An_Introduction_to_Machine_Learning.ipynb

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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/aryakamwys/Cyber-Academy/blob/main/An_Introduction_to_Machine_Learning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    },
    {
      "cell_type": "markdown",
      "source": [
        "# **An Introduction to Machine Learning**"
      ],
      "metadata": {
        "id": "ic8YVRkBQMkV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Train Test Split**"
      ],
      "metadata": {
        "id": "Pv1joFKeJF3i"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Untuk latihan membagi dataset terdiri dari tahapan-tahapan sebagai berikut:\n",
        "\n",
        "1. Persiapkan dataset ke dalam Notebook.\n",
        "2. Impor library SKLearn.\n",
        "3. Buat variabel untuk menampung data training dan data testing.\n",
        "4. Panggil fungsi train_test_split()."
      ],
      "metadata": {
        "id": "1zaFU7MrRHpO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import sklearn\n",
        "from sklearn import datasets"
      ],
      "metadata": {
        "id": "0O3aBdcTJH1g"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# load iris dataset\n",
        "iris = datasets.load_iris()"
      ],
      "metadata": {
        "id": "xUSpy52DJL6x"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# pisahkan atribut dan label pada iris dataset\n",
        "x=iris.data\n",
        "y=iris.target"
      ],
      "metadata": {
        "id": "0QAdc4FEJN-8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# membagi dataset menjadi training dan testing\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=1)"
      ],
      "metadata": {
        "id": "rd23fw3JJPmr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# menghitung panjang/jumlah data pada x_test\n",
        "len(x_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IreVc5rGJiei",
        "outputId": "f92758bd-362f-4b17-a6ff-aeac059cee66"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "30"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ketika kita print panjang dari x_test, kita bisa melihat bahwa panjang dari atribut test set adalah 30 sampel, sesuai dengan parameter yang kita masukkan pada fungsi train_test_split yaitu 0.2 atau 20% dari 150 sampel."
      ],
      "metadata": {
        "id": "FWZYC8teJptu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Cross Validation Split**"
      ],
      "metadata": {
        "id": "AoHb8MUhJ6Gz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Tahapan yang dilakukan pada codelab ini sebagai berikut:\n",
        "1. Impor library yang dibutuhkan.\n",
        "2. Pisahkan antara atribut dan label pada dataset.\n",
        "3. Buat model decision tree.\n",
        "4. Hitung hasil cross validation dari model dengan fungsi cross_val_score()."
      ],
      "metadata": {
        "id": "y900wgQvRSkv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import sklearn\n",
        "from sklearn import datasets\n",
        "\n",
        "# Load iris dataset\n",
        "iris = datasets.load_iris()"
      ],
      "metadata": {
        "id": "vFlLNFlDJ7jh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# mendefinisikan atribut dan label pada dataset\n",
        "x=iris.data\n",
        "y=iris.target"
      ],
      "metadata": {
        "id": "lIMhaN8OKEWX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import tree\n",
        "\n",
        "# membuat model dengan decision tree classifier\n",
        "clf = tree.DecisionTreeClassifier()"
      ],
      "metadata": {
        "id": "C3lyUfmMKGCE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Setelah dataset dan model siap, kita bisa menggunakan cross validation untuk mengevaluasi performa dari model machine learning. Fungsi cross_val_score() seperti di bawah menerima 4 parameter yaitu, ‘clf’ yang merupakan model machine learning, ‘X’ yang merupakan atribut dari dataset, ‘y’ yang merupakan label dari dataset, dan ‘cv’ yang merupakan jumlah fold yang akan dipakai pada cross validation."
      ],
      "metadata": {
        "id": "4rvGVsp3Px64"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "# mengevaluasi performa model dengan cross_val_score\n",
        "scores = cross_val_score(clf, x, y, cv=5)\n",
        "print(scores)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BEUWnY0lKIBX",
        "outputId": "d8e3b63c-5242-4f40-dbbd-b0e05ef29a95"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.96666667 0.96666667 0.9        1.         1.        ]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Elemen pertama dari larik menunjukkan nilai 0.96666 yang berarti ketika fold pertama dijadikan validation set dan fold lainnya dijadikan train set, hasil dari pengujian tersebut adalah akurasi sebesar 0.96666."
