Final_Code_project.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Final Code.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "8NCt5haZsJ2f"
      },
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import numpy as np"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fb3hetTrsQjb",
        "outputId": "26dd3723-5ad6-4fe6-8b18-34a1d3e2f3ab"
      },
      "source": [
        "df = pd.read_csv(\"heart.csv\")\n",
        "df=df.drop('slope',axis=1)\n",
        "df=df.drop('oldpeak',axis=1)\n",
        "df=df.drop('exang',axis=1)\n",
        "df=df.drop('thal',axis=1)\n",
        "df.info()\n"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 303 entries, 0 to 302\n",
            "Data columns (total 10 columns):\n",
            " #   Column    Non-Null Count  Dtype\n",
            "---  ------    --------------  -----\n",
            " 0   age       303 non-null    int64\n",
            " 1   sex       303 non-null    int64\n",
            " 2   cp        303 non-null    int64\n",
            " 3   trestbps  303 non-null    int64\n",
            " 4   chol      303 non-null    int64\n",
            " 5   fbs       303 non-null    int64\n",
            " 6   restecg   303 non-null    int64\n",
            " 7   thalach   303 non-null    int64\n",
            " 8   ca        303 non-null    int64\n",
            " 9   target    303 non-null    int64\n",
            "dtypes: int64(10)\n",
            "memory usage: 23.8 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MUk8GQVhULNa"
      },
      "source": [
        "from sklearn.metrics import precision_score,recall_score,f1_score\n",
        "from sklearn.metrics import matthews_corrcoef,cohen_kappa_score,roc_auc_score\n",
        "from sklearn.metrics import accuracy_score\n",
        "def all_metrices(y_test,y_pred):\n",
        "  acc=accuracy_score(y_test, y_pred)\n",
        "  auc=roc_auc_score(y_test,y_pred)\n",
        "  precision=precision_score(y_test, y_pred)\n",
        "  recall=recall_score(y_test, y_pred)\n",
        "  f1=f1_score(y_test, y_pred)\n",
        "  mcc=matthews_corrcoef(y_test,y_pred)\n",
        "  kappa=cohen_kappa_score(y_test,y_pred)\n",
        "\n",
        "  print('Accuracy: %.2f' % (acc*100))\n",
        "  print('Roc_Auc Score: %.2f' % (auc*100))\n",
        "  print('Precision: %.2f' % (precision*100))\n",
        "  print('recall: %.2f' % (recall*100))\n",
        "  print('fl score: %.2f' % (f1*100))\n",
        "  print('Mcc Score: %.2f' % (mcc))\n",
        "  print('Kappa score: %.2f' % (kappa))\n",
        "  print('\\n')\n",
        "  "
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5it2JY7yuJUH",
        "outputId": "f7bbd99f-55bd-44c2-9fbf-0f6715d1f252"
      },
      "source": [
        "categorical_val = []\n",
        "continous_val = []\n",
        "for column in df.columns:\n",
        "    print('==============================')\n",
        "    print(f\"{column} : {df[column].unique()}\")\n",
        "    if len(df[column].unique()) <= 10:\n",
        "        categorical_val.append(column)\n",
        "    else:\n",
        "        continous_val.append(column)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==============================\n",
            "age : [63 37 41 56 57 44 52 54 48 49 64 58 50 66 43 69 59 42 61 40 71 51 65 53\n",
            " 46 45 39 47 62 34 35 29 55 60 67 68 74 76 70 38 77]\n",
            "==============================\n",
            "sex : [1 0]\n",
            "==============================\n",
            "cp : [3 2 1 0]\n",
            "==============================\n",
            "trestbps : [145 130 120 140 172 150 110 135 160 105 125 142 155 104 138 128 108 134\n",
            " 122 115 118 100 124  94 112 102 152 101 132 148 178 129 180 136 126 106\n",
            " 156 170 146 117 200 165 174 192 144 123 154 114 164]\n",
            "==============================\n",
            "chol : [233 250 204 236 354 192 294 263 199 168 239 275 266 211 283 219 340 226\n",
            " 247 234 243 302 212 175 417 197 198 177 273 213 304 232 269 360 308 245\n",
            " 208 264 321 325 235 257 216 256 231 141 252 201 222 260 182 303 265 309\n",
            " 186 203 183 220 209 258 227 261 221 205 240 318 298 564 277 214 248 255\n",
            " 207 223 288 160 394 315 246 244 270 195 196 254 126 313 262 215 193 271\n",
            " 268 267 210 295 306 178 242 180 228 149 278 253 342 157 286 229 284 224\n",
            " 206 167 230 335 276 353 225 330 290 172 305 188 282 185 326 274 164 307\n",
            " 249 341 407 217 174 281 289 322 299 300 293 184 409 259 200 327 237 218\n",
            " 319 166 311 169 187 176 241 131]\n",
            "==============================\n",
            "fbs : [1 0]\n",
            "==============================\n",
            "restecg : [0 1 2]\n",
            "==============================\n",
            "thalach : [150 187 172 178 163 148 153 173 162 174 160 139 171 144 158 114 151 161\n",
            " 179 137 157 123 152 168 140 188 125 170 165 142 180 143 182 156 115 149\n",
            " 146 175 186 185 159 130 190 132 147 154 202 166 164 184 122 169 138 111\n",
            " 145 194 131 133 155 167 192 121  96 126 105 181 116 108 129 120 112 128\n",
            " 109 113  99 177 141 136  97 127 103 124  88 195 106  95 117  71 118 134\n",
            "  90]\n",
            "==============================\n",
            "ca : [0 2 1 3 4]\n",
            "==============================\n",
            "target : [1 0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fT6OX9gPuQUy"
      },
      "source": [
        "categorical_val.remove('target')\n",
        "dataset = pd.get_dummies(df, columns = categorical_val)"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2bVCmHlRuU2Z"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "s_sc = StandardScaler()\n",
        "col_to_scale = ['age', 'trestbps', 'chol', 'thalach']\n",
        "dataset[col_to_scale] = s_sc.fit_transform(dataset[col_to_scale])"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ggxz6zS3uZv1"
      },
      "source": [
        "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
        "\n",
        "def print_score(clf, X_train, y_train, X_test, y_test, train=True):\n",
        "    if train:\n",
        "        pred = clf.predict(X_train)\n",
        "        \n",
        "        clf_report = pd.DataFrame(classification_report(y_train, pred, output_dict=True))\n",
        "        print(\"Train Result:\\n================================================\")\n",
        "        print(f\"Accuracy Score: {accuracy_score(y_train, pred) * 100:.2f}%\")\n",
        "        print(\"_______________________________________________\")\n",
        "        print(f\"CLASSIFICATION REPORT:\\n{clf_report}\")\n",
        "        print(\"_______________________________________________\")\n",
        "        print(f\"Confusion Matrix: \\n {confusion_matrix(y_train, pred)}\\n\")\n",
        "        all_metrices(y_train,pred)\n",
        "    elif train==False:\n",
        "        pred = clf.predict(X_test)\n",
        "        \n",
        "        clf_report = pd.DataFrame(classification_report(y_test, pred, output_dict=True))\n",
        "        print(\"Test Result:\\n================================================\")        \n",
        "        print(f\"Accuracy Score: {accuracy_score(y_test, pred) * 100:.2f}%\")\n",
        "        print(\"_______________________________________________\")\n",
        "        print(f\"CLASSIFICATION REPORT:\\n{clf_report}\")\n",
        "        print(\"_______________________________________________\")\n",
        "        print(f\"Confusion Matrix: \\n {confusion_matrix(y_test, pred)}\\n\")\n",
        "        all_metrices(y_test,pred)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eUXHv02oue6C",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a323f5c5-6096-4ace-83ef-76e5300165d5"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from imblearn.over_sampling import RandomOverSampler\n",
        "from imblearn.under_sampling import RandomUnderSampler\n",
        "\n",
        "\n",
        "X = dataset.drop('target', axis=1)#Dummy Variables Dataset\n",
        "y = dataset.target\n",
        "\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/externals/six.py:31: FutureWarning: The module is deprecated in version 0.21 and will be removed in version 0.23 since we've dropped support for Python 2.7. Please rely on the official version of six (https://pypi.org/project/six/).\n",
            "  \"(https://pypi.org/project/six/).\", FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.neighbors.base module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.neighbors. Anything that cannot be imported from sklearn.neighbors is now part of the private API.\n",
            "  warnings.warn(message, FutureWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EG5gKb7JXYlM"
      },
      "source": [
        "#**Models Without Grid Search and Cross Validation**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pVZcr4gYsbV8",
        "outputId": "31a0fa97-bb42-4bce-fa2b-f3612f964862"
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "lr_clf = LogisticRegression()\n",
        "lr_clf.fit(X_train, y_train)\n",
        "\n",
        "print_score(lr_clf, X_train, y_train, X_test, y_test, train=True)\n",
