185150400111051_NAUFAL_ZULFIKAR_PRIAMBODO.ipynb

{
  "nbformat": 4,
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  "metadata": {
    "colab": {
      "name": "185150400111051_NAUFAL ZULFIKAR PRIAMBODO.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/nanangyudi/2020pds_b/blob/main/185150400111051_NAUFAL_ZULFIKAR_PRIAMBODO.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3pGwHb3T8SBS"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EvhfB8Vc_i6-"
      },
      "source": [
        "# Tujuan Bisnis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n0o-jB5FA9LS"
      },
      "source": [
        "Tujuan dari eksplorasi data ini adalah untuk memprediksi KUALITAS dari Wine sesuai dengan data yang ada"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wVsy_aj6_o5n"
      },
      "source": [
        "# Analisa Pendekatan"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P0Q9TncaBA4k"
      },
      "source": [
        "Analisa dari data yang telah terkumpul menggunakan Linear Regression"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C6fpMfig_uR1"
      },
      "source": [
        "# Pengumpulan Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AbZSTSyUArW2"
      },
      "source": [
        "Dataset ini kami dapatkan dari situs https://www.kaggle.com/\r\n",
        "\r\n",
        "Dataset ini berisi:\r\n",
        "1. fixed acidity : asan tetap\r\n",
        "2. volatile acidity : tingkat volatile acidity\r\n",
        "3. citric acid : tingkat asam sitrat\r\n",
        "4. residual sugar : tingkat residu gula\r\n",
        "5. chlorides : tingkat klorida\r\n",
        "6. free sulfur dioxide : tingkat kebasan sulfur dioksida\r\n",
        "7. total sulfur dioxide : total kadar sulfur dioksida\r\n",
        "8. density : Masa jenis \r\n",
        "9. pH : tingkat pH\r\n",
        "10. sulphates : tingkat sulfat\r\n",
        "11. alcohol : tingkat alkohol\r\n",
        "12. quality : kualitas \r\n",
        "\r\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oyvtodZC_ycn"
      },
      "source": [
        "# Pemahaman Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KGMIkDAQa-Nm"
      },
      "source": [
        "# Import di library pyhton\r\n",
        "import pandas as pd\r\n",
        "import seaborn as sns\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "from sklearn.ensemble import RandomForestClassifier\r\n",
        "from sklearn.svm import SVC\r\n",
        "from sklearn.linear_model import SGDClassifier\r\n",
        "from sklearn.metrics import confusion_matrix, classification_report\r\n",
        "from sklearn.preprocessing import StandardScaler, LabelEncoder\r\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score\r\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ujm1inwcbNv7"
      },
      "source": [
        "url = 'https://raw.githubusercontent.com/naufalzulfikar/projek/main/winequality-red.csv'\r\n",
        "df = pd.read_csv(url)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "2QNsU1HabdaF",
        "outputId": "72af9067-ed44-443c-c11a-7506e91dfdfa"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>fixed acidity</th>\n",
              "      <th>volatile acidity</th>\n",
              "      <th>citric acid</th>\n",
              "      <th>residual sugar</th>\n",
              "      <th>chlorides</th>\n",
              "      <th>free sulfur dioxide</th>\n",
              "      <th>total sulfur dioxide</th>\n",
              "      <th>density</th>\n",
              "      <th>pH</th>\n",
              "      <th>sulphates</th>\n",
              "      <th>alcohol</th>\n",
              "      <th>quality</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7.4</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.076</td>\n",
              "      <td>11.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.9978</td>\n",
              "      <td>3.51</td>\n",
              "      <td>0.56</td>\n",
              "      <td>9.4</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7.8</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2.6</td>\n",
              "      <td>0.098</td>\n",
              "      <td>25.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>0.9968</td>\n",
              "      <td>3.20</td>\n",
              "      <td>0.68</td>\n",
              "      <td>9.8</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.8</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.04</td>\n",
              "      <td>2.3</td>\n",
              "      <td>0.092</td>\n",
              "      <td>15.0</td>\n",
              "      <td>54.0</td>\n",
              "      <td>0.9970</td>\n",
              "      <td>3.26</td>\n",
              "      <td>0.65</td>\n",
              "      <td>9.8</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11.2</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.56</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.075</td>\n",
