ML_8주차_실습.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jEQQFbkcfCiu"
      },
      "source": [
        "# Linear Regression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-UyHqUe4-wg6",
        "outputId": "b3335fb5-a587-4654-cca1-bce719d3deca"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-04-17 09:45:59--  https://docs.google.com/uc?export=download&confirm=&id=1f09CkI3Hd7h6VdwAOhAaja5G_HwtPBZZ\n",
            "Resolving docs.google.com (docs.google.com)... 173.194.214.100, 173.194.214.102, 173.194.214.138, ...\n",
            "Connecting to docs.google.com (docs.google.com)|173.194.214.100|:443... connected.\n",
            "HTTP request sent, awaiting response... 303 See Other\n",
            "Location: https://doc-10-8k-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/k8i0lo6d9md5hhu05oc0tgqf1ea1vhsg/1681724700000/00253795494504996225/*/1f09CkI3Hd7h6VdwAOhAaja5G_HwtPBZZ?e=download&uuid=a6b1d71c-c8f2-4368-918d-a34f33032b83 [following]\n",
            "Warning: wildcards not supported in HTTP.\n",
            "--2023-04-17 09:45:59--  https://doc-10-8k-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/k8i0lo6d9md5hhu05oc0tgqf1ea1vhsg/1681724700000/00253795494504996225/*/1f09CkI3Hd7h6VdwAOhAaja5G_HwtPBZZ?e=download&uuid=a6b1d71c-c8f2-4368-918d-a34f33032b83\n",
            "Resolving doc-10-8k-docs.googleusercontent.com (doc-10-8k-docs.googleusercontent.com)... 173.194.218.132, 2607:f8b0:400c:c14::84\n",
            "Connecting to doc-10-8k-docs.googleusercontent.com (doc-10-8k-docs.googleusercontent.com)|173.194.218.132|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 428120 (418K) [text/csv]\n",
            "Saving to: ‘weight_height.csv’\n",
            "\n",
            "weight_height.csv   100%[===================>] 418.09K  --.-KB/s    in 0.005s  \n",
            "\n",
            "2023-04-17 09:45:59 (82.0 MB/s) - ‘weight_height.csv’ saved [428120/428120]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "! wget --load-cookies ~/cookies.txt \"https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies ~/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=1f09CkI3Hd7h6VdwAOhAaja5G_HwtPBZZ' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\\1\\n/p')&id=1f09CkI3Hd7h6VdwAOhAaja5G_HwtPBZZ\" -O weight_height.csv && rm -rf ~/cookies.txt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "mOLjcQmN-wg9"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv(\"./weight_height.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "80TK3OVhAZS3",
        "outputId": "9b82b943-6208-4c73-8709-d9e25f8c943e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Gender     Height      Weight\n",
              "0   Male  73.847017  241.893563\n",
              "1   Male  68.781904  162.310473\n",
              "2   Male  74.110105  212.740856\n",
              "3   Male  71.730978  220.042470\n",
              "4   Male  69.881796  206.349801"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8ff09424-0f90-4c4a-a659-94f62b3cf15b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Gender</th>\n",
              "      <th>Height</th>\n",
              "      <th>Weight</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Male</td>\n",
              "      <td>73.847017</td>\n",
              "      <td>241.893563</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Male</td>\n",
              "      <td>68.781904</td>\n",
              "      <td>162.310473</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Male</td>\n",
              "      <td>74.110105</td>\n",
              "      <td>212.740856</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Male</td>\n",
              "      <td>71.730978</td>\n",
              "      <td>220.042470</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Male</td>\n",
              "      <td>69.881796</td>\n",
              "      <td>206.349801</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8ff09424-0f90-4c4a-a659-94f62b3cf15b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "    .colab-df-convert:hover {\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      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",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8ff09424-0f90-4c4a-a659-94f62b3cf15b 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-8ff09424-0f90-4c4a-a659-94f62b3cf15b');\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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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "5zW_UrbsGs7o",
        "outputId": "6164bcca-4712-40c5-d9f7-3b1e908371f3"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.scatter(df.Height, df.Weight)\n",
