AlphabetSoupCharity_Optimization.ipynb

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        "### Preprocessing the Data for a Neural Network"
      ]
    },
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              "  <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",
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            "text/plain": [
              "        EIN                                      NAME APPLICATION_TYPE  \\\n",
              "0  10520599              BLUE KNIGHTS MOTORCYCLE CLUB              T10   \n",
              "1  10531628    AMERICAN CHESAPEAKE CLUB CHARITABLE TR               T3   \n",
              "2  10547893        ST CLOUD PROFESSIONAL FIREFIGHTERS               T5   \n",
              "3  10553066            SOUTHSIDE ATHLETIC ASSOCIATION               T3   \n",
              "4  10556103  GENETIC RESEARCH INSTITUTE OF THE DESERT               T3   \n",
              "\n",
              "        AFFILIATION CLASSIFICATION      USE_CASE  ORGANIZATION  STATUS  \\\n",
              "0       Independent          C1000    ProductDev   Association       1   \n",
              "1       Independent          C2000  Preservation  Co-operative       1   \n",
              "2  CompanySponsored          C3000    ProductDev   Association       1   \n",
              "3  CompanySponsored          C2000  Preservation         Trust       1   \n",
              "4       Independent          C1000     Heathcare         Trust       1   \n",
              "\n",
              "      INCOME_AMT SPECIAL_CONSIDERATIONS  ASK_AMT  IS_SUCCESSFUL  \n",
              "0              0                      N     5000              1  \n",
              "1         1-9999                      N   108590              1  \n",
              "2              0                      N     5000              0  \n",
              "3    10000-24999                      N     6692              1  \n",
              "4  100000-499999                      N   142590              1  "
            ]
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          "execution_count": 1,
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      ],
      "source": [
        "# Import dependencies\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler,OneHotEncoder\n",
        "import pandas as pd\n",
        "import tensorflow as tf\n",
        "\n",
        "#  Import and read the charity_data.csv.\n",
        "import pandas as pd \n",
        "application_df = pd.read_csv(\"https://2u-data-curriculum-team.s3.amazonaws.com/dataviz-online/module_19/charity_data.csv\")\n",
        "application_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "c0pgaRo7euIQ"
      },
      "outputs": [],
      "source": [
        "# Drop the non-beneficial ID columns, 'EIN' and 'NAME'.\n",
        "application_df = application_df.drop(columns=['EIN', 'NAME'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
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        {
          "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"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Determine the number of unique values in each column.\n",
        "application_df.nunique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
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        "outputId": "1ca5cc17-cc31-46bd-c2af-3a4e44e99c90"
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        {
          "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",
              "T25        3\n",
              "T14        3\n",
              "T29        2\n",
              "T15        2\n",
              "T17        1\n",
              "Name: APPLICATION_TYPE, dtype: int64"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Look at APPLICATION_TYPE value counts for binning\n",
        "application_counts = application_df.APPLICATION_TYPE.value_counts()\n",
        "application_counts"
      ]
    },
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      "cell_type": "code",
      "execution_count": 5,
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        "id": "tEjGANrOfPd1",
        "outputId": "d26b5e7c-f36c-40c0-f92e-f47d61dae9fd"
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      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fab68880e90>"
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          "execution_count": 5,
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          "data": {
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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Visualize the value counts of APPLICATION_TYPE\n",
        "application_counts.plot.density()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MuWchw2NfbQq",
        "outputId": "6a941545-64df-41ab-fd57-0ab1133fcbc8"
      },
      "outputs": [
        {
          "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",
              "Other      120\n",
              "Name: APPLICATION_TYPE, dtype: int64"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Determine which values to replace if counts are less than 100.\n",
        "replace_application = list(application_counts[application_counts < 100].index)\n",
        "\n",
        "# Replace in dataframe\n",
        "for app in replace_application:\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()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O7KuHE04fdUo",
        "outputId": "ecbbd0de-5f29-4195-a2de-4288beb3f181"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "C1000    17326\n",
              "C2000     6074\n",
              "C1200     4837\n",
              "C3000     1918\n",
              "C2100     1883\n",
              "         ...  \n",
              "C4120        1\n",
              "C8210        1\n",
              "C2561        1\n",
              "C4500        1\n",
              "C2150        1\n",
              "Name: CLASSIFICATION, Length: 71, dtype: int64"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Look at CLASSIFICATION value counts for binning\n",
        "classification_counts = application_df.CLASSIFICATION.value_counts()\n",
        "classification_counts"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4kNRHZdgVyBl",
        "outputId": "a0828f7e-7399-42bc-eb8f-20b1f963751a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([17326,  6074,  4837,  1918,  1883,   777,   287,   194,   116,\n",
              "         114,   104,    95,    75,    58,    50,    36,    34,    32,\n",
              "          30,    20,    18,    16,    15,    14,    11,    10,     9,\n",
              "           7,     6,     5,     3,     2,     1])"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "classification_counts.unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "tyLBKk0wfiP0",
        "outputId": "8bd2f238-20f0-4a3f-98b7-3011a8a4afa6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fab687d9e10>"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Visualize the value counts of CLASSIFICATION\n",
        "classification_counts.plot.density()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B49M1LRGfj2Z",
        "outputId": "a4db54f3-d142-4955-d228-8abf456b23b9"
      },
      "outputs": [
        {
          "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"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Determine which values to replace if counts are less than 1000.\n",
        "replace_class = list(classification_counts[classification_counts < 1000].index)\n",
        "\n",
        "# Replace in dataframe\n",
        "for cls in replace_class:\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()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qAHxU_hRjSM5",
        "outputId": "b059092a-9fd2-4279-f335-5b76bf3b19c0"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "5000        25398\n",
              "10478           3\n",
              "15583           3\n",
              "63981           3\n",
              "6725            3\n",
              "            ...  \n",
              "5371754         1\n",
              "30060           1\n",
              "43091152        1\n",
              "18683           1\n",
              "36500179        1\n",
              "Name: ASK_AMT, Length: 8747, dtype: int64"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# # Look at ASK_AMT value counts\n",
        "# amount_counts = application_df.ASK_AMT.value_counts()\n",
        "# amount_counts\n",
        "application_df.ASK_AMT.value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "9WJ2wVYIjkc4"
