaM5w4RyvafCGv6HHPItUsqtgidfOyXjv+ptpw+ThowdxR1y0RZZb7rzC4Fg7Tc9ijp7Lzc6SXuVWfMGWzaRQpbvISpPWev7SKAVoOvs3RzHn2/KY0kMlv3iNVg6GKhsfhobvYzFMk89ChXJibRAVrwtseZxvy+Nu5zAusRegVDC5z097vf/Ours4VkpxaqgiX7R9AaK9znDros3aDChFW5THIoH+GKk0fSi67rZlXelFG0bqbm9zKxO08/C9lSRWEHECbqRrr77W9JODFrXHUhBK3bA+jJFFVcZoDnwES0M6N/0d+74dbJh8Srfbbk7VMjGUYu1Jwe1nS+I4wfTWhFDmd1U3c7KVRrD0Z29OnnloZKF1qUNuVcVLRbGcSC7Nzgmakw89TT1uQ2zZ303GfSKm6pn4MNlmeGgwAxwxA4zpNNoUmZD3f4EmGjE4z5Z3zJPwIhVGqY5nTUG0mga/9taELwwjyy0XpHh0pic5Sud7TlevqwJCe2XD9yN60FbGfU9D9xDMBfi2Y0jSRjaCw/c5PQ7fHzB9tGL2uTcPwdGNbzuGdJgC6KkEuZVxH2uE7qJu9gWItN/0UqR0skPn4gJlo1Dp0qNPd4Zpco+nksfbqjv0KisNHy5l65L80p553/UKb6/hRQGjo3zRi7UYgd4MoAPZaOFAH234/qgZiNqbh/ahe4Acr5cT9n8Ood8lvFWjt4ns15+jKSuHbUXFnKA5o9a5hvYRgVank6yITTRSIVbmt6XJDNCI0athewieqMo0O1Wmv8OwnVWXINVD9xBcHWAA/4xj+NPKuC/r5YVNpMjd7tqXfXY8EVsn5u2BNv7iC9bP/4qtf3vGhUqnFTM8ndBkBvjcaKNcc/LFjFw2G628E9okKbLc8mBweqi8c0VEBUnrIrinoftUsYbvuys5bX1/+zI/3x3rvBstETqyxn2s6aZY+wJEisy47/Bc5eCIGYhr35JkkUCfZB8EWtgSKs36j9AHucU0qCMQNbiMDPVuO9e877xGubOYPXozQJ7SUUqFA33nDWoazQAtGDF7n8UHPg///4xd2xny6zspfPBHZP/5Gaa8HaxzcPj919AbjrL+wmswVPfbbk7QMnAS/MKU2Z0JVbBLVHiteYwvX2WUpMhElSk7PswOdQzcpp9stJgXO8lkLQPsaVc5CI4ktWJGvVhM1EQtA51gb7g9477j6xZpNoYoGxuNFg6aPs7QcyhK8XRGT4o6zdN/EipCNU3P4pbiEWSi+L33CBv8zV3KLae7AmVjYqg+xNHQxWh1aPQpnqH7VBijHJQqGx8EWmKW6w0n4iVpyqAza0g+Wgdrn+nFhxk1NypSd7v9AeEkyBGdjnNf9rXvLQn0SWb1COwonvbWUGf6wxna0YJLPjrZaOzvMhTspzlijXJn3QX6AoIB3gr0nYd4K0PJaqN2b4v62nnr/kR+c7AHMGlYOZ5ps9GPVJL17mtMrHgLgJ3DRtI6cw4bJp8CtPf2orEpxUmhL+ywKLvWJVO0zO9I0ZIiE9V5nt4M1Ug4Fr15CE59lDsy2WK09tgrsC54knHCzFAa5VoGuw1vOCkv2rTSiaF5ehgYPeP2zPvgd2VzaNh+qpaJy+bgX0ObJD3grQI6ZpsPBtbwvdWDrjL8OFHk9dPpP1g8xxq+jz7HvTOOQjl9MUTZYmbeW9st97THQbR9ASIdMLvfI6A3+9r3lgT6HjSYARZ5DjDvwCfc2LqPG1v38R+t+8KJapF2G218YniYrmUy315EEwa/9da0L62LkdhhZd5Hrt+OVignkrUv9JGIojkeM7gjlRXgrYDfuUdawZgZAAAgAElEQVTvPrgTgLEfb8Cx7YMOP9Or9uP85J8UeoPvP7l0PI3/cjNHbvsNtd/7OZ5bHqFU2fhs+Ggav/49tuDFiYq5c5XFGr4vs6e2p2RTinGao0Pmd6RoyxwTZf0drdGBptBOYKlOxIt0TlZBXMP3O+LYTz0RU/VMTGCT0UomKurwr/VZmKRlMC4Jc6x91V4dL/g92BJoJQMV3nr2XD2X6VomBjBc2ZnezehUOorcndE0TapNH6UR29P2h/biOV3Po01mgIOmjxP6UCinJ5pSocx7X5fM+3i/M5pSXGzLxw+84etafMeq0jmy08hAfyyxk0DfC0fMAMu87i5bSFpDOBfb8znflsckLYN/Blr4s68OiB1cTg6dPJ/3tS+D6bxGubMsNJyoDklGkRn3kf/tPEfvPrwbgOG1R8l55Snwtvd+s9a9AsAc08mX9JxwhryZnYd/dDlGXiFjNSdNGOw02jhg+pikZfRY9nW2LYdJWgbnRNmeNtkmaBkxt0xNRo9+WKcevfsYLa2L9KXQcexpG86dgWDBpWQM3UPnxMzoVeO+qGdzoubkG/aipLxnX0X26GsMP5Wmj5O0zHDOglKK7zpcTNQyuNZe1C/LAFOpMDx872Gf6aUVk5J+nk4Z283w/WdJWlbXk+GhKbjOmfdW6eNhcZwjzrblkI3GGn9Dl98j1tB9ibKRiTqmCXkS6HuQp3R+kTGCFSNO4rHMUTyWOYobHS68mDzhdYcTso4awcIgVo9AU4rvO1w4UOHs9VgfnK/aCsJbc1pDonvM7nv00Yrm1EesoY/8b2Sgtx3azSEt2OYhE2ag17nJfusPAGi1bpyb/o6/ZARfGTaZG5wlUa/6raGn/wlNU8Sz7WahsnF3xjBOcqa+0lj7PH3X4fvDhg8HKmqN/3gN7dSjP5aJeJYyu5NxmoOPuxm+bzUN9plexmvOPifiWcZrTjJD+RaxppWGaDZ+njGc8hQlUiUqsgyulW3feappiGbjZxnDuq1Vn85O17MxgVdDnZF4Mu5TKbJ4zkedhu+TUSgnHtEy7xvMAIdNPyfEWfq4874AkfabPgojMu4tmlKM1pwcNH3dLjFMJgn0vTBLz+YLehafGh7WhOolr/E3ECDYm7eC41DNHu7V2IgdCOxKcYOjBA1Y5nXTGtqHvfMa5c6KlY1GjPCVpBXQ87vp0Wf+/X84UDwEh2GSMfsSAkWlZP7jL9gO7Sbz739GGQFazrkcYmybC+0XH9aSnXgC/bFkDbnt7HTFbIayacuUvU+9tiylkY/epUef6qp4nZ2u5xAg9vD9LqMNk/YLn2TQlQovo+yccT9QRZbB3RJR0vl48kU9OHz/j1AwSlUti0TMCtVWiBy+95oGG0MXY6nu0Y+Ikgi9M9Q5SOS9rX0BXvXVh8+1raZBjenvknFv6W7fkFSQQN8LSim+7RhCDhov+I6yz/Dypr+BXDTO7lQy8yu2PL6oZ3OGntNtcBmvO7nMVoDb9LPM6466Rrmz4oieCrSvobcCfYYKDu9bHz6t4SiOzf/gQLGLMt2J5sig8fJ/RxkGuS89RuY/3yZQMIS26bO7fV+rXSbBdeWjemjnsVak2ShWOjsCHQvn1JoB2jD7ND9vGabZqTb9+E0zYg39sT15WklWsYbvkz0/bznPlsdwZWdamsxl56vgFsRHzQBbAq0UoKcsyWugKtJslGsZWP3HVNayiNdY5aAkYvj+kOHlds8hdhltTNcyye1joZyeRKthsiOciBf/d6ZIszHXlkel6WOh5wCfBFrDw/YjYxznL+rZDFW2cGcs1STQ91KBsvFvjiG0YbLEc4gmDOZGKZmpKcUtzlJ+4Czp8TWvtBcyUtnD5SF76jFZPRVrmVfnHn2wnXr4AiCj4q8czczCY3eEg51vwnQ8J5+FrXIvyuel5axLoYe61YVKJz+U6DdFzxyQc5onahk0dCqcY/XA+1o4BoJz/CbBylrh8rfHeN6zVLMzVgWH75uiDN9bJ60JPWQPJ2qGnsWvMkf2+7K5eOlKUaB0PjM81BNgqp7Zr4lo/eX0iA1YBkKP3hq+92DyO98RFnkOstf0MteWy0+cpSl//yHKhgPVYWvr3vToARbYi7nOXkS9GeDutkqe9wVLK8cqHTxZz+ThzFHHbImjBPo+mK1nc5qeRTMGNuArfSyZGTmED7HnQC1WYDnaTaDPVzoNZgCjzUPm+2+yb9hooGMyWtPF/4qRmYORnYdn5pwe26mUCretu2V1/Sla4RwrOa9zGeLeGKq1z9PXmH7sqPDFz7E0yxYcvl/fafjeNE12Gh5cyhZ1K+LjTaHSsS75jrdhe0tkoD/Wo0+xzAqNgL7lDxYs+g9HCf/ucB2TPQY0pRiu7BwyfRimGSq728ZwZe8yrx7Pa11qL+Bu5zCKlM7W0Hkn1tD9sSaBvg+UUvy7YwjFSud8W15SSmaO151cbS8kF63DrmnRFHVaS18XJdDnhfaW92/5B1prE7tnnAl0XAFg5uRTe8O91H7v5+CI70r2C3o2hUpnRgK7yx1LkVumWntQP+87io1g4Ze+shIrD5k+3EZwDX1/9BLPCM29vuVv7DBNUWX6acRI+Txnuoi82EmXKYdkC249m82U0CZVA8E4zcEkLYPxoW13zzzGeyKMDJX0rjL97DO9eDD7NNVVrmdwf8YITtWzKFR60la79NXAuKxLYwXKxuMZo5J6kv+6vZCv2Qp6fM3wWvpQgG8wAyggj45D9wCezX/D1G3sHT8RaOuyvMwYUpZQG+fa85jbz5t+dGes5sQGfBxo5edGJR+Herf/z1ES7o33hXWhtMfw0ogR3rryWCvR7JyqZ7Eh0MIOoy2c6Z6q+fl0ZWXeD1f2tJlySIX/dwyGxBOhlOIuZ1m/TaWM0BwQCFbIszpKPRXK6Ume0rnVORTDNAfMtObAuKxLc6n4kMbzmp2r49WbAXLQ0COem28G/8SN3lY8p36JSnvwhJeMhLSBzK4UY0NLWD42PMzUs7g/YzgnJGnJTqmyoQheSMCxXVrX2SW24Jp6a7kjRFbEkx49tI9+Ha/D9gNZf+ZLWEmZ+01v+OK4p+Jf8RooQR4k0Ke1bKtojtE+dN9hOZ5hULL5XQCqx02i6ZIFVBo+stFSurHMQPFFPRsduN5exE8cpX3e7jKSQ2kMUTbqQomOx7IqXmeTtAzGKgfrA81UhzKIdwY82FEx6zAcb07QgrstzNIH5zp50TsjItbS70hx2d3+NPjP9oOYUooiZaMmtMyrGaM90Jsm2X/5HSWfbgSg6qxLCNiCpXaHaf1b/vJY+aotn+WZY8N7UCdb5PRHf/bolVJcbM/HBF73N+AxDfaaXsZpjqQVykl3J+tZLM8cwyTp0YsIJaHM++2Gh0rTx4QUlt3tTxLo09yQUNEca4mXFeiz3lpJ1ruvk+cIJsvV6Rpu00+A5CwvSwdKqZQGusjpj/7OYj5Dz6FQ6bzjb+DjQCsGyRuCHCwGSgKaGDiszHur6NVgneqST36as0q5WrXx85WOc+PfyH7njwSKSrFdtAAIDut3t4ueSNxA6dFDcDOfC2x5tIbWJMPgPWkJkUyRa90nDJCyzckmgT7NWQl5n4cCfWFzM7l/+jWGM5P6BYvJzS0EoIFAeOe2eDZrED2z1uMr2pO9+tNcWx4OFNXh3sngPGkJkUyRm84M1uWoEujTXHugD1Z3GvreGyhvG01f/x4B1zCy0dAJZuRXSo8+qawpkCKlD4i58Fylc05oHXKx0o/rZWRCxMsqhTtc2ZOasDuQSKBPc8Vaxx59ceV+Wk+/gLZpZwDBeep8pVNnBsK7rR0vc/Sp5lI2ctEY3U9r6KO52Baszzf5OC0KI0SixmhOdILlvAcrueRPc9YcfXNou4rczDyaLrq+w2Py0TkU6s0XKp1MSUpKCl0p7ssYTsYAOp7DNAcPZIyQsrdCxGmIZuM/M0b0e55NKg2cM5Tolc4fTvsF3wB7x7KLBUqnDRO36e9SEU/0TYlmP2Y7UMVruOYgawBdfAgx0I3QHAPqgj3ZBu9vlsbsuz4m94X/RjU39PjYbDScgfady3ILh3Z5TGQgkkAvhBDHFwn0A1DG+2+QsaWC3D88CobR7WOVz4urvhaATFTUtcKR1fIkEU8IIY4vEugHIPvBzwFw7thI5v+90u1jMzb9HVd9HdAxoEcqkB69EEIctyTQDzDK04x+tArfsLEE8orIfnMF9t2fRn+waZJR8VdcjcEh/liBPk969EIIcdySQD/A2Cr3AuAbP4WGf7kZgNwVv0I11Xd97L4d2Cv3UBQqc9tTj14BpdKjF0KI44oE+gHGdnA3AP5hY/GPmUTz3HnoDbXk/eERMAIdHptZ8VcA8svGAcTM/rbuL1E27AOgsIsQQohjRwL9AGM7ZAX6YPBuPfsy2k48BcfOTeQ9ez+qtRkA1ViH8+MK/CUjKHKNBGKXYbXul9K3Qghx/JFAP8DYDn2OaXcSGBJaJqdpNM77Id4J03Hu2EjBY4vQD+8jc8PbqECA1tMvYLqexQJ7Mefb8qK+Zp7Sucnh4lpH8TH8TYQQQgwEg7cUUDrytaG7D+IfOQG09mF4MzOb+gW3kf3mSrLWrqbwicWYdgeGI4O2U85GU4qL7PndvvRZttxUt14IIcQAJD36AcR2eD/KMPCXjen6Q02n+YJvUP+NHwVvNjfSdsrZmBlZx7aRQggh0or06AeQ8Pz88HExH+OdOovakhFkrH+Lli9dfqyaJoQQIk1JoB9A2hPxxnb7uEDpSJq/+s1j0SQhhBBpTobuBxDbod2Yuo6/ZER/N0UIIcQgIYF+oAgEsB3ei790FNhkGZwQQojkkEA/QOjugyi/D/+wMf3dFCGEEIOIBPoBonOhHCGEECIZJNAPEPEm4gkhhBCJkEA/QNgO7cZUCv/QUf3dFCGEEIOIBPqBwDCwHdpNYMgwcGb2d2uEEEIMIhLoBwCtthqtrVUS8YQQQiSdBPpjwTAgEIj5Y0nEE0IIkSoS6I+B/KfvoeCxReBri/pzuyTiCSGESBEJ9Cmmmhtw7PoYe+Ueste82OXnWsNRMv75NqZuwz9cAr0QQojkkkCfYvbdn4b/P+sff8G+6+P2HwYC5L74IFpzA00XXY+ZmdMPLRRCCDGYpXRTm3vvvZdNmzahlGLx4sVMmzYNgKqqKn784x+HH7d//35uueUWzjjjDG699Vba2trw+XzcdtttTJ8+nW3btnHXXXcBUF5ezt13353KZieV4/NgYG+6+F/Jfn05uS89Tu0PH8DMyCLrrZU49nxK25Qv4pn1lX5uqRBCiMEoZT369evXs3fvXlauXMnSpUtZunRp+GelpaUsX76c5cuX88wzz1BWVsacOXN49dVXueyyy1i+fDk/+tGPeOihhwBYunQpixcvZsWKFTQ1NbFu3bpUNTvp7J9vxbQ7aD39AlrO+Rp6nZvsv/wO+/aNZK9dTaColMYrvg9K9XdThRBCDEIp69FXVFRw3nnnATB+/Hjq6+tpamoiJ6fj8PTq1au54IILyM7O5pvfbN96tbKyktLSUrxeLwcPHgyPBpx77rlUVFRwzjnnpKrpSaOa6rFV7cd7wlSw2WmZcwWO7R+SueF/cW6uwNRtNHzj/2FmZPd3U4UQQgxSKevR19TUUFhYGL5dVFSE2+3u8rhVq1Zx5ZVXhm+73W6uuOIKnnjiCW6++WZqa2vJy8sL/7y4uDjq6wxE9j3B+XnfuMnBO2x2Gq/6AaZuQ/N6aLr4X/EPH9+PLRRCCDHYxdWj//jjj3G73Zx77rn86le/4qOPPuKmm25i5syZcb+RaZpd7tu4cSPjxo3r0Mt3uVz88Y9/ZN26ddx2223cd999Pb5OZ4WFWdhsetxti5fLlZvYEw7tACB7xhfItp7rmgw3/hSqDpF73uXkHodD9gkfRxGVHMfkkOOYHHIckyMVxzGuQP/zn/+cX/ziF2zYsIEtW7bw05/+lJ/97Gf8/ve/j/mckpISampqwrerq6txuVwdHrN27VpmzZoVvr1+/XrKy8vJz8/nnHPOYeHChRQVFVFXVxd+TFVVFSUlJd22t7a2JZ5fKyEuVy5ud2NCzyn85CN0u5Oa7KEQ+dyyk4L/apqS3MqBrzfHUXQlxzE55DgmhxzH5OjrcYx1kRDX0L3T6WTMmDG8/fbbXH311ZxwwgloWvdPnT17NmvWrAFg69atlJSUdJmf37JlCxMnTgzffuONN1i9ejUA27dvp6ysDLvdzrhx49iwYUP4MWeddVY8ze5X1vy8b3Q52Oz93RwhhBDHqbh69K2trbz++uu89dZb3HjjjdTV1dHQ0NDtc2bMmMHkyZOZN28eSimWLFnCyy+/TG5uLnPnzgWC8/HFxcXh59xwww0sWrSIN998E6/XG15St3jxYu68804Mw2D69OmcccYZvfx1jx377k+AiPl5IYQQoh8oM45J7/fee4/f//73XHLJJVx00UU88sgjjB49mksvvfRYtDFhqRhCSnRIJeeVJ8l8bw213/s5/tHlSW9PupIhvuSQ45gcchyTQ45jcqRq6D6uHv3pp5/OlClTyMnJoaamhlmzZjFjxoxeN+Z4EFw/78Q/QrLqhRBC9J+45ujvueceXn/9derq6pg3bx7PPfdceFhddKWa6rFVH8A3ZiLoKS0+KIQQQnQrrkD/ySefcNVVV/H666/zta99jQcffJC9e/