스마트폰_모델별_가격_예측.ipynb

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        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from scipy.stats import f_oneway\n",
        "\n",
        "pd.set_option(\"display.max_rows\" ,100)\n",
        "pd.set_option(\"display.max_columns\" ,100)\n",
        "sns.set_style(\"whitegrid\")"
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      "source": [
        "targetUrl = \"https://raw.githubusercontent.com/oprostep/2024-Spring-ML/main/smartphones.csv\"\n",
        "\n",
        "# Parsing\n",
        "df = pd.read_csv(targetUrl, sep=',')\n",
        "df.head()"
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              "                                     Smartphone     Brand           Model  \\\n",
              "0            Realme C55 8/256GB Sunshower Libre    Realme             C55   \n",
              "1      Samsung Galaxy M23 5G 4/128GB Azul Libre   Samsung      Galaxy M23   \n",
              "2  Motorola Moto G13 4/128GB Azul Lavanda Libre  Motorola        Moto G13   \n",
              "3      Xiaomi Redmi Note 11S 6/128GB Gris Libre    Xiaomi  Redmi Note 11S   \n",
              "4       Nothing Phone (2) 12/512GB Blanco Libre   Nothing       Phone (2)   \n",
              "\n",
              "    RAM  Storage   Color Free  Final Price  \n",
              "0   8.0    256.0  Yellow  Yes       231.60  \n",
              "1   4.0    128.0    Blue  Yes       279.00  \n",
              "2   4.0    128.0    Blue  Yes       179.01  \n",
              "3   6.0    128.0    Gray  Yes       279.99  \n",
              "4  12.0    512.0   White  Yes       799.00  "
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              "      <td>Samsung Galaxy M23 5G 4/128GB Azul Libre</td>\n",
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              "      <td>Xiaomi Redmi Note 11S 6/128GB Gris Libre</td>\n",
              "      <td>Xiaomi</td>\n",
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 1816,\n  \"fields\": [\n    {\n      \"column\": \"Smartphone\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1816,\n        \"samples\": [\n          \"Samsung Galaxy A03s 3/32GB Negro Libre\",\n          \"Samsung Galaxy S23 128GB Verde Libre\",\n          \"Apple iPhone 14 Pro 512GB Negro Espacial Libre\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Brand\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 37,\n        \"samples\": [\n          \"Ulefone\",\n          \"CAT\",\n          \"Nothing\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Model\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 383,\n        \"samples\": [\n          \"Galaxy XCover 5\",\n          \"8T\",\n          \"MyPhone Blade\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RAM\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.668069942275087,\n        \"min\": 1.0,\n        \"max\": 12.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          8.0,\n          4.0,\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Storage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 139.41160529993135,\n        \"min\": 2.0,\n        \"max\": 1000.0,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          3.0,\n          12.0,\n          256.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Color\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 17,\n        \"samples\": [\n          \"Yellow\",\n          \"Blue\",\n          \"Green\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Free\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"No\",\n          \"Yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Final Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 398.6061825798195,\n        \"min\": 60.46,\n        \"max\": 2271.28,\n        \"num_unique_values\": 1022,\n        \"samples\": [\n          238.72,\n          1193.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          "execution_count": 2
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      "outputs": [
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1816 entries, 0 to 1815\n",
            "Data columns (total 8 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   Smartphone   1816 non-null   object \n",
            " 1   Brand        1816 non-null   object \n",
            " 2   Model        1816 non-null   object \n",
            " 3   RAM          1333 non-null   float64\n",
            " 4   Storage      1791 non-null   float64\n",
            " 5   Color        1816 non-null   object \n",
            " 6   Free         1816 non-null   object \n",
            " 7   Final Price  1816 non-null   float64\n",
            "dtypes: float64(3), object(5)\n",
            "memory usage: 113.6+ KB\n"
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        "\n",
        "\n",
        "\"RAM\" , \"Storage\"에 결측치"
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              "              count     mean      std    min     25%     50%      75%      max\n",
              "RAM          1333.0    5.961    2.668   1.00    4.00    6.00    8.000    12.00\n",
              "Storage      1791.0  162.652  139.412   2.00   64.00  128.00  256.000  1000.00\n",
              "Final Price  1816.0  492.176  398.606  60.46  200.99  349.99  652.718  2271.28"
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
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              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>RAM</th>\n",
              "      <td>1333.0</td>\n",
              "      <td>5.961</td>\n",
              "      <td>2.668</td>\n",
              "      <td>1.00</td>\n",
              "      <td>4.00</td>\n",
              "      <td>6.00</td>\n",
              "      <td>8.000</td>\n",
              "      <td>12.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Storage</th>\n",
              "      <td>1791.0</td>\n",
              "      <td>162.652</td>\n",
              "      <td>139.412</td>\n",
              "      <td>2.00</td>\n",
              "      <td>64.00</td>\n",
              "      <td>128.00</td>\n",
              "      <td>256.000</td>\n",
              "      <td>1000.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Final Price</th>\n",
              "      <td>1816.0</td>\n",
              "      <td>492.176</td>\n",
              "      <td>398.606</td>\n",