        "print_score(lr_clf, X_train, y_train, X_test, y_test, train=False)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train Result:\n",
            "================================================\n",
            "Accuracy Score: 80.99%\n",
            "_______________________________________________\n",
            "CLASSIFICATION REPORT:\n",
            "                    0           1  accuracy   macro avg  weighted avg\n",
            "precision    0.805825    0.812950  0.809917    0.809387      0.809741\n",
            "recall       0.761468    0.849624  0.809917    0.805546      0.809917\n",
            "f1-score     0.783019    0.830882  0.809917    0.806951      0.809324\n",
            "support    109.000000  133.000000  0.809917  242.000000    242.000000\n",
            "_______________________________________________\n",
            "Confusion Matrix: \n",
            " [[ 83  26]\n",
            " [ 20 113]]\n",
            "\n",
            "Accuracy: 80.99\n",
            "Roc_Auc Score: 80.55\n",
            "Precision: 81.29\n",
            "recall: 84.96\n",
            "fl score: 83.09\n",
            "Mcc Score: 0.61\n",
            "Kappa score: 0.61\n",
            "\n",
            "\n",
            "Test Result:\n",
            "================================================\n",
            "Accuracy Score: 86.89%\n",
            "_______________________________________________\n",
            "CLASSIFICATION REPORT:\n",
            "                   0          1  accuracy  macro avg  weighted avg\n",
            "precision   0.838710   0.900000  0.868852   0.869355      0.870862\n",
            "recall      0.896552   0.843750  0.868852   0.870151      0.868852\n",
            "f1-score    0.866667   0.870968  0.868852   0.868817      0.868923\n",
            "support    29.000000  32.000000  0.868852  61.000000     61.000000\n",
            "_______________________________________________\n",
            "Confusion Matrix: \n",
            " [[26  3]\n",
            " [ 5 27]]\n",
            "\n",
            "Accuracy: 86.89\n",
            "Roc_Auc Score: 87.02\n",
            "Precision: 90.00\n",
            "recall: 84.38\n",
            "fl score: 87.10\n",
            "Mcc Score: 0.74\n",
            "Kappa score: 0.74\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W1DSafp7svB8",
        "outputId": "8c2d3661-0729-4dcc-c212-2b9c71fa5f64"
      },
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "knn_clf = KNeighborsClassifier()\n",
        "knn_clf.fit(X_train, y_train)\n",
        "\n",
        "print_score(knn_clf, X_train, y_train, X_test, y_test, train=True)\n",
        "print_score(knn_clf, X_train, y_train, X_test, y_test, train=False)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train Result:\n",
            "================================================\n",
            "Accuracy Score: 84.71%\n",
            "_______________________________________________\n",
            "CLASSIFICATION REPORT:\n",
            "                    0           1  accuracy   macro avg  weighted avg\n",
            "precision    0.833333    0.858209  0.847107    0.845771      0.847005\n",
            "recall       0.825688    0.864662  0.847107    0.845175      0.847107\n",
            "f1-score     0.829493    0.861423  0.847107    0.845458      0.847041\n",
            "support    109.000000  133.000000  0.847107  242.000000    242.000000\n",
            "_______________________________________________\n",
            "Confusion Matrix: \n",
            " [[ 90  19]\n",
            " [ 18 115]]\n",
            "\n",
            "Accuracy: 84.71\n",
            "Roc_Auc Score: 84.52\n",
            "Precision: 85.82\n",
            "recall: 86.47\n",
            "fl score: 86.14\n",
            "Mcc Score: 0.69\n",
            "Kappa score: 0.69\n",
            "\n",
            "\n",
            "Test Result:\n",
            "================================================\n",
            "Accuracy Score: 83.61%\n",
            "_______________________________________________\n",
            "CLASSIFICATION REPORT:\n",
            "                   0          1  accuracy  macro avg  weighted avg\n",
            "precision   0.851852   0.823529  0.836066   0.837691      0.836994\n",
            "recall      0.793103   0.875000  0.836066   0.834052      0.836066\n",
            "f1-score    0.821429   0.848485  0.836066   0.834957      0.835622\n",
            "support    29.000000  32.000000  0.836066  61.000000     61.000000\n",
            "_______________________________________________\n",
            "Confusion Matrix: \n",
            " [[23  6]\n",
            " [ 4 28]]\n",
            "\n",
            "Accuracy: 83.61\n",
            "Roc_Auc Score: 83.41\n",
            "Precision: 82.35\n",
            "recall: 87.50\n",
            "fl score: 84.85\n",
            "Mcc Score: 0.67\n",
            "Kappa score: 0.67\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rJdtczFlsfYg",
        "outputId": "ae6c6374-6f82-4610-f116-e1d3e74b254c"
      },
      "source": [
        "from sklearn.svm import SVC\n",
        "\n",
        "\n",
        "svm_clf = SVC(kernel='rbf', gamma=0.1, C=1.0)\n",
        "svm_clf.fit(X_train, y_train)\n",
        "\n",
        "print_score(svm_clf, X_train, y_train, X_test, y_test, train=True)\n",
        "print_score(svm_clf, X_train, y_train, X_test, y_test, train=False)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train Result:\n",
            "================================================\n",
            "Accuracy Score: 85.95%\n",
            "_______________________________________________\n",
            "CLASSIFICATION REPORT:\n",
            "                    0           1  accuracy   macro avg  weighted avg\n",
            "precision    0.871287    0.851064  0.859504    0.861175      0.860173\n",
            "recall       0.807339    0.902256  0.859504    0.854798      0.859504\n",
            "f1-score     0.838095    0.875912  0.859504    0.857004      0.858879\n",
            "support    109.000000  133.000000  0.859504  242.000000    242.000000\n",
            "_______________________________________________\n",
            "Confusion Matrix: \n",
            " [[ 88  21]\n",
            " [ 13 120]]\n",
            "\n",
            "Accuracy: 85.95\n",
            "Roc_Auc Score: 85.48\n",
            "Precision: 85.11\n",
            "recall: 90.23\n",
            "fl score: 87.59\n",
            "Mcc Score: 0.72\n",
            "Kappa score: 0.71\n",
            "\n",
            "\n",
            "Test Result:\n",
            "================================================\n",
            "Accuracy Score: 88.52%\n",
            "_______________________________________________\n",
            "CLASSIFICATION REPORT:\n",
            "                   0          1  accuracy  macro avg  weighted avg\n",
            "precision   0.866667   0.903226  0.885246   0.884946      0.885845\n",
            "recall      0.896552   0.875000  0.885246   0.885776      0.885246\n",
            "f1-score    0.881356   0.888889  0.885246   0.885122      0.885308\n",
            "support    29.000000  32.000000  0.885246  61.000000     61.000000\n",
            "_______________________________________________\n",
            "Confusion Matrix: \n",
            " [[26  3]\n",
            " [ 4 28]]\n",
            "\n",
            "Accuracy: 88.52\n",
            "Roc_Auc Score: 88.58\n",
            "Precision: 90.32\n",
            "recall: 87.50\n",
            "fl score: 88.89\n",
            "Mcc Score: 0.77\n",
            "Kappa score: 0.77\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1pcuqIKCt5nn",
        "outputId": "c6a1fc75-da7e-4bdc-bb50-cb7e613c72fc"
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "\n",
        "tree_clf = DecisionTreeClassifier(random_state=42)\n",
        "tree_clf.fit(X_train, y_train)\n",
        "\n",
        "print_score(tree_clf, X_train, y_train, X_test, y_test, train=True)\n",
        "print_score(tree_clf, X_train, y_train, X_test, y_test, train=False)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train Result:\n",
            "================================================\n",
            "Accuracy Score: 100.00%\n",
            "_______________________________________________\n",
            "CLASSIFICATION REPORT:\n",
            "               0      1  accuracy  macro avg  weighted avg\n",
            "precision    1.0    1.0       1.0        1.0           1.0\n",
            "recall       1.0    1.0       1.0        1.0           1.0\n",
            "f1-score     1.0    1.0       1.0        1.0           1.0\n",
            "support    109.0  133.0       1.0      242.0         242.0\n",
            "_______________________________________________\n",
            "Confusion Matrix: \n",
            " [[109   0]\n",
            " [  0 133]]\n",
            "\n",
            "Accuracy: 100.00\n",
            "Roc_Auc Score: 100.00\n",
            "Precision: 100.00\n",
            "recall: 100.00\n",
            "fl score: 100.00\n",
            "Mcc Score: 1.00\n",
            "Kappa score: 1.00\n",
            "\n",
            "\n",
            "Test Result:\n",
            "================================================\n",