      },
      "outputs": [],
      "source": [
        "# # Visualize the value counts of ASK_AMT\n",
        "# amount_counts.plot.density()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "l_8hxGqFn_Hl"
      },
      "outputs": [],
      "source": [
        "# # Set bins for ASK_AMT value column\n",
        "# bins = [0, 4999, 5000, 1000000000]\n",
        "# labels = ['<5000', '=5000', '>5000']\n",
        "# application_df.ASK_AMT = pd.cut(application_df.ASK_AMT, bins=bins, labels=labels)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "Z9iMKb39o1ie"
      },
      "outputs": [],
      "source": [
        "# # Look at ASK_AMT value counts after binned.\n",
        "# application_df = application_df.astype({'ASK_AMT':'object'})\n",
        "# application_df.ASK_AMT.value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "KrNEiPOCjMOf"
      },
      "outputs": [],
      "source": [
        "# # Look at STATUS value counts\n",
        "# application_df.STATUS.value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "v3m5A3yzkEtW"
      },
      "outputs": [],
      "source": [
        "# # Only keep activate STATUS cases.           \n",
        "# application_df = application_df[application_df.STATUS == 1]\n",
        "# application_df = application_df.drop(labels='STATUS', axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7MQFp6FTgayv",
        "outputId": "6ccf3d14-dd19-457c-d66a-419e345b7896"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Independent         18480\n",
              "CompanySponsored    15705\n",
              "Family/Parent          64\n",
              "National               33\n",
              "Regional               13\n",
              "Other                   4\n",
              "Name: AFFILIATION, dtype: int64"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# # Look at AFFILIATION value counts\n",
        "# affiliation_counts = application_df.AFFILIATION.value_counts()\n",
        "# affiliation_counts\n",
        "application_df.AFFILIATION.value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "uVkG8KvZhOSL"
      },
      "outputs": [],
      "source": [
        "# # Visualize the value counts of AFFILIATION\n",
        "# affiliation_counts.plot.density()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "aRr1fgEohWBp"
      },
      "outputs": [],
      "source": [
        "# # Determine which values to replace if counts are less than 50.\n",
        "# replace_affiliation = list(affiliation_counts[affiliation_counts < 50].index)\n",
        "\n",
        "# # Replace in dataframe\n",
        "# for cls in replace_affiliation:\n",
        "#     application_df.AFFILIATION = application_df.AFFILIATION.replace(cls,\"Others\")\n",
        "    \n",
        "# # Check to make sure binning was successful\n",
        "# application_df.AFFILIATION.value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6MS64mKKh75N",
        "outputId": "aacbf761-2b5a-49bf-e2c9-62e479268eb1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Preservation     28095\n",
              "ProductDev        5671\n",
              "CommunityServ      384\n",
              "Heathcare          146\n",
              "Other                3\n",
              "Name: USE_CASE, dtype: int64"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Look at USE_CASE value counts\n",
        "application_df.USE_CASE.value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uM6-vUFWiNwR",
        "outputId": "0c16d44c-520a-4541-b20c-f757af265bb1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Trust           23515\n",
              "Association     10255\n",
              "Co-operative      486\n",
              "Corporation        43\n",
              "Name: ORGANIZATION, dtype: int64"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Look at ORGANIZATION value counts\n",
        "application_df.ORGANIZATION.value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LUsu8XcSiT4Z",
        "outputId": "be460484-708e-463a-ef42-84fc57670a09"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0                24388\n",
              "25000-99999       3747\n",
              "100000-499999     3374\n",
              "1M-5M              955\n",
              "1-9999             728\n",
              "10000-24999        543\n",
              "10M-50M            240\n",
              "5M-10M             185\n",
              "50M+               139\n",
              "Name: INCOME_AMT, dtype: int64"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Look at INCOME_AMT value counts\n",
        "application_df.INCOME_AMT.value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_2YwzVmylDFI",
        "outputId": "5f6ed917-213a-4eea-e836-0bcc3120a494"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "APPLICATION_TYPE          object\n",
              "AFFILIATION               object\n",
              "CLASSIFICATION            object\n",
              "USE_CASE                  object\n",
              "ORGANIZATION              object\n",
              "STATUS                     int64\n",
              "INCOME_AMT                object\n",
              "SPECIAL_CONSIDERATIONS    object\n",
              "ASK_AMT                    int64\n",
              "IS_SUCCESSFUL              int64\n",
              "dtype: object"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "application_df.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "Z5u27Z-ufgc7"
      },
      "outputs": [],
      "source": [
        "# Change INCOME_AMT to numerical data.\n",
        "income_dict = {\n",
        "    '0' : 0,\n",
        "    '1-9999' : 10000,\n",
        "    '10000-24999' : 25000,\n",
        "    '25000-99999' : 100000,\n",
        "    '100000-499999' : 500000,\n",
        "    '1M-5M': 5000000,\n",
        "    '5M-10M' : 10000000,\n",
        "    '10M-50M' : 50000000,\n",
        "    '50M+' : 100000000\n",
        "}\n",
        "application_df = application_df.replace({'INCOME_AMT' : income_dict})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oxEztJMUlIvW",
        "outputId": "d69183c6-9244-4e59-caa3-60d033e37f8c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "APPLICATION_TYPE          object\n",
              "AFFILIATION               object\n",
              "CLASSIFICATION            object\n",
              "USE_CASE                  object\n",
              "ORGANIZATION              object\n",
              "STATUS                     int64\n",
              "INCOME_AMT                 int64\n",
              "SPECIAL_CONSIDERATIONS    object\n",
              "ASK_AMT                    int64\n",
              "IS_SUCCESSFUL              int64\n",
              "dtype: object"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "application_df.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cJ8LRnnPr91Y",
        "outputId": "6fa57d1b-3b66-40e9-87e3-a266dfc9a75e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "APPLICATION_TYPE            10\n",
              "AFFILIATION                  6\n",
              "CLASSIFICATION               6\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"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Determine the number of unique values in each column in new DataFrame.\n",
        "application_df.nunique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "bi2bj62RpSfE",
        "outputId": "651f341f-8fd2-4df1-ef90-11a974fb4e2b"
      },
      "outputs": [
        {
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            "text/plain": [
              "  APPLICATION_TYPE       AFFILIATION CLASSIFICATION      USE_CASE  \\\n",
              "0              T10       Independent          C1000    ProductDev   \n",
              "1               T3       Independent          C2000  Preservation   \n",
              "2               T5  CompanySponsored          C3000    ProductDev   \n",
              "3               T3  CompanySponsored          C2000  Preservation   \n",
              "4               T3       Independent          C1000     Heathcare   \n",
              "\n",
              "   ORGANIZATION  STATUS  INCOME_AMT SPECIAL_CONSIDERATIONS  ASK_AMT  \\\n",
              "0   Association       1           0                      N     5000   \n",
              "1  Co-operative       1       10000                      N   108590   \n",
              "2   Association       1           0                      N     5000   \n",