              "      <td>60.46</td>\n",
              "      <td>200.99</td>\n",
              "      <td>349.99</td>\n",
              "      <td>652.718</td>\n",
              "      <td>2271.28</td>\n",
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              "\n",
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              "      const quickchartButtonEl =\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 271.93075098880104,\n        \"min\": 1333.0,\n        \"max\": 1816.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1333.0,\n          1791.0,\n          1816.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 248.17438837841425,\n        \"min\": 5.961,\n        \"max\": 492.176,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          5.961,\n          162.652,\n          492.176\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 201.10003771589237,\n        \"min\": 2.668,\n        \"max\": 398.606,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2.668,\n          139.412,\n          398.606\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"min\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 34.04424376210072,\n        \"min\": 1.0,\n        \"max\": 60.46,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1.0,\n          2.0,\n          60.46\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"25%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 100.97138224929544,\n        \"min\": 4.0,\n        \"max\": 200.99,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          4.0,\n          64.0,\n          200.99\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"50%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 174.40024856633661,\n        \"min\": 6.0,\n        \"max\": 349.99,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          6.0,\n          128.0,\n          349.99\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"75%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 325.20518421657016,\n        \"min\": 8.0,\n        \"max\": 652.718,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          8.0,\n          256.0,\n          652.718\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"max\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1132.596056029392,\n        \"min\": 12.0,\n        \"max\": 2271.28,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          12.0,\n          1000.0,\n          2271.28\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "id": "OM8nG00CFnSK"
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Cleaning"
      ],
      "metadata": {
        "id": "gFzTOGbWFnSK"
      },
      "id": "gFzTOGbWFnSK"
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop(\"Smartphone\" ,axis =1 , inplace =True)\n",
        "df[df.duplicated()]"
      ],
      "metadata": {
        "id": "RM9EVJ8BFnSL",
        "outputId": "dc7aca89-48e7-4369-ea6b-513fd141aa57",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        }
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Brand           Model   RAM  Storage   Color Free  Final Price\n",
              "58      Xiaomi        Redmi A1   2.0     32.0   Black  Yes       109.00\n",
              "234   Motorola         Edge 40   8.0    256.0    Gold  Yes       491.64\n",
              "348   Motorola         Razr 40   8.0    256.0    Gold  Yes       899.01\n",
              "465    Samsung      Galaxy S21   8.0    256.0    Gray  Yes       649.00\n",
              "562    Samsung      Galaxy S20   6.0    128.0    Blue  Yes       649.00\n",
              "699    Samsung      Galaxy A14   4.0    128.0   Black  Yes       209.00\n",
              "790      Apple       iPhone SE   NaN     64.0   Black  Yes       439.00\n",
              "812    Samsung  Galaxy Z Fold4  12.0    256.0   Green  Yes      1799.00\n",
              "847      Cubot             X70  12.0    256.0   Black  Yes       249.95\n",
              "1005    Realme               7   8.0    128.0    Blue  Yes       239.00\n",
              "1226   Samsung      Galaxy S20   6.0    128.0   Green  Yes       649.00\n",
              "1269   Samsung      Galaxy S20   6.0    128.0  Orange  Yes       649.00\n",
              "1274    Xiaomi              10   6.0    128.0    Gray  Yes       349.00\n",
              "1561   Samsung      Galaxy S20   6.0    128.0     Red  Yes       649.00\n",
              "1581   Samsung      Galaxy S22   8.0    128.0   Black  Yes       889.99\n",
              "1809    Xiaomi   Redmi Note 10   8.0    128.0    Blue  Yes       349.00"
            ],
            "text/html": [
              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Brand</th>\n",
              "      <th>Model</th>\n",
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              "      <th>Color</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>Xiaomi</td>\n",
              "      <td>Redmi A1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>32.0</td>\n",
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              "      <td>Yes</td>\n",
              "      <td>109.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>234</th>\n",
              "      <td>Motorola</td>\n",
              "      <td>Edge 40</td>\n",
              "      <td>8.0</td>\n",
              "      <td>256.0</td>\n",
              "      <td>Gold</td>\n",
              "      <td>Yes</td>\n",
              "      <td>491.64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>348</th>\n",
              "      <td>Motorola</td>\n",
              "      <td>Razr 40</td>\n",
              "      <td>8.0</td>\n",
              "      <td>256.0</td>\n",
              "      <td>Gold</td>\n",
              "      <td>Yes</td>\n",
              "      <td>899.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>465</th>\n",
              "      <td>Samsung</td>\n",
              "      <td>Galaxy S21</td>\n",
              "      <td>8.0</td>\n",
              "      <td>256.0</td>\n",
              "      <td>Gray</td>\n",
              "      <td>Yes</td>\n",
              "      <td>649.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>562</th>\n",
              "      <td>Samsung</td>\n",
              "      <td>Galaxy S20</td>\n",
              "      <td>6.0</td>\n",
              "      <td>128.0</td>\n",
              "      <td>Blue</td>\n",