            "Accuracy Score: 78.69%\n",
            "_______________________________________________\n",
            "CLASSIFICATION REPORT:\n",
            "                   0          1  accuracy  macro avg  weighted avg\n",
            "precision   0.750000   0.827586  0.786885   0.788793      0.790701\n",
            "recall      0.827586   0.750000  0.786885   0.788793      0.786885\n",
            "f1-score    0.786885   0.786885  0.786885   0.786885      0.786885\n",
            "support    29.000000  32.000000  0.786885  61.000000     61.000000\n",
            "_______________________________________________\n",
            "Confusion Matrix: \n",
            " [[24  5]\n",
            " [ 8 24]]\n",
            "\n",
            "Accuracy: 78.69\n",
            "Roc_Auc Score: 78.88\n",
            "Precision: 82.76\n",
            "recall: 75.00\n",
            "fl score: 78.69\n",
            "Mcc Score: 0.58\n",
            "Kappa score: 0.57\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aryutzxTwNrW",
        "outputId": "ff5799a9-9ebc-4e97-be5c-c2d353070a4b"
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "\n",
        "rf_clf = RandomForestClassifier(n_estimators=2000, random_state=42)\n",
        "rf_clf.fit(X_train, y_train)\n",
        "\n",
        "print_score(rf_clf, X_train, y_train, X_test, y_test, train=True)\n",
        "print_score(rf_clf, X_train, y_train, X_test, y_test, train=False)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train Result:\n",
            "================================================\n",
            "Accuracy Score: 100.00%\n",
            "_______________________________________________\n",
            "CLASSIFICATION REPORT:\n",
            "               0      1  accuracy  macro avg  weighted avg\n",
            "precision    1.0    1.0       1.0        1.0           1.0\n",
            "recall       1.0    1.0       1.0        1.0           1.0\n",
            "f1-score     1.0    1.0       1.0        1.0           1.0\n",
            "support    109.0  133.0       1.0      242.0         242.0\n",
            "_______________________________________________\n",
            "Confusion Matrix: \n",
            " [[109   0]\n",
            " [  0 133]]\n",
            "\n",
            "Accuracy: 100.00\n",
            "Roc_Auc Score: 100.00\n",
            "Precision: 100.00\n",
            "recall: 100.00\n",
            "fl score: 100.00\n",
            "Mcc Score: 1.00\n",
            "Kappa score: 1.00\n",
            "\n",
            "\n",
            "Test Result:\n",
            "================================================\n",
            "Accuracy Score: 86.89%\n",
            "_______________________________________________\n",
            "CLASSIFICATION REPORT:\n",
            "                   0          1  accuracy  macro avg  weighted avg\n",
            "precision   0.838710   0.900000  0.868852   0.869355      0.870862\n",
            "recall      0.896552   0.843750  0.868852   0.870151      0.868852\n",
            "f1-score    0.866667   0.870968  0.868852   0.868817      0.868923\n",
            "support    29.000000  32.000000  0.868852  61.000000     61.000000\n",
            "_______________________________________________\n",
            "Confusion Matrix: \n",
            " [[26  3]\n",
            " [ 5 27]]\n",
            "\n",
            "Accuracy: 86.89\n",
            "Roc_Auc Score: 87.02\n",
            "Precision: 90.00\n",
            "recall: 84.38\n",
            "fl score: 87.10\n",
            "Mcc Score: 0.74\n",
            "Kappa score: 0.74\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nV0DKmQ0wWgV",
        "outputId": "34efd410-7a8c-491b-f07f-73d663d4f917"
      },
      "source": [
        "from xgboost import XGBClassifier\n",
        "\n",
        "xgb_clf = XGBClassifier()\n",
        "xgb_clf.fit(X_train, y_train)\n",
        "\n",
        "print_score(xgb_clf, X_train, y_train, X_test, y_test, train=True)\n",
        "print_score(xgb_clf, X_train, y_train, X_test, y_test, train=False)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train Result:\n",
            "================================================\n",
            "Accuracy Score: 95.87%\n",
            "_______________________________________________\n",
            "CLASSIFICATION REPORT:\n",
            "                    0           1  accuracy   macro avg  weighted avg\n",
            "precision    0.971429    0.948905  0.958678    0.960167      0.959050\n",
            "recall       0.935780    0.977444  0.958678    0.956612      0.958678\n",
            "f1-score     0.953271    0.962963  0.958678    0.958117      0.958598\n",
            "support    109.000000  133.000000  0.958678  242.000000    242.000000\n",
            "_______________________________________________\n",
            "Confusion Matrix: \n",
            " [[102   7]\n",
            " [  3 130]]\n",
            "\n",
            "Accuracy: 95.87\n",
            "Roc_Auc Score: 95.66\n",
            "Precision: 94.89\n",
            "recall: 97.74\n",
            "fl score: 96.30\n",
            "Mcc Score: 0.92\n",
            "Kappa score: 0.92\n",
            "\n",
            "\n",
            "Test Result:\n",
            "================================================\n",
            "Accuracy Score: 85.25%\n",
            "_______________________________________________\n",
            "CLASSIFICATION REPORT:\n",
            "                   0          1  accuracy  macro avg  weighted avg\n",
            "precision   0.812500   0.896552  0.852459   0.854526      0.856593\n",
            "recall      0.896552   0.812500  0.852459   0.854526      0.852459\n",
            "f1-score    0.852459   0.852459  0.852459   0.852459      0.852459\n",
            "support    29.000000  32.000000  0.852459  61.000000     61.000000\n",
            "_______________________________________________\n",
            "Confusion Matrix: \n",
            " [[26  3]\n",
            " [ 6 26]]\n",
            "\n",
            "Accuracy: 85.25\n",
            "Roc_Auc Score: 85.45\n",
            "Precision: 89.66\n",
            "recall: 81.25\n",
            "fl score: 85.25\n",
            "Mcc Score: 0.71\n",
            "Kappa score: 0.71\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TdhV41K5B4Cb",
        "outputId": "91cb7241-98da-427e-e55e-d03dbb5fea47"
      },
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "import numpy\n",
        "model = Sequential()\n",
        "model.add(Dense(20,activation='relu',input_dim=20))\n",
        "model.add(Dense(20, activation='relu'))\n",
        "model.add(Dense(1,activation='sigmoid'))\n",
        "\n",
        "model.compile(loss='binary_crossentropy',optimizer='sgd',metrics=['accuracy'])\n",
        "model.fit(X_train,y_train,epochs=400)\n",
        "Y_pred_nn = model.predict(X_test)\n",
        "rounded = [round(x[0]) for x in Y_pred_nn]\n",
        "Y_pred_nn = rounded\n",
        "# score_nn = round(accuracy_score(Y_pred_nn,y_test)*100,2)\n",
        "\n",
        "# print(\"The accuracy score achieved using Neural Network is: \"+str(score_nn)+\" %\")\n",
        "all_metrices(y_test,Y_pred_nn)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/400\n",
            "8/8 [==============================] - 14s 3ms/step - loss: 0.6864 - accuracy: 0.6195\n",
            "Epoch 2/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6821 - accuracy: 0.5801\n",
            "Epoch 3/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6791 - accuracy: 0.6161\n",
            "Epoch 4/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6762 - accuracy: 0.5979\n",
            "Epoch 5/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6706 - accuracy: 0.6133\n",
            "Epoch 6/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6753 - accuracy: 0.5880\n",
            "Epoch 7/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6681 - accuracy: 0.6196\n",
            "Epoch 8/400\n",
            "8/8 [==============================] - 0s 2ms/step - loss: 0.6653 - accuracy: 0.6257\n",
            "Epoch 9/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6708 - accuracy: 0.6065\n",
            "Epoch 10/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6690 - accuracy: 0.6111\n",
            "Epoch 11/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6638 - accuracy: 0.6310\n",
            "Epoch 12/400\n",
            "8/8 [==============================] - 0s 2ms/step - loss: 0.6598 - accuracy: 0.6390\n",
            "Epoch 13/400\n",
            "8/8 [==============================] - 0s 2ms/step - loss: 0.6602 - accuracy: 0.6594\n",
            "Epoch 14/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6576 - accuracy: 0.6343\n",
            "Epoch 15/400\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.6591 - accuracy: 0.6191\n",
            "Epoch 16/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6497 - accuracy: 0.6503\n",
            "Epoch 17/400\n",
            "8/8 [==============================] - 0s 2ms/step - loss: 0.6527 - accuracy: 0.6604\n",
            "Epoch 18/400\n",
            "8/8 [==============================] - 0s 2ms/step - loss: 0.6419 - accuracy: 0.6860\n",
            "Epoch 19/400\n",