        "plt.xlabel('Height (Feet)')\n",
        "plt.ylabel('Weight (Pound)')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "nSmB1STVGz9e"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X, y = df.Height.values, df.Weight.values\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1213)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m1IaQ-PxG2eq",
        "outputId": "04c97729-0ea5-4641-b726-87f61bb94191"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((8000,), (2000,))"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "X_train.shape, X_test.shape # X의 경우 (데이터의 크기, 피처의 개수) 의 형태로 shape이 구성되어야 함\n",
        "# But, 현재 (8000, )으로 차원이 하나 부족함"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "WFaa8sBIG4mE"
      },
      "outputs": [],
      "source": [
        "X_train = X_train.reshape(-1,1)\n",
        "X_test = X_test.reshape(-1,1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cWoEdZmHVkws",
        "outputId": "4119c1f3-3dd0-48ad-8c77-8733b881f880"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((8000, 1), (2000, 1))"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "X_train.shape, X_test.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "kGHvdPxRG6H4",
        "outputId": "8e358a60-73dd-4f3f-b724-6698f69e08f2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "lr = LinearRegression()\n",
        "lr.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GK_dYXyfqjNm"
      },
      "source": [
        "* MSE: 실제 값과 예측값의 차이를 기준으로 오차가 어느 정도인지 계산\n",
        "* reference: https://bitcodic.tistory.com/m/108"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "95QiiI7JG8OR",
        "outputId": "e81230d2-02cb-4ad3-a231-15b3a857c849"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE: 151.34\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "pred = lr.predict(X_test)\n",
        "mse = mean_squared_error(y_test, pred)\n",
        "print(f\"MSE: {mse:.2f}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p5GuLtN1HhOW"
      },
      "source": [
        "## 젠더 특성을 고려하기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "mt0amcjU-5xl",
        "outputId": "eb4f99ec-f565-4d72-dab3-948fe2c66e5b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "male_df = df[df.Gender == \"Male\"]\n",
        "female_df = df[df.Gender == \"Female\"]\n",
        "\n",
        "plt.scatter(male_df.Height, male_df.Weight, color= \"Blue\", label=\"Male\")\n",
        "plt.scatter(female_df.Height, female_df.Weight, color= \"Red\", label=\"Female\")\n",
        "plt.xlabel('Height (Feet)')\n",
        "plt.ylabel('Weight (Pound)')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iDKvDl43AV-3"
      },
      "source": [
        "## Model Engineering\n",
        "* 데이터의 특성을 반영한 인공지능 모델을 개발하는 것이 중요\n",
        "* 단순하게 모든 데이터를 다 활용해줄 수도 있지만, 데이터 분포를 보면 남자와 여자의 분포가 다름\n",
        "* Goal: 키가 주어졌을 때, 몸무게를 예측하는 것 (이 때, 성별에 따른 데이터의 분포를 반영)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "NaUBy710DA5S"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_male, y_male = male_df.Height.values, male_df.Weight.values\n",
        "X_male_train, X_male_test, y_male_train, y_male_test = \\\n",
        "train_test_split(X_male, y_male, test_size=0.2, random_state=1213)\n",
        "\n",
        "X_male_train = X_male_train.reshape(-1,1)\n",
        "X_male_test = X_male_test.reshape(-1,1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "bSECuZ5UDX-T"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_female, y_female = female_df.Height.values, female_df.Weight.values\n",
        "X_female_train, X_female_test, y_female_train, y_female_test = \\\n",
        "train_test_split(X_female, y_female, test_size=0.2, random_state=1213)\n",
        "\n",
        "X_female_train = X_female_train.reshape(-1,1)\n",
        "X_female_test = X_female_test.reshape(-1,1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "pLI44OKODcdl",
        "outputId": "9fe4a3fe-cdaf-45bc-81a4-7868ca391623"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "lr_male = LinearRegression()\n",
        "lr_male.fit(X_male_train, y_male_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "7LyqLgP_DvgG",
        "outputId": "4c6ef1ad-4c32-49d2-db8b-8b173e2c9e02"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "lr_female = LinearRegression()\n",
        "lr_female.fit(X_female_train, y_female_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dFr8MVqADzld",
        "outputId": "d18f5fd0-25ec-4531-f302-7b0aee028da2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE male: 95.48\n"
          ]
        }
      ],
      "source": [
