CART.ipynb

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      "source": [
        "################################################\n",
        "# Decision Tree Classification: CART\n",
        "################################################\n",
        "\n",
        " 1. Exploratory Data Analysis\n",
        " 2. Data Preprocessing & Feature Engineering\n",
        " 3. Modeling using CART\n",
        " 4. Hyperparameter Optimization with GridSearchCV\n",
        " 5. Final Model\n",
        " 6. Feature Importance\n",
        " 7. Analyzing Model Complexity with Learning Curves (BONUS)\n",
        " 8. Visualizing the Decision Tree\n",
        " 9. Extracting Decision Rules\n",
        " 10. Extracting Python/SQL/Excel Codes of Decision Rules\n",
        " 11. Prediction using Python Codes\n",
        " 12. Saving and Loading Model"
      ],
      "metadata": {
        "id": "ZohCVlc5Fm7f"
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      "execution_count": null,
      "metadata": {
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          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1cc48a6d-0e14-4aad-f5a0-1f4243ec514d"
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      "outputs": [
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          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: pydotplus in /usr/local/lib/python3.7/dist-packages (2.0.2)\n",
            "Requirement already satisfied: pyparsing>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from pydotplus) (3.0.9)\n"
          ]
        }
      ],
      "source": [
        "pip install pydotplus"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install skompiler"
      ],
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            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting skompiler\n",
            "  Downloading SKompiler-0.6.tar.gz (45 kB)\n",
            "\u001b[K     |████████████████████████████████| 45 kB 1.4 MB/s \n",
            "\u001b[?25hRequirement already satisfied: scikit-learn>=0.22 in /usr/local/lib/python3.7/dist-packages (from skompiler) (1.0.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.22->skompiler) (3.1.0)\n",
            "Requirement already satisfied: scipy>=1.1.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.22->skompiler) (1.7.3)\n",
            "Requirement already satisfied: numpy>=1.14.6 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.22->skompiler) (1.21.6)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.22->skompiler) (1.1.0)\n",
            "Building wheels for collected packages: skompiler\n",
            "  Building wheel for skompiler (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for skompiler: filename=SKompiler-0.6-py3-none-any.whl size=54286 sha256=980f97ae593d0abea09bfebab86e22876056e7068bca0509c4d69ff2a4355646\n",
            "  Stored in directory: /root/.cache/pip/wheels/47/1c/59/b80a730f4afd2144bad854df4b167b812486c9d4c1bd4cf4c5\n",
            "Successfully built skompiler\n",
            "Installing collected packages: skompiler\n",
            "Successfully installed skompiler-0.6\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install astor"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/"
        },
        "id": "e9wUQ7ROF1oj",
        "outputId": "8425e35b-8876-47f5-995c-1ff9db342ca9"
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            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: astor in /usr/local/lib/python3.7/dist-packages (0.8.1)\n"
          ]
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install joblib"
      ],
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          "base_uri": "https://localhost:8080/"
        },
        "id": "-YoQSbXjF34y",
        "outputId": "653218a9-9041-42b6-91c4-d01f11858447"
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      "execution_count": null,
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            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (1.1.0)\n"
          ]
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    {
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      "source": [
        "import warnings\n",
        "import joblib\n",
        "import pydotplus\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "from matplotlib import pyplot as plt\n",
        "from sklearn.tree import DecisionTreeClassifier, export_graphviz, export_text\n",
        "from sklearn.metrics import classification_report, roc_auc_score\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV, cross_validate, validation_curve\n",
        "from skompiler import skompile\n",
        "import graphviz\n",
        "\n",
        "pd.set_option('display.max_columns', None)\n",
        "pd.set_option('display.width', 500)\n",
        "\n",
        "warnings.simplefilter(action='ignore', category=Warning)"
      ],
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        "id": "up92XhmuF5em"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount(\"/content/gdrive\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KQIH5cmmF8XV",
        "outputId": "8bd71715-15e4-4f40-9f82-89707fb80ed0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"/content/gdrive/My Drive/machine_learning/diabetes.csv\")\n",
        "df.head()"
      ],
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          "height": 206
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        "id": "M3_wk33MF-vz",
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              "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  DiabetesPedigreeFunction  Age  Outcome\n",
              "0            6      148             72             35        0  33.6                     0.627   50        1\n",
              "1            1       85             66             29        0  26.6                     0.351   31        0\n",
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              "3            1       89             66             23       94  28.1                     0.167   21        0\n",
              "4            0      137             40             35      168  43.1                     2.288   33        1"
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        "df.info()"
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 768 entries, 0 to 767\n",
            "Data columns (total 9 columns):\n",
            " #   Column                    Non-Null Count  Dtype  \n",
            "---  ------                    --------------  -----  \n",
            " 0   Pregnancies               768 non-null    int64  \n",
            " 1   Glucose                   768 non-null    int64  \n",
            " 2   BloodPressure             768 non-null    int64  \n",
            " 3   SkinThickness             768 non-null    int64  \n",
            " 4   Insulin                   768 non-null    int64  \n",
            " 5   BMI                       768 non-null    float64\n",