            "8/8 [==============================] - 0s 2ms/step - loss: 0.6334 - accuracy: 0.6612\n",
            "Epoch 20/400\n",
            "8/8 [==============================] - 0s 2ms/step - loss: 0.6362 - accuracy: 0.6798\n",
            "Epoch 21/400\n",
            "8/8 [==============================] - 0s 2ms/step - loss: 0.6310 - accuracy: 0.6822\n",
            "Epoch 22/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6382 - accuracy: 0.6909\n",
            "Epoch 23/400\n",
            "8/8 [==============================] - 0s 2ms/step - loss: 0.6274 - accuracy: 0.6902\n",
            "Epoch 24/400\n",
            "8/8 [==============================] - 0s 2ms/step - loss: 0.6398 - accuracy: 0.6567\n",
            "Epoch 25/400\n",
            "8/8 [==============================] - 0s 2ms/step - loss: 0.6186 - accuracy: 0.7246\n",
            "Epoch 26/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6155 - accuracy: 0.7193\n",
            "Epoch 27/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6199 - accuracy: 0.7116\n",
            "Epoch 28/400\n",
            "8/8 [==============================] - 0s 2ms/step - loss: 0.6185 - accuracy: 0.7078\n",
            "Epoch 29/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6102 - accuracy: 0.7125\n",
            "Epoch 30/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6188 - accuracy: 0.6865\n",
            "Epoch 31/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6113 - accuracy: 0.7252\n",
            "Epoch 32/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.6081 - accuracy: 0.7199\n",
            "Epoch 33/400\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.6155 - accuracy: 0.7303\n",
            "Epoch 34/400\n",
            "8/8 [==============================] - 0s 2ms/step - loss: 0.6013 - accuracy: 0.7489\n",
            "Epoch 35/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.5995 - accuracy: 0.7263\n",
            "Epoch 36/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.5788 - accuracy: 0.7819\n",
            "Epoch 37/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.5767 - accuracy: 0.7659\n",
            "Epoch 38/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.5743 - accuracy: 0.7685\n",
            "Epoch 39/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.5715 - accuracy: 0.7876\n",
            "Epoch 40/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.5654 - accuracy: 0.7923\n",
            "Epoch 41/400\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.5691 - accuracy: 0.7818\n",
            "Epoch 42/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.5559 - accuracy: 0.7934\n",
            "Epoch 43/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.5777 - accuracy: 0.7647\n",
            "Epoch 44/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.5540 - accuracy: 0.7830\n",
            "Epoch 45/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.5694 - accuracy: 0.7839\n",
            "Epoch 46/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.5557 - accuracy: 0.7954\n",
            "Epoch 47/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.5540 - accuracy: 0.7909\n",
            "Epoch 48/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.5516 - accuracy: 0.8036\n",
            "Epoch 49/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.5600 - accuracy: 0.7752\n",
            "Epoch 50/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.5362 - accuracy: 0.8123\n",
            "Epoch 51/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.5437 - accuracy: 0.7920\n",
            "Epoch 52/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.5233 - accuracy: 0.8110\n",
            "Epoch 53/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.5287 - accuracy: 0.8025\n",
            "Epoch 54/400\n",
            "8/8 [==============================] - 0s 2ms/step - loss: 0.5298 - accuracy: 0.7942\n",
            "Epoch 55/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.5343 - accuracy: 0.8088\n",
            "Epoch 56/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.5141 - accuracy: 0.8277\n",
            "Epoch 57/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.5097 - accuracy: 0.8322\n",
            "Epoch 58/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.5206 - accuracy: 0.7989\n",
            "Epoch 59/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.5071 - accuracy: 0.8016\n",
            "Epoch 60/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.4995 - accuracy: 0.8182\n",
            "Epoch 61/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.5264 - accuracy: 0.7936\n",
            "Epoch 62/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.5258 - accuracy: 0.7699\n",
            "Epoch 63/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.5009 - accuracy: 0.8079\n",
            "Epoch 64/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.4768 - accuracy: 0.8211\n",
            "Epoch 65/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.4824 - accuracy: 0.8283\n",
            "Epoch 66/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.4909 - accuracy: 0.8030\n",
            "Epoch 67/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.4910 - accuracy: 0.7980\n",
            "Epoch 68/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.4767 - accuracy: 0.8089\n",
            "Epoch 69/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.4870 - accuracy: 0.7855\n",
            "Epoch 70/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.4834 - accuracy: 0.7765\n",
            "Epoch 71/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.4797 - accuracy: 0.7866\n",
            "Epoch 72/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.4369 - accuracy: 0.8179\n",
            "Epoch 73/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.4625 - accuracy: 0.8226\n",
            "Epoch 74/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.4380 - accuracy: 0.8255\n",
            "Epoch 75/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.4690 - accuracy: 0.7951\n",
            "Epoch 76/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.4502 - accuracy: 0.8115\n",
            "Epoch 77/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.4584 - accuracy: 0.8095\n",
            "Epoch 78/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.4475 - accuracy: 0.8324\n",
            "Epoch 79/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.4684 - accuracy: 0.7838\n",
            "Epoch 80/400\n",
            "8/8 [==============================] - 0s 2ms/step - loss: 0.4272 - accuracy: 0.8170\n",
            "Epoch 81/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.4421 - accuracy: 0.8280\n",
            "Epoch 82/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.4323 - accuracy: 0.8543\n",
            "Epoch 83/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.4687 - accuracy: 0.7928\n",
            "Epoch 84/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.4581 - accuracy: 0.8026\n",
            "Epoch 85/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.4443 - accuracy: 0.8105\n",
            "Epoch 86/400\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.4581 - accuracy: 0.7839\n",
            "Epoch 87/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.4440 - accuracy: 0.8093\n",
            "Epoch 88/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.4399 - accuracy: 0.8187\n",
            "Epoch 89/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.4310 - accuracy: 0.8157\n",
            "Epoch 90/400\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.4188 - accuracy: 0.8083\n",
            "Epoch 91/400\n",
            "8/8 [==============================] - 0s 2ms/step - loss: 0.4413 - accuracy: 0.8135\n",
            "Epoch 92/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.4433 - accuracy: 0.8231\n",
            "Epoch 93/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.4214 - accuracy: 0.8175\n",
            "Epoch 94/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.4494 - accuracy: 0.8234\n",
            "Epoch 95/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.4336 - accuracy: 0.7959\n",
            "Epoch 96/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.4226 - accuracy: 0.8288\n",
            "Epoch 97/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.4426 - accuracy: 0.7968\n",
            "Epoch 98/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3988 - accuracy: 0.8565\n",
            "Epoch 99/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.4144 - accuracy: 0.8354\n",
            "Epoch 100/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3957 - accuracy: 0.8489\n",
            "Epoch 101/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.4244 - accuracy: 0.8181\n",
            "Epoch 102/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.4178 - accuracy: 0.8360\n",
            "Epoch 103/400\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.4064 - accuracy: 0.8450\n",
            "Epoch 104/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.4093 - accuracy: 0.8174\n",
            "Epoch 105/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3993 - accuracy: 0.8389\n",
            "Epoch 106/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3989 - accuracy: 0.8526\n",
