AlphabetSoupCharity_Optimization.ipynb

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      "source": [
        "### Deliverable 1: Preprocessing the Data for a Neural Network"
      ]
    },
    {
      "cell_type": "code",
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      "source": [
        "# Import our dependencies\n",
        "\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",
        "from tensorflow.keras import layers\n",
        "\n",
        "#  Import and read the charity_data.csv.\n",
        "import pandas as pd \n",
        "application_df = pd.read_csv(\"https://raw.githubusercontent.com/Aryanakh7/Neural_Networks/main/charity_data.csv\")\n",
        "application_df.head()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<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": {
            "tags": []
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          "execution_count": 25
        }
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      "source": [
        "# Drop the non-beneficial ID columns, 'EIN' and 'NAME'.\n",
        "application_df = application_df.drop([\"EIN\", \"NAME\", \"STATUS\", \"SPECIAL_CONSIDERATIONS\"], 1)\n",
        "application_df"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <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>INCOME_AMT</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>0</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>1-9999</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>0</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>10000-24999</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>100000-499999</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",
              "    </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>0</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>0</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>0</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>0</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>1M-5M</td>\n",
              "      <td>36500179</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>34299 rows × 8 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 8 columns]"
            ]
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            "tags": []
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          "execution_count": 26
        }
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        "outputId": "30af9c29-5dae-45fc-d1c7-02158e5f9b5a"
      },
      "source": [
        "# Determine the number of unique values in each column.\n",
        "application_cat = application_df.dtypes[application_df.dtypes == \"object\"].index.tolist()\n",
        "application_df[application_cat].nunique()"
      ],
      "execution_count": 27,
      "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",
              "INCOME_AMT           9\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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          "base_uri": "https://localhost:8080/"
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        "id": "EJ__reK-sFYF",
        "outputId": "b6283b07-67e5-43be-f4cc-59231722a746"
      },
      "source": [
        "# Look at APPLICATION_TYPE value counts for binning\n",
        "application_counts = application_df.APPLICATION_TYPE.value_counts()\n",
        "application_counts "
      ],
      "execution_count": 28,
      "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",
              "T29        2\n",
              "T15        2\n",
              "T17        1\n",
              "Name: APPLICATION_TYPE, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
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        "id": "JKzImx6QsFYM",
        "outputId": "5c16110d-1a43-4722-be00-76b15888f306"
      },
      "source": [
        "# Visualize the value counts of APPLICATION_TYPE\n",
        "application_counts.plot.density()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f066826ba90>"
            ]
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          "metadata": {
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          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ry5q0z71sFYO",
        "outputId": "efa06188-c095-4713-9ffd-44f980fd571c"
      },
      "source": [
        "# Determine which values to replace if counts are less than ...?\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()"
      ],
      "execution_count": 30,
      "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",
              "Other      120\n",
              "Name: APPLICATION_TYPE, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "57xm1k-osFYQ",
        "outputId": "a7a115fe-b593-4438-84ca-c67fff4b5bb9"
      },
      "source": [
        "# Look at CLASSIFICATION value counts for binning\n",
        "classification_counts = application_df.CLASSIFICATION.value_counts()\n",
        "classification_counts"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "C1000    17326\n",
              "C2000     6074\n",
              "C1200     4837\n",
              "C3000     1918\n",
              "C2100     1883\n",
              "         ...  \n",
              "C1570        1\n",
              "C2570        1\n",
              "C2380        1\n",
              "C1248        1\n",
              "C6100        1\n",
              "Name: CLASSIFICATION, Length: 71, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "YBMms0SdsFYW",
        "outputId": "1dcbea7b-274b-42ba-a537-50f3a7b16171"
