AlphabetSoupCharity_ModelStarterCode.ipynb

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        "## Preprocessing"
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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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              "      <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",
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              "      <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",
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              "      <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",
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              "    <tr>\n",
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              "      <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",
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              "    <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",
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              "      <td>6692</td>\n",
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              "    <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",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "application_df",
              "summary": "{\n  \"name\": \"application_df\",\n  \"rows\": 34299,\n  \"fields\": [\n    {\n      \"column\": \"EIN\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 245147183,\n        \"min\": 10520599,\n        \"max\": 996086871,\n        \"num_unique_values\": 34299,\n        \"samples\": [\n          271598055,\n          900109768,\n          352562499\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NAME\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 19568,\n        \"samples\": [\n          \"LOCAL 12 USW GOODYEAR INSTITUTE FORCAREER DEVELOPMENT\",\n          \"INTERNATION ASSOCIATION OF ELECTRICAL INSPECTORS\",\n          \"BRICKLAYERS & ALLIED CRAFTWORKERS LOCAL 13 VACATION FUND\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"APPLICATION_TYPE\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 17,\n        \"samples\": [\n          \"T10\",\n          \"T3\",\n          \"T6\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AFFILIATION\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Independent\",\n          \"CompanySponsored\",\n          \"Other\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CLASSIFICATION\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 71,\n        \"samples\": [\n          \"C1500\",\n          \"C1000\",\n          \"C1570\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"USE_CASE\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Preservation\",\n          \"Other\",\n          \"Heathcare\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ORGANIZATION\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Co-operative\",\n          \"Corporation\",\n          \"Association\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"STATUS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"INCOME_AMT\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"1M-5M\",\n          \"1-9999\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SPECIAL_CONSIDERATIONS\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Y\",\n          \"N\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ASK_AMT\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 87130452,\n        \"min\": 5000,\n        \"max\": 8597806340,\n        \"num_unique_values\": 8747,\n        \"samples\": [\n          1328927,\n          42942\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IS_SUCCESSFUL\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 1
        }
      ],
      "source": [
        "# Import our dependencies\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "import pandas as pd\n",
        "import tensorflow as tf\n",
        "\n",
        "# Import and read the charity_data.csv.\n",
        "import pandas as pd\n",
        "application_df = pd.read_csv(\"https://static.bc-edx.com/data/dl-1-2/m21/lms/starter/charity_data.csv\")\n",
        "application_df.head()"
      ]
    },
    {
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      "source": [
        "# Make a copy of the DF before droping any columns\n",
        "application_df_copy = application_df.copy()\n",
        "\n",
        "# Show a snippet of the copy DF\n",
        "application_df_copy.head()"
      ],
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          "height": 452
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        "id": "CXFDgK3E0Qim",
        "outputId": "0a0f1ccc-8e3a-4838-e217-12a3eb210899"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "        EIN                                      NAME APPLICATION_TYPE  \\\n",
              "0  10520599              BLUE KNIGHTS MOTORCYCLE CLUB              T10   \n",
              "1  10531628    AMERICAN CHESAPEAKE CLUB CHARITABLE TR               T3   \n",
              "2  10547893        ST CLOUD PROFESSIONAL FIREFIGHTERS               T5   \n",
              "3  10553066            SOUTHSIDE ATHLETIC ASSOCIATION               T3   \n",
              "4  10556103  GENETIC RESEARCH INSTITUTE OF THE DESERT               T3   \n",
              "\n",
              "        AFFILIATION CLASSIFICATION      USE_CASE  ORGANIZATION  STATUS  \\\n",
              "0       Independent          C1000    ProductDev   Association       1   \n",
              "1       Independent          C2000  Preservation  Co-operative       1   \n",
              "2  CompanySponsored          C3000    ProductDev   Association       1   \n",
              "3  CompanySponsored          C2000  Preservation         Trust       1   \n",
              "4       Independent          C1000     Heathcare         Trust       1   \n",
              "\n",
              "      INCOME_AMT SPECIAL_CONSIDERATIONS  ASK_AMT  IS_SUCCESSFUL  \n",
              "0              0                      N     5000              1  \n",
              "1         1-9999                      N   108590              1  \n",