              "      <td>Yes</td>\n",
              "      <td>649.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>699</th>\n",
              "      <td>Samsung</td>\n",
              "      <td>Galaxy A14</td>\n",
              "      <td>4.0</td>\n",
              "      <td>128.0</td>\n",
              "      <td>Black</td>\n",
              "      <td>Yes</td>\n",
              "      <td>209.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>790</th>\n",
              "      <td>Apple</td>\n",
              "      <td>iPhone SE</td>\n",
              "      <td>NaN</td>\n",
              "      <td>64.0</td>\n",
              "      <td>Black</td>\n",
              "      <td>Yes</td>\n",
              "      <td>439.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>812</th>\n",
              "      <td>Samsung</td>\n",
              "      <td>Galaxy Z Fold4</td>\n",
              "      <td>12.0</td>\n",
              "      <td>256.0</td>\n",
              "      <td>Green</td>\n",
              "      <td>Yes</td>\n",
              "      <td>1799.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>847</th>\n",
              "      <td>Cubot</td>\n",
              "      <td>X70</td>\n",
              "      <td>12.0</td>\n",
              "      <td>256.0</td>\n",
              "      <td>Black</td>\n",
              "      <td>Yes</td>\n",
              "      <td>249.95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1005</th>\n",
              "      <td>Realme</td>\n",
              "      <td>7</td>\n",
              "      <td>8.0</td>\n",
              "      <td>128.0</td>\n",
              "      <td>Blue</td>\n",
              "      <td>Yes</td>\n",
              "      <td>239.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1226</th>\n",
              "      <td>Samsung</td>\n",
              "      <td>Galaxy S20</td>\n",
              "      <td>6.0</td>\n",
              "      <td>128.0</td>\n",
              "      <td>Green</td>\n",
              "      <td>Yes</td>\n",
              "      <td>649.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1269</th>\n",
              "      <td>Samsung</td>\n",
              "      <td>Galaxy S20</td>\n",
              "      <td>6.0</td>\n",
              "      <td>128.0</td>\n",
              "      <td>Orange</td>\n",
              "      <td>Yes</td>\n",
              "      <td>649.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1274</th>\n",
              "      <td>Xiaomi</td>\n",
              "      <td>10</td>\n",
              "      <td>6.0</td>\n",
              "      <td>128.0</td>\n",
              "      <td>Gray</td>\n",
              "      <td>Yes</td>\n",
              "      <td>349.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1561</th>\n",
              "      <td>Samsung</td>\n",
              "      <td>Galaxy S20</td>\n",
              "      <td>6.0</td>\n",
              "      <td>128.0</td>\n",
              "      <td>Red</td>\n",
              "      <td>Yes</td>\n",
              "      <td>649.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1581</th>\n",
              "      <td>Samsung</td>\n",
              "      <td>Galaxy S22</td>\n",
              "      <td>8.0</td>\n",
              "      <td>128.0</td>\n",
              "      <td>Black</td>\n",
              "      <td>Yes</td>\n",
              "      <td>889.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1809</th>\n",
              "      <td>Xiaomi</td>\n",
              "      <td>Redmi Note 10</td>\n",
              "      <td>8.0</td>\n",
              "      <td>128.0</td>\n",
              "      <td>Blue</td>\n",
              "      <td>Yes</td>\n",
              "      <td>349.00</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-a1ca8ecf-fa53-40b9-962a-27de74ab7f63')\"\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",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-a1ca8ecf-fa53-40b9-962a-27de74ab7f63 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-a1ca8ecf-fa53-40b9-962a-27de74ab7f63');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-3f49baf2-6f44-4ada-abf4-c6d7e7eca9ec\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3f49baf2-6f44-4ada-abf4-c6d7e7eca9ec')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-3f49baf2-6f44-4ada-abf4-c6d7e7eca9ec button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df[df\",\n  \"rows\": 16,\n  \"fields\": [\n    {\n      \"column\": \"Brand\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Xiaomi\",\n          \"Motorola\",\n          \"Realme\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Model\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 13,\n        \"samples\": [\n          \"Galaxy S22\",\n          \"7\",\n          \"Redmi A1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RAM\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.596701204000403,\n        \"min\": 2.0,\n        \"max\": 12.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          8.0,\n          12.0,\n          6.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Storage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 73.40844638050856,\n        \"min\": 32.0,\n        \"max\": 256.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          256.0,\n          64.0,\n          32.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Color\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Black\",\n          \"Gold\",\n          \"Orange\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Free\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Final Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 402.22096212105026,\n        \"min\": 109.0,\n        \"max\": 1799.0,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          439.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "id": "RM9EVJ8BFnSL"
    },
    {
      "cell_type": "code",
      "source": [
        "plt.title(\"Distribute of Storage\")\n",
        "sns.countplot(data = df ,x=\"Storage\")\n",
        "plt.xlabel(\"Type of Storage\")\n",
        "plt.ylabel(\"Count\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "ZmAszzuGFnSO",
        "outputId": "28804026-19b2-4d7a-aa7c-35f25121ddca"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "id": "ZmAszzuGFnSO"
    },
