데이터_모델_불러오기(0222).ipynb

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            ".. _breast_cancer_dataset:\n",
            "\n",
            "Breast cancer wisconsin (diagnostic) dataset\n",
            "--------------------------------------------\n",
            "\n",
            "**Data Set Characteristics:**\n",
            "\n",
            "    :Number of Instances: 569\n",
            "\n",
            "    :Number of Attributes: 30 numeric, predictive attributes and the class\n",
            "\n",
            "    :Attribute Information:\n",
            "        - radius (mean of distances from center to points on the perimeter)\n",
            "        - texture (standard deviation of gray-scale values)\n",
            "        - perimeter\n",
            "        - area\n",
            "        - smoothness (local variation in radius lengths)\n",
            "        - compactness (perimeter^2 / area - 1.0)\n",
            "        - concavity (severity of concave portions of the contour)\n",
            "        - concave points (number of concave portions of the contour)\n",
            "        - symmetry\n",
            "        - fractal dimension (\"coastline approximation\" - 1)\n",
            "\n",
            "        The mean, standard error, and \"worst\" or largest (mean of the three\n",
            "        worst/largest values) of these features were computed for each image,\n",
            "        resulting in 30 features.  For instance, field 0 is Mean Radius, field\n",
            "        10 is Radius SE, field 20 is Worst Radius.\n",
            "\n",
            "        - class:\n",
            "                - WDBC-Malignant\n",
            "                - WDBC-Benign\n",
            "\n",
            "    :Summary Statistics:\n",
            "\n",
            "    ===================================== ====== ======\n",
            "                                           Min    Max\n",
            "    ===================================== ====== ======\n",
            "    radius (mean):                        6.981  28.11\n",
            "    texture (mean):                       9.71   39.28\n",
            "    perimeter (mean):                     43.79  188.5\n",
            "    area (mean):                          143.5  2501.0\n",
            "    smoothness (mean):                    0.053  0.163\n",
            "    compactness (mean):                   0.019  0.345\n",
            "    concavity (mean):                     0.0    0.427\n",
            "    concave points (mean):                0.0    0.201\n",
            "    symmetry (mean):                      0.106  0.304\n",
            "    fractal dimension (mean):             0.05   0.097\n",
            "    radius (standard error):              0.112  2.873\n",
            "    texture (standard error):             0.36   4.885\n",
            "    perimeter (standard error):           0.757  21.98\n",
            "    area (standard error):                6.802  542.2\n",
            "    smoothness (standard error):          0.002  0.031\n",
            "    compactness (standard error):         0.002  0.135\n",
            "    concavity (standard error):           0.0    0.396\n",
            "    concave points (standard error):      0.0    0.053\n",
            "    symmetry (standard error):            0.008  0.079\n",
            "    fractal dimension (standard error):   0.001  0.03\n",
            "    radius (worst):                       7.93   36.04\n",
            "    texture (worst):                      12.02  49.54\n",
            "    perimeter (worst):                    50.41  251.2\n",
            "    area (worst):                         185.2  4254.0\n",
            "    smoothness (worst):                   0.071  0.223\n",
            "    compactness (worst):                  0.027  1.058\n",
            "    concavity (worst):                    0.0    1.252\n",
            "    concave points (worst):               0.0    0.291\n",
            "    symmetry (worst):                     0.156  0.664\n",
            "    fractal dimension (worst):            0.055  0.208\n",
            "    ===================================== ====== ======\n",
            "\n",
            "    :Missing Attribute Values: None\n",
            "\n",
            "    :Class Distribution: 212 - Malignant, 357 - Benign\n",
            "\n",
            "    :Creator:  Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian\n",
            "\n",
            "    :Donor: Nick Street\n",
            "\n",
            "    :Date: November, 1995\n",
            "\n",
            "This is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.\n",