              "3         Trust       1       25000                      N     6692   \n",
              "4         Trust       1      500000                      N   142590   \n",
              "\n",
              "   IS_SUCCESSFUL  \n",
              "0              1  \n",
              "1              1  \n",
              "2              0  \n",
              "3              1  \n",
              "4              1  "
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Show the first 5 rows of application_df\n",
        "application_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gaesOGzWfncM",
        "outputId": "33792b6c-4f9e-4854-bd1d-2cd6bc9c74d4"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "APPLICATION_TYPE          10\n",
              "AFFILIATION                6\n",
              "CLASSIFICATION             6\n",
              "USE_CASE                   5\n",
              "ORGANIZATION               4\n",
              "SPECIAL_CONSIDERATIONS     2\n",
              "dtype: int64"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Generate our categorical variable lists\n",
        "application_cat = application_df.dtypes[application_df.dtypes == 'object'].index.tolist()\n",
        "application_df[application_cat].nunique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
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          "height": 314
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        "id": "qjaDfN20jyvj",
        "outputId": "8881933d-e9de-429d-b4bd-e5d2d567c192"
      },
      "outputs": [
        {
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              "      <th>APPLICATION_TYPE_T10</th>\n",
              "      <th>APPLICATION_TYPE_T19</th>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "    <tr>\n",
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              "<p>5 rows × 33 columns</p>\n",
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              "4                  0.0  ...                 1.0             0.0   \n",
              "\n",
              "   USE_CASE_Preservation  USE_CASE_ProductDev  ORGANIZATION_Association  \\\n",
              "0                    0.0                  1.0                       1.0   \n",
              "1                    1.0                  0.0                       0.0   \n",
              "2                    0.0                  1.0                       1.0   \n",
              "3                    1.0                  0.0                       0.0   \n",
              "4                    0.0                  0.0                       0.0   \n",
              "\n",
              "   ORGANIZATION_Co-operative  ORGANIZATION_Corporation  ORGANIZATION_Trust  \\\n",
              "0                        0.0                       0.0                 0.0   \n",
              "1                        1.0                       0.0                 0.0   \n",
              "2                        0.0                       0.0                 0.0   \n",
              "3                        0.0                       0.0                 1.0   \n",
              "4                        0.0                       0.0                 1.0   \n",
              "\n",
              "   SPECIAL_CONSIDERATIONS_N  SPECIAL_CONSIDERATIONS_Y  \n",
              "0                       1.0                       0.0  \n",
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              "4                       1.0                       0.0  \n",
              "\n",
              "[5 rows x 33 columns]"
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Create a OneHotEncoder instance\n",
        "enc = OneHotEncoder(sparse=False)\n",
        "\n",
        "# Fit and transform the OneHotEncoder using the categorical variable list\n",
        "encode_df = pd.DataFrame(enc.fit_transform(application_df[application_cat]))\n",
        "\n",
        "# Add the encoded variable names to the dataframe\n",
        "encode_df.columns = enc.get_feature_names_out(application_cat)\n",
        "encode_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
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        "id": "qaxXJVghj7XZ",
        "outputId": "662830a6-62f1-438e-9a8a-8625d3c6477b"
      },
      "outputs": [
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              "   STATUS  INCOME_AMT  ASK_AMT  IS_SUCCESSFUL  APPLICATION_TYPE_Other  \\\n",
              "0       1           0     5000              1                     0.0   \n",
              "1       1       10000   108590              1                     0.0   \n",
              "2       1           0     5000              0                     0.0   \n",
              "3       1       25000     6692              1                     0.0   \n",
              "4       1      500000   142590              1                     0.0   \n",
              "\n",
              "   APPLICATION_TYPE_T10  APPLICATION_TYPE_T19  APPLICATION_TYPE_T3  \\\n",
              "0                   1.0                   0.0                  0.0   \n",
              "1                   0.0                   0.0                  1.0   \n",
              "2                   0.0                   0.0                  0.0   \n",
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              "4                   0.0                   0.0                  1.0   \n",
              "\n",
              "   APPLICATION_TYPE_T4  APPLICATION_TYPE_T5  ...  USE_CASE_Heathcare  \\\n",
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              "2                  0.0                  1.0  ...                 0.0   \n",
              "3                  0.0                  0.0  ...                 0.0   \n",
              "4                  0.0                  0.0  ...                 1.0   \n",
              "\n",
              "   USE_CASE_Other  USE_CASE_Preservation  USE_CASE_ProductDev  \\\n",
              "0             0.0                    0.0                  1.0   \n",
              "1             0.0                    1.0                  0.0   \n",
              "2             0.0                    0.0                  1.0   \n",
              "3             0.0                    1.0                  0.0   \n",
              "4             0.0                    0.0                  0.0   \n",
              "\n",
              "   ORGANIZATION_Association  ORGANIZATION_Co-operative  \\\n",
              "0                       1.0                        0.0   \n",
              "1                       0.0                        1.0   \n",
              "2                       1.0                        0.0   \n",
              "3                       0.0                        0.0   \n",
              "4                       0.0                        0.0   \n",
              "\n",
              "   ORGANIZATION_Corporation  ORGANIZATION_Trust  SPECIAL_CONSIDERATIONS_N  \\\n",
              "0                       0.0                 0.0                       1.0   \n",
              "1                       0.0                 0.0                       1.0   \n",
              "2                       0.0                 0.0                       1.0   \n",
              "3                       0.0                 1.0                       1.0   \n",
              "4                       0.0                 1.0                       1.0   \n",
              "\n",
              "   SPECIAL_CONSIDERATIONS_Y  \n",
              "0                       0.0  \n",
              "1                       0.0  \n",
              "2                       0.0  \n",
              "3                       0.0  \n",
              "4                       0.0  \n",
              "\n",
              "[5 rows x 37 columns]"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Merge one-hot encoded features and drop the originals\n",
        "application_df = application_df.merge(encode_df, left_index=True, right_index=True)\n",
        "application_df = application_df.drop(labels=application_cat, axis=1)\n",
        "application_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "fpTA1BPUgtAq"
      },
      "outputs": [],
      "source": [
        "# Split our preprocessed data into our features and target arrays\n",
        "y = application_df.IS_SUCCESSFUL.values\n",
        "X = application_df.drop(columns=\"IS_SUCCESSFUL\").values\n",
        "\n",
        "# Split the preprocessed data into a training and testing dataset\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42, stratify=y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "OeHGNRdjuZpo"
