ML_Mobile_Price_Classification.ipynb

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    "language_info": {
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      "cell_type": "markdown",
      "source": [
        "# Import Library"
      ],
      "metadata": {
        "id": "R5z-99GnULYK"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8cHwePs5W2xM"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import preprocessing\n",
        "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV\n",
        "from sklearn.metrics import precision_score, recall_score, confusion_matrix, classification_report, accuracy_score, f1_score"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **MENAMPILKAN DATA**"
      ],
      "metadata": {
        "id": "SnNoc34HUQFg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "csv_path = \"/content/train.csv\"\n",
        "df = pd.read_csv(csv_path)\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "id": "bNeUc7iGX29j",
        "outputId": "9933bdfb-760b-4ebc-f9cb-14e5a74e298a"
      },
      "execution_count": null,
      "outputs": [
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              "   battery_power  blue  clock_speed  dual_sim  fc  four_g  int_memory  m_dep  \\\n",
              "0            842     0          2.2         0   1       0           7    0.6   \n",
              "1           1021     1          0.5         1   0       1          53    0.7   \n",
              "2            563     1          0.5         1   2       1          41    0.9   \n",
              "3            615     1          2.5         0   0       0          10    0.8   \n",
              "4           1821     1          1.2         0  13       1          44    0.6   \n",
              "\n",
              "   mobile_wt  n_cores  ...  px_height  px_width   ram  sc_h  sc_w  talk_time  \\\n",
              "0        188        2  ...         20       756  2549     9     7         19   \n",
              "1        136        3  ...        905      1988  2631    17     3          7   \n",
              "2        145        5  ...       1263      1716  2603    11     2          9   \n",
              "3        131        6  ...       1216      1786  2769    16     8         11   \n",
              "4        141        2  ...       1208      1212  1411     8     2         15   \n",
              "\n",
              "   three_g  touch_screen  wifi  price_range  \n",
              "0        0             0     1            1  \n",
              "1        1             1     0            2  \n",
              "2        1             1     0            2  \n",
              "3        1             0     0            2  \n",
              "4        1             1     0            1  \n",
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              "(2000, 21)"
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          "execution_count": 5
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 2000 entries, 0 to 1999\n",
            "Data columns (total 21 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   battery_power  2000 non-null   int64  \n",
            " 1   blue           2000 non-null   int64  \n",
            " 2   clock_speed    2000 non-null   float64\n",
            " 3   dual_sim       2000 non-null   int64  \n",
            " 4   fc             2000 non-null   int64  \n",
            " 5   four_g         2000 non-null   int64  \n",
            " 6   int_memory     2000 non-null   int64  \n",
            " 7   m_dep          2000 non-null   float64\n",
            " 8   mobile_wt      2000 non-null   int64  \n",
            " 9   n_cores        2000 non-null   int64  \n",
            " 10  pc             2000 non-null   int64  \n",
            " 11  px_height      2000 non-null   int64  \n",
            " 12  px_width       2000 non-null   int64  \n",
            " 13  ram            2000 non-null   int64  \n",
            " 14  sc_h           2000 non-null   int64  \n",
            " 15  sc_w           2000 non-null   int64  \n",
            " 16  talk_time      2000 non-null   int64  \n",
            " 17  three_g        2000 non-null   int64  \n",
            " 18  touch_screen   2000 non-null   int64  \n",
            " 19  wifi           2000 non-null   int64  \n",
            " 20  price_range    2000 non-null   int64  \n",
            "dtypes: float64(2), int64(19)\n",
            "memory usage: 328.2 KB\n"
          ]
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      "source": [
        "important_cols = [\"ram\", \"blue\", \"dual_sim\",\"four_g\",\"three_g\",\"touch_screen\",\"wifi\",\"n_cores\", \"price_range\"]\n",
        "\n",
        "df = df[important_cols]"
      ],
      "metadata": {
        "id": "2UI09oidIw5r"
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      "execution_count": null,
