Car Price Predict [SVM Regression].ipynb

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      "source": [
        "## Import Dataset"
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      "source": [
        "import pandas as pd\n",
        "import missingno as msno\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.svm import SVR\n",
        "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.pipeline import Pipeline"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "url = \"https://raw.githubusercontent.com/WiraDKP/supervised_learning/master/05%20-%20SVM/data/carprice.csv\"\n",
        "data = pd.read_csv(url, index_col=\"ID\")\n",
        "\n",
        "data.head()"
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              "                                Name    Location  Year  Kilometers_Driven  \\\n",
              "ID                                                                          \n",
              "1   Hyundai Creta 1.6 CRDi SX Option        Pune  2015              41000   \n",
              "2                       Honda Jazz V     Chennai  2011              46000   \n",
              "3                  Maruti Ertiga VDI     Chennai  2012              87000   \n",
              "4    Audi A4 New 2.0 TDI Multitronic  Coimbatore  2013              40670   \n",
              "6             Nissan Micra Diesel XV      Jaipur  2013              86999   \n",
              "\n",
              "   Fuel_Type Transmission Owner_Type  Mileage_kmpl  Engine_CC  Power_bhp  \\\n",
              "ID                                                                         \n",
              "1     Diesel       Manual      First         19.67     1582.0     126.20   \n",
              "2     Petrol       Manual      First         18.20     1199.0      88.70   \n",
              "3     Diesel       Manual      First         20.77     1248.0      88.76   \n",
              "4     Diesel    Automatic     Second         15.20     1968.0     140.80   \n",
              "6     Diesel       Manual      First         23.08     1461.0      63.10   \n",
              "\n",
              "    Seats  Price  \n",
              "ID                \n",
              "1     5.0  12.50  \n",
              "2     5.0   4.50  \n",
              "3     7.0   6.00  \n",
              "4     5.0  17.74  \n",
              "6     5.0   3.50  "
            ],
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              "      <th>Power_bhp</th>\n",
              "      <th>Seats</th>\n",
              "      <th>Price</th>\n",
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              "      <th>ID</th>\n",
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              "      <th>1</th>\n",
              "      <td>Hyundai Creta 1.6 CRDi SX Option</td>\n",
              "      <td>Pune</td>\n",
              "      <td>2015</td>\n",
              "      <td>41000</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>19.67</td>\n",
              "      <td>1582.0</td>\n",
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              "      <td>5.0</td>\n",
              "      <td>12.50</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Honda Jazz V</td>\n",
              "      <td>Chennai</td>\n",
              "      <td>2011</td>\n",
              "      <td>46000</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>18.20</td>\n",
              "      <td>1199.0</td>\n",
              "      <td>88.70</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Maruti Ertiga VDI</td>\n",
              "      <td>Chennai</td>\n",
              "      <td>2012</td>\n",
              "      <td>87000</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>20.77</td>\n",
              "      <td>1248.0</td>\n",
              "      <td>88.76</td>\n",
              "      <td>7.0</td>\n",
              "      <td>6.00</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Audi A4 New 2.0 TDI Multitronic</td>\n",
              "      <td>Coimbatore</td>\n",
              "      <td>2013</td>\n",
              "      <td>40670</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Automatic</td>\n",
              "      <td>Second</td>\n",
              "      <td>15.20</td>\n",
              "      <td>1968.0</td>\n",
              "      <td>140.80</td>\n",
              "      <td>5.0</td>\n",
              "      <td>17.74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Nissan Micra Diesel XV</td>\n",
              "      <td>Jaipur</td>\n",
              "      <td>2013</td>\n",
              "      <td>86999</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>23.08</td>\n",
              "      <td>1461.0</td>\n",
              "      <td>63.10</td>\n",
              "      <td>5.0</td>\n",