      ],
      "metadata": {
        "id": "4NiScAgBdPjm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **SKLearn Decision Tree**"
      ],
      "metadata": {
        "id": "yiShLcHNQIYN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Tahapan yang ada pada latihan ini antara lain:\n",
        "\n",
        "1. Ubah dataset ke dalam dataframe.\n",
        "2. Hapus kolom 'Id' pada dataframe serta pisahkan antara atribut dan label.\n",
        "3. Bagi dataset menjadi data latih dan data uji.\n",
        "4. Buat dan latih model Decision Tree.\n",
        "5. Lakukan pengujian model dengan menggunakan data uji.\n",
        "6. Lakukan prediksi dengan model yang telah dilatih.\n",
        "7. Visualisasi model Decision Tree yang telah dilatih."
      ],
      "metadata": {
        "id": "djHoMId3RgeN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pertama kita akan mengimpor library yang dibutuhkan dan mempersiapkan dataset. Dataset dapat anda unduh di tautan berikut (https://www.kaggle.com/datasets/uciml/iris). Setelah data diunduh, masukkan berkas Iris.csv ke dalam Google Colab. Lalu jangan lupa konversi dataset menjadi Pandas dataframe."
      ],
      "metadata": {
        "id": "KvM1yKC2RwEr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Membaca file iris.csv\n",
        "iris = pd.read_csv('Iris.csv')"
      ],
      "metadata": {
        "id": "2OwaQJerKLCK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Melihat informasi dataset\n",
        "iris.info()\n",
        "\n",
        "# melihat informasi dataset pada 5 baris pertama\n",
        "iris.head()"
      ],
      "metadata": {
        "id": "2g2szsdadN7B"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# menghilangkan kolom yang tidak penting\n",
        "iris.drop('Id',axis=1,inplace=True)"
      ],
      "metadata": {
        "id": "wOkBPTnpT87n"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Sebelum melatih model kita perlu memisahkan atribut dengan label. Selain itu, kita juga perlu membagi dataset menjadi data latih dan data uji."
      ],
      "metadata": {
        "id": "DY7oJwohVY72"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# memisahkan atribut dan label\n",
        "X = iris[['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm' ]]\n",
        "y = iris['Species']\n",
        "\n",
        "# Membagi dataset menjadi data latih & data uji\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=0.1, random_state=123)"
      ],
      "metadata": {
        "id": "sTOuZbphT_Ii"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Selanjutnya, definisikan model decision tree yang akan kita gunakan. Kemudian,  latih model menggunakan data latih menggunakan fungsi fit()."
      ],
      "metadata": {
        "id": "68jFoq7RV5L6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "# membuat model Decision Tree\n",
        "tree_model = DecisionTreeClassifier()\n",
        "\n",
        "# Melatih model dengan menggunakan data latih\n",
        "tree_model = tree_model.fit(X_train, y_train)"
      ],
      "metadata": {
        "id": "m202-T2EUGuO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluasi Model\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "y_pred = tree_model.predict(X_test)\n",
        "\n",
        "acc_secore = round(accuracy_score(y_pred, y_test), 3)\n",
        "\n",
        "print('Accuracy: ', acc_secore)"
      ],
      "metadata": {
        "id": "qGZIWgRwUJZ_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# prediksi model dengan tree_model.predict([[SepalLength, SepalWidth, PetalLength, PetalWidth]])\n",
        "print(tree_model.predict([[6.2, 3.4, 5.4, 2.3]])[0])"
      ],
      "metadata": {
        "id": "Mspa3g91UKxG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import export_graphviz\n",
        "export_graphviz(\n",
        "    tree_model,\n",
        "    out_file = \"iris_tree.dot\",\n",
        "    feature_names = ['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm'],\n",
        "    class_names = ['Iris-setosa', 'Iris-versicolor', 'Iris-virginica' ],\n",
        "    rounded= True,\n",
        "    filled =True)"
      ],
      "metadata": {
        "id": "1iy6_b0nUM6F"
      },
      "execution_count": null,
      "outputs": []
    },
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)"
      ],
      "metadata": {
        "id": "VQKsQtm7UjsW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **SKLearn Linear Regression**"
      ],
      "metadata": {
        "id": "xwSvFsaFWHgZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Berikut adalah tahapan latihan yang akan Anda lakukan:\n",
        "1. Impor library yang dibutuhkan.\n",
        "2. Buat dataset dummy dengan Numpy Array.\n",
        "3. Buat plot dari model."