      },
      "outputs": [],
      "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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "02XXS-S5t37m"
      },
      "source": [
        "### Compile, Train and Evaluate the Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "427EgihxoEIz",
        "outputId": "fb8a1a4b-e9e4-4020-b1d1-50cc22c912a8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(25724, 36)\n"
          ]
        }
      ],
      "source": [
        "# Check X_train_scaled samples\n",
        "print(X_train_scaled.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L3qHRZKity7_",
        "outputId": "a068dd83-47a7-4f95-f72b-185c5b289a14"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense (Dense)               (None, 200)               7400      \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 100)               20100     \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 50)                5050      \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 1)                 51        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 32,601\n",
            "Trainable params: 32,601\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "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",
        "# hidden_nodes_layer3 = 10\n",
        "hidden_nodes_layer1 = 200\n",
        "hidden_nodes_layer2 = 100\n",
        "hidden_nodes_layer3 = 50\n",
        "\n",
        "nn = tf.keras.models.Sequential()\n",
        "\n",
        "# First hidden layer\n",
        "nn.add(tf.keras.layers.Dense(units=hidden_nodes_layer1, input_dim=number_input_features, activation=\"tanh\"))\n",
        "\n",
        "# Second hidden layer\n",
        "nn.add(tf.keras.layers.Dense(units=hidden_nodes_layer2, activation=\"tanh\"))\n",
        "\n",
        "# Third hidden layer\n",
        "nn.add(tf.keras.layers.Dense(units=hidden_nodes_layer3, activation=\"tanh\"))\n",
        "\n",
        "# Output layer\n",
        "nn.add(tf.keras.layers.Dense(units=1, activation=\"sigmoid\"))\n",
        "\n",
        "# Check the structure of the model\n",
        "nn.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7p4sMPo9w_cz"
      },
      "source": [
        "### Save model's weights"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "EyvoszUUw9DJ"
      },
      "outputs": [],
      "source": [
        "# Import checkpoint dependencies\n",
        "import os\n",
        "from tensorflow.keras.callbacks import ModelCheckpoint\n",
        "\n",
        "# Define the checkpoint path and filenames\n",
        "os.makedirs(\"checkpoints_optimzation/\", exist_ok=True)\n",
        "checkpoint_path = \"checkpoints_optimzation/weights.{epoch:02d}.hdf5\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "id": "I6w366GUxXlI"
      },
      "outputs": [],
      "source": [
        "# Compile the model\n",
        "nn.compile(loss=\"binary_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])\n",
        "\n",
        "# batch_size = 804\n",
        "batch_size = 8040\n",
        "\n",
        "# Creat a callback that saves the model's weights every 5 epochs\n",
        "cp_callback = ModelCheckpoint(\n",
        "    filepath=checkpoint_path,\n",
        "    verbose=1,\n",
        "    save_weights_only=True,\n",
        "    # save_freq= int(batch_size * 5))\n",
        "    save_freq= 20)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hMofhmz8yLiU",
        "outputId": "ff4fa06b-912b-49f8-e0ae-2cbf2cbf6067"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/500\n",
            "4/4 [==============================] - 1s 32ms/step - loss: 0.5438 - accuracy: 0.7398\n",
            "Epoch 2/500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.5416 - accuracy: 0.7401\n",
            "Epoch 3/500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.5412 - accuracy: 0.7400\n",
            "Epoch 4/500\n",
            "4/4 [==============================] - 0s 33ms/step - loss: 0.5403 - accuracy: 0.7402\n",
            "Epoch 5/500\n",
            "3/4 [=====================>........] - ETA: 0s - loss: 0.5398 - accuracy: 0.7403\n",
            "Epoch 5: saving model to /drive/My Drive/checkpoints_optimzation/weights.05.hdf5\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.5398 - accuracy: 0.7401\n",
            "Epoch 6/500\n",
            "4/4 [==============================] - 0s 31ms/step - loss: 0.5402 - accuracy: 0.7400\n",
            "Epoch 7/500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.5397 - accuracy: 0.7389\n",
            "Epoch 8/500\n",
            "4/4 [==============================] - 0s 33ms/step - loss: 0.5399 - accuracy: 0.7402\n",
            "Epoch 9/500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.5398 - accuracy: 0.7399\n",
            "Epoch 10/500\n",
            "3/4 [=====================>........] - ETA: 0s - loss: 0.5384 - accuracy: 0.7410\n",
            "Epoch 10: saving model to /drive/My Drive/checkpoints_optimzation/weights.10.hdf5\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.5398 - accuracy: 0.7398\n",
            "Epoch 11/500\n",
            "4/4 [==============================] - 0s 33ms/step - loss: 0.5395 - accuracy: 0.7402\n",
            "Epoch 12/500\n",
            "4/4 [==============================] - 0s 31ms/step - loss: 0.5397 - accuracy: 0.7399\n",
            "Epoch 13/500\n",
            "4/4 [==============================] - 0s 31ms/step - loss: 0.5395 - accuracy: 0.7396\n",
            "Epoch 14/500\n",
            "4/4 [==============================] - 0s 33ms/step - loss: 0.5394 - accuracy: 0.7396\n",
            "Epoch 15/500\n",
            "3/4 [=====================>........] - ETA: 0s - loss: 0.5388 - accuracy: 0.7405\n",
            "Epoch 15: saving model to /drive/My Drive/checkpoints_optimzation/weights.15.hdf5\n",
            "4/4 [==============================] - 0s 41ms/step - loss: 0.5393 - accuracy: 0.7401\n",
            "Epoch 16/500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.5392 - accuracy: 0.7403\n",
            "Epoch 17/500\n",
            "4/4 [==============================] - 0s 32ms/step - loss: 0.5397 - accuracy: 0.7403\n",
            "Epoch 18/500\n",
            "4/4 [==============================] - 0s 31ms/step - loss: 0.5392 - accuracy: 0.7399\n",
            "Epoch 19/500\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.5395 - accuracy: 0.7400\n",
            "Epoch 20/500\n",
            "3/4 [=====================>........] - ETA: 0s - loss: 0.5381 - accuracy: 0.7415\n",
            "Epoch 20: saving model to /drive/My Drive/checkpoints_optimzation/weights.20.hdf5\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.5396 - accuracy: 0.7402\n",
            "Epoch 21/500\n",
            "4/4 [==============================] - 0s 32ms/step - loss: 0.5393 - accuracy: 0.7399\n",
            "Epoch 22/500\n",
            "4/4 [==============================] - 0s 32ms/step - loss: 0.5392 - accuracy: 0.7399\n",
            "Epoch 23/500\n",
            "4/4 [==============================] - 0s 33ms/step - loss: 0.5399 - accuracy: 0.7390\n",
            "Epoch 24/500\n",
            "4/4 [==============================] - 0s 33ms/step - loss: 0.5401 - accuracy: 0.7402\n",
            "Epoch 25/500\n",
            "3/4 [=====================>........] - ETA: 0s - loss: 0.5409 - accuracy: 0.7393\n",
            "Epoch 25: saving model to /drive/My Drive/checkpoints_optimzation/weights.25.hdf5\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.5399 - accuracy: 0.7399\n",
            "Epoch 26/500\n",
            "4/4 [==============================] - 0s 32ms/step - loss: 0.5402 - accuracy: 0.7398\n",
            "Epoch 27/500\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.5398 - accuracy: 0.7400\n",
            "Epoch 28/500\n",
            "4/4 [==============================] - 0s 33ms/step - loss: 0.5402 - accuracy: 0.7401\n",
            "Epoch 29/500\n",
            "4/4 [==============================] - 0s 31ms/step - loss: 0.5400 - accuracy: 0.7397\n",
            "Epoch 30/500\n",
            "3/4 [=====================>........] - ETA: 0s - loss: 0.5392 - accuracy: 0.7400\n",
            "Epoch 30: saving model to /drive/My Drive/checkpoints_optimzation/weights.30.hdf5\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.5398 - accuracy: 0.7396\n",
            "Epoch 31/500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.5396 - accuracy: 0.7394\n",
