codingws3_ml_ans.ipynb

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        "<a href=\"https://colab.research.google.com/github/beatriceadel/ppsdcw3/blob/main/codingws3_ml_ans.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": [
        "#**Machine Learning w/ Python**"
      ],
      "metadata": {
        "id": "4-TJLve5j5xY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Apa itu Machine Learning?**\n",
        "\n",
        "\n",
        ">The use and development of computer systems that are able to **learn and adapt without following explicit instructions**, by using **algorithms and statistical models** to analyse and draw inferences from **patterns in data**. \n",
        "\n",
        "-*Oxford Dictionaries*\n",
        "\n",
        "<img src=\"https://volcanohong.github.io/content/images/2016/ml_process.png\" width=600 height=300/>"
      ],
      "metadata": {
        "id": "P4Ch4mz-Qm5N"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Libraries & Tools Python untuk Machine Learning**\n",
        "\n",
        "\n",
        "*   **NumPy & SciPy**: General-purpose numerical computation\n",
        "*   **Pandas**: Data analysis & manipulation\n",
        "*   **Matplotlib & Seaborn**: Data visualization\n",
        "*   **Scikit-Learn**: Machine learning functions and algorithms\n",
        "*   **Tensorflow**: Machine learning models\n",
        "*   **PyTorch**: Machine learning, computer vision, & natural language processing\n",
        "*   **Keras**: Machine learning, deep learning, neural networks\n",
        "\n"
      ],
      "metadata": {
        "id": "wBpEygDBRTvJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Studi Kasus**\n",
        "\n",
        "Untuk workshop ini, kita akan mengaplikasikan beberapa macam algoritma pada dataset **'Breast Cancer Wisconsin'** yang diambil dari [*UCI Machine Learning Repository*](https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Original))\n",
        "\n",
        "Dataset dapat di download melalui link [ini](https://raw.githubusercontent.com/beatriceadel/ppsdcw3/main/breast-cancer-wisconsin.csv)\n",
        "\n",
        "Setelah data sudah didownload, kita bisa memulai dengan mengimport libraries yang akan kita gunakan: NumPy, Pandas, Matplotlib, dan Seaborn."
      ],
      "metadata": {
        "id": "k07RoFpkVKAF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import library - library yang akan digunakan\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "2VExtS1IboeA"
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      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Mengidentifikasi Masalah\n",
        "\n",
        "Pertama - tama, dalam membangun sebuah machine learning model, kita perlu mengidentifikasi terlebih dahulu masalah apa yang ingin kita selesaikan dengan model yang kita buat.\n",
        "\n",
        "**Masalah - masalah paling umum dalam Machine Learning:**\n",
        "\n",
        "*   *Classification*\n",
        "*   Regression\n",
        "*   Clustering\n",
        "\n",
        "<img src=\"https://www.researchgate.net/profile/Frank-Nielsen-3/publication/314626729/figure/fig1/AS:810830673244160@1570328505835/The-three-pillars-of-learning-in-data-science-clustering-flat-or-hierarchical.ppm\" width=600 height=400/>"
      ],
      "metadata": {
        "id": "IL4497yvW-QF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Membaca data\n",
        "url = \"https://raw.githubusercontent.com/beatriceadel/ppsdcw3/main/breast-cancer-wisconsin.csv\"\n",
        "data = pd.read_csv(url)\n",
        "data"
      ],
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          id  clump_thickness  size_uniformity  ...  normal_nucleoli  mitoses  class\n",
              "0    1000025                5                1  ...                1        1      2\n",
              "1    1002945                5                4  ...                2        1      2\n",
              "2    1015425                3                1  ...                1        1      2\n",
              "3    1016277                6                8  ...                7        1      2\n",
              "4    1017023                4                1  ...                1        1      2\n",
              "..       ...              ...              ...  ...              ...      ...    ...\n",
              "694   776715                3                1  ...                1        1      2\n",
              "695   841769                2                1  ...                1        1      2\n",
              "696   888820                5               10  ...               10        2      4\n",
              "697   897471                4                8  ...                6        1      4\n",
              "698   897471                4                8  ...                4        1      4\n",
              "\n",
              "[699 rows x 11 columns]"
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    {
      "cell_type": "markdown",
      "source": [
        "## Memproses Data\n",
        "Dalam machine learning, sebelum kita membangun model, kita juga perlu melakukan **data pre-processing.**\n"
      ],
      "metadata": {
        "id": "X8vNYexrWzLz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Memeriksa Missing Values\n",
        "\n",
        "Missing values atau data yang kosong dapat mengganggu proses komputer dalam mempelajari data. Maka dari itu, ada beberapa cara yang dapat kita lakukan untuk mengatasi missing values:\n",
        "\n",
        "\n",
        "*   Menghapus instansi data (row) yang mengandung missing value\n",
        "*   Mengganti missing value dengan rata - rata dari data atribut tersebut\n",
        "*   Mengganti missing value dengan sebuah konstanta (constant)\n",
        "\n",
        "Kita dapat memfilter missing values dengan menggunakan cara yang sama seperti ketika kita membuat kondisi saat menulis `if` statement."
