meeting_7.ipynb

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      "name": "meeting_7.ipynb",
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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        "<a href=\"https://colab.research.google.com/github/Mr94t3z/pembelajaran-mesin/blob/master/meeting_7.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ANN\n",
        "\n",
        "https://www.analyticsvidhya.com/blog/2021/10/implementing-artificial-neural-networkclassification-in-python-from-scratch/"
      ],
      "metadata": {
        "id": "jwsEvj9Z5C92"
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      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "tvMXCeaO4VPW"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import tensorflow as tf"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "url = 'https://raw.githubusercontent.com/Mr94t3z/pembelajaran-mesin/master/datasets/Churn_Modelling.csv'\n",
        "data = pd.read_csv(url, sep=',')\n",
        "\n",
        "data.head()"
      ],
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        "outputId": "20770910-5a30-4e9b-9dd5-2d8e46938c57"
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      "execution_count": 2,
      "outputs": [
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            "text/plain": [
              "   RowNumber  CustomerId   Surname  CreditScore Geography  Gender  Age  \\\n",
              "0          1    15634602  Hargrave          619    France  Female   42   \n",
              "1          2    15647311      Hill          608     Spain  Female   41   \n",
              "2          3    15619304      Onio          502    France  Female   42   \n",
              "3          4    15701354      Boni          699    France  Female   39   \n",
              "4          5    15737888  Mitchell          850     Spain  Female   43   \n",
              "\n",
              "   Tenure    Balance  NumOfProducts  HasCrCard  IsActiveMember  \\\n",
              "0       2       0.00              1          1               1   \n",
              "1       1   83807.86              1          0               1   \n",
              "2       8  159660.80              3          1               0   \n",
              "3       1       0.00              2          0               0   \n",
              "4       2  125510.82              1          1               1   \n",
              "\n",
              "   EstimatedSalary  Exited  \n",
              "0        101348.88       1  \n",
              "1        112542.58       0  \n",
              "2        113931.57       1  \n",
              "3         93826.63       0  \n",
              "4         79084.10       0  "
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              "      <td>Boni</td>\n",
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              "      <td>15737888</td>\n",
              "      <td>Mitchell</td>\n",
              "      <td>850</td>\n",
              "      <td>Spain</td>\n",
              "      <td>Female</td>\n",
              "      <td>43</td>\n",
              "      <td>2</td>\n",
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    },
    {
      "cell_type": "code",
      "source": [
        "#Generating Matrix of Features\n",
        "X = data.iloc[:,3:-1].values\n",
        "\n",
        "#Generating Dependent Variable Vectors\n",
        "Y = data.iloc[:,-1].values"
      ],
      "metadata": {
        "id": "SLJMAvA67C4a"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TJr1WLgR7HtE",
        "outputId": "12f5f539-6797-4126-caac-aa0e9305aa2a"
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              "(10000, 10)"
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          "metadata": {},
          "execution_count": 4
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        "X"
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        "id": "WDQbqVPjFtbM",
        "outputId": "9ec5352f-a3b9-4ff0-a89c-03998544f6df"
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      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[619, 'France', 'Female', ..., 1, 1, 101348.88],\n",
              "       [608, 'Spain', 'Female', ..., 0, 1, 112542.58],\n",
              "       [502, 'France', 'Female', ..., 1, 0, 113931.57],\n",
              "       ...,\n",
              "       [709, 'France', 'Female', ..., 0, 1, 42085.58],\n",
              "       [772, 'Germany', 'Male', ..., 1, 0, 92888.52],\n",
              "       [792, 'France', 'Female', ..., 1, 0, 38190.78]], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Encoding Categorical Variable Gender\n",
        "\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "LE1 = LabelEncoder()\n",
        "\n",
        "X[:,2] = np.array(LE1.fit_transform(X[:,2]))\n",
        "\n",
        "print(LE1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tG1xAfrm8h8_",
        "outputId": "f0fe41d0-4a81-4a4f-8046-bc8e4da3ed8b"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LabelEncoder()\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0rSpJHx68nhH",