emum1py5qf98r8vBBCiH4WV6C3pvHXrl3LnDlzAPB6valrVZpzhDau8Y09qZ9bIoQQ4ngXV6AfO3YsF110Ec3NzUyaNIk//elP5Ofnp7ptacu+eyumQ+bnhRBC9L+4C+bs2LGD8eODgeuEE07gl7/8ZUoblq60+iPYqg/iPfFkmZ8XQgjR7+KKRB6Ph3feeYeHHnoIpRQnn3wyJ5xwQqrblpYcOzcB4J1wcj+3RAghhIhz6P6nP/0pTU1NzJs3j6uvvpqamhruuOOOVLctLdl3fASA98Tp/dwSIYQQIs4efU1NDf/93/8dvn3uuecyf/78lDUqbRkBHJ9tJlAwhIBreH+3RgghhIivR9/a2kpra2v4dktLC21tbSlrVLqyHdiF1tqMd8J0OA53pRNCCDHwxNWjv+aaa7jwwguZMmUKENyk5oc//GFKG5aOHDtC8/Mnyvy8EEKIgSGuQH/llVcye/Zstm7dilKKn/70pyxfvjzVbUs7jp0fYWoavvFT+7spQgghBBBnoAcoKyujrKwsfHvz5s0paVC6Uq1N2PbvxD/qRMzM7P5ujhBCCAHEOUcfTRyb3h1X7J9tQZmmLKsTQggxoPQ60CtJNuvAEV5WJ4FeCCHEwNHt0P0555wTNaCbpkltbW3KGpV2TBPHjo8wsnLxDx/b360RQgghwroN9C+88MKxakda06sPoDccxTNtNmh6fzdHCCGECOs20A8fLkVf4iHD9kIIIQaqXs/Ri3ZWoPdNkLK3QgghBhYJ9H2k1dVg3/Mp/qGjMfIK+7s5QgghRAcS6Psi4CdvxYMov4/WMy7s79YIIYQQXUig74PsN17Evnc7nmmz8cyc09/NEUIIIbqQQN9Ljk8/IOv/XsVfXEbT178rm9gIIYQYkCTQ94JW5yZ31aOYNjsN1/4I05nZ300SQgghopJAnyjDIO+FX6G1NtH01X8jUDamv1skhBBCxCSBPkF6zSHs+3fSVj4Dz2lf7u/mCCGEEN2SQJ8g1eYBIFAyXOblhRBCDHgS6BOk/F4ATLujn1sihBBC9EwCfaK8bQCYdmc/N0QIIYTomQT6BFk9emzSoxdCCDHwSaBPkPLJ0L0QQoj0IYE+QRLohRBCpBMJ9ImSQC+EECKNSKBPkMzRCyGESCcS6BMkQ/dCCCHSiQT6BEmgF0IIkU4k0CfKZ62jl0AvhBBi4JNAnyCrR48EeiGEEGlAAn2CZOheCCFEOpFAn6BwrXvJuhdCCJEGJNAnSnr0Qggh0ogE+gTJHL0QQoh0IoE+QcrnxdR10PT+booQQgjRIwn0CVI+r8zPCyGESBsS6BPl82I6ZC96IYQQ6UECfYKU3yt17oUQQqQNCfQJUj6vZNwLIYRIGxLoEySBXgghRDqRQJ8I0wS/V5bWCSGESBsS6BMR8KNMU7LuhRBCpA0J9AmQOvdCCCHSjS2VL37vvfeyadMmlFIsXryYadOmAVBVVcWPf/zj8OP279/PLbfcwoUXXsjtt9/Ovn37CAQCLFy4kJkzZzJ//nxaWlrIysoC4NZbb2XKlCmpbHpUSraoFUIIkWZSFujXr1/P3r17WblyJbt27WLx4sWsXLkSgNLSUpYvXw6A3+9n/vz5zJkzh1deeYXMzExefPFFdu7cyW233cZLL70EwH333ceJJ56YqubGJ9yjl3X0Qggh0kPKAn1FRQXnnXceAOPHj6e+vp6mpiZycnI6PG716tVccMEFZGdnc+mll3LJJZcAUFRURF1dXaqa1ytS514IIUS6SdkcfU1NDYWFheHbRUVFuN3uLo9btWoVV155JQB2ux2nM9hbfvbZZ8NBH+Dhhx/m2muv5c4778Tj8aSq2d2SOXohhBDpJqVz9JFM0+xy38aNGxk3blyXXv7zzz/P1q1bWbZsGQDXX3895eXljBo1iiVLlvD888/zrW99K+Z7FRZmYbMlf9OZwpzg4crKzyHLlZv01z9euOTYJYUcx+SQ45gcchyTIxXHMWWBvqSkhJqamvDt6upqXC5Xh8esXbuWWbNmdbhv1apVvPPOOzz++OPY7XYA5s6dG/75nDlzeO2117p979ralr42vwuXK5c6dx0FQLMXWtyNSX+P44HLlYtbjl2fyXFMDjmOySHHMTn6ehxjXSSkbOh+9uzZrFmzBoCtW7dSUlLSpee+ZcsWJk6cGL69f/9+VqxYwaOPPhoewjdNkwULFtDQ0ADA+++/z4QJE1LV7G7J0L0QQoh0k7Ie/YwZM5g8eTLz5s1DKcWSJUt4+eWXyc3NDffQ3W43xcXF4eesWrWKuro6vvOd74Tve+qpp7j66qtZsGABmZmZlJaWctNNN6Wq2d2SQC+EECLdKDPa5HmaS8UQksuVS+NrfyL35WU0XHkjbad+KenvcTyQIb7kkOOYHHIck0OOY3Kk3dD9oGT16GU/eiGEEGlCAn0ClD+0jl5q3QshhEgTEugTIHP0Qggh0o0E+gRIoBdCCJFuJNAnQkrgCiGESDMS6BNgzdHLfvRCCCHShQT6BMjQvRBCiHQjgT4Bsh+9EEKIdCOBPhHSoxdCCJFmJNAnQPajF0IIkW4k0CdA+byYug5a8rfAFUIIIVJBAn0ClN8rGfdCCCHSigT6RPi8MmwvhBAirUigT4DyeSURTwghRFqRQJ8A5ZdAL4QQIr1IoE+A8socvRBCiPQigT5epgl+L8he9EIIIdKIBPp4+X0o05QevRBCiLQigT5eUhVPCCFEGpJAHy+v1LkXQgiRfiTQx0vK3wohhEhDEujjZfXoZY5eCCFEGpFAHy+ZoxdCCJGGJNDHS+bohRBCpCEJ9PEKBXrsso5eCCFE+pBAHy8ZuhdCCJGGJNDHS4buhRBCpCEJ9PGyevSSdS+EECKNSKCPV3iOXgK9EEKI9CGBPl4yRy+EECINSaCPl8zRCyGESEMS6OMllfGEEEKkIQn08bJq3ct+9EIIIdKIBPp4SY9eCCFEGpJAHy9JxhNCCJGGJNDHS5LxhBBCpCEJ9PGS/eiFEEKkIQn08fJKZTwhhBDpRwJ9vLweTE0HXe/vlgghhBBxk0AfL59X5ueFEEKkHQn08fK2yfy8EEKItCOBPl7SoxdCCJGGJNDHyyuBXgghRPqRQB8vX5tk3AshhEg7EujjYZrB5XXSoxdCCJFmJNDHI+AH05CheyGEEGlHAn0clNS5F0IIkaYk0MchHOhljl4IIUSakUAfD7/UuRdCCJGeJNDHoX3o3tnPLRFCCCESI4E+DjJHL4QQIl3ZUvni9957L5s2bUIpxeLFi5k2bRoAVVVV/PjHPw4/bv/+/dxyyy1ceOGF3H777ezbt49AIMDChQuZOXMm27Zt46677gKgvLycu+++O5XN7kICvRBCiHSVsh79+vXr2bt3LytXrmTp0qUsXbo0/LPS0lKWL1/O8uXLeeaZZygrK2POnDm88sorZGZm8uKLL7J06VJ+8YtfALB06VIWL17MihUraGpqYt26dalqdnSyF70QQog0lbJAX1FRwXnnnQfA+PHjqa+vp6mpqcvjVq9ezQUXXEB2djaXXnopt912GwBFRUXU1dXh9Xo5ePBgeDTg3HPPpaKiIlXNjkr5JeteCCFEekrZ0H1NTQ2TJ08O3y4qKsLtdpOTk9PhcatWreLpp58GwG63h+9/9tlnueSSS6itrSUvLy98f3FxMW63u9v3LizMwmZL4r7xGQqAnMI8cly5yXvd45RLjmFSyHFMDjmOySHHMTlScRxTOkcfyTTNLvdt3LiRcePGdQn+zz//PFu3bmXZsmUcPXq0x9fprLa2pW+N7STjaAO5QEObQZu7MamvfbxxuXJxyzHsMzmOySHHMTnkOCZHX49jrIuElA3dl5SUUFNTE75dXV2Ny+Xq8Ji1a9cya9asDvetWrWKd955h8cffxy73R4ewrdUVVVRUlKSqmZHJ3P0Qggh0lTKAv3s2bNZs2YNAFu3bqWkpKRLz33Lli1MnDgxfHv//v2sWLGCRx99FKczuGbdbrczbtw4NmzYAMAbb7zBWWedlapmRyXr6IUQQqSrlA3dz5gxg8mTJzNv3jyUUixZsoSXX36Z3Nxc5s6dC4Db7aa4uDj8nFWrVlFXV8d3vvOd8H1PPfUUixcv5s4778QwDKZPn84ZZ5yRqmZHJcvrhBBCpCtlxjPpnWaSPVeUveYFstaupva79+AfM7HnJ4iYZC4vOeQ4Joccx+SQ45gcaTdHP6jIHL0QQog0JYE+DjJ0L4QQIsA7bFQAAAw7SURBVF1JoI+D8rUBEuiFEEKkHwn0cZD96IUQQqQrCfTxkDl6IYQQaUoCfRzCte4l0AshhEgzEujjoHxe0HXQj1nFYCGEECIpJNDHw+cFqYonhBAiDUmgj4Pye2V+XgghRFqSQB8H5fOCQwK9EEKI9COBPg7K1yZD90IIIdKSBPo4SI9eCCFEupJA3xPTlGQ8IYQQaUsCfU8CfpRpgkMCvRBCiPQjgb4HVrEcGboXQgiRjiTQ9yRc/lZ69EIIIdKPBPoeWBvaSI9eCCFEOpJA3wMlPXohhBBpTAJ9D6RHL4QQIp1JoO+B9OiFEEKkMwn0PQjkFWLanTBiTH83RQghhEiY7LvaA6N4KDVLfodraCG4G/u7OUIIIURCpEcfD9mHXgghRJqSQC+EEEIMYhLohRBCiEFMAr0QQggxiEmgF0IIIQYxCfRCCCHEICaBXgghhBjEJNALIYQQg5gEeiGEEGIQk0AvhBBCDGIS6IUQQohBTAK9EEIIMYgp0zTN/m6EEEIIIVJDevRCCCHEICaBXgghhBjEJNALIYQQg5gEeiGEEGIQk0AvhBBCDGIS6IUQQohBzNbfDRjo7r33XjZt2oRSisWLFzNt2rT+blJa+eUvf8kHH3yA3+/nu9/9LlOnTmXhwoUEAgFcLhf/+Z//icPh6O9mDngej4dLLrmEG264gVmzZskx7IVXX32VJ598EpvNxn/8x39QXl4uxzFBzc3N3HrrrdTX1+Pz+bjxxhtxuVzcddddAJSXl3P33Xf3byMHuB07dnDDDTewYMECrrvuOiorK6N+Dl999VWeffZZNE3j6quv5qqrrur1e0qPvhvr169n7969rFy5kqVLl7J06dL+blJaee+999i5cycrV67kySef5N577+Xhhx/mG9/4Bi+88AKjR4/mpZde6u9mpoUnnniC/Px8ADmGvVBbW8tjjz3GCy+8wLJly3j77bflOPbC6tWrGTt2LMuXL+ehhx4KnxcXL17MihUraGpqYt26df3dzAGrpaWFe+65h1mzZoXvi/Y5bGlp4bHHHuN3v/sdy5cv59lnn6Wurq7X7yuBvhsVFRWcd955AIwfP576+nqampr6uVXp47TTTuOhhx4CIC8vj9bWVt5//32+/OUvA3DuuedSUVHRn01MC7t27eKzzz7jS1/6EoAcw16oqKhg1qxZ5OTkUFJSwj333CPHsRcKCwvDAaehoYGCggIOHjwYHumU49g9h8PBb3/7W0pKSsL3Rfscbtq0ialTp5Kbm0tGRgYzZszgww8/7PX7SqDvRk1NDYWFheHbRUVFuN3ufmxRetF1naysLABeeuklzj77bFpbW8PDo8XFxXI843D//fezaNGi8G05hok7cOAAHo+H733ve3zjG9+goqJCjmMvXHzxxRw6dIi5c+dy3XXXsXDhQvLy8sI/l+PYPZvNRkZGRof7on0Oa2pqKCoqCj+mr7FH5ugTINWCe+ett97ipZde4umnn+b8888P3y/Hs2d/+tOfOPnkkxk5cmTUn8sxjF9dXR2PPvoohw4d4vrrr+9w7OQ4xueVV15h2LBhPPXUU2zbto0bb7yR3Nzc8M/lOPZNrOPX1+Mqgb4bJSUl1NTUhG9XV1fjcrn6sUXp529/+xvLli3jySefJDc3l6ysLDweDxkZGVRVVXUYwhJdrV27lv3797N27VoOHz6Mw+GQY9gLxcXFnHLKKdhsNkaNGkV2dja6rstxTNCHH37ImWeeCcDEiRNpa2vD7/eHfy7HMXHRvs/RYs/JJ5/c6/eQoftuzJ49mzVr1gCwdetWSkpKyMnJ6edWpY/GxkZ++ctf8utf/5qCggIAzjjjjPAxfeONNzjrrLP6s4kD3oMPPsgf//hH/vCHP3DVVVdxww03yDHshTPPPJP33nsPwzCora2lpaVFjmMvjB49mk2bNgFw8OBBsrOzGT9+PBs2bADkOPZGtM/h9OnT2bJlCw0NDTQ3N/Phhx8yc+bMXr+H7F7XgwceeIANGzaglGLJkiVMnDixv5uUNlauXMkjjzzC2LFjw/f94he/4I477qCtrY1hw4Zx3333Ybfb+7GV6eORRx5h+PDhnHnmmdx6661yDBO0YsWKcGb997//faZOnSrHMUHNzc0sXryYI0eO4Pf7+eEPf4jL5eLOO+/EMAymT5/Obbfd1t/NHLA+/vhj7r//fg4ePIjNZqO0tJQHHniARYsWdfkc/vWvf+Wpp55CKcV1113HpZde2uv3lUAvhBBCDGIydC+EEEIMYhLohRBCiEFMAr0QQggxiEmgF0IIIQYxCfRCCCHEICaBXogB6sCBA5SXl/Pqq692uH/OnDlJef3y8vIOxU5SYc2aNXz5y19m1apVHe5ftGgRF1xwAfPnz+/w7+jRo0l77/nz5/Puu+8m7fWESFdSGU+IAWzMmDE89thjzJkzJy2LNa1bt45vfetbUbfY/Pa3v92nrTeFEPGRQC/EAFZSUsKZZ57J448/zsKFCzv87OWXX+bdd9/lgQceAII92O9///vous6yZcsYOnQoW7ZsYfr06ZSXl/Pmm29SV1fHb3/7W4YOHQrAsmXLeO+992hubub+++/nxBNPZNu2bdx///34/X58Ph933nknJ510EvPnz2fixIl8+umnPPvss+i6Hm7L2rVreeyxx8jIyCAzM5N77rmHjRs3sm7dOj744AN0Xeeaa66J63d+5JFH2L9/P7W1tbjdbk4//XQWLVpEIBDg3nvvZevWrQCcfvrp3HzzzQA8/vjjvP3222iaxmWXXcZ1110HBHet+93vfseePXu48cYbueyyy3jttdd46qmnyMrKwjRN7rvvvph7CQgxGEigF2KA++Y3v8nXvvY1rrzySsaNGxfXczZv3syvfvUrMjMzOe200zjttNNYvnw5ixYt4q9//SsLFiwAgtsv/+AHP2DVqlU8+uijPPzww/zkJz/hscceY9SoUWzbto3Fixfz8ssvA8G63M8991yH92ptbeWOO+7gpZdeYujQoTz33HM8+OCD3Hfffaxdu5ZTTz014Z77zp07WbVqFYZhcPHFF3P55Zfz2WefceDAAV588UUMw2DevHmcccYZaJrG2rVr+cMf/oBhGNx0003hKmKmafKb3/yGDRs2cPfdd3PZZZexbNky7rnnHqZPn86mTZuoqqqSQC8GNQn0QgxwDoeDhQsXsnTpUp566qm4njN+/Pjw/gIFBQWccsopAJSWltLU1BR+3OzZswGYMWMGTz/9NEeOHGH37t3cfvvt4cc0NTVhGEb4cZ3t2bOH4uLi8CjBF77wBVasWNFjG5988skO+Qfjx4/nrrvuAoK9dZsteHqaMmUKu3btYtOmTcyaNQulFLquM3PmTLZs2QLAqaeeiq7r4dEMyxe+8AUAhg4dSkNDAwBf//rXWbRoEeeffz7nn38+06dP77GtQqQzCfRCpIFzzjmHF198kTfffDN8n1Kqw2N8Pl/4/yOH1Tvfjqx6rWla+D6lFA6HA7vdzvLly6O2I1ot+M7tsF6rJ93N0VsXFpGv1937xKrkbV0sRD5mwYIFXHLJJfztb3/jzjvv5KqrrmLevHk9tleIdCVZ90KkicWLF/Nf//VfeL1eAHJycjh8+DAAR44cYefOnQm/ZkVFBRDcfvTEE08kNzeXESNGsG7dOgB2797No48+2u1rjBkzhiNHjnDo0KHwa/a1l/zPf/6TQCCA1+tly5YtlP//9u4YRWEoisLwDwERQcEFKEgKS5WUtkGLVDYSEZcQJF0KS1OIdukEe1eiYOEGbK1sXIAvOp3FMDNON+PjfAt473aHe5rbbNJut9ntdjweD4wxHA4HWq0WnU6H/X7P7XbDGMNkMuFyuXz5bp7nrFYryuUyg8GAKIqe19hEbKWNXuRN1Ot1+v3+s5rudrtsNhuGwyGu6z7r+d9yHIfT6cR2u+V6vbJcLgFYLBbM53PW6zXGGJIk+fGdYrFImqbEcUyhUKBUKpGm6cv/P1f3AFEUAVCr1ZhOp5zPZ4IgwHVdGo0Gx+OR0WjE/X7H9308zwOg1+sxHo8BCILg25vojuNQrVYJw5BKpQLAbDZ7OavIO9P1OhH5V7IswxhDHMd/PYqIFVTdi4iIWEwbvYiIiMW00YuIiFhMQS8iImIxBb2IiIjFFPQiIiIWU9CLiIhYTEEvIiJisQ81FbPVkg2dsQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "id": "0yjZKbH6rhBW",