      ],
      "metadata": {
        "id": "v97DA050jtoI"
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    {
      "cell_type": "code",
      "source": [
        "# Menyeleksi missing values\n",
        "missing_values = data[\"bare_nucleoli\"] == \"?\"\n",
        "data[missing_values]"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 615
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        "id": "m7L7F0XKkqP9",
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      "execution_count": 3,
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              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>158</th>\n",
              "      <td>1193683</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>?</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>164</th>\n",
              "      <td>1197510</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>?</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>235</th>\n",
              "      <td>1241232</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>?</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>249</th>\n",
              "      <td>169356</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>?</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>275</th>\n",
              "      <td>432809</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>?</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>292</th>\n",
              "      <td>563649</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>?</td>\n",
              "      <td>6</td>\n",
              "      <td>10</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
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              "    <tr>\n",
              "      <th>294</th>\n",
              "      <td>606140</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>?</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>297</th>\n",
              "      <td>61634</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>?</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
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              "      <th>315</th>\n",
              "      <td>704168</td>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>?</td>\n",
              "      <td>4</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>321</th>\n",
              "      <td>733639</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>?</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>411</th>\n",
              "      <td>1238464</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>?</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>617</th>\n",
              "      <td>1057067</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>?</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "          id  clump_thickness  size_uniformity  ...  normal_nucleoli  mitoses  class\n",
              "23   1057013                8                4  ...                3        1      4\n",
              "40   1096800                6                6  ...                8        1      2\n",
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              "297    61634                5                4  ...                3        1      2\n",
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              "411  1238464                1                1  ...                1        1      2\n",
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              "\n",
              "[16 rows x 11 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### **Latihan**\n",
        "\n",
        "Coba periksa apakah kolom ```size_uniformity``` dan ```shape_uniformity```mengandung missing values atau tidak. \n"
      ],
      "metadata": {
        "id": "zJMkR0HzvKcu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Jawaban\n",
        "missing_values = (data[\"size_uniformity\"] == \"?\") | (data[\"shape_uniformity\"] == \"?\") \n",
        "data[missing_values]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113
        },
        "id": "6ZjWIUsqv9I4",
        "outputId": "7df4ecef-588e-46d1-900a-5c8b09bd365b"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>clump_thickness</th>\n",
              "      <th>size_uniformity</th>\n",
              "      <th>shape_uniformity</th>\n",
              "      <th>marginal_adhesion</th>\n",
              "      <th>epithelial_size</th>\n",
              "      <th>bare_nucleoli</th>\n",
              "      <th>bland_chromatin</th>\n",
              "      <th>normal_nucleoli</th>\n",
              "      <th>mitoses</th>\n",
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              "\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ef0f2757-2d48-4312-8780-d6ef1536f868 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
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              "\n",
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              "          const element = document.querySelector('#df-ef0f2757-2d48-4312-8780-d6ef1536f868');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "  "