        "outputId": "2a28ee41-d86f-4f90-d306-b44c3728174a"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "(10000, 10)"
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          "metadata": {},
          "execution_count": 7
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        "X"
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      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[619, 'France', 0, ..., 1, 1, 101348.88],\n",
              "       [608, 'Spain', 0, ..., 0, 1, 112542.58],\n",
              "       [502, 'France', 0, ..., 1, 0, 113931.57],\n",
              "       ...,\n",
              "       [709, 'France', 0, ..., 0, 1, 42085.58],\n",
              "       [772, 'Germany', 1, ..., 1, 0, 92888.52],\n",
              "       [792, 'France', 0, ..., 1, 0, 38190.78]], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Encoding Categorical variable Geography\n",
        "\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "ct =ColumnTransformer(transformers=[('encoder',OneHotEncoder(),[1])],remainder=\"passthrough\")\n",
        "X = np.array(ct.fit_transform(X))"
      ],
      "metadata": {
        "id": "O1XgmYxt8sYD"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GthyJUPW8zBF",
        "outputId": "d97f238c-019a-4678-8427-a3838bf0ca29"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
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    },
    {
      "cell_type": "code",
      "source": [
        "X"
      ],
      "metadata": {
        "colab": {
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        },
        "id": "uQuyysglGFi4",
        "outputId": "8ee4dc59-3f1d-425d-aef4-a09664d9d211"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.0, 0.0, 0.0, ..., 1, 1, 101348.88],\n",
              "       [0.0, 0.0, 1.0, ..., 0, 1, 112542.58],\n",
              "       [1.0, 0.0, 0.0, ..., 1, 0, 113931.57],\n",
              "       ...,\n",
              "       [1.0, 0.0, 0.0, ..., 0, 1, 42085.58],\n",
              "       [0.0, 1.0, 0.0, ..., 1, 0, 92888.52],\n",
              "       [1.0, 0.0, 0.0, ..., 1, 0, 38190.78]], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Splitting dataset into training and testing dataset\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=0.2,random_state=0)"
      ],
      "metadata": {
        "id": "aLcsVsM_9C6-"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Performing Feature Scaling\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "X_train = sc.fit_transform(X_train)\n",
        "X_test = sc.transform(X_test)"
      ],
      "metadata": {
        "id": "GM9urW_n9Dtr"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Initialising ANN\n",
        "ann = tf.keras.models.Sequential()\n",
        "\n",
        "#Adding First Hidden Layer\n",
        "ann.add(tf.keras.layers.Dense(units=6,activation=\"relu\"))\n",
        "\n",
        "#Adding Second Hidden Layer\n",
        "ann.add(tf.keras.layers.Dense(units=6,activation=\"relu\"))\n",
        "\n",
        "#Adding Output Layer\n",
        "ann.add(tf.keras.layers.Dense(units=1,activation=\"sigmoid\"))\n",
        "\n",
        "#Compiling ANN\n",
        "ann.compile(optimizer=\"adam\",loss=\"binary_crossentropy\",metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "iv9gz2Bo9VV1"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Fitting ANN\n",
        "ann.fit(X_train,Y_train,batch_size=32,epochs = 100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6M0-t9z99iEY",
        "outputId": "8fe38fae-42c5-4d1d-81fc-3491a258baf4"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "250/250 [==============================] - 1s 1ms/step - loss: 0.6646 - accuracy: 0.6260\n",
            "Epoch 2/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.4923 - accuracy: 0.8059\n",
            "Epoch 3/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.4509 - accuracy: 0.8144\n",
            "Epoch 4/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.4343 - accuracy: 0.8177\n",
            "Epoch 5/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.4254 - accuracy: 0.8216\n",
            "Epoch 6/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.4179 - accuracy: 0.8261\n",
            "Epoch 7/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.4086 - accuracy: 0.8322\n",
            "Epoch 8/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3919 - accuracy: 0.8401\n",
            "Epoch 9/100\n",
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            "Epoch 76/100\n",
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            "Epoch 78/100\n",
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            "Epoch 79/100\n",
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            "Epoch 80/100\n",
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            "Epoch 81/100\n",
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            "Epoch 82/100\n",
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            "Epoch 83/100\n",