        "outputId": "629a7bf6-d59a-4f9d-ca0b-93d2081aea10"
      },
      "source": [
        "# Model Loss Graph\n",
        "plt.style.use('seaborn')\n",
        "plt.title('Loss \\n',fontsize=20)\n",
        "plt.plot(fit_model.history['loss'], 'orchid', label='Training Loss')\n",
        "plt.plot(fit_model.history['val_loss'],'royalblue', label='Validation Loss')\n",
        "plt.xlabel('Number of Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E9vmvJXJ1jI-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "2273662d-99ab-4d5d-a585-ca99a0ff09fc"
      },
      "source": [
        "# Export our model to HDF5 file\n",
        "from google.colab import files\n",
        "\n",
        "nn_model.save('/content/Alphabet_Soup_model1.h5')\n",
        "files.download('/content/Alphabet_Soup_model1.h5')"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_6b33d88a-a5f7-4fd6-b2e7-8ea63a1bfaf6\", \"Alphabet_Soup_model1.h5\", 103352)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "It0pLk1aDD_K"
      },
      "source": [
        "# Model Evaluation 2"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yIqz_wAEDFo9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7181e8d3-1575-4f7b-b4d9-82af4d38db6f"
      },
      "source": [
        "# Define the model - deep neural net, i.e., the number of input features and hidden nodes for each layer.\n",
        "number_input_features = len( X_train_scaled[0])\n",
        "hidden_nodes_layer1=1450\n",
        "hidden_nodes_layer2=80\n",
        "\n",
        "nn_model = tf.keras.models.Sequential()\n",
        "\n",
        "# First hidden layer\n",
        "nn_model.add(tf.keras.layers.Dense(units=hidden_nodes_layer1, input_dim=number_input_features, activation='relu'))\n",
        "\n",
        "# Second hidden layer\n",
        "nn_model.add(tf.keras.layers.Dense(units=hidden_nodes_layer2, activation='relu'))\n",
        "\n",
        "# Output layer\n",
        "nn_model.add(tf.keras.layers.Dense(units=1, activation='sigmoid'))\n",
        "\n",
        "# Check the structure of the model\n",
        "nn_model.summary()"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_4\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_13 (Dense)             (None, 1450)              63800     \n",
            "_________________________________________________________________\n",
            "dense_14 (Dense)             (None, 80)                116080    \n",
            "_________________________________________________________________\n",
            "dense_15 (Dense)             (None, 1)                 81        \n",
            "=================================================================\n",
            "Total params: 179,961\n",
            "Trainable params: 179,961\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M9zqjrf8DRDm"
      },
      "source": [
        "# Compile the model\n",
        "nn_model.compile(loss = 'binary_crossentropy', optimizer = 'adam', metrics=['accuracy', tf.keras.metrics.Recall()])"
      ],
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tIC2jHSoDRnj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e12ea7d1-33bb-4ec0-bf3f-c32814a47c58"
      },
      "source": [
        "fit_model = nn_model.fit(X_train_scaled,y_train,validation_split=0.15, epochs=100)"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "684/684 [==============================] - 4s 5ms/step - loss: 0.5695 - accuracy: 0.7194 - recall_4: 0.7871 - val_loss: 0.5520 - val_accuracy: 0.7344 - val_recall_4: 0.7566\n",
            "Epoch 2/100\n",
            "684/684 [==============================] - 3s 5ms/step - loss: 0.5581 - accuracy: 0.7273 - recall_4: 0.7824 - val_loss: 0.5494 - val_accuracy: 0.7357 - val_recall_4: 0.7566\n",
            "Epoch 3/100\n",
            "684/684 [==============================] - 3s 5ms/step - loss: 0.5548 - accuracy: 0.7285 - recall_4: 0.7780 - val_loss: 0.5519 - val_accuracy: 0.7372 - val_recall_4: 0.7581\n",
            "Epoch 4/100\n",
            "684/684 [==============================] - 3s 4ms/step - loss: 0.5530 - accuracy: 0.7310 - recall_4: 0.7883 - val_loss: 0.5521 - val_accuracy: 0.7359 - val_recall_4: 0.7805\n",
            "Epoch 5/100\n",
            "684/684 [==============================] - 3s 5ms/step - loss: 0.5518 - accuracy: 0.7309 - recall_4: 0.7932 - val_loss: 0.5495 - val_accuracy: 0.7344 - val_recall_4: 0.7785\n",
            "Epoch 6/100\n",
            "684/684 [==============================] - 3s 5ms/step - loss: 0.5505 - accuracy: 0.7308 - recall_4: 0.7871 - val_loss: 0.5473 - val_accuracy: 0.7339 - val_recall_4: 0.7561\n",
            "Epoch 7/100\n",
            "684/684 [==============================] - 3s 5ms/step - loss: 0.5502 - accuracy: 0.7317 - recall_4: 0.7890 - val_loss: 0.5445 - val_accuracy: 0.7354 - val_recall_4: 0.7710\n",
            "Epoch 8/100\n",
            "684/684 [==============================] - 3s 5ms/step - loss: 0.5489 - accuracy: 0.7325 - recall_4: 0.7905 - val_loss: 0.5447 - val_accuracy: 0.7370 - val_recall_4: 0.7651\n",
            "Epoch 9/100\n",
            "684/684 [==============================] - 4s 5ms/step - loss: 0.5480 - accuracy: 0.7322 - recall_4: 0.7976 - val_loss: 0.5468 - val_accuracy: 0.7375 - val_recall_4: 0.7870\n",
            "Epoch 10/100\n",
            "684/684 [==============================] - 4s 5ms/step - loss: 0.5476 - accuracy: 0.7322 - recall_4: 0.8020 - val_loss: 0.5452 - val_accuracy: 0.7346 - val_recall_4: 0.7775\n",
            "Epoch 11/100\n",
            "684/684 [==============================] - 3s 5ms/step - loss: 0.5467 - accuracy: 0.7335 - recall_4: 0.8036 - val_loss: 0.5467 - val_accuracy: 0.7359 - val_recall_4: 0.7820\n",
            "Epoch 12/100\n",
            "684/684 [==============================] - 3s 5ms/step - loss: 0.5464 - accuracy: 0.7339 - recall_4: 0.8061 - val_loss: 0.5459 - val_accuracy: 0.7391 - val_recall_4: 0.7715\n",
            "Epoch 13/100\n",
            "684/684 [==============================] - 4s 5ms/step - loss: 0.5456 - accuracy: 0.7342 - recall_4: 0.8034 - val_loss: 0.5454 - val_accuracy: 0.7357 - val_recall_4: 0.7820\n",
            "Epoch 14/100\n",
            "684/684 [==============================] - 3s 4ms/step - loss: 0.5451 - accuracy: 0.7343 - recall_4: 0.7994 - val_loss: 0.5467 - val_accuracy: 0.7352 - val_recall_4: 0.7641\n",
            "Epoch 15/100\n",
            "684/684 [==============================] - 3s 4ms/step - loss: 0.5457 - accuracy: 0.7337 - recall_4: 0.8028 - val_loss: 0.5457 - val_accuracy: 0.7388 - val_recall_4: 0.7865\n",
            "Epoch 16/100\n",
            "684/684 [==============================] - 3s 5ms/step - loss: 0.5445 - accuracy: 0.7342 - recall_4: 0.8044 - val_loss: 0.5434 - val_accuracy: 0.7362 - val_recall_4: 0.7850\n",
            "Epoch 17/100\n",
            "684/684 [==============================] - 3s 5ms/step - loss: 0.5443 - accuracy: 0.7343 - recall_4: 0.8040 - val_loss: 0.5456 - val_accuracy: 0.7349 - val_recall_4: 0.7750\n",
            "Epoch 18/100\n",
            "684/684 [==============================] - 3s 5ms/step - loss: 0.5444 - accuracy: 0.7345 - recall_4: 0.8046 - val_loss: 0.5439 - val_accuracy: 0.7383 - val_recall_4: 0.7889\n",
            "Epoch 19/100\n",
            "684/684 [==============================] - 3s 5ms/step - loss: 0.5435 - accuracy: 0.7353 - recall_4: 0.8069 - val_loss: 0.5449 - val_accuracy: 0.7378 - val_recall_4: 0.7845\n",
            "Epoch 20/100\n",
            "684/684 [==============================] - 3s 5ms/step - loss: 0.5434 - accuracy: 0.7346 - recall_4: 0.8090 - val_loss: 0.5434 - val_accuracy: 0.7367 - val_recall_4: 0.7899\n",
            "Epoch 21/100\n",
            "684/684 [==============================] - 3s 4ms/step - loss: 0.5427 - accuracy: 0.7348 - recall_4: 0.8076 - val_loss: 0.5458 - val_accuracy: 0.7378 - val_recall_4: 0.7949\n",
            "Epoch 22/100\n",
            "684/684 [==============================] - 3s 4ms/step - loss: 0.5428 - accuracy: 0.7355 - recall_4: 0.8091 - val_loss: 0.5444 - val_accuracy: 0.7372 - val_recall_4: 0.7880\n",
            "Epoch 23/100\n",
            "684/684 [==============================] - 3s 5ms/step - loss: 0.5424 - accuracy: 0.7351 - recall_4: 0.8069 - val_loss: 0.5435 - val_accuracy: 0.7378 - val_recall_4: 0.7880\n",
            "Epoch 24/100\n",
            "684/684 [==============================] - 3s 5ms/step - loss: 0.5419 - accuracy: 0.7360 - recall_4: 0.8038 - val_loss: 0.5487 - val_accuracy: 0.7359 - val_recall_4: 0.7865\n",