      "outputs": []
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              "       ram  blue  dual_sim  four_g  three_g  touch_screen  wifi  n_cores  \\\n",
              "0     2549     0         0       0        0             0     1        2   \n",
              "1     2631     1         1       1        1             1     0        3   \n",
              "2     2603     1         1       1        1             1     0        5   \n",
              "3     2769     1         0       0        1             0     0        6   \n",
              "4     1411     1         0       1        1             1     0        2   \n",
              "...    ...   ...       ...     ...      ...           ...   ...      ...   \n",
              "1995   668     1         1       1        1             1     0        6   \n",
              "1996  2032     1         1       0        1             1     1        4   \n",
              "1997  3057     0         1       1        1             1     0        8   \n",
              "1998   869     0         0       1        1             1     1        5   \n",
              "1999  3919     1         1       1        1             1     1        6   \n",
              "\n",
              "      price_range  \n",
              "0               1  \n",
              "1               2  \n",
              "2               2  \n",
              "3               2  \n",
              "4               1  \n",
              "...           ...  \n",
              "1995            0  \n",
              "1996            2  \n",
              "1997            3  \n",
              "1998            0  \n",
              "1999            3  \n",
              "\n",
              "[2000 rows x 9 columns]"
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1411</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1995</th>\n",
              "      <td>668</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1996</th>\n",
              "      <td>2032</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1997</th>\n",
              "      <td>3057</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1998</th>\n",
              "      <td>869</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1999</th>\n",
              "      <td>3919</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2000 rows × 9 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8ca1f8a8-1c40-4de7-9240-e519b5a40402')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8ca1f8a8-1c40-4de7-9240-e519b5a40402 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-8ca1f8a8-1c40-4de7-9240-e519b5a40402');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **TAHAP PRAPROSES**"
      ],
      "metadata": {
        "id": "rei5E4WbUbxS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Memeriksa Missing Value"
      ],
      "metadata": {
        "id": "dy5qpv80h7tJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe(include=\"all\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "W1WkiDvvZ6TL",
        "outputId": "fd1c125f-bd7a-4b22-d1ed-ce8b8346e4db"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               ram       blue     dual_sim       four_g      three_g  \\\n",
              "count  2000.000000  2000.0000  2000.000000  2000.000000  2000.000000   \n",
              "mean   2124.213000     0.4950     0.509500     0.521500     0.761500   \n",
              "std    1084.732044     0.5001     0.500035     0.499662     0.426273   \n",
              "min     256.000000     0.0000     0.000000     0.000000     0.000000   \n",
              "25%    1207.500000     0.0000     0.000000     0.000000     1.000000   \n",
              "50%    2146.500000     0.0000     1.000000     1.000000     1.000000   \n",
              "75%    3064.500000     1.0000     1.000000     1.000000     1.000000   \n",
              "max    3998.000000     1.0000     1.000000     1.000000     1.000000   \n",
              "\n",
              "       touch_screen         wifi      n_cores  price_range  \n",
              "count   2000.000000  2000.000000  2000.000000  2000.000000  \n",
              "mean       0.503000     0.507000     4.520500     1.500000  \n",
              "std        0.500116     0.500076     2.287837     1.118314  \n",
              "min        0.000000     0.000000     1.000000     0.000000  \n",
              "25%        0.000000     0.000000     3.000000     0.750000  \n",
              "50%        1.000000     1.000000     4.000000     1.500000  \n",
              "75%        1.000000     1.000000     7.000000     2.250000  \n",
              "max        1.000000     1.000000     8.000000     3.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-769acf2a-bfca-4ff0-8da0-a5121e97411d\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ram</th>\n",