            "https://goo.gl/U2Uwz2\n",
            "\n",
            "Features are computed from a digitized image of a fine needle\n",
            "aspirate (FNA) of a breast mass.  They describe\n",
            "characteristics of the cell nuclei present in the image.\n",
            "\n",
            "Separating plane described above was obtained using\n",
            "Multisurface Method-Tree (MSM-T) [K. P. Bennett, \"Decision Tree\n",
            "Construction Via Linear Programming.\" Proceedings of the 4th\n",
            "Midwest Artificial Intelligence and Cognitive Science Society,\n",
            "pp. 97-101, 1992], a classification method which uses linear\n",
            "programming to construct a decision tree.  Relevant features\n",
            "were selected using an exhaustive search in the space of 1-4\n",
            "features and 1-3 separating planes.\n",
            "\n",
            "The actual linear program used to obtain the separating plane\n",
            "in the 3-dimensional space is that described in:\n",
            "[K. P. Bennett and O. L. Mangasarian: \"Robust Linear\n",
            "Programming Discrimination of Two Linearly Inseparable Sets\",\n",
            "Optimization Methods and Software 1, 1992, 23-34].\n",
            "\n",
            "This database is also available through the UW CS ftp server:\n",
            "\n",
            "ftp ftp.cs.wisc.edu\n",
            "cd math-prog/cpo-dataset/machine-learn/WDBC/\n",
            "\n",
            ".. topic:: References\n",
            "\n",
            "   - W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction \n",
            "     for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on \n",
            "     Electronic Imaging: Science and Technology, volume 1905, pages 861-870,\n",
            "     San Jose, CA, 1993.\n",
            "   - O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and \n",
            "     prognosis via linear programming. Operations Research, 43(4), pages 570-577, \n",
            "     July-August 1995.\n",
            "   - W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques\n",
            "     to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) \n",
            "     163-171.\n"
          ]
        }
      ],
      "source": [
        "from sklearn.datasets import load_breast_cancer\n",
        "\n",
        "breast_cancer_data = load_breast_cancer()\n",
        "print(breast_cancer_data['DESCR'])\n"
      ]
    },
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      "source": [
        "# 데이터와 레이블을 데이터 프레임으로 만드시오\n",
        "import pandas as pd\n",
        "\n",
        "df_data = pd.DataFrame(breast_cancer_data['data'])\n",
        "df_label = pd.DataFrame(breast_cancer_data['target'])\n",
        "df_data.head()"
      ],
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        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "scaler = MinMaxScaler()\n",
        "\n",
        "# scaler.fit(df_data)\n",
        "# data_scaled = scaler.fit_transform(df_data)\n",
        "\n",
        "data_scaled = scaler.fit_transform(df_data)\n",
        "data_scaled"
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              "array([[0.52103744, 0.0226581 , 0.54598853, ..., 0.91202749, 0.59846245,\n",
              "        0.41886396],\n",
              "       [0.64314449, 0.27257355, 0.61578329, ..., 0.63917526, 0.23358959,\n",
              "        0.22287813],\n",
              "       [0.60149557, 0.3902604 , 0.59574321, ..., 0.83505155, 0.40370589,\n",
              "        0.21343303],\n",
              "       ...,\n",
              "       [0.45525108, 0.62123774, 0.44578813, ..., 0.48728522, 0.12872068,\n",
              "        0.1519087 ],\n",
              "       [0.64456434, 0.66351031, 0.66553797, ..., 0.91065292, 0.49714173,\n",
              "        0.45231536],\n",
              "       [0.03686876, 0.50152181, 0.02853984, ..., 0.        , 0.25744136,\n",
              "        0.10068215]])"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_data_scaled = pd.DataFrame(data_scaled)\n",
        "df_data_scaled.describe()"
      ],
      "metadata": {
        "id": "Xw4t3s1u_YVw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "outputId": "a8fe0fb7-b030-4f3c-f18e-68f769642c7f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               0           1           2           3           4           5   \\\n",
              "count  569.000000  569.000000  569.000000  569.000000  569.000000  569.000000   \n",
              "mean     0.338222    0.323965    0.332935    0.216920    0.394785    0.260601   \n",
              "std      0.166787    0.145453    0.167915    0.149274    0.126967    0.161992   \n",