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [id, clump_thickness, size_uniformity, shape_uniformity, marginal_adhesion, epithelial_size, bare_nucleoli, bland_chromatin, normal_nucleoli, mitoses, class]\n",
              "Index: []"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Menghapus data dengan missing values\n",
        "cleaned_data = data[data.bare_nucleoli!= \"?\"]\n",
        "cleaned_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "XBwpbbTxqAdv",
        "outputId": "127540f4-b11f-4d51-8170-a2ff7615fcff"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1016277</td>\n",
              "      <td>6</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1017023</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>694</th>\n",
              "      <td>776715</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>695</th>\n",
              "      <td>841769</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>696</th>\n",
              "      <td>888820</td>\n",
              "      <td>5</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "      <td>8</td>\n",
              "      <td>10</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>697</th>\n",
              "      <td>897471</td>\n",
              "      <td>4</td>\n",
              "      <td>8</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>10</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>698</th>\n",
              "      <td>897471</td>\n",
              "      <td>4</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>10</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>683 rows × 11 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e5ac9d77-0117-4d0b-ae5c-4cbed366d989')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e5ac9d77-0117-4d0b-ae5c-4cbed366d989 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e5ac9d77-0117-4d0b-ae5c-4cbed366d989');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "          id  clump_thickness  size_uniformity  ...  normal_nucleoli  mitoses  class\n",
              "0    1000025                5                1  ...                1        1      2\n",
              "1    1002945                5                4  ...                2        1      2\n",
              "2    1015425                3                1  ...                1        1      2\n",
              "3    1016277                6                8  ...                7        1      2\n",
              "4    1017023                4                1  ...                1        1      2\n",
              "..       ...              ...              ...  ...              ...      ...    ...\n",
              "694   776715                3                1  ...                1        1      2\n",
              "695   841769                2                1  ...                1        1      2\n",
              "696   888820                5               10  ...               10        2      4\n",
              "697   897471                4                8  ...                6        1      4\n",
              "698   897471                4                8  ...                4        1      4\n",
              "\n",
              "[683 rows x 11 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Melihat korelasi antar fitur/kolom (bar chart)\n",
        "\n",
        "Terkadang ada fitur / kolom di dataset yang kita miliki yang sebenarnya tidak relevan untuk proses training. \n",
        "\n",
        "Kita dapat melihat fitur mana saja yang relevan dan tidak melalui visualisasi data dengan menggunakan [Matplotlib](https://matplotlib.org/) dan [Seaborn](http://seaborn.pydata.org/). "
      ],
      "metadata": {
        "id": "L4Rdj6KHt4OW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Kita dapat melihat korelasi antara satu fitur dengan target fitur kita (class) dengan menggunakan **bar chart**."
      ],
      "metadata": {
        "id": "LWEv9uXi0dav"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Menampilkan bar chart untuk melihat korelasi clump_thickness dengan class\n",
        "\n",
        "fig = plt.figure(figsize=(10,10))\n",
        "ax = sns.countplot(x='clump_thickness', hue='class', data=cleaned_data)\n",
        "ax.set(xlabel='Clump Thickness', ylabel='No of cases')\n",
        "fig.suptitle(\"Clump Thickness w.r.t. Class\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 695
        },
        "id": "fNS0XmVp0wmv",
        "outputId": "5ac753e8-9e7c-4d34-b4e3-7dbd3d0029b0"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0.98, 'Clump Thickness w.r.t. Class')"
            ]
          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### **Latihan**\n",
        "\n",
        "Tampilkan bar chart yang menunjukkan korelasi antara fitur `size_uniformity` dengan target fitur kita (`class`)\n"
      ],
      "metadata": {
        "id": "yVq6HiVu26JW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Jawaban \n",
        "fig = plt.figure(figsize=(10,10))\n",