              "      <th>blue</th>\n",
              "      <th>dual_sim</th>\n",
              "      <th>four_g</th>\n",
              "      <th>three_g</th>\n",
              "      <th>touch_screen</th>\n",
              "      <th>wifi</th>\n",
              "      <th>n_cores</th>\n",
              "      <th>price_range</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2000.000000</td>\n",
              "      <td>2000.0000</td>\n",
              "      <td>2000.000000</td>\n",
              "      <td>2000.000000</td>\n",
              "      <td>2000.000000</td>\n",
              "      <td>2000.000000</td>\n",
              "      <td>2000.000000</td>\n",
              "      <td>2000.000000</td>\n",
              "      <td>2000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2124.213000</td>\n",
              "      <td>0.4950</td>\n",
              "      <td>0.509500</td>\n",
              "      <td>0.521500</td>\n",
              "      <td>0.761500</td>\n",
              "      <td>0.503000</td>\n",
              "      <td>0.507000</td>\n",
              "      <td>4.520500</td>\n",
              "      <td>1.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1084.732044</td>\n",
              "      <td>0.5001</td>\n",
              "      <td>0.500035</td>\n",
              "      <td>0.499662</td>\n",
              "      <td>0.426273</td>\n",
              "      <td>0.500116</td>\n",
              "      <td>0.500076</td>\n",
              "      <td>2.287837</td>\n",
              "      <td>1.118314</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>256.000000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1207.500000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2146.500000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3064.500000</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>2.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>3998.000000</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-769acf2a-bfca-4ff0-8da0-a5121e97411d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-769acf2a-bfca-4ff0-8da0-a5121e97411d 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-769acf2a-bfca-4ff0-8da0-a5121e97411d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WzRtF5RQaKX7",
        "outputId": "a467927e-eccf-4d98-cccb-6e830adf3109"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ram             0\n",
              "blue            0\n",
              "dual_sim        0\n",
              "four_g          0\n",
              "three_g         0\n",
              "touch_screen    0\n",
              "wifi            0\n",
              "n_cores         0\n",
              "price_range     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Visualisasi"
      ],
      "metadata": {
        "id": "IZVYJo5HUhNB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "plt.figure(figsize=(10,8))\n",
        "sns.heatmap(df.corr(), cmap=\"RdBu\",annot=True, fmt=\".1f\")\n",
        "plt.title(\"Correlations Between Variables\", size=15)\n",
        "plt.show();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 557
        },
        "id": "yB2a5gXCIYjk",
        "outputId": "6297cbf4-58ea-4fda-ec47-e7b85a83e51c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.pairplot(df[[\"ram\", \"price_range\"]]);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "id": "shcQBQ8lKX6a",
        "outputId": "4d30bd1f-2eb4-4cb4-d8a2-2e4f5f4c0d10"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*"
      ],
      "metadata": {
        "id": "LfeQl728UG4u"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **MEMBAGI DATA Train dan Test**"
      ],
      "metadata": {
        "id": "ylffV2F5UmgX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Memisahkan Data fitur X dan Data prediksi Y\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "7YXi0s43mQbL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = df.iloc[:,0:7].values\n",
        "y = df.iloc[:, -1].values"
      ],
      "metadata": {
        "id": "ihzrkPEJmSJZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print (x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YsrysIgqn3-h",
        "outputId": "f16228c8-88af-4df6-9b75-550d0c115d69"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[2549    0    0 ...    0    0    1]\n",
            " [2631    1    1 ...    1    1    0]\n",
            " [2603    1    1 ...    1    1    0]\n",
            " ...\n",
            " [3057    0    1 ...    1    1    0]\n",
            " [ 869    0    0 ...    1    1    1]\n",
            " [3919    1    1 ...    1    1    1]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print (y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vKIAhUAdn50U",