            "Epoch 25/100\n",
            "684/684 [==============================] - 3s 5ms/step - loss: 0.5421 - accuracy: 0.7351 - recall_4: 0.8105 - val_loss: 0.5468 - val_accuracy: 0.7391 - val_recall_4: 0.7919\n",
            "Epoch 26/100\n",
            "684/684 [==============================] - 3s 5ms/step - loss: 0.5420 - accuracy: 0.7360 - recall_4: 0.8106 - val_loss: 0.5449 - val_accuracy: 0.7380 - val_recall_4: 0.7795\n",
            "Epoch 27/100\n",
            "684/684 [==============================] - 3s 5ms/step - loss: 0.5424 - accuracy: 0.7355 - recall_4: 0.8124 - val_loss: 0.5437 - val_accuracy: 0.7375 - val_recall_4: 0.7835\n",
            "Epoch 28/100\n",
            "684/684 [==============================] - 3s 4ms/step - loss: 0.5416 - accuracy: 0.7364 - recall_4: 0.8048 - val_loss: 0.5412 - val_accuracy: 0.7385 - val_recall_4: 0.7885\n",
            "Epoch 29/100\n",
            "684/684 [==============================] - 3s 4ms/step - loss: 0.5419 - accuracy: 0.7354 - recall_4: 0.8093 - val_loss: 0.5452 - val_accuracy: 0.7375 - val_recall_4: 0.7885\n",
            "Epoch 30/100\n",
            "684/684 [==============================] - 3s 5ms/step - loss: 0.5414 - accuracy: 0.7366 - recall_4: 0.8079 - val_loss: 0.5466 - val_accuracy: 0.7378 - val_recall_4: 0.7909\n",
            "Epoch 31/100\n",
            "684/684 [==============================] - 3s 5ms/step - loss: 0.5412 - accuracy: 0.7357 - recall_4: 0.8085 - val_loss: 0.5442 - val_accuracy: 0.7378 - val_recall_4: 0.7865\n",
            "Epoch 32/100\n",
            "684/684 [==============================] - 3s 5ms/step - loss: 0.5415 - accuracy: 0.7361 - recall_4: 0.8066 - val_loss: 0.5435 - val_accuracy: 0.7406 - val_recall_4: 0.7919\n",
            "Epoch 33/100\n",
            "684/684 [==============================] - 4s 5ms/step - loss: 0.5412 - accuracy: 0.7351 - recall_4: 0.8069 - val_loss: 0.5446 - val_accuracy: 0.7385 - val_recall_4: 0.7855\n",
            "Epoch 34/100\n",
            "684/684 [==============================] - 3s 5ms/step - loss: 0.5403 - accuracy: 0.7359 - recall_4: 0.8085 - val_loss: 0.5475 - val_accuracy: 0.7385 - val_recall_4: 0.7825\n",
            "Epoch 35/100\n",
            "684/684 [==============================] - 3s 4ms/step - loss: 0.5405 - accuracy: 0.7358 - recall_4: 0.8104 - val_loss: 0.5479 - val_accuracy: 0.7378 - val_recall_4: 0.7850\n",
            "Epoch 36/100\n",
            "684/684 [==============================] - 3s 5ms/step - loss: 0.5409 - accuracy: 0.7360 - recall_4: 0.8055 - val_loss: 0.5442 - val_accuracy: 0.7380 - val_recall_4: 0.7889\n",
            "Epoch 37/100\n",
            "684/684 [==============================] - 3s 5ms/step - loss: 0.5397 - accuracy: 0.7364 - recall_4: 0.8040 - val_loss: 0.5483 - val_accuracy: 0.7372 - val_recall_4: 0.7780\n",
            "Epoch 38/100\n",
            "684/684 [==============================] - 3s 5ms/step - loss: 0.5399 - accuracy: 0.7365 - recall_4: 0.8017 - val_loss: 0.5443 - val_accuracy: 0.7391 - val_recall_4: 0.7989\n",
            "Epoch 39/100\n",
            "684/684 [==============================] - 3s 5ms/step - loss: 0.5406 - accuracy: 0.7364 - recall_4: 0.8064 - val_loss: 0.5464 - val_accuracy: 0.7346 - val_recall_4: 0.7775\n",
            "Epoch 40/100\n",
            "684/684 [==============================] - 3s 5ms/step - loss: 0.5397 - accuracy: 0.7373 - recall_4: 0.8035 - val_loss: 0.5478 - val_accuracy: 0.7370 - val_recall_4: 0.7725\n",
            "Epoch 41/100\n",
            "684/684 [==============================] - 3s 5ms/step - loss: 0.5405 - accuracy: 0.7365 - recall_4: 0.8016 - val_loss: 0.5459 - val_accuracy: 0.7388 - val_recall_4: 0.7830\n",
            "Epoch 42/100\n",
            "684/684 [==============================] - 3s 5ms/step - loss: 0.5405 - accuracy: 0.7364 - recall_4: 0.8099 - val_loss: 0.5476 - val_accuracy: 0.7370 - val_recall_4: 0.7825\n",
            "Epoch 43/100\n",
            "684/684 [==============================] - 3s 5ms/step - loss: 0.5391 - accuracy: 0.7360 - recall_4: 0.8101 - val_loss: 0.5464 - val_accuracy: 0.7378 - val_recall_4: 0.7865\n",
            "Epoch 44/100\n",
            "684/684 [==============================] - 3s 5ms/step - loss: 0.5401 - accuracy: 0.7363 - recall_4: 0.8100 - val_loss: 0.5445 - val_accuracy: 0.7375 - val_recall_4: 0.7835\n",
            "Epoch 45/100\n",
            "684/684 [==============================] - 3s 5ms/step - loss: 0.5388 - accuracy: 0.7362 - recall_4: 0.8109 - val_loss: 0.5482 - val_accuracy: 0.7367 - val_recall_4: 0.7899\n",
            "Epoch 46/100\n",
            "684/684 [==============================] - 3s 5ms/step - loss: 0.5397 - accuracy: 0.7367 - recall_4: 0.8070 - val_loss: 0.5498 - val_accuracy: 0.7375 - val_recall_4: 0.7870\n",
            "Epoch 47/100\n",
            "684/684 [==============================] - 3s 5ms/step - loss: 0.5390 - accuracy: 0.7366 - recall_4: 0.8093 - val_loss: 0.5493 - val_accuracy: 0.7393 - val_recall_4: 0.7885\n",
            "Epoch 48/100\n",
            "684/684 [==============================] - 3s 5ms/step - loss: 0.5480 - accuracy: 0.7362 - recall_4: 0.8105 - val_loss: 0.5445 - val_accuracy: 0.7383 - val_recall_4: 0.7830\n",
            "Epoch 49/100\n",
            "684/684 [==============================] - 3s 5ms/step - loss: 0.5441 - accuracy: 0.7370 - recall_4: 0.8059 - val_loss: 0.5471 - val_accuracy: 0.7393 - val_recall_4: 0.7939\n",
            "Epoch 50/100\n",
            "684/684 [==============================] - 3s 5ms/step - loss: 0.5393 - accuracy: 0.7372 - recall_4: 0.8103 - val_loss: 0.5457 - val_accuracy: 0.7380 - val_recall_4: 0.7860\n",
            "Epoch 51/100\n",
            "684/684 [==============================] - 3s 5ms/step - loss: 0.5385 - accuracy: 0.7366 - recall_4: 0.8116 - val_loss: 0.5456 - val_accuracy: 0.7370 - val_recall_4: 0.7885\n",
            "Epoch 52/100\n",
            "684/684 [==============================] - 3s 5ms/step - loss: 0.5380 - accuracy: 0.7373 - recall_4: 0.8106 - val_loss: 0.5465 - val_accuracy: 0.7385 - val_recall_4: 0.7845\n",
            "Epoch 53/100\n",
            "684/684 [==============================] - 3s 5ms/step - loss: 0.5381 - accuracy: 0.7368 - recall_4: 0.8064 - val_loss: 0.5492 - val_accuracy: 0.7380 - val_recall_4: 0.7785\n",
            "Epoch 54/100\n",
            "684/684 [==============================] - 3s 4ms/step - loss: 0.5383 - accuracy: 0.7365 - recall_4: 0.8090 - val_loss: 0.5467 - val_accuracy: 0.7380 - val_recall_4: 0.7820\n",
            "Epoch 55/100\n",
            "684/684 [==============================] - 3s 4ms/step - loss: 0.5385 - accuracy: 0.7370 - recall_4: 0.8082 - val_loss: 0.5454 - val_accuracy: 0.7380 - val_recall_4: 0.7894\n",
            "Epoch 56/100\n",
            "684/684 [==============================] - 3s 4ms/step - loss: 0.5382 - accuracy: 0.7369 - recall_4: 0.8111 - val_loss: 0.5480 - val_accuracy: 0.7380 - val_recall_4: 0.7899\n",
            "Epoch 57/100\n",
            "684/684 [==============================] - 3s 5ms/step - loss: 0.5392 - accuracy: 0.7376 - recall_4: 0.8105 - val_loss: 0.5488 - val_accuracy: 0.7370 - val_recall_4: 0.7860\n",
            "Epoch 58/100\n",
            "684/684 [==============================] - 3s 5ms/step - loss: 0.5390 - accuracy: 0.7368 - recall_4: 0.8105 - val_loss: 0.5512 - val_accuracy: 0.7367 - val_recall_4: 0.7865\n",
            "Epoch 59/100\n",
            "684/684 [==============================] - 3s 5ms/step - loss: 0.5378 - accuracy: 0.7371 - recall_4: 0.8127 - val_loss: 0.5482 - val_accuracy: 0.7367 - val_recall_4: 0.7855\n",
            "Epoch 60/100\n",
            "684/684 [==============================] - 3s 5ms/step - loss: 0.5374 - accuracy: 0.7370 - recall_4: 0.8137 - val_loss: 0.5518 - val_accuracy: 0.7365 - val_recall_4: 0.7845\n",
            "Epoch 61/100\n",
            "684/684 [==============================] - 4s 5ms/step - loss: 0.5374 - accuracy: 0.7373 - recall_4: 0.8104 - val_loss: 0.5502 - val_accuracy: 0.7362 - val_recall_4: 0.7840\n",
            "Epoch 62/100\n",
            "684/684 [==============================] - 3s 5ms/step - loss: 0.5372 - accuracy: 0.7373 - recall_4: 0.8118 - val_loss: 0.5515 - val_accuracy: 0.7362 - val_recall_4: 0.7870\n",
            "Epoch 63/100\n",