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            "Epoch 84/100\n",
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            "Epoch 85/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3328 - accuracy: 0.8627\n",
            "Epoch 86/100\n",
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            "Epoch 87/100\n",
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            "Epoch 88/100\n",
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            "Epoch 89/100\n",
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            "Epoch 90/100\n",
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            "Epoch 91/100\n",
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            "Epoch 92/100\n",
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            "Epoch 96/100\n",
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            "Epoch 97/100\n",
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            "Epoch 98/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3320 - accuracy: 0.8635\n",
            "Epoch 99/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3318 - accuracy: 0.8621\n",
            "Epoch 100/100\n",
            "250/250 [==============================] - 0s 1ms/step - loss: 0.3320 - accuracy: 0.8631\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f294dde8ed0>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# SVM"
      ],
      "metadata": {
        "id": "EEfnFtytiHBS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "url = 'https://raw.githubusercontent.com/Mr94t3z/pembelajaran-mesin/master/datasets/diabetes.csv'\n",
        "df = pd.read_csv(url, sep=',')\n",
        "\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "bd-W846LiO_J",
        "outputId": "44070c40-072d-430d-edba-db775ab91a1e"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
              "0            6      148             72             35        0  33.6   \n",
              "1            1       85             66             29        0  26.6   \n",
              "2            8      183             64              0        0  23.3   \n",
              "3            1       89             66             23       94  28.1   \n",
              "4            0      137             40             35      168  43.1   \n",
              "\n",
              "   DiabetesPedigreeFunction  Age  Outcome  \n",
              "0                     0.627   50        1  \n",
              "1                     0.351   31        0  \n",
              "2                     0.672   32        1  \n",
              "3                     0.167   21        0  \n",
              "4                     2.288   33        1  "
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    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hNXlpWYclE04",
        "outputId": "ef0dcd49-ae30-458c-c9bd-cf1e99e81443"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 768 entries, 0 to 767\n",
            "Data columns (total 9 columns):\n",
            " #   Column                    Non-Null Count  Dtype  \n",
            "---  ------                    --------------  -----  \n",
            " 0   Pregnancies               768 non-null    int64  \n",
            " 1   Glucose                   768 non-null    int64  \n",
            " 2   BloodPressure             768 non-null    int64  \n",
            " 3   SkinThickness             768 non-null    int64  \n",
            " 4   Insulin                   768 non-null    int64  \n",
            " 5   BMI                       768 non-null    float64\n",
            " 6   DiabetesPedigreeFunction  768 non-null    float64\n",
            " 7   Age                       768 non-null    int64  \n",
            " 8   Outcome                   768 non-null    int64  \n",
            "dtypes: float64(2), int64(7)\n",
            "memory usage: 54.1 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# set independent variable/attribute/predictor/feature\n",
        "X = df[df.columns[:8]]\n",
        " \n",
        "# set dependent variable/clas/target/label\n",
        "y = df['Outcome']"
      ],
      "metadata": {
        "id": "2xuMqZSXlLpJ"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "NzEuLuMIlhQX",
        "outputId": "551f1967-eb8c-44d7-e443-e00750108f77"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
              "0              6      148             72             35        0  33.6   \n",
              "1              1       85             66             29        0  26.6   \n",
              "2              8      183             64              0        0  23.3   \n",
              "3              1       89             66             23       94  28.1   \n",
              "4              0      137             40             35      168  43.1   \n",
              "..           ...      ...            ...            ...      ...   ...   \n",
              "763           10      101             76             48      180  32.9   \n",
