AlphabetSoupCharity.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/annakthrnlee/Neural_Network_Charity_Analysis/blob/main/AlphabetSoupCharity.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rml1ldRH329N",
        "outputId": "f0e92f30-7c3b-423e-a868-b67e1f76d84a"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "path_csv = \"C://Users//Desktop//Master//Neural_Network_Charity_Analysis\""
      ],
      "metadata": {
        "id": "uZf5i11cUfmf"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "### Deliverable 1: Preprocessing the Data for a Neural Network"
      ],
      "metadata": {
        "id": "ZYdyEbSU4Ez8"
      },
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Import our 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"
      ],
      "metadata": {
        "id": "0UIRy4dW4LUV"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Import and read the charity.csv - google colab style ;)\n",
        "from google.colab import files\n",
        " \n",
        " \n",
        "uploaded = files.upload()"
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          "height": 74
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        "outputId": "56c5fee5-aa0c-4785-876d-4398f75c1f29"
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      "execution_count": 74,
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              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
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          "name": "stdout",
          "text": [
            "Saving charity_data.csv to charity_data (10).csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#  Import and read the charity_data.csv\n",
        "import pandas as pd \n",
        "application_df = pd.read_csv(\"charity_data.csv\")\n",
        "application_df.head()"
      ],
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        "id": "NebacPi64vi2",
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      "execution_count": 75,
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              "        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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              "      <td>C2000</td>\n",
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              "      <td>C3000</td>\n",
              "      <td>ProductDev</td>\n",
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              "      <td>0</td>\n",
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              "      <td>C2000</td>\n",
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              "      <td>10000-24999</td>\n",
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              "      <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",
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        "# Drop the non-beneficial ID columns, 'EIN' and 'NAME'\n",
        "application_df.drop(['EIN', 'NAME'], axis=1, inplace=True)\n",
        "application_df.head()"
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      "execution_count": 76,
      "outputs": [
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          "data": {
            "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         1-9999                      N   108590   \n",
              "2   Association       1              0                      N     5000   \n",
              "3         Trust       1    10000-24999                      N     6692   \n",
              "4         Trust       1  100000-499999                      N   142590   \n",
              "\n",
              "   IS_SUCCESSFUL  \n",
              "0              1  \n",
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            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Determine the number of unique values in each column\n",
        "application_df.nunique()"
      ],
      "metadata": {
        "id": "iGTTNFE36z79",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1d7de30e-5cb5-461b-e5d5-5067f7d413ad"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "APPLICATION_TYPE            17\n",
              "AFFILIATION                  6\n",
              "CLASSIFICATION              71\n",
              "USE_CASE                     5\n",
              "ORGANIZATION                 4\n",
              "STATUS                       2\n",
              "INCOME_AMT                   9\n",
              "SPECIAL_CONSIDERATIONS       2\n",
              "ASK_AMT                   8747\n",
              "IS_SUCCESSFUL                2\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Look at APPLICATION_TYPE value counts for binning\n",
        "application_value_counts = application_df.loc[:, \"APPLICATION_TYPE\"].value_counts()\n",
        "application_value_counts"
      ],
      "metadata": {
        "id": "cID3daeo7ds3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4f8eee7d-db55-47e3-9004-a93d83c6d8f4"
      },
      "execution_count": 78,
      "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",