            " 6   DiabetesPedigreeFunction  768 non-null    float64\n",
            " 7   Age                       768 non-null    int64  \n",
            " 8   Outcome                   768 non-null    int64  \n",
            "dtypes: float64(2), int64(7)\n",
            "memory usage: 54.1 KB\n"
          ]
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    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
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        },
        "id": "dK42kMMmGxgF",
        "outputId": "d3535455-7906-465c-b391-41f7d51c3903"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pregnancies                 0\n",
              "Glucose                     0\n",
              "BloodPressure               0\n",
              "SkinThickness               0\n",
              "Insulin                     0\n",
              "BMI                         0\n",
              "DiabetesPedigreeFunction    0\n",
              "Age                         0\n",
              "Outcome                     0\n",
              "dtype: int64"
            ]
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          "metadata": {},
          "execution_count": 9
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    {
      "cell_type": "code",
      "source": [
        "y = df[\"Outcome\"]\n",
        "X = df.drop([\"Outcome\"], axis=1)\n",
        "\n",
        "cart_model = DecisionTreeClassifier(random_state=1).fit(X, y)\n",
        "\n",
        "#random_state=1 ortak çıktı elde edilmek için yazıldı özel bir amacı yok"
      ],
      "metadata": {
        "id": "_ViVDocNGzWr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Confusion matrix için y_pred:\n",
        "y_pred = cart_model.predict(X)"
      ],
      "metadata": {
        "id": "tsFOIne-G3Ko"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# AUC için y_prob:\n",
        "y_prob = cart_model.predict_proba(X)[:, 1]"
      ],
      "metadata": {
        "id": "vpRQQLOwG4_u"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Confusion matrix\n",
        "print(classification_report(y, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sA5OaOF-G6yP",
        "outputId": "71b47680-8bba-43a0-9b3a-f776f6d35f0b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00       500\n",
            "           1       1.00      1.00      1.00       268\n",
            "\n",
            "    accuracy                           1.00       768\n",
            "   macro avg       1.00      1.00      1.00       768\n",
            "weighted avg       1.00      1.00      1.00       768\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# MODEL BAŞARISI 1 ÇIKTI NASIL YANİ ?? bu terslikte bir iş var gibi :)\n",
        "\n",
        "## Başarımı nasıl doğru değerlendiririm ??"
      ],
      "metadata": {
        "id": "vsnYmNueJN4z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Holdout Yöntemi ile Başarı Değerlendirme\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=45)"
      ],
      "metadata": {
        "id": "pbNSYDt8G-aS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cart_model = DecisionTreeClassifier(random_state=17).fit(X_train, y_train)"
      ],
      "metadata": {
        "id": "B6fww9zWJy_b"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# AUC\n",
        "roc_auc_score(y, y_prob)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CW8jmKk3G8TL",
        "outputId": "51caf725-dd21-46ee-8775-17acc062bc01"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Train Hatası\n",
        "y_pred = cart_model.predict(X_train)\n",
        "y_prob = cart_model.predict_proba(X_train)[:, 1]\n",
        "print(classification_report(y_train, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_fIn8ydzHB1y",
        "outputId": "c4d61e9b-150a-4a6a-c039-9ddd868f5f2b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00       346\n",
            "           1       1.00      1.00      1.00       191\n",
            "\n",
            "    accuracy                           1.00       537\n",
            "   macro avg       1.00      1.00      1.00       537\n",
            "weighted avg       1.00      1.00      1.00       537\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "roc_auc_score(y_train, y_prob)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bSbdIQPYHDs_",
        "outputId": "86ede013-506a-42f6-9ce6-d2013f8a46c5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Test Hatası\n",
        "y_pred = cart_model.predict(X_test)  # modelin hiç görmediği test setini gönderiyoruz\n",
        "y_prob = cart_model.predict_proba(X_test)[:, 1]\n",
        "print(classification_report(y_test, y_pred))\n",
        "roc_auc_score(y_test, y_prob)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fo9BfC8mHGJQ",
        "outputId": "551ecadd-dda3-4251-8ad0-371218b0d205"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.77      0.75      0.76       154\n",
            "           1       0.53      0.56      0.54        77\n",
            "\n",
            "    accuracy                           0.69       231\n",
            "   macro avg       0.65      0.66      0.65       231\n",
            "weighted avg       0.69      0.69      0.69       231\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6558441558441559"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model göremediği test setinde çalışınca overfit oldu. ! \n",
        "\n",
        "acc değeri : 0.69 iyi de değil kötüde değil"
      ],
      "metadata": {
        "id": "RuKIMs0iKQc6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# CV ile Başarı Değerlendirme\n",
        "cart_model = DecisionTreeClassifier(random_state=17).fit(X, y)\n",
        "\n",
        "#cart_model sonunda fit yazdık o yüzden ezberledi gibi bir soru olabilir fakat cv yöntemi kendi tekrar parçalayıp böldüğü için sorun olmayacaktır.\n",
        "\n",
        "cv_results = cross_validate(cart_model,\n",
        "                            X, y,\n",
        "                            cv=5,\n",
        "                            scoring=[\"accuracy\", \"f1\", \"roc_auc\"])"
      ],
      "metadata": {
        "id": "xKtv7HT0HIKR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cv_results['test_accuracy'].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B3I23uPqLIKg",
        "outputId": "9966e184-293a-4e0a-9b83-428955320df5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7058568882098294"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cv_results['test_f1'].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uSzFcZ5ALJ_C",
        "outputId": "9e8e7831-6156-4d87-a02e-c9716611c7a0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5710621194523633"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cv_results['test_roc_auc'].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2OBzCiuRLLpA",