    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame(df.Storage.value_counts())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        },
        "id": "GLuNYFzeFnSO",
        "outputId": "b2a47837-1895-4039-9226-e8ba96d37915"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         count\n",
              "Storage       \n",
              "128.0      732\n",
              "256.0      412\n",
              "64.0       332\n",
              "32.0       166\n",
              "512.0       94\n",
              "16.0        24\n",
              "1000.0      17\n",
              "4.0          6\n",
              "8.0          3\n",
              "2.0          2\n",
              "3.0          2\n",
              "12.0         1"
            ],
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              "      <th>Storage</th>\n",
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              "      <th>128.0</th>\n",
              "      <td>732</td>\n",
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              "    <tr>\n",
              "      <th>256.0</th>\n",
              "      <td>412</td>\n",
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              "      <td>332</td>\n",
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              "      <th>32.0</th>\n",
              "      <td>166</td>\n",
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              "      <th>512.0</th>\n",
              "      <td>94</td>\n",
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              "      <td>24</td>\n",
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              "      <td>17</td>\n",
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              "      <td>6</td>\n",
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              "      <td>3</td>\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "        document.querySelector('#df-6473df0e-a498-4e75-a941-3f0cbfacc26d button.colab-df-convert');\n",
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              "                                                    [key], {});\n",
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              "\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",
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              "\n",
              "<div id=\"df-bb410ef8-fb69-4e79-af62-ca231b6d9f8d\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-bb410ef8-fb69-4e79-af62-ca231b6d9f8d')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-bb410ef8-fb69-4e79-af62-ca231b6d9f8d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 12,\n  \"fields\": [\n    {\n      \"column\": \"Storage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 302.0981553312523,\n        \"min\": 2.0,\n        \"max\": 1000.0,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          3.0,\n          2.0,\n          128.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 230,\n        \"min\": 1,\n        \"max\": 732,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          24,\n          732,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "id": "GLuNYFzeFnSO"
    },
    {
      "cell_type": "code",
      "source": [
        "print('Storage 결측치 비율: {:.2}%'.format(df['Storage'].isna().sum()/len(df)*100))"
      ],
      "metadata": {
        "id": "uClEpMd6FcXc",
        "outputId": "55c68fb0-42c2-40c1-864e-27ada5726f5a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "uClEpMd6FcXc",
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Storage 결측치 비율: 1.4%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 결측치 비율이 충분히 작으므로 최빈값으로 일괄 처리\n",
        "print(\"storage의 최빈값: {mode}\".format(mode=df['Storage'].mode()[0]))\n",
        "df['Storage'] =  df['Storage'].fillna(df['Storage'].mode()[0])"
      ],
      "metadata": {
        "id": "kqxVwPVEF9yC",
        "outputId": "ec5e37a9-299b-4a9a-89e6-505340a0c6d6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "kqxVwPVEF9yC",
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "storage의 최빈값: 128.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.title(\"Distribute of RAM\")\n",
        "sns.countplot(data = df ,x=\"RAM\")\n",
        "plt.xlabel(\"Type of Ram\")\n",
        "plt.ylabel(\"Count\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "Ft9RK_rUFnSN",
        "outputId": "9e00080b-e280-4bcf-88b6-85197b5aafe9"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "id": "Ft9RK_rUFnSN"
    },
    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame(df.RAM.value_counts())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "OYDCWgxBFnSN",
        "outputId": "25a143e1-f5ee-4dd1-ca78-1ae41e3a3817"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      count\n",
              "RAM        \n",
              "8.0     381\n",
              "4.0     380\n",
              "6.0     259\n",
              "3.0     112\n",
              "12.0    109\n",
              "2.0      88\n",
              "1.0       4"
            ],
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              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-5c06df02-07cf-4edd-9e68-b1a5d9dc3f1c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"RAM\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.8483144114694956,\n        \"min\": 1.0,\n        \"max\": 12.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          8.0,\n          4.0,\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 149,\n        \"min\": 4,\n        \"max\": 381,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          381,\n          380,\n          88\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "id": "OYDCWgxBFnSN"
    },
    {
      "cell_type": "code",
      "source": [
        "print('RAM 결측치 비율: {:.4}%'.format(df['RAM'].isna().sum()/len(df)*100))"
      ],
      "metadata": {
        "id": "aEGM-4ryGbYM",
        "outputId": "609e4014-ebc1-4bb8-ad21-d78df8f484ba",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "aEGM-4ryGbYM",
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RAM 결측치 비율: 26.6%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 결측치 비율이 크므로 데이터분포를 통한 imputation\n",
        "distribution = df['RAM'].value_counts(normalize=True)\n",
        "impu_val = np.random.choice(distribution.index, size = df['RAM'].isna().sum(), p = distribution.values)\n",
        "df.loc[df[df['RAM'].isna()].index, 'RAM'] = impu_val"