              "2              0                      N     5000              0  \n",
              "3    10000-24999                      N     6692              1  \n",
              "4  100000-499999                      N   142590              1  "
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              "    <tr>\n",
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              "type": "dataframe",
              "variable_name": "application_df_copy",
              "summary": "{\n  \"name\": \"application_df_copy\",\n  \"rows\": 34299,\n  \"fields\": [\n    {\n      \"column\": \"EIN\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 245147183,\n        \"min\": 10520599,\n        \"max\": 996086871,\n        \"num_unique_values\": 34299,\n        \"samples\": [\n          271598055,\n          900109768,\n          352562499\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NAME\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 19568,\n        \"samples\": [\n          \"LOCAL 12 USW GOODYEAR INSTITUTE FORCAREER DEVELOPMENT\",\n          \"INTERNATION ASSOCIATION OF ELECTRICAL INSPECTORS\",\n          \"BRICKLAYERS & ALLIED CRAFTWORKERS LOCAL 13 VACATION FUND\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"APPLICATION_TYPE\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 17,\n        \"samples\": [\n          \"T10\",\n          \"T3\",\n          \"T6\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AFFILIATION\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Independent\",\n          \"CompanySponsored\",\n          \"Other\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CLASSIFICATION\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 71,\n        \"samples\": [\n          \"C1500\",\n          \"C1000\",\n          \"C1570\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"USE_CASE\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Preservation\",\n          \"Other\",\n          \"Heathcare\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ORGANIZATION\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Co-operative\",\n          \"Corporation\",\n          \"Association\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"STATUS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"INCOME_AMT\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"1M-5M\",\n          \"1-9999\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SPECIAL_CONSIDERATIONS\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Y\",\n          \"N\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ASK_AMT\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 87130452,\n        \"min\": 5000,\n        \"max\": 8597806340,\n        \"num_unique_values\": 8747,\n        \"samples\": [\n          1328927,\n          42942\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IS_SUCCESSFUL\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 261
        },
        "id": "9rp3Z0PBwtV1",
        "outputId": "9d5b1ba3-6ead-4c37-ae9d-7058f80829d9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "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",
              "1              1  \n",
              "2              0  \n",
              "3              1  \n",
              "4              1  "
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              "      <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>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b0e320d3-1836-4701-9283-4c6b925f8430')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-b0e320d3-1836-4701-9283-4c6b925f8430 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-b0e320d3-1836-4701-9283-4c6b925f8430');\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",
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              "\n",
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              "\n",
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              "     width=\"24px\">\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e1155165-df86-42c3-b1bf-7b15c24be9aa button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "application_df_copy",
              "summary": "{\n  \"name\": \"application_df_copy\",\n  \"rows\": 34299,\n  \"fields\": [\n    {\n      \"column\": \"APPLICATION_TYPE\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 17,\n        \"samples\": [\n          \"T10\",\n          \"T3\",\n          \"T6\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AFFILIATION\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Independent\",\n          \"CompanySponsored\",\n          \"Other\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CLASSIFICATION\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 71,\n        \"samples\": [\n          \"C1500\",\n          \"C1000\",\n          \"C1570\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"USE_CASE\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Preservation\",\n          \"Other\",\n          \"Heathcare\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ORGANIZATION\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Co-operative\",\n          \"Corporation\",\n          \"Association\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"STATUS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"INCOME_AMT\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"1M-5M\",\n          \"1-9999\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SPECIAL_CONSIDERATIONS\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Y\",\n          \"N\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ASK_AMT\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 87130452,\n        \"min\": 5000,\n        \"max\": 8597806340,\n        \"num_unique_values\": 8747,\n        \"samples\": [\n          1328927,\n          42942\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IS_SUCCESSFUL\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "# Drop the non-beneficial ID columns, 'EIN' and 'NAME'.\n",
        "application_df_copy = application_df_copy.drop(columns=['EIN', 'NAME'])\n",
