BreastCancer.ipynb

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  "metadata": {
    "colab": {
      "name": "BreastCancer.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMSU6pA45wFYQRynXQ4vlsj",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/oktaviacitra/classification/blob/main/BreastCancer.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Read File CSV"
      ],
      "metadata": {
        "id": "yS0C-voXxpns"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tIANL-I-3uTY",
        "outputId": "82c305ad-6943-4e8c-ffb0-4cd79f6dfd6a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "\n",
        "origin_path = \"/content/drive\"\n",
        "drive.mount(origin_path)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "folder_path = origin_path + \"/MyDrive/Learning Journey/Breast Cancer\"\n",
        "dataset_path = folder_path + \"/dataset.csv\"\n",
        "dataframe = pd.read_csv(dataset_path)\n",
        "dataframe.head(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 254
        },
        "id": "B2rzrwE34d4v",
        "outputId": "4c3e9ef2-3453-4a1c-af1f-3429d4d8b6c5"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
              "0    842302         M        17.99         10.38           122.8     1001.0   \n",
              "1    842517         M        20.57         17.77           132.9     1326.0   \n",
              "2  84300903         M        19.69         21.25           130.0     1203.0   \n",
              "\n",
              "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
              "0          0.11840           0.27760          0.3001              0.14710   \n",
              "1          0.08474           0.07864          0.0869              0.07017   \n",
              "2          0.10960           0.15990          0.1974              0.12790   \n",
              "\n",
              "   ...  radius_worst  texture_worst  perimeter_worst  area_worst  \\\n",
              "0  ...         25.38          17.33            184.6      2019.0   \n",
              "1  ...         24.99          23.41            158.8      1956.0   \n",
              "2  ...         23.57          25.53            152.5      1709.0   \n",
              "\n",
              "   smoothness_worst  compactness_worst  concavity_worst  concave points_worst  \\\n",
              "0            0.1622             0.6656           0.7119                0.2654   \n",
              "1            0.1238             0.1866           0.2416                0.1860   \n",
              "2            0.1444             0.4245           0.4504                0.2430   \n",
              "\n",
              "   symmetry_worst  fractal_dimension_worst  \n",
              "0          0.4601                  0.11890  \n",
              "1          0.2750                  0.08902  \n",
              "2          0.3613                  0.08758  \n",
              "\n",
              "[3 rows x 32 columns]"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>diagnosis</th>\n",
              "      <th>radius_mean</th>\n",
              "      <th>texture_mean</th>\n",
              "      <th>perimeter_mean</th>\n",
              "      <th>area_mean</th>\n",
              "      <th>smoothness_mean</th>\n",
              "      <th>compactness_mean</th>\n",
              "      <th>concavity_mean</th>\n",
              "      <th>concave points_mean</th>\n",
              "      <th>...</th>\n",
              "      <th>radius_worst</th>\n",
              "      <th>texture_worst</th>\n",
              "      <th>perimeter_worst</th>\n",
              "      <th>area_worst</th>\n",
              "      <th>smoothness_worst</th>\n",
              "      <th>compactness_worst</th>\n",
              "      <th>concavity_worst</th>\n",
              "      <th>concave points_worst</th>\n",
              "      <th>symmetry_worst</th>\n",
              "      <th>fractal_dimension_worst</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>842302</td>\n",
              "      <td>M</td>\n",
              "      <td>17.99</td>\n",
              "      <td>10.38</td>\n",
              "      <td>122.8</td>\n",
              "      <td>1001.0</td>\n",
              "      <td>0.11840</td>\n",
              "      <td>0.27760</td>\n",
              "      <td>0.3001</td>\n",
              "      <td>0.14710</td>\n",
              "      <td>...</td>\n",
              "      <td>25.38</td>\n",
              "      <td>17.33</td>\n",
              "      <td>184.6</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>0.1622</td>\n",
              "      <td>0.6656</td>\n",
              "      <td>0.7119</td>\n",
              "      <td>0.2654</td>\n",
              "      <td>0.4601</td>\n",
              "      <td>0.11890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>842517</td>\n",
              "      <td>M</td>\n",
              "      <td>20.57</td>\n",
              "      <td>17.77</td>\n",
              "      <td>132.9</td>\n",
              "      <td>1326.0</td>\n",
              "      <td>0.08474</td>\n",
              "      <td>0.07864</td>\n",
              "      <td>0.0869</td>\n",
              "      <td>0.07017</td>\n",
              "      <td>...</td>\n",
              "      <td>24.99</td>\n",
              "      <td>23.41</td>\n",
              "      <td>158.8</td>\n",
              "      <td>1956.0</td>\n",
              "      <td>0.1238</td>\n",
              "      <td>0.1866</td>\n",
              "      <td>0.2416</td>\n",
              "      <td>0.1860</td>\n",
              "      <td>0.2750</td>\n",
              "      <td>0.08902</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>84300903</td>\n",
              "      <td>M</td>\n",
              "      <td>19.69</td>\n",
              "      <td>21.25</td>\n",
              "      <td>130.0</td>\n",
              "      <td>1203.0</td>\n",
              "      <td>0.10960</td>\n",
              "      <td>0.15990</td>\n",
              "      <td>0.1974</td>\n",
              "      <td>0.12790</td>\n",
              "      <td>...</td>\n",
              "      <td>23.57</td>\n",
              "      <td>25.53</td>\n",
              "      <td>152.5</td>\n",