            "Epoch 107/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.4527 - accuracy: 0.8105\n",
            "Epoch 108/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.4197 - accuracy: 0.8288\n",
            "Epoch 109/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.4045 - accuracy: 0.8433\n",
            "Epoch 110/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3957 - accuracy: 0.8303\n",
            "Epoch 111/400\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.4116 - accuracy: 0.7996\n",
            "Epoch 112/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.4263 - accuracy: 0.8233\n",
            "Epoch 113/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.4234 - accuracy: 0.8187\n",
            "Epoch 114/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.4370 - accuracy: 0.8133\n",
            "Epoch 115/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3740 - accuracy: 0.8356\n",
            "Epoch 116/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.4067 - accuracy: 0.8174\n",
            "Epoch 117/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3851 - accuracy: 0.8539\n",
            "Epoch 118/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.4133 - accuracy: 0.7999\n",
            "Epoch 119/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.4192 - accuracy: 0.8173\n",
            "Epoch 120/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.4035 - accuracy: 0.8210\n",
            "Epoch 121/400\n",
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            "8/8 [==============================] - 0s 3ms/step - loss: 0.3867 - accuracy: 0.8477\n",
            "Epoch 298/400\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3362 - accuracy: 0.8529\n",
            "Epoch 299/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3561 - accuracy: 0.8542\n",
            "Epoch 300/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3703 - accuracy: 0.8538\n",
            "Epoch 301/400\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.3403 - accuracy: 0.8613\n",
            "Epoch 302/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3943 - accuracy: 0.8228\n",
            "Epoch 303/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3615 - accuracy: 0.8407\n",
            "Epoch 304/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3817 - accuracy: 0.8503\n",
            "Epoch 305/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3382 - accuracy: 0.8643\n",
            "Epoch 306/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3864 - accuracy: 0.8463\n",
            "Epoch 307/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3689 - accuracy: 0.8495\n",
            "Epoch 308/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.4103 - accuracy: 0.8135\n",
            "Epoch 309/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3456 - accuracy: 0.8701\n",
            "Epoch 310/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3793 - accuracy: 0.8487\n",
            "Epoch 311/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3629 - accuracy: 0.8434\n",
            "Epoch 312/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3291 - accuracy: 0.8677\n",
            "Epoch 313/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3837 - accuracy: 0.8329\n",
            "Epoch 314/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3612 - accuracy: 0.8418\n",
            "Epoch 315/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3734 - accuracy: 0.8379\n",
            "Epoch 316/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3740 - accuracy: 0.8363\n",
            "Epoch 317/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3549 - accuracy: 0.8569\n",
            "Epoch 318/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3754 - accuracy: 0.8393\n",
            "Epoch 319/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3170 - accuracy: 0.8722\n",
            "Epoch 320/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3488 - accuracy: 0.8517\n",
            "Epoch 321/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.4101 - accuracy: 0.8084\n",
            "Epoch 322/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3605 - accuracy: 0.8479\n",
            "Epoch 323/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3847 - accuracy: 0.8371\n",
            "Epoch 324/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3343 - accuracy: 0.8468\n",
            "Epoch 325/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.4040 - accuracy: 0.8256\n",
            "Epoch 326/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3477 - accuracy: 0.8707\n",
            "Epoch 327/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3376 - accuracy: 0.8529\n",
            "Epoch 328/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3385 - accuracy: 0.8718\n",
            "Epoch 329/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3681 - accuracy: 0.8417\n",
            "Epoch 330/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3664 - accuracy: 0.8354\n",
            "Epoch 331/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3691 - accuracy: 0.8707\n",
            "Epoch 332/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3532 - accuracy: 0.8511\n",
            "Epoch 333/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3586 - accuracy: 0.8364\n",
            "Epoch 334/400\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3399 - accuracy: 0.8674\n",
            "Epoch 335/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3237 - accuracy: 0.8722\n",
            "Epoch 336/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3747 - accuracy: 0.8336\n",
            "Epoch 337/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3442 - accuracy: 0.8339\n",
            "Epoch 338/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3316 - accuracy: 0.8736\n",
            "Epoch 339/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3482 - accuracy: 0.8518\n",
            "Epoch 340/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3373 - accuracy: 0.8607\n",
            "Epoch 341/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3522 - accuracy: 0.8572\n",
            "Epoch 342/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3642 - accuracy: 0.8428\n",
            "Epoch 343/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.4021 - accuracy: 0.8214\n",
            "Epoch 344/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3779 - accuracy: 0.8492\n",
            "Epoch 345/400\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3331 - accuracy: 0.8741\n",
            "Epoch 346/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3479 - accuracy: 0.8502\n",
            "Epoch 347/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3366 - accuracy: 0.8590\n",
            "Epoch 348/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3701 - accuracy: 0.8514\n",
            "Epoch 349/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3318 - accuracy: 0.8700\n",
            "Epoch 350/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3557 - accuracy: 0.8304\n",
            "Epoch 351/400\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3522 - accuracy: 0.8616\n",
            "Epoch 352/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3568 - accuracy: 0.8350\n",
            "Epoch 353/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3430 - accuracy: 0.8559\n",
            "Epoch 354/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3087 - accuracy: 0.8765\n",
            "Epoch 355/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3225 - accuracy: 0.8853\n",
            "Epoch 356/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3212 - accuracy: 0.8773\n",
            "Epoch 357/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3392 - accuracy: 0.8642\n",
            "Epoch 358/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3551 - accuracy: 0.8397\n",
            "Epoch 359/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3329 - accuracy: 0.8709\n",
            "Epoch 360/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3551 - accuracy: 0.8484\n",
            "Epoch 361/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3433 - accuracy: 0.8638\n",
            "Epoch 362/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.2987 - accuracy: 0.8776\n",
            "Epoch 363/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3616 - accuracy: 0.8437\n",
            "Epoch 364/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3675 - accuracy: 0.8593\n",
            "Epoch 365/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3620 - accuracy: 0.8448\n",
            "Epoch 366/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3292 - accuracy: 0.8586\n",
            "Epoch 367/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3406 - accuracy: 0.8553\n",
            "Epoch 368/400\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3927 - accuracy: 0.8318\n",
            "Epoch 369/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3110 - accuracy: 0.8903\n",
            "Epoch 370/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3176 - accuracy: 0.8702\n",
            "Epoch 371/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3559 - accuracy: 0.8597\n",