      ],
      "metadata": {
        "id": "6R8iN0EFWOaZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "# Data jumlah kamar\n",
        "bedrooms = np.array([1,1,2,2,3,4,4,5,5,5])\n",
        "\n",
        "# Data harga rumah dalam dollar\n",
        "house_price = np.array([15000, 18000, 27000, 34000, 50000, 68000, 65000, 81000,85000, 90000])\n"
      ],
      "metadata": {
        "id": "JpKFHbQlU9Au"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Menampilkan scatter plot dari dataset\n",
        "plt.scatter(bedrooms, house_price)\n",
        "plt.xlabel(\"Jumlah Kamar\")\n",
        "plt.ylabel(\"Harga Rumah ($)\")\n",
        "plt.title(\"Hubungan Jumlah Kamar dengan Harga Rumah\")\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "r7CHQ00_WdEI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Melatih model dengan Linear Regression\n",
        "bedrooms = bedrooms.reshape(-1, 1)\n",
        "linreg = LinearRegression()\n",
        "linreg.fit(bedrooms, house_price)"
      ],
      "metadata": {
        "id": "s_CGfClEWfHS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Menampilkan plot hubungan antara jumlah kamar dengan harga rumah\n",
        "plt.scatter(bedrooms, house_price, label=\"Data Aktual\")\n",
        "plt.plot(bedrooms, linreg.predict(bedrooms), color='red', label=\"Prediksi\")\n",
        "plt.xlabel(\"Jumlah Kamar\")\n",
        "plt.ylabel(\"Harga Rumah ($)\")\n",
        "plt.title(\"Hubungan Jumlah Kamar dengan Harga Rumah\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "jf6ZQSz9WgLt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Menampilkan akurasi model\n",
        "accuracy = linreg.score(bedrooms, house_price)\n",
        "print(f\"Akurasi model (R2 score): {accuracy}\")"
      ],
      "metadata": {
        "id": "9O8rPz_VXOX5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Prediksi harga rumah dengan data baru\n",
        "new_bedrooms = np.array([3, 6]).reshape(-1, 1)\n",
        "predicted_prices = linreg.predict(new_bedrooms)\n",
        "print(f\"Prediksi harga rumah untuk jumlah kamar {new_bedrooms.flatten()}: {predicted_prices}\")"
      ],
      "metadata": {
        "id": "vF-hbcPRXQlu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **SKLearn Logistic Regression**"
      ],
      "metadata": {
        "id": "beJd1_wwvAEO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pada latihan ini kita akan menggunakan logistic regression untuk memprediksi apakah seseorang akan membeli setelah melihat iklan sebuah produk.\n",
        "\n",
        "Tahapan yang dilalui dalam latihan kali ini adalah sebagai berikut:\n",
        "\n",
        "1. Ubah dataset menjadi Dataframe.\n",
        "2. Hapus kolom 'User ID'.\n",
        "3. Pisahkan atribut dan label.\n",
        "4. Latih model Logistic Regression.\n",
        "5. Evaluasi akurasi model."
      ],
      "metadata": {
        "id": "GvCjXXQuvFOF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dataset untuk latihan bisa Anda unduh pada tautan berikut https://www.kaggle.com/datasets/dragonheir/logistic-regression. Seperti biasa, unggah dataset Social_Network_Ads pada session storage Google Colab."
