[2023]_Midterm_Lab_ipynb의_사본.ipynb

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        "<a href=\"https://colab.research.google.com/github/kaeba0616/BED/blob/main/%5B2023%5D_Midterm_Lab_ipynb%EC%9D%98_%EC%82%AC%EB%B3%B8.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "# Midterm Examination - Lab Session\n",
        "### Basic Engineering Design\n",
        "##### Seoul National University of Science and Technology\n",
        "##### 25. Oct. 2023.\n",
        "---\n",
        "| Problem | Score |\n",
        "|---------|-------|\n",
        "| 1       |(&emsp;&emsp;) / 50    |\n",
        "| 2       |(&emsp;&emsp;) / 50    |\n",
        "| Total   |(&emsp;&emsp;) / 100    |\n",
        "\n",
        "---\n",
        "* This examination contains **two problems**.\n",
        "\n",
        "* You have **45 minutes** to complete the examination.\n",
        "       \n",
        "* No need to consider colors.\n",
        "---"
      ]
    },
    {
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      "source": [
        "# Load Packages"
      ]
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    {
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      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
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      "source": [
        "## Load Dataset"
      ]
    },
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        "outputId": "b876e0e7-28e2-4141-c568-5b781f2e0363"
      },
      "source": [
        "# Load marketing data\n",
        "data = pd.read_csv('https://raw.githubusercontent.com/Kaushik-Varma/Marketing_Data_Analysis/master/Marketing_Analysis.csv',\n",
        "                   header=2).set_index('customerid').sample(n=500, random_state=0)\n",
        "\n",
        "# Extract job  & Education in newly from \"jobedu\" column.\n",
        "data['job']= data[\"jobedu\"].apply(lambda x: x.split(\",\")[0])\n",
        "data['education']= data[\"jobedu\"].apply(lambda x: x.split(\",\")[1])\n",
        "\n",
        "# Drop the \"jobedu\" column from the dataframe.\n",
        "data.drop('jobedu', axis = 1, inplace = True)\n",
        "\n",
        "# Column-wise filtration\n",
        "selectedAttritbute = ['age',\t'salary',\t'balance',\t'marital',\t'targeted',\t'job',\t'education']\n",
        "data = data[selectedAttritbute]\n",
        "\n",
        "data.head()"
      ],
      "execution_count": 2,
      "outputs": [
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          "output_type": "execute_result",
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            "text/plain": [
              "             age  salary  balance  marital targeted          job  education\n",
              "customerid                                                                 \n",
              "14002       37.0  100000     1403  married      yes   management   tertiary\n",
              "32047       36.0   70000     -253   single      yes     services  secondary\n",
              "13319       47.0   20000     -406  married      yes  blue-collar    primary\n",
              "42992       57.0   16000      501  married      yes    housemaid    primary\n",
              "14238       33.0   20000     -406  married      yes  blue-collar  secondary"
            ],
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    {
      "cell_type": "markdown",
      "source": [
        "# Problem 1. Statics and Data Visualization  [50 Points]"
      ],
      "metadata": {
        "id": "YoE_HOzHz_Ke"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eAZ_ZEusEXWr"
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      "source": [
        "## 1-1. Visualization [15 Points]\n",
        "결혼 여부에 따른 나이의 분포를 KDE plot을 이용하여 나타내시오 (Plot the kernel density estimate (KDE) plot of `age` in condition of `maritial`).\n",
        "- Use `kdeplot` of Seaborn"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "C3dCTD1QDGWq",
        "outputId": "9f5900c3-073e-49c0-fe29-16af6abf3898"
      },
      "source": [
        "ax = sns.kdeplot(data=data, x='age', hue=\"marital\" )\n",
        "plt.show()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JKaaEDCTJRRF"
      },
      "source": [
        "## 1-2. Visualization and statistical test. [35 Points]\n",
        "* [10 pts] 아래 완성된 그림을 참조하여 `marital` 상태에 따라 3개의 그룹을 나누어진 여섯 개의 `targeted`과 `balance`의 관계를 나타내고 있는 boxplot을 그리시오. (Referring to the completed figure below, draw a boxplot showing the relationship between six `targeted` and `balance`, divided into three groups according to `marital` status.)\n",
        " * **hint**, use `catplot` instead of boxplot\n",
        "* [25 pts] `targeted`에 따른 `balance`의 차이가 존재하는 지 여부를 **적절한** 통계검정을 통해 p-value를 산출하시오. (Get p-value with proper statistical test whether there is a difference in `balance` according to `targeted`)\n",
        " * 옳지 않은 통계검정을 사용할 경우 감점 (If you use wrong statistical test, grade will be deducted)\n",
        " * 각 axes의 중간에 각 통계 검증의 p-value를 text 형태로 출력하시오. (Add p-value of each test in the middle of corresponding axes.)  "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "id": "RP5Yg8hOJgXP",
        "outputId": "805d0a02-76c5-4822-c145-5a13d86fef69"
      },
      "source": [
        "from scipy.stats import ttest_ind\n",
        "\n",
        "# 1번 문항\n",
        "g = sns.catplot(data=data, x=\"targeted\", y=\"balance\", kind=\"box\", col='marital')\n",
        "\n",
        "# 2번 문항\n",
        "marital = ['married', 'single', 'divorced']\n",
        "for ma, ax in zip(marital, g.axes[0]):\n",
        "    yesSalary = data[(data['targeted']=='yes') & (data['marital'] == ma)]['balance'].dropna()\n",
        "    noSalary = data[(data['targeted']=='no') & (data['marital'] == ma)]['balance'].dropna()\n",