        "\n",
        "# Show the df without the dropped columns\n",
        "application_df_copy.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w1Q5EohSwtV2",
        "outputId": "2d9481f8-0f92-4afc-844f-0d50963467aa"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "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\n"
          ]
        }
      ],
      "source": [
        "# Determine the number of unique values in each column.\n",
        "unique_values = application_df_copy.nunique()\n",
        "\n",
        "# Display the unique values per column\n",
        "print(unique_values)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nXBDkMGVwtV2",
        "outputId": "0b86e0e8-3496-43f3-eefc-c72a4cdb0952"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "APPLICATION_TYPE\n",
              "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: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "# Look at APPLICATION_TYPE value counts to identify and replace with \"Other\"\n",
        "application_count = application_df_copy['APPLICATION_TYPE'].value_counts()\n",
        "\n",
        "# Show the data\n",
        "application_count"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "56TfLXEEwtV2",
        "outputId": "a29af065-ca5d-4a1c-d04f-c0eea42a0fa9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "APPLICATION_TYPE\n",
              "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: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "# Choose a cutoff value and create a list of application types to be replaced\n",
        "# use the variable name `application_types_to_replace`\n",
        "cutoff_value = 500\n",
        "application_types_to_replace = application_count[application_count < cutoff_value].index.tolist()\n",
        "\n",
        "# Replace in dataframe\n",
        "for app in application_types_to_replace:\n",
        "    application_df_copy['APPLICATION_TYPE'] = application_df_copy['APPLICATION_TYPE'].replace(app,\"Other\")\n",
        "\n",
        "# Check to make sure replacement was successful\n",
        "application_df_copy['APPLICATION_TYPE'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lX9jcwInwtV2",
        "outputId": "e79740c2-71e0-4f83-b818-040b9cfd60a5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CLASSIFICATION\n",
              "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: count, Length: 71, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "# Look at CLASSIFICATION value counts to identify and replace with \"Other\"\n",
        "classification_count = application_df_copy['CLASSIFICATION'].value_counts()\n",
        "\n",
        "# Show the data\n",
        "classification_count"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b6Ys6UwuwtV2",
        "outputId": "2dc8a75f-eab5-494f-807a-88bb7da8302a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CLASSIFICATION\n",
              "C1000    17326\n",
              "C2000     6074\n",
              "C1200     4837\n",
              "C3000     1918\n",
              "C2100     1883\n",
              "C7000      777\n",
              "C1700      287\n",
              "C4000      194\n",
              "C5000      116\n",
              "C1270      114\n",
              "C2700      104\n",
              "C2800       95\n",
              "C7100       75\n",
              "C1300       58\n",
              "C1280       50\n",
              "C1230       36\n",
              "C1400       34\n",
              "C7200       32\n",
              "C2300       32\n",
              "C1240       30\n",
              "C8000       20\n",
              "C7120       18\n",
              "C1500       16\n",
              "C1800       15\n",
              "C6000       15\n",
              "C1250       14\n",
              "C8200       11\n",
              "C1238       10\n",
              "C1278       10\n",
              "C1235        9\n",
              "C1237        9\n",
              "C7210        7\n",
              "C2400        6\n",
              "C1720        6\n",
              "C4100        6\n",
              "C1257        5\n",
              "C1600        5\n",
              "C1260        3\n",
              "C2710        3\n",
              "C0           3\n",
              "C3200        2\n",
              "C1234        2\n",
              "C1246        2\n",
              "C1267        2\n",
              "C1256        2\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "# You may find it helpful to look at CLASSIFICATION value counts >1\n",
        "classification_gt1 = classification_count[classification_count>1]\n",
        "\n",
        "# Show the new count data\n",
        "classification_gt1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iYM7u8mYwtV3",
        "outputId": "d9151409-f129-4c22-f0e4-d105bf0d8a1c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CLASSIFICATION\n",
              "C1000    17326\n",
              "C2000     6074\n",
              "C1200     4837\n",
              "Other     2261\n",
              "C3000     1918\n",
              "C2100     1883\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "# Choose a cutoff value and create a list of classifications to be replaced\n",
        "# use the variable name `classifications_to_replace`\n",
        "cutoff_cv = 1000\n",
        "classifications_to_replace = classification_count[classification_count < cutoff_cv].index.tolist()\n",
        "\n",
        "# Replace in dataframe\n",
        "for cls in classifications_to_replace:\n",
        "    application_df_copy['CLASSIFICATION'] = application_df_copy['CLASSIFICATION'].replace(cls,\"Other\")\n",
        "\n",
        "# Check to make sure replacement was successful\n",
        "application_df_copy['CLASSIFICATION'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Make a copy of the DF before turning categorical values into numeric\n",
        "asoup_df = application_df_copy.copy()\n",
        "\n",
        "# Show a snippet of the copy DF\n",
        "asoup_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 261