        "ax = sns.countplot(x='size_uniformity', hue='class', data=cleaned_data)\n",
        "ax.set(xlabel='Size Uniformity', ylabel='No of cases')\n",
        "fig.suptitle(\"Size Uniformity w.r.t. Class\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 695
        },
        "id": "8W8djo3f2p63",
        "outputId": "ecc4d0fa-844c-4984-9f57-ff123af9c84e"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0.98, 'Size Uniformity w.r.t. Class')"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Melihat korelasi antar fitur/kolom (heatmap)\n",
        "\n",
        "Untuk melihat korelasi antara semua fitur, bentuk visualisasi yang paling mudah adalah dengan menggunakan **heatmap.** Dalam sebuah heatmap, angka *+1* menunjukkan tingkat korelasi positif yang paling kuat, angka *-1* mennjukkan tingkat korelasi negatif yang paling kuat, dan angka *0* mengindikasikan bahwa tidak ada korelasi antara kedua fitur tersebut. "
      ],
      "metadata": {
        "id": "kaTLIkLo31_l"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Menampilkan heatmap\n",
        "\n",
        "plt.figure(figsize=(10, 10))\n",
        "sns.heatmap(cleaned_data.astype(float).corr(), linewidths=0.1, square=True, linecolor='white', annot=True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 632
        },
        "id": "lelLAYCTyF4m",
        "outputId": "1a793be4-7bcd-45ed-ec98-3ccac70c04fc"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dari heatmap di atas, kita dapat melihat bahwa fitur \"id\" dan \"mitoses\" tidak memiliki korelasi yang kuat dengan \"class\". Jadi, kita dapat menyeleksi fitur/kolom yang akan kita gunakan dalam proses training sebagai berikut:"
      ],
      "metadata": {
        "id": "Wf0c1myv4Mh4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Seleksi fitur\n",
        "features_selection = ['clump_thickness', 'size_uniformity', 'shape_uniformity', 'marginal_adhesion', 'epithelial_size', 'bare_nucleoli', 'bland_chromatin', 'normal_nucleoli']"
      ],
      "metadata": {
        "id": "YtzWXO-fCJWr"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Membangun Machine Learning Model\n",
        "\n",
        "Untuk membangun machine learning model kali ini, kita akan mencoba menggunakan beberapa algoritma yang tersedia di [scikit-learn](https://scikit-learn.org/stable/index.html). Pertama, kita import terlebih dahulu tools yang akan kita gunakan.\n",
        "\n",
        "*   `train_test_split`, `cross_val_score` dari `sklearn.model_selection`\n",
        "*   `accuracy_score` dari `sklearn.metrics` \n",
        "*   `time` \n",
        "\n"
      ],
      "metadata": {
        "id": "idy0kbfk8Isb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import dari scikit-learn\n",
        "\n",
        "from sklearn.model_selection import train_test_split, cross_val_score\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "import time"
      ],
      "metadata": {
        "id": "lVlc33Sl_kxu"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Kita ingin membuat model berdasarkan fungsi **y = f(x)** dimana ketika input x (data baru) diterima, machine learning model kita dapat menentukan y (class) berdasarkan data yang sudah dipelajari di proses training. \n",
        "\n",
        "Untuk itu, kita juga perlu membagi dataset kita menjadi \"train set\" dan \"test set\". Train set akan digunakan untuk melatih machine learning model kita, sedangkan test set akan digunakan untuk mengevaluasi model kita. \n",
        "\n",
        "Dalam percobaan kita, kita akan menggunakan fungsi `train_test_split` untuk membagi secara random (`random_state = 42`), dan kita akan tentukan jumlah test set sebanyak 20% dari total data (`test_size = 0.2`)"
      ],
      "metadata": {
        "id": "xi4pGp89CyPo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Menentukan pembagian data\n",
        "\n",
        "x = cleaned_data.loc[:,features_selection]\n",
        "y = cleaned_data.loc[:, 'class']\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2, random_state = 42)\n",
        "\n",
        "accuracy = []\n",
        "cvs = []"
      ],
      "metadata": {
        "id": "KGrkM5fRDoNS"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Setelah itu, kita perlu:\n",
        "\n",
        "1.   Menentukan algoritma klasifikasi / classifier (`clf`) yang akan digunakan. \n",
        "2.   Melatih classifier `clf` dengan train dataset `x_train` dan `y_train`.\n",
        "3.   Menggunakan data `x_test` untuk melihat hasil prediksi model.\n",
        "4.   Mengestimasi akurasi model dengan test dataset dan **cross-validation**.\n",
        "\n",
        "\n",
        "\n",
        "Apa itu cross-validation? \n",
        "\n",
        "Cross-validation adalah salah satu cara untuk mengevaluasi machine learning model. Dalam cross-validation, dataset dibagi menjadi beberapa subset (misal *n*), *1* subset akan disimpan untuk validasi atau test, dan *n-1* subset lainnya akan digunakan untuk proses training. Proses ini diulang hingga semua subset sempat digunakan untuk validasi, dan rata - rata akurasi dari tiap repetisinya adalah hasil akurasi akhir."