              "T25        3\n",
              "T14        3\n",
              "T29        2\n",
              "T15        2\n",
              "T17        1\n",
              "Name: APPLICATION_TYPE, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize the value counts of APPLICATION_TYPE\n",
        "application_value_counts.plot(kind=\"density\",  color=\"pink\")"
      ],
      "metadata": {
        "id": "ERmDzzYITQlR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "88aed278-0ff1-4723-bb5f-4ded5f000474"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f235f929850>"
            ]
          },
          "metadata": {},
          "execution_count": 79
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Determine which values to replace if counts are less than ...?\n",
        "replace_application = list(application_value_counts[application_value_counts < 500].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()"
      ],
      "metadata": {
        "id": "Rape5l-HVMKa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f910d23a-0ee3-4007-c39b-5564cbb83190"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "T3       27037\n",
              "T4        1542\n",
              "T6        1216\n",
              "T5        1173\n",
              "T19       1065\n",
              "T8         737\n",
              "T7         725\n",
              "T10        528\n",
              "Other      276\n",
              "Name: APPLICATION_TYPE, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Look at CLASSIFICATION value counts for binning\n",
        "classification_value_counts = application_df.loc[:, \"CLASSIFICATION\"].value_counts()\n",
        "classification_value_counts"
      ],
      "metadata": {
        "id": "OxxaCqtCYEXu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3e059536-beb0-4b50-a162-ade4f82dd945"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "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"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize the value counts of CLASSIFICATION\n",
        "classification_value_counts.plot(kind=\"density\", color=\"pink\")"
      ],
      "metadata": {
        "id": "YE3BhyrKYKIe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "outputId": "5ca07b29-e83f-400a-9f41-b9475b0cb837"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f235f8e56d0>"
            ]
          },
          "metadata": {},
          "execution_count": 82
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Determine which values to replace if counts are less than ..?\n",
        "replace_class = list(classification_value_counts[classification_value_counts < 500].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()"
      ],
      "metadata": {
        "id": "9Mk9nn5ZYqlD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e770bc00-6a66-4d85-cd43-d9425785680d"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "C1000    17326\n",
              "C2000     6074\n",
              "C1200     4837\n",
              "C3000     1918\n",
              "C2100     1883\n",
              "Other     1484\n",
              "C7000      777\n",
              "Name: CLASSIFICATION, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Generate our categorical variable lists\n",
        "application_cat = application_df.dtypes[application_df.dtypes == 'object'].index.tolist()\n",
        "application_cat"
      ],
      "metadata": {
        "id": "JCrs9-gZZc8O",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ea086472-80c5-4b08-a8bf-1431063fb6a2"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['APPLICATION_TYPE',\n",
              " 'AFFILIATION',\n",
              " 'CLASSIFICATION',\n",
              " 'USE_CASE',\n",
              " 'ORGANIZATION',\n",
              " 'INCOME_AMT',\n",
              " 'SPECIAL_CONSIDERATIONS']"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "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()"
      ],
      "metadata": {
        "id": "S0grLcvdZwz2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 353
        },
        "outputId": "adcb8633-fd9f-432e-8252-a6681de44796"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   APPLICATION_TYPE_Other  APPLICATION_TYPE_T10  APPLICATION_TYPE_T19  \\\n",
              "0                     0.0                   1.0                   0.0   \n",
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    {
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        "# Merge one-hot encoded features and drop the originals\n",
        "application_df = application_df.merge(encode_df,left_index=True, right_index=True)\n",
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        "application_df.head()"
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          "height": 353
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      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
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              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>5000</td>\n",