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              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 5953,\n  \"fields\": [\n    {\n      \"column\": \"ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1738,\n        \"min\": 1,\n        \"max\": 6018,\n        \"num_unique_values\": 5953,\n        \"samples\": [\n          4089,\n          4299,\n          1985\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1849,\n        \"samples\": [\n          \"BMW X5 xDrive 30d Design Pure Experience 5 Seater\",\n          \"Tata Tigor 1.05 Revotorq XZ Option\",\n          \"Tata Sumo DX\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Location\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 11,\n        \"samples\": [\n          \"Kochi\",\n          \"Pune\",\n          \"Hyderabad\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 1998,\n        \"max\": 2019,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          2015,\n          2006,\n          2010\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Kilometers_Driven\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 91743,\n        \"min\": 171,\n        \"max\": 6500000,\n        \"num_unique_values\": 3062,\n        \"samples\": [\n          51366,\n          160538,\n          72500\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fuel_Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Diesel\",\n          \"Petrol\",\n          \"Electric\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Transmission\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Automatic\",\n          \"Manual\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Owner_Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Second\",\n          \"Third\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Mileage_kmpl\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.511393402407908,\n        \"min\": 0.0,\n        \"max\": 28.4,\n        \"num_unique_values\": 418,\n        \"samples\": [\n          15.74,\n          10.9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Engine_CC\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 601.5846333481196,\n        \"min\": 72.0,\n        \"max\": 5998.0,\n        \"num_unique_values\": 146,\n        \"samples\": [\n          2993.0,\n          1978.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Power_bhp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 53.896718590633405,\n        \"min\": 34.2,\n        \"max\": 560.0,\n        \"num_unique_values\": 359,\n        \"samples\": [\n          130.2,\n          75.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Seats\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8106283142322979,\n        \"min\": 0.0,\n        \"max\": 10.0,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          9.0,\n          7.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.230162689342361,\n        \"min\": 0.44,\n        \"max\": 160.0,\n        \"num_unique_values\": 1371,\n        \"samples\": [\n          10.99,\n          14.49\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## CEK MISSING VALUES"
      ],
      "metadata": {
        "id": "gtqeX_vW9HeK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "msno.matrix(data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 700
        },
        "id": "4S4n0mtgu_Kk",
        "outputId": "1b0f84f2-e910-445c-a969-2f3bd7e65f74"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 3
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2500x1000 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.drop(columns=\"Name\", inplace=True)"
      ],
      "metadata": {
        "id": "abN2J_cwvTUy"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "4n_McCHIv2Nj",
        "outputId": "6499f647-7038-4437-ef1a-8c1ae8dcc491"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Location  Year  Kilometers_Driven Fuel_Type Transmission Owner_Type  \\\n",
              "ID                                                                          \n",
              "1         Pune  2015              41000    Diesel       Manual      First   \n",
              "2      Chennai  2011              46000    Petrol       Manual      First   \n",
              "3      Chennai  2012              87000    Diesel       Manual      First   \n",
              "4   Coimbatore  2013              40670    Diesel    Automatic     Second   \n",
              "6       Jaipur  2013              86999    Diesel       Manual      First   \n",
              "\n",
              "    Mileage_kmpl  Engine_CC  Power_bhp  Seats  Price  \n",
              "ID                                                    \n",
              "1          19.67     1582.0     126.20    5.0  12.50  \n",
              "2          18.20     1199.0      88.70    5.0   4.50  \n",
              "3          20.77     1248.0      88.76    7.0   6.00  \n",
              "4          15.20     1968.0     140.80    5.0  17.74  \n",
              "6          23.08     1461.0      63.10    5.0   3.50  "
            ],
            "text/html": [
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Location</th>\n",
              "      <th>Year</th>\n",
              "      <th>Kilometers_Driven</th>\n",
              "      <th>Fuel_Type</th>\n",
              "      <th>Transmission</th>\n",
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              "      <th>Mileage_kmpl</th>\n",
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              "      <th></th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Pune</td>\n",
              "      <td>2015</td>\n",
              "      <td>41000</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>19.67</td>\n",
              "      <td>1582.0</td>\n",
              "      <td>126.20</td>\n",
              "      <td>5.0</td>\n",
              "      <td>12.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Chennai</td>\n",
              "      <td>2011</td>\n",
              "      <td>46000</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>18.20</td>\n",
              "      <td>1199.0</td>\n",
              "      <td>88.70</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Chennai</td>\n",
              "      <td>2012</td>\n",
              "      <td>87000</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>20.77</td>\n",
