dataprep.ipynb

{
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      "execution_count": 1,
      "id": "f2e9ebd1",
      "metadata": {
        "id": "f2e9ebd1"
      },
      "outputs": [],
      "source": [
        "# import libraries\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from IPython.display import Image\n",
        "from IPython.display import display\n",
        "from time import gmtime, strftime\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "49556f41",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "49556f41",
        "outputId": "9c3ebdb1-a9e1-4e61-cccf-54e505504245"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Success: Data loaded into dataframe.\n"
          ]
        }
      ],
      "source": [
        "try:\n",
        "  raw_data = pd.read_csv('./Dummy-Data.csv',index_col=0)\n",
        "  print('Success: Data loaded into dataframe.')\n",
        "except Exception as e:\n",
        "    print('Data load error: ',e)\n",
        "    "
      ]
    },
    {
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      "outputs": [
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          "data": {
            "text/plain": [
              "       Ins_Age Ins_Gender   Ht   Wt  IssueDate  height      weight        bmi  \\\n",
              "AppID                                                                           \n",
              "56372       31       Male  510  185        NaN  1.7780   84.090909  26.600239   \n",
              "34565       35       Male  510  205        NaN  1.7780   93.181818  29.475940   \n",
              "57732       45     Female  510  125        NaN  1.7780   56.818182  17.973134   \n",
              "87324       38       Male  503  175        NaN  1.6002   79.545455  31.064677   \n",
              "12323       39     Female  600  252        NaN  1.8288  114.545455  34.248806   \n",
              "\n",
              "       quote  \n",
              "AppID         \n",
              "56372  500.0  \n",
              "34565  500.0  \n",
              "57732  900.0  \n",
              "87324  500.0  \n",
              "12323  450.0  "
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              "            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",
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              "  "
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      ],
      "source": [
        "#preprocess raw data\n",
        "#convert height into meters   \n",
        "raw_data[\"height\"] = (((raw_data[\"Ht\"]%100) + ((raw_data[\"Ht\"]//100)*12))*0.0254)\n",
        "raw_data[\"weight\"] = (raw_data[\"Wt\"]/2.2)\n",
        "raw_data[\"bmi\"] = raw_data[\"weight\"]/(raw_data[\"height\"]**2)\n",
        "\n",
        "def insurance_quote(data):\n",
        "    if  (data[\"Ins_Age\"]>17 and data[\"Ins_Age\"]<40) and (data[\"bmi\"]<17.49 or data[\"bmi\"]>38.5 ):\n",
        "        quote = 750\n",
        "    elif (data[\"Ins_Age\"]>39 and data[\"Ins_Age\"]<60) and (data[\"bmi\"]<18.49 or data[\"bmi\"]>38.5 ):\n",
        "        quote = 1000\n",
        "    elif (data[\"Ins_Age\"]>60) and (data[\"bmi\"]<18.49 or data[\"bmi\"]>45.5 ):\n",
        "        quote = 2000\n",
        "    else:\n",
        "        quote = 500\n",
        "    if (data[\"Ins_Gender\"] == \"Female\"):\n",
        "        quote = 0.9*quote\n",
        "    return quote \n",
        "    \n",
        "\n",
        "\n",
        "raw_data[\"quote\"] = raw_data.apply(insurance_quote, axis=1)\n",
        "\n",
        "raw_data.head(5)"
      ]
    },
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          "height": 143
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Ins_Age  Ins_Gender  height     weight        bmi  quote\n",
              "AppID                                                          \n",
              "56372       31           0   1.778  84.090909  26.600239  500.0\n",
              "34565       35           0   1.778  93.181818  29.475940  500.0"
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              "            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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "#converting gender into number for training\n",
        "raw_data['Ins_Gender'].replace({'Female':1,'Male':0},inplace=True)\n",
        "#dropping unwanted collumns Ht Wt IssueDate\n",
        "preprocessed_traintest_data = raw_data.drop(columns=['Ht', 'Wt', 'IssueDate'])\n",
        "preprocessed_traintest_data.to_csv('preprocessed_data.csv',sep=',', index=False, header=False)\n",
        "preprocessed_traintest_data.head(2)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "db4b2f64",
      "metadata": {
        "id": "db4b2f64"
      },
      "source": [
        "Now creating a training dataset for predicting BMI from age gender, height."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "7154b38f",
      "metadata": {
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          "height": 112
        },
        "id": "7154b38f",
        "outputId": "735fe831-8db8-49c7-8d91-3b2d0664e241"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Ins_Age  Ins_Gender  height        bmi\n",
              "AppID                                        \n",
              "56372       31           0   1.778  26.600239"
            ],
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              "    <tr>\n",
              "      <th>56372</th>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "      <td>1.778</td>\n",
              "      <td>26.600239</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2729d8c8-703a-47d6-9cda-a59d12ffbcf4')\"\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",
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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",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2729d8c8-703a-47d6-9cda-a59d12ffbcf4 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-2729d8c8-703a-47d6-9cda-a59d12ffbcf4');\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",
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              "          docLink.innerHTML = docLinkHtml;\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "#dropping height and weight\n",
        "quote_traintest = preprocessed_traintest_data.drop(preprocessed_traintest_data.columns[[3, 5]], axis=1)\n",
        "quote_traintest.head(1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "6ea2c362",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6ea2c362",
        "outputId": "64c52de7-c681-4b6d-b0b9-a6c83263f4b6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(70, 4) (30, 4)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method NDFrame.head of        Ins_Age  Ins_Gender  height        bmi\n",
              "AppID                                        \n",
              "45764       31           1  1.8542  26.441992\n",
              "24435       33           0  1.7526  31.224404\n",
              "64574       25           0  1.8542  21.153593\n",
              "34523       32           0  1.8542  16.526245\n",
              "99946       30           0  1.7272  21.331436\n",
              "...        ...         ...     ...        ...\n",
              "99452       46           0  1.8542  23.797792\n",
              "99918       19           0  1.8034  20.964497\n",
              "53242       28           1  1.7272  25.140621\n",
              "12323       39           1  1.8288  34.248806\n",
              "99946       40           0  1.6002  31.952239\n",
              "\n",
              "[70 rows x 4 columns]>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "#test train split\n",
        "train_data, test_data = np.split(quote_traintest.sample(frac=1, random_state=1729), [int(0.7 * len(quote_traintest))])\n",
        "print(train_data.shape, test_data.shape)\n",
        "train_data.head"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "69c3406a",
      "metadata": {
        "id": "69c3406a"
      },
      "outputs": [],
      "source": [
        "pd.concat([train_data['bmi'], train_data.drop(['bmi'], axis=1)], axis=1).to_csv('train1.csv',sep=',', index=False, header=False)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "05a47117",
      "metadata": {
        "id": "05a47117"
      },
      "outputs": [],
      "source": [
        "pd.concat([test_data['bmi'], test_data.drop(['bmi'], axis=1)], axis=1).to_csv('test1.csv',sep=',', index=False, header=False)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8319668c",
      "metadata": {
        "id": "8319668c"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "conda_python3",
      "language": "python",
      "name": "conda_python3"
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      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.8"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}