              "      <td>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>...</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\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>...</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\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>...</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>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 45 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-130675a8-d4bb-47d2-b628-fa92a3a40bbd')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-130675a8-d4bb-47d2-b628-fa92a3a40bbd button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-130675a8-d4bb-47d2-b628-fa92a3a40bbd');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "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=0, stratify=y)"
      ],
      "metadata": {
        "id": "4Q6_KuaiaLhe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2319e41a-1d51-4a81-ed25-737ed77c3714"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "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)"
      ],
      "metadata": {
        "id": "EUmHnmSAlQRX"
      },
      "execution_count": 88,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "### Deliverable 2: Compile, Train, and Evaluate the Model"
      ],
      "metadata": {
        "id": "3HtK2mvqlEIT"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the model - deep neural net\n",
        "number_input_features = len(X_train[0])\n",
        "hidden_nodes_layer1 = 80\n",
        "hidden_nodes_layer2 = 30\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()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vEbBCQLUFa1U",
        "outputId": "9d58448a-9c59-44a5-a598-6c280b136cfc"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_6 (Dense)             (None, 80)                3600      \n",
            "                                                                 \n",
            " dense_7 (Dense)             (None, 30)                2430      \n",
            "                                                                 \n",
            " dense_8 (Dense)             (None, 1)                 31        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 6,061\n",
            "Trainable params: 6,061\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Compile the model\n",
        "nn.compile(loss=\"binary_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])"
      ],
      "metadata": {
        "id": "P-iH651iJf3F"
      },
      "execution_count": 90,
      "outputs": []
    },
    {
      "cell_type": "code",
      "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/\", exist_ok=True)\n",
        "checkpoint_path = \"checkpoints/weights.{epoch:02d}hdf5\""
      ],
      "metadata": {
        "id": "5Wp_G77NLP4W"
      },
      "execution_count": 92,
      "outputs": []
    },
    {
      "cell_type": "code",
      "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='epoch',\n",
        "    period=5)\n",
        "\n",
        "# Train the model\n",
        "fit_model = nn.fit(X_train_scaled, y_train, epochs=100, callbacks=[cp_callback])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UCSvScRFLUEU",
        "outputId": "155c0818-7ad1-4c5f-f283-15c40674209b"
      },
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:`period` argument is deprecated. Please use `save_freq` to specify the frequency in number of batches seen.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.5866 - accuracy: 0.7189\n",
            "Epoch 2/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.5666 - accuracy: 0.7274\n",
            "Epoch 3/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.5562 - accuracy: 0.7273\n",
            "Epoch 4/100\n",
            "804/804 [==============================] - 2s 2ms/step - loss: 0.5531 - accuracy: 0.7313\n",
            "Epoch 5/100\n",
            "796/804 [============================>.] - ETA: 0s - loss: 0.5507 - accuracy: 0.7323\n",
            "Epoch 5: saving model to checkpoints/weights.05hdf5\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.5512 - accuracy: 0.7317\n",
            "Epoch 6/100\n",
            "804/804 [==============================] - 2s 2ms/step - loss: 0.5536 - accuracy: 0.7304\n",
            "Epoch 7/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.5501 - accuracy: 0.7316\n",
            "Epoch 8/100\n",
            "804/804 [==============================] - 2s 2ms/step - loss: 0.5492 - accuracy: 0.7311\n",
            "Epoch 9/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.5489 - accuracy: 0.7316\n",
            "Epoch 10/100\n",
            "793/804 [============================>.] - ETA: 0s - loss: 0.5495 - accuracy: 0.7323\n",
            "Epoch 10: saving model to checkpoints/weights.10hdf5\n",
            "804/804 [==============================] - 2s 2ms/step - loss: 0.5493 - accuracy: 0.7321\n",