      ],
      "metadata": {
        "id": "DBufMi1BvYTV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# membaca dataset dan mengubahnya menjadi dataframe\n",
        "df = pd.read_csv('Social_Network_Ads.csv')"
      ],
      "metadata": {
        "id": "DFGIWA2bvKu0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "id": "4YB_Tdz_vV-c"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "id": "WFkKLRN6vruF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# drop kolom yang tidak diperlukan\n",
        "data = df.drop(columns=['User ID'])\n",
        "\n",
        "# jalankan proses one-hot encoding dengan pd.get_dummies()\n",
        "data = pd.get_dummies(data, dtype=\"int\")\n",
        "data"
      ],
      "metadata": {
        "id": "1OnVHVPevukp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# pisahkan atribut dan label\n",
        "predictions = ['Age' , 'EstimatedSalary' , 'Gender_Female' , 'Gender_Male']\n",
        "X = data[predictions]\n",
        "y = data['Purchased']"
      ],
      "metadata": {
        "id": "xzYlg44bv1cn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# lakukan normalisasi terhadap data yang kita miliki\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler = StandardScaler()\n",
        "scaler.fit(X)\n",
        "scaled_data = scaler.transform(X)\n",
        "scaled_data = pd.DataFrame(scaled_data, columns= X.columns)\n",
        "scaled_data.head()"
      ],
      "metadata": {
        "id": "SvB62Rtyv4Bi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# bagi data menjadi train dan test untuk setiap atribut dan label\n",
        "X_train, X_test, y_train, y_test = train_test_split(scaled_data, y, test_size=0.2, random_state=1)"
      ],
      "metadata": {
        "id": "TJm61cqdv-zw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import linear_model\n",
        "\n",
        "# latih model dengan fungsi fit\n",
        "model = linear_model.LogisticRegression()\n",
        "model.fit(X_train, y_train)"
      ],
      "metadata": {
        "id": "CYrDiZKZwFGF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# uji akurasi model\n",
        "model.score(X_test, y_test)"
      ],
      "metadata": {
        "id": "SeE9HbnSwHIb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Prediksi dengan data baru\n",
        "new_data = pd.DataFrame({\n",
        "    'Age': [30, 40],\n",
        "    'EstimatedSalary': [87000, 120000],\n",
        "    'Gender_Female': [0, 1],\n",
        "    'Gender_Male': [1, 0]\n",
        "})"
      ],
      "metadata": {
        "id": "nc23WV_wwKnH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Menormalkan data baru menggunakan scaler yang sudah fit\n",
        "scaled_new_data = scaler.transform(new_data)\n",
        "\n",
        "# Melakukan prediksi\n",
        "predictions = model.predict(scaled_new_data)\n",
        "print(f\"Prediksi untuk data baru: {predictions}\")"
      ],
      "metadata": {
        "id": "iplEzhWewimr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **SKLearn K-means**"
      ],
      "metadata": {
        "id": "MNo158Glw9UM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Tahapan pada latihan kali ini adalah sebagai berikut:\n",
        "\n",
        "1. Konversi data menjadi Dataframe.\n",
        "2. Lakukan preprocessing data.\n",
        "3. Hilangkan kolom 'CustomerID' dan 'gender'.\n",
        "4. Latih model K-Means.\n",
        "5. Buat plot untuk Elbow dan Cluster."
      ],
      "metadata": {
        "id": "SeYzggvZxOn5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dataset yang akan Anda gunakan adalah data pengunjung sebuah mall fiktif. Dataset bisa Anda dapatkan pada tautan berikut https://www.kaggle.com/datasets/vjchoudhary7/customer-segmentation-tutorial-in-python."
      ],
      "metadata": {
        "id": "DdmbP3WdxYBG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "#ubah file csv menjadi dataframe\n",
        "df = pd.read_csv('Mall_Customers.csv')\n",
        "\n",
        "# tampilkan 3 baris pertama\n",
        "df.head(3)"
      ],
      "metadata": {
        "id": "UVIsNfpMxN5p"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# ubah nama kolom\n",
        "df = df.rename(columns={'Gender': 'gender', 'Age': 'age',\n",
        "                        'Annual Income (k$)': 'annual_income',\n",
        "                        'Spending Score (1-100)': 'spending_score'})\n",
        "\n",
        "# ubah data kategorik menjadi data numerik\n",
        "df['gender'].replace(['Female', 'Male'], [0,1], inplace=True)\n",
        "\n",
        "# tampilkan data yang sudah di preprocess\n",
        "df.head(3)"
      ],
      "metadata": {
        "id": "DFrfi8BKw-9F"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.cluster import KMeans\n",
        "\n",