              "      <td>1248.0</td>\n",
              "      <td>88.76</td>\n",
              "      <td>7.0</td>\n",
              "      <td>6.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Coimbatore</td>\n",
              "      <td>2013</td>\n",
              "      <td>40670</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Automatic</td>\n",
              "      <td>Second</td>\n",
              "      <td>15.20</td>\n",
              "      <td>1968.0</td>\n",
              "      <td>140.80</td>\n",
              "      <td>5.0</td>\n",
              "      <td>17.74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Jaipur</td>\n",
              "      <td>2013</td>\n",
              "      <td>86999</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>23.08</td>\n",
              "      <td>1461.0</td>\n",
              "      <td>63.10</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.50</td>\n",
              "    </tr>\n",
              "  </tbody>\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-7c8c9eba-544d-4a2a-aadb-8862c172b9ff 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-7c8c9eba-544d-4a2a-aadb-8862c172b9ff');\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-04497828-f277-453d-b751-7d1ddbeee8d1\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-04497828-f277-453d-b751-7d1ddbeee8d1')\"\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-04497828-f277-453d-b751-7d1ddbeee8d1 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 5953,\n  \"fields\": [\n    {\n      \"column\": \"ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1738,\n        \"min\": 1,\n        \"max\": 6018,\n        \"num_unique_values\": 5953,\n        \"samples\": [\n          4089,\n          4299,\n          1985\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Location\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 11,\n        \"samples\": [\n          \"Kochi\",\n          \"Pune\",\n          \"Hyderabad\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 1998,\n        \"max\": 2019,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          2015,\n          2006,\n          2010\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Kilometers_Driven\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 91743,\n        \"min\": 171,\n        \"max\": 6500000,\n        \"num_unique_values\": 3062,\n        \"samples\": [\n          51366,\n          160538,\n          72500\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fuel_Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Diesel\",\n          \"Petrol\",\n          \"Electric\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Transmission\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Automatic\",\n          \"Manual\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Owner_Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Second\",\n          \"Third\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Mileage_kmpl\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.511393402407908,\n        \"min\": 0.0,\n        \"max\": 28.4,\n        \"num_unique_values\": 418,\n        \"samples\": [\n          15.74,\n          10.9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Engine_CC\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 601.5846333481196,\n        \"min\": 72.0,\n        \"max\": 5998.0,\n        \"num_unique_values\": 146,\n        \"samples\": [\n          2993.0,\n          1978.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Power_bhp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 53.896718590633405,\n        \"min\": 34.2,\n        \"max\": 560.0,\n        \"num_unique_values\": 359,\n        \"samples\": [\n          130.2,\n          75.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Seats\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8106283142322979,\n        \"min\": 0.0,\n        \"max\": 10.0,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          9.0,\n          7.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.230162689342361,\n        \"min\": 0.44,\n        \"max\": 160.0,\n        \"num_unique_values\": 1371,\n        \"samples\": [\n          10.99,\n          14.49\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# SPLITTING"
      ],
      "metadata": {
        "id": "Y8Tiau9BwTk-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = data.drop(columns=\"Price\")\n",
        "y = data[\"Price\"]\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "arb2avSiwKoZ",
        "outputId": "2748b591-0826-4cfd-ceee-d5220ba4d695"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((4762, 10), (1191, 10), (4762,), (1191,))"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Training"
      ],
      "metadata": {
        "id": "v7RPaPsmw6sf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g1vW51NNxTYT",
        "outputId": "8eb37195-20d0-4cc3-e50c-c74a188e12cb"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 4762 entries, 4380 to 866\n",
            "Data columns (total 10 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   Location           4762 non-null   object \n",
            " 1   Year               4762 non-null   int64  \n",
            " 2   Kilometers_Driven  4762 non-null   int64  \n",
            " 3   Fuel_Type          4762 non-null   object \n",
            " 4   Transmission       4762 non-null   object \n",
            " 5   Owner_Type         4762 non-null   object \n",
            " 6   Mileage_kmpl       4760 non-null   float64\n",