        "outputId": "ebd04a2f-df8c-4028-c80c-ae092f13c260"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6719440950384347"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 4. Hyperparameter Optimization with GridSearchCV\n",
        "cart_model.get_params()\n",
        "\n",
        "#overfit önüne geçmek için max_depth ve min_samples_split önemli parametredir!"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MeruOKOxLNYK",
        "outputId": "c45ea2a2-e934-49ec-ad41-95ebaefcff88"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'ccp_alpha': 0.0,\n",
              " 'class_weight': None,\n",
              " 'criterion': 'gini',\n",
              " 'max_depth': None,\n",
              " 'max_features': None,\n",
              " 'max_leaf_nodes': None,\n",
              " 'min_impurity_decrease': 0.0,\n",
              " 'min_samples_leaf': 1,\n",
              " 'min_samples_split': 2,\n",
              " 'min_weight_fraction_leaf': 0.0,\n",
              " 'random_state': 17,\n",
              " 'splitter': 'best'}"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cart_params = {'max_depth': range(1, 11),\n",
        "               \"min_samples_split\": range(2, 20)}"
      ],
      "metadata": {
        "id": "bieVkl3uQaPJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cart_best_grid = GridSearchCV(cart_model,\n",
        "                              cart_params,\n",
        "                              scoring=\"f1\",\n",
        "                              cv=5,\n",
        "                              n_jobs=-1,\n",
        "                              verbose=True).fit(X, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1sBaUgAFSRL8",
        "outputId": "3feaf2c1-6e88-43b0-a252-a3626aeef515"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 180 candidates, totalling 900 fits\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cart_best_grid.best_params_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kI72ICgZRcKr",
        "outputId": "0260b513-cf7a-4ceb-e1d5-e605a9688601"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'max_depth': 4, 'min_samples_split': 2}"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cart_best_grid.best_score_\n",
        "\n",
        "# 0.6395752751155839 bu değer GridSeacrh içindeki scoring dir ön tanımlı olarak accuracy verir. scoring =\"f1\" de diyip değiştiredebilirsin.\n",
        "# scoring =\"roc_auc\" göre yaparsan farklı sonuç alırsın."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pjaiIzbGReG2",
        "outputId": "7f43ba76-0b1b-4513-d22b-7111fe2169b6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6395752751155839"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cart_best_grid = GridSearchCV(cart_model,\n",
        "                              cart_params,\n",
        "                              cv=5,\n",
        "                              n_jobs=-1,\n",
        "                              verbose=2).fit(X, y)\n",
        "\n",
        "# verbose raporlamanın türünü ifade ediyor. ister verbose=True de ister verbose=1. verbose=2 dersen eşleştirme dökumanı çıkar.\n",
        "#cart_best_grid  GridSeacrh içinde en iyi modeli saklar"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QrEjvvrvP8L0",
        "outputId": "991cc604-d532-475f-9ead-cb2082e0c108"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 180 candidates, totalling 900 fits\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cart_best_grid.best_params_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tGAmu0Q0SdZv",
        "outputId": "ac2071d2-77f3-40c8-c0ad-56f36d577f17"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'max_depth': 5, 'min_samples_split': 4}"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cart_best_grid.best_score_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qlDLqbtSSgiJ",
        "outputId": "5d4e1ab2-ef09-4d40-a89a-c47267f148a1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7500806383159324"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "random = X.sample(1, random_state=45)\n",
        "random"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "vWePnfr_TjbL",
        "outputId": "909c515b-8393-44d1-e0ef-f374c33db639"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  DiabetesPedigreeFunction  Age\n",
              "195            5      158             84             41      210  39.4                     0.395   29"
            ],
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cart_best_grid.predict(random)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-BluserkLQ32",
        "outputId": "06785613-095c-4e6b-e4d7-bc90c0279d57"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1])"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 5. Final Model\n",
        "cart_final = DecisionTreeClassifier(**cart_best_grid.best_params_, random_state=17).fit(X, y)\n",
        "cart_final.get_params()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UOuWkn1WLT4E",
        "outputId": "d086e094-95fd-477c-f389-7ad0dd08eb53"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'ccp_alpha': 0.0,\n",
              " 'class_weight': None,\n",
              " 'criterion': 'gini',\n",
              " 'max_depth': 5,\n",
              " 'max_features': None,\n",
              " 'max_leaf_nodes': None,\n",
              " 'min_impurity_decrease': 0.0,\n",
              " 'min_samples_leaf': 1,\n",
              " 'min_samples_split': 4,\n",
              " 'min_weight_fraction_leaf': 0.0,\n",
              " 'random_state': 17,\n",
              " 'splitter': 'best'}"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cart_final = cart_model.set_params(**cart_best_grid.best_params_).fit(X, y)\n",
        "cart_final\n",
        "# (cart_best_grid.best_params_) parametreleri var olan modele göre set edebilirim. "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xAnBjsZyLX9c",
        "outputId": "68b35cd3-39ed-4857-fc91-7752b2ffc850"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(max_depth=5, min_samples_split=4, random_state=17)"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cv_results = cross_validate(cart_final,\n",
        "                            X, y,\n",
        "                            cv=5,\n",
        "                            scoring=[\"accuracy\", \"f1\", \"roc_auc\"])\n",
        "cv_results #sonuç karışık geldiği için içinden ortalama alıp seçmen gerekiyor"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n_6i4Dl1LaCg",
        "outputId": "5f2c713b-01a7-473c-f404-1789e3c90bfd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'fit_time': array([0.01401925, 0.02041006, 0.01624393, 0.00905991, 0.00643492]),\n",
              " 'score_time': array([0.01883006, 0.01742101, 0.00999808, 0.00844932, 0.00800538]),\n",
              " 'test_accuracy': array([0.74675325, 0.68831169, 0.75324675, 0.79084967, 0.77124183]),\n",
              " 'test_f1': array([0.59793814, 0.55555556, 0.59574468, 0.69230769, 0.63157895]),\n",