            "Epoch 372/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3475 - accuracy: 0.8600\n",
            "Epoch 373/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3302 - accuracy: 0.8650\n",
            "Epoch 374/400\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3736 - accuracy: 0.8414\n",
            "Epoch 375/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3108 - accuracy: 0.8964\n",
            "Epoch 376/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3490 - accuracy: 0.8399\n",
            "Epoch 377/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3823 - accuracy: 0.8223\n",
            "Epoch 378/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3151 - accuracy: 0.8611\n",
            "Epoch 379/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3494 - accuracy: 0.8403\n",
            "Epoch 380/400\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3621 - accuracy: 0.8347\n",
            "Epoch 381/400\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3049 - accuracy: 0.8640\n",
            "Epoch 382/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3757 - accuracy: 0.8293\n",
            "Epoch 383/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3400 - accuracy: 0.8659\n",
            "Epoch 384/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3524 - accuracy: 0.8438\n",
            "Epoch 385/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3400 - accuracy: 0.8604\n",
            "Epoch 386/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3236 - accuracy: 0.8739\n",
            "Epoch 387/400\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3452 - accuracy: 0.8288\n",
            "Epoch 388/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3467 - accuracy: 0.8588\n",
            "Epoch 389/400\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3393 - accuracy: 0.8599\n",
            "Epoch 390/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3635 - accuracy: 0.8359\n",
            "Epoch 391/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3699 - accuracy: 0.8412\n",
            "Epoch 392/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3314 - accuracy: 0.8529\n",
            "Epoch 393/400\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3826 - accuracy: 0.8220\n",
            "Epoch 394/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3491 - accuracy: 0.8445\n",
            "Epoch 395/400\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.2988 - accuracy: 0.8834\n",
            "Epoch 396/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3427 - accuracy: 0.8583\n",
            "Epoch 397/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3556 - accuracy: 0.8575\n",
            "Epoch 398/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3163 - accuracy: 0.8666\n",
            "Epoch 399/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3549 - accuracy: 0.8555\n",
            "Epoch 400/400\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3092 - accuracy: 0.8697\n",
            "Accuracy: 83.61\n",
            "Roc_Auc Score: 83.89\n",
            "Precision: 89.29\n",
            "recall: 78.12\n",
            "fl score: 83.33\n",
            "Mcc Score: 0.68\n",
            "Kappa score: 0.67\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ggKz3TpnXQBh"
      },
      "source": [
        "#**Models With Grid Search and Cross Validation**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZI_SLtgnYpZQ"
      },
      "source": [
        "#**Logistic Regression**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hQ4VtdCEWvBI",
        "outputId": "9aadcf87-0485-4be2-ceba-ecedb442278d"
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "  \n",
        "# Creating the hyperparameter grid\n",
        "c_space = np.logspace(-5, 8, 15)\n",
        "param_grid = {'C': c_space}\n",
        "  \n",
        "# Instantiating logistic regression classifier\n",
        "logreg = LogisticRegression()\n",
        "  \n",
        "# Instantiating the GridSearchCV object\n",
        "logreg_cv = GridSearchCV(logreg, param_grid, cv = 5)\n",
        "  \n",
        "logreg_cv.fit(X_train, y_train)\n",
        "  \n",
        "# Print the tuned parameters and score\n",
        "print(\"Tuned Logistic Regression Parameters: {}\".format(logreg_cv.best_params_)) \n",
        "print(\"Best score is {}\".format(logreg_cv.best_score_))"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Tuned Logistic Regression Parameters: {'C': 0.05179474679231213}\n",
            "Best score is 0.7935374149659864\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ag_kTEDvwl8k",
        "outputId": "4f7aecea-4973-4fd7-9690-9588d3ceec43"
      },
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "import numpy as np\n",
        "params = {\"C\": [0.05179474679231213]\n",
        "          }\n",
        "\n",
        "lr_clf = LogisticRegression()\n",
        "\n",
        "lr_cv = GridSearchCV(lr_clf, params, scoring=\"accuracy\", n_jobs=-1, verbose=1, cv=3)\n",
        "lr_cv.fit(X_train, y_train)#Dummy Variables splitted dataset\n",
        "best_params = lr_cv.best_params_\n",
        "print(f\"Best parameters: {best_params}\")\n",
        "lr_clf = LogisticRegression(**best_params)\n",
        "\n",
        "lr_clf.fit(X_train, y_train)\n",
        "\n",
        "print_score(lr_clf, X_train, y_train, X_test, y_test, train=True)\n",
        "print_score(lr_clf, X_train, y_train, X_test, y_test, train=False)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 3 folds for each of 1 candidates, totalling 3 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Best parameters: {'C': 0.05179474679231213}\n",
            "Train Result:\n",
            "================================================\n",
            "Accuracy Score: 80.17%\n",
            "_______________________________________________\n",
            "CLASSIFICATION REPORT:\n",
            "                    0           1  accuracy   macro avg  weighted avg\n",
            "precision    0.835165    0.781457  0.801653    0.808311      0.805648\n",
            "recall       0.697248    0.887218  0.801653    0.792233      0.801653\n",
            "f1-score     0.760000    0.830986  0.801653    0.795493      0.799013\n",
            "support    109.000000  133.000000  0.801653  242.000000    242.000000\n",
            "_______________________________________________\n",
            "Confusion Matrix: \n",
            " [[ 76  33]\n",
            " [ 15 118]]\n",
            "\n",
            "Accuracy: 80.17\n",
            "Roc_Auc Score: 79.22\n",
            "Precision: 78.15\n",
            "recall: 88.72\n",
            "fl score: 83.10\n",
            "Mcc Score: 0.60\n",
            "Kappa score: 0.59\n",
            "\n",
            "\n",
            "Test Result:\n",
            "================================================\n",
            "Accuracy Score: 88.52%\n",
            "_______________________________________________\n",
            "CLASSIFICATION REPORT:\n",
            "                   0          1  accuracy  macro avg  weighted avg\n",
            "precision   0.866667   0.903226  0.885246   0.884946      0.885845\n",
            "recall      0.896552   0.875000  0.885246   0.885776      0.885246\n",
            "f1-score    0.881356   0.888889  0.885246   0.885122      0.885308\n",
            "support    29.000000  32.000000  0.885246  61.000000     61.000000\n",
            "_______________________________________________\n",
            "Confusion Matrix: \n",
            " [[26  3]\n",
            " [ 4 28]]\n",
            "\n",
            "Accuracy: 88.52\n",
            "Roc_Auc Score: 88.58\n",
            "Precision: 90.32\n",
            "recall: 87.50\n",
            "fl score: 88.89\n",
            "Mcc Score: 0.77\n",
            "Kappa score: 0.77\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Done   3 out of   3 | elapsed:    1.2s finished\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kVsWZvkOYwtq"
      },
      "source": [
        "#**KNN**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2oxO8b7L4yG2",
        "outputId": "653e1a78-2feb-43eb-abe4-3e516829573e"
      },
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn import metrics\n",
        "knn = KNeighborsClassifier()\n",
        "mean_acc = np.zeros(20)\n",
        "for i in range(1,21):\n",
        "    #Train Model and Predict  \n",
        "    knn = KNeighborsClassifier(n_neighbors = i).fit(X_train,y_train)\n",
        "    yhat= knn.predict(X_test)\n",
        "    mean_acc[i-1] = metrics.accuracy_score(y_test, yhat)\n",
        "   \n",
        "mean_acc\n",
        "\n"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.81967213, 0.78688525, 0.86885246, 0.83606557, 0.83606557,\n",
              "       0.81967213, 0.83606557, 0.81967213, 0.83606557, 0.81967213,\n",
              "       0.81967213, 0.78688525, 0.81967213, 0.80327869, 0.83606557,\n",
              "       0.83606557, 0.85245902, 0.85245902, 0.83606557, 0.81967213])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 387
        },
        "id": "mIJPTFrmUqM_",
        "outputId": "47c75333-1742-4829-b11a-c5eab05e65ca"
      },
      "source": [
        "loc = np.arange(1,21,step=1.0)\n",