        "pred_male = lr_male.predict(X_male_test)\n",
        "mse_male = mean_squared_error(y_male_test, pred_male)\n",
        "print(f\"MSE male: {mse_male:.2f}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "01tl_k54ENAH",
        "outputId": "f30a22f4-109b-4a73-9b6a-e9a95e793bfb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE female: 102.56\n"
          ]
        }
      ],
      "source": [
        "pred_female = lr_female.predict(X_female_test)\n",
        "mse_female = mean_squared_error(y_female_test, pred_female)\n",
        "print(f\"MSE female: {mse_female:.2f}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "idJgjdf0ES_G"
      },
      "source": [
        "## Conclusion\n",
        "* Gender를 고려하지 않을 경우: 151.34\n",
        "* 고려했을 경우: 95.48(Male), 102.56(Female)\n",
        "\n",
        "* 데이터의 특징을 잘 고려한 모델이 더 좋은 성능을 확보할 수 있음"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NiJHDEgFfCjE"
      },
      "source": [
        "# Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zd0DD2J5WOi2",
        "outputId": "17fda707-2bb3-4b0c-8c4a-a97e3ef2c604"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-04-17 09:46:02--  https://docs.google.com/uc?export=download&confirm=&id=1NdCDgf8g99c6ug_yYFHdHNHWEtoGOmUz\n",
            "Resolving docs.google.com (docs.google.com)... 173.194.214.100, 173.194.214.102, 173.194.214.138, ...\n",
            "Connecting to docs.google.com (docs.google.com)|173.194.214.100|:443... connected.\n",
            "HTTP request sent, awaiting response... 303 See Other\n",
            "Location: https://doc-14-8k-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/5bshiv4hahbrtatmb1p8kfh71fibf2e8/1681724700000/00253795494504996225/*/1NdCDgf8g99c6ug_yYFHdHNHWEtoGOmUz?e=download&uuid=392bb7fb-d31f-4658-8cd6-ce537a12c434 [following]\n",
            "Warning: wildcards not supported in HTTP.\n",
            "--2023-04-17 09:46:02--  https://doc-14-8k-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/5bshiv4hahbrtatmb1p8kfh71fibf2e8/1681724700000/00253795494504996225/*/1NdCDgf8g99c6ug_yYFHdHNHWEtoGOmUz?e=download&uuid=392bb7fb-d31f-4658-8cd6-ce537a12c434\n",
            "Resolving doc-14-8k-docs.googleusercontent.com (doc-14-8k-docs.googleusercontent.com)... 173.194.218.132, 2607:f8b0:400c:c14::84\n",
            "Connecting to doc-14-8k-docs.googleusercontent.com (doc-14-8k-docs.googleusercontent.com)|173.194.218.132|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 61194 (60K) [text/csv]\n",
            "Saving to: ‘train.csv’\n",
            "\n",
            "train.csv           100%[===================>]  59.76K  --.-KB/s    in 0.001s  \n",
            "\n",
            "2023-04-17 09:46:02 (83.4 MB/s) - ‘train.csv’ saved [61194/61194]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# train.csv\n",
        "! wget --load-cookies ~/cookies.txt \"https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies ~/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=1NdCDgf8g99c6ug_yYFHdHNHWEtoGOmUz' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\\1\\n/p')&id=1NdCDgf8g99c6ug_yYFHdHNHWEtoGOmUz\" -O train.csv && rm -rf ~/cookies.txt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g_446mjiWOi4",
        "outputId": "ccd21dba-bfc1-4715-fe1e-60ac1a63d468"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-04-17 09:46:02--  https://docs.google.com/uc?export=download&confirm=&id=1nIWM6BOp8Xu1rlF6AUQCSfelPuiBu7tF\n",
            "Resolving docs.google.com (docs.google.com)... 173.194.214.100, 173.194.214.102, 173.194.214.138, ...\n",
            "Connecting to docs.google.com (docs.google.com)|173.194.214.100|:443... connected.\n",
            "HTTP request sent, awaiting response... 303 See Other\n",
            "Location: https://doc-04-8k-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/2tl27sdin0kpme0g4naiodv7fh4mfb0s/1681724700000/00253795494504996225/*/1nIWM6BOp8Xu1rlF6AUQCSfelPuiBu7tF?e=download&uuid=5f026392-c63a-4473-a4cd-f67c74f70bc8 [following]\n",
            "Warning: wildcards not supported in HTTP.\n",
            "--2023-04-17 09:46:03--  https://doc-04-8k-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/2tl27sdin0kpme0g4naiodv7fh4mfb0s/1681724700000/00253795494504996225/*/1nIWM6BOp8Xu1rlF6AUQCSfelPuiBu7tF?e=download&uuid=5f026392-c63a-4473-a4cd-f67c74f70bc8\n",
            "Resolving doc-04-8k-docs.googleusercontent.com (doc-04-8k-docs.googleusercontent.com)... 173.194.218.132, 2607:f8b0:400c:c14::84\n",
            "Connecting to doc-04-8k-docs.googleusercontent.com (doc-04-8k-docs.googleusercontent.com)|173.194.218.132|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 28629 (28K) [text/csv]\n",