      },
      "source": [
        "# Visualize the value counts of CLASSIFICATION\n",
        "classification_counts.plot.density()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f066837cf90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2snhgK0LsFYa",
        "outputId": "70b10171-8df7-430b-ac3b-66a3b1b96081"
      },
      "source": [
        "# Determine which values to replace if counts are less than ..?\n",
        "replace_class = list(classification_counts [classification_counts < 1200].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()"
      ],
      "execution_count": 33,
      "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": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "axEbFfG6sFYe",
        "outputId": "a3072525-2b5d-4f8f-b6dd-fbfdfe7c3bd1"
      },
      "source": [
        "# Generate our categorical variable lists\n",
        "application_cat = application_df.dtypes[application_df.dtypes == \"object\"].index.tolist()\n",
        "application_df[application_cat].nunique()"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "APPLICATION_TYPE    10\n",
              "AFFILIATION          6\n",
              "CLASSIFICATION       6\n",
              "USE_CASE             5\n",
              "ORGANIZATION         4\n",
              "INCOME_AMT           9\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "_URBejXVsFYg",
        "outputId": "8cf730b6-f908-4950-8747-43466dac6834"
      },
      "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(application_cat)\n",
        "encode_df.head()"
      ],
      "execution_count": 35,
      "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_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",
              "      <th>APPLICATION_TYPE_T6</th>\n",
              "      <th>APPLICATION_TYPE_T7</th>\n",
              "      <th>APPLICATION_TYPE_T8</th>\n",
              "      <th>APPLICATION_TYPE_T9</th>\n",
              "      <th>AFFILIATION_CompanySponsored</th>\n",
              "      <th>AFFILIATION_Family/Parent</th>\n",
              "      <th>AFFILIATION_Independent</th>\n",
              "      <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",
              "      <th>CLASSIFICATION_Other</th>\n",
              "      <th>USE_CASE_CommunityServ</th>\n",
              "      <th>USE_CASE_Heathcare</th>\n",
              "      <th>USE_CASE_Other</th>\n",
              "      <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",
              "      <th>ORGANIZATION_Trust</th>\n",
              "      <th>INCOME_AMT_0</th>\n",
              "      <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",
              "      <th>INCOME_AMT_1M-5M</th>\n",
              "      <th>INCOME_AMT_25000-99999</th>\n",
              "      <th>INCOME_AMT_50M+</th>\n",
              "      <th>INCOME_AMT_5M-10M</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   APPLICATION_TYPE_Other  ...  INCOME_AMT_5M-10M\n",
              "0                     0.0  ...                0.0\n",
              "1                     0.0  ...                0.0\n",
              "2                     0.0  ...                0.0\n",
              "3                     0.0  ...                0.0\n",
              "4                     0.0  ...                0.0\n",
              "\n",
              "[5 rows x 40 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "Tyn64Rb8sFYl",
        "outputId": "1c19fa39-36c0-4857-875c-aeb07519be7b"
      },
      "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(application_cat,1)\n",
        "application_df.head()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\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>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",
              "      <th>APPLICATION_TYPE_T6</th>\n",
              "      <th>APPLICATION_TYPE_T7</th>\n",
              "      <th>APPLICATION_TYPE_T8</th>\n",
              "      <th>APPLICATION_TYPE_T9</th>\n",
              "      <th>AFFILIATION_CompanySponsored</th>\n",
              "      <th>AFFILIATION_Family/Parent</th>\n",
              "      <th>AFFILIATION_Independent</th>\n",
              "      <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",
              "      <th>CLASSIFICATION_Other</th>\n",
              "      <th>USE_CASE_CommunityServ</th>\n",
              "      <th>USE_CASE_Heathcare</th>\n",
              "      <th>USE_CASE_Other</th>\n",
              "      <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",
              "      <th>ORGANIZATION_Trust</th>\n",
              "      <th>INCOME_AMT_0</th>\n",
              "      <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",
              "      <th>INCOME_AMT_1M-5M</th>\n",
              "      <th>INCOME_AMT_25000-99999</th>\n",
              "      <th>INCOME_AMT_50M+</th>\n",
              "      <th>INCOME_AMT_5M-10M</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5000</td>\n",
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              "      <td>1.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>108590</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>1.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>1.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5000</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6692</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>142590</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   ASK_AMT  IS_SUCCESSFUL  ...  INCOME_AMT_50M+  INCOME_AMT_5M-10M\n",