        "# menghilangkan kolom customer id dan gender\n",
        "X = df.drop(['CustomerID', 'gender'], axis=1)\n",
        "\n",
        "#membuat list yang berisi inertia\n",
        "clusters = []\n",
        "for i in range(1,11):\n",
        "  km = KMeans(n_clusters=i).fit(X)\n",
        "  clusters.append(km.inertia_)"
      ],
      "metadata": {
        "id": "Xg4q9nkBxv11"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import seaborn as sns\n",
        "\n",
        "# membuat plot inertia\n",
        "fig, ax = plt.subplots(figsize=(8, 4))\n",
        "sns.lineplot(x=list(range(1, 11)), y=clusters, ax=ax)\n",
        "ax.set_title('Cari Elbow')\n",
        "ax.set_xlabel('Clusters')\n",
        "ax.set_ylabel('Inertia')"
      ],
      "metadata": {
        "id": "wfeWEBYZxw-0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# membuat objek KMeans\n",
        "km5 = KMeans(n_clusters=5).fit(X)\n",
        "\n",
        "# menambahkan kolom label pada dataset\n",
        "X['Labels'] = km5.labels_\n",
        "\n",
        "# membuat plot KMeans dengan 5 klaster\n",
        "plt.figure(figsize=(8,4))\n",
        "sns.scatterplot(x=X['annual_income'], y=X['spending_score'], hue=X['Labels'],\n",
        "                palette=sns.color_palette('hls', 5))\n",
        "plt.title('KMeans dengan 5 Cluster')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "IKig9wzWx-ek"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **SKLearn SVM untuk Klasifikasi**"
      ],
      "metadata": {
        "id": "dFf2ULCu0ByS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dataset berisi 8 kolom atribut dan 1 kolom label yang berisi 2 kelas yaitu 1 dan 0. Angka 1 menandakan bahwa orang tersebut positif diabetes dan 0 menandakan sebaliknya. Terdapat 768 sampel yang merupakan 768 pasien perempuan keturunan suku Indian Pima.\n",
        "\n",
        "Model machine learning yang akan kita buat bertujuan untuk mengklasifikasikan apakah seorang pasien positif diabetes atau tidak.\n",
        "\n",
        "Tahapan latihan kali ini adalah:\n",
        "\n",
        "1. Ubah data ke dalam Dataframe.\n",
        "2. Bagi dataset.\n",
        "3. Lakukan standarisasi.\n",
        "4. Buat dan latih model.\n",
        "5. Evaluasi model."
      ],
      "metadata": {
        "id": "RdzofEig0EQi"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Tahap pertama yang perlu kita lakukan adalah mengunduh dataset Pima Indian Diabetes dari tautan berikut https://www.kaggle.com/datasets/uciml/pima-indians-diabetes-database. Setelah mengunduh dataset, jangan lupa masukkan dataset ke dalam Google Colab."
      ],
      "metadata": {
        "id": "AZOYjTSU1SG1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv('diabetes.csv')"
      ],
      "metadata": {
        "id": "GYZz0h1W0Dx5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "id": "TfsECCus1Zgu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "id": "FTCJBM301bB4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# memisahkan atribut pada dataset dan menyimpannya pada sebuah variabel\n",
        "X = df[df.columns[:8]]\n",
        "\n",
        "# memisahkan label pada dataset dan menyimpannya pada sebuah variabel\n",
        "y = df['Outcome']"
      ],
      "metadata": {
        "id": "pnBjE74R1c2y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "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": "L0Ynq45q1eah"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "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": "PzgBroOJ1gIJ"
      },
      "execution_count": null,
      "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()\n",
        "clf.fit(X_train, y_train)"
      ],
      "metadata": {
        "id": "F5N6uSD91h39"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Menampilkan skor akurasi prediksi\n",
        "clf.score(X_test, y_test)"
      ],
      "metadata": {
        "id": "_958XHSi1kVY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Studi Kasus**"
      ],
      "metadata": {
        "id": "wZz_rVq4gbVq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "Tahapan latihan kali ini adalah:\n",
        "\n",
        "1. Mengimpor Pustaka.\n",
        "2. Memuat Dataset.\n",
        "3. Menghapus Kolom yang Tidak Diperlukan.\n",
        "4. Visualisasi Box Plot Sebelum Menghapus Outlier.\n",
        "5. Menghitung IQR untuk Setiap Kolom.\n",
        "6. Mendefinisikan Mask untuk Outlier dan Menghapus Outlier\n",
        "7. Visualisasi Box Plot Setelah Menghapus Outlier.\n",
        "8. Mendefinisikan Fitur dan Target untuk Data yang Sudah Dibersihkan.\n",
        "9. Membagi Dataset yang Sudah Dibersihkan ke dalam Training dan Testing Sets.\n",
        "10. Inisialisasi dan Pelatihan Model Random Forest.\n",
        "11. Inisialisasi dan Pelatihan Model Regresi Linear."