            "Epoch 11/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.5484 - accuracy: 0.7311\n",
            "Epoch 12/100\n",
            "804/804 [==============================] - 2s 2ms/step - loss: 0.5463 - accuracy: 0.7327\n",
            "Epoch 13/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.5463 - accuracy: 0.7331\n",
            "Epoch 14/100\n",
            "804/804 [==============================] - 2s 2ms/step - loss: 0.5441 - accuracy: 0.7332\n",
            "Epoch 15/100\n",
            "779/804 [============================>.] - ETA: 0s - loss: 0.5445 - accuracy: 0.7340\n",
            "Epoch 15: saving model to checkpoints/weights.15hdf5\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.5452 - accuracy: 0.7328\n",
            "Epoch 16/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.5455 - accuracy: 0.7348\n",
            "Epoch 17/100\n",
            "804/804 [==============================] - 2s 2ms/step - loss: 0.5449 - accuracy: 0.7332\n",
            "Epoch 18/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.5435 - accuracy: 0.7333\n",
            "Epoch 19/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.5429 - accuracy: 0.7350\n",
            "Epoch 20/100\n",
            "804/804 [==============================] - ETA: 0s - loss: 0.5426 - accuracy: 0.7345\n",
            "Epoch 20: saving model to checkpoints/weights.20hdf5\n",
            "804/804 [==============================] - 2s 2ms/step - loss: 0.5426 - accuracy: 0.7345\n",
            "Epoch 21/100\n",
            "804/804 [==============================] - 2s 2ms/step - loss: 0.5428 - accuracy: 0.7348\n",
            "Epoch 22/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.5417 - accuracy: 0.7353\n",
            "Epoch 23/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.5416 - accuracy: 0.7357\n",
            "Epoch 24/100\n",
            "804/804 [==============================] - 2s 2ms/step - loss: 0.5418 - accuracy: 0.7351\n",
            "Epoch 25/100\n",
            "790/804 [============================>.] - ETA: 0s - loss: 0.5403 - accuracy: 0.7355\n",
            "Epoch 25: saving model to checkpoints/weights.25hdf5\n",
            "804/804 [==============================] - 2s 2ms/step - loss: 0.5407 - accuracy: 0.7350\n",
            "Epoch 26/100\n",
            "804/804 [==============================] - 2s 2ms/step - loss: 0.5406 - accuracy: 0.7372\n",
            "Epoch 27/100\n",
            "804/804 [==============================] - 2s 2ms/step - loss: 0.5407 - accuracy: 0.7347\n",
            "Epoch 28/100\n",
            "804/804 [==============================] - 2s 2ms/step - loss: 0.5404 - accuracy: 0.7362\n",
            "Epoch 29/100\n",
            "804/804 [==============================] - 2s 2ms/step - loss: 0.5403 - accuracy: 0.7373\n",
            "Epoch 30/100\n",
            "799/804 [============================>.] - ETA: 0s - loss: 0.5401 - accuracy: 0.7356\n",
            "Epoch 30: saving model to checkpoints/weights.30hdf5\n",
            "804/804 [==============================] - 2s 2ms/step - loss: 0.5398 - accuracy: 0.7358\n",
            "Epoch 31/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.5405 - accuracy: 0.7360\n",
            "Epoch 32/100\n",
            "804/804 [==============================] - 2s 2ms/step - loss: 0.5389 - accuracy: 0.7372\n",
            "Epoch 33/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.5393 - accuracy: 0.7369\n",
            "Epoch 34/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.5389 - accuracy: 0.7356\n",
            "Epoch 35/100\n",
            "792/804 [============================>.] - ETA: 0s - loss: 0.5387 - accuracy: 0.7369\n",
            "Epoch 35: saving model to checkpoints/weights.35hdf5\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.5387 - accuracy: 0.7366\n",
            "Epoch 36/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.5387 - accuracy: 0.7373\n",
            "Epoch 37/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.5384 - accuracy: 0.7360\n",
            "Epoch 38/100\n",
            "804/804 [==============================] - 2s 2ms/step - loss: 0.5384 - accuracy: 0.7362\n",
            "Epoch 39/100\n",
            "804/804 [==============================] - 2s 2ms/step - loss: 0.5384 - accuracy: 0.7363\n",
            "Epoch 40/100\n",
            "786/804 [============================>.] - ETA: 0s - loss: 0.5394 - accuracy: 0.7359\n",
            "Epoch 40: saving model to checkpoints/weights.40hdf5\n",
            "804/804 [==============================] - 2s 2ms/step - loss: 0.5386 - accuracy: 0.7365\n",
            "Epoch 41/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.5377 - accuracy: 0.7376\n",
            "Epoch 42/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.5378 - accuracy: 0.7375\n",
            "Epoch 43/100\n",
            "804/804 [==============================] - 2s 2ms/step - loss: 0.5378 - accuracy: 0.7384\n",
            "Epoch 44/100\n",