              "0     5000              1  ...              0.0                0.0\n",
              "1   108590              1  ...              0.0                0.0\n",
              "2     5000              0  ...              0.0                0.0\n",
              "3     6692              1  ...              0.0                0.0\n",
              "4   142590              1  ...              0.0                0.0\n",
              "\n",
              "[5 rows x 42 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4Y3IO2XysFYp"
      },
      "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\"],1).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=10)"
      ],
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k89FALcMsFYv"
      },
      "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": 38,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pf8WSKbFsFY2"
      },
      "source": [
        "### Deliverable 2: Compile, Train and Evaluate the Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "su3T3mAEsFY3",
        "outputId": "f166f0ba-2b9d-491e-d332-06ff31754968"
      },
      "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[0])\n",
        "hidden_nodes_layer1 =  14\n",
        "hidden_nodes_layer2 = 7\n",
        "\n",
        "nn = tf.keras.models.Sequential()\n",
        "\n",
        "# First hidden layer\n",
        "nn.add(\n",
        "    tf.keras.layers.Dense(units=hidden_nodes_layer1, input_dim=number_input_features, activation=\"relu\")\n",
        ")\n",
        "\n",
        "# Second hidden layer\n",
        "nn.add(tf.keras.layers.Dense(units=hidden_nodes_layer2, activation=\"relu\"))\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()"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_6 (Dense)              (None, 14)                588       \n",
            "_________________________________________________________________\n",
            "dense_7 (Dense)              (None, 7)                 105       \n",
            "_________________________________________________________________\n",
            "dense_8 (Dense)              (None, 1)                 8         \n",
            "=================================================================\n",
            "Total params: 701\n",
            "Trainable params: 701\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "khsr5EyiXp7M"
      },
      "source": [
        "# Import checkpoint dependencies\n",
        "import os\n",
        "from tensorflow.keras.callbacks import ModelCheckpoint"
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G4tAjFIfsFZC"
      },
      "source": [
        "# Compile the model\n",
        "nn.compile(loss=\"binary_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])"
      ],
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DdvlbE7Fn126"
      },
      "source": [
        "\n",
        "os.makedirs(\"checkpoints_optimized/\",exist_ok=True)\n",
        "checkpoint_path = \"checkpoints_optimized/weights.{epoch:02d}.hdf5\""
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UTgj7uzKsFZD",
        "outputId": "53265774-6adb-4e1d-8088-17740180b2d9"
      },
      "source": [
        "# Create 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=3000)\n",
        "\n",
        "\n",
        "# Train the model\n",
        "fit_model = nn.fit(X_train,y_train,epochs=100, callbacks=[cp_callback]) "
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 2/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 3/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6908 - accuracy: 0.5341\n",
            "Epoch 4/100\n",
            "578/804 [====================>.........] - ETA: 0s - loss: 0.6909 - accuracy: 0.5336\n",
            "Epoch 00004: saving model to checkpoints_optimized/weights.04.hdf5\n",
            "804/804 [==============================] - 1s 1ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 5/100\n",
            "804/804 [==============================] - 1s 1ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 6/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6908 - accuracy: 0.5341\n",
            "Epoch 7/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 8/100\n",
            "369/804 [============>.................] - ETA: 0s - loss: 0.6905 - accuracy: 0.5363\n",
            "Epoch 00008: saving model to checkpoints_optimized/weights.08.hdf5\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 9/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 10/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 11/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6908 - accuracy: 0.5341\n",
            "Epoch 12/100\n",
            "140/804 [====>.........................] - ETA: 1s - loss: 0.6904 - accuracy: 0.5371\n",
            "Epoch 00012: saving model to checkpoints_optimized/weights.12.hdf5\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 13/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 14/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 15/100\n",
            "712/804 [=========================>....] - ETA: 0s - loss: 0.6909 - accuracy: 0.5336\n",
            "Epoch 00015: saving model to checkpoints_optimized/weights.15.hdf5\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 16/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 17/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6908 - accuracy: 0.5341\n",
            "Epoch 18/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 19/100\n",