            "684/684 [==============================] - 3s 5ms/step - loss: 0.5374 - accuracy: 0.7376 - recall_4: 0.8111 - val_loss: 0.5475 - val_accuracy: 0.7365 - val_recall_4: 0.7865\n",
            "Epoch 64/100\n",
            "684/684 [==============================] - 3s 5ms/step - loss: 0.5369 - accuracy: 0.7373 - recall_4: 0.8110 - val_loss: 0.5476 - val_accuracy: 0.7354 - val_recall_4: 0.7865\n",
            "Epoch 65/100\n",
            "684/684 [==============================] - 3s 5ms/step - loss: 0.5379 - accuracy: 0.7372 - recall_4: 0.8121 - val_loss: 0.5491 - val_accuracy: 0.7370 - val_recall_4: 0.7795\n",
            "Epoch 66/100\n",
            "684/684 [==============================] - 3s 5ms/step - loss: 0.5372 - accuracy: 0.7378 - recall_4: 0.8120 - val_loss: 0.5520 - val_accuracy: 0.7375 - val_recall_4: 0.7810\n",
            "Epoch 67/100\n",
            "684/684 [==============================] - 4s 5ms/step - loss: 0.5368 - accuracy: 0.7371 - recall_4: 0.8119 - val_loss: 0.5530 - val_accuracy: 0.7365 - val_recall_4: 0.7855\n",
            "Epoch 68/100\n",
            "684/684 [==============================] - 3s 5ms/step - loss: 0.5369 - accuracy: 0.7376 - recall_4: 0.8100 - val_loss: 0.5509 - val_accuracy: 0.7359 - val_recall_4: 0.7865\n",
            "Epoch 69/100\n",
            "684/684 [==============================] - 4s 5ms/step - loss: 0.5367 - accuracy: 0.7382 - recall_4: 0.8047 - val_loss: 0.5492 - val_accuracy: 0.7372 - val_recall_4: 0.7760\n",
            "Epoch 70/100\n",
            "684/684 [==============================] - 3s 5ms/step - loss: 0.5363 - accuracy: 0.7375 - recall_4: 0.8081 - val_loss: 0.5513 - val_accuracy: 0.7354 - val_recall_4: 0.7820\n",
            "Epoch 71/100\n",
            "684/684 [==============================] - 3s 5ms/step - loss: 0.5381 - accuracy: 0.7377 - recall_4: 0.8051 - val_loss: 0.5478 - val_accuracy: 0.7391 - val_recall_4: 0.7904\n",
            "Epoch 72/100\n",
            "684/684 [==============================] - 3s 5ms/step - loss: 0.5371 - accuracy: 0.7371 - recall_4: 0.8127 - val_loss: 0.5545 - val_accuracy: 0.7365 - val_recall_4: 0.7865\n",
            "Epoch 73/100\n",
            "684/684 [==============================] - 4s 5ms/step - loss: 0.5367 - accuracy: 0.7378 - recall_4: 0.8119 - val_loss: 0.5472 - val_accuracy: 0.7359 - val_recall_4: 0.7845\n",
            "Epoch 74/100\n",
            "684/684 [==============================] - 3s 5ms/step - loss: 0.5367 - accuracy: 0.7370 - recall_4: 0.8053 - val_loss: 0.5500 - val_accuracy: 0.7385 - val_recall_4: 0.7860\n",
            "Epoch 75/100\n",
            "684/684 [==============================] - 3s 5ms/step - loss: 0.5380 - accuracy: 0.7377 - recall_4: 0.8111 - val_loss: 0.5525 - val_accuracy: 0.7359 - val_recall_4: 0.7865\n",
            "Epoch 76/100\n",
            "684/684 [==============================] - 3s 5ms/step - loss: 0.5360 - accuracy: 0.7372 - recall_4: 0.8099 - val_loss: 0.5566 - val_accuracy: 0.7367 - val_recall_4: 0.7855\n",
            "Epoch 77/100\n",
            "684/684 [==============================] - 4s 5ms/step - loss: 0.5361 - accuracy: 0.7378 - recall_4: 0.8140 - val_loss: 0.5532 - val_accuracy: 0.7375 - val_recall_4: 0.7855\n",
            "Epoch 78/100\n",
            "684/684 [==============================] - 3s 5ms/step - loss: 0.5355 - accuracy: 0.7379 - recall_4: 0.8131 - val_loss: 0.5498 - val_accuracy: 0.7378 - val_recall_4: 0.7865\n",
            "Epoch 79/100\n",
            "684/684 [==============================] - 3s 5ms/step - loss: 0.5359 - accuracy: 0.7383 - recall_4: 0.8128 - val_loss: 0.5499 - val_accuracy: 0.7375 - val_recall_4: 0.7735\n",
            "Epoch 80/100\n",
            "684/684 [==============================] - 3s 5ms/step - loss: 0.5350 - accuracy: 0.7372 - recall_4: 0.8096 - val_loss: 0.5559 - val_accuracy: 0.7383 - val_recall_4: 0.7850\n",
            "Epoch 81/100\n",
            "684/684 [==============================] - 3s 5ms/step - loss: 0.5356 - accuracy: 0.7375 - recall_4: 0.8114 - val_loss: 0.5543 - val_accuracy: 0.7362 - val_recall_4: 0.7835\n",
            "Epoch 82/100\n",
            "684/684 [==============================] - 4s 5ms/step - loss: 0.5357 - accuracy: 0.7379 - recall_4: 0.8110 - val_loss: 0.5522 - val_accuracy: 0.7372 - val_recall_4: 0.7835\n",
            "Epoch 83/100\n",
            "684/684 [==============================] - 3s 5ms/step - loss: 0.5355 - accuracy: 0.7382 - recall_4: 0.8100 - val_loss: 0.5558 - val_accuracy: 0.7359 - val_recall_4: 0.7870\n",
            "Epoch 84/100\n",
            "684/684 [==============================] - 3s 5ms/step - loss: 0.5365 - accuracy: 0.7372 - recall_4: 0.8102 - val_loss: 0.5519 - val_accuracy: 0.7367 - val_recall_4: 0.7825\n",
            "Epoch 85/100\n",
            "684/684 [==============================] - 4s 5ms/step - loss: 0.5353 - accuracy: 0.7378 - recall_4: 0.8119 - val_loss: 0.5924 - val_accuracy: 0.7380 - val_recall_4: 0.7810\n",
            "Epoch 86/100\n",
            "684/684 [==============================] - 3s 5ms/step - loss: 0.5496 - accuracy: 0.7377 - recall_4: 0.8096 - val_loss: 0.5669 - val_accuracy: 0.7354 - val_recall_4: 0.7780\n",
            "Epoch 87/100\n",
            "684/684 [==============================] - 3s 5ms/step - loss: 0.5351 - accuracy: 0.7378 - recall_4: 0.8081 - val_loss: 0.5543 - val_accuracy: 0.7367 - val_recall_4: 0.7840\n",
            "Epoch 88/100\n",
            "684/684 [==============================] - 3s 5ms/step - loss: 0.5359 - accuracy: 0.7384 - recall_4: 0.8081 - val_loss: 0.5521 - val_accuracy: 0.7367 - val_recall_4: 0.7790\n",
            "Epoch 89/100\n",
            "684/684 [==============================] - 3s 5ms/step - loss: 0.5357 - accuracy: 0.7374 - recall_4: 0.8112 - val_loss: 0.5547 - val_accuracy: 0.7370 - val_recall_4: 0.7805\n",
            "Epoch 90/100\n",
            "684/684 [==============================] - 3s 4ms/step - loss: 0.5350 - accuracy: 0.7378 - recall_4: 0.8097 - val_loss: 0.5538 - val_accuracy: 0.7372 - val_recall_4: 0.7870\n",
            "Epoch 91/100\n",
            "684/684 [==============================] - 3s 5ms/step - loss: 0.5354 - accuracy: 0.7377 - recall_4: 0.8120 - val_loss: 0.5584 - val_accuracy: 0.7378 - val_recall_4: 0.7875\n",
            "Epoch 92/100\n",
            "684/684 [==============================] - 4s 5ms/step - loss: 0.5358 - accuracy: 0.7380 - recall_4: 0.8130 - val_loss: 0.5585 - val_accuracy: 0.7380 - val_recall_4: 0.7870\n",
            "Epoch 93/100\n",
            "684/684 [==============================] - 4s 5ms/step - loss: 0.5355 - accuracy: 0.7380 - recall_4: 0.8126 - val_loss: 0.5502 - val_accuracy: 0.7385 - val_recall_4: 0.7855\n",
            "Epoch 94/100\n",
            "684/684 [==============================] - 3s 5ms/step - loss: 0.5348 - accuracy: 0.7377 - recall_4: 0.8122 - val_loss: 0.5548 - val_accuracy: 0.7380 - val_recall_4: 0.7870\n",
            "Epoch 95/100\n",
            "684/684 [==============================] - 4s 5ms/step - loss: 0.5350 - accuracy: 0.7381 - recall_4: 0.8131 - val_loss: 0.5586 - val_accuracy: 0.7362 - val_recall_4: 0.7855\n",
            "Epoch 96/100\n",
            "684/684 [==============================] - 3s 5ms/step - loss: 0.5365 - accuracy: 0.7377 - recall_4: 0.8136 - val_loss: 0.5515 - val_accuracy: 0.7375 - val_recall_4: 0.7865\n",
            "Epoch 97/100\n",
            "684/684 [==============================] - 4s 5ms/step - loss: 0.5402 - accuracy: 0.7375 - recall_4: 0.8137 - val_loss: 0.5620 - val_accuracy: 0.7367 - val_recall_4: 0.7845\n",
            "Epoch 98/100\n",
            "684/684 [==============================] - 3s 5ms/step - loss: 0.5347 - accuracy: 0.7376 - recall_4: 0.8142 - val_loss: 0.5554 - val_accuracy: 0.7372 - val_recall_4: 0.7790\n",
            "Epoch 99/100\n",
            "684/684 [==============================] - 3s 5ms/step - loss: 0.5342 - accuracy: 0.7380 - recall_4: 0.8108 - val_loss: 0.5599 - val_accuracy: 0.7362 - val_recall_4: 0.7889\n",
            "Epoch 100/100\n",
            "684/684 [==============================] - 3s 4ms/step - loss: 0.5362 - accuracy: 0.7374 - recall_4: 0.8143 - val_loss: 0.5579 - val_accuracy: 0.7380 - val_recall_4: 0.7845\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ASt0xBh9DVYZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "22795912-c9c8-43f7-a5d3-31c6afd68565"
      },
      "source": [
        "# Evaluate the model using the test data\n",