            "Saving to: ‘test.csv’\n",
            "\n",
            "test.csv            100%[===================>]  27.96K  --.-KB/s    in 0s      \n",
            "\n",
            "2023-04-17 09:46:03 (93.0 MB/s) - ‘test.csv’ saved [28629/28629]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# test.csv\n",
        "! wget --load-cookies ~/cookies.txt \"https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies ~/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=1nIWM6BOp8Xu1rlF6AUQCSfelPuiBu7tF' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\\1\\n/p')&id=1nIWM6BOp8Xu1rlF6AUQCSfelPuiBu7tF\" -O test.csv && rm -rf ~/cookies.txt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "BoBaVMzqWOi5"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "\n",
        "train_df = pd.read_csv(\"./train.csv\")\n",
        "test_df = pd.read_csv(\"./test.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "GHvtC-8rWOi6",
        "outputId": "9357ed56-438b-42c8-b840-01db40885d43"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "   PassengerId  Survived  Pclass  \\\n",
              "0            1         0       3   \n",
              "1            2         1       1   \n",
              "2            3         1       3   \n",
              "3            4         1       1   \n",
              "4            5         0       3   \n",
              "\n",
              "                                                Name     Sex   Age  SibSp  \\\n",
              "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
              "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
              "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
              "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
              "4                           Allen, Mr. William Henry    male  35.0      0   \n",
              "\n",
              "   Parch            Ticket     Fare Cabin Embarked  \n",
              "0      0         A/5 21171   7.2500   NaN        S  \n",
              "1      0          PC 17599  71.2833   C85        C  \n",
              "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
              "3      0            113803  53.1000  C123        S  \n",
              "4      0            373450   8.0500   NaN        S  "
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              "\n",
              "  <div id=\"df-62027b79-a66a-433e-a3ee-bfac5ba3fc5e\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "      <th>PassengerId</th>\n",
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              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
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              "      <th>SibSp</th>\n",
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              "      <td>1</td>\n",
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              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
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              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
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              "      <td>26.0</td>\n",
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              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-62027b79-a66a-433e-a3ee-bfac5ba3fc5e')\"\n",
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              "      </button>\n",
              "      \n",
              "  <style>\n",
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              "      display:flex;\n",
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              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "\n",
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              "    }\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-62027b79-a66a-433e-a3ee-bfac5ba3fc5e 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-62027b79-a66a-433e-a3ee-bfac5ba3fc5e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "train_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "6l1kKgY6fCjF",
        "outputId": "09685d5d-685a-4052-8fd5-ac92e4767962"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   PassengerId  Pclass                                          Name     Sex  \\\n",
              "0          892       3                              Kelly, Mr. James    male   \n",
              "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
              "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
              "3          895       3                              Wirz, Mr. Albert    male   \n",
              "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