            "804/804 [==============================] - 2s 2ms/step - loss: 0.5373 - accuracy: 0.7378\n",
            "Epoch 45/100\n",
            "785/804 [============================>.] - ETA: 0s - loss: 0.5371 - accuracy: 0.7389\n",
            "Epoch 45: saving model to checkpoints/weights.45hdf5\n",
            "804/804 [==============================] - 2s 2ms/step - loss: 0.5374 - accuracy: 0.7383\n",
            "Epoch 46/100\n",
            "804/804 [==============================] - 2s 2ms/step - loss: 0.5374 - accuracy: 0.7385\n",
            "Epoch 47/100\n",
            "804/804 [==============================] - 2s 2ms/step - loss: 0.5370 - accuracy: 0.7385\n",
            "Epoch 48/100\n",
            "804/804 [==============================] - 2s 2ms/step - loss: 0.5372 - accuracy: 0.7379\n",
            "Epoch 49/100\n",
            "804/804 [==============================] - 2s 2ms/step - loss: 0.5367 - accuracy: 0.7386\n",
            "Epoch 50/100\n",
            "795/804 [============================>.] - ETA: 0s - loss: 0.5363 - accuracy: 0.7388\n",
            "Epoch 50: saving model to checkpoints/weights.50hdf5\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.5368 - accuracy: 0.7385\n",
            "Epoch 51/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.5361 - accuracy: 0.7382\n",
            "Epoch 52/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.5366 - accuracy: 0.7386\n",
            "Epoch 53/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.5370 - accuracy: 0.7385\n",
            "Epoch 54/100\n",
            "804/804 [==============================] - 2s 2ms/step - loss: 0.5362 - accuracy: 0.7384\n",
            "Epoch 55/100\n",
            "781/804 [============================>.] - ETA: 0s - loss: 0.5368 - accuracy: 0.7394\n",
            "Epoch 55: saving model to checkpoints/weights.55hdf5\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.5366 - accuracy: 0.7393\n",
            "Epoch 56/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.5364 - accuracy: 0.7391\n",
            "Epoch 57/100\n",
            "804/804 [==============================] - 2s 2ms/step - loss: 0.5358 - accuracy: 0.7377\n",
            "Epoch 58/100\n",
            "804/804 [==============================] - 2s 2ms/step - loss: 0.5363 - accuracy: 0.7382\n",
            "Epoch 59/100\n",
            "804/804 [==============================] - 2s 2ms/step - loss: 0.5360 - accuracy: 0.7382\n",
            "Epoch 60/100\n",
            "797/804 [============================>.] - ETA: 0s - loss: 0.5352 - accuracy: 0.7388\n",
            "Epoch 60: saving model to checkpoints/weights.60hdf5\n",
            "804/804 [==============================] - 2s 2ms/step - loss: 0.5358 - accuracy: 0.7385\n",
            "Epoch 61/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.5355 - accuracy: 0.7380\n",
            "Epoch 62/100\n",
            "804/804 [==============================] - 2s 2ms/step - loss: 0.5360 - accuracy: 0.7381\n",
            "Epoch 63/100\n",
            "804/804 [==============================] - 2s 2ms/step - loss: 0.5356 - accuracy: 0.7393\n",
            "Epoch 64/100\n",
            "804/804 [==============================] - 2s 2ms/step - loss: 0.5352 - accuracy: 0.7388\n",
            "Epoch 65/100\n",
            "799/804 [============================>.] - ETA: 0s - loss: 0.5358 - accuracy: 0.7385\n",
            "Epoch 65: saving model to checkpoints/weights.65hdf5\n",
            "804/804 [==============================] - 2s 2ms/step - loss: 0.5354 - accuracy: 0.7387\n",
            "Epoch 66/100\n",
            "804/804 [==============================] - 2s 2ms/step - loss: 0.5351 - accuracy: 0.7386\n",
            "Epoch 67/100\n",
            "804/804 [==============================] - 2s 2ms/step - loss: 0.5349 - accuracy: 0.7386\n",
            "Epoch 68/100\n",
            "804/804 [==============================] - 2s 2ms/step - loss: 0.5350 - accuracy: 0.7396\n",
            "Epoch 69/100\n",
            "804/804 [==============================] - 2s 2ms/step - loss: 0.5346 - accuracy: 0.7391\n",
            "Epoch 70/100\n",
            "788/804 [============================>.] - ETA: 0s - loss: 0.5346 - accuracy: 0.7387\n",
            "Epoch 70: saving model to checkpoints/weights.70hdf5\n",
            "804/804 [==============================] - 2s 2ms/step - loss: 0.5348 - accuracy: 0.7386\n",
            "Epoch 71/100\n",
            "804/804 [==============================] - 2s 2ms/step - loss: 0.5351 - accuracy: 0.7392\n",
            "Epoch 72/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.5353 - accuracy: 0.7393\n",
            "Epoch 73/100\n",
            "804/804 [==============================] - 2s 2ms/step - loss: 0.5344 - accuracy: 0.7388\n",
            "Epoch 74/100\n",
            "804/804 [==============================] - 2s 2ms/step - loss: 0.5345 - accuracy: 0.7389\n",
            "Epoch 75/100\n",