              "      <td>17.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>0.9980</td>\n",
              "      <td>3.16</td>\n",
              "      <td>0.58</td>\n",
              "      <td>9.8</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7.4</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.076</td>\n",
              "      <td>11.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.9978</td>\n",
              "      <td>3.51</td>\n",
              "      <td>0.56</td>\n",
              "      <td>9.4</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   fixed acidity  volatile acidity  citric acid  ...  sulphates  alcohol  quality\n",
              "0            7.4              0.70         0.00  ...       0.56      9.4        5\n",
              "1            7.8              0.88         0.00  ...       0.68      9.8        5\n",
              "2            7.8              0.76         0.04  ...       0.65      9.8        5\n",
              "3           11.2              0.28         0.56  ...       0.58      9.8        6\n",
              "4            7.4              0.70         0.00  ...       0.56      9.4        5\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8ahOshPfbstv",
        "outputId": "c510f29c-fbc3-46a7-8b35-98ca93211c2e"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1599 entries, 0 to 1598\n",
            "Data columns (total 12 columns):\n",
            " #   Column                Non-Null Count  Dtype  \n",
            "---  ------                --------------  -----  \n",
            " 0   fixed acidity         1599 non-null   float64\n",
            " 1   volatile acidity      1599 non-null   float64\n",
            " 2   citric acid           1599 non-null   float64\n",
            " 3   residual sugar        1599 non-null   float64\n",
            " 4   chlorides             1599 non-null   float64\n",
            " 5   free sulfur dioxide   1599 non-null   float64\n",
            " 6   total sulfur dioxide  1599 non-null   float64\n",
            " 7   density               1599 non-null   float64\n",
            " 8   pH                    1599 non-null   float64\n",
            " 9   sulphates             1599 non-null   float64\n",
            " 10  alcohol               1599 non-null   float64\n",
            " 11  quality               1599 non-null   int64  \n",
            "dtypes: float64(11), int64(1)\n",
            "memory usage: 150.0 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UkV25Xlx_4bn"
      },
      "source": [
        "# Eksplorasi Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        },
        "id": "-9pnqkNPb1kH",
        "outputId": "9cb2a03c-ce08-4ee4-844a-745abaa8a93f"
      },
      "source": [
        "# Fixed Acidity tidak memberikan spesifikasi apapun untuk mengklasifikasikan kualitas\r\n",
        "fig = plt.figure(figsize = (10,6))\r\n",
        "sns.barplot(x = 'quality', y = 'fixed acidity', data = df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4767a3b080>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        },
        "id": "odB0eAJncAF8",
        "outputId": "0db1f2fe-6b69-4fe2-a173-24f56fc2a4eb"
      },
      "source": [
        "# Di sini kita melihat bahwa tren yang  menurun dalam volatile acidity karena kita meningkatkan kualitasnya\r\n",
        "fig = plt.figure(figsize = (10,6))\r\n",
        "sns.barplot(x = 'quality', y = 'volatile acidity', data = df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4742caecf8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        },
        "id": "UCGeKWO6csQA",
        "outputId": "163fa781-6ab8-4914-d563-8239c9f47092"
      },
      "source": [
        "# Komposisi asam sitrat semakin tinggi seiring semakin tinggi kualitas anggur\r\n",
        "fig = plt.figure(figsize = (10,6))\r\n",
        "sns.barplot(x = 'quality', y = 'citric acid', data = df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f47425a4630>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        },
        "id": "uNGOaDqac1DE",
        "outputId": "4d80c8cc-d149-424c-bfcb-120320279730"
      },
      "source": [
        "fig = plt.figure(figsize = (10,6))\r\n",
        "sns.barplot(x = 'quality', y = 'residual sugar', data = df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f474249f3c8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        },
        "id": "JJHhezD2c6mb",
        "outputId": "197b6bdf-e3d6-43df-cade-2f1f14bfb369"
      },
      "source": [
        "# Komposisi klorida juga turun karena kualitas anggur semakin tinggi\r\n",
        "fig = plt.figure(figsize = (10,6))\r\n",
        "sns.barplot(x = 'quality', y = 'chlorides', data = df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f47424f3ac8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        },