        },
        "id": "DITWJFdFSnVX",
        "outputId": "c35c6095-246e-4fae-e120-ee05c8b36bd1"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "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",
              "1              1  \n",
              "2              0  \n",
              "3              1  \n",
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          "metadata": {},
          "execution_count": 10
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    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
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        "id": "KrKvKXLawtV3",
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          "data": {
            "text/plain": [
              "   STATUS  ASK_AMT  IS_SUCCESSFUL  APPLICATION_TYPE_Other  \\\n",
              "0       1     5000              1                       0   \n",
              "1       1   108590              1                       0   \n",
              "2       1     5000              0                       0   \n",
              "3       1     6692              1                       0   \n",
              "4       1   142590              1                       0   \n",
              "\n",
              "   APPLICATION_TYPE_T10  APPLICATION_TYPE_T19  APPLICATION_TYPE_T3  \\\n",
              "0                     1                     0                    0   \n",
              "1                     0                     0                    1   \n",
              "2                     0                     0                    0   \n",
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              "4                     0                     0                    1   \n",
              "\n",
              "   APPLICATION_TYPE_T4  APPLICATION_TYPE_T5  APPLICATION_TYPE_T6  ...  \\\n",
              "0                    0                    0                    0  ...   \n",
              "1                    0                    0                    0  ...   \n",
              "2                    0                    1                    0  ...   \n",
              "3                    0                    0                    0  ...   \n",
              "4                    0                    0                    0  ...   \n",
              "\n",
              "   INCOME_AMT_1-9999  INCOME_AMT_10000-24999  INCOME_AMT_100000-499999  \\\n",
              "0                  0                       0                         0   \n",
              "1                  1                       0                         0   \n",
              "2                  0                       0                         0   \n",
              "3                  0                       1                         0   \n",
              "4                  0                       0                         1   \n",
              "\n",
              "   INCOME_AMT_10M-50M  INCOME_AMT_1M-5M  INCOME_AMT_25000-99999  \\\n",
              "0                   0                 0                       0   \n",
              "1                   0                 0                       0   \n",
              "2                   0                 0                       0   \n",
              "3                   0                 0                       0   \n",
              "4                   0                 0                       0   \n",
              "\n",
              "   INCOME_AMT_50M+  INCOME_AMT_5M-10M  SPECIAL_CONSIDERATIONS_N  \\\n",
              "0                0                  0                         1   \n",
              "1                0                  0                         1   \n",
              "2                0                  0                         1   \n",
              "3                0                  0                         1   \n",
              "4                0                  0                         1   \n",
              "\n",
              "   SPECIAL_CONSIDERATIONS_Y  \n",
              "0                         0  \n",
              "1                         0  \n",
              "2                         0  \n",
              "3                         0  \n",
              "4                         0  \n",
              "\n",
              "[5 rows x 44 columns]"
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>5000</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>6692</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
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              "      <td>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</td>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 44 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dd4a2d1e-64a7-41c0-91b2-2fd1298cd0ac')\"\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "      fill: #D2E3FC;\n",
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              "\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",
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              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-dd4a2d1e-64a7-41c0-91b2-2fd1298cd0ac 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-dd4a2d1e-64a7-41c0-91b2-2fd1298cd0ac');\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",
              "\n",
              "\n",
              "<div id=\"df-70f20c4c-12b8-436d-aeb2-15aa949268e7\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-70f20c4c-12b8-436d-aeb2-15aa949268e7')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
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              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-70f20c4c-12b8-436d-aeb2-15aa949268e7 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "asoup_df"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "# Convert categorical data to numeric with `pd.get_dummies`\n",
        "asoup_df = pd.get_dummies(asoup_df, dtype=int)\n",
        "\n",
        "# Show the head of the DF\n",
        "asoup_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273