            " 7   Engine_CC          4732 non-null   float64\n",
            " 8   Power_bhp          4646 non-null   float64\n",
            " 9   Seats              4726 non-null   float64\n",
            "dtypes: float64(4), int64(2), object(4)\n",
            "memory usage: 409.2+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## SETTING PIPELINE DAN PARAMETER"
      ],
      "metadata": {
        "id": "1eZNlDFj8phD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "preprocessor = ColumnTransformer(\n",
        "    transformers=[\n",
        "        ('num', Pipeline([\n",
        "            ('imputer', SimpleImputer(strategy='median')),\n",
        "            ('scaler', StandardScaler())\n",
        "        ]), ['Year', 'Kilometers_Driven', 'Mileage_kmpl', 'Engine_CC', 'Power_bhp', 'Seats']),\n",
        "\n",
        "        ('cat', Pipeline([\n",
        "            ('imputer', SimpleImputer(strategy='most_frequent')),\n",
        "            ('onehot', OneHotEncoder(handle_unknown='ignore'))\n",
        "        ]), ['Location', 'Fuel_Type', 'Transmission', 'Owner_Type'])\n",
        "    ]\n",
        ")\n",
        "\n",
        "pipeline = Pipeline([\n",
        "    ('preprocessor', preprocessor),\n",
        "    ('algo', SVR(max_iter=500))\n",
        "])\n",
        "\n",
        "parameter = {\n",
        "    'algo__gamma': ['scale', 'auto', 0.01, 0.1, 1, 10],\n",
        "    'algo__C': [0.1, 1, 10, 100, 1000]\n",
        "}"
      ],
      "metadata": {
        "id": "TA4f_9Ojw2Fo"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## TRAIN MODEL"
      ],
      "metadata": {
        "id": "4aHuIOyt8yoS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = GridSearchCV(pipeline, param_grid=parameter, cv=3, n_jobs=-1, verbose=1)\n",
        "model.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "id": "KCA9zo5vzCKW",
        "outputId": "f1601505-bcfe-451b-d2f8-efe0eb596ce4"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 3 folds for each of 30 candidates, totalling 90 fits\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/sklearn/svm/_base.py:305: ConvergenceWarning: Solver terminated early (max_iter=500).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=3,\n",
              "             estimator=Pipeline(steps=[('preprocessor',\n",
              "                                        ColumnTransformer(transformers=[('num',\n",
              "                                                                         Pipeline(steps=[('imputer',\n",
              "                                                                                          SimpleImputer(strategy='median')),\n",
              "                                                                                         ('scaler',\n",
              "                                                                                          StandardScaler())]),\n",
              "                                                                         ['Year',\n",
              "                                                                          'Kilometers_Driven',\n",
              "                                                                          'Mileage_kmpl',\n",
              "                                                                          'Engine_CC',\n",
              "                                                                          'Power_bhp',\n",
              "                                                                          'Seats']),\n",
              "                                                                        ('cat',\n",
              "                                                                         Pipeline(steps=[('imputer',\n",
              "                                                                                          SimpleImputer(strategy='most_frequent')),\n",
              "                                                                                         ('onehot',\n",
              "                                                                                          OneHotEncoder(handle_unknown='ignore'))]),\n",
              "                                                                         ['Location',\n",
              "                                                                          'Fuel_Type',\n",
              "                                                                          'Transmission',\n",
              "                                                                          'Owner_Type'])])),\n",
              "                                       ('algo', SVR(max_iter=500))]),\n",
              "             n_jobs=-1,\n",
              "             param_grid={'algo__C': [0.1, 1, 10, 100, 1000],\n",
              "                         'algo__gamma': ['scale', 'auto', 0.01, 0.1, 1, 10]},\n",
              "             verbose=1)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-1 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-1 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-1 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-1 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 0.5em;\n",
              "  text-align: center;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=3,\n",
              "             estimator=Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
              "                                        ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