        "id": "Kso11gzGdHAY",
        "outputId": "2508f84c-41e1-4e2a-cef2-4b411d4b5491"
      },
      "source": [
        "fig = plt.figure(figsize = (10,6))\r\n",
        "sns.barplot(x = 'quality', y = 'free sulfur dioxide', data = df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f47423a3fd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        },
        "id": "lLgVOmHKdJ3S",
        "outputId": "d1152085-b601-4c09-a1c6-01bc9addaf89"
      },
      "source": [
        "fig = plt.figure(figsize = (10,6))\r\n",
        "sns.barplot(x = 'quality', y = 'total sulfur dioxide', data = df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4742337748>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        },
        "id": "J0MFsZxudPHZ",
        "outputId": "10a2b8f6-dec3-4ac0-8a41-8959d7243054"
      },
      "source": [
        "# Kadar sulfat semakin tinggi seiring dengan kualitas wine\r\n",
        "fig = plt.figure(figsize = (10,6))\r\n",
        "sns.barplot(x = 'quality', y = 'sulphates', data = df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f474230af60>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        },
        "id": "UbmxBvQYdYbW",
        "outputId": "737b3fc3-55bc-4c00-99e5-4df6fe13c129"
      },
      "source": [
        "# Tingkat alkohol juga meningkat seiring dengan peningkatan kualitas anggur\r\n",
        "fig = plt.figure(figsize = (10,6))\r\n",
        "sns.barplot(x = 'quality', y = 'alcohol', data = df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4742239b70>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WELmjalS_65g"
      },
      "source": [
        "# Pemodelan Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nFyLVr_2dl-b"
      },
      "source": [
        "# Membuat klasifikasi biner untuk variabel respon.\r\n",
        "# Membagi wine sebagai yang baik dan yang buruk dengan memberikan batasan kualitas\r\n",
        "bins = (2, 6.5, 8)\r\n",
        "group_names = ['bad', 'good']\r\n",
        "df['quality'] = pd.cut(df['quality'], bins = bins, labels = group_names)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rzjNExGieClP"
      },
      "source": [
        "# Menetapkan label ke variabel kualitas\r\n",
        "label_quality = LabelEncoder()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DnlMosJeeM3N"
      },
      "source": [
        "df['quality'] = label_quality.fit_transform(df['quality'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CZY7m8VDeXrt",
        "outputId": "c8027ba6-a346-4ae5-e5a7-017dc1c1cedd"
      },
      "source": [
        "df['quality'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    1382\n",
              "1     217\n",
              "Name: quality, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "Ryd_JPv1eegv",
        "outputId": "384d5f44-5fca-4436-9bca-ce28f0be0eb5"
      },
      "source": [
        "sns.countplot(df['quality'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4742caea90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KIx3jowTekut"
      },
      "source": [
        "# Pisahkan dataset sebagai variabel respon dan variabel fitur\r\n",
        "X = df.drop('quality', axis = 1)\r\n",
        "y = df['quality']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E6xtyxHxetMJ"
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S0F9gQ5UexwO"
      },
      "source": [
        "sc = StandardScaler()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EAAYkNl2e1dk"
      },
      "source": [
        "X_train = sc.fit_transform(X_train)\r\n",
        "X_test = sc.fit_transform(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QqepV2C-e3H2"
      },
      "source": [
        "rfc = RandomForestClassifier(n_estimators=200)\r\n",
        "rfc.fit(X_train, y_train)\r\n",
        "pred_rfc = rfc.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aZBfy3uCe7ZN",
        "outputId": "7adb4412-decc-4855-f677-551ec475ee5f"
      },
      "source": [
        "print(classification_report(y_test, pred_rfc))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.90      0.97      0.93       273\n",
            "           1       0.67      0.38      0.49        47\n",
            "\n",
            "    accuracy                           0.88       320\n",
            "   macro avg       0.78      0.68      0.71       320\n",
            "weighted avg       0.87      0.88      0.87       320\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OBdS7gVBfBFk",
        "outputId": "d5d7aa2e-802a-43dd-989d-60e5f6454e94"
      },
      "source": [
        "print(confusion_matrix(y_test, pred_rfc))\r\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[264   9]\n",
            " [ 29  18]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_9W2Md1VfJeG"
      },
      "source": [
        "sgd = SGDClassifier(penalty=None)\r\n",