      ],
      "metadata": {
        "id": "xgyjDShDGkZF"
      },
      "id": "xgyjDShDGkZF",
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#df.RAM = df.RAM.astype(\"category\")\n",
        "#df.Storage = df.Storage.astype(\"category\")"
      ],
      "metadata": {
        "id": "bJ8Q3XmRFnSQ"
      },
      "execution_count": 14,
      "outputs": [],
      "id": "bJ8Q3XmRFnSQ"
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe(include=\"O\").T"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 174
        },
        "id": "92OWYGNxFnSQ",
        "outputId": "5a6f1531-4a06-4218-a7a5-81af210a1c67"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      count unique        top  freq\n",
              "Brand  1816     37    Samsung   458\n",
              "Model  1816    383  iPhone 13    60\n",
              "Color  1816     17      Black   537\n",
              "Free   1816      2        Yes  1772"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2397bd07-1ac6-4887-9138-74536ef7177d\" 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>count</th>\n",
              "      <th>unique</th>\n",
              "      <th>top</th>\n",
              "      <th>freq</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Brand</th>\n",
              "      <td>1816</td>\n",
              "      <td>37</td>\n",
              "      <td>Samsung</td>\n",
              "      <td>458</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Model</th>\n",
              "      <td>1816</td>\n",
              "      <td>383</td>\n",
              "      <td>iPhone 13</td>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Color</th>\n",
              "      <td>1816</td>\n",
              "      <td>17</td>\n",
              "      <td>Black</td>\n",
              "      <td>537</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Free</th>\n",
              "      <td>1816</td>\n",
              "      <td>2</td>\n",
              "      <td>Yes</td>\n",
              "      <td>1772</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-2397bd07-1ac6-4887-9138-74536ef7177d')\"\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",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-2397bd07-1ac6-4887-9138-74536ef7177d 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-2397bd07-1ac6-4887-9138-74536ef7177d');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-9b9d7cb8-9cc8-48df-b1c8-d91ab78f2a17\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9b9d7cb8-9cc8-48df-b1c8-d91ab78f2a17')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-9b9d7cb8-9cc8-48df-b1c8-d91ab78f2a17 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"1816\",\n        \"max\": \"1816\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"1816\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"unique\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 2,\n        \"max\": 383,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          383\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"top\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"iPhone 13\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"freq\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"60\",\n        \"max\": \"1772\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"60\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "id": "92OWYGNxFnSQ"
    },
    {
      "cell_type": "code",
      "source": [
        "df[['RAM', 'Storage', 'Final Price']].corr().style.background_gradient(axis=None, vmin=0.5, vmax=1, cmap=\"RdBu\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "3Kb5HM5uAncl",
        "outputId": "e30bbb19-9d78-4fd1-e48d-5eb4761056f2"
      },
      "id": "3Kb5HM5uAncl",
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x78abc22790c0>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_eaf51_row0_col0, #T_eaf51_row1_col1, #T_eaf51_row2_col2 {\n",
              "  background-color: #053061;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_eaf51_row0_col1, #T_eaf51_row0_col2, #T_eaf51_row1_col0, #T_eaf51_row2_col0 {\n",
              "  background-color: #67001f;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_eaf51_row1_col2, #T_eaf51_row2_col1 {\n",
              "  background-color: #fcd7c2;\n",
              "  color: #000000;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_eaf51\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_eaf51_level0_col0\" class=\"col_heading level0 col0\" >RAM</th>\n",
              "      <th id=\"T_eaf51_level0_col1\" class=\"col_heading level0 col1\" >Storage</th>\n",
              "      <th id=\"T_eaf51_level0_col2\" class=\"col_heading level0 col2\" >Final Price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_eaf51_level0_row0\" class=\"row_heading level0 row0\" >RAM</th>\n",
              "      <td id=\"T_eaf51_row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
              "      <td id=\"T_eaf51_row0_col1\" class=\"data row0 col1\" >0.379667</td>\n",
              "      <td id=\"T_eaf51_row0_col2\" class=\"data row0 col2\" >0.401861</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_eaf51_level0_row1\" class=\"row_heading level0 row1\" >Storage</th>\n",
              "      <td id=\"T_eaf51_row1_col0\" class=\"data row1 col0\" >0.379667</td>\n",
              "      <td id=\"T_eaf51_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
              "      <td id=\"T_eaf51_row1_col2\" class=\"data row1 col2\" >0.696851</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_eaf51_level0_row2\" class=\"row_heading level0 row2\" >Final Price</th>\n",
              "      <td id=\"T_eaf51_row2_col0\" class=\"data row2 col0\" >0.401861</td>\n",
              "      <td id=\"T_eaf51_row2_col1\" class=\"data row2 col1\" >0.696851</td>\n",
              "      <td id=\"T_eaf51_row2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure(figsize = (6,3))\n",
        "g = sns.regplot(x = df.RAM, y = df['Final Price'], x_jitter = True, line_kws={\"color\": \"red\"}, scatter_kws={'s':3})\n",
        "plt.title('Price vs RAM')\n",
        "g.grid(False)\n",
        "sns.despine()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        },
        "id": "B13-C52BAu6U",
        "outputId": "827f927a-390b-4b59-b8e2-120f90070c33"
      },
      "id": "B13-C52BAu6U",
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure(figsize = (6,3))\n",