              " 'test_roc_auc': array([0.81537037, 0.72333333, 0.80518519, 0.83962264, 0.8054717 ])}"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cv_results['test_accuracy'].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CNK0BcPwLbk1",
        "outputId": "4a4d8f1e-c0fa-47e8-a680-3f768cc53d20"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7500806383159324"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cv_results['test_f1'].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1WAHHHLnLdoA",
        "outputId": "7f745b83-6eab-42a4-c9d5-c044ac4dfb51"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.614625004082526"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cv_results['test_roc_auc'].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y0qQQbLhLe92",
        "outputId": "dcdc2beb-da5c-4ad0-accc-476ce85b5fba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.797796645702306"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 6. Feature Importance\n",
        "cart_final.feature_importances_\n",
        "\n",
        "def plot_importance(model, features, num=len(X), save=False):\n",
        "    feature_imp = pd.DataFrame({'Value': model.feature_importances_, 'Feature': features.columns})\n",
        "    plt.figure(figsize=(10, 10))\n",
        "    sns.set(font_scale=1)\n",
        "    sns.barplot(x=\"Value\", y=\"Feature\", data=feature_imp.sort_values(by=\"Value\",\n",
        "                                                                     ascending=False)[0:num])\n",
        "    plt.title('Features')\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "    if save:\n",
        "        plt.savefig('importances.png')\n",
        "\n",
        "\n",
        "plot_importance(cart_final, X, num=5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 725
        },
        "id": "4NKqnXhnLgYg",
        "outputId": "74b5809e-4718-466c-9782-cd91ede05e80"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 7. Analyzing Model Complexity with Learning Curves (BONUS)\n",
        "train_score, test_score = validation_curve(cart_final, X, y,\n",
        "                                           param_name=\"max_depth\",\n",
        "                                           param_range=range(1, 11),\n",
        "                                           scoring=\"roc_auc\",\n",
        "                                           cv=10)"
      ],
      "metadata": {
        "id": "lvmX1aquLnO5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mean_train_score = np.mean(train_score, axis=1)\n",
        "mean_test_score = np.mean(test_score, axis=1)"
      ],
      "metadata": {
        "id": "H5TgpqoxLsXI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(range(1, 11), mean_train_score,\n",
        "         label=\"Training Score\", color='b')\n",
        "\n",
        "plt.plot(range(1, 11), mean_test_score,\n",
        "         label=\"Validation Score\", color='g')\n",
        "\n",
        "plt.title(\"Validation Curve for CART\")\n",
        "plt.xlabel(\"Number of max_depth\")\n",
        "plt.ylabel(\"AUC\")\n",
        "plt.tight_layout()\n",
        "plt.legend(loc='best')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "id": "5U4mNlBQLt4j",
        "outputId": "7129461c-2b20-4158-e2c1-8cb480f23a0b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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zaNGbuLm5k5AQx2+/7WTRojc5eHA/H3zwDj169OTYsaOAgpdeeo3Onf0B+OijJWzZ8iuOjk6EhfXlwIF9LF/+1VVx5ufn4uHhYXzs6+t3xbE83nvvLTIzMwAYPnwk9933IEVFhbz11utcuJCJwWBg2rT7GDUqus7zfuqpZ/n446UcOnQAtVpDYGAgTz75bKMkPCFamt5goPBSFRl5ZWTmlZGRX/N/XnElf+02KAAHWwucLvdgfDzsr+jVWNbq6ViYNyzRmAJJSFfYfTSbXUeym6TuQb1UDAy9+enF5eXlfPLJlwBUV1fz9tvvYWlpjVar5d//fpw9e36jf/8BV73uxInjfPHFN3h5dSA2dhE//vgdjzwy96pyaWmpPPfcAp5++j988cVyvvhiOQsXLmLXrh389tsuVqz4BisrK55//plrxnj//Q8xd+4sQkJ6ERLSi+HDR9KpU2cAXn75BSIjB/Lqq28BUFxcDMB7771NQEAXXn/9bQoKCnj44RkEBXUnICDwqvNeseJT7OzsjI+XLv2Ar776vM7zEcKUVVZrycovNyadjLwyMvPLqFLrgJqE4+lig6+nPf2CvXBxsDImHCc7SxztLDE3M5k5aY1GElIrERU1xvi1Xq9n6dL3OXLkMGCgsLCQM2dO15mQevW6DS+vminyPXuGsG9fUp31+/l1olu37pfLhbJ7904AkpP3c9ddw7GxsQFg1KgxrFixvM467r13JiNHjubAgX3s37+Xhx+ewdtvf0C3bt35/fcjvPvuEmNZZ2dnAPbv38vjj/8TAHd3dyIjB3Lw4H5jQrryvHfv3kF5eTnbtm0BQKNRExjY9TotJ0TL0RsMFBRXkmFMOuVk5JWSX/znjfg2Vub4etgxMESFj6cdPp72+LjbN3gIrS2RhHSFgaG31otpSra2Nsavv/vu/ygtLeHjj1dgZWVFbOyrqNXVdb7O0tLS+LVSaYZOp7tGOasryimvWe563N09GDlyNCNHjsbKyopt2zYbE93NuPK8DQZ48sn59O17+03XJ0RTqajSkplf09PJvCIBVWsu93oU4OViS+cOjgzq5Y2vhz2+nva4OlrJLQiXSUJqhUpLS3F3d8fKyor8/Dx27drO+PETm+S9wsL6snz5R0yZci+Wlpb88kvCNcvu3LmNAQMGY2ZmRnV1NefOpTF48FBsbW0JCenF999/zfTp9wM1Q3bOzs6Eh/cjLm41Dz/8CIWFBSQm7mby5Ol11j9o0BC+++7/CAkJxcrKmoqKcvLy8ozXuoRoDnq9gbziSmPS+WO4reDSn70eO2tzfD3tGdxLhY9nTeLxdrfDyqL99XpuhCSkVmjSpKksWDCf++6bjIeHV5P2GAYNGsrRo0eYOXMqjo6O9OwZSmlp3TNstm7dzNKlH2BpaYVOpyU8PIKJE6cAsGDBK7zzTiz33TcZpdKMu+8eyYwZD/DPfz7FW2+9xsyZUzEYDMyZ8zgBAV3qrH/GjAdYvvwj/v73+1EqlYCChx6aJQlJNJnyKg2ZVwy1ZeSVk1VQZrw/R6lQ0MHNlgBvR4b29sbX0x4fD3tcHKTXczNk2ncrnfbbnNO+KyrKsbW1Q6/X88Ybr+Du7sHs2Y81y3vfLJn2XT9Tm9psCrQ6PWcyijlfUMGptEIy88soLPlzKNzexsKYcHw87fDzdMDb3bZVzWJrDDLtW7SoV15ZSE7OBaqrqwkKCubee+9v6ZCEaBQXS6s5eraQI6mFHDtXRLVah1KpQOVmS1cfZ+68PNzm42GPs72l9HqamCQkcV2vv/52S4cgRKPQ6fWcvVDCkdSaJJSRV7PigKujFZE9O9ArwI3BfX0pLals4UjbJ0lIQog2raRCze9/9ILSiiiv0qJUKOjq48SkO7vQK8ANb3c7Y+/H2socGcxsGZKQhBBtit5g4HxOqbEXdC67BAPgaGdJWFcPenVxo0dnV2yt5defqZHviBCi1Suv0nAsrYijqYUcPVtISYUGBRDg7cj4wf706uKOr5c9SrkGZNIkIQkhWh2DwUBmfjlHUgs4mlpISlYJeoMBO2tzQgPcCO3iRoi/Kw62ltevTJgMSUhCiFahSq3l+LmLHLncC7pYWjMlu5OXA6MjO9GrixsBKkeUSukFtVaSkEzIk0/OY/DgIYwf/zfjcwaDgcmTx/PccwsIC+tb5+teffVFuncPZuLEKfXuh3TlCt/12bFjG+7u7vToEQLAyZPH+e67r1m4cNEtnF1tBw/u53//W4xGo0GjUePm5s577y29fMOrEDWf/ZyiCo6mFnLkbCGn0ovR6Q1YW5rR09+VXgFuhAS44eJgdf3KRKsgCcmEjBkzjm+/XVkrISUnH0CpVNC7d58G1XHla2/Wzp3b6N492JiQunfv0ajJSKvV8p//PM3ixR8ZF0c9ffpko93jUbN3kgzVtEZqjY6T6cWXk1CBcRHSju523H27L70C3Aj0cWqTK10LSUi1JGUfIDF7X5PUHam6nQhV3T2cPwwePJT//vd1zp1LMy6Hs27dWkaPHsvZs6n8979vUFVViVqtZvz4Cfztb9OuquPK/ZA0Gg3vvluz35GTkzNduwYZy6WmptSqb9y4e5g8eTpJSYns2rWD/fv3Ehe3hilTpuPl1YElS9437oG0fn0833zzFQqFAm9vH55++jlcXFzr3X/pShUVFVRWVuDq6mp87soFWM+dS+P999+mqKiw1h5JmZkZvPXWaxQXX8TMzIzZs+caVzgfNCicBx+cRWLibiIiIrn//pm8++5/SU09g1qtJiwsnCee+BdmZu3rrvrWoKC4kiOXp2WfPH8RtVaPpbmS4E4uRPXzIzTADXdnm+tXJFo9SUgmxMLCgrvvHkVCwloee+wfVFSUs3Pndlau/B57e3vee28plpaWVFRUMHv2TMLD+9e7jtuaNT+RnX2BlSt/QKvVMnfuLFSqmtXMVSrVVfX16xdJREQkgwYNMQ4BQs3w2h/Onk1h2bIPWb58Je7u7nzyyf949923ePnl14GG7b/k6OjIuHH3MHXqBHr37kNo6G2MGBGFl1cHtFot8+c/yezZj3HXXcMBuHSpZu+kl156npiYe4iOHk9a2lkef3wWK1f+iIuLCwBWVlZ8+mnNXkmxsYvo3bsP8+e/gF6v56WXnmfdurWMG3dPY3yrxC3S6fUcTilk84FMTpy/CICnsw1DbvOmVxc3gvyc292SPEISUi0Rqr7X7cU0tTFjxvHUU0/wyCOPs3nzr4SG3oanpxdFRYV8+OEbpKScRqFQUlCQT0rK6XoT0sGDBxg1KvrylufmjBw5iiNHDgFQVVV1w/XV1LmfyMiBuLvX9HpiYibwwAN/rs7d0P2X/v3vZ5gy5V4OHtzPnj27Wbnycz799Cs0Gg06nc6YjACcnJypqCgnJeU0o0ePA8DfP4DAwCCOHTvKoEFDAIw7zQLs2rWd48d/59tv/894vp6eXvWem2h6pRVqdhy+wLbkLApLqnF1tGLCkADCu3vSwVV2/m3vJCGZmK5du+Hm5sGePb+RkLCWSZNqftl/9NESXF3d+Oyz/8Pc3Jx///tx1Gr1Tb/PX+v717/m3lJ9f2jo/ksAHTv60LGjD2PHjufJJ+exe/cO+vWLvOn3trH58xeawWDgtdfepmNHn5uuTzSetOwSthzIJOlEHlqdnuBOLkwb3o3bAt0wk4ks4rJm+ySkpaUxZcoURo4cyZQpUzh37txVZfLz83n00UcZO3Yso0aNYs2aNcZjixcvJjIykpiYGGJiYnjppZeaK/RmN2bMOD777GMyMtIZPHgoAGVlpXh6emFubs7ZsykcPpx83Xr69g1nw4YEtFot1dVV/PrrBuOxq+s7ZDxmZ2dHWVlZnXX26RNOYuJuCgsLAIiLW83tt/e7ofOrqKhg7949/LHQfGlpKdnZWahUHfHz64SZmRlbtmwylr90qRhbWzsCA7uxfn08UHOdKTX1ND17htb5HoMHD2Xlyi+MCbG4uJgLF7JuKE5xazRaPYnHclj05X5e+WI/+0/lM/g2Fa/8PYL/Ny2MPt08JBmJWpqth7Rw4UKmT59OTEwMa9asYcGCBXz55Ze1yrzxxhuEhITwv//9j6KiIiZMmEC/fv2M1z3Gjx/PM88801wht5i7745iyZL3GTfuHiwsLACYOfNhXnllAevWrcHX14/evcOuW8+4cRNISUlhxoxJODk50717Ty5eLLxufSNHjubVV19i69bNxkkNfwgICGTOnMf517/mXp7U0JH/9/+eu8EzNPDzz9/z7rtvXt47SceIEaMYOvROAN5447+8++6brFjxCQqFkmnTZhAVNYaFCxfx1luv8f33X2NmZsbzz79svH70V//851MsXvweDzwwDYVCgYWFJfPmPYm3d8cbjFXcqKKSKrYdusCOQ1mUVGjwcrVl+vCuDAhRyXI9ol7Nsh9SYWEhI0eOJCkpCTOzmmGciIgINm7cWGum1ZgxY3j99dfp1asXAHPmzKFfv3489NBDLF68mIqKiltKSLIfUvsh+yHVr7H3QzIYDJzOKGbzgUwOni7AYDBwW6A7d/XtSI/Orq1qyR7ZK6p+rX4/pOzsbLy8vIxTbs3MzPD09CQ7O7tWQurZsycJCQmEhoaSmZlJcnIyPj5/XgNYt24du3btwsPDgyeeeIKwsOv3EoQQTadarSPxeA5bDmSSmV+OnbU5I/r5cmdYRzxkqra4QSbVf54/fz6vvfYaMTExeHt7ExkZaUxiU6dOZc6cOVhYWLB7924ee+wxEhISrjlkU5e6MnNenhJz89Y5jt1a424uDWkfpVKJh4dDM0Rjem7lvC8UlJGw+xyb9p6nvEqLv7cjT0zuzZCwjlhbmtSvlZvSXj8TDdVU7dMsnxyVSkVubi46nc44ZJeXl2e8NvQHV1dX3n77z83gZs2aRWBgIAAeHh7G5wcOHIhKpeLMmTP069fwC+p1Ddnp9Xo0Gl2r2wlShuzq15D2MRgM6PX6djk8czPDLnqDgd/PFrL5QBZHzxZiplTQN8iDYX19COzohEKhoPRSZavfS0iG7OrX6ofs3NzcCA4OJj4+npiYGOLj4wkODq41XAdw8eJFHBwcMDc3JzExkdOnT/PBBx8AkJubi5dXzX0kJ06cICsrC3//+u+ZaYiaqclazM0tbrku0brodFqUSrn58noqqjTsOpLNloNZ5BVX4mRnybiBnbkjrCPO9rKOnGg8zda3fvHFF5k/fz5Lly7F0dGR2NhYoKYXNG/ePEJDQzly5AivvvoqSqUSFxcXli1bho1NzTj0O++8w7Fjx1AqlVhYWPDmm2/W6jXdLBsbe0pLi3F2dkOhkCGw9sJg0FNaehEbm2v/tdbeZeaVsflgJonHclBr9AT6OHHPkAD6BnnIWnKiSTTLLDtTUdeQncFg4OLFfNTqKqD1NIVSqUSvlyG7a7l++yiwtLTGxcWj1Q3XNoZrDbtodXoOnSlg84FMTmUUY2GuJKKHF8P6+NCpQ/u4riJDdvVr9UN2pkyhUODq6tnSYdww+aGpn7TPjblUrmbHoSy2HbrAxdJq3J2smXRnFwb38sbeRoazRfNo9wlJiPbKYDBwNruEzQcy2XciD53eQM/OLswY0Y3burjLRnei2UlCEqKd0Wh1bN6XzuptKZzLKcXa0ow7wjpyV5+OqNzsWjo80Y5JQhKinSitULPlYBabD2RSVqlB5WbLjBHdiOzZARsr+VUgWp58CoVo4/KLK9m4N4OdRy6g1urp1cWNycODUDlbtcsJHcJ0SUISoo06n1PK+qTz7DuZh1KhoH9PL6L6+dHRw14mfQiTJAlJiDbEYDBw7FwRG5LSOX7uItaWZoy83Y/h4T64Olq3dHhC1EsSkhBtgE6vZ9+JPNYnpZORV4aTvSWT7ujC0N4dZcsH0WrIJ1WIVqxarWPHkQts3JtBYUkVKjdbHhzVnf49O2Ahi++KVkYSkhCtUEm5ms0HMtlyMJPyKi2BPk5Mv7srtwW6t6q9h4S4kiQkIVqRvIsV/LI3g11Hs9Fo9YR1dWdURCcCfZxaOjQhbpkkJCFagbTsEtYnpXPgVB5mSgUDQjowsp+f3Mgq2hRJSEKYKIPBwO9pRazfc56T6cXYWJkzKqITw8N9ZNsH0YifmGYAACAASURBVCZJQhLCxGh1f8yYO09mfjkuDlZMvjOQob29ZUUF0abJp1sIE1Gl1rLjcDYb96VTVFJNR3c7Hh4TTEQPL9l/SLQLkpCEaGGXytVs2p/B1oNZVFRrCfJ15r4RQYR2cZMZc6JdkYQkRAvJKargl73p7D6ag06np0+QB1ERfnTxlhlzon2ShCREM0u9cIkNe9I5eDofMzMlg0JrZsx5udq2dGhCtChJSEI0A73BwNHUQtYnpXM6oxhbK3PGDOjEsL6+ONlZtnR4QpgESUhCNCG93sCe4zms35NOVkE5bo5WTBvWlcG3qbC2lB8/Ia4kPxFCNAGDwcDRs4X8sC2VrPxyfDzsmTW2B7d395QZc0JcgyQkIRpZWnYJP2xN4WR6MZ4uNjw2PoS+QR6yGZ4Q19FsCSktLY358+dTXFyMs7MzsbGxdO7cuVaZ/Px8FixYQGZmJlqtljlz5hATEwOATqdj0aJF7Ny5E4VCwezZs5k0aVJzhS/EdeUXV/LzjrMkHc/FwdaCe+/uxtDe3tIjEqKBmi0hLVy4kOnTpxMTE8OaNWtYsGABX375Za0yb7zxBiEhIfzvf/+jqKiICRMm0K9fP1QqFXFxcaSnp7Nx40aKi4sZP348kZGR+Pj4NNcpCFGnskoN8b+dY8vBTJQKBdEDOjMqwk9WVRDiBjXLn26FhYUcP36c6OhoAKKjozl+/DhFRUW1yp08eZLBgwcD4OrqSvfu3Vm/fj0ACQkJTJo0CaVSiaurK8OHD2fDhg3NEb4QdVJrdKzfc55nliXy6/4MBoR04PVHIpkwJECSkRA3oVl+arKzs/Hy8sLMzAwAMzMzPD09yc7OxtXV1ViuZ8+eJCQkEBoaSmZmJsnJycYeUHZ2Nt7e3sayKpWKnJycG4rDzc2+Ec7GdHh4OLR0CCatqdpHpzew/WAGX60/SUFxJbf38GLmmB506uDYJO/XVOTzc23SNvVrqvYxqT/j5s+fz2uvvUZMTAze3t5ERkYak1hjKCwsQ683NFp9LcnDw4H8/NKWDsNkNVX7/J5WyA9bU8nIK6NzBwcemhZG904uAK3q+yGfn2uTtqnfrbSPUqmot2PQLAlJpVKRm5uLTqfDzMwMnU5HXl4eKpWqVjlXV1fefvtt4+NZs2YRGBhorOPChQv06tULuLrHJERTSs8t5YetKRw7dxF3J2vmxPQkvLunrDUnRCNqlmtIbm5uBAcHEx8fD0B8fDzBwcG1husALl68iFarBSAxMZHTp08brztFRUXxww8/oNfrKSoqYtOmTYwcObI5whftWMGlSj6JO85Ln+/jXE4p04Z15dVZ/ekX7CXJSIhG1mxDdi+++CLz589n6dKlODo6EhsbC9T0gubNm0doaChHjhzh1VdfRalU4uLiwrJly7CxsQEgJiaGw4cPM2LECADmzp2Lr69vc4Uv2pnyKg3rEs+zaX8mCgWM6t+J0f39sLW2aOnQhGizFAaDoW1cVGkAuYbUftxs+2i0erYczCT+t3NUVGkZENqBewYH4Opo3QRRthz5/FybtE39Wv01JCFMnd5gYO/xXH7ecZaCS1WEBLgy6Y5AfD3b1sxMIUyZJCTR7p04V8T3W1M5n1uKn5c9T47qTc/Ortd/oRCiUUlCEu1WZl4ZP2xL5ejZQtwcrZk1tgcRPWSyghAtRRKSaHeKSqpYtfMsvx3NwdbanMl3BjKsb0cszBvvnjchxI2ThCTajYoqLQl7zvPr/gwMBhgZ4ceYyE7Yycw5IUyCJCTR5ml1erYmZxG3+xxllRoie3pxz5AA3J1sWjo0IcQVJCGJNstgMLDvZB4/bU8lv7iK4E4uTL4zkE4dZJ0yIUyRJCTRJh1NLeCTVUdIyy7Fx8Oef0++jZ7+rrJJnhAmTBKSaFMullazcuMpks8U4OpoxcNjgons2QGlUhKREKZOEpJoEwwGA3uO5/L1r6fRaPXcPzqYAcGeWFrIzDkhWgtJSKLVu1Su5ssNJ0k+U0BgRyceGhNMaJCXLP8iRCsjCUm0antP5LJy42mq1Dom3xnIiNt9ZXhOiFZKEpJolUor1Hy18TT7T+bhr3Lg4TE98Ha3a+mwhBC3QBKSaHUOnMrnq19OUl6lZeLQAKIi/DBTNsvWXkKIJiQJSbQaZZUavt50mj3Hcunk5cBTU4PxkdW4hWgzJCGJVuFQSgFfrD9JWaWG8YP8GR3ZCXMz6RUJ0ZZIQhImraJKwzebzrD79xx8POz51+Tb8POSlRaEaIskIQmTdfRsISvWn+RSmZroAZ0ZN7Cz9IqEaMMkIQmTU1mt5bstKew4fAFvdzsenxCKv8qxpcMSQjQxSUjCpBw/V8TnCScoKq1mVH8/xg/yl32KhGgnJCEJk1Cl1vLDtlS2HszCy9WWZ2f0JbCjU0uHJYRoRs2WkNLS0pg/fz7FxcU4OzsTGxtL586da5UpLCzk2WefJTs7G61WS0REBM8//zzm5uYsXryYr7/+Gk9PTwD69OnDwoULmyt80YROpV/ks4QTFBRXMeJ2XyYMCZA16IRoh5otIS1cuJDp06cTExPDmjVrWLBgAV9++WWtMsuWLaNLly58/PHHaDQapk+fzsaNGxk9ejQA48eP55lnnmmukEUTq9bo+Gl7Kpv3Z+LhbMMz9/ahm69zS4clhGgh9U5ZOnPmDJ988kmdxz755BNSU1Mb9CaFhYUcP36c6OhoAKKjozl+/DhFRUW1yikUCsrLy9Hr9ajVajQaDV5eXg16D9G6pGRe4sXP9rJpfyZ39fHhpYf6STISop2rNyEtWbIElUpV57GOHTuyZMmSBr1JdnY2Xl5emJnVDMOYmZnh6elJdnZ2rXKPPfYYaWlpDBo0yPivb9++xuPr1q1j7NixPPTQQyQnJzfovYVp0Wh1fL8lhddXHkCnN/D/poVx74huWFnKEJ0Q7V29Q3aHDh0iNja2zmPDhw+/5rGbtWHDBoKCgvjiiy8oLy9n1qxZbNiwgaioKKZOncqcOXOwsLBg9+7dPPbYYyQkJODi4tLg+t3c2tYyMx4eresG0dPpF3n3m4Nk5pURFdmZB6N7YGtt0WTv19rap7lJ+1ybtE39mqp96k1Ily5dQnmNRSsVCgUlJSUNehOVSkVubi46nQ4zMzN0Oh15eXlX9b5WrlzJa6+9hlKpxMHBgbvuuoukpCSioqLw8PAwlhs4cCAqlYozZ87Qr1+/BsUAUFhYhl5vaHB5U+bh4dBq9vvRaPWs3Z1Gwp7zONtb8e8ptxHi70Z5aRXlpVVN8p6tqX1agrTPtUnb1O9W2kepVNTbMah3yM7Hx+eaQ2PJycl07NixQUG4ubkRHBxMfHw8APHx8QQHB+Pq6nrV++3YsQMAtVpNYmIiXbt2BSA3N9dY7sSJE2RlZeHv79+g9xct53xOKS9/sY91iecZGKrilYcjCPF3a+mwhBAmqN4e0qRJk3j++ed55513CAkJMT5/7NgxXnjhBWbMmNHgN3rxxReZP38+S5cuxdHR0TjcN2vWLObNm0doaCjPPfccCxcuZOzYseh0OiIiIpg8eTIA77zzDseOHUOpVGJhYcGbb75Zq9ckTItWpyf+t3OsSzyPva0F//hbL24LdG/psIQQJkxhMBjqHcNatGgRX3/9NR06dMDT05O8vDxyc3OZNm0azz//fHPF2ShkyK55ZOSVsTz+OOl5ZUT29GL63d2wa8JrRXUx5fYxBdI+1yZtU7+mHLK77n1Izz//PDNmzCAxMZFLly7h7OxMZGQknTp1uqmARNul0+tJ2JPO2l1p2Fmb8/iEUPp0k16sEKJhGnRjbOfOna9aVUGIK2UVlLM8/jjnckrpF+zJvXd3w8HWsqXDEkK0IvUmpKFDh6JQKGq/wNwcb29voqOjjdd3RPtlMBj4dV8GP25PxdrSnEfHh3B7d8+WDksI0QrVm5Deeuutq57TarVkZGSwYsUKSkpK+Pvf/95kwQnTVqXW8nnCSfadzCOsqzszo7rjaCe9IiHEzak3IdV3j0+/fv2YM2eOJKR2KvdiBR/+fJQLBeVMuqMLURF+V/WmhRDiRtz04qr+/v4UFhY2ZiyilTicUsDHccdRKuDfk3vT09/1+i8SQojruOmEdOTIETp06NCYsQgTpzcYiN99jjW70vD1sufxe0Jxd7Zp6bCEEG1EvQnpxx9/vOo5rVZLVlYWP//8M08++WSTBSZMS0WVlk/jj3MopYDInh2YGRUkexYJIRpVvQlpzZo1V7/A3ByVSkVsbCwDBgxossCE6cgqKOfDn49SUFzJ9OFdGdbXR64XCSEaXb0J6auvvqrz+ZMnT7JmzRrmz5/Prl27miQwYRr2n8xjecIJrMyVPDW1N0F+DV9dXQghbkSDryEVFRURFxfH6tWrOXnyJOHh4fznP/9pythEC9LrDazaeZZ1iecJ8HZk7j2huDhYtXRYQog2rN6EpNFo2LJlC6tWrWLXrl34+fkxZswYsrKyeO+993Bzk1Wb26KySg0frz3G72lFDLnNm3vv7oaFeb0LwwshxC2rNyENHDgQhULBhAkTeOKJJ+jZsycA33zzTbMEJ5pfem4pH/58lOKyamZGBTG0d8O2GBFCiFtV75+9QUFBlJaWcvjwYY4ePcqlS5eaKy7RAvYcy+G1r2q2Fn/m3j6SjIQQzeq6kxqysrJYvXo1n332GYsWLWLQoEFUVFSg1WqbK0bRxLQ6PT9sTeXX/Rl083Hi0XtCcZIlgIQQzey6FwY6duzI3Llz2bhxIytWrMDDwwOlUsm4ceN48803myNG0YRKytX899tD/Lo/g+F9fXhqWpgkIyFEi7ihlRrCw8MJDw/n+eef59dff2X16tVNFZdoBmcvlLBk1VHKKjXMiu5BZIisvCGEaDk3tXSQlZUV0dHRREdHN3Y8opnsOHyBlRtP4WRnxXMz+tKpg0NLhySEaOduei070TpptHq+2XSabYcu0KOzC3NiQrC3ad7txYUQoi6SkNqRi6XVLF11lNQLJYzq78fEIV1QKmUJICGEaZCE1E6czihm6erfqVbrZFdXIYRJkoTUxhkMBrYczOLbzWdwc7Lmqam98fGwb+mwhBDiKs2WkNLS0pg/fz7FxcU4OzsTGxtL586da5UpLCzk2WefJTs7G61WS0REBM8//zzm5ubodDoWLVrEzp07USgUzJ49m0mTJjVX+K2SWqPjq19Osfv3HG7r4sassT2wtZbrRUII09RsC5QtXLiQ6dOn88svvzB9+nQWLFhwVZlly5bRpUsX4uLiWLt2LceOHWPjxo0AxMXFkZ6ezsaNG/nuu+9YvHgxmZmZzRV+q1NwqZLXVx5k9+85jBvYmSf+1kuSkRDCpDVLQiosLOT48ePGaeLR0dEcP36coqKiWuUUCgXl5eXo9XrUajUajQYvLy8AEhISmDRpEkqlEldXV4YPH86GDRuaI/xW58S5Il5esZ+84grmTezF+MEBKGX/IiGEiWuWIbvs7Gy8vLwwM6vZYdTMzAxPT0+ys7NxdXU1lnvsscd44oknGDRoEJWVldx777307dvXWIe3t7exrEqlIicn54bicHNrW9dOPDxq3ztkMBhYtS2VL9Ydo6OnA/95sB8d2/H1or+2j6hN2ufapG3q11TtY1KTGjZs2EBQUBBffPEF5eXlzJo1iw0bNhAVFdUo9RcWlqHXGxqlrpbm4eFAfn6p8XG1Wsfn60+w90Qe4UEePDg6GEsMtcq0J39tH1GbtM+1SdvU71baR6lU1NsxaJYhO5VKRW5uLjqdDgCdTkdeXh4qlapWuZUrVzJu3DiUSiUODg7cddddJCUlGeu4cOGCsWx2djYdOshSNwC5FytY9NV+9p3M4293dOHR8SHYWJnU3xpCCHFdzZKQ3NzcCA4OJj4+HoD4+HiCg4NrDdcB+Pj4sGPHDgDUajWJiYl07doVgKioKH744Qf0ej1FRUVs2rSJkSNHNkf4Ju1IaiGvrNhPcWk1/5p8G6P7d0Ih14uEEK1Qs/0Z/eKLLzJ//nyWLl2Ko6MjsbGxAMyaNYt58+YRGhrKc889x8KFCxk7diw6nY6IiAgmT54MQExMDIcPH2bEiBEAzJ07F19f3+YK3+To9QbW7k5jzc40fD3tmTshFA9nm5YOSwghbprCYDC0jYsqDdBWriFVVmv5cuNpko7l0L+nFzOjumNlYdbSYZkUuQ5QP2mfa5O2qV9TXkOSCw2tjF5v4IMfj3Am6xLThnVleLiPDNEJIdoESUitTMKe85zKKOYfU8K4zd+lpcMRQohG02wrNYhbd/ZCCWt2pdEv2JNht7ff62dCiLZJElIrUaXW8nHcMZztLbl/ZJAM0wkh2hxJSK3E15vOkF9cyd+jZYFUIUTbJAmpFdh/Mo9dR7IZE9mJID+5biSEaJskIZm4opIqVqw/ib/KkXED/Vs6HCGEaDIyy86E6fUGPok7jk5vYPa4Hpibyd8PULOIrAEDeoO+9tcY0BsMGAwGnHTWLR2mEOIGSUIyYeuTaqZ4Pzi6O14uti0dTp3KNRWsT9tEemkWBgwYDHr01CSFK782PofemDSulVQMhsuJBf1fXltTpqFszG1wtLTH0dIBh8v/13ztYHze0coBBwt7zJRyY7EQLU0SkolKyy5h9c40wrt7MihUdf0XNDO9QU9i9j7WpK6nUltFgFMnzBXmKBVKFChQKBQoFQoUKGu+vvycAkVNmSu/Npb/62svf12rjtpfG+u98jgKlFZ6cooLKakupURdRmbpBUrUpVTpqus8HzsL2ysS1p/Jq9ZzVg7YW9ihVEhPVYimIAnJBFWptXy89hiOdpbMjDK9Kd7pJZl8d3o150rS6eLUmSlB99DR3rSS5rWWN1Hr1JSoyyhRl1KqLqVEXfrn48vJK+1SOiXqUjR6zVWvV6DA3tKu7oT1l+RlZ25rct87IUyZJCQT9O3mM+RdrOTp6WHYmdAU73JNBXFnf2FX1h7sLe24P3gK/Tr0aVW/dC3NLHG3ccXdxrXecgaDgWpdda2EVZPEyiipLqVUU0pJdRk55XmUqkvRGnRX1aFUKC8nKXvcrF3xtPXA09a95p+NB3YWkrCEuJIkJBOz/2QeOw6b1hRvvUHPnuwDrElNoFxTwR0+AxkTcDc25m13dXGFQoG1uTXW5tZ42nrUW9ZgMFCprazd2/ojiVWXckldQlZZNocLjtW6BmZrbvNnkrL5I1l54GHjhrW5VVOfohAmRxKSCSkqqeKLDSfp3MGBmEGmMcU7vTST70+tJq0knQCnzkzpNh4fB+/rv7AdUSgU2FrYYmthSwc7r2uW0+l1FFQVkV9RQF5FPrmVBeRVFHD6Yip7cw7WKutk6WhMUJ627njZeuBp446bjSvmSvmxFW2TfLJNhN5g4NP442h0eh4Z17PFp3hXaCqIO7uRnVmJ2Fu0zuE5U2OmNMPL1gMvWw8guNYxtU5NfmUhuRX55F1OWHkVBRzKP0q5psJYTqlQ4mrtUpOkruhVedq642zlJBMuRKsmCclE/JKUzsn0Yh4Y1R0v15ab4q036EnKOcjqlHWUayoY4jOAaP8R2Fq03eE5U2BpZklHe1Wdk0PKNRV/JqnKP5NVSnEaap3aWM5CaY6HjfufScrmz2Rlb2Enf0wIkycJyQScyynh5x1n6RvkweBeLTdbLaP0At+fXsXZS+cJcOrE5G734CvDcy3OzsIWfyc//J38aj1vMBi4pC6p1aPKq8wnuzyXowUn0F0x0cLG3Np4ncrL1oMh5uHY4dzcpyJEvSQhtbBqtY6P1h6/PMW7e4v8FVuhqSQ+7Rd2ZCZiZ2HLjODJRHToI8M/Jk6hUOBs5YSzlRPdXLrUOqbT6yiqKiavsvYQYEpxGvtzDxGftpGO9ir6q8K53SsMB8tr7+IpRHORhNTCvt1yhryiCp6aFoa9TfNO8TYYDCTlHGB1SgJlmnKG+EReHp4zzVUhRMOZKc3wsHXDw9aNnm61j5VrKjhVfpJNZ3bz05k4VqWsI8QtmP6qvvR06y6TJkSLkU9eCzpwKp/thy4wKsKP4E7NO8U7s/QC359eTeqlc/g7dmJu74fxdejYrDGIlmFnYcvIrkPp49yHC2U5JOUcYG/OQY4UHMPewo7bvcKIUIXLcK1odpKQWsjF0mpWrD9Bpw4O3DMkoNnet1JbSfzZjWzP/K1meK77JCJUfWV4rp3ytu/APYFjGBcQxYmi0+zJOcDOrES2Zu6SIT3R7JotIaWlpTF//nyKi4txdnYmNjaWzp071yrz9NNPc+rUKePjU6dOsWTJEoYNG8bixYv5+uuv8fT0BKBPnz4sXLiwucJvVFdO8Z49tnlW8TYYDOzNOciq1HWUqcsZ3LE/YwNGyvCcAGqG+ELcgwlxD6ZcU8GB3EPsyT4gQ3qiWSkMBoOhOd7o/vvvZ+LEicTExLBmzRp++uknvvzyy2uWP3nyJDNnzmTnzp1YWlqyePFiKioqeOaZZ246hsLCMvT6Zjndem1ISuf7rSnMjApiaO+bGya71lptdckqy+a7U6tIvXSOzo5+TOk2Hj9Hn5t639biRtqnPWpo+1w5pFeiLm0XQ3ry2anfrbSPUqnAze3ave1m+VOnsLCQ48eP8/nnnwMQHR3NK6+8QlFREa6uda8p9uOPPzJ27FgsLS2bI8Rmcz6nlJ+2p9KnmwdDbmvaH+hKbSXr0n5le+Zv2Jhbc2/3v9FfFS7Dc6LBZEhPNKdmSUjZ2dl4eXlhZlaz54yZmRmenp5kZ2fXmZDUajVxcXGsWLGi1vPr1q1j165deHh48MQTTxAWFtYc4Teaao2Oj+OO4WBrwQOjmm6Kt8FgYF9uMqtS1lGqLmPQ5eE5OxmeEzdJhvREczDJT86mTZvw9vYmOPjP5VWmTp3KnDlzsLCwYPfu3Tz22GMkJCTg4tLw2Wn1dRWbw9IfD5NdWMGiRwbg71f/atMN4eHhcNVz6cVZLD/4HSfyzxDo2pln+86li2unW36v1qiu9hF/utn28cCBzt4jmRg2koxLF9h+bg87ziVx5OgxHKzsGex3O3f4R9LZxbeRI24+8tmpX1O1T7MkJJVKRW5uLjqdDjMzM3Q6HXl5eahUda9K8NNPPzFx4sRaz3l4/Lni8sCBA1GpVJw5c4Z+/fo1OI6WvIaUfDqf9YnniOrnh7eL9S2PUf91HLdSW0VC2q9sy9yNjbk107tPJFJ1O0qdsl2Oh8t1gPo1VvtY48BI77sZ3uEu45DexpQdJJzZ2mqH9OSzU79Wfw3Jzc2N4OBg4uPjiYmJIT4+nuDg4DqH63Jycjhw4ADvvPNOredzc3Px8qpZSfnEiRNkZWXh728aK2JfT3FZNZ+vP4mfl32jT/E2GAzszz3EqpR4StRlDPTux9guUdhb2DXq+whRHxnSE42h2T4ZL774IvPnz2fp0qU4OjoSGxsLwKxZs5g3bx6hoaEArFq1ijvvvBMnJ6dar3/nnXc4duwYSqUSCwsL3nzzzVq9JlOlNxhYHn8ctUbHI+N6YmHeeBMKLpTl8P3p1ZwpPoufgw+P9HqATo6td5hEtA12FrYM8RnAEJ8BcuOtuCHNNu3bFLTEkN3Gvel8uyWF+0cGcUdY46yEUKWtYmvuDhJOb8HGzJpxXaIY4N1PZs9dQYZd6tfc7aPT64xDekfzj6E16Ex2SE8+O/Vr9UN27VV6bik/bk8lrKs7Q3s3zl+DZZpyPkj+mAtlOQzw7sc4GZ4TrcD1hvTCvXozzHeIbP7YzklCaiLVGh0frT2GnU3jTfEu05SzOPkT8iryeXbI43Q0l+E50fpcOaSXXZ7L7qwkdmfvZW/OQbq7dGWY3xCCXbvJ/k3tkCSkJvL91hSyCyt4ckpvHGxv/ebeck0FHyZ/Qk5FHnNCH6C3qocMK4hWT2Xnxd+6jWO0/3B2XUhiW8YulhxejrddB4b5DSHcq7dMgmhH5DvdBA6dKWDrwSxG3O5LT/9bv9+oQlPB4kOfkF2ey+xeDxDs1q0RohTCdNha2DKi053c5TuY/bmH2Jy+g69OfM/a1A3c4TuQQd79ZdfidkASUiO7VFbNZwkn8PW0Z+LQLtd/wXVUaCpZfOhTsstymBV6Pz3dghohSiFMk7nSnP6qcCI69OVE0Wk2p+9gTep6NpzbzABVP+70HYSbza3/kSdMkySkRqQ3GFi+7gTVGh2zG2GKd6W2kg8PfUpWWTazQu8jxD34+i8Sog1QKBT0cAuih1sQmaUX2Jyxg+1Zv7Etczd9PHsxzG+I3OLQBklCakSb92fye1oR943oRkf3W5v5VpOMlpNZdoFZofcR6t6jkaIUonXxcfBmZo+pjAuIYlvmbnZlJXEg7zBdnQMY5jeEnm7d5ZaHNkISUiPJyCvjh20p9A50v+X7jSq1VSw5tJz00kz+HiLJSAgAF2tn7gkcQ1TnYSRe2MuWjF0sO7ICL1tPhvkOpl+HPliYWbR0mOIWSEJqBGqNjo/XHsPO2oIHRt/aFO8qbRVLDy/nfGkmD4fM4DaPno0YqRCtn425NXf5DWGoz0CS846wKWMHX5/6ibizvzDUZwCDO0Zibyn35rVGkpAawQ9bU8kqKOffk2/D8RameFdpq1hy+DPOlWTwUM976e0R0ohRCtG2mCnNCO8QRl+v3pwpTmVT+g7i0zbyy/mtRKrCudN3MJ627i0dprgBkpBu0eGUAjYfzOTucF9CAtxuup4qbTVLD3/OuZJ0Huw5nTDP0EaMUoi2S6FQ0M0lkG4ugWSX57IlfQe/XdjLzqw93ObRk2F+Qwhw6tzSYYoGkIR0Cy6Vq/ks4QQ+Hvb87Y6bX8W7Wqdm2ZHPSSs5zwM9ptHHs1cjRilE+6Gy8+Le4ElEB0SxI3M3O7ISOZT/O/6OnRjuN4ReHj1lAoQJk4R0kwwGA5+tO0GVWsfT03pgYW52U/WodWr+d/gzUorTeKDnNPp63dbIkQrR/jhZOTC2SxQjZ2oKCgAAFMxJREFUOt9FYvY+tqbv5JPfv8Ldxo1hvoPprwrH0uzWV1ARjUsS0k3afCCTo2cLuffubnT0uLmVitU6Nf87soKU4jRm9phKuFfvRo5SiPbNysySO3wGMqRjJIfzj7EpfTvfnV5NfNpGhnSMZIjPABwtZXdYUyEJ6SZk5pXx/dZUenVx464+NzfFW63T8NGRLzhzMZX7e0zh9g5hjRylEOIPSoWSMM9QenuEcPbSeTanb2fDuS38mr6dfl59GOY3mA52Xi0dZrsnCekGabQ6Poo7hq2VGQ+NDr6pKd41yWgFpy6mcF/wZPp16NMEkQoh/kqhUNDFuTNdnDuTW5HPloydJGXv57fsvYS4BTPcbwju7jJs3lIkId2gH7amkpVfzj8n3Yaj3Y2PQWt0Gj4++gWnLqZwb/AkIlR9myBKIcT1eNl6MC1oAtH+I9iZlcj2zN94L/kjVqf50NUxkEBnfwKcOsuirs1IEtINOJJayKYDmQzv60OvLjc+xVuj1/Lx719youg093afRKQqvAmiFELcCAdLe0b7381wvzvYm3OAAwWH2JKxk1/Tt6FAgbd9BwKd/eni5E+gsz9OVo4tHXKbJQmpgUrK1Xy27jgdPeyYdOeNr+Kt0Wv59OiXHC88xfTuExngfXsTRCmEuFmWZv+/vXsPiqr++wD+5uJyMy67CiyggCi0Ps8vubqaGFcBCYEiRnNAHzMcJ8rMLLHpNzhhFmORmlhpSENZ/RQQ09BHG+/484LXkIVwg0RYWFxugnHb/T5/kOdxU/mBAuegn9eM4+6ePed8zhfdN9/vnnO+o+DvOA0veM5CTZ0GVa3Xca25EsrmKvxbVYxjN04BAMaYSTDR2hUTrVzhZu2KsWYSmkxwkFAg9QNjDNsLFbjdqcXKl/9rwKd494bRtyjRlOFljxcxw0E+RJUSQgaDyEjEXWwLAFqdFjfaalHR/DuUzVX49WYpTquKAQCWoqfgZu3KhZTDaHu61ukhUSD1w/nyBlxRajA/dBKcBniKd4+uB1kl36FEo8A8jxfg7zhtiKokhAwVI0MjOFuOg7PlOISOD4CO6VB/uwHXmitx7a+Quqi+AqD3XnsTrFy4HtR4SyeMollv+4VaqR/sJeaID3RDiI/TgNbT6rTYXrIDv94sxVz3WMx0nD5EFRJChpOhgSGkFnaQWthh5l+/ZGr+bIKypfKvkKrEVU0ZAGCUoTFcLMf39qKsXOFqNR6mxqZ8li9YwxZIlZWVSElJQXNzM6ytrZGeng4XFxe997z77rsoLy/nnpeXlyMzMxMhISHQarVYu3YtTpw4AQMDAyxZsgTx8fHDUrvT2NED7hlpdVpsv7oDl29eRbx7DJ5zenaIqiOECIHEzAYSMxvuMo5bXW1QtlRB+Vcv6n+rDuMAGAwNDOE02qH3RAlrV7hZueAp0cNdXP+4MWCMseHY0YIFCxAXF4eYmBjs2bMHeXl5yMnJeeD7y8rKsHDhQpw4cQIikQgFBQXYu3cvtm3bhubmZsTGxuL777+Hk1P/ey0aTRt0uqE/3N4w+h6XGn7FS5OiETTOf9D3MXbsU2houDXo231cUPv0jdrnwYaqbTp6OlDZch3XWiqhbK5EZet19Oh6AAB25ra930H9dTafxMxm0Pc/WB6lfQwNDSCRPDh8h6WHpNFoUFpaiuzsbABAVFQU0tLS0NjYCLFYfN91cnNzMWfOHIhEvdf6FBYWIj4+HoaGhhCLxQgNDcWBAwfw6quvDsch9JtWp0V26Q+41PAr4iZGDUkYEUJGHlNjU8gk7pBJ3AH0nux0vfVGbw+qpRIX1JdRVHsGAGBjYg03axdMtJ4AV8vxGGMmfiKG+YYlkFQqFezs7GBk1Ht2mpGREWxtbaFSqe4bSF1dXdi7dy+++eYbvW04ODhwz6VSKerq6gZUR1/JPBi0Oi0+P/MNLqqvIHFKHOY8HTqk+xs7lu7B1Rdqn75R+zzYcLWNg50NpqF3qhmdTofrLbVQNFRAcfMayhquobj+Evfe0SILjLUQY6yFBLbmEoy16P1j+9ffZqOGL7CGqn0EeVLDL7/8AgcHB8hkskHd7lAO2Wl1WuQo/oXi+kuIdYvENIl8SIdEaMilb9Q+faP2eTA+28YCVvC18YWvjS/YRIaGPzWovnUDjR3N0HQ0QdPRiOuNtbhYexXdum79dY3NITazgcTUBmJTG0hMxZCY9T4Wm9rAbJB6WCN+yE4qlaK+vh5arRZGRkbQarVQq9WQSqX3fX9eXh7i4uLu2UZtbS2eeaZ3rqC/95j4pGM6fKvYieL6S4hxm41ZzoF8l0QIGeEMDAxgaz7mvrPeMsbQ1t0OTUcjNH82obGjiQusunY1rmrKeQusRzEsgSSRSCCTybBv3z7ExMRg3759kMlk9x2uq6urw/nz55GRkaH3ekREBHbt2oWwsDA0Nzfjl19+wY4dO4aj/D7dCaNz9RcRPSECYc5BfJdECHnMGRgY4CnRaDwlGg0Xy/H3LH+YwDI3NusNKzPxXaFlA4mZeNgCa9iG7NasWYOUlBRs2bIFlpaWSE9PBwAkJSVh2bJl+Mc/esdRd+/ejaCgIFhZWemtHxMTg8uXLyMsLAwAkJycjHHjxg1X+felYzp8p9iFs3UXEOUajnCXYF7rIYQQ4OEC605o1d9ugEJTjq4HBFaA2zRMlwzNBf7Ddtq3EAzmd0g6psOOslycVhXjeddZiHSdNSjb7S/6DqBv1D59o/Z5MGqb/w8srmf1ZyP3+L+lk/Cc7cyH2q4gvkN63OiYDj+U5eG0qhiRLqHDHkaEEDKU7u5hOVvqj0QNZWDTHQAHSMd0+LE8H6dU5xDhEkJhRAghg4QCaQB0TId/le9GUe1ZhDsHI8o1jG47Twghg4QCqZ8YY9j52x6crD2DMOcgzJkQTmFECCGDiAKpHxhj2FWxBydq/o3Q8QGInhBBYUQIIYOMAqkfztVfxLEbpxAy7jnEukVSGBFCyBCgs+z6YZL1BPzP5Jfha+dJYUQIIUOEAqkfbEyt4WfvxXcZhBDyWKMhO0IIIYJAgUQIIUQQKJAIIYQIAgUSIYQQQaBAIoQQIggUSIQQQgThiTrt29Dw8bqG6HE7nsFG7dM3ap8Ho7bp28O2z39a74maD4kQQohw0ZAdIYQQQaBAIoQQIggUSIQQQgSBAokQQoggUCARQggRBAokQgghgkCBRAghRBAokAghhAgCBRIhhBBBoEAaQZqampCUlITw8HDMmTMHr7/+OhobG/kuS5A2b94MDw8P/Pbbb3yXIiidnZ1ITU1FWFgY5syZg3/+8598lyQoR44cQWxsLGJiYhAdHY2DBw/yXRJv0tPTERwcfM//o8rKSsydOxfh4eGYO3cuqqqqBm+njIwYTU1N7PTp09zzjz/+mK1evZrHioSppKSELV68mAUFBbHy8nK+yxGUtLQ09uGHHzKdTscYY6yhoYHnioRDp9MxX19f7t+MQqFgnp6eTKvV8lwZP86dO8dqa2vv+X+UmJjICgoKGGOMFRQUsMTExEHbJ/WQRhBra2vI5XLuuaenJ2pra3msSHi6urrwwQcfYM2aNXyXIjjt7e0oKCjAm2++CQOD3ptcjhkzhueqhMXQ0BC3bt0CANy6dQu2trYwNHwyPyZ9fX0hlUr1XtNoNCgtLUVUVBQAICoqCqWlpYM2UvNE3e37caLT6fDDDz8gODiY71IEZePGjYiOjoaTkxPfpQhOdXU1rK2tsXnzZpw5cwYWFhZ488034evry3dpgmBgYIANGzbgtddeg7m5Odrb27F161a+yxIUlUoFOzs7GBkZAQCMjIxga2sLlUoFsVj8yNt/MqP/MZCWlgZzc3MkJCTwXYpgXLx4ESUlJZg/fz7fpQiSVqtFdXU1Jk+ejPz8fKxcuRJvvPEG2tra+C5NEHp6evDVV19hy5YtOHLkCL744gssX74c7e3tfJf2xKBAGoHS09Pxxx9/YMOGDU/scML9nDt3DkqlEiEhIQgODkZdXR0WL16MkydP8l2aIEilUhgbG3PDLVOmTIGNjQ0qKyt5rkwYFAoF1Go1fHx8AAA+Pj4wMzODUqnkuTLhkEqlqK+vh1arBdD7S45arb5naO9h0afZCJORkYGSkhJkZmZCJBLxXY6gLFmyBCdPnsThw4dx+PBh2NvbIysrC/7+/nyXJghisRhyuRxFRUUAes+W0mg0cHZ25rkyYbC3t0ddXR1+//13AIBSqYRGo8H48eN5rkw4JBIJZDIZ9u3bBwDYt28fZDLZoAzXATRB34hSUVGBqKgouLi4wNTUFADg5OSEzMxMnisTpuDgYHz55Zdwd3fnuxTBqK6uxnvvvYfm5mYYGxtj+fLlCAgI4Lsswfjpp5+wbds27qSPZcuWITQ0lOeq+LF27VocPHgQN2/ehI2NDaytrfHzzz9DqVQiJSUFra2tsLS0RHp6OiZMmDAo+6RAIoQQIgg0ZEcIIUQQKJAIIYQIAgUSIYQQQaBAIoQQIggUSIQQQgSBAomQv0lJScFnn33Gy74ZY1i9ejX8/Pzw0ksv8VLDQCUmJmLXrl1Dsm0+fxZk+FEgEcELDg7G9OnTcfv2be61Xbt2ITExkceqhsb58+dRVFSEY8eOITc3l+9yhlV+fj5efvllvssgPKJAIiOCTqdDTk4O32UM2J1brPRXTU0NHB0dYW5uPkQVESJcFEhkRFi8eDG2b9+O1tbWe5bduHEDHh4e6Onp4V67exgpPz8f8+bNw7p16+Dr64uQkBBcuHAB+fn5CAgIwPTp07F79269bTY1NWHRokXw8vJCQkICampquGVKpRKLFi3C1KlTER4ejsLCQm5ZSkoKUlNTkZSUBE9PT5w5c+aeeuvr67F06VJMnToVs2bNws6dOwH09vref/99XLp0CV5eXti0adM96w70WI4ePYrY2Fh4e3sjICAAn3/+ObessLAQwcHB3M1Vjx07hhkzZvzHqQSKiooQEREBHx8ffPDBB/j7tfW5ubmYPXs2/Pz8sHjxYr228/DwQE5ODkJCQiCXy5Geng6dTgelUonU1FTu2O++A3lrayuWLFkCLy8vxMfH4/r1633WR0awQZtZiZAhEhQUxIqKilhycjLLyMhgjDG2c+dOlpCQwBhjrLq6mrm7u7Pu7m5unYSEBLZz507GGGN5eXlMJpOx3Nxc1tPTwzIyMlhAQABbs2YN6+zsZCdOnGCenp6sra2NMcbYqlWrmKenJzt79izr7OxkaWlpbN68eYwxxtrb29lzzz3HcnNzWXd3N7t69SqbOnUqq6io4Nb19vZmxcXFTKvVso6OjnuOZ/78+Sw1NZV1dHSw0tJSJpfL2alTp7ha7+zrfgZ6LKdPn2ZlZWVMq9UyhULBpk+fzg4dOsRtb8WKFWzVqlWssbGRzZgxgx0+fLjPn4VGo2Genp5s//79rKuri2VnZzOZTMa19aFDh1hoaCi7du0a6+7uZpmZmWzu3Lnc+u7u7iwhIYE1NTWxmpoaFhYWpvdz+vuxr1q1ik2dOpVdvnyZdXd3sxUrVrDly5f3WSMZuaiHREaMZcuW4bvvvnuoycCcnJwQFxcHIyMjREZGQqVSITk5GSKRCP7+/hCJRHq/eQcGBsLPzw8ikQhvvfUWLl26BJVKhaNHj8LR0RFxcXEwNjbG5MmTER4ejgMHDnDrhoSEwMfHB4aGhjAxMdGrQ6VS4cKFC1i5ciVMTEwgk8kQHx+PPXv2DMmxyOVyeHh4wNDQEE8//TSef/55nD17lttWamoqTp8+jQULFiA4OBhBQUF97vv48eOYNGkSIiIiMGrUKCxcuFBvkr8ff/wRS5YsgZubG4yNjbF06VIoFAq9XlJSUhKsra3h4OCABQsWcDfqfJDQ0FA888wzMDY2RnR0NBQKRb/biowsNEEfGTHc3d0RGBiIrVu3ws3NbUDrSiQS7vGdG9Pe/UFqYmKiN++Nvb0999jCwgJWVlZQq9WoqanBlStX9IaUtFotoqOjued93YpfrVbDysoKo0eP5l5zcHBASUnJkBzL5cuX8cknn6CiogLd3d3o6upCREQE915LS0tEREQgOzv7vkOE96v/7rYxMDDQO97a2lqsW7cO6enp3GuMMdTX18PR0RGAfvs4OjpCrVb3uc+7j83U1FTv5BbyeKFAIiPKsmXL8MILL+CVV17hXrtzAkBHRwf3Qd/Q0PBI+6mrq+Met7e3o6WlBba2tpBKpfDz80N2dvZDbdfW1hYtLS1oa2vjar0zC+dQePvtt5GQkICvv/4aJiYm+PDDD9HU1MQtVygUyMvLQ1RUFNauXYusrKw+tzd27Fi9tmGMQaVScc+lUimWLl2qF9B/p1KpMGnSJAC9AWZrawsA3B22yZOLhuzIiOLs7IzIyEh8++233GtisRh2dnbYs2cPtFotcnNzUV1d/Uj7OXbsGIqLi9HV1YWNGzdiypQpkEqlCAwMRFVVFQoKCtDd3Y3u7m5cuXKl35O4SaVSeHl5ISMjA52dnSgrK0Nubm6fH+CPor29HVZWVjAxMcGVK1f0hsc6Ozvxzjvv4K233sJHH30EtVqNHTt29Lm9gIAAVFRU4ODBg+jp6UFOTg5u3rzJLZ83bx62bt2KiooKAMCtW7ewf/9+vW1kZWWhpaUFKpUKOTk5iIyMBNDb86uvr0dXV9dgHT4ZYSiQyIiTnJx8z7BNWloasrKyIJfLce3aNXh5eT3SPqKiopCZmQm5XI6rV69i/fr1AIDRo0cjKysLhYWFmDlzJvz9/fHJJ58M6EM0IyMDNTU1mDlzJl5//XW88cYbePbZZx+p3gdJTU3Fpk2b4OXlhczMTMyePZtb9umnn8Le3h7z58+HSCTC+vXrsXHjRlRVVT1we2KxGBs3bsSnn34KuVyOP/74A97e3tzyWbNm4dVXX8WKFSvg7e2NqKgoHD9+XG8bISEhePHFFxEbG4vAwEDuAuBp06Zh4sSJ8Pf3h1wuH9yGICMCzYdECBk2Hh4eOHjwIM1SS+6LekiEEEIEgU5qIIToKS4uRlJS0n2XXbx4cZirIU8SGrIjhBAiCDRkRwghRBAokAghhAgCBRIhhBBBoEAihBAiCBRIhBBCBIECiRBCiCD8H1+SxOiMHtz9AAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def val_curve_params(model, X, y, param_name, param_range, scoring=\"roc_auc\", cv=10):\n",
        "    train_score, test_score = validation_curve(\n",
        "        model, X=X, y=y, param_name=param_name, param_range=param_range, scoring=scoring, cv=cv)\n",
        "\n",
        "    mean_train_score = np.mean(train_score, axis=1)\n",
        "    mean_test_score = np.mean(test_score, axis=1)\n",
        "\n",