        "outputId": "dca82670-effb-4eb8-f778-e09740865b4e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1 2 2 ... 3 0 3]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Membagi data train dan test menggunakan sklearn\n",
        "from sklearn.model_selection import train_test_split\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.25, random_state = 0)"
      ],
      "metadata": {
        "id": "wb7n-Zwdn-V5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#melihat jumlah data train\n",
        "x_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0iCmaRQnoNfa",
        "outputId": "9bad4d59-8767-433f-f8c4-061eeb9a4593"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1500, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#melihat jumlah data test\n",
        "x_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iN66F9GkoP4S",
        "outputId": "4929754c-2ef1-456c-a9f2-df3f129592b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(500, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*"
      ],
      "metadata": {
        "id": "_RAvAjbeygwi"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# STANDARISASI DATA"
      ],
      "metadata": {
        "id": "H1wg0WRlUtHO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "label = LabelEncoder()\n",
        "y_train =  label.fit_transform(y_train)\n",
        "y_test = label.transform(y_test)"
      ],
      "metadata": {
        "id": "ylQKfgvhLdDY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sc = StandardScaler()\n",
        "x_train = sc.fit_transform (x_train)\n",
        "x_test = sc.transform(x_test)"
      ],
      "metadata": {
        "id": "oA1tju0xoS7Z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **MEMBANGUN MODEL DENGAN ALGORTIMA**\n",
        "\n",
        "# **& MENAMPILKAN NILAI AKURASI, PRECISION, RECALL, DAN F1 SCORE**"
      ],
      "metadata": {
        "id": "GiNTEOHAU2yJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Menggunakan Regresi**"
      ],
      "metadata": {
        "id": "ScBflTzIpMkh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "clf_lm = LogisticRegression()\n",
        "clf_lm.fit(x_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dbxyuN1IpL3x",
        "outputId": "a8c2d342-e058-4706-ff24-a777e5f3f4f9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(clf_lm.coef_)\n",
        "print(clf_lm.intercept_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UJmXcGQIs2Ja",
        "outputId": "56dde3e9-a010-4e74-f8b4-139384e088e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[-5.87980761e+00 -2.33551975e-02  7.50173890e-02  4.40303464e-02\n",
            "  -1.14106836e-01  7.62248133e-04 -1.45591893e-02]\n",
            " [-1.67213389e+00 -2.22803636e-02  1.00412462e-01  3.72550393e-02\n",
            "  -6.84087102e-03  1.78766764e-02  2.13676690e-02]\n",
            " [ 1.88530844e+00 -1.86289098e-02 -2.77734697e-02 -1.36150844e-01\n",
            "   7.53010571e-02 -5.93769563e-02  8.88174542e-03]\n",
            " [ 5.66663306e+00  6.42644709e-02 -1.47656382e-01  5.48654584e-02\n",
            "   4.56466503e-02  4.07380318e-02 -1.56902251e-02]]\n",
            "[-1.77887952  1.77013505  1.64565053 -1.63690606]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = clf_lm.predict(x_test)"
      ],
      "metadata": {
        "id": "EVWNEhk8q3mi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Accuracy Score: {:.4f}'.format(accuracy_score(y_pred, y_test)))\n",
        "print(\"\\n\",classification_report(y_pred, y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D9DwQip9q-gS",
        "outputId": "189ff8d4-4a7e-4f36-e816-2a1d1bb73391"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy Score: 0.7620\n",
            "\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.84      0.86      0.85       121\n",
            "           1       0.66      0.63      0.65       115\n",
            "           2       0.63      0.69      0.66       115\n",
            "           3       0.89      0.84      0.86       149\n",
            "\n",
            "    accuracy                           0.76       500\n",
            "   macro avg       0.76      0.76      0.75       500\n",
            "weighted avg       0.77      0.76      0.76       500\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Menggunakan Algortima Decision Tree**"
      ],
      "metadata": {
        "id": "SVrCfDsNPRDl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "clf_dc = DecisionTreeClassifier() \n",
        "clf_dc.fit(x_train, y_train) \n",
        "\n",
        "y_pred = clf_dc.predict(x_test)"
      ],
      "metadata": {
        "id": "P6re1wrZPVg0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Accuracy Score: {:.4f}'.format(accuracy_score(y_test, y_pred)))\n",
        "print(\"\\n\",classification_report(y_pred, y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "npQgjF-kSnR7",