              "764            2      122             70             27        0  36.8   \n",
              "765            5      121             72             23      112  26.2   \n",
              "766            1      126             60              0        0  30.1   \n",
              "767            1       93             70             31        0  30.4   \n",
              "\n",
              "     DiabetesPedigreeFunction  Age  \n",
              "0                       0.627   50  \n",
              "1                       0.351   31  \n",
              "2                       0.672   32  \n",
              "3                       0.167   21  \n",
              "4                       2.288   33  \n",
              "..                        ...  ...  \n",
              "763                     0.171   63  \n",
              "764                     0.340   27  \n",
              "765                     0.245   30  \n",
              "766                     0.349   47  \n",
              "767                     0.315   23  \n",
              "\n",
              "[768 rows x 8 columns]"
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              "      <th>Pregnancies</th>\n",
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              "      <td>0.340</td>\n",
              "      <td>27</td>\n",
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              "      <th>765</th>\n",
              "      <td>5</td>\n",
              "      <td>121</td>\n",
              "      <td>72</td>\n",
              "      <td>23</td>\n",
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              "      <td>26.2</td>\n",
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              "<p>768 rows × 8 columns</p>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jdE_D6R5lmWx",
        "outputId": "02db2067-2b1a-4593-e1e6-7869f35ca6de"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      1\n",
              "1      0\n",
              "2      1\n",
              "3      0\n",
              "4      1\n",
              "      ..\n",
              "763    0\n",
              "764    0\n",
              "765    0\n",
              "766    1\n",
              "767    0\n",
              "Name: Outcome, Length: 768, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        " \n",
        "# standarisasi nilai-nilai dari dataset\n",
        "scaler = StandardScaler()\n",
        "scaler.fit(X)\n",
        "X = scaler.transform(X)"
      ],
      "metadata": {
        "id": "iabHlBTTlnwG"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i1m3-VfMlzd7",
        "outputId": "4f1bbe8a-3e30-49d5-e623-d7e9aa9cd5b5"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.63994726,  0.84832379,  0.14964075, ...,  0.20401277,\n",
              "         0.46849198,  1.4259954 ],\n",
              "       [-0.84488505, -1.12339636, -0.16054575, ..., -0.68442195,\n",
              "        -0.36506078, -0.19067191],\n",
              "       [ 1.23388019,  1.94372388, -0.26394125, ..., -1.10325546,\n",
              "         0.60439732, -0.10558415],\n",
              "       ...,\n",
              "       [ 0.3429808 ,  0.00330087,  0.14964075, ..., -0.73518964,\n",
              "        -0.68519336, -0.27575966],\n",
              "       [-0.84488505,  0.1597866 , -0.47073225, ..., -0.24020459,\n",
              "        -0.37110101,  1.17073215],\n",
              "       [-0.84488505, -0.8730192 ,  0.04624525, ..., -0.20212881,\n",
              "        -0.47378505, -0.87137393]])"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        " \n",
        "# memisahkan data untuk training dan testing\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    X, y, test_size=0.33, random_state=42)"
      ],
      "metadata": {
        "id": "IuuE-7LKl_1k"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVC\n",
        " \n",
        "# membuat objek SVC dan memanggil fungsi fit untuk melatih model\n",
        "clf = SVC(kernel='rbf')\n",
        "clf.fit(X_train, y_train)\n",
        "\n",
        "#Predict the response for test dataset\n",
        "y_pred = clf.predict(X_test)"
      ],
      "metadata": {
        "id": "Yq_mqJESmDDx"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Menampilkan skor akurasi prediksi\n",
        "clf.score(X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QOVW1UaGmOc6",
        "outputId": "57c3c0b1-5dd8-4ff3-af60-9a5e7009d8b3"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7559055118110236"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2qHB3vq8mTgR",
        "outputId": "7e8822b0-b836-4ada-f622-e207f5d39314"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.80      0.85      0.82       168\n",
            "           1       0.66      0.58      0.62        86\n",
            "\n",
            "    accuracy                           0.76       254\n",
            "   macro avg       0.73      0.71      0.72       254\n",
            "weighted avg       0.75      0.76      0.75       254\n",
            "\n"
          ]
        }
      ]
    }
  ]
}