        "g = sns.regplot(x = df.Storage, y = df['Final Price'], x_jitter = True, line_kws={\"color\": \"red\"}, scatter_kws={'s':3})\n",
        "plt.title('Price vs Storage')\n",
        "g.grid(False)\n",
        "sns.despine()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        },
        "id": "JYCFbb97CoQK",
        "outputId": "dc13512e-97d6-496a-bc84-44183ee47f71"
      },
      "id": "JYCFbb97CoQK",
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1. 가장 인기 많은 브랜드"
      ],
      "metadata": {
        "id": "n_XN-Az4FnSS"
      },
      "id": "n_XN-Az4FnSS"
    },
    {
      "cell_type": "code",
      "source": [
        "df.Brand.value_counts()[0:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T2JHQ0-8FnSS",
        "outputId": "dabd4bb3-2a8e-4338-dd25-63e396f2ab25"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Brand\n",
              "Samsung    458\n",
              "Xiaomi     351\n",
              "Apple      292\n",
              "Realme     117\n",
              "OPPO        92\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "id": "T2JHQ0-8FnSS"
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,5))\n",
        "plt.title(\"Popular Brands\")\n",
        "figure = sns.barplot(x=df.Brand.value_counts()[0:10].index ,y= df.Brand.value_counts()[0:10].values)\n",
        "for cotain in figure.containers :\n",
        "    figure.bar_label(cotain)\n",
        "plt.xlabel(\"Brands\")\n",
        "plt.ylabel(\"Frequence\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "_-P4xtxlFnST",
        "outputId": "764074ed-2793-42de-fa89-4c69c2097f3e"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "id": "_-P4xtxlFnST"
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2. 브랜드별 평균 가격"
      ],
      "metadata": {
        "id": "kiYRZL2pFnSU"
      },
      "id": "kiYRZL2pFnSU"
    },
    {
      "cell_type": "code",
      "source": [
        "df.groupby(\"Brand\")[\"Final Price\"].mean().sort_values(ascending=False).round(3)[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5ceG1ff8FnSV",
        "outputId": "4e6816f6-41e6-47cc-ec3f-0d459bafa2d5"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Brand\n",
              "Apple      842.397\n",
              "Lenovo     757.180\n",
              "Asus       751.573\n",
              "Nothing    657.749\n",
              "Samsung    639.754\n",
              "Name: Final Price, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "id": "5ceG1ff8FnSV"
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "fig ,ax =plt.subplots(nrows=1 ,ncols= 2 ,sharex=True, figsize=(20,10) )\n",
        "fig.set_label(\"avg.Price of Brand\")\n",
        "\n",
        "#ax[0]\n",
        "figure = sns.barplot(x =df.groupby(\"Brand\")[\"Final Price\"].mean().sort_values(ascending=False).round(3)[0:5].index ,\n",
        "            y=df.groupby(\"Brand\")[\"Final Price\"].mean().sort_values(ascending=False).round(3)[0:5].values ,\n",
        "                    ax= ax[1])\n",
        "\n",
        "\n",
        "for cotain in figure.containers :\n",
        "    figure.bar_label(cotain)\n",
        "ax[0].set_ylabel(\"avg.Price\",fontsize=30)\n",
        "ax[0].set_xlabel(\"Brand\",fontsize=30)\n",
        "#ax[1]\n",
        "ax[0].plot(df.groupby(\"Brand\")[\"Final Price\"].mean().sort_values(ascending=False).round(3)[0:5].index ,\n",
        "            df.groupby(\"Brand\")[\"Final Price\"].mean().sort_values(ascending=False).round(3)[0:5].values ,\n",
        "                     \"o--\")\n",
        "\n",
        "\n",
        "for cotain in figure.containers :\n",
        "    figure.bar_label(cotain)\n",
        "ax[1].set_ylabel(\"avg.Price\",fontsize=30)\n",
        "ax[1].set_xlabel(\"Brand\",fontsize=30)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 680
        },
        "id": "KFQXm_P9FnSV",
        "outputId": "17c2f6d7-1040-4e13-dc52-36b442a9800b"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1000 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "id": "KFQXm_P9FnSV"
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 3. RAM과 가격의 상관관계"
      ],
      "metadata": {
        "id": "JRdOMMxAFnSW"
      },
      "id": "JRdOMMxAFnSW"
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.stats import f_oneway\n",
        "\n",
        "cleaned_df = df.dropna(subset=['RAM', 'Final Price'])\n",
        "\n",
        "storage_categories = cleaned_df['RAM'].unique()\n",
        "price_arrays = [cleaned_df[cleaned_df['RAM'] == category]['Final Price'] for category in storage_categories]\n",
        "\n",
        "f_statistic, p_value = f_oneway(*price_arrays)\n",
        "\n",
        "print(\"F 검정:\", f_statistic)\n",
        "print(\"P 계수:\", p_value)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "98LwKaunFnSW",
        "outputId": "f9e3d5f5-7282-4cae-936c-01a51afc53e9"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "F 검정: 59.87099870487069\n",
            "P 계수: 8.00211261067631e-68\n"
          ]
        }
      ],
      "id": "98LwKaunFnSW"
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "F 검정과 P계수로부터 유의미한 상관관계가 있다고 볼 수 있다."
      ],
      "metadata": {
        "id": "0jxEQp-kFnSX"
      },
      "id": "0jxEQp-kFnSX"
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 4. 용량과 가격의 상관관계"
      ],
      "metadata": {
        "id": "ecqWLgaVFnSY"
      },
      "id": "ecqWLgaVFnSY"
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.stats import f_oneway\n",
        "\n",
        "cleaned_df = df.dropna(subset=['Storage', 'Final Price'])\n",
        "\n",
        "storage_categories = cleaned_df['Storage'].unique()\n",
        "price_arrays = [cleaned_df[cleaned_df['Storage'] == category]['Final Price'] for category in storage_categories]\n",
        "\n",
        "f_statistic, p_value = f_oneway(*price_arrays)\n",
        "\n",
        "print(\"F-statistic:\", f_statistic)\n",
        "print(\"P-value:\", p_value)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6-y8tC95FnSY",
        "outputId": "d9b6c894-c113-4e0d-ec24-5b8fe44cc06c"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "F-statistic: 169.0103782135786\n",
            "P-value: 6.228979859033764e-268\n"
          ]
        }
      ],
      "id": "6-y8tC95FnSY"
    },
    {
      "cell_type": "markdown",
      "source": [
        "F 검정과 P계수로부터 유의미한 상관관계가 있다고 볼 수 있다."