        "\n",
        "\n",
        "    stats, p = ttest_ind(yesSalary, noSalary)\n",
        "\n",
        "    ax.text(x= 0.23,\n",
        "          y= 23000,\n",
        "          s=f'p-value:{p:.6f}',\n",
        "          fontsize=10)\n"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 3 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aXMvhLkxYpF-"
      },
      "source": [
        "# Problem 2. Data Preprocessing [50 Points]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kY4V5SOLYvRN"
      },
      "source": [
        "## 2-1 Filtering and Outlier Detection [30 Points]\n",
        "* [15 pts] Remove samples with `unknown` `education`.\n",
        "* [15 pts] Exclude the outlier with too much or less balance.\n",
        " * Assume that `balance` of outlier is greater than `Q3 + (2 * IQR)` and smaller than `Q1 - (2 * IQR)`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bmzry94eY6R5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b1f772ec-4e1c-4c4b-b4a1-c01a948a4b99"
      },
      "source": [
        "print(\"before filtering: %d\" % len(data))\n",
        "\n",
        "# Remove samples with `unknown` `education`.\n",
        "dataFilt = data[data[\"education\"] != 'unknown']\n",
        "print(\"after removing samples: %d\" % len(dataFilt))\n",
        "\n",
        "# Remove outliers\n",
        "attribute = 'balance'\n",
        "\n",
        "Q1 = dataFilt[attribute].quantile(.25)\n",
        "Q3 = dataFilt[attribute].quantile(.75)\n",
        "IQR = Q3 - Q1\n",
        "outlierStep = 2 * IQR\n",
        "\n",
        "dataFilt = dataFilt[(dataFilt[attribute] > Q1 - outlierStep) &\n",
        "                    (dataFilt[attribute] < Q3 + outlierStep)]\n",
        "\n",
        "print(\"after removing outlier: %d\" % len(dataFilt))"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "before filtering: 500\n",
            "after removing samples: 478\n",
            "after removing outlier: 435\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**[범위 외 내용으로 수정 필요 없음. No need to modify, out of scope]**\n",
        "* Encode `marital`, `targeted`, `job` attributes using one-hot encoding.\n",
        "* Encode `education` attribute using ordinal encoding with order of (`primary`, `secondary`, `tertiary`)."
      ],
      "metadata": {
        "id": "Nt30JkJvmJ4t"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Encode using one-hot encoding.\n",
        "dataEncode = pd.get_dummies(dataFilt, columns=['marital', 'targeted', 'job'])\n",
        "\n",
        "# Encode using ordinal encoding\n",
        "eduMap = {'primary': 0, 'secondary':1, 'tertiary':2}\n",
        "dataEncode['education'] = data.education.map(eduMap)"
      ],
      "metadata": {
        "id": "K2YTObZsmI9G"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 275
        },
        "id": "CCF1IVmMY4R6",
        "outputId": "65e2d450-6305-45a3-8d23-8631eafa84b7"
      },
      "source": [
        "dataEncode.head(5)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             age  salary  balance  education  marital_divorced  \\\n",
              "customerid                                                       \n",
              "14002       37.0  100000     1403        2.0                 0   \n",
              "32047       36.0   70000     -253        1.0                 0   \n",
              "13319       47.0   20000     -406        0.0                 0   \n",
              "42992       57.0   16000      501        0.0                 0   \n",
              "14238       33.0   20000     -406        1.0                 0   \n",
              "\n",
              "            marital_married  marital_single  targeted_no  targeted_yes  \\\n",
              "customerid                                                               \n",
              "14002                     1               0            0             1   \n",
              "32047                     0               1            0             1   \n",
              "13319                     1               0            0             1   \n",
              "42992                     1               0            0             1   \n",
              "14238                     1               0            0             1   \n",
              "\n",
              "            job_admin.  job_blue-collar  job_entrepreneur  job_housemaid  \\\n",
              "customerid                                                                 \n",
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              "\n",
              "            job_management  job_retired  job_self-employed  job_services  \\\n",
              "customerid                                                                 \n",
              "14002                    1            0                  0             0   \n",
              "32047                    0            0                  0             1   \n",
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              "42992                    0            0                  0             0   \n",
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              "\n",
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              "customerid                                               \n",
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              "  </style>\n",
              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-88868aa7-7302-482c-a910-cf572194ee70 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-88868aa7-7302-482c-a910-cf572194ee70');\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",
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              "\n",