        "sgd.fit(X_train, y_train)\r\n",
        "pred_sgd = sgd.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5ehLzqfVfN21",
        "outputId": "90880e6e-2b70-4ba3-c90b-a1ab33419f27"
      },
      "source": [
        "print(classification_report(y_test, pred_sgd))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.88      0.91      0.89       273\n",
            "           1       0.33      0.26      0.29        47\n",
            "\n",
            "    accuracy                           0.82       320\n",
            "   macro avg       0.61      0.58      0.59       320\n",
            "weighted avg       0.80      0.82      0.81       320\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NBkkArhEfQN2",
        "outputId": "87f08466-976b-4a5b-d3ff-dd4ba7c38c01"
      },
      "source": [
        "print(confusion_matrix(y_test, pred_sgd))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[249  24]\n",
            " [ 35  12]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VZTzINdMfSiT"
      },
      "source": [
        "svc = SVC()\r\n",
        "svc.fit(X_train, y_train)\r\n",
        "pred_svc = svc.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sS1WX5C0fUb1",
        "outputId": "8f9a2940-6b09-4784-97fb-4f4ff93c0d76"
      },
      "source": [
        "print(classification_report(y_test, pred_svc))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.88      0.98      0.93       273\n",
            "           1       0.71      0.26      0.37        47\n",
            "\n",
            "    accuracy                           0.88       320\n",
            "   macro avg       0.80      0.62      0.65       320\n",
            "weighted avg       0.86      0.88      0.85       320\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DywJWiRZfmBs"
      },
      "source": [
        "# Evaluasi Data\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F04FS6KYftLV"
      },
      "source": [
        "param = {\r\n",
        "    'C': [0.1,0.8,0.9,1,1.1,1.2,1.3,1.4],\r\n",
        "    'kernel':['linear', 'rbf'],\r\n",
        "    'gamma' :[0.1,0.8,0.9,1,1.1,1.2,1.3,1.4]\r\n",
        "}\r\n",
        "grid_svc = GridSearchCV(svc, param_grid=param, scoring='accuracy', cv=10)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2v-IiNV6fz_0",
        "outputId": "7292f6bf-b817-4406-d15f-d356ff0af745"
      },
      "source": [
        "grid_svc.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=10, error_score=nan,\n",
              "             estimator=SVC(C=1.0, break_ties=False, cache_size=200,\n",
              "                           class_weight=None, coef0=0.0,\n",
              "                           decision_function_shape='ovr', degree=3,\n",
              "                           gamma='scale', kernel='rbf', max_iter=-1,\n",
              "                           probability=False, random_state=None, shrinking=True,\n",
              "                           tol=0.001, verbose=False),\n",
              "             iid='deprecated', n_jobs=None,\n",
              "             param_grid={'C': [0.1, 0.8, 0.9, 1, 1.1, 1.2, 1.3, 1.4],\n",
              "                         'gamma': [0.1, 0.8, 0.9, 1, 1.1, 1.2, 1.3, 1.4],\n",
              "                         'kernel': ['linear', 'rbf']},\n",
              "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
              "             scoring='accuracy', verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_nkNIwkhgGO6",
        "outputId": "cf2ea39b-528c-4691-e0ea-a4bf59f804ba"
      },
      "source": [
        "grid_svc.best_params_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'C': 1.2, 'gamma': 0.9, 'kernel': 'rbf'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YbtwB747gIeS",
        "outputId": "36e31465-adc6-4ffd-94ed-0d75a6ac6b8c"
      },
      "source": [
        "svc2 = SVC(C = 1.2, gamma =  0.9, kernel= 'rbf')\r\n",
        "svc2.fit(X_train, y_train)\r\n",
        "pred_svc2 = svc2.predict(X_test)\r\n",
        "print(classification_report(y_test, pred_svc2))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.90      0.99      0.94       273\n",
            "           1       0.89      0.34      0.49        47\n",
            "\n",
            "    accuracy                           0.90       320\n",
            "   macro avg       0.89      0.67      0.72       320\n",
            "weighted avg       0.90      0.90      0.88       320\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "01ahQYsvgNPu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7fd0588e-5a82-4d2e-8502-24973a27d085"
      },
      "source": [
        "rfc_eval = cross_val_score(estimator = rfc, X = X_train, y = y_train, cv = 10)\r\n",
        "rfc_eval.mean()\r\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9140255905511812"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    }
  ]
}