      ],
      "metadata": {
        "id": "I3FfOHtwFnSZ"
      },
      "id": "I3FfOHtwFnSZ"
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 5. 통신사 약정이 스마트폰 가격에 영향을 미치는가?"
      ],
      "metadata": {
        "id": "fB-2S66LFnSa"
      },
      "id": "fB-2S66LFnSa"
    },
    {
      "cell_type": "code",
      "source": [
        "df.Free.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q2FEdTCiFnSb",
        "outputId": "3a7b2fde-b517-4abb-fc2a-441c69a32efd"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Free\n",
              "Yes    1772\n",
              "No       44\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "id": "Q2FEdTCiFnSb"
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.stats import ttest_ind\n",
        "\n",
        "cleaned_df = df.dropna(subset=['Free', 'Final Price'])\n",
        "\n",
        "price_with_contract = cleaned_df[cleaned_df['Free'] == 'Yes']['Final Price']\n",
        "price_without_contract = cleaned_df[cleaned_df['Free'] == 'No']['Final Price']\n",
        "\n",
        "t_statistic, p_value = ttest_ind(price_with_contract, price_without_contract)\n",
        "\n",
        "print(\"T-statistic:\", t_statistic)\n",
        "print(\"P-value:\", p_value)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JypISYdWFnSb",
        "outputId": "42e00fb1-0456-4459-c772-9a0c4da95cd5"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "T-statistic: -1.9036352316879213\n",
            "P-value: 0.05711582745245739\n"
          ]
        }
      ],
      "id": "JypISYdWFnSb"
    },
    {
      "cell_type": "markdown",
      "source": [
        "유의미한 상관관계가 없다고 볼 수 있다!"
      ],
      "metadata": {
        "id": "CO1F05W2FnSc"
      },
      "id": "CO1F05W2FnSc"
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 모델 구성"
      ],
      "metadata": {
        "id": "Zxxf74paFnSc"
      },
      "id": "Zxxf74paFnSc"
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.metrics import mean_absolute_error\n",
        "from sklearn.metrics import r2_score\n",
        "from sklearn.linear_model import Ridge\n",
        "from sklearn.linear_model import Lasso\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.preprocessing import StandardScaler"
      ],
      "metadata": {
        "id": "t2vUL14QFnSd"
      },
      "execution_count": 27,
      "outputs": [],
      "id": "t2vUL14QFnSd"
    },
    {
      "cell_type": "code",
      "source": [
        "lr_reg = LinearRegression()\n",
        "ridge_reg = Ridge(alpha=0.1)\n",
        "lasso_reg = Lasso(alpha=0.1)\n",
        "tree_reg = DecisionTreeRegressor(random_state=1)\n",
        "forest_reg = RandomForestRegressor(n_estimators=100,random_state=1)"
      ],
      "metadata": {
        "id": "w1ybfzgOFnSd"
      },
      "execution_count": 28,
      "outputs": [],
      "id": "w1ybfzgOFnSd"
    },
    {
      "cell_type": "code",
      "source": [
        "brand_freq = df['Brand'].value_counts(normalize=True).to_dict()\n",
        "df['brand_freq'] = df['Brand'].map(brand_freq)"
      ],
      "metadata": {
        "id": "hohYh2XaFnSe"
      },
      "execution_count": 29,
      "outputs": [],
      "id": "hohYh2XaFnSe"
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop(columns=['Brand'] , inplace= True)"
      ],
      "metadata": {
        "id": "eAC-cNQEFnSe"
      },
      "execution_count": 30,
      "outputs": [],
      "id": "eAC-cNQEFnSe"
    },
    {
      "cell_type": "code",
      "source": [
        "color_freq = df['Color'].value_counts(normalize=True).to_dict()\n",
        "df['color_freq'] = df['Color'].map(color_freq)\n",
        "df.drop(columns=['Color'] , inplace= True)"
      ],
      "metadata": {
        "id": "Mo9yqd8_FnSe"
      },
      "execution_count": 31,
      "outputs": [],
      "id": "Mo9yqd8_FnSe"
    },
    {
      "cell_type": "code",
      "source": [
        "model_freq = df['Model'].value_counts(normalize=True).to_dict()\n",
        "df['model_freq'] = df['Model'].map(model_freq)\n",
        "df.drop(columns=['Model'] , inplace= True)"
      ],
      "metadata": {
        "id": "Sa-GOEWiFnSe"
      },
      "execution_count": 32,
      "outputs": [],
      "id": "Sa-GOEWiFnSe"
    },
    {
      "cell_type": "code",
      "source": [
        "model_freq = df['Free'].value_counts(normalize=True).to_dict()\n",
        "df['Free_freq'] = df['Free'].map(model_freq)\n",
        "df.drop(columns=['Free'] , inplace= True)"
      ],
      "metadata": {
        "id": "zMGVaDoBFnSf"
      },
      "execution_count": 33,
      "outputs": [],
      "id": "zMGVaDoBFnSf"
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "sc = MinMaxScaler()\n",