            "Epoch 32/500\n",
            "4/4 [==============================] - 0s 33ms/step - loss: 0.5400 - accuracy: 0.7397\n",
            "Epoch 33/500\n",
            "4/4 [==============================] - 0s 33ms/step - loss: 0.5394 - accuracy: 0.7398\n",
            "Epoch 34/500\n",
            "4/4 [==============================] - 0s 32ms/step - loss: 0.5399 - accuracy: 0.7395\n",
            "Epoch 35/500\n",
            "3/4 [=====================>........] - ETA: 0s - loss: 0.5382 - accuracy: 0.7401\n",
            "Epoch 35: saving model to /drive/My Drive/checkpoints_optimzation/weights.35.hdf5\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.5394 - accuracy: 0.7390\n",
            "Epoch 36/500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.5394 - accuracy: 0.7405\n",
            "Epoch 37/500\n",
            "4/4 [==============================] - 0s 31ms/step - loss: 0.5395 - accuracy: 0.7401\n",
            "Epoch 38/500\n",
            "4/4 [==============================] - 0s 31ms/step - loss: 0.5394 - accuracy: 0.7403\n",
            "Epoch 39/500\n",
            "4/4 [==============================] - 0s 32ms/step - loss: 0.5394 - accuracy: 0.7399\n",
            "Epoch 40/500\n",
            "3/4 [=====================>........] - ETA: 0s - loss: 0.5387 - accuracy: 0.7408\n",
            "Epoch 40: saving model to /drive/My Drive/checkpoints_optimzation/weights.40.hdf5\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.5393 - accuracy: 0.7399\n",
            "Epoch 41/500\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.5393 - accuracy: 0.7398\n",
            "Epoch 42/500\n",
            "4/4 [==============================] - 0s 33ms/step - loss: 0.5391 - accuracy: 0.7398\n",
            "Epoch 43/500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.5394 - accuracy: 0.7403\n",
            "Epoch 44/500\n",
            "4/4 [==============================] - 0s 32ms/step - loss: 0.5390 - accuracy: 0.7399\n",
            "Epoch 45/500\n",
            "3/4 [=====================>........] - ETA: 0s - loss: 0.5395 - accuracy: 0.7381\n",
            "Epoch 45: saving model to /drive/My Drive/checkpoints_optimzation/weights.45.hdf5\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.5392 - accuracy: 0.7391\n",
            "Epoch 46/500\n",
            "4/4 [==============================] - 0s 31ms/step - loss: 0.5393 - accuracy: 0.7398\n",
            "Epoch 47/500\n",
            "4/4 [==============================] - 0s 33ms/step - loss: 0.5391 - accuracy: 0.7400\n",
            "Epoch 48/500\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.5389 - accuracy: 0.7402\n",
            "Epoch 49/500\n",
            "4/4 [==============================] - 0s 33ms/step - loss: 0.5391 - accuracy: 0.7402\n",
            "Epoch 50/500\n",
            "3/4 [=====================>........] - ETA: 0s - loss: 0.5387 - accuracy: 0.7404\n",
            "Epoch 50: saving model to /drive/My Drive/checkpoints_optimzation/weights.50.hdf5\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.5391 - accuracy: 0.7395\n",
            "Epoch 51/500\n",
            "4/4 [==============================] - 0s 31ms/step - loss: 0.5392 - accuracy: 0.7404\n",
            "Epoch 52/500\n",
            "4/4 [==============================] - 0s 33ms/step - loss: 0.5393 - accuracy: 0.7392\n",
            "Epoch 53/500\n",
            "4/4 [==============================] - 0s 33ms/step - loss: 0.5393 - accuracy: 0.7393\n",
            "Epoch 54/500\n",
            "4/4 [==============================] - 0s 31ms/step - loss: 0.5395 - accuracy: 0.7399\n",
            "Epoch 55/500\n",
            "3/4 [=====================>........] - ETA: 0s - loss: 0.5398 - accuracy: 0.7392\n",
            "Epoch 55: saving model to /drive/My Drive/checkpoints_optimzation/weights.55.hdf5\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.5396 - accuracy: 0.7392\n",
            "Epoch 56/500\n",
            "4/4 [==============================] - 0s 32ms/step - loss: 0.5393 - accuracy: 0.7399\n",
            "Epoch 57/500\n",
            "4/4 [==============================] - 0s 31ms/step - loss: 0.5393 - accuracy: 0.7390\n",
            "Epoch 58/500\n",
            "4/4 [==============================] - 0s 33ms/step - loss: 0.5389 - accuracy: 0.7395\n",
            "Epoch 59/500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.5390 - accuracy: 0.7403\n",
            "Epoch 60/500\n",
            "3/4 [=====================>........] - ETA: 0s - loss: 0.5400 - accuracy: 0.7393\n",
            "Epoch 60: saving model to /drive/My Drive/checkpoints_optimzation/weights.60.hdf5\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.5392 - accuracy: 0.7401\n",
            "Epoch 61/500\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.5395 - accuracy: 0.7399\n",
            "Epoch 62/500\n",
            "4/4 [==============================] - 0s 32ms/step - loss: 0.5389 - accuracy: 0.7402\n",
            "Epoch 63/500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.5393 - accuracy: 0.7402\n",
            "Epoch 64/500\n",
            "4/4 [==============================] - 0s 33ms/step - loss: 0.5394 - accuracy: 0.7400\n",
            "Epoch 65/500\n",
            "3/4 [=====================>........] - ETA: 0s - loss: 0.5393 - accuracy: 0.7399\n",
            "Epoch 65: saving model to /drive/My Drive/checkpoints_optimzation/weights.65.hdf5\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.5389 - accuracy: 0.7401\n",
            "Epoch 66/500\n",
            "4/4 [==============================] - 0s 33ms/step - loss: 0.5393 - accuracy: 0.7400\n",
            "Epoch 67/500\n",
            "4/4 [==============================] - 0s 32ms/step - loss: 0.5388 - accuracy: 0.7404\n",
            "Epoch 68/500\n",
            "4/4 [==============================] - 0s 33ms/step - loss: 0.5393 - accuracy: 0.7402\n",
            "Epoch 69/500\n",
            "4/4 [==============================] - 0s 33ms/step - loss: 0.5390 - accuracy: 0.7400\n",
            "Epoch 70/500\n",
            "3/4 [=====================>........] - ETA: 0s - loss: 0.5390 - accuracy: 0.7404\n",
            "Epoch 70: saving model to /drive/My Drive/checkpoints_optimzation/weights.70.hdf5\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 0.5389 - accuracy: 0.7402\n",
            "Epoch 71/500\n",
            "4/4 [==============================] - 0s 33ms/step - loss: 0.5394 - accuracy: 0.7398\n",
            "Epoch 72/500\n",
            "4/4 [==============================] - 0s 32ms/step - loss: 0.5389 - accuracy: 0.7399\n",
            "Epoch 73/500\n",
            "4/4 [==============================] - 0s 32ms/step - loss: 0.5389 - accuracy: 0.7406\n",
            "Epoch 74/500\n",
            "4/4 [==============================] - 0s 32ms/step - loss: 0.5389 - accuracy: 0.7395\n",
            "Epoch 75/500\n",
            "3/4 [=====================>........] - ETA: 0s - loss: 0.5398 - accuracy: 0.7386\n",
            "Epoch 75: saving model to /drive/My Drive/checkpoints_optimzation/weights.75.hdf5\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.5390 - accuracy: 0.7396\n",
            "Epoch 76/500\n",
            "4/4 [==============================] - 0s 32ms/step - loss: 0.5390 - accuracy: 0.7397\n",
            "Epoch 77/500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.5388 - accuracy: 0.7403\n",
            "Epoch 78/500\n",
            "4/4 [==============================] - 0s 32ms/step - loss: 0.5388 - accuracy: 0.7401\n",
            "Epoch 79/500\n",
            "4/4 [==============================] - 0s 33ms/step - loss: 0.5389 - accuracy: 0.7397\n",
            "Epoch 80/500\n",
            "3/4 [=====================>........] - ETA: 0s - loss: 0.5380 - accuracy: 0.7415\n",
            "Epoch 80: saving model to /drive/My Drive/checkpoints_optimzation/weights.80.hdf5\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.5388 - accuracy: 0.7402\n",
            "Epoch 81/500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.5388 - accuracy: 0.7400\n",
            "Epoch 82/500\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.5386 - accuracy: 0.7405\n",
            "Epoch 83/500\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.5388 - accuracy: 0.7399\n",
            "Epoch 84/500\n",
            "4/4 [==============================] - 0s 33ms/step - loss: 0.5395 - accuracy: 0.7390\n",
            "Epoch 85/500\n",
            "3/4 [=====================>........] - ETA: 0s - loss: 0.5382 - accuracy: 0.7401\n",
            "Epoch 85: saving model to /drive/My Drive/checkpoints_optimzation/weights.85.hdf5\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.5393 - accuracy: 0.7397\n",
            "Epoch 86/500\n",
            "4/4 [==============================] - 0s 33ms/step - loss: 0.5388 - accuracy: 0.7399\n",
            "Epoch 87/500\n",
            "4/4 [==============================] - 0s 33ms/step - loss: 0.5393 - accuracy: 0.7401\n",
            "Epoch 88/500\n",
            "4/4 [==============================] - 0s 33ms/step - loss: 0.5390 - accuracy: 0.7401\n",
            "Epoch 89/500\n",
            "4/4 [==============================] - 0s 32ms/step - loss: 0.5389 - accuracy: 0.7393\n",
            "Epoch 90/500\n",
            "3/4 [=====================>........] - ETA: 0s - loss: 0.5387 - accuracy: 0.7404\n",
            "Epoch 90: saving model to /drive/My Drive/checkpoints_optimzation/weights.90.hdf5\n",
            "4/4 [==============================] - 0s 41ms/step - loss: 0.5389 - accuracy: 0.7397\n",
            "Epoch 91/500\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.5391 - accuracy: 0.7404\n",
            "Epoch 92/500\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.5387 - accuracy: 0.7402\n",
            "Epoch 93/500\n",
            "4/4 [==============================] - 0s 33ms/step - loss: 0.5388 - accuracy: 0.7400\n",
            "Epoch 94/500\n",
            "4/4 [==============================] - 0s 33ms/step - loss: 0.5389 - accuracy: 0.7399\n",
            "Epoch 95/500\n",
            "3/4 [=====================>........] - ETA: 0s - loss: 0.5389 - accuracy: 0.7402\n",
            "Epoch 95: saving model to /drive/My Drive/checkpoints_optimzation/weights.95.hdf5\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.5390 - accuracy: 0.7399\n",
            "Epoch 96/500\n",
            "4/4 [==============================] - 0s 32ms/step - loss: 0.5388 - accuracy: 0.7402\n",
            "Epoch 97/500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.5387 - accuracy: 0.7402\n",
            "Epoch 98/500\n",
            "4/4 [==============================] - 0s 33ms/step - loss: 0.5390 - accuracy: 0.7396\n",
            "Epoch 99/500\n",
            "4/4 [==============================] - 0s 33ms/step - loss: 0.5389 - accuracy: 0.7397\n",
            "Epoch 100/500\n",
            "3/4 [=====================>........] - ETA: 0s - loss: 0.5388 - accuracy: 0.7400\n",
            "Epoch 100: saving model to /drive/My Drive/checkpoints_optimzation/weights.100.hdf5\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.5388 - accuracy: 0.7405\n",
            "Epoch 101/500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.5387 - accuracy: 0.7405\n",
            "Epoch 102/500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.5393 - accuracy: 0.7401\n",
            "Epoch 103/500\n",
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            "4/4 [==============================] - 0s 42ms/step - loss: 0.5372 - accuracy: 0.7406\n",
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            "4/4 [==============================] - 0s 43ms/step - loss: 0.5373 - accuracy: 0.7409\n",
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            "4/4 [==============================] - 0s 39ms/step - loss: 0.5384 - accuracy: 0.7396\n",
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            "4/4 [==============================] - 0s 41ms/step - loss: 0.5374 - accuracy: 0.7407\n",
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            "4/4 [==============================] - 0s 45ms/step - loss: 0.5374 - accuracy: 0.7406\n",
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            "4/4 [==============================] - 0s 38ms/step - loss: 0.5370 - accuracy: 0.7409\n",
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            "4/4 [==============================] - 0s 39ms/step - loss: 0.5377 - accuracy: 0.7385\n",
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            "4/4 [==============================] - 0s 40ms/step - loss: 0.5370 - accuracy: 0.7411\n",
            "Epoch 371/500\n",
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            "Epoch 375/500\n",
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            "Epoch 375: saving model to /drive/My Drive/checkpoints_optimzation/weights.375.hdf5\n",
            "4/4 [==============================] - 0s 41ms/step - loss: 0.5383 - accuracy: 0.7403\n",
            "Epoch 376/500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.5374 - accuracy: 0.7403\n",
            "Epoch 377/500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.5376 - accuracy: 0.7407\n",
            "Epoch 378/500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.5374 - accuracy: 0.7403\n",
            "Epoch 379/500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.5372 - accuracy: 0.7412\n",
            "Epoch 380/500\n",
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            "Epoch 380: saving model to /drive/My Drive/checkpoints_optimzation/weights.380.hdf5\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 0.5368 - accuracy: 0.7411\n",
            "Epoch 381/500\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.5373 - accuracy: 0.7402\n",
            "Epoch 382/500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.5370 - accuracy: 0.7407\n",
            "Epoch 383/500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.5369 - accuracy: 0.7405\n",
            "Epoch 384/500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.5368 - accuracy: 0.7408\n",
            "Epoch 385/500\n",
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            "Epoch 385: saving model to /drive/My Drive/checkpoints_optimzation/weights.385.hdf5\n",
            "4/4 [==============================] - 0s 41ms/step - loss: 0.5367 - accuracy: 0.7408\n",
            "Epoch 386/500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.5369 - accuracy: 0.7411\n",
            "Epoch 387/500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.5369 - accuracy: 0.7412\n",
            "Epoch 388/500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.5369 - accuracy: 0.7411\n",
            "Epoch 389/500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.5375 - accuracy: 0.7409\n",
            "Epoch 390/500\n",
            "3/4 [=====================>........] - ETA: 0s - loss: 0.5384 - accuracy: 0.7405\n",
            "Epoch 390: saving model to /drive/My Drive/checkpoints_optimzation/weights.390.hdf5\n",
            "4/4 [==============================] - 0s 41ms/step - loss: 0.5383 - accuracy: 0.7407\n",
            "Epoch 391/500\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.5388 - accuracy: 0.7403\n",
            "Epoch 392/500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.5375 - accuracy: 0.7404\n",
            "Epoch 393/500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.5378 - accuracy: 0.7404\n",
            "Epoch 394/500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.5373 - accuracy: 0.7408\n",
            "Epoch 395/500\n",
            "2/4 [==============>...............] - ETA: 0s - loss: 0.5373 - accuracy: 0.7414\n",
            "Epoch 395: saving model to /drive/My Drive/checkpoints_optimzation/weights.395.hdf5\n",
            "4/4 [==============================] - 0s 42ms/step - loss: 0.5370 - accuracy: 0.7410\n",
            "Epoch 396/500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.5373 - accuracy: 0.7406\n",
            "Epoch 397/500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.5367 - accuracy: 0.7403\n",
            "Epoch 398/500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.5367 - accuracy: 0.7397\n",
            "Epoch 399/500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.5368 - accuracy: 0.7404\n",
            "Epoch 400/500\n",
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            "Epoch 400: saving model to /drive/My Drive/checkpoints_optimzation/weights.400.hdf5\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 0.5367 - accuracy: 0.7407\n",
            "Epoch 401/500\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 0.5367 - accuracy: 0.7410\n",
            "Epoch 402/500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.5369 - accuracy: 0.7409\n",
            "Epoch 403/500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.5366 - accuracy: 0.7407\n",