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              "      --hover-fill-color: #174EA6;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
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              "  }\n",
              "\n",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    60% {\n",
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              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-a0da4af2-1eeb-469b-b41b-5b4bbf9d35be button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RDVmABMId3A0"
      },
      "source": [
        "**[범위 외 내용으로 수정 필요 없음. No need to modify, out of scope]**\n",
        "* Apply robust scaling."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 244
        },
        "id": "AKxEOAkedCVo",
        "outputId": "3d54c581-4c73-45a1-e489-f47be515cc37"
      },
      "source": [
        "from sklearn.preprocessing import RobustScaler\n",
        "\n",
        "# create a scaler object\n",
        "scaler = RobustScaler()\n",
        "\n",
        "# fit and transform the data\n",
        "dataScale = pd.DataFrame(scaler.fit_transform(dataEncode), columns=dataEncode.columns)\n",
        "\n",
        "dataScale.head()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        age  salary   balance  education  marital_divorced  marital_married  \\\n",
              "0 -0.066667    0.80  1.260338        1.0               0.0              0.0   \n",
              "1 -0.133333    0.20 -0.668608        0.0               0.0             -1.0   \n",
              "2  0.600000   -0.80 -0.846826       -1.0               0.0              0.0   \n",
              "3  1.266667   -0.88  0.209668       -1.0               0.0              0.0   \n",
              "4 -0.333333   -0.80 -0.846826        0.0               0.0              0.0   \n",
              "\n",
              "   marital_single  targeted_no  targeted_yes  job_admin.  job_blue-collar  \\\n",
              "0             0.0          0.0           0.0         0.0              0.0   \n",
              "1             1.0          0.0           0.0         0.0              0.0   \n",
              "2             0.0          0.0           0.0         0.0              1.0   \n",
              "3             0.0          0.0           0.0         0.0              0.0   \n",
              "4             0.0          0.0           0.0         0.0              1.0   \n",
              "\n",
              "   job_entrepreneur  job_housemaid  job_management  job_retired  \\\n",
              "0               0.0            0.0             1.0          0.0   \n",
              "1               0.0            0.0             0.0          0.0   \n",
              "2               0.0            0.0             0.0          0.0   \n",
              "3               0.0            1.0             0.0          0.0   \n",
              "4               0.0            0.0             0.0          0.0   \n",
              "\n",
              "   job_self-employed  job_services  job_student  job_technician  \\\n",
              "0                0.0           0.0          0.0             0.0   \n",
              "1                0.0           1.0          0.0             0.0   \n",
              "2                0.0           0.0          0.0             0.0   \n",
              "3                0.0           0.0          0.0             0.0   \n",
              "4                0.0           0.0          0.0             0.0   \n",
              "\n",
              "   job_unemployed  \n",
              "0             0.0  \n",
              "1             0.0  \n",
              "2             0.0  \n",
              "3             0.0  \n",
              "4             0.0  "
            ],
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              "\n",
              "  <div id=\"df-b4e1ef5c-eb45-4ea7-8cea-4fcc27556ff6\" class=\"colab-df-container\">\n",
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              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>salary</th>\n",
              "      <th>balance</th>\n",
              "      <th>education</th>\n",
              "      <th>marital_divorced</th>\n",
              "      <th>marital_married</th>\n",
              "      <th>marital_single</th>\n",
              "      <th>targeted_no</th>\n",
              "      <th>targeted_yes</th>\n",
              "      <th>job_admin.</th>\n",
              "      <th>job_blue-collar</th>\n",
              "      <th>job_entrepreneur</th>\n",
              "      <th>job_housemaid</th>\n",
              "      <th>job_management</th>\n",
              "      <th>job_retired</th>\n",
              "      <th>job_self-employed</th>\n",
              "      <th>job_services</th>\n",
              "      <th>job_student</th>\n",
              "      <th>job_technician</th>\n",
              "      <th>job_unemployed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-0.066667</td>\n",
              "      <td>0.80</td>\n",
              "      <td>1.260338</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.133333</td>\n",
              "      <td>0.20</td>\n",
              "      <td>-0.668608</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.600000</td>\n",
              "      <td>-0.80</td>\n",
              "      <td>-0.846826</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.266667</td>\n",
              "      <td>-0.88</td>\n",
              "      <td>0.209668</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.333333</td>\n",
              "      <td>-0.80</td>\n",
              "      <td>-0.846826</td>\n",