        "model_loss, model_accuracy, model_Recall= nn_model.evaluate(X_test_scaled,y_test,verbose=2)\n",
        "print(f\"Loss: {model_loss}, Accuracy: {model_accuracy}\")"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "268/268 - 0s - loss: 0.5760 - accuracy: 0.7318 - recall_4: 0.8021\n",
            "Loss: 0.575981616973877, Accuracy: 0.7317784428596497\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "id": "fVZwcsPvsC0O",
        "outputId": "c6b003d4-2141-48d8-cafa-31ac53ba0cae"
      },
      "source": [
        "# Model Accuracy Graph\n",
        "plt.style.use('seaborn')\n",
        "plt.title('Accuracy \\n',fontsize=20)\n",
        "plt.plot(fit_model.history['accuracy'], 'tomato', label='Training Accuracy')\n",
        "plt.plot(fit_model.history['val_accuracy'], 'turquoise',label='Validation Accuracy')\n",
        "plt.xlabel('Number of Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "id": "88zMXnfysDIU",
        "outputId": "7948e8c4-53d6-47ae-f6ff-2307c7992ec5"
      },
      "source": [
        "# Model Loss Graph\n",
        "plt.style.use('seaborn')\n",
        "plt.title('Loss \\n',fontsize=20)\n",
        "plt.plot(fit_model.history['loss'], 'orchid', label='Training Loss')\n",
        "plt.plot(fit_model.history['val_loss'],'royalblue', label='Validation Loss')\n",
        "plt.xlabel('Number of Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "xfhvaC3wsNpW",
        "outputId": "f16e7ab2-b328-44af-e346-0f2beee52275"
      },
      "source": [
        "# Export our model to HDF5 file\n",
        "from google.colab import files\n",
        "\n",
        "nn_model.save('/content/Alphabet_Soup_model2.h5')\n",
        "files.download('/content/Alphabet_Soup_model2.h5')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_e51b26fe-e0bf-4a78-8378-6710b036a4bd\", \"Alphabet_Soup_model2.h5\", 2190896)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q3Qz-crSHTHM"
      },
      "source": [
        "# Model Evaluation 3"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yQ2MHXVTDb2a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e21c6ac4-d0e3-4a08-f464-98c804313f2d"
      },
      "source": [
        "# Define the model - deep neural net, i.e., the number of input features and hidden nodes for each layer.\n",
        "number_input_features = len( X_train_scaled[0])\n",
        "hidden_nodes_layer1=3\n",
        "hidden_nodes_layer2=15\n",
        "hidden_nodes_layer3=3\n",
        "\n",
        "nn_model = tf.keras.models.Sequential()\n",
        "\n",
        "# First hidden layer\n",
        "nn_model.add(tf.keras.layers.Dense(units=hidden_nodes_layer1, input_dim=number_input_features, activation='relu'))\n",
        "\n",
        "# Second hidden layer\n",
        "nn_model.add(tf.keras.layers.Dense(units=hidden_nodes_layer2, activation='relu'))\n",
        "\n",
        "# Third hidden layer\n",
        "nn_model.add(tf.keras.layers.Dense(units=hidden_nodes_layer3, activation='relu'))\n",
        "\n",
        "# Output layer\n",
        "nn_model.add(tf.keras.layers.Dense(units=1, activation='sigmoid'))\n",
        "\n",
        "# Check the structure of the model\n",
        "nn_model.summary()"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_5\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_16 (Dense)             (None, 3)                 132       \n",
            "_________________________________________________________________\n",
            "dense_17 (Dense)             (None, 15)                60        \n",
            "_________________________________________________________________\n",
            "dense_18 (Dense)             (None, 3)                 48        \n",
            "_________________________________________________________________\n",
            "dense_19 (Dense)             (None, 1)                 4         \n",
            "=================================================================\n",
            "Total params: 244\n",
            "Trainable params: 244\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3dhAy5ZRHZ-A"
      },
      "source": [
        "# Compile the model\n",
        "nn_model.compile(loss = 'binary_crossentropy', optimizer = 'adam', metrics=['accuracy', tf.keras.metrics.Recall()])"
      ],
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vwok5whqHtaI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fbeda453-d5d7-482d-ae5e-652f6ae4303b"
      },
      "source": [
        "fit_model = nn_model.fit( X_train_scaled,y_train,validation_split=0.15,epochs=100)"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.6441 - accuracy: 0.6804 - recall_5: 0.7185 - val_loss: 0.5935 - val_accuracy: 0.7269 - val_recall_5: 0.7710\n",
            "Epoch 2/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5882 - accuracy: 0.7188 - recall_5: 0.7939 - val_loss: 0.5734 - val_accuracy: 0.7315 - val_recall_5: 0.7899\n",
            "Epoch 3/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5774 - accuracy: 0.7215 - recall_5: 0.8056 - val_loss: 0.5683 - val_accuracy: 0.7318 - val_recall_5: 0.7899\n",
            "Epoch 4/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5737 - accuracy: 0.7225 - recall_5: 0.8075 - val_loss: 0.5664 - val_accuracy: 0.7318 - val_recall_5: 0.7894\n",
            "Epoch 5/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5709 - accuracy: 0.7235 - recall_5: 0.8006 - val_loss: 0.5643 - val_accuracy: 0.7300 - val_recall_5: 0.7959\n",
            "Epoch 6/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5688 - accuracy: 0.7247 - recall_5: 0.7930 - val_loss: 0.5609 - val_accuracy: 0.7344 - val_recall_5: 0.7740\n",
            "Epoch 7/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5676 - accuracy: 0.7265 - recall_5: 0.7894 - val_loss: 0.5606 - val_accuracy: 0.7344 - val_recall_5: 0.7735\n",
            "Epoch 8/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5672 - accuracy: 0.7270 - recall_5: 0.7897 - val_loss: 0.5604 - val_accuracy: 0.7346 - val_recall_5: 0.7730\n",
            "Epoch 9/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5667 - accuracy: 0.7269 - recall_5: 0.7913 - val_loss: 0.5602 - val_accuracy: 0.7354 - val_recall_5: 0.7735\n",
            "Epoch 10/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5667 - accuracy: 0.7271 - recall_5: 0.7884 - val_loss: 0.5599 - val_accuracy: 0.7352 - val_recall_5: 0.7750\n",
            "Epoch 11/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5664 - accuracy: 0.7272 - recall_5: 0.7910 - val_loss: 0.5598 - val_accuracy: 0.7334 - val_recall_5: 0.7730\n",
            "Epoch 12/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5659 - accuracy: 0.7277 - recall_5: 0.7936 - val_loss: 0.5606 - val_accuracy: 0.7341 - val_recall_5: 0.7770\n",
            "Epoch 13/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5657 - accuracy: 0.7276 - recall_5: 0.7920 - val_loss: 0.5604 - val_accuracy: 0.7344 - val_recall_5: 0.7765\n",
            "Epoch 14/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5654 - accuracy: 0.7280 - recall_5: 0.7971 - val_loss: 0.5605 - val_accuracy: 0.7336 - val_recall_5: 0.7750\n",
            "Epoch 15/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5653 - accuracy: 0.7278 - recall_5: 0.7934 - val_loss: 0.5605 - val_accuracy: 0.7339 - val_recall_5: 0.7790\n",
            "Epoch 16/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5653 - accuracy: 0.7274 - recall_5: 0.7943 - val_loss: 0.5611 - val_accuracy: 0.7334 - val_recall_5: 0.7800\n",
            "Epoch 17/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5650 - accuracy: 0.7277 - recall_5: 0.7960 - val_loss: 0.5597 - val_accuracy: 0.7344 - val_recall_5: 0.7800\n",
            "Epoch 18/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5647 - accuracy: 0.7276 - recall_5: 0.7952 - val_loss: 0.5596 - val_accuracy: 0.7339 - val_recall_5: 0.7775\n",
            "Epoch 19/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5643 - accuracy: 0.7280 - recall_5: 0.7932 - val_loss: 0.5601 - val_accuracy: 0.7339 - val_recall_5: 0.7785\n",
            "Epoch 20/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5638 - accuracy: 0.7280 - recall_5: 0.7950 - val_loss: 0.5605 - val_accuracy: 0.7328 - val_recall_5: 0.7740\n",
            "Epoch 21/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5637 - accuracy: 0.7277 - recall_5: 0.7948 - val_loss: 0.5613 - val_accuracy: 0.7331 - val_recall_5: 0.7909\n",