        "plt.figure(figsize = (10, 6))\n",
        "plt.plot(range(1,21), mean_acc)\n",
        "plt.xticks(loc)\n",
        "plt.xlabel('Number of Neighbors ')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.show()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eTs1yKD_UvxN",
        "outputId": "198aa16f-2fdb-4b18-a01b-2e4b91b1e179"
      },
      "source": [
        "grid_params = { 'n_neighbors' : [13,16,19],\n",
        "               'weights' : ['uniform','distance'],\n",
        "               'metric' : ['minkowski','euclidean','manhattan']}\n",
        "gs = GridSearchCV(KNeighborsClassifier(), grid_params, verbose = 1, cv=3, n_jobs = -1)\n",
        "g_res = gs.fit(X_train, y_train)\n",
        "print_score(g_res, X_train, y_train, X_test, y_test, train=True)\n",
        "print_score(g_res, X_train, y_train, X_test, y_test, train=False)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 3 folds for each of 18 candidates, totalling 54 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Train Result:\n",
            "================================================\n",
            "Accuracy Score: 100.00%\n",
            "_______________________________________________\n",
            "CLASSIFICATION REPORT:\n",
            "               0      1  accuracy  macro avg  weighted avg\n",
            "precision    1.0    1.0       1.0        1.0           1.0\n",
            "recall       1.0    1.0       1.0        1.0           1.0\n",
            "f1-score     1.0    1.0       1.0        1.0           1.0\n",
            "support    109.0  133.0       1.0      242.0         242.0\n",
            "_______________________________________________\n",
            "Confusion Matrix: \n",
            " [[109   0]\n",
            " [  0 133]]\n",
            "\n",
            "Accuracy: 100.00\n",
            "Roc_Auc Score: 100.00\n",
            "Precision: 100.00\n",
            "recall: 100.00\n",
            "fl score: 100.00\n",
            "Mcc Score: 1.00\n",
            "Kappa score: 1.00\n",
            "\n",
            "\n",
            "Test Result:\n",
            "================================================\n",
            "Accuracy Score: 88.52%\n",
            "_______________________________________________\n",
            "CLASSIFICATION REPORT:\n",
            "                   0          1  accuracy  macro avg  weighted avg\n",
            "precision   0.892857   0.878788  0.885246   0.885823      0.885477\n",
            "recall      0.862069   0.906250  0.885246   0.884159      0.885246\n",
            "f1-score    0.877193   0.892308  0.885246   0.884750      0.885122\n",
            "support    29.000000  32.000000  0.885246  61.000000     61.000000\n",
            "_______________________________________________\n",
            "Confusion Matrix: \n",
            " [[25  4]\n",
            " [ 3 29]]\n",
            "\n",
            "Accuracy: 88.52\n",
            "Roc_Auc Score: 88.42\n",
            "Precision: 87.88\n",
            "recall: 90.62\n",
            "fl score: 89.23\n",
            "Mcc Score: 0.77\n",
            "Kappa score: 0.77\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Done  54 out of  54 | elapsed:    0.4s finished\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FczvHjyMY1Sf"
      },
      "source": [
        "#**SVM**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G3NC8eXvxkpx",
        "outputId": "e5bfa6b7-5c10-40b3-fbd8-68e4f44ca41d"
      },
      "source": [
        "from sklearn.utils.fixes import scipy\n",
        "param_grid = {'C': [0.1, 1, 10, 100, 1000], \n",
        "              'gamma': [1, 0.1, 0.01, 0.001, 0.0001],\n",
        "              'kernel': ['rbf']} \n",
        "  \n",
        "grid = GridSearchCV(SVC(), param_grid, refit = True, verbose = 3)\n",
        "grid.fit(X_train, y_train)\n",
        "print(grid.best_params_)\n",
        "print(grid.best_estimator_)\n"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Fitting 5 folds for each of 25 candidates, totalling 125 fits\n",
            "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
            "[CV] .......... C=0.1, gamma=1, kernel=rbf, score=0.551, total=   0.0s\n",
            "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
            "[CV] .......... C=0.1, gamma=1, kernel=rbf, score=0.551, total=   0.0s\n",
            "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
            "[CV] .......... C=0.1, gamma=1, kernel=rbf, score=0.562, total=   0.0s\n",
            "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
            "[CV] .......... C=0.1, gamma=1, kernel=rbf, score=0.542, total=   0.0s\n",
            "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
            "[CV] .......... C=0.1, gamma=1, kernel=rbf, score=0.542, total=   0.0s\n",
            "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
            "[CV] ........ C=0.1, gamma=0.1, kernel=rbf, score=0.796, total=   0.0s\n",
            "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
            "[CV] ........ C=0.1, gamma=0.1, kernel=rbf, score=0.714, total=   0.0s\n",
            "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
            "[CV] ........ C=0.1, gamma=0.1, kernel=rbf, score=0.750, total=   0.0s\n",
            "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
            "[CV] ........ C=0.1, gamma=0.1, kernel=rbf, score=0.667, total=   0.0s\n",
            "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
            "[CV] ........ C=0.1, gamma=0.1, kernel=rbf, score=0.792, total=   0.0s\n",
            "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
            "[CV] ....... C=0.1, gamma=0.01, kernel=rbf, score=0.551, total=   0.0s\n",
            "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
            "[CV] ....... C=0.1, gamma=0.01, kernel=rbf, score=0.551, total=   0.0s\n",
            "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
            "[CV] ....... C=0.1, gamma=0.01, kernel=rbf, score=0.562, total=   0.0s\n",
            "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
            "[CV] ....... C=0.1, gamma=0.01, kernel=rbf, score=0.542, total=   0.0s\n",
            "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
            "[CV] ....... C=0.1, gamma=0.01, kernel=rbf, score=0.542, total=   0.0s\n",
            "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
            "[CV] ...... C=0.1, gamma=0.001, kernel=rbf, score=0.551, total=   0.0s\n",
            "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
            "[CV] ...... C=0.1, gamma=0.001, kernel=rbf, score=0.551, total=   0.0s\n",
            "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
            "[CV] ...... C=0.1, gamma=0.001, kernel=rbf, score=0.562, total=   0.0s\n",
            "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
            "[CV] ...... C=0.1, gamma=0.001, kernel=rbf, score=0.542, total=   0.0s\n",
            "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
            "[CV] ...... C=0.1, gamma=0.001, kernel=rbf, score=0.542, total=   0.0s\n",
            "[CV] C=0.1, gamma=0.0001, kernel=rbf .................................\n",
            "[CV] ..... C=0.1, gamma=0.0001, kernel=rbf, score=0.551, total=   0.0s\n",
            "[CV] C=0.1, gamma=0.0001, kernel=rbf .................................\n",
            "[CV] ..... C=0.1, gamma=0.0001, kernel=rbf, score=0.551, total=   0.0s\n",
            "[CV] C=0.1, gamma=0.0001, kernel=rbf .................................\n",
            "[CV] ..... C=0.1, gamma=0.0001, kernel=rbf, score=0.562, total=   0.0s\n",
            "[CV] C=0.1, gamma=0.0001, kernel=rbf .................................\n",
            "[CV] ..... C=0.1, gamma=0.0001, kernel=rbf, score=0.542, total=   0.0s\n",
            "[CV] C=0.1, gamma=0.0001, kernel=rbf .................................\n",
            "[CV] ..... C=0.1, gamma=0.0001, kernel=rbf, score=0.542, total=   0.0s\n",
            "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
            "[CV] ............ C=1, gamma=1, kernel=rbf, score=0.714, total=   0.0s\n",
            "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
            "[CV] ............ C=1, gamma=1, kernel=rbf, score=0.673, total=   0.0s\n",
            "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
            "[CV] ............ C=1, gamma=1, kernel=rbf, score=0.646, total=   0.0s\n",
            "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
            "[CV] ............ C=1, gamma=1, kernel=rbf, score=0.667, total=   0.0s\n",
            "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
            "[CV] ............ C=1, gamma=1, kernel=rbf, score=0.771, total=   0.0s\n",
            "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
            "[CV] .......... C=1, gamma=0.1, kernel=rbf, score=0.755, total=   0.0s\n",
            "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
            "[CV] .......... C=1, gamma=0.1, kernel=rbf, score=0.755, total=   0.0s\n",
            "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
            "[CV] .......... C=1, gamma=0.1, kernel=rbf, score=0.750, total=   0.0s\n",
            "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
            "[CV] .......... C=1, gamma=0.1, kernel=rbf, score=0.729, total=   0.0s\n",