            "510/804 [==================>...........] - ETA: 0s - loss: 0.6909 - accuracy: 0.5335\n",
            "Epoch 00019: saving model to checkpoints_optimized/weights.19.hdf5\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 20/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 21/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 22/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 23/100\n",
            "294/804 [=========>....................] - ETA: 0s - loss: 0.6906 - accuracy: 0.5356\n",
            "Epoch 00023: saving model to checkpoints_optimized/weights.23.hdf5\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 24/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 25/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6908 - accuracy: 0.5341\n",
            "Epoch 26/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 27/100\n",
            " 80/804 [=>............................] - ETA: 1s - loss: 0.6910 - accuracy: 0.5332\n",
            "Epoch 00027: saving model to checkpoints_optimized/weights.27.hdf5\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6908 - accuracy: 0.5341\n",
            "Epoch 28/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 29/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 30/100\n",
            "683/804 [========================>.....] - ETA: 0s - loss: 0.6911 - accuracy: 0.5320\n",
            "Epoch 00030: saving model to checkpoints_optimized/weights.30.hdf5\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6908 - accuracy: 0.5341\n",
            "Epoch 31/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 32/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 33/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 34/100\n",
            "450/804 [===============>..............] - ETA: 0s - loss: 0.6911 - accuracy: 0.5326\n",
            "Epoch 00034: saving model to checkpoints_optimized/weights.34.hdf5\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 35/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6908 - accuracy: 0.5341\n",
            "Epoch 36/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 37/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 38/100\n",
            "252/804 [========>.....................] - ETA: 0s - loss: 0.6907 - accuracy: 0.5350\n",
            "Epoch 00038: saving model to checkpoints_optimized/weights.38.hdf5\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 39/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 40/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6908 - accuracy: 0.5341\n",
            "Epoch 41/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 42/100\n",
            " 32/804 [>.............................] - ETA: 1s - loss: 0.6919 - accuracy: 0.5264\n",
            "Epoch 00042: saving model to checkpoints_optimized/weights.42.hdf5\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 43/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 44/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 45/100\n",
            "600/804 [=====================>........] - ETA: 0s - loss: 0.6909 - accuracy: 0.5336\n",
            "Epoch 00045: saving model to checkpoints_optimized/weights.45.hdf5\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 46/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 47/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 48/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6908 - accuracy: 0.5341\n",
            "Epoch 49/100\n",
            "400/804 [=============>................] - ETA: 0s - loss: 0.6902 - accuracy: 0.5388\n",
            "Epoch 00049: saving model to checkpoints_optimized/weights.49.hdf5\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 50/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 51/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 52/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 53/100\n",
            "160/804 [====>.........................] - ETA: 1s - loss: 0.6922 - accuracy: 0.5234\n",
            "Epoch 00053: saving model to checkpoints_optimized/weights.53.hdf5\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 54/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6908 - accuracy: 0.5341\n",
            "Epoch 55/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 56/100\n",
            "766/804 [===========================>..] - ETA: 0s - loss: 0.6908 - accuracy: 0.5342\n",
            "Epoch 00056: saving model to checkpoints_optimized/weights.56.hdf5\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6908 - accuracy: 0.5341\n",
            "Epoch 57/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 58/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 59/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 60/100\n",
            "553/804 [===================>..........] - ETA: 0s - loss: 0.6907 - accuracy: 0.5351\n",
            "Epoch 00060: saving model to checkpoints_optimized/weights.60.hdf5\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 61/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6908 - accuracy: 0.5341\n",
            "Epoch 62/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 63/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 64/100\n",
            "340/804 [===========>..................] - ETA: 0s - loss: 0.6910 - accuracy: 0.5327\n",