              "\n",
              "    Age  SibSp  Parch   Ticket     Fare Cabin Embarked  \n",
              "0  34.5      0      0   330911   7.8292   NaN        Q  \n",
              "1  47.0      1      0   363272   7.0000   NaN        S  \n",
              "2  62.0      0      0   240276   9.6875   NaN        Q  \n",
              "3  27.0      0      0   315154   8.6625   NaN        S  \n",
              "4  22.0      1      1  3101298  12.2875   NaN        S  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8e95f84f-1b25-48c5-a11f-ac75c0e8f907\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>892</td>\n",
              "      <td>3</td>\n",
              "      <td>Kelly, Mr. James</td>\n",
              "      <td>male</td>\n",
              "      <td>34.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>330911</td>\n",
              "      <td>7.8292</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>893</td>\n",
              "      <td>3</td>\n",
              "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
              "      <td>female</td>\n",
              "      <td>47.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>363272</td>\n",
              "      <td>7.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>894</td>\n",
              "      <td>2</td>\n",
              "      <td>Myles, Mr. Thomas Francis</td>\n",
              "      <td>male</td>\n",
              "      <td>62.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>240276</td>\n",
              "      <td>9.6875</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>895</td>\n",
              "      <td>3</td>\n",
              "      <td>Wirz, Mr. Albert</td>\n",
              "      <td>male</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>315154</td>\n",
              "      <td>8.6625</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>896</td>\n",
              "      <td>3</td>\n",
              "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
              "      <td>female</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3101298</td>\n",
              "      <td>12.2875</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8e95f84f-1b25-48c5-a11f-ac75c0e8f907')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8e95f84f-1b25-48c5-a11f-ac75c0e8f907 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-8e95f84f-1b25-48c5-a11f-ac75c0e8f907');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "test_df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "muZdy5joWOi7"
      },
      "source": [
        "## Data Analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 850
        },
        "id": "1MnfCtsOWOi9",
        "outputId": "bb724927-7ced-4be7-c2db-a56e062b5cfe"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1000 with 4 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "plt.figure(figsize=(15, 10))\n",
        "plt.subplot(221)\n",
        "sns.barplot(x = 'SibSp', y = 'Survived', data = train_df)\n",
        "plt.subplot(222)\n",
        "sns.barplot(x = 'Pclass', y = 'Survived', data = train_df)\n",
        "plt.subplot(223)\n",
        "sns.barplot(x = 'Sex', y = 'Survived', data = train_df)\n",
        "plt.subplot(224)\n",
        "sns.barplot(x = 'Embarked', y = 'Survived', data = train_df)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IFLkhLgCWOi-"
      },
      "source": [
        "## Processing Data\n",
        "* 이전까지 활용한 데이터는 실제 데이터가 아닌, scikit-learn에서 제공한 데이터 → 이미 전처리 되어 있음\n",
        "* 실제로 우리가 연구/개발을 하면서 마주하게 될 데이터는 전처리 필요\n",
        "* 실제 머신러닝 개발 코드: 머신러닝 관련 코드(10%), 전처리/데이터수집/관리(90%)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ve0fxxTuWOi_"
      },
      "source": [
        "### 결측값 처리\n",
        "* 결측값: 데이터가 없는 상태 (데이터 수집/보관시 여러 문제들로 인하여 데이터가 사라진 것)\n",
        "* pandas의 isna()를 통해서 결측값 확인\n",
        "\n",
        "#### 1. 결측값 0으로 처리\n",
        "* 결측값이 ML 학습 시 에러 유발 → 0으로 채움\n",
        "\n",
        "#### 2. 결측값을 평균으로 처리\n",
        "* 1번의 경우 잘못된 데이터가 학습될 가능성이 큼\n",
        "* 결측값을 평균으로 처리 → 결측 데이터의 영향을 최소화할 수 있음\n",
        "\n",
        "#### 3. 빈도 높은 값으로 결측값 채우기\n",
        "* 결측값이 실수/정수 데이터가 아닌 경우 → 가장 빈도 높은 값으로 채움"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "alJtQcuVWOjB",
        "outputId": "06ba079a-f4bf-406a-dd32-308dcb70aa94"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId      0\n",
              "Survived         0\n",
              "Pclass           0\n",
              "Name             0\n",
              "Sex              0\n",
              "Age            177\n",
              "SibSp            0\n",
              "Parch            0\n",