        },
        "id": "85JnmTOVwtV3",
        "outputId": "73329594-8d58-4652-c5e1-9507f1be5934"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   STATUS  ASK_AMT  APPLICATION_TYPE_Other  APPLICATION_TYPE_T10  \\\n",
              "0       1     5000                       0                     1   \n",
              "1       1   108590                       0                     0   \n",
              "2       1     5000                       0                     0   \n",
              "3       1     6692                       0                     0   \n",
              "4       1   142590                       0                     0   \n",
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              "0                         0  \n",
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              "      <td>142590</td>\n",
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              "      <td>...</td>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 43 columns</p>\n",
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              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "X"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "# Split our preprocessed data into our features and target arrays\n",
        "# Define features set\n",
        "# Make a copy of the dataset\n",
        "X = asoup_df.copy()\n",
        "\n",
        "# Drop the column\n",
        "X.drop(columns=['IS_SUCCESSFUL'], axis=1, inplace=True)\n",
        "\n",
        "# Display the values the features columns\n",
        "X.head()\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define Target Vector\n",
        "y = asoup_df['IS_SUCCESSFUL'].values\n",
        "\n",
        "# Show first 5 values in the array\n",
        "y[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R9Xd1PeDXDnj",
        "outputId": "6b42a87a-0d29-40f5-c4d4-8aaf3e779c1d"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 0, 1, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the preprocessed data into a training and testing dataset\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)"
      ],
      "metadata": {
        "id": "wtdIz11NVVap"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "4vIg4k6BwtV3"
      },
      "outputs": [],
      "source": [
        "# Create a StandardScaler instances\n",
        "scaler = StandardScaler()\n",
        "\n",
        "# Fit the StandardScaler\n",
        "X_scaler = scaler.fit(X_train)\n",
        "\n",
        "# Scale the data\n",
        "# Transform the training data\n",
        "X_train_scaled = X_scaler.transform(X_train)\n",
        "\n",
        "# Transform the testing data\n",
        "X_test_scaled = X_scaler.transform(X_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oRfVwcAZwtV3"
      },
      "source": [
        "## Compile, Train and Evaluate the Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xcFkGKmewtV3",
        "outputId": "a29e4769-9c9e-4a90-fdd7-e61b3d496fba"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense (Dense)               (None, 80)                3520      \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 30)                2430      \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 1)                 31        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 5981 (23.36 KB)\n",
            "Trainable params: 5981 (23.36 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "# Define the model - deep neural net, i.e., the number of input features and hidden nodes for each layer.\n",
        "nn = tf.keras.models.Sequential()\n",
        "\n",
        "# First input layer\n",
        "nn.add(tf.keras.layers.Dense(units=80, activation='relu', input_dim=X_train_scaled.shape[1]))\n",
        "\n",
        "# Second hidden layer\n",
        "nn.add(tf.keras.layers.Dense(units=30, 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()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "B1tugtGywtV4"
      },
      "outputs": [],
      "source": [
        "# Compile the model\n",
        "nn.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Import ModelCheckpoint from Tensorflow\n",
        "from tensorflow.keras.callbacks import ModelCheckpoint\n",
        "\n",
        "# Create a callback that saves the model's weights every five epochs\n",
        "checkpoint_callback = ModelCheckpoint(filepath='checkpoint_weights.h5', save_weights_only=True, save_freq=5)"
      ],
      "metadata": {
        "id": "ulVbmHunABkt"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W6hgOdXJwtV4",
        "outputId": "8c6ed664-086a-4160-89f6-81a0050872ec"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "804/804 [==============================] - 8s 6ms/step - loss: 0.5710 - accuracy: 0.7208 - val_loss: 0.5598 - val_accuracy: 0.7265\n",
            "Epoch 2/100\n",
            "804/804 [==============================] - 5s 6ms/step - loss: 0.5554 - accuracy: 0.7276 - val_loss: 0.5598 - val_accuracy: 0.7269\n",
            "Epoch 3/100\n",
            "804/804 [==============================] - 3s 4ms/step - loss: 0.5534 - accuracy: 0.7297 - val_loss: 0.5545 - val_accuracy: 0.7242\n",
            "Epoch 4/100\n",
            "804/804 [==============================] - 4s 5ms/step - loss: 0.5509 - accuracy: 0.7312 - val_loss: 0.5543 - val_accuracy: 0.7273\n",
            "Epoch 5/100\n",
            "804/804 [==============================] - 4s 5ms/step - loss: 0.5496 - accuracy: 0.7309 - val_loss: 0.5569 - val_accuracy: 0.7277\n",
            "Epoch 6/100\n",
            "804/804 [==============================] - 5s 6ms/step - loss: 0.5480 - accuracy: 0.7327 - val_loss: 0.5553 - val_accuracy: 0.7272\n",
            "Epoch 7/100\n",
            "804/804 [==============================] - 3s 4ms/step - loss: 0.5477 - accuracy: 0.7321 - val_loss: 0.5535 - val_accuracy: 0.7300\n",
            "Epoch 8/100\n",
            "804/804 [==============================] - 4s 4ms/step - loss: 0.5466 - accuracy: 0.7328 - val_loss: 0.5590 - val_accuracy: 0.7275\n",
            "Epoch 9/100\n",