              "                                                                         Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                                                          SimpleImputer(strategy=&#x27;median&#x27;)),\n",
              "                                                                                         (&#x27;scaler&#x27;,\n",
              "                                                                                          StandardScaler())]),\n",
              "                                                                         [&#x27;Year&#x27;,\n",
              "                                                                          &#x27;Kilometers_Driven&#x27;,\n",
              "                                                                          &#x27;Mileage_kmpl&#x27;,\n",
              "                                                                          &#x27;Engine_CC&#x27;,\n",
              "                                                                          &#x27;Power_bhp&#x27;,\n",
              "                                                                          &#x27;Seats&#x27;]),\n",
              "                                                                        (&#x27;cat&#x27;,\n",
              "                                                                         Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                                                          SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
              "                                                                                         (&#x27;onehot&#x27;,\n",
              "                                                                                          OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
              "                                                                         [&#x27;Location&#x27;,\n",
              "                                                                          &#x27;Fuel_Type&#x27;,\n",
              "                                                                          &#x27;Transmission&#x27;,\n",
              "                                                                          &#x27;Owner_Type&#x27;])])),\n",
              "                                       (&#x27;algo&#x27;, SVR(max_iter=500))]),\n",
              "             n_jobs=-1,\n",
              "             param_grid={&#x27;algo__C&#x27;: [0.1, 1, 10, 100, 1000],\n",
              "                         &#x27;algo__gamma&#x27;: [&#x27;scale&#x27;, &#x27;auto&#x27;, 0.01, 0.1, 1, 10]},\n",
              "             verbose=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>GridSearchCV</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=3,\n",
              "             estimator=Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
              "                                        ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
              "                                                                         Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                                                          SimpleImputer(strategy=&#x27;median&#x27;)),\n",
              "                                                                                         (&#x27;scaler&#x27;,\n",
              "                                                                                          StandardScaler())]),\n",
              "                                                                         [&#x27;Year&#x27;,\n",
              "                                                                          &#x27;Kilometers_Driven&#x27;,\n",
              "                                                                          &#x27;Mileage_kmpl&#x27;,\n",
              "                                                                          &#x27;Engine_CC&#x27;,\n",
              "                                                                          &#x27;Power_bhp&#x27;,\n",
              "                                                                          &#x27;Seats&#x27;]),\n",
              "                                                                        (&#x27;cat&#x27;,\n",
              "                                                                         Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                                                          SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
              "                                                                                         (&#x27;onehot&#x27;,\n",
              "                                                                                          OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
              "                                                                         [&#x27;Location&#x27;,\n",
              "                                                                          &#x27;Fuel_Type&#x27;,\n",
              "                                                                          &#x27;Transmission&#x27;,\n",
              "                                                                          &#x27;Owner_Type&#x27;])])),\n",
              "                                       (&#x27;algo&#x27;, SVR(max_iter=500))]),\n",
              "             n_jobs=-1,\n",