            "Epoch 22/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5632 - accuracy: 0.7283 - recall_5: 0.7956 - val_loss: 0.5609 - val_accuracy: 0.7334 - val_recall_5: 0.7780\n",
            "Epoch 23/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5631 - accuracy: 0.7277 - recall_5: 0.7928 - val_loss: 0.5597 - val_accuracy: 0.7336 - val_recall_5: 0.7780\n",
            "Epoch 24/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5626 - accuracy: 0.7277 - recall_5: 0.7946 - val_loss: 0.5597 - val_accuracy: 0.7321 - val_recall_5: 0.7715\n",
            "Epoch 25/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5620 - accuracy: 0.7292 - recall_5: 0.7903 - val_loss: 0.5588 - val_accuracy: 0.7336 - val_recall_5: 0.7735\n",
            "Epoch 26/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5617 - accuracy: 0.7285 - recall_5: 0.7906 - val_loss: 0.5593 - val_accuracy: 0.7323 - val_recall_5: 0.7725\n",
            "Epoch 27/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5615 - accuracy: 0.7290 - recall_5: 0.7931 - val_loss: 0.5582 - val_accuracy: 0.7331 - val_recall_5: 0.7735\n",
            "Epoch 28/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5615 - accuracy: 0.7284 - recall_5: 0.7903 - val_loss: 0.5587 - val_accuracy: 0.7336 - val_recall_5: 0.7730\n",
            "Epoch 29/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5611 - accuracy: 0.7294 - recall_5: 0.7942 - val_loss: 0.5592 - val_accuracy: 0.7336 - val_recall_5: 0.7745\n",
            "Epoch 30/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5610 - accuracy: 0.7287 - recall_5: 0.7903 - val_loss: 0.5593 - val_accuracy: 0.7328 - val_recall_5: 0.7725\n",
            "Epoch 31/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5607 - accuracy: 0.7292 - recall_5: 0.7912 - val_loss: 0.5577 - val_accuracy: 0.7323 - val_recall_5: 0.7770\n",
            "Epoch 32/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5599 - accuracy: 0.7293 - recall_5: 0.7949 - val_loss: 0.5573 - val_accuracy: 0.7336 - val_recall_5: 0.7680\n",
            "Epoch 33/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5596 - accuracy: 0.7289 - recall_5: 0.7889 - val_loss: 0.5573 - val_accuracy: 0.7328 - val_recall_5: 0.7800\n",
            "Epoch 34/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5598 - accuracy: 0.7295 - recall_5: 0.7905 - val_loss: 0.5569 - val_accuracy: 0.7334 - val_recall_5: 0.7855\n",
            "Epoch 35/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5598 - accuracy: 0.7292 - recall_5: 0.7902 - val_loss: 0.5563 - val_accuracy: 0.7334 - val_recall_5: 0.7710\n",
            "Epoch 36/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5595 - accuracy: 0.7289 - recall_5: 0.7904 - val_loss: 0.5566 - val_accuracy: 0.7334 - val_recall_5: 0.7670\n",
            "Epoch 37/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5592 - accuracy: 0.7293 - recall_5: 0.7870 - val_loss: 0.5572 - val_accuracy: 0.7326 - val_recall_5: 0.7720\n",
            "Epoch 38/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5586 - accuracy: 0.7296 - recall_5: 0.7881 - val_loss: 0.5558 - val_accuracy: 0.7331 - val_recall_5: 0.7700\n",
            "Epoch 39/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5585 - accuracy: 0.7292 - recall_5: 0.7876 - val_loss: 0.5562 - val_accuracy: 0.7334 - val_recall_5: 0.7661\n",
            "Epoch 40/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5583 - accuracy: 0.7293 - recall_5: 0.7868 - val_loss: 0.5558 - val_accuracy: 0.7341 - val_recall_5: 0.7651\n",
            "Epoch 41/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5581 - accuracy: 0.7289 - recall_5: 0.7872 - val_loss: 0.5554 - val_accuracy: 0.7336 - val_recall_5: 0.7531\n",
            "Epoch 42/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5579 - accuracy: 0.7294 - recall_5: 0.7853 - val_loss: 0.5555 - val_accuracy: 0.7344 - val_recall_5: 0.7760\n",
            "Epoch 43/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5572 - accuracy: 0.7293 - recall_5: 0.7866 - val_loss: 0.5555 - val_accuracy: 0.7339 - val_recall_5: 0.7810\n",
            "Epoch 44/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5568 - accuracy: 0.7292 - recall_5: 0.7837 - val_loss: 0.5546 - val_accuracy: 0.7328 - val_recall_5: 0.7750\n",
            "Epoch 45/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5562 - accuracy: 0.7290 - recall_5: 0.7877 - val_loss: 0.5532 - val_accuracy: 0.7328 - val_recall_5: 0.7646\n",
            "Epoch 46/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5563 - accuracy: 0.7287 - recall_5: 0.7871 - val_loss: 0.5531 - val_accuracy: 0.7328 - val_recall_5: 0.7621\n",
            "Epoch 47/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5561 - accuracy: 0.7296 - recall_5: 0.7860 - val_loss: 0.5541 - val_accuracy: 0.7331 - val_recall_5: 0.7670\n",
            "Epoch 48/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5561 - accuracy: 0.7303 - recall_5: 0.7907 - val_loss: 0.5540 - val_accuracy: 0.7339 - val_recall_5: 0.7750\n",
            "Epoch 49/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5560 - accuracy: 0.7301 - recall_5: 0.7878 - val_loss: 0.5541 - val_accuracy: 0.7339 - val_recall_5: 0.7695\n",
            "Epoch 50/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5564 - accuracy: 0.7297 - recall_5: 0.7871 - val_loss: 0.5531 - val_accuracy: 0.7331 - val_recall_5: 0.7661\n",
            "Epoch 51/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5562 - accuracy: 0.7293 - recall_5: 0.7864 - val_loss: 0.5530 - val_accuracy: 0.7346 - val_recall_5: 0.7675\n",
            "Epoch 52/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5559 - accuracy: 0.7296 - recall_5: 0.7889 - val_loss: 0.5528 - val_accuracy: 0.7331 - val_recall_5: 0.7735\n",
            "Epoch 53/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5559 - accuracy: 0.7304 - recall_5: 0.7913 - val_loss: 0.5532 - val_accuracy: 0.7326 - val_recall_5: 0.7675\n",
            "Epoch 54/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5558 - accuracy: 0.7311 - recall_5: 0.7890 - val_loss: 0.5531 - val_accuracy: 0.7339 - val_recall_5: 0.7596\n",
            "Epoch 55/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5555 - accuracy: 0.7292 - recall_5: 0.7859 - val_loss: 0.5538 - val_accuracy: 0.7339 - val_recall_5: 0.7611\n",
            "Epoch 56/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5555 - accuracy: 0.7295 - recall_5: 0.7877 - val_loss: 0.5535 - val_accuracy: 0.7321 - val_recall_5: 0.7800\n",
            "Epoch 57/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5553 - accuracy: 0.7302 - recall_5: 0.7872 - val_loss: 0.5533 - val_accuracy: 0.7336 - val_recall_5: 0.7616\n",
            "Epoch 58/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5558 - accuracy: 0.7312 - recall_5: 0.7883 - val_loss: 0.5537 - val_accuracy: 0.7344 - val_recall_5: 0.7675\n",
            "Epoch 59/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5553 - accuracy: 0.7308 - recall_5: 0.7913 - val_loss: 0.5524 - val_accuracy: 0.7339 - val_recall_5: 0.7695\n",
            "Epoch 60/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5556 - accuracy: 0.7293 - recall_5: 0.7906 - val_loss: 0.5527 - val_accuracy: 0.7334 - val_recall_5: 0.7651\n",
            "Epoch 61/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5552 - accuracy: 0.7305 - recall_5: 0.7871 - val_loss: 0.5524 - val_accuracy: 0.7339 - val_recall_5: 0.7685\n",
            "Epoch 62/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5554 - accuracy: 0.7292 - recall_5: 0.7842 - val_loss: 0.5538 - val_accuracy: 0.7334 - val_recall_5: 0.7596\n",
            "Epoch 63/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5552 - accuracy: 0.7311 - recall_5: 0.7865 - val_loss: 0.5528 - val_accuracy: 0.7344 - val_recall_5: 0.7735\n",
            "Epoch 64/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5553 - accuracy: 0.7296 - recall_5: 0.7866 - val_loss: 0.5525 - val_accuracy: 0.7336 - val_recall_5: 0.7700\n",
            "Epoch 65/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5555 - accuracy: 0.7305 - recall_5: 0.7855 - val_loss: 0.5539 - val_accuracy: 0.7326 - val_recall_5: 0.7621\n",
            "Epoch 66/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5549 - accuracy: 0.7311 - recall_5: 0.7875 - val_loss: 0.5533 - val_accuracy: 0.7328 - val_recall_5: 0.7949\n",
            "Epoch 67/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5552 - accuracy: 0.7316 - recall_5: 0.7897 - val_loss: 0.5536 - val_accuracy: 0.7331 - val_recall_5: 0.7656\n",