              "      <td>1709.0</td>\n",
              "      <td>0.1444</td>\n",
              "      <td>0.4245</td>\n",
              "      <td>0.4504</td>\n",
              "      <td>0.2430</td>\n",
              "      <td>0.3613</td>\n",
              "      <td>0.08758</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3 rows × 32 columns</p>\n",
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              "\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-dfce8a98-b85f-49d7-a3b8-423b84cf03d6 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-dfce8a98-b85f-49d7-a3b8-423b84cf03d6');\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",
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            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Preprocessing Data"
      ],
      "metadata": {
        "id": "JEiLgjbRxuK2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "X = StandardScaler().fit_transform(dataframe.iloc[:, 2:])\n",
        "y = pd.factorize(dataframe.iloc[:, 1])[0]\n",
        "\n",
        "X.shape, y.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2s1e_axrRusE",
        "outputId": "a9127b7c-387e-4213-9cab-011132165359"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((569, 30), (569,))"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Select Best Feature"
      ],
      "metadata": {
        "id": "SxLW8DH1xy1z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.feature_selection import SelectKBest,f_classif\n",
        "\n",
        "pipeline = Pipeline(\n",
        "    [\n",
        "     ('selector',SelectKBest(f_classif)),\n",
        "     ('model',RandomForestClassifier(max_depth=2, random_state=0))\n",
        "    ]\n",
        ")"
      ],
      "metadata": {
        "id": "-vowPdQ5HSEq"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "search = GridSearchCV(\n",
        "    estimator = pipeline,\n",
        "    param_grid = {'selector__k':[i for i in range(2, 30)]},\n",
        "    n_jobs=-1,\n",
        "    scoring=\"neg_mean_squared_error\",\n",
        "    cv=5,\n",
        "    verbose=3\n",
        ")"
      ],
      "metadata": {
        "id": "ElAb4zFBJRfM"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "search.fit(X, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QSq5Nn14JtIa",
        "outputId": "9056616b-86cd-4047-a568-b7d28f74ffe1"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 28 candidates, totalling 140 fits\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5,\n",
              "             estimator=Pipeline(steps=[('selector', SelectKBest()),\n",
              "                                       ('model',\n",
              "                                        RandomForestClassifier(max_depth=2,\n",
              "                                                               random_state=0))]),\n",
              "             n_jobs=-1,\n",
              "             param_grid={'selector__k': [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,\n",
              "                                         14, 15, 16, 17, 18, 19, 20, 21, 22, 23,\n",
              "                                         24, 25, 26, 27, 28, 29]},\n",
              "             scoring='neg_mean_squared_error', verbose=3)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "search.best_params_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j-12M9h9JlkR",
        "outputId": "a7d15604-b33a-4a92-eae7-455361a6d9ff"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'selector__k': 25}"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "search.best_score_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XUjlQ92KiDQ8",
        "outputId": "546d7bca-3c14-44e1-ef76-aa232e538c3a"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-0.04216736531594473"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_selection import chi2\n",
        "\n",
        "X_new = SelectKBest(f_classif, k=25).fit_transform(X, y)\n",
        "X_new.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wt50Ywq1hQv4",
        "outputId": "b9a6f43c-b60f-42d2-f569-fc290064746f"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(569, 25)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Splitting Data"
      ],
      "metadata": {
        "id": "jNUuGi6lx-cx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_new, y, test_size=0.3, random_state=42)\n",
        "X_train.shape, X_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RnxlVSWLwmXW",
        "outputId": "4527388c-3a26-4d8f-ad5d-bda709429cfb"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((398, 25), (171, 25))"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = RandomForestClassifier(max_depth=2, random_state=0)\n",
        "model.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t8h0vJ_yh-PX",
        "outputId": "59131e25-f7a6-414b-ff48-91e40bf1bf3d"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(max_depth=2, random_state=0)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Evaluation"
      ],
      "metadata": {
        "id": "VRptUydoyLIe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report\n",
        "\n",
        "y_pred = model.predict(X_test)\n",
        "print(classification_report(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8cjLEFq1xIRS",
        "outputId": "91d1c52a-0833-4f1e-b7cb-9f2e78a0c3f7"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      0.92      0.94        63\n",
            "           1       0.95      0.98      0.97       108\n",
            "\n",
            "    accuracy                           0.96       171\n",
            "   macro avg       0.96      0.95      0.96       171\n",
            "weighted avg       0.96      0.96      0.96       171\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
        "\n",
        "cm = confusion_matrix(y_test, y_pred, labels=model.classes_)\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=model.classes_)\n",
        "disp.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "RrsiumxGxOgP",
        "outputId": "db929244-9ed5-4c2e-bea5-bcd88e1513ce"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7faab6ce1090>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}