              "             param_grid={&#x27;algo__C&#x27;: [0.1, 1, 10, 100, 1000],\n",
              "                         &#x27;algo__gamma&#x27;: [&#x27;scale&#x27;, &#x27;auto&#x27;, 0.01, 0.1, 1, 10]},\n",
              "             verbose=1)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>best_estimator_: Pipeline</div></div></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
              "                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
              "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
              "                                                                  (&#x27;scaler&#x27;,\n",
              "                                                                   StandardScaler())]),\n",
              "                                                  [&#x27;Year&#x27;, &#x27;Kilometers_Driven&#x27;,\n",
              "                                                   &#x27;Mileage_kmpl&#x27;, &#x27;Engine_CC&#x27;,\n",
              "                                                   &#x27;Power_bhp&#x27;, &#x27;Seats&#x27;]),\n",
              "                                                 (&#x27;cat&#x27;,\n",
              "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
              "                                                                  (&#x27;onehot&#x27;,\n",
              "                                                                   OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
              "                                                  [&#x27;Location&#x27;, &#x27;Fuel_Type&#x27;,\n",
              "                                                   &#x27;Transmission&#x27;,\n",
              "                                                   &#x27;Owner_Type&#x27;])])),\n",
              "                (&#x27;algo&#x27;, SVR(C=10, gamma=0.1, max_iter=500))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>preprocessor: ColumnTransformer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for preprocessor: ColumnTransformer</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
              "                                 Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                  SimpleImputer(strategy=&#x27;median&#x27;)),\n",
              "                                                 (&#x27;scaler&#x27;, StandardScaler())]),\n",
              "                                 [&#x27;Year&#x27;, &#x27;Kilometers_Driven&#x27;, &#x27;Mileage_kmpl&#x27;,\n",
              "                                  &#x27;Engine_CC&#x27;, &#x27;Power_bhp&#x27;, &#x27;Seats&#x27;]),\n",
              "                                (&#x27;cat&#x27;,\n",
              "                                 Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
              "                                                 (&#x27;onehot&#x27;,\n",
              "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
              "                                 [&#x27;Location&#x27;, &#x27;Fuel_Type&#x27;, &#x27;Transmission&#x27;,\n",
              "                                  &#x27;Owner_Type&#x27;])])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>num</div></div></label><div class=\"sk-toggleable__content fitted\"><pre>[&#x27;Year&#x27;, &#x27;Kilometers_Driven&#x27;, &#x27;Mileage_kmpl&#x27;, &#x27;Engine_CC&#x27;, &#x27;Power_bhp&#x27;, &#x27;Seats&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SimpleImputer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>cat</div></div></label><div class=\"sk-toggleable__content fitted\"><pre>[&#x27;Location&#x27;, &#x27;Fuel_Type&#x27;, &#x27;Transmission&#x27;, &#x27;Owner_Type&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SimpleImputer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>OneHotEncoder</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div> </div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SVR</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.svm.SVR.html\">?<span>Documentation for SVR</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>SVR(C=10, gamma=0.1, max_iter=500)</pre></div> </div></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## EVALUASI MODEL"
      ],
      "metadata": {
        "id": "0DrlFNEQ495g"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error\n",
        "import numpy as np\n",
        "\n",
        "# Menggunakan model yang sudah di-fit untuk melakukan prediksi\n",
        "y_pred_train = model.predict(X_train)\n",
        "y_pred_test = model.predict(X_test)\n",
        "\n",
        "# Menghitung metrik untuk data pelatihan (train)\n",
        "r2_train = r2_score(y_train, y_pred_train)\n",
        "mae_train = mean_absolute_error(y_train, y_pred_train)\n",
        "mse_train = mean_squared_error(y_train, y_pred_train)\n",
        "rmse_train = np.sqrt(mse_train)\n",
        "mape_train = np.mean(np.abs((y_train - y_pred_train) / y_train)) * 100\n",
        "\n",
        "# Menghitung metrik untuk data pengujian (test)\n",
        "r2_test = r2_score(y_test, y_pred_test)\n",
        "mae_test = mean_absolute_error(y_test, y_pred_test)\n",
        "mse_test = mean_squared_error(y_test, y_pred_test)\n",
        "rmse_test = np.sqrt(mse_test)\n",
        "mape_test = np.mean(np.abs((y_test - y_pred_test) / y_test)) * 100\n",
        "\n",
        "# Menampilkan hasil evaluasi\n",
        "print(\"Train Evaluation Metrics:\")\n",
        "print(f\"R²: {r2_train:.4f}\")\n",
        "print(f\"MAE: {mae_train:.4f}\")\n",
        "print(f\"MSE: {mse_train:.4f}\")\n",