              "Ticket           0\n",
              "Fare             0\n",
              "Cabin          687\n",
              "Embarked         2\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "train_df.isna().sum() # 훈련데이터에서의 결측 값 데이터 확인"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pl-roYZpWOjC",
        "outputId": "4690f5df-7282-4981-9b54-89713d7d5aa7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId      0\n",
              "Pclass           0\n",
              "Name             0\n",
              "Sex              0\n",
              "Age             86\n",
              "SibSp            0\n",
              "Parch            0\n",
              "Ticket           0\n",
              "Fare             1\n",
              "Cabin          327\n",
              "Embarked         0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "test_df.isna().sum() # 테스트 데이터에서의 결측값 확인"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BAt_zobOWOjC"
      },
      "source": [
        "* Age/Fare은 연속성있는 데이터이기 때문에 평균값으로, Embarked는 빈도 높은 값으로 결측치 채워줌\n",
        "* Cabin은 훈련에 영향을 줄 정도로 결측값의 양이 많아 사용 X"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "Qh4AxJixWOjD"
      },
      "outputs": [],
      "source": [
        "AverageAge = train_df.Age.mean()\n",
        "train_df.Age = train_df.Age.fillna(AverageAge)\n",
        "\n",
        "AverageAge = test_df.Age.mean()\n",
        "test_df.Age = test_df.Age.fillna(AverageAge)\n",
        "\n",
        "AverageFare = test_df.Fare.mean()\n",
        "test_df.Fare = test_df.Fare.fillna(AverageFare)\n",
        "\n",
        "FrequentEmbarked = train_df.Embarked.value_counts().idxmax()\n",
        "train_df.Embarked = train_df.Embarked.fillna(FrequentEmbarked)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tncfqbYRWOjE",
        "outputId": "12802c79-36ea-44dd-b94f-3f1ce79efaf7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId      0\n",
              "Survived         0\n",
              "Pclass           0\n",
              "Name             0\n",
              "Sex              0\n",
              "Age              0\n",
              "SibSp            0\n",
              "Parch            0\n",
              "Ticket           0\n",
              "Fare             0\n",
              "Cabin          687\n",
              "Embarked         0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "train_df.isna().sum() # 잘 바뀌었는지 확인"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TwP8rjhufuwy",
        "outputId": "478a8dd8-9624-4d32-cf7f-5d5f3708e4fe"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId      0\n",
              "Pclass           0\n",
              "Name             0\n",
              "Sex              0\n",
              "Age              0\n",
              "SibSp            0\n",
              "Parch            0\n",
              "Ticket           0\n",
              "Fare             0\n",
              "Cabin          327\n",
              "Embarked         0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "test_df.isna().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0B4YZQBdWOjE"
      },
      "source": [
        "## 데이터 전처리하기\n",
        "\n",
        "### 1. 실수값 변환\n",
        "* 실수값이 아닌 데이터의 경우, 실수값 변환 필요\n",
        "* Sex, Embarked의 dtype: string → 실수값으로 데이터 변환\n",
        "\n",
        "### 2. Normalization 수행\n",
        "* 특정 데이터만 데이터의 범위가 너무 클 경우, 머신러닝 훈련 시 범위가 큰 데이터에 영향을 크게 받게됨\n",
        "* → 사용할 모든 데이터를 0 ~ 1 사이의 값으로 변경"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "nhpC77l_WOjF"
      },
      "outputs": [],
      "source": [
        "column_to_use = [\"Pclass\", \"Sex\", \"Age\", \"SibSp\",\"Parch\", \"Fare\", \"Embarked\"]\n",
        "train_x = train_df[column_to_use].copy()\n",
        "test_x = test_df[column_to_use].copy()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wuf9e5_FWOjF"
      },
      "source": [
        "#### copy()\n",
        "* 파이썬의 리스트 or 판다스 객체와 같은 경우, 변수 자체에 값을 할당하는 C언어와 달리 변수에 데이터가 저장된 위치를 저장함\n",
        "* copy()를 쓰지 않을 경우 train에 train_df가 복사되어 들어가는게 아니라, train_df의 위치가 저장되어 train_df와 train이 결국에는 같은 데이터를 지칭하게 됨\n",
        "* 그러므로 train을 변형하면 train_df까지 바꾸어야 할지, train변수를 복사한것으로 간주해야 할지 모르는 문제가 발생\n",
        "* 따라서 copy() 함수를 통해 train변수에 train_df의 위치를 저장하는 게 아니라, train_df의 데이터 자체를 복사해 넣어줌"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "HPlYU3X0WOjG"
      },
      "outputs": [],
      "source": [
        "train_x[\"Sex\"] = train_x.Sex.apply(lambda x: 0 if x==\"male\" else 1)\n",
        "test_x[\"Sex\"] = test_x.Sex.apply(lambda x: 0 if x==\"male\" else 1)\n",
        "\n",