        "df.RAM = df.RAM.astype(\"float\")\n",
        "df.Storage = df.Storage.astype(\"float\")\n",
        "numerical_columns = ['RAM' , 'Storage']\n",
        "df[numerical_columns] = sc.fit_transform(df[numerical_columns])"
      ],
      "metadata": {
        "id": "go213G8YFnSg"
      },
      "execution_count": 34,
      "outputs": [],
      "id": "go213G8YFnSg"
    },
    {
      "cell_type": "markdown",
      "source": [
        "최종 학습 데이터셋 구성"
      ],
      "metadata": {
        "id": "gG93MfW7ItFd"
      },
      "id": "gG93MfW7ItFd"
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.drop('Final Price', axis=1)\n",
        "y = df['Final Price']\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
      ],
      "metadata": {
        "id": "6lBJYCP6FnSg"
      },
      "execution_count": 35,
      "outputs": [],
      "id": "6lBJYCP6FnSg"
    },
    {
      "cell_type": "code",
      "source": [
        "lr_reg.fit(X_train ,y_train)\n",
        "y_pred = lr_reg.predict(X_test)\n",
        "y_prdict_train = lr_reg.predict(X_train)\n",
        "r2_train = r2_score(y_train, y_prdict_train)\n",
        "\n",
        "r2_test = r2_score(y_test, y_pred)\n",
        "print(f'R2 Score for train: {r2_train:.4f}')\n",
        "print(f'R2 Score for test: {r2_test:.4f}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mXrWzjVoFnSh",
        "outputId": "e93d5da8-b0de-4b92-b2e1-fb0fc2e5b127"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R2 Score for train: 0.6475\n",
            "R2 Score for test: 0.6332\n"
          ]
        }
      ],
      "id": "mXrWzjVoFnSh"
    },
    {
      "cell_type": "code",
      "source": [
        "ridge_reg.fit(X_train ,y_train)\n",
        "y_pred = ridge_reg.predict(X_test)\n",
        "y_prdict_train =ridge_reg.predict(X_train)\n",
        "r2_train = r2_score(y_train, y_prdict_train)\n",
        "\n",
        "r2_test = r2_score(y_test, y_pred)\n",
        "print(f'R2 Score for train: {r2_train:.4f}')\n",
        "print(f'R2 Score for test: {r2_test:.4f}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EJHuJ5szFnSi",
        "outputId": "31d3e47f-01cd-4585-e011-a7d8fe028376"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R2 Score for train: 0.6108\n",
            "R2 Score for test: 0.6229\n"
          ]
        }
      ],
      "id": "EJHuJ5szFnSi"
    },
    {
      "cell_type": "code",
      "source": [
        "lasso_reg.fit(X_train ,y_train)\n",
        "y_pred = lasso_reg.predict(X_test)\n",
        "y_prdict_train = lasso_reg.predict(X_train)\n",
        "r2_train = r2_score(y_train, y_prdict_train)\n",
        "\n",
        "r2_test = r2_score(y_test, y_pred)\n",
        "print(f'R2 Score for train: {r2_train:.4f}')\n",
        "print(f'R2 Score for test: {r2_test:.4f}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j2ucyZeTFnSi",
        "outputId": "413c2e4f-9d92-4014-83cb-dc376702234d"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R2 Score for train: 0.6466\n",
            "R2 Score for test: 0.6357\n"
          ]
        }
      ],
      "id": "j2ucyZeTFnSi"
    },
    {
      "cell_type": "code",
      "source": [
        "tree_reg.fit(X_train ,y_train)\n",
        "y_pred = tree_reg.predict(X_test)\n",
        "y_prdict_train =tree_reg.predict(X_train)\n",
        "r2_train = r2_score(y_train, y_prdict_train)\n",
        "\n",
        "r2_test = r2_score(y_test, y_pred)\n",
        "print(f'R2 Score for train: {r2_train:.4f}')\n",
        "print(f'R2 Score for test: {r2_test:.4f}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5R620BYkFnSj",
        "outputId": "61e0ac6c-1250-4939-de62-9c7794169d69"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R2 Score for train: 0.9758\n",
            "R2 Score for test: 0.7041\n"
          ]
        }
      ],
      "id": "5R620BYkFnSj"
    },
    {
      "cell_type": "code",
      "source": [
        "forest_reg.fit(X_train ,y_train)\n",
        "y_pred = forest_reg.predict(X_test)\n",
        "y_prdict_train = forest_reg.predict(X_train)\n",
        "r2_train = r2_score(y_train, y_prdict_train)\n",
        "\n",
        "r2_test = r2_score(y_test, y_pred)\n",
        "print(f'R2 Score for train: {r2_train:.4f}')\n",
        "print(f'R2 Score for test: {r2_test:.4f}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p-lxIafFJozD",
        "outputId": "9fde1a6e-3404-4c3b-cdc1-0225f7e5d68e"
      },
      "id": "p-lxIafFJozD",
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R2 Score for train: 0.9565\n",
            "R2 Score for test: 0.7670\n"
          ]
        }
      ]
    }
  ]
}