            "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
            "[CV] .......... C=1, gamma=0.1, kernel=rbf, score=0.812, total=   0.0s\n",
            "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
            "[CV] ......... C=1, gamma=0.01, kernel=rbf, score=0.816, total=   0.0s\n",
            "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
            "[CV] ......... C=1, gamma=0.01, kernel=rbf, score=0.735, total=   0.0s\n",
            "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
            "[CV] ......... C=1, gamma=0.01, kernel=rbf, score=0.792, total=   0.0s\n",
            "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
            "[CV] ......... C=1, gamma=0.01, kernel=rbf, score=0.750, total=   0.0s\n",
            "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
            "[CV] ......... C=1, gamma=0.01, kernel=rbf, score=0.771, total=   0.0s\n",
            "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
            "[CV] ........ C=1, gamma=0.001, kernel=rbf, score=0.551, total=   0.0s\n",
            "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
            "[CV] ........ C=1, gamma=0.001, kernel=rbf, score=0.551, total=   0.0s\n",
            "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
            "[CV] ........ C=1, gamma=0.001, kernel=rbf, score=0.562, total=   0.0s\n",
            "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
            "[CV] ........ C=1, gamma=0.001, kernel=rbf, score=0.542, total=   0.0s\n",
            "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
            "[CV] ........ C=1, gamma=0.001, kernel=rbf, score=0.542, total=   0.0s\n",
            "[CV] C=1, gamma=0.0001, kernel=rbf ...................................\n",
            "[CV] ....... C=1, gamma=0.0001, kernel=rbf, score=0.551, total=   0.0s\n",
            "[CV] C=1, gamma=0.0001, kernel=rbf ...................................\n",
            "[CV] ....... C=1, gamma=0.0001, kernel=rbf, score=0.551, total=   0.0s\n",
            "[CV] C=1, gamma=0.0001, kernel=rbf ...................................\n",
            "[CV] ....... C=1, gamma=0.0001, kernel=rbf, score=0.562, total=   0.0s\n",
            "[CV] C=1, gamma=0.0001, kernel=rbf ...................................\n",
            "[CV] ....... C=1, gamma=0.0001, kernel=rbf, score=0.542, total=   0.0s\n",
            "[CV] C=1, gamma=0.0001, kernel=rbf ...................................\n",
            "[CV] ....... C=1, gamma=0.0001, kernel=rbf, score=0.542, total=   0.0s\n",
            "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
            "[CV] ........... C=10, gamma=1, kernel=rbf, score=0.673, total=   0.0s\n",
            "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
            "[CV] ........... C=10, gamma=1, kernel=rbf, score=0.592, total=   0.0s\n",
            "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
            "[CV] ........... C=10, gamma=1, kernel=rbf, score=0.667, total=   0.0s\n",
            "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
            "[CV] ........... C=10, gamma=1, kernel=rbf, score=0.688, total=   0.0s\n",
            "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
            "[CV] ........... C=10, gamma=1, kernel=rbf, score=0.708, total=   0.0s\n",
            "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
            "[CV] ......... C=10, gamma=0.1, kernel=rbf, score=0.714, total=   0.0s\n",
            "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
            "[CV] ......... C=10, gamma=0.1, kernel=rbf, score=0.816, total=   0.0s\n",
            "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
            "[CV] ......... C=10, gamma=0.1, kernel=rbf, score=0.708, total=   0.0s\n",
            "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
            "[CV] ......... C=10, gamma=0.1, kernel=rbf, score=0.771, total=   0.0s\n",
            "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
            "[CV] ......... C=10, gamma=0.1, kernel=rbf, score=0.771, total=   0.0s\n",
            "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
            "[CV] ........ C=10, gamma=0.01, kernel=rbf, score=0.776, total=   0.0s\n",
            "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
            "[CV] ........ C=10, gamma=0.01, kernel=rbf, score=0.776, total=   0.0s\n",
            "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
            "[CV] ........ C=10, gamma=0.01, kernel=rbf, score=0.792, total=   0.0s\n",
            "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
            "[CV] ........ C=10, gamma=0.01, kernel=rbf, score=0.750, total=   0.0s\n",
            "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
            "[CV] ........ C=10, gamma=0.01, kernel=rbf, score=0.792, total=   0.0s\n",
            "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
            "[CV] ....... C=10, gamma=0.001, kernel=rbf, score=0.816, total=   0.0s\n",
            "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
            "[CV] ....... C=10, gamma=0.001, kernel=rbf, score=0.735, total=   0.0s\n",
            "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
            "[CV] ....... C=10, gamma=0.001, kernel=rbf, score=0.792, total=   0.0s\n",
            "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
            "[CV] ....... C=10, gamma=0.001, kernel=rbf, score=0.750, total=   0.0s\n",
            "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
            "[CV] ....... C=10, gamma=0.001, kernel=rbf, score=0.792, total=   0.0s\n",
            "[CV] C=10, gamma=0.0001, kernel=rbf ..................................\n",
            "[CV] ...... C=10, gamma=0.0001, kernel=rbf, score=0.551, total=   0.0s\n",
            "[CV] C=10, gamma=0.0001, kernel=rbf ..................................\n",
            "[CV] ...... C=10, gamma=0.0001, kernel=rbf, score=0.551, total=   0.0s\n",
            "[CV] C=10, gamma=0.0001, kernel=rbf ..................................\n",
            "[CV] ...... C=10, gamma=0.0001, kernel=rbf, score=0.562, total=   0.0s\n",
            "[CV] C=10, gamma=0.0001, kernel=rbf ..................................\n",
            "[CV] ...... C=10, gamma=0.0001, kernel=rbf, score=0.542, total=   0.0s\n",
            "[CV] C=10, gamma=0.0001, kernel=rbf ..................................\n",
            "[CV] ...... C=10, gamma=0.0001, kernel=rbf, score=0.542, total=   0.0s\n",
            "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
            "[CV] .......... C=100, gamma=1, kernel=rbf, score=0.673, total=   0.0s\n",
            "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
            "[CV] .......... C=100, gamma=1, kernel=rbf, score=0.592, total=   0.0s\n",
            "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
            "[CV] .......... C=100, gamma=1, kernel=rbf, score=0.667, total=   0.0s\n",
            "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
            "[CV] .......... C=100, gamma=1, kernel=rbf, score=0.688, total=   0.0s\n",
            "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
            "[CV] .......... C=100, gamma=1, kernel=rbf, score=0.708, total=   0.0s\n",
            "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
            "[CV] ........ C=100, gamma=0.1, kernel=rbf, score=0.653, total=   0.0s\n",
            "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
            "[CV] ........ C=100, gamma=0.1, kernel=rbf, score=0.694, total=   0.0s\n",
            "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
            "[CV] ........ C=100, gamma=0.1, kernel=rbf, score=0.646, total=   0.0s\n",
            "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
            "[CV] ........ C=100, gamma=0.1, kernel=rbf, score=0.750, total=   0.0s\n",
            "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
            "[CV] ........ C=100, gamma=0.1, kernel=rbf, score=0.688, total=   0.0s\n",
            "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
            "[CV] ....... C=100, gamma=0.01, kernel=rbf, score=0.735, total=   0.0s\n",
            "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
            "[CV] ....... C=100, gamma=0.01, kernel=rbf, score=0.796, total=   0.0s\n",
            "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
            "[CV] ....... C=100, gamma=0.01, kernel=rbf, score=0.708, total=   0.0s\n",
            "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
            "[CV] ....... C=100, gamma=0.01, kernel=rbf, score=0.750, total=   0.0s\n",
            "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
            "[CV] ....... C=100, gamma=0.01, kernel=rbf, score=0.771, total=   0.0s\n",
            "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
            "[CV] ...... C=100, gamma=0.001, kernel=rbf, score=0.735, total=   0.0s\n",
            "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
            "[CV] ...... C=100, gamma=0.001, kernel=rbf, score=0.755, total=   0.0s\n",
            "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
            "[CV] ...... C=100, gamma=0.001, kernel=rbf, score=0.792, total=   0.0s\n