      ],
      "metadata": {
        "id": "X1juNSilEyP7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Nearest Neighbor\n",
        "\n",
        "Nearest Neighbor adalah alogritma yang cara kerjanya adalah mencari \"tetangga\" terdekat untuk menentukan data yang baru diterima akan diklasifikasikan ke kelompok yang mana. \n",
        "\n",
        "<img src=\"http://3.bp.blogspot.com/-ZslDMqm5M9o/T8ja_f_fALI/AAAAAAAAAt4/z7w55YAZXpw/s1600/p1.png\" width=400 height=300/>\n",
        "\n",
        "Kita dapat menggunakan algoritma ini dengan mengimport `KNeighborsClassifier` dari  `sklearn.neighbors`"
      ],
      "metadata": {
        "id": "5Boc-g7a_1k6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Nearest Neighbor\n",
        "\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "start = time.time()\n",
        "\n",
        "clf = KNeighborsClassifier()\n",
        "clf.fit(x_train, y_train)\n",
        "prediction = clf.predict(x_test)\n",
        "scores = cross_val_score(clf, x, y, cv=10)\n",
        "\n",
        "end = time.time()\n",
        "\n",
        "accuracy.append(accuracy_score(prediction, y_test))\n",
        "cvs.append(np.mean(scores))\n",
        "\n",
        "print(\"Accuracy: {0:.2%}\".format(accuracy_score(prediction, y_test)))\n",
        "print(\"Cross validation score: {0:.2%} (+/- {1:.2%})\".format(np.mean(scores), np.std(scores)*2))\n",
        "print(\"Execution time: {0:.5} seconds \\n\".format(end-start))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gBbKX1OzA-lH",
        "outputId": "adf3750b-ac96-4900-de0b-35e713689f4f"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 95.62%\n",
            "Cross validation score: 97.22% (+/- 4.01%)\n",
            "Execution time: 0.078441 seconds \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Decision Tree\n",
        "\n",
        "Decision Tree adalah algoritma yang cara kerjanya adalah menyusun suatu struktur hierarki untuk menentukan data yang baru diterima akan diklasifikasikan ke kelompok yang mana. \n",
        "\n",
        "<img src=\"https://miro.medium.com/max/6810/1*1tGLoeGg4cDwQXSLSgD5Zg.png\" width=300 height=150/>\n",
        "\n",
        "Kita dapat menggunakan algoritma ini dengan mengimport `DecisionTreeClassifier` dari `sklearn.tree`"
      ],
      "metadata": {
        "id": "xHK6KRKE_8QW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Decision Tree\n",
        "\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "start = time.time()\n",
        "\n",
        "clf = DecisionTreeClassifier()\n",
        "clf.fit(x_train, y_train)\n",
        "prediction = clf.predict(x_test)\n",
        "scores = cross_val_score(clf, x, y, cv=10)\n",
        "\n",
        "end = time.time()\n",
        "\n",
        "accuracy.append(accuracy_score(prediction, y_test))\n",
        "cvs.append(np.mean(scores))\n",
        "\n",
        "print(\"Accuracy: {0:.2%}\".format(accuracy_score(prediction, y_test)))\n",
        "print(\"Cross validation score: {0:.2%} (+/- {1:.2%})\".format(np.mean(scores), np.std(scores)*2))\n",
        "print(\"Execution time: {0:.5} seconds \\n\".format(end-start))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yC4G1RUDJGsh",
        "outputId": "1252dc46-b732-4fa2-88fe-3e3bd0226ab5"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 94.16%\n",
            "Cross validation score: 94.74% (+/- 4.91%)\n",
            "Execution time: 0.052164 seconds \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Random Forest\n",
        "\n",
        "Random Forest adalah algoritma yang dibangun dengan beberapa decision tree, dimana output dari seluruh decision tree digabung menjadi 1 output. \n",
        "\n",
        "<img src=\"https://miro.medium.com/max/2612/0*f_qQPFpdofWGLQqc.png\" width=300 height=200/>\n",
        "\n",
        "Kita dapat menggunakan algoritma ini dengan mengimport `RandomForestClassifier` dari `sklearn.ensemble`"
      ],
      "metadata": {