      ],
      "metadata": {
        "id": "TWnKrHYFRg8B"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "data = pd.read_csv('house_price.csv')\n",
        "\n",
        "data"
      ],
      "metadata": {
        "id": "OF5AKetIQKL5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.info()"
      ],
      "metadata": {
        "id": "1aW3VtoZQVT_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = data.drop(['date', 'yr_renovated', 'street', 'city', 'statezip', 'country', 'yr_built'], axis=1)"
      ],
      "metadata": {
        "id": "u4jRgMlgQcVM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15, 10))\n",
        "sns.boxplot(data=data)\n",
        "plt.xticks(rotation=90)\n",
        "plt.title('Box Plot Before Removing Outliers')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "7deMgYXNQdde"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Q1 = data.quantile(0.25)\n",
        "Q3 = data.quantile(0.75)\n",
        "IQR = Q3 - Q1\n",
        "\n",
        "outliers = ((data < (Q1 - 1.5 * IQR)) | (data > (Q3 + 1.5 * IQR)))\n",
        "\n",
        "data_clean = data[~outliers.any(axis=1)]\n",
        "\n",
        "print(f'Original dataset shape: {data.shape}')\n",
        "print(f'Cleaned dataset shape: {data_clean.shape}')"
      ],
      "metadata": {
        "id": "uz_WlFVZQhS7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15, 10))\n",
        "sns.boxplot(data=data_clean)\n",
        "plt.xticks(rotation=90)\n",
        "plt.title('Box Plot After Removing Outliers')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "26aTAoCDQyLR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_clean = data_clean.drop(['price'], axis=1)\n",
        "y_clean = data_clean['price']\n",
        "\n",
        "X_train_clean, X_test_clean, y_train_clean, y_test_clean = train_test_split(X_clean, y_clean, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "DF48rEwyQ4nn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rf_model_clean = RandomForestRegressor(n_estimators=100, random_state=42)\n",
        "rf_model_clean.fit(X_train_clean, y_train_clean)\n",
        "\n",
        "rf_y_pred_clean = rf_model_clean.predict(X_test_clean)\n",
        "\n",
        "rf_mae_clean = mean_absolute_error(y_test_clean, rf_y_pred_clean)\n",
        "rf_mse_clean = mean_squared_error(y_test_clean, rf_y_pred_clean)\n",
        "rf_rmse_clean = mean_squared_error(y_test_clean, rf_y_pred_clean, squared=False)\n",
        "rf_r2_clean = r2_score(y_test_clean, rf_y_pred_clean)\n",
        "\n",
        "print('Random Forest Model Performance after Outlier Removal:')\n",
        "print(f'Mean Absolute Error: {rf_mae_clean}')\n",
        "print(f'Mean Squared Error: {rf_mse_clean}')\n",
        "print(f'Root Mean Squared Error: {rf_rmse_clean}')\n",
        "print(f'R-squared: {rf_r2_clean}\\n')"
      ],
      "metadata": {
        "id": "3Umh6fxCRAF0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lr_model_clean = LinearRegression()\n",
        "lr_model_clean.fit(X_train_clean, y_train_clean)\n",
        "\n",
        "lr_y_pred_clean = lr_model_clean.predict(X_test_clean)\n",
        "\n",
        "lr_mae_clean = mean_absolute_error(y_test_clean, lr_y_pred_clean)\n",
        "lr_mse_clean = mean_squared_error(y_test_clean, lr_y_pred_clean)\n",
        "lr_rmse_clean = mean_squared_error(y_test_clean, lr_y_pred_clean, squared=False)\n",
        "lr_r2_clean = r2_score(y_test_clean, lr_y_pred_clean)\n",
        "\n",
        "print('Linear Regression Model Performance after Outlier Removal:')\n",
        "print(f'Mean Absolute Error: {lr_mae_clean}')\n",
        "print(f'Mean Squared Error: {lr_mse_clean}')\n",
        "print(f'Root Mean Squared Error: {lr_rmse_clean}')\n",
        "print(f'R-squared: {lr_r2_clean}')"
      ],
      "metadata": {
        "id": "Biqxx4j_pqKL"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}