              "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
              "25%      0.223342    0.218465    0.216847    0.117413    0.304595    0.139685   \n",
              "50%      0.302381    0.308759    0.293345    0.172895    0.390358    0.224679   \n",
              "75%      0.416442    0.408860    0.416765    0.271135    0.475490    0.340531   \n",
              "max      1.000000    1.000000    1.000000    1.000000    1.000000    1.000000   \n",
              "\n",
              "               6           7           8           9   ...          20  \\\n",
              "count  569.000000  569.000000  569.000000  569.000000  ...  569.000000   \n",
              "mean     0.208058    0.243137    0.379605    0.270379  ...    0.296663   \n",
              "std      0.186785    0.192857    0.138456    0.148702  ...    0.171940   \n",
              "min      0.000000    0.000000    0.000000    0.000000  ...    0.000000   \n",
              "25%      0.069260    0.100944    0.282323    0.163016  ...    0.180719   \n",
              "50%      0.144189    0.166501    0.369697    0.243892  ...    0.250445   \n",
              "75%      0.306232    0.367793    0.453030    0.340354  ...    0.386339   \n",
              "max      1.000000    1.000000    1.000000    1.000000  ...    1.000000   \n",
              "\n",
              "               21          22          23          24          25          26  \\\n",
              "count  569.000000  569.000000  569.000000  569.000000  569.000000  569.000000   \n",
              "mean     0.363998    0.283138    0.170906    0.404138    0.220212    0.217403   \n",
              "std      0.163813    0.167352    0.139932    0.150779    0.152649    0.166633   \n",
              "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
              "25%      0.241471    0.167837    0.081130    0.300007    0.116337    0.091454   \n",
              "50%      0.356876    0.235320    0.123206    0.397081    0.179110    0.181070   \n",
              "75%      0.471748    0.373475    0.220901    0.494156    0.302520    0.305831   \n",
              "max      1.000000    1.000000    1.000000    1.000000    1.000000    1.000000   \n",
              "\n",
              "               27          28          29  \n",
              "count  569.000000  569.000000  569.000000  \n",
              "mean     0.393836    0.263307    0.189596  \n",
              "std      0.225884    0.121954    0.118466  \n",
              "min      0.000000    0.000000    0.000000  \n",
              "25%      0.223127    0.185098    0.107700  \n",
              "50%      0.343402    0.247782    0.163977  \n",
              "75%      0.554639    0.318155    0.242949  \n",
              "max      1.000000    1.000000    1.000000  \n",
              "\n",
              "[8 rows x 30 columns]"
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              "\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>...</th>\n",
              "      <th>20</th>\n",
              "      <th>21</th>\n",
              "      <th>22</th>\n",
              "      <th>23</th>\n",
              "      <th>24</th>\n",
              "      <th>25</th>\n",
              "      <th>26</th>\n",
              "      <th>27</th>\n",
              "      <th>28</th>\n",
              "      <th>29</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.338222</td>\n",
              "      <td>0.323965</td>\n",
              "      <td>0.332935</td>\n",
              "      <td>0.216920</td>\n",
              "      <td>0.394785</td>\n",
              "      <td>0.260601</td>\n",
              "      <td>0.208058</td>\n",
              "      <td>0.243137</td>\n",
              "      <td>0.379605</td>\n",
              "      <td>0.270379</td>\n",
              "      <td>...</td>\n",
              "      <td>0.296663</td>\n",
              "      <td>0.363998</td>\n",
              "      <td>0.283138</td>\n",
              "      <td>0.170906</td>\n",
              "      <td>0.404138</td>\n",
              "      <td>0.220212</td>\n",
              "      <td>0.217403</td>\n",
              "      <td>0.393836</td>\n",
              "      <td>0.263307</td>\n",
              "      <td>0.189596</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.166787</td>\n",
              "      <td>0.145453</td>\n",
              "      <td>0.167915</td>\n",
              "      <td>0.149274</td>\n",
              "      <td>0.126967</td>\n",
              "      <td>0.161992</td>\n",
              "      <td>0.186785</td>\n",
              "      <td>0.192857</td>\n",
              "      <td>0.138456</td>\n",
              "      <td>0.148702</td>\n",
              "      <td>...</td>\n",