            "Epoch 404/500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.5369 - accuracy: 0.7411\n",
            "Epoch 405/500\n",
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            "Epoch 405: saving model to /drive/My Drive/checkpoints_optimzation/weights.405.hdf5\n",
            "4/4 [==============================] - 0s 41ms/step - loss: 0.5376 - accuracy: 0.7381\n",
            "Epoch 406/500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.5369 - accuracy: 0.7407\n",
            "Epoch 407/500\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.5368 - accuracy: 0.7411\n",
            "Epoch 408/500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.5370 - accuracy: 0.7413\n",
            "Epoch 409/500\n",
            "4/4 [==============================] - 0s 45ms/step - loss: 0.5373 - accuracy: 0.7408\n",
            "Epoch 410/500\n",
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            "Epoch 410: saving model to /drive/My Drive/checkpoints_optimzation/weights.410.hdf5\n",
            "4/4 [==============================] - 0s 42ms/step - loss: 0.5369 - accuracy: 0.7413\n",
            "Epoch 411/500\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.5376 - accuracy: 0.7402\n",
            "Epoch 412/500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.5369 - accuracy: 0.7413\n",
            "Epoch 413/500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.5368 - accuracy: 0.7405\n",
            "Epoch 414/500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.5370 - accuracy: 0.7412\n",
            "Epoch 415/500\n",
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            "Epoch 415: saving model to /drive/My Drive/checkpoints_optimzation/weights.415.hdf5\n",
            "4/4 [==============================] - 0s 45ms/step - loss: 0.5366 - accuracy: 0.7411\n",
            "Epoch 416/500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.5367 - accuracy: 0.7413\n",
            "Epoch 417/500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.5367 - accuracy: 0.7402\n",
            "Epoch 418/500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.5380 - accuracy: 0.7406\n",
            "Epoch 419/500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.5373 - accuracy: 0.7397\n",
            "Epoch 420/500\n",
            "3/4 [=====================>........] - ETA: 0s - loss: 0.5373 - accuracy: 0.7397\n",
            "Epoch 420: saving model to /drive/My Drive/checkpoints_optimzation/weights.420.hdf5\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.5375 - accuracy: 0.7397\n",
            "Epoch 421/500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.5366 - accuracy: 0.7409\n",
            "Epoch 422/500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.5373 - accuracy: 0.7408\n",
            "Epoch 423/500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.5377 - accuracy: 0.7396\n",
            "Epoch 424/500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.5367 - accuracy: 0.7411\n",
            "Epoch 425/500\n",
            "2/4 [==============>...............] - ETA: 0s - loss: 0.5331 - accuracy: 0.7441\n",
            "Epoch 425: saving model to /drive/My Drive/checkpoints_optimzation/weights.425.hdf5\n",
            "4/4 [==============================] - 0s 44ms/step - loss: 0.5376 - accuracy: 0.7411\n",
            "Epoch 426/500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.5373 - accuracy: 0.7413\n",
            "Epoch 427/500\n",
            "4/4 [==============================] - 0s 43ms/step - loss: 0.5371 - accuracy: 0.7411\n",
            "Epoch 428/500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.5369 - accuracy: 0.7408\n",
            "Epoch 429/500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.5368 - accuracy: 0.7411\n",
            "Epoch 430/500\n",
            "2/4 [==============>...............] - ETA: 0s - loss: 0.5413 - accuracy: 0.7380\n",
            "Epoch 430: saving model to /drive/My Drive/checkpoints_optimzation/weights.430.hdf5\n",
            "4/4 [==============================] - 0s 41ms/step - loss: 0.5369 - accuracy: 0.7410\n",
            "Epoch 431/500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.5365 - accuracy: 0.7408\n",
            "Epoch 432/500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.5367 - accuracy: 0.7409\n",
            "Epoch 433/500\n",
            "4/4 [==============================] - 0s 43ms/step - loss: 0.5370 - accuracy: 0.7407\n",
            "Epoch 434/500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.5372 - accuracy: 0.7410\n",
            "Epoch 435/500\n",
            "3/4 [=====================>........] - ETA: 0s - loss: 0.5359 - accuracy: 0.7413\n",
            "Epoch 435: saving model to /drive/My Drive/checkpoints_optimzation/weights.435.hdf5\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 0.5370 - accuracy: 0.7408\n",
            "Epoch 436/500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.5367 - accuracy: 0.7410\n",
            "Epoch 437/500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.5370 - accuracy: 0.7413\n",
            "Epoch 438/500\n",
            "4/4 [==============================] - 0s 33ms/step - loss: 0.5367 - accuracy: 0.7411\n",
            "Epoch 439/500\n",
            "4/4 [==============================] - 0s 44ms/step - loss: 0.5364 - accuracy: 0.7410\n",
            "Epoch 440/500\n",
            "3/4 [=====================>........] - ETA: 0s - loss: 0.5360 - accuracy: 0.7425\n",
            "Epoch 440: saving model to /drive/My Drive/checkpoints_optimzation/weights.440.hdf5\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.5369 - accuracy: 0.7412\n",
            "Epoch 441/500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.5371 - accuracy: 0.7416\n",
            "Epoch 442/500\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.5369 - accuracy: 0.7408\n",
            "Epoch 443/500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.5365 - accuracy: 0.7406\n",
            "Epoch 444/500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.5365 - accuracy: 0.7406\n",
            "Epoch 445/500\n",
            "3/4 [=====================>........] - ETA: 0s - loss: 0.5361 - accuracy: 0.7412\n",
            "Epoch 445: saving model to /drive/My Drive/checkpoints_optimzation/weights.445.hdf5\n",
            "4/4 [==============================] - 0s 44ms/step - loss: 0.5365 - accuracy: 0.7413\n",
            "Epoch 446/500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.5367 - accuracy: 0.7410\n",
            "Epoch 447/500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.5366 - accuracy: 0.7405\n",
            "Epoch 448/500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.5364 - accuracy: 0.7407\n",
            "Epoch 449/500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.5364 - accuracy: 0.7408\n",
            "Epoch 450/500\n",
            "3/4 [=====================>........] - ETA: 0s - loss: 0.5363 - accuracy: 0.7408\n",
            "Epoch 450: saving model to /drive/My Drive/checkpoints_optimzation/weights.450.hdf5\n",
            "4/4 [==============================] - 0s 41ms/step - loss: 0.5362 - accuracy: 0.7409\n",
            "Epoch 451/500\n",
            "4/4 [==============================] - 0s 43ms/step - loss: 0.5366 - accuracy: 0.7411\n",
            "Epoch 452/500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.5365 - accuracy: 0.7407\n",
            "Epoch 453/500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.5364 - accuracy: 0.7414\n",
            "Epoch 454/500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.5364 - accuracy: 0.7414\n",
            "Epoch 455/500\n",
            "3/4 [=====================>........] - ETA: 0s - loss: 0.5356 - accuracy: 0.7411\n",
            "Epoch 455: saving model to /drive/My Drive/checkpoints_optimzation/weights.455.hdf5\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 0.5366 - accuracy: 0.7409\n",
            "Epoch 456/500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.5368 - accuracy: 0.7405\n",
            "Epoch 457/500\n",
            "4/4 [==============================] - 0s 41ms/step - loss: 0.5366 - accuracy: 0.7409\n",
            "Epoch 458/500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.5367 - accuracy: 0.7411\n",
            "Epoch 459/500\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 0.5364 - accuracy: 0.7408\n",
            "Epoch 460/500\n",
            "2/4 [==============>...............] - ETA: 0s - loss: 0.5365 - accuracy: 0.7412\n",