            "Epoch 00064: saving model to checkpoints_optimized/weights.64.hdf5\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 65/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 66/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 67/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6908 - accuracy: 0.5341\n",
            "Epoch 68/100\n",
            " 99/804 [==>...........................] - ETA: 1s - loss: 0.6908 - accuracy: 0.5347\n",
            "Epoch 00068: saving model to checkpoints_optimized/weights.68.hdf5\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 69/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 70/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 71/100\n",
            "705/804 [=========================>....] - ETA: 0s - loss: 0.6907 - accuracy: 0.5354\n",
            "Epoch 00071: saving model to checkpoints_optimized/weights.71.hdf5\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 72/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 73/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6908 - accuracy: 0.5341\n",
            "Epoch 74/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 75/100\n",
            "499/804 [=================>............] - ETA: 0s - loss: 0.6909 - accuracy: 0.5340\n",
            "Epoch 00075: saving model to checkpoints_optimized/weights.75.hdf5\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 76/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 77/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 78/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6908 - accuracy: 0.5341\n",
            "Epoch 79/100\n",
            "276/804 [=========>....................] - ETA: 0s - loss: 0.6911 - accuracy: 0.5326\n",
            "Epoch 00079: saving model to checkpoints_optimized/weights.79.hdf5\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 80/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6908 - accuracy: 0.5341\n",
            "Epoch 81/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 82/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 83/100\n",
            " 62/804 [=>............................] - ETA: 1s - loss: 0.6901 - accuracy: 0.5393\n",
            "Epoch 00083: saving model to checkpoints_optimized/weights.83.hdf5\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 84/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 85/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 86/100\n",
            "648/804 [=======================>......] - ETA: 0s - loss: 0.6909 - accuracy: 0.5336\n",
            "Epoch 00086: saving model to checkpoints_optimized/weights.86.hdf5\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 87/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6908 - accuracy: 0.5341\n",
            "Epoch 88/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 89/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 90/100\n",
            "439/804 [===============>..............] - ETA: 0s - loss: 0.6909 - accuracy: 0.5336\n",
            "Epoch 00090: saving model to checkpoints_optimized/weights.90.hdf5\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 91/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6908 - accuracy: 0.5341\n",
            "Epoch 92/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 93/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6908 - accuracy: 0.5341\n",
            "Epoch 94/100\n",
            "195/804 [======>.......................] - ETA: 0s - loss: 0.6917 - accuracy: 0.5277\n",
            "Epoch 00094: saving model to checkpoints_optimized/weights.94.hdf5\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 95/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6908 - accuracy: 0.5341\n",
            "Epoch 96/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 97/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 98/100\n",
            "  1/804 [..............................] - ETA: 7s - loss: 0.6750 - accuracy: 0.6562\n",
            "Epoch 00098: saving model to checkpoints_optimized/weights.98.hdf5\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 99/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n",
            "Epoch 100/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.6909 - accuracy: 0.5341\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zvTpBmutsFZJ",
        "outputId": "f1c598c6-8a3d-49c1-b5a2-d5e9fdcc9511"
      },
      "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}\")"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "268/268 - 0s - loss: 0.7471 - accuracy: 0.5511\n",
            "Loss: 0.7470656037330627, Accuracy: 0.5511370301246643\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xcKw8N30r0lu"
      },
      "source": [
        "nn.save('AlphabetSoupCharity_Optimization.h5')"
      ],
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UQtdrWfua1mZ"
      },
      "source": [
        "from keras.models import load_model\n",
        "model = load_model('AlphabetSoupCharity_Optimization.h5')"
      ],
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "N76CqvmNYige",
        "outputId": "8ce60f59-2db1-496c-8ad4-2e3ec00dc2be"
      },
      "source": [
        "from google.colab import files\n",
        "files.download('AlphabetSoupCharity_Optimization.h5')\n",
        "\n",
        "from google.colab import files\n"
      ],
      "execution_count": 60,
      "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": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_91a20ded-a2bd-4970-acc2-6565444fdc15\", \"AlphabetSoupCharity_Optimization.h5\", 39464)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}