            "804/804 [==============================] - 5s 6ms/step - loss: 0.5464 - accuracy: 0.7327 - val_loss: 0.5549 - val_accuracy: 0.7272\n",
            "Epoch 10/100\n",
            "804/804 [==============================] - 3s 4ms/step - loss: 0.5458 - accuracy: 0.7327 - val_loss: 0.5546 - val_accuracy: 0.7228\n",
            "Epoch 11/100\n",
            "804/804 [==============================] - 3s 4ms/step - loss: 0.5448 - accuracy: 0.7330 - val_loss: 0.5567 - val_accuracy: 0.7258\n",
            "Epoch 12/100\n",
            "804/804 [==============================] - 4s 5ms/step - loss: 0.5449 - accuracy: 0.7338 - val_loss: 0.5544 - val_accuracy: 0.7271\n",
            "Epoch 13/100\n",
            "804/804 [==============================] - 5s 6ms/step - loss: 0.5449 - accuracy: 0.7348 - val_loss: 0.5530 - val_accuracy: 0.7318\n",
            "Epoch 14/100\n",
            "804/804 [==============================] - 4s 5ms/step - loss: 0.5442 - accuracy: 0.7345 - val_loss: 0.5540 - val_accuracy: 0.7230\n",
            "Epoch 15/100\n",
            "804/804 [==============================] - 4s 5ms/step - loss: 0.5440 - accuracy: 0.7341 - val_loss: 0.5535 - val_accuracy: 0.7284\n",
            "Epoch 16/100\n",
            "804/804 [==============================] - 5s 6ms/step - loss: 0.5437 - accuracy: 0.7334 - val_loss: 0.5530 - val_accuracy: 0.7306\n",
            "Epoch 17/100\n",
            "804/804 [==============================] - 4s 4ms/step - loss: 0.5433 - accuracy: 0.7336 - val_loss: 0.5534 - val_accuracy: 0.7312\n",
            "Epoch 18/100\n",
            "804/804 [==============================] - 5s 6ms/step - loss: 0.5431 - accuracy: 0.7349 - val_loss: 0.5552 - val_accuracy: 0.7289\n",
            "Epoch 19/100\n",
            "804/804 [==============================] - 4s 5ms/step - loss: 0.5428 - accuracy: 0.7352 - val_loss: 0.5540 - val_accuracy: 0.7305\n",
            "Epoch 20/100\n",
            "804/804 [==============================] - 4s 5ms/step - loss: 0.5424 - accuracy: 0.7344 - val_loss: 0.5540 - val_accuracy: 0.7298\n",
            "Epoch 21/100\n",
            "804/804 [==============================] - 4s 4ms/step - loss: 0.5424 - accuracy: 0.7360 - val_loss: 0.5524 - val_accuracy: 0.7318\n",
            "Epoch 22/100\n",
            "804/804 [==============================] - 4s 5ms/step - loss: 0.5414 - accuracy: 0.7337 - val_loss: 0.5537 - val_accuracy: 0.7268\n",
            "Epoch 23/100\n",
            "804/804 [==============================] - 5s 7ms/step - loss: 0.5420 - accuracy: 0.7365 - val_loss: 0.5526 - val_accuracy: 0.7338\n",
            "Epoch 24/100\n",
            "804/804 [==============================] - 3s 4ms/step - loss: 0.5419 - accuracy: 0.7358 - val_loss: 0.5541 - val_accuracy: 0.7275\n",
            "Epoch 25/100\n",
            "804/804 [==============================] - 4s 5ms/step - loss: 0.5413 - accuracy: 0.7361 - val_loss: 0.5533 - val_accuracy: 0.7314\n",
            "Epoch 26/100\n",
            "804/804 [==============================] - 4s 5ms/step - loss: 0.5408 - accuracy: 0.7361 - val_loss: 0.5544 - val_accuracy: 0.7301\n",
            "Epoch 27/100\n",
            "804/804 [==============================] - 4s 5ms/step - loss: 0.5409 - accuracy: 0.7363 - val_loss: 0.5556 - val_accuracy: 0.7287\n",
            "Epoch 28/100\n",
            "804/804 [==============================] - 3s 4ms/step - loss: 0.5402 - accuracy: 0.7373 - val_loss: 0.5553 - val_accuracy: 0.7299\n",
            "Epoch 29/100\n",
            "804/804 [==============================] - 3s 4ms/step - loss: 0.5401 - accuracy: 0.7372 - val_loss: 0.5551 - val_accuracy: 0.7297\n",
            "Epoch 30/100\n",
            "804/804 [==============================] - 5s 6ms/step - loss: 0.5404 - accuracy: 0.7367 - val_loss: 0.5528 - val_accuracy: 0.7319\n",
            "Epoch 31/100\n",
            "804/804 [==============================] - 4s 5ms/step - loss: 0.5402 - accuracy: 0.7350 - val_loss: 0.5594 - val_accuracy: 0.7257\n",
            "Epoch 32/100\n",
            "804/804 [==============================] - 3s 4ms/step - loss: 0.5406 - accuracy: 0.7361 - val_loss: 0.5531 - val_accuracy: 0.7310\n",
            "Epoch 33/100\n",
            "804/804 [==============================] - 4s 5ms/step - loss: 0.5398 - accuracy: 0.7373 - val_loss: 0.5542 - val_accuracy: 0.7283\n",
            "Epoch 34/100\n",
            "804/804 [==============================] - 4s 5ms/step - loss: 0.5400 - accuracy: 0.7363 - val_loss: 0.5533 - val_accuracy: 0.7322\n",
            "Epoch 35/100\n",
            "804/804 [==============================] - 4s 5ms/step - loss: 0.5396 - accuracy: 0.7374 - val_loss: 0.5548 - val_accuracy: 0.7300\n",
            "Epoch 36/100\n",
            "804/804 [==============================] - 4s 5ms/step - loss: 0.5396 - accuracy: 0.7376 - val_loss: 0.5537 - val_accuracy: 0.7299\n",
            "Epoch 37/100\n",
            "804/804 [==============================] - 5s 6ms/step - loss: 0.5393 - accuracy: 0.7375 - val_loss: 0.5546 - val_accuracy: 0.7273\n",
            "Epoch 38/100\n",
            "804/804 [==============================] - 4s 5ms/step - loss: 0.5393 - accuracy: 0.7370 - val_loss: 0.5553 - val_accuracy: 0.7300\n",
            "Epoch 39/100\n",
            "804/804 [==============================] - 4s 5ms/step - loss: 0.5388 - accuracy: 0.7370 - val_loss: 0.5544 - val_accuracy: 0.7313\n",
            "Epoch 40/100\n",
            "804/804 [==============================] - 5s 6ms/step - loss: 0.5388 - accuracy: 0.7379 - val_loss: 0.5546 - val_accuracy: 0.7268\n",
            "Epoch 41/100\n",
            "804/804 [==============================] - 3s 4ms/step - loss: 0.5390 - accuracy: 0.7374 - val_loss: 0.5539 - val_accuracy: 0.7285\n",
            "Epoch 42/100\n",
            "804/804 [==============================] - 4s 5ms/step - loss: 0.5393 - accuracy: 0.7376 - val_loss: 0.5556 - val_accuracy: 0.7238\n",
            "Epoch 43/100\n",
            "804/804 [==============================] - 4s 5ms/step - loss: 0.5387 - accuracy: 0.7378 - val_loss: 0.5550 - val_accuracy: 0.7256\n",