        "id": "tjRfuhyxJYC8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Random Forest\n",
        "\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "start = time.time()\n",
        "\n",
        "clf = RandomForestClassifier()\n",
        "clf.fit(x_train, y_train)\n",
        "prediction = clf.predict(x_test)\n",
        "scores = cross_val_score(clf, x, y, cv=10)\n",
        "\n",
        "end = time.time()\n",
        "\n",
        "accuracy.append(accuracy_score(prediction, y_test))\n",
        "cvs.append(np.mean(scores))\n",
        "\n",
        "print(\"Accuracy: {0:.2%}\".format(accuracy_score(prediction, y_test)))\n",
        "print(\"Cross validation score: {0:.2%} (+/- {1:.2%})\".format(np.mean(scores), np.std(scores)*2))\n",
        "print(\"Execution time: {0:.5} seconds \\n\".format(end-start))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YO9nHfx1JZNz",
        "outputId": "a2922ffc-c9da-4318-fa2e-6eb3c5f9ff1d"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 96.35%\n",
            "Cross validation score: 96.94% (+/- 4.76%)\n",
            "Execution time: 1.5953 seconds \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Naive Bayes (Latihan)\n",
        "\n",
        "Naive Bayes adalah algoritma yang mengaplikasikan **Bayes' Theorem**, dimana cara kerjanya adalah menghitung probabilitas sesuatu berdasarkan data yang sudah diketahui. Probabilitas inilah yang menentukan data yang baru akan diklasifikasikan ke kelompok yang mana. Kita dapat menggunakan algoritma ini dengan mengimport `GaussianNB` dari `sklearn.naive_bayes`"
      ],
      "metadata": {
        "id": "sIPRYUg1Ip1s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Naive Bayes (Latihan)\n",
        "\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "\n",
        "start = time.time()\n",
        "\n",
        "clf = GaussianNB()\n",
        "clf.fit(x_train, y_train)\n",
        "prediction = clf.predict(x_test)\n",
        "scores = cross_val_score(clf, x, y, cv=10)\n",
        "\n",
        "end = time.time()\n",
        "\n",
        "accuracy.append(accuracy_score(prediction, y_test))\n",
        "cvs.append(np.mean(scores))\n",
        "\n",
        "print(\"Accuracy: {0:.2%}\".format(accuracy_score(prediction, y_test)))\n",
        "print(\"Cross validation score: {0:.2%} (+/- {1:.2%})\".format(np.mean(scores), np.std(scores)*2))\n",
        "print(\"Execution time: {0:.5} seconds \\n\".format(end-start))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wFp0PpDhIpB6",
        "outputId": "7789230e-9a8a-4c11-a285-6ce6dab74a54"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 97.08%\n",
            "Cross validation score: 96.49% (+/- 3.49%)\n",
            "Execution time: 0.056579 seconds \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Perbandingan hasil akurasi\n",
        "\n",
        "col = {'accuracy': accuracy, 'cvs': cvs}\n",
        "index = ['KNeighbors', 'GaussianNB', 'DecisionTree', 'RandomForest']\n",
        "\n",
        "df = pd.DataFrame(col, index=index)\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "NEuJQv28Ji1f",
        "outputId": "b94e5320-021f-46c3-9972-c4e30a9cbe2d"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<style scoped>\n",
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              "      <th></th>\n",
              "      <th>accuracy</th>\n",
              "      <th>cvs</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>KNeighbors</th>\n",
              "      <td>0.956204</td>\n",
              "      <td>0.972229</td>\n",
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              "      <td>0.969352</td>\n",
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            "text/plain": [
              "              accuracy       cvs\n",
              "KNeighbors    0.956204  0.972229\n",
              "GaussianNB    0.941606  0.947357\n",
              "DecisionTree  0.963504  0.969352\n",
              "RandomForest  0.970803  0.964919"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    }
  ]
}