            "Epoch 68/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5552 - accuracy: 0.7310 - recall_5: 0.7872 - val_loss: 0.5520 - val_accuracy: 0.7341 - val_recall_5: 0.7710\n",
            "Epoch 69/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5551 - accuracy: 0.7297 - recall_5: 0.7855 - val_loss: 0.5539 - val_accuracy: 0.7346 - val_recall_5: 0.7631\n",
            "Epoch 70/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5552 - accuracy: 0.7306 - recall_5: 0.7865 - val_loss: 0.5540 - val_accuracy: 0.7344 - val_recall_5: 0.7760\n",
            "Epoch 71/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5550 - accuracy: 0.7299 - recall_5: 0.7880 - val_loss: 0.5528 - val_accuracy: 0.7349 - val_recall_5: 0.7755\n",
            "Epoch 72/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5551 - accuracy: 0.7302 - recall_5: 0.7881 - val_loss: 0.5522 - val_accuracy: 0.7339 - val_recall_5: 0.7666\n",
            "Epoch 73/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5548 - accuracy: 0.7300 - recall_5: 0.7856 - val_loss: 0.5522 - val_accuracy: 0.7334 - val_recall_5: 0.7591\n",
            "Epoch 74/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5547 - accuracy: 0.7303 - recall_5: 0.7889 - val_loss: 0.5521 - val_accuracy: 0.7331 - val_recall_5: 0.7621\n",
            "Epoch 75/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5549 - accuracy: 0.7304 - recall_5: 0.7859 - val_loss: 0.5525 - val_accuracy: 0.7326 - val_recall_5: 0.7646\n",
            "Epoch 76/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5548 - accuracy: 0.7295 - recall_5: 0.7855 - val_loss: 0.5535 - val_accuracy: 0.7354 - val_recall_5: 0.7700\n",
            "Epoch 77/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5550 - accuracy: 0.7303 - recall_5: 0.7884 - val_loss: 0.5530 - val_accuracy: 0.7321 - val_recall_5: 0.7621\n",
            "Epoch 78/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5551 - accuracy: 0.7302 - recall_5: 0.7875 - val_loss: 0.5531 - val_accuracy: 0.7341 - val_recall_5: 0.7785\n",
            "Epoch 79/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5548 - accuracy: 0.7302 - recall_5: 0.7889 - val_loss: 0.5532 - val_accuracy: 0.7344 - val_recall_5: 0.7899\n",
            "Epoch 80/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5549 - accuracy: 0.7303 - recall_5: 0.7924 - val_loss: 0.5556 - val_accuracy: 0.7341 - val_recall_5: 0.7456\n",
            "Epoch 81/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5549 - accuracy: 0.7303 - recall_5: 0.7884 - val_loss: 0.5531 - val_accuracy: 0.7344 - val_recall_5: 0.7775\n",
            "Epoch 82/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5548 - accuracy: 0.7305 - recall_5: 0.7885 - val_loss: 0.5530 - val_accuracy: 0.7359 - val_recall_5: 0.7845\n",
            "Epoch 83/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5552 - accuracy: 0.7300 - recall_5: 0.7907 - val_loss: 0.5534 - val_accuracy: 0.7349 - val_recall_5: 0.7690\n",
            "Epoch 84/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5550 - accuracy: 0.7298 - recall_5: 0.7901 - val_loss: 0.5530 - val_accuracy: 0.7349 - val_recall_5: 0.7656\n",
            "Epoch 85/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5550 - accuracy: 0.7297 - recall_5: 0.7848 - val_loss: 0.5533 - val_accuracy: 0.7346 - val_recall_5: 0.7894\n",
            "Epoch 86/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5550 - accuracy: 0.7309 - recall_5: 0.7920 - val_loss: 0.5534 - val_accuracy: 0.7367 - val_recall_5: 0.7695\n",
            "Epoch 87/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5551 - accuracy: 0.7296 - recall_5: 0.7910 - val_loss: 0.5528 - val_accuracy: 0.7362 - val_recall_5: 0.7750\n",
            "Epoch 88/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5549 - accuracy: 0.7308 - recall_5: 0.7896 - val_loss: 0.5529 - val_accuracy: 0.7344 - val_recall_5: 0.7680\n",
            "Epoch 89/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5550 - accuracy: 0.7300 - recall_5: 0.7901 - val_loss: 0.5528 - val_accuracy: 0.7346 - val_recall_5: 0.7720\n",
            "Epoch 90/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5548 - accuracy: 0.7312 - recall_5: 0.7909 - val_loss: 0.5531 - val_accuracy: 0.7352 - val_recall_5: 0.7755\n",
            "Epoch 91/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5548 - accuracy: 0.7309 - recall_5: 0.7911 - val_loss: 0.5524 - val_accuracy: 0.7352 - val_recall_5: 0.7695\n",
            "Epoch 92/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5549 - accuracy: 0.7302 - recall_5: 0.7906 - val_loss: 0.5537 - val_accuracy: 0.7349 - val_recall_5: 0.7626\n",
            "Epoch 93/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5549 - accuracy: 0.7307 - recall_5: 0.7901 - val_loss: 0.5527 - val_accuracy: 0.7362 - val_recall_5: 0.7800\n",
            "Epoch 94/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5547 - accuracy: 0.7299 - recall_5: 0.7917 - val_loss: 0.5533 - val_accuracy: 0.7362 - val_recall_5: 0.7700\n",
            "Epoch 95/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5548 - accuracy: 0.7304 - recall_5: 0.7897 - val_loss: 0.5560 - val_accuracy: 0.7341 - val_recall_5: 0.7377\n",
            "Epoch 96/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5553 - accuracy: 0.7295 - recall_5: 0.7910 - val_loss: 0.5533 - val_accuracy: 0.7354 - val_recall_5: 0.7631\n",
            "Epoch 97/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5547 - accuracy: 0.7309 - recall_5: 0.7913 - val_loss: 0.5525 - val_accuracy: 0.7349 - val_recall_5: 0.7636\n",
            "Epoch 98/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5546 - accuracy: 0.7300 - recall_5: 0.7893 - val_loss: 0.5539 - val_accuracy: 0.7344 - val_recall_5: 0.7745\n",
            "Epoch 99/100\n",
            "684/684 [==============================] - 1s 2ms/step - loss: 0.5546 - accuracy: 0.7301 - recall_5: 0.7907 - val_loss: 0.5567 - val_accuracy: 0.7339 - val_recall_5: 0.7720\n",
            "Epoch 100/100\n",
            "684/684 [==============================] - 2s 2ms/step - loss: 0.5548 - accuracy: 0.7300 - recall_5: 0.7925 - val_loss: 0.5524 - val_accuracy: 0.7365 - val_recall_5: 0.7675\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uAeEUdrHHgtT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "442902ba-c26a-44fa-bbed-dbe77c3cde12"
      },
      "source": [
        "# Evaluate the model using the test data\n",
        "model_loss, model_accuracy, model_Recall= nn_model.evaluate(X_test_scaled,y_test,verbose=2)\n",
        "print(f\"Loss: {model_loss}, Accuracy: {model_accuracy}\")"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "268/268 - 0s - loss: 0.5613 - accuracy: 0.7255 - recall_5: 0.7732\n",
            "Loss: 0.5612754821777344, Accuracy: 0.7254810333251953\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xRzY-NApz_YR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "outputId": "c81cffa3-019a-4662-df60-4a831bbd3418"
      },
      "source": [
        "# Model Accuracy Graph\n",
        "plt.style.use('seaborn')\n",
        "plt.title('Accuracy \\n',fontsize=20)\n",
        "plt.plot(fit_model.history['accuracy'], 'tomato', label='Training Accuracy')\n",
        "plt.plot(fit_model.history['val_accuracy'], 'turquoise',label='Validation Accuracy')\n",
        "plt.xlabel('Number of Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_CzZU5Ju32Fq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "outputId": "76be5792-4c08-46cf-838b-813b3942040a"
      },
      "source": [
        "# Model Loss Graph\n",
        "plt.style.use('seaborn')\n",
        "plt.title('Loss \\n',fontsize=20)\n",
        "plt.plot(fit_model.history['loss'], 'orchid', label='Training Loss')\n",
        "plt.plot(fit_model.history['val_loss'],'royalblue', label='Validation Loss')\n",
        "plt.xlabel('Number of Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vfXTiRXfHvPf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "526c6345-9e70-4ddf-d6e3-d21e459467ec"
      },
      "source": [
        "# Export our model to HDF5 file\n",
        "from google.colab import files\n",
        "\n",
        "nn_model.save('/content/Alphabet_Soup_model3.h5')\n",
        "files.download('/content/Alphabet_Soup_model3.h5')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_a6aa1b5d-65e3-40d6-ac8d-426c1ee64ed8\", \"Alphabet_Soup_model3.h5\", 41624)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}