        "    plt.plot(param_range, mean_train_score,\n",
        "             label=\"Training Score\", color='b')\n",
        "\n",
        "    plt.plot(param_range, mean_test_score,\n",
        "             label=\"Validation Score\", color='g')\n",
        "\n",
        "    plt.title(f\"Validation Curve for {type(model).__name__}\")\n",
        "    plt.xlabel(f\"Number of {param_name}\")\n",
        "    plt.ylabel(f\"{scoring}\")\n",
        "    plt.tight_layout()\n",
        "    plt.legend(loc='best')\n",
        "    plt.show(block=True)"
      ],
      "metadata": {
        "id": "FrO0J9_ALvzF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "val_curve_params(cart_final, X, y, \"max_depth\", range(1, 11), scoring=\"f1\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "id": "NU9luOcjor34",
        "outputId": "bd0857fd-00af-476a-fb5d-9581c0da6589"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cart_val_params = [[\"max_depth\", range(1, 11)], [\"min_samples_split\", range(2, 20)]]\n",
        "\n",
        "for i in range(len(cart_val_params)):\n",
        "    val_curve_params(cart_model, X, y, cart_val_params[i][0], cart_val_params[i][1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 569
        },
        "id": "_Y9VNPKXLyAg",
        "outputId": "28659a9e-88c6-49c0-deac-cee727c04728"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 8. Visualizing the Decision Tree\n",
        "def tree_graph(model, col_names, file_name):\n",
        "    tree_str = export_graphviz(model, feature_names=col_names, filled=True, out_file=None)\n",
        "    graph = pydotplus.graph_from_dot_data(tree_str)\n",
        "    graph.write_png(file_name)"
      ],
      "metadata": {
        "id": "-PRMc7far72O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tree_graph(model=cart_final, col_names=X.columns, file_name=\"cart_final.png\")\n",
        "\n",
        "cart_final.get_params()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h8CEk6QIL0AL",
        "outputId": "3b4b3720-59aa-486c-de14-745d72d91267"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'ccp_alpha': 0.0,\n",
              " 'class_weight': None,\n",
              " 'criterion': 'gini',\n",
              " 'max_depth': 5,\n",
              " 'max_features': None,\n",
              " 'max_leaf_nodes': None,\n",
              " 'min_impurity_decrease': 0.0,\n",
              " 'min_samples_leaf': 1,\n",
              " 'min_samples_split': 4,\n",
              " 'min_weight_fraction_leaf': 0.0,\n",
              " 'random_state': 17,\n",
              " 'splitter': 'best'}"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 9. Extracting Decision Rules\n",
        "tree_rules = export_text(cart_final, feature_names=list(X.columns))\n",
        "print(tree_rules)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YIXQikbTL8yL",
        "outputId": "b29e55e9-9bd2-462e-a02e-16213905b119"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "|--- Glucose <= 127.50\n",
            "|   |--- Age <= 28.50\n",
            "|   |   |--- BMI <= 45.40\n",
            "|   |   |   |--- BMI <= 30.95\n",
            "|   |   |   |   |--- Pregnancies <= 7.50\n",
            "|   |   |   |   |   |--- class: 0\n",
            "|   |   |   |   |--- Pregnancies >  7.50\n",
            "|   |   |   |   |   |--- class: 1\n",
            "|   |   |   |--- BMI >  30.95\n",
            "|   |   |   |   |--- DiabetesPedigreeFunction <= 0.50\n",
            "|   |   |   |   |   |--- class: 0\n",
            "|   |   |   |   |--- DiabetesPedigreeFunction >  0.50\n",
            "|   |   |   |   |   |--- class: 0\n",
            "|   |   |--- BMI >  45.40\n",
            "|   |   |   |--- BloodPressure <= 99.00\n",
            "|   |   |   |   |--- class: 1\n",
            "|   |   |   |--- BloodPressure >  99.00\n",
            "|   |   |   |   |--- class: 0\n",
            "|   |--- Age >  28.50\n",
            "|   |   |--- BMI <= 26.35\n",
            "|   |   |   |--- BMI <= 9.65\n",
            "|   |   |   |   |--- class: 1\n",
            "|   |   |   |--- BMI >  9.65\n",
            "|   |   |   |   |--- class: 0\n",
            "|   |   |--- BMI >  26.35\n",
            "|   |   |   |--- Glucose <= 99.50\n",
            "|   |   |   |   |--- Glucose <= 28.50\n",
            "|   |   |   |   |   |--- class: 1\n",
            "|   |   |   |   |--- Glucose >  28.50\n",
            "|   |   |   |   |   |--- class: 0\n",
            "|   |   |   |--- Glucose >  99.50\n",
            "|   |   |   |   |--- DiabetesPedigreeFunction <= 0.56\n",
            "|   |   |   |   |   |--- class: 0\n",
            "|   |   |   |   |--- DiabetesPedigreeFunction >  0.56\n",
            "|   |   |   |   |   |--- class: 1\n",
            "|--- Glucose >  127.50\n",
            "|   |--- BMI <= 29.95\n",
            "|   |   |--- Glucose <= 145.50\n",
            "|   |   |   |--- Insulin <= 132.50\n",
            "|   |   |   |   |--- BMI <= 28.15\n",
            "|   |   |   |   |   |--- class: 0\n",
            "|   |   |   |   |--- BMI >  28.15\n",
            "|   |   |   |   |   |--- class: 1\n",
            "|   |   |   |--- Insulin >  132.50\n",
            "|   |   |   |   |--- class: 0\n",
            "|   |   |--- Glucose >  145.50\n",
            "|   |   |   |--- Age <= 25.50\n",
            "|   |   |   |   |--- class: 0\n",
            "|   |   |   |--- Age >  25.50\n",
            "|   |   |   |   |--- Age <= 61.00\n",
            "|   |   |   |   |   |--- class: 1\n",
            "|   |   |   |   |--- Age >  61.00\n",
            "|   |   |   |   |   |--- class: 0\n",
            "|   |--- BMI >  29.95\n",
            "|   |   |--- Glucose <= 157.50\n",
            "|   |   |   |--- Age <= 30.50\n",
            "|   |   |   |   |--- BloodPressure <= 61.00\n",
            "|   |   |   |   |   |--- class: 1\n",
            "|   |   |   |   |--- BloodPressure >  61.00\n",
            "|   |   |   |   |   |--- class: 0\n",
            "|   |   |   |--- Age >  30.50\n",
            "|   |   |   |   |--- DiabetesPedigreeFunction <= 0.43\n",
            "|   |   |   |   |   |--- class: 1\n",
            "|   |   |   |   |--- DiabetesPedigreeFunction >  0.43\n",
            "|   |   |   |   |   |--- class: 1\n",
            "|   |   |--- Glucose >  157.50\n",
            "|   |   |   |--- Insulin <= 629.50\n",
            "|   |   |   |   |--- DiabetesPedigreeFunction <= 0.30\n",
            "|   |   |   |   |   |--- class: 1\n",
            "|   |   |   |   |--- DiabetesPedigreeFunction >  0.30\n",
            "|   |   |   |   |   |--- class: 1\n",
            "|   |   |   |--- Insulin >  629.50\n",
            "|   |   |   |   |--- class: 0\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 10. Extracting Python Codes of Decision Rules\n",
        "\n"
      ],
      "metadata": {
        "id": "Sbg7jYI1MAGA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "AgnjtAciMFmA"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}