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              "      <td>1.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b4e1ef5c-eb45-4ea7-8cea-4fcc27556ff6')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-b4e1ef5c-eb45-4ea7-8cea-4fcc27556ff6 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-b4e1ef5c-eb45-4ea7-8cea-4fcc27556ff6');\n",
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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",
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              "            title=\"Suggest charts.\"\n",
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              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
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              "      --bg-color: #E8F0FE;\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --hover-bg-color: #434B5C;\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-7cda9895-49d2-4b7f-8a99-f2412b150a39 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8tDuZim2ehnQ"
      },
      "source": [
        "## 2-2 kNN imputation [20 Points]\n",
        "* [15 pts] 2명의 이웃 정보를 동등한 비율로 고려하는 kNN 기법을 이용하여 결측치를 imputation 시키시오. (Apply kNN imputation with two numbers of nearest neighbor using uniform weight)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        },
        "id": "eKlGI1OYebWj",
        "outputId": "741b8eaf-658f-4407-e8e8-41920b28cce9"
      },
      "source": [
        "index = dataScale['age'].isna()\n",
        "dataScale.loc[index, :]"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     age  salary   balance  education  marital_divorced  marital_married  \\\n",
              "337  NaN     1.2  0.157251        0.0               0.0              0.0   \n",
              "\n",
              "     marital_single  targeted_no  targeted_yes  job_admin.  job_blue-collar  \\\n",
              "337             0.0          0.0           0.0         0.0              0.0   \n",
              "\n",
              "     job_entrepreneur  job_housemaid  job_management  job_retired  \\\n",
              "337               1.0            0.0             0.0          0.0   \n",
              "\n",
              "     job_self-employed  job_services  job_student  job_technician  \\\n",
              "337                0.0           0.0          0.0             0.0   \n",
              "\n",
              "     job_unemployed  \n",
              "337             0.0  "
            ],
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              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-cb421df4-73dd-43bc-8a2a-060662a50d70 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-cb421df4-73dd-43bc-8a2a-060662a50d70');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ffdvbPO-fa5q"
      },
      "source": [
        "from sklearn.impute import KNNImputer\n",
        "\n",
        "dataImp = dataScale.copy()\n",
        "# create a imputer object\n",
        "imp_knn = KNNImputer(n_neighbors=2, weights='uniform')\n",
        "\n",
        "# fit and transform the data\n",
        "dataImp[:] = imp_knn.fit_transform(dataImp)"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        },
        "id": "ScC4cOMtf4ZA",
        "outputId": "998de5e2-baa7-424b-d0e7-a62da644c096"
      },
      "source": [
        "dataImp.loc[index, :]"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     age  salary   balance  education  marital_divorced  marital_married  \\\n",
              "337 -0.3     1.2  0.157251        0.0               0.0              0.0   \n",
              "\n",
              "     marital_single  targeted_no  targeted_yes  job_admin.  job_blue-collar  \\\n",
              "337             0.0          0.0           0.0         0.0              0.0   \n",
              "\n",
              "     job_entrepreneur  job_housemaid  job_management  job_retired  \\\n",
              "337               1.0            0.0             0.0          0.0   \n",
              "\n",
              "     job_self-employed  job_services  job_student  job_technician  \\\n",
              "337                0.0           0.0          0.0             0.0   \n",
              "\n",
              "     job_unemployed  \n",
              "337             0.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a8fcbb67-fe80-4915-89f2-cf3647411e4b\" 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>age</th>\n",
              "      <th>salary</th>\n",
              "      <th>balance</th>\n",
              "      <th>education</th>\n",
              "      <th>marital_divorced</th>\n",
              "      <th>marital_married</th>\n",
              "      <th>marital_single</th>\n",
              "      <th>targeted_no</th>\n",
              "      <th>targeted_yes</th>\n",
              "      <th>job_admin.</th>\n",
              "      <th>job_blue-collar</th>\n",
              "      <th>job_entrepreneur</th>\n",
              "      <th>job_housemaid</th>\n",
              "      <th>job_management</th>\n",
              "      <th>job_retired</th>\n",
              "      <th>job_self-employed</th>\n",
              "      <th>job_services</th>\n",
              "      <th>job_student</th>\n",
              "      <th>job_technician</th>\n",
              "      <th>job_unemployed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>337</th>\n",
              "      <td>-0.3</td>\n",
              "      <td>1.2</td>\n",
              "      <td>0.157251</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a8fcbb67-fe80-4915-89f2-cf3647411e4b')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-a8fcbb67-fe80-4915-89f2-cf3647411e4b 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-a8fcbb67-fe80-4915-89f2-cf3647411e4b');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    }
  ]
}