            "791/804 [============================>.] - ETA: 0s - loss: 0.5352 - accuracy: 0.7395\n",
            "Epoch 75: saving model to checkpoints/weights.75hdf5\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.5354 - accuracy: 0.7392\n",
            "Epoch 76/100\n",
            "804/804 [==============================] - 2s 2ms/step - loss: 0.5342 - accuracy: 0.7382\n",
            "Epoch 77/100\n",
            "804/804 [==============================] - 2s 2ms/step - loss: 0.5344 - accuracy: 0.7388\n",
            "Epoch 78/100\n",
            "804/804 [==============================] - 2s 2ms/step - loss: 0.5340 - accuracy: 0.7393\n",
            "Epoch 79/100\n",
            "804/804 [==============================] - 2s 2ms/step - loss: 0.5341 - accuracy: 0.7396\n",
            "Epoch 80/100\n",
            "803/804 [============================>.] - ETA: 0s - loss: 0.5345 - accuracy: 0.7394\n",
            "Epoch 80: saving model to checkpoints/weights.80hdf5\n",
            "804/804 [==============================] - 2s 2ms/step - loss: 0.5344 - accuracy: 0.7395\n",
            "Epoch 81/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.5343 - accuracy: 0.7397\n",
            "Epoch 82/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.5340 - accuracy: 0.7408\n",
            "Epoch 83/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.5335 - accuracy: 0.7394\n",
            "Epoch 84/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.5340 - accuracy: 0.7394\n",
            "Epoch 85/100\n",
            "797/804 [============================>.] - ETA: 0s - loss: 0.5340 - accuracy: 0.7409\n",
            "Epoch 85: saving model to checkpoints/weights.85hdf5\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.5343 - accuracy: 0.7407\n",
            "Epoch 86/100\n",
            "804/804 [==============================] - 2s 2ms/step - loss: 0.5340 - accuracy: 0.7400\n",
            "Epoch 87/100\n",
            "804/804 [==============================] - 2s 2ms/step - loss: 0.5339 - accuracy: 0.7394\n",
            "Epoch 88/100\n",
            "804/804 [==============================] - 2s 2ms/step - loss: 0.5335 - accuracy: 0.7404\n",
            "Epoch 89/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.5339 - accuracy: 0.7388\n",
            "Epoch 90/100\n",
            "801/804 [============================>.] - ETA: 0s - loss: 0.5338 - accuracy: 0.7401\n",
            "Epoch 90: saving model to checkpoints/weights.90hdf5\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.5337 - accuracy: 0.7403\n",
            "Epoch 91/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.5335 - accuracy: 0.7395\n",
            "Epoch 92/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.5335 - accuracy: 0.7396\n",
            "Epoch 93/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.5333 - accuracy: 0.7395\n",
            "Epoch 94/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.5330 - accuracy: 0.7400\n",
            "Epoch 95/100\n",
            "782/804 [============================>.] - ETA: 0s - loss: 0.5331 - accuracy: 0.7401\n",
            "Epoch 95: saving model to checkpoints/weights.95hdf5\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.5334 - accuracy: 0.7397\n",
            "Epoch 96/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.5331 - accuracy: 0.7403\n",
            "Epoch 97/100\n",
            "804/804 [==============================] - 1s 2ms/step - loss: 0.5331 - accuracy: 0.7402\n",
            "Epoch 98/100\n",
            "804/804 [==============================] - 2s 2ms/step - loss: 0.5332 - accuracy: 0.7406\n",
            "Epoch 99/100\n",
            "804/804 [==============================] - 2s 3ms/step - loss: 0.5332 - accuracy: 0.7403\n",
            "Epoch 100/100\n",
            "794/804 [============================>.] - ETA: 0s - loss: 0.5328 - accuracy: 0.7402\n",
            "Epoch 100: saving model to checkpoints/weights.100hdf5\n",
            "804/804 [==============================] - 2s 2ms/step - loss: 0.5330 - accuracy: 0.7398\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "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}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x2TSfJJVJySf",
        "outputId": "ba533433-454a-42dd-8070-33afee1a0163"
      },
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "268/268 - 0s - loss: 0.5626 - accuracy: 0.7296 - 347ms/epoch - 1ms/step\n",
            "Loss: 0.5625913739204407, Accuracy: 0.7295626997947693\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Export the model to HDF5 file\n",
        "nn.save(\"AlphabetSoupCharity.h5\")"
      ],
      "metadata": {
        "id": "XlpM9R67M7sp"
      },
      "execution_count": 112,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Import the model to a new object\n",
        "nn_imported = tf.keras.models.load_model('AlphabetSoupCharity.h5')"
      ],
      "metadata": {
        "id": "SCLnxj8JOULu"
      },
      "execution_count": 113,
      "outputs": []
    }
  ]
}