        "Embark2Float = {\"S\":0, \"C\":1, \"Q\":2}\n",
        "train_x.Embarked = train_x.Embarked.apply(lambda x:Embark2Float[x])\n",
        "test_x.Embarked = test_x.Embarked.apply(lambda x:Embark2Float[x])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "exyLQ5ZBWOjG"
      },
      "outputs": [],
      "source": [
        "train_y = train_df.Survived\n",
        "# test_df는 실제 테스트용 데이터이기 때문에, y값에 해당하는 Survived가 존재하지 않는다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "qUQBm8W9WOjH",
        "outputId": "bdf97ecb-3532-4da7-954b-d57001e4e086"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Pclass  Sex        Age  SibSp  Parch     Fare  Embarked\n",
              "0         3    0  22.000000      1      0   7.2500         0\n",
              "1         1    1  38.000000      1      0  71.2833         1\n",
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              "3         1    1  35.000000      1      0  53.1000         0\n",
              "4         3    0  35.000000      0      0   8.0500         0\n",
              "..      ...  ...        ...    ...    ...      ...       ...\n",
              "886       2    0  27.000000      0      0  13.0000         0\n",
              "887       1    1  19.000000      0      0  30.0000         0\n",
              "888       3    1  29.699118      1      2  23.4500         0\n",
              "889       1    0  26.000000      0      0  30.0000         1\n",
              "890       3    0  32.000000      0      0   7.7500         2\n",
              "\n",
              "[891 rows x 7 columns]"
            ],
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              "          const element = document.querySelector('#df-701590e6-94b1-42cf-b33b-bdd4c036efa6');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ],
      "source": [
        "train_x"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "FkGikAzEWOjH"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "ss = StandardScaler()\n",
        "train_x = ss.fit_transform(train_x)\n",
        "test_x = ss.transform(test_x)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zgA1HspbWOjH",
        "outputId": "8f0933ae-f962-47d9-93f9-48480d52ae3e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((891, 7), (418, 7))"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "train_x.shape, test_x.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_Hw-LAH1WOjI"
      },
      "source": [
        "## Model Training\n",
        "* train_df에서 validation 데이터 분리"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "01TJlO9ZWOjI"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "train_x, val_x, train_y, val_y = train_test_split(train_x, train_y, test_size=0.2, random_state=1213)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9Z43Gav7WOjJ",
        "outputId": "24ee2a93-c7f3-470e-bbb5-b2fd68829c2a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuray: 0.82\n"
          ]
        }
      ],
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "lr = LogisticRegression(max_iter=100, penalty=\"l2\")\n",
        "lr.fit(train_x, train_y)\n",
        "print(f\"Accuray: {lr.score(val_x, val_y):.2f}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        },
        "id": "4m7KkrTKWOjJ",
        "outputId": "58c7060b-d8c7-4e31-928a-b5a5f325b2b3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Feature Importance')"
            ]
          },
          "metadata": {},
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.bar(column_to_use, np.abs(lr.coef_)[0], color ='maroon', width = 0.4)\n",
        "plt.title(\"Feature Importance\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "15ASYvbqgNk5"
      },
      "source": [
        "# 실습과제 1\n",
        "\n",
        "* Linear Regression의 파라미터를 바꿔보며, 성능을 관찰해보세요.\n",
        "* dataset은 weight_height.csv를 사용하시면 됩니다.\n",
        "\n",
        "* *reference: https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "PNU3m3TJgrEj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ffdd7406-de28-444f-892a-7e917d9bf042"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE: 151.3418\n",
            "MSE: 552.8934\n",
            "MSE: 151.3418\n",
            "MSE: 264561.2265\n"
          ]
        }
      ],
      "source": [
        "# Linear Regression\n",
        "# default\n",
        "lr = LinearRegression(fit_intercept=True, copy_X=True, n_jobs=None, positive=False)\n",
        "lr.fit(X_train, y_train)\n",
        "pred = lr.predict(X_test)\n",
        "mse = mean_squared_error(y_test, pred)\n",
        "print(f\"MSE: {mse:.4f}\")\n",
        "# fit_intercept = False\n",