            "Epoch 44/100\n",
            "804/804 [==============================] - 4s 5ms/step - loss: 0.5384 - accuracy: 0.7380 - val_loss: 0.5561 - val_accuracy: 0.7304\n",
            "Epoch 45/100\n",
            "804/804 [==============================] - 4s 5ms/step - loss: 0.5382 - accuracy: 0.7381 - val_loss: 0.5569 - val_accuracy: 0.7275\n",
            "Epoch 46/100\n",
            "804/804 [==============================] - 3s 4ms/step - loss: 0.5378 - accuracy: 0.7375 - val_loss: 0.5551 - val_accuracy: 0.7282\n",
            "Epoch 47/100\n",
            "804/804 [==============================] - 5s 6ms/step - loss: 0.5385 - accuracy: 0.7365 - val_loss: 0.5544 - val_accuracy: 0.7300\n",
            "Epoch 48/100\n",
            "804/804 [==============================] - 4s 5ms/step - loss: 0.5380 - accuracy: 0.7372 - val_loss: 0.5551 - val_accuracy: 0.7283\n",
            "Epoch 49/100\n",
            "804/804 [==============================] - 4s 5ms/step - loss: 0.5380 - accuracy: 0.7383 - val_loss: 0.5563 - val_accuracy: 0.7276\n",
            "Epoch 50/100\n",
            "804/804 [==============================] - 4s 5ms/step - loss: 0.5378 - accuracy: 0.7381 - val_loss: 0.5542 - val_accuracy: 0.7293\n",
            "Epoch 51/100\n",
            "804/804 [==============================] - 4s 5ms/step - loss: 0.5380 - accuracy: 0.7381 - val_loss: 0.5550 - val_accuracy: 0.7304\n",
            "Epoch 52/100\n",
            "804/804 [==============================] - 3s 4ms/step - loss: 0.5374 - accuracy: 0.7371 - val_loss: 0.5548 - val_accuracy: 0.7286\n",
            "Epoch 53/100\n",
            "804/804 [==============================] - 4s 5ms/step - loss: 0.5371 - accuracy: 0.7386 - val_loss: 0.5595 - val_accuracy: 0.7261\n",
            "Epoch 54/100\n",
            "804/804 [==============================] - 5s 6ms/step - loss: 0.5374 - accuracy: 0.7386 - val_loss: 0.5545 - val_accuracy: 0.7290\n",
            "Epoch 55/100\n",
            "804/804 [==============================] - 4s 5ms/step - loss: 0.5369 - accuracy: 0.7381 - val_loss: 0.5565 - val_accuracy: 0.7292\n",
            "Epoch 56/100\n",
            "804/804 [==============================] - 3s 4ms/step - loss: 0.5368 - accuracy: 0.7382 - val_loss: 0.5555 - val_accuracy: 0.7286\n",
            "Epoch 57/100\n",
            "804/804 [==============================] - 4s 5ms/step - loss: 0.5374 - accuracy: 0.7390 - val_loss: 0.5562 - val_accuracy: 0.7290\n",
            "Epoch 58/100\n",
            "804/804 [==============================] - 4s 5ms/step - loss: 0.5372 - accuracy: 0.7392 - val_loss: 0.5554 - val_accuracy: 0.7237\n",
            "Epoch 59/100\n",
            "804/804 [==============================] - 3s 4ms/step - loss: 0.5373 - accuracy: 0.7380 - val_loss: 0.5551 - val_accuracy: 0.7303\n",
            "Epoch 60/100\n",
            "804/804 [==============================] - 4s 4ms/step - loss: 0.5371 - accuracy: 0.7380 - val_loss: 0.5539 - val_accuracy: 0.7296\n",
            "Epoch 61/100\n",
            "804/804 [==============================] - 4s 6ms/step - loss: 0.5363 - accuracy: 0.7386 - val_loss: 0.5578 - val_accuracy: 0.7286\n",
            "Epoch 62/100\n",
            "804/804 [==============================] - 4s 5ms/step - loss: 0.5369 - accuracy: 0.7385 - val_loss: 0.5549 - val_accuracy: 0.7279\n",
            "Epoch 63/100\n",
            "804/804 [==============================] - 4s 5ms/step - loss: 0.5371 - accuracy: 0.7388 - val_loss: 0.5562 - val_accuracy: 0.7287\n",
            "Epoch 64/100\n",
            "804/804 [==============================] - 4s 5ms/step - loss: 0.5366 - accuracy: 0.7390 - val_loss: 0.5566 - val_accuracy: 0.7289\n",
            "Epoch 65/100\n",
            "804/804 [==============================] - 4s 5ms/step - loss: 0.5366 - accuracy: 0.7391 - val_loss: 0.5556 - val_accuracy: 0.7292\n",
            "Epoch 66/100\n",
            "804/804 [==============================] - 3s 4ms/step - loss: 0.5364 - accuracy: 0.7382 - val_loss: 0.5578 - val_accuracy: 0.7294\n",
            "Epoch 67/100\n",
            "804/804 [==============================] - 3s 4ms/step - loss: 0.5364 - accuracy: 0.7384 - val_loss: 0.5562 - val_accuracy: 0.7291\n",
            "Epoch 68/100\n",
            "804/804 [==============================] - 6s 7ms/step - loss: 0.5363 - accuracy: 0.7394 - val_loss: 0.5568 - val_accuracy: 0.7291\n",
            "Epoch 69/100\n",
            "804/804 [==============================] - 4s 5ms/step - loss: 0.5364 - accuracy: 0.7394 - val_loss: 0.5571 - val_accuracy: 0.7298\n",
            "Epoch 70/100\n",
            "804/804 [==============================] - 3s 4ms/step - loss: 0.5360 - accuracy: 0.7399 - val_loss: 0.5604 - val_accuracy: 0.7296\n",
            "Epoch 71/100\n",
            "804/804 [==============================] - 3s 4ms/step - loss: 0.5361 - accuracy: 0.7393 - val_loss: 0.5584 - val_accuracy: 0.7250\n",
            "Epoch 72/100\n",
            "804/804 [==============================] - 5s 6ms/step - loss: 0.5361 - accuracy: 0.7397 - val_loss: 0.5570 - val_accuracy: 0.7279\n",
            "Epoch 73/100\n",
            "804/804 [==============================] - 3s 4ms/step - loss: 0.5357 - accuracy: 0.7393 - val_loss: 0.5580 - val_accuracy: 0.7285\n",
            "Epoch 74/100\n",
            "804/804 [==============================] - 3s 4ms/step - loss: 0.5362 - accuracy: 0.7389 - val_loss: 0.5584 - val_accuracy: 0.7271\n",
            "Epoch 75/100\n",
            "804/804 [==============================] - 3s 4ms/step - loss: 0.5356 - accuracy: 0.7402 - val_loss: 0.5593 - val_accuracy: 0.7304\n",
            "Epoch 76/100\n",
            "804/804 [==============================] - 5s 6ms/step - loss: 0.5358 - accuracy: 0.7394 - val_loss: 0.5569 - val_accuracy: 0.7299\n",
            "Epoch 77/100\n",
            "804/804 [==============================] - 3s 4ms/step - loss: 0.5357 - accuracy: 0.7398 - val_loss: 0.5582 - val_accuracy: 0.7298\n",