              "      <td>0.171940</td>\n",
              "      <td>0.163813</td>\n",
              "      <td>0.167352</td>\n",
              "      <td>0.139932</td>\n",
              "      <td>0.150779</td>\n",
              "      <td>0.152649</td>\n",
              "      <td>0.166633</td>\n",
              "      <td>0.225884</td>\n",
              "      <td>0.121954</td>\n",
              "      <td>0.118466</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.223342</td>\n",
              "      <td>0.218465</td>\n",
              "      <td>0.216847</td>\n",
              "      <td>0.117413</td>\n",
              "      <td>0.304595</td>\n",
              "      <td>0.139685</td>\n",
              "      <td>0.069260</td>\n",
              "      <td>0.100944</td>\n",
              "      <td>0.282323</td>\n",
              "      <td>0.163016</td>\n",
              "      <td>...</td>\n",
              "      <td>0.180719</td>\n",
              "      <td>0.241471</td>\n",
              "      <td>0.167837</td>\n",
              "      <td>0.081130</td>\n",
              "      <td>0.300007</td>\n",
              "      <td>0.116337</td>\n",
              "      <td>0.091454</td>\n",
              "      <td>0.223127</td>\n",
              "      <td>0.185098</td>\n",
              "      <td>0.107700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.302381</td>\n",
              "      <td>0.308759</td>\n",
              "      <td>0.293345</td>\n",
              "      <td>0.172895</td>\n",
              "      <td>0.390358</td>\n",
              "      <td>0.224679</td>\n",
              "      <td>0.144189</td>\n",
              "      <td>0.166501</td>\n",
              "      <td>0.369697</td>\n",
              "      <td>0.243892</td>\n",
              "      <td>...</td>\n",
              "      <td>0.250445</td>\n",
              "      <td>0.356876</td>\n",
              "      <td>0.235320</td>\n",
              "      <td>0.123206</td>\n",
              "      <td>0.397081</td>\n",
              "      <td>0.179110</td>\n",
              "      <td>0.181070</td>\n",
              "      <td>0.343402</td>\n",
              "      <td>0.247782</td>\n",
              "      <td>0.163977</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.416442</td>\n",
              "      <td>0.408860</td>\n",
              "      <td>0.416765</td>\n",
              "      <td>0.271135</td>\n",
              "      <td>0.475490</td>\n",
              "      <td>0.340531</td>\n",
              "      <td>0.306232</td>\n",
              "      <td>0.367793</td>\n",
              "      <td>0.453030</td>\n",
              "      <td>0.340354</td>\n",
              "      <td>...</td>\n",
              "      <td>0.386339</td>\n",
              "      <td>0.471748</td>\n",
              "      <td>0.373475</td>\n",
              "      <td>0.220901</td>\n",
              "      <td>0.494156</td>\n",
              "      <td>0.302520</td>\n",
              "      <td>0.305831</td>\n",
              "      <td>0.554639</td>\n",
              "      <td>0.318155</td>\n",
              "      <td>0.242949</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 30 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3b177e23-d355-424a-850a-eb065e2e2c5a')\"\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",
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              "  </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",
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              "    }\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-3b177e23-d355-424a-850a-eb065e2e2c5a 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-3b177e23-d355-424a-850a-eb065e2e2c5a');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "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(df_data_scaled, df_label, test_size=0.2, random_state=62)"
      ],
      "metadata": {
        "id": "tSqmBfCZAqeO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2. 모델 읽어오기"
      ],
      "metadata": {
        "id": "RoRLy8FIBLuS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "RzlLVT5oBKeP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bcbb8f77-1d69-4a1a-d901-7bca4bf21c83"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import joblib\n",
        "\n",
        "path = '/content/drive/MyDrive/2022_1_Study_AI/2022_1_Study_AI/data'\n",
        "filename = '/breast_cancer_v1.pkl'\n",
        "\n",
        "clf = joblib.load(path+filename)\n",
        "clf.predict(X_test)"
      ],
      "metadata": {
        "id": "4A-TU9JYBVQ9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6450294a-8b4e-47f0-f9a1-83e79fe6632f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    }
  ]
}