        "lr = LinearRegression(fit_intercept=False, copy_X=True, n_jobs=None, positive=False)\n",
        "lr.fit(X_train, y_train)\n",
        "pred = lr.predict(X_test)\n",
        "mse = mean_squared_error(y_test, pred)\n",
        "print(f\"MSE: {mse:.4f}\")\n",
        "# copy_X = False\n",
        "lr = LinearRegression(fit_intercept=True, copy_X=False, n_jobs=None, positive=False)\n",
        "lr.fit(X_train, y_train)\n",
        "pred = lr.predict(X_test)\n",
        "mse = mean_squared_error(y_test, pred)\n",
        "print(f\"MSE: {mse:.4f}\")\n",
        "# n_jobs = -1 \n",
        "lr = LinearRegression(fit_intercept=True, copy_X=False, n_jobs=-1, positive=False)\n",
        "lr.fit(X_train, y_train)\n",
        "pred = lr.predict(X_test)\n",
        "mse = mean_squared_error(y_test, pred)\n",
        "print(f\"MSE: {mse:.4f}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J1e46Eyygy2f"
      },
      "source": [
        "# 실습과제 2\n",
        "\n",
        "* Logistic Regression의 파라미터를 바꿔보며, 성능을 관찰해보세요.\n",
        "* dataset은 titanic(i.e. train.csv)를 사용하시면 됩니다.\n",
        "\n",
        "* *reference: https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "id": "nvEIZoHwgtQm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eac95ee3-ae62-4bbe-c5f0-734f59dd18df"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "# Tolerance\n",
            "max_iter :  1e-05\n",
            "Accuray: 0.8156\n",
            "max_iter :  1e-07\n",
            "Accuray: 0.8156\n",
            "max_iter :  1e-08\n",
            "Accuray: 0.8156\n",
            "max_iter :  1e-10\n",
            "Accuray: 0.8156\n",
            "# max_iter\n",
            "max_iter :  100\n",
            "Accuray: 0.8156\n",
            "max_iter :  200\n",
            "Accuray: 0.8156\n",
            "max_iter :  300\n",
            "Accuray: 0.8156\n",
            "max_iter :  400\n",
            "Accuray: 0.8156\n",
            "max_iter :  500\n",
            "Accuray: 0.8156\n",
            "max_iter :  600\n",
            "Accuray: 0.8156\n",
            "# C\n",
            "C : 0.01\n",
            "Accuray: 0.8156\n",
            "C : 0.1\n",
            "Accuray: 0.8268\n",
            "C : 0.5\n",
            "Accuray: 0.8212\n",
            "C : 1.0\n",
            "Accuray: 0.8156\n",
            "C : 1.2\n",
            "Accuray: 0.8156\n",
            "C : 1.5\n",
            "Accuray: 0.8156\n",
            "# optimization methods :\n",
            "lbfgs\n",
            "Accuray: 0.8156\n",
            "newton-cg\n",
            "Accuray: 0.8156\n",
            "newton-cholesky\n",
            "Accuray: 0.8156\n",
            "sag\n",
            "Accuray: 0.8156\n",
            "saga\n",
            "Accuray: 0.8156\n"
          ]
        }
      ],
      "source": [
        "# Logistic Regression\n",
        "# default \n",
        "\n",
        "# Tolerance\n",
        "Tolerance = [0.00001, 0.0000001, 0.00000001, 0.0000000001]\n",
        "print('# Tolerance')\n",
        "for tol in Tolerance :\n",
        "    lr = LogisticRegression(max_iter=100, penalty=\"l2\", tol=tol)\n",
        "    lr.fit(train_x, train_y)\n",
        "    print('max_iter : ', tol)\n",
        "    print(f\"Accuray: {lr.score(val_x, val_y):.4f}\")\n",
        "\n",
        "# max_iter\n",
        "max_iter = [100, 200, 300, 400, 500, 600]\n",
        "print('# max_iter')\n",
        "for it in max_iter :\n",
        "    lr = LogisticRegression(max_iter=it, penalty=\"l2\")\n",
        "    lr.fit(train_x, train_y)\n",
        "    print('max_iter : ', it)\n",
        "    print(f\"Accuray: {lr.score(val_x, val_y):.4f}\")\n",
        "\n",
        "# C\n",
        "C = [0.01, 0.1, 0.5, 1.0, 1.2, 1.5]\n",
        "print('# C')\n",
        "for c in C :\n",
        "    lr = LogisticRegression(max_iter=100, penalty=\"l2\", C = c)\n",
        "    lr.fit(train_x, train_y)\n",
        "    print('C :', c)\n",
        "    print(f\"Accuray: {lr.score(val_x, val_y):.4f}\")\n",
        "\n",
        "# Optimization methods\n",
        "optim = ['lbfgs', 'newton-cg', 'newton-cholesky', 'sag', 'saga']\n",
        "print('# optimization methods :')\n",
        "for opt in optim :\n",
        "    lr = LogisticRegression(max_iter=100, penalty=\"l2\", solver=opt)\n",
        "    lr.fit(train_x, train_y)\n",
        "    print(opt)\n",
        "    print(f\"Accuray: {lr.score(val_x, val_y):.4f}\")\n",
        "\n",
        "# C = 0.1일때 Accuracy가 상승"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "L7Benexum5gg"
      },
      "execution_count": null,
      "outputs": []
    }
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