            "Epoch 78/100\n",
            "804/804 [==============================] - 3s 4ms/step - loss: 0.5354 - accuracy: 0.7395 - val_loss: 0.5588 - val_accuracy: 0.7280\n",
            "Epoch 79/100\n",
            "804/804 [==============================] - 4s 5ms/step - loss: 0.5358 - accuracy: 0.7399 - val_loss: 0.5569 - val_accuracy: 0.7280\n",
            "Epoch 80/100\n",
            "804/804 [==============================] - 4s 5ms/step - loss: 0.5350 - accuracy: 0.7398 - val_loss: 0.5611 - val_accuracy: 0.7283\n",
            "Epoch 81/100\n",
            "804/804 [==============================] - 4s 5ms/step - loss: 0.5359 - accuracy: 0.7399 - val_loss: 0.5564 - val_accuracy: 0.7262\n",
            "Epoch 82/100\n",
            "804/804 [==============================] - 4s 5ms/step - loss: 0.5350 - accuracy: 0.7399 - val_loss: 0.5571 - val_accuracy: 0.7307\n",
            "Epoch 83/100\n",
            "804/804 [==============================] - 5s 6ms/step - loss: 0.5353 - accuracy: 0.7401 - val_loss: 0.5574 - val_accuracy: 0.7300\n",
            "Epoch 84/100\n",
            "804/804 [==============================] - 3s 4ms/step - loss: 0.5352 - accuracy: 0.7398 - val_loss: 0.5570 - val_accuracy: 0.7290\n",
            "Epoch 85/100\n",
            "804/804 [==============================] - 3s 4ms/step - loss: 0.5351 - accuracy: 0.7400 - val_loss: 0.5570 - val_accuracy: 0.7283\n",
            "Epoch 86/100\n",
            "804/804 [==============================] - 4s 5ms/step - loss: 0.5349 - accuracy: 0.7387 - val_loss: 0.5593 - val_accuracy: 0.7283\n",
            "Epoch 87/100\n",
            "804/804 [==============================] - 4s 5ms/step - loss: 0.5354 - accuracy: 0.7396 - val_loss: 0.5587 - val_accuracy: 0.7297\n",
            "Epoch 88/100\n",
            "804/804 [==============================] - 4s 5ms/step - loss: 0.5353 - accuracy: 0.7392 - val_loss: 0.5601 - val_accuracy: 0.7278\n",
            "Epoch 89/100\n",
            "804/804 [==============================] - 4s 5ms/step - loss: 0.5352 - accuracy: 0.7386 - val_loss: 0.5584 - val_accuracy: 0.7324\n",
            "Epoch 90/100\n",
            "804/804 [==============================] - 5s 6ms/step - loss: 0.5350 - accuracy: 0.7400 - val_loss: 0.5574 - val_accuracy: 0.7314\n",
            "Epoch 91/100\n",
            "804/804 [==============================] - 3s 4ms/step - loss: 0.5350 - accuracy: 0.7398 - val_loss: 0.5573 - val_accuracy: 0.7291\n",
            "Epoch 92/100\n",
            "804/804 [==============================] - 4s 5ms/step - loss: 0.5350 - accuracy: 0.7398 - val_loss: 0.5574 - val_accuracy: 0.7314\n",
            "Epoch 93/100\n",
            "804/804 [==============================] - 4s 5ms/step - loss: 0.5351 - accuracy: 0.7393 - val_loss: 0.5595 - val_accuracy: 0.7296\n",
            "Epoch 94/100\n",
            "804/804 [==============================] - 5s 6ms/step - loss: 0.5347 - accuracy: 0.7392 - val_loss: 0.5593 - val_accuracy: 0.7293\n",
            "Epoch 95/100\n",
            "804/804 [==============================] - 3s 4ms/step - loss: 0.5349 - accuracy: 0.7401 - val_loss: 0.5578 - val_accuracy: 0.7310\n",
            "Epoch 96/100\n",
            "804/804 [==============================] - 3s 4ms/step - loss: 0.5351 - accuracy: 0.7390 - val_loss: 0.5582 - val_accuracy: 0.7280\n",
            "Epoch 97/100\n",
            "804/804 [==============================] - 4s 5ms/step - loss: 0.5347 - accuracy: 0.7390 - val_loss: 0.5603 - val_accuracy: 0.7290\n",
            "Epoch 98/100\n",
            "804/804 [==============================] - 4s 5ms/step - loss: 0.5350 - accuracy: 0.7395 - val_loss: 0.5597 - val_accuracy: 0.7237\n",
            "Epoch 99/100\n",
            "804/804 [==============================] - 4s 5ms/step - loss: 0.5345 - accuracy: 0.7397 - val_loss: 0.5623 - val_accuracy: 0.7283\n",
            "Epoch 100/100\n",
            "804/804 [==============================] - 3s 4ms/step - loss: 0.5348 - accuracy: 0.7402 - val_loss: 0.5613 - val_accuracy: 0.7307\n"
          ]
        }
      ],
      "source": [
        "# Train the model\n",
        "fit_model = nn.fit(X_train_scaled, y_train, epochs=100, validation_data=(X_test_scaled, y_test), callbacks=[checkpoint_callback] )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wLCfbTIEwtV4",
        "outputId": "eb36dec6-f3e0-41fa-f7f2-081cf413d784"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "268/268 - 1s - loss: 0.5613 - accuracy: 0.7307 - 511ms/epoch - 2ms/step\n",
            "Loss: 0.56126469373703, Accuracy: 0.7307288646697998\n"
          ]
        }
      ],
      "source": [
        "# Evaluate the model using the test data\n",
        "model_loss, model_accuracy = nn.evaluate(X_test_scaled,y_test,verbose=2)\n",
        "print(f\"Loss: {model_loss}, Accuracy: {model_accuracy}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "id": "PV11AICfwtV4",
        "outputId": "45bc2090-7655-436a-f9c3-60c9ba3c10ed"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
            "  saving_api.save_model(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "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",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_a1cc72b6-39ea-4bc4-a21f-93d706df51e2\", \"AlphabetSoupCharity.h5\", 102392)"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Export our model to HDF5 file\n",
        "# Load Dependencies\n",
        "import os\n",
        "from google.colab import files\n",
        "\n",
        "# Create a directory\n",
        "os.makedirs('/nn_models', exist_ok=True)\n",
        "\n",
        "# Save the model as an h5 file\n",
        "nn.save('/nn_models/AlphabetSoupCharity.h5')\n",
        "\n",
        "# Download the model file\n",
        "files.download('/nn_models/AlphabetSoupCharity.h5')"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.-1.-1"
    },
    "vscode": {
      "interpreter": {
        "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
      }
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}