        "outputId": "1cec3a11-ebef-442a-8b22-103d19f4f03a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy Score: 0.6580\n",
            "\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.79      0.81      0.80       121\n",
            "           1       0.53      0.52      0.52       111\n",
            "           2       0.51      0.50      0.51       128\n",
            "           3       0.77      0.78      0.78       140\n",
            "\n",
            "    accuracy                           0.66       500\n",
            "   macro avg       0.65      0.65      0.65       500\n",
            "weighted avg       0.66      0.66      0.66       500\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Menggunakan Algoritma SVM**"
      ],
      "metadata": {
        "id": "u1S8UWgSos3C"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVC \n",
        "clf_svm = SVC() \n",
        "clf_svm.fit(x_train, y_train) \n",
        "\n",
        "y_pred = clf_svm.predict(x_test)"
      ],
      "metadata": {
        "id": "VU_GCR0UoiYz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Accuracy Score: {:.4f}'.format(accuracy_score(y_test, y_pred)))\n",
        "print(\"\\n\",classification_report(y_pred, y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eDTr5cVtpEOa",
        "outputId": "5545cde6-33fb-4c3f-bcfb-389dcab34d50"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy Score: 0.7580\n",
            "\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.85      0.86      0.85       122\n",
            "           1       0.67      0.62      0.65       119\n",
            "           2       0.61      0.68      0.64       112\n",
            "           3       0.88      0.84      0.86       147\n",
            "\n",
            "    accuracy                           0.76       500\n",
            "   macro avg       0.75      0.75      0.75       500\n",
            "weighted avg       0.76      0.76      0.76       500\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Menggunakan Algortima KNN**"
      ],
      "metadata": {
        "id": "KqMdgHryozaq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "*KNN dengan Metric Euclidean*"
      ],
      "metadata": {
        "id": "tBQePuXAy8nQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "clf_knn = KNeighborsClassifier(n_neighbors = 5, metric = 'euclidean', p=2)\n",
        "clf_knn.fit(x_train, y_train)\n",
        "\n",
        "y_pred = clf_knn.predict(x_test)"
      ],
      "metadata": {
        "id": "32EWAouZodcC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Accuracy Score: {:.4f}'.format(accuracy_score(y_test, y_pred)))\n",
        "print(\"\\n\",classification_report(y_pred, y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N7ztd2_xoyZR",
        "outputId": "a58aed3d-9e13-4a0a-cd75-b81238ed9468"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy Score: 0.7020\n",
            "\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.80      0.81      0.80       122\n",
            "           1       0.59      0.52      0.56       124\n",
            "           2       0.58      0.60      0.59       122\n",
            "           3       0.81      0.86      0.84       132\n",
            "\n",
            "    accuracy                           0.70       500\n",
            "   macro avg       0.70      0.70      0.70       500\n",
            "weighted avg       0.70      0.70      0.70       500\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*"
      ],
      "metadata": {
        "id": "X7M9kJrZT8_9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **KESIMPULAN**"
      ],
      "metadata": {
        "id": "Mm-VPHH8VVvG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Perbandingan Model**"
      ],
      "metadata": {
        "id": "jd5EqjQBT3ca"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "models = [\n",
        "    clf_lm,\n",
        "    clf_dc,\n",
        "    clf_svm,\n",
        "    clf_knn\n",
        "]\n",
        "\n",
        "results = []\n",
        "results = pd.DataFrame(columns=[\"Models\",\"Accuracy\"])\n",
        "\n",
        "for model in models:\n",
        "    Name = model.__class__.__name__\n",
        "    y_pred = model.predict(x_test)\n",
        "    Accuracy = accuracy_score(y_test,y_pred)\n",
        "    result = pd.DataFrame([[Name, Accuracy*100]], columns = [\"Models\",\"Accuracy\"])\n",
        "    results = results.append(result)"
      ],
      "metadata": {
        "id": "t6c4LAY7OP1l"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(x='Accuracy', y='Models', data=results, color = \"b\")\n",
        "plt.xlabel('Accuracy %')\n",
        "plt.title('Model Accuracy Score ');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "XqlEcVEoOUq9",
        "outputId": "5716f2cd-976b-4342-92e1-d90cc982d833"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}