            "Epoch 460: saving model to /drive/My Drive/checkpoints_optimzation/weights.460.hdf5\n",
            "4/4 [==============================] - 0s 42ms/step - loss: 0.5364 - accuracy: 0.7414\n",
            "Epoch 461/500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.5366 - accuracy: 0.7412\n",
            "Epoch 462/500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.5362 - accuracy: 0.7415\n",
            "Epoch 463/500\n",
            "4/4 [==============================] - 0s 44ms/step - loss: 0.5367 - accuracy: 0.7410\n",
            "Epoch 464/500\n",
            "4/4 [==============================] - 0s 42ms/step - loss: 0.5364 - accuracy: 0.7410\n",
            "Epoch 465/500\n",
            "3/4 [=====================>........] - ETA: 0s - loss: 0.5361 - accuracy: 0.7421\n",
            "Epoch 465: saving model to /drive/My Drive/checkpoints_optimzation/weights.465.hdf5\n",
            "4/4 [==============================] - 0s 41ms/step - loss: 0.5370 - accuracy: 0.7411\n",
            "Epoch 466/500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.5368 - accuracy: 0.7409\n",
            "Epoch 467/500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.5368 - accuracy: 0.7411\n",
            "Epoch 468/500\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 0.5367 - accuracy: 0.7403\n",
            "Epoch 469/500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.5361 - accuracy: 0.7414\n",
            "Epoch 470/500\n",
            "3/4 [=====================>........] - ETA: 0s - loss: 0.5371 - accuracy: 0.7414\n",
            "Epoch 470: saving model to /drive/My Drive/checkpoints_optimzation/weights.470.hdf5\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 0.5371 - accuracy: 0.7415\n",
            "Epoch 471/500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.5365 - accuracy: 0.7411\n",
            "Epoch 472/500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.5368 - accuracy: 0.7405\n",
            "Epoch 473/500\n",
            "4/4 [==============================] - 0s 42ms/step - loss: 0.5363 - accuracy: 0.7411\n",
            "Epoch 474/500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.5372 - accuracy: 0.7417\n",
            "Epoch 475/500\n",
            "3/4 [=====================>........] - ETA: 0s - loss: 0.5376 - accuracy: 0.7402\n",
            "Epoch 475: saving model to /drive/My Drive/checkpoints_optimzation/weights.475.hdf5\n",
            "4/4 [==============================] - 0s 41ms/step - loss: 0.5363 - accuracy: 0.7413\n",
            "Epoch 476/500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.5364 - accuracy: 0.7408\n",
            "Epoch 477/500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.5360 - accuracy: 0.7413\n",
            "Epoch 478/500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.5361 - accuracy: 0.7410\n",
            "Epoch 479/500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.5364 - accuracy: 0.7411\n",
            "Epoch 480/500\n",
            "3/4 [=====================>........] - ETA: 0s - loss: 0.5375 - accuracy: 0.7409\n",
            "Epoch 480: saving model to /drive/My Drive/checkpoints_optimzation/weights.480.hdf5\n",
            "4/4 [==============================] - 0s 42ms/step - loss: 0.5366 - accuracy: 0.7413\n",
            "Epoch 481/500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.5367 - accuracy: 0.7406\n",
            "Epoch 482/500\n",
            "4/4 [==============================] - 0s 41ms/step - loss: 0.5364 - accuracy: 0.7411\n",
            "Epoch 483/500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.5370 - accuracy: 0.7382\n",
            "Epoch 484/500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.5374 - accuracy: 0.7409\n",
            "Epoch 485/500\n",
            "3/4 [=====================>........] - ETA: 0s - loss: 0.5353 - accuracy: 0.7422\n",
            "Epoch 485: saving model to /drive/My Drive/checkpoints_optimzation/weights.485.hdf5\n",
            "4/4 [==============================] - 0s 41ms/step - loss: 0.5365 - accuracy: 0.7413\n",
            "Epoch 486/500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.5366 - accuracy: 0.7406\n",
            "Epoch 487/500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.5366 - accuracy: 0.7410\n",
            "Epoch 488/500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.5365 - accuracy: 0.7411\n",
            "Epoch 489/500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.5365 - accuracy: 0.7413\n",
            "Epoch 490/500\n",
            "3/4 [=====================>........] - ETA: 0s - loss: 0.5376 - accuracy: 0.7408\n",
            "Epoch 490: saving model to /drive/My Drive/checkpoints_optimzation/weights.490.hdf5\n",
            "4/4 [==============================] - 0s 41ms/step - loss: 0.5371 - accuracy: 0.7411\n",
            "Epoch 491/500\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 0.5372 - accuracy: 0.7413\n",
            "Epoch 492/500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.5374 - accuracy: 0.7411\n",
            "Epoch 493/500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.5364 - accuracy: 0.7413\n",
            "Epoch 494/500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.5368 - accuracy: 0.7411\n",
            "Epoch 495/500\n",
            "3/4 [=====================>........] - ETA: 0s - loss: 0.5365 - accuracy: 0.7410\n",
            "Epoch 495: saving model to /drive/My Drive/checkpoints_optimzation/weights.495.hdf5\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 0.5365 - accuracy: 0.7409\n",
            "Epoch 496/500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.5367 - accuracy: 0.7407\n",
            "Epoch 497/500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.5366 - accuracy: 0.7406\n",
            "Epoch 498/500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.5365 - accuracy: 0.7411\n",
            "Epoch 499/500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.5366 - accuracy: 0.7409\n",
            "Epoch 500/500\n",
            "3/4 [=====================>........] - ETA: 0s - loss: 0.5368 - accuracy: 0.7396\n",
            "Epoch 500: saving model to /drive/My Drive/checkpoints_optimzation/weights.500.hdf5\n",
            "4/4 [==============================] - 0s 46ms/step - loss: 0.5366 - accuracy: 0.7392\n"
          ]
        }
      ],
      "source": [
        "# Train the model\n",
        "fit_model = nn.fit(X_train_scaled, y_train, epochs=500, callbacks=[cp_callback], batch_size=batch_size)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3TtOuRm0yNxB",
        "outputId": "324ddcce-6825-48eb-f285-2c7f51e3c4cf"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "268/268 - 0s - loss: 0.5566 - accuracy: 0.7233 - 420ms/epoch - 2ms/step\n",
            "Loss: 0.5566207766532898, Accuracy: 0.7232652902603149\n"
          ]
        }
      ],
      "source": [
        "# Evaluate the model using the test data\n",
        "model_loss, model_accuracy = nn.evaluate(X_test_scaled,y_test,verbose=2)\n",
        "print(f\"Loss: {model_loss}, Accuracy: {model_accuracy}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iPK9YPOaaZbf"
      },
      "source": [
        "Proprocessing Optimization Attempts: \n",
        "   - original: loss: 0.5701 - accuracy: 0.7221 \n",
        "   - bucketing application_accounts < 100: loss: 0.5694 - accuracy: 0.7227 √\n",
        "   - bucketing classification_counts < 150: loss: 0.5708 - accuracy: 0.7220 X\n",
        "   - bucketing classification_counts < 500: loss: 0.5728 - accuracy: 0.7203 X\n",
        "   - filtering active status: loss: 0.6929 - accuracy: 0.5582 X\n",
        "   - bucketing affiliation_counts < 50: loss: 0.5705 - accuracy: 0.7206 X\n",
        "   - binning and encoding ASK_AMT: loss: 0.5727 - accuracy: 0.7195 X\n",
        "   - changing INCOME_AMT to numeric data: loss: 0.5727 - accuracy: 0.7232 √\n",
        "\n",
        "Model Optimization Attempts: \n",
        "   - adding hidden layer: loss: 0.5726 - accuracy: 0.7238 √\n",
        "   - changing activation function to tanh: loss: 0.5685 - accuracy: 0.7243 √\n",
        "   - adding neurons to hidden layers: loss: 0.5704 - accuracy: 0.7250 √\n",
        "   - decreasing batch size: loss: 0.5744 - accuracy: 0.7222 X\n",
        "   - increasing batch size: loss: 0.5433 - accuracy: 0.7392 √\n",
        "   - adding epochs: loss: 0.5438 - accuracy: 0.7398 √"
      ]
    }
  ],
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