        "print(f\"RMSE: {rmse_train:.4f}\")\n",
        "print(f\"MAPE: {mape_train:.4f}%\")\n",
        "\n",
        "print(\"\\nTest Evaluation Metrics:\")\n",
        "print(f\"R²: {r2_test:.4f}\")\n",
        "print(f\"MAE: {mae_test:.4f}\")\n",
        "print(f\"MSE: {mse_test:.4f}\")\n",
        "print(f\"RMSE: {rmse_test:.4f}\")\n",
        "print(f\"MAPE: {mape_test:.4f}%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zlCzDxsP4Jci",
        "outputId": "39eee084-2281-4f3c-de0c-6f24bf562594"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Evaluation Metrics:\n",
            "R²: 0.8595\n",
            "MAE: 2.4473\n",
            "MSE: 17.2220\n",
            "RMSE: 4.1499\n",
            "MAPE: 44.2437%\n",
            "\n",
            "Test Evaluation Metrics:\n",
            "R²: 0.8197\n",
            "MAE: 2.6553\n",
            "MSE: 25.2685\n",
            "RMSE: 5.0268\n",
            "MAPE: 46.1385%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## PREDIKSI DATA BARU"
      ],
      "metadata": {
        "id": "E-jTJXDW6cyi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "new_data = {\n",
        "    'Location': ['Pune', 'Chennai', 'Coimbatore', 'Jaipur'],\n",
        "    'Year': [2016, 2017, 2018, 2019],\n",
        "    'Kilometers_Driven': [35000, 42000, 49000, 33000],\n",
        "    'Fuel_Type': ['Diesel', 'Petrol', 'Diesel', 'Petrol'],\n",
        "    'Transmission': ['Manual', 'Automatic', 'Manual', 'Automatic'],\n",
        "    'Owner_Type': ['First', 'Second', 'First', 'First'],\n",
        "    'Mileage_kmpl': [18.5, 16.0, 19.0, 17.5],\n",
        "    'Engine_CC': [1500.0, 1300.0, 1800.0, 1200.0],\n",
        "    'Power_bhp': [110.0, 90.0, 150.0, 85.0],\n",
        "    'Seats': [5, 5, 7, 5]\n",
        "}\n",
        "\n",
        "new_df = pd.DataFrame(new_data)\n",
        "new_df['Price_Predicted'] = model.predict(new_df)\n",
        "new_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "mkZBVYin51tU",
        "outputId": "0dc63937-0fa0-4de8-d5f4-907458066fb2"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Location  Year  Kilometers_Driven Fuel_Type Transmission Owner_Type  \\\n",
              "0        Pune  2016              35000    Diesel       Manual      First   \n",
              "1     Chennai  2017              42000    Petrol    Automatic     Second   \n",
              "2  Coimbatore  2018              49000    Diesel       Manual      First   \n",
              "3      Jaipur  2019              33000    Petrol    Automatic      First   \n",
              "\n",
              "   Mileage_kmpl  Engine_CC  Power_bhp  Seats  Price_Predicted  \n",
              "0          18.5     1500.0      110.0      5         7.979001  \n",
              "1          16.0     1300.0       90.0      5         5.793520  \n",
              "2          19.0     1800.0      150.0      7        14.694482  \n",
              "3          17.5     1200.0       85.0      5         6.937122  "
            ],
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              "summary": "{\n  \"name\": \"new_df\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"Location\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Chennai\",\n          \"Jaipur\",\n          \"Pune\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 2016,\n        \"max\": 2019,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2017,\n          2019,\n          2016\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Kilometers_Driven\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7274,\n        \"min\": 33000,\n        \"max\": 49000,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          42000,\n          33000,\n          35000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fuel_Type\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Petrol\",\n          \"Diesel\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Transmission\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Automatic\",\n          \"Manual\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Owner_Type\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Second\",\n          \"First\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Mileage_kmpl\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.3228756555322954,\n        \"min\": 16.0,\n        \"max\": 19.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          16.0,\n          17.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Engine_CC\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 264.5751311064591,\n        \"min\": 1200.0,\n        \"max\": 1800.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1300.0,\n          1200.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Power_bhp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 29.545163168726393,\n        \"min\": 85.0,\n        \"max\": 150.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          90.0,\n          85.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Seats\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 5,\n        \"max\": 7,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          7,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price_Predicted\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.9965722377656725,\n        \"min\": 5.793520381886442,\n        \"max\": 14.69448153698345,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          5.793520381886442,\n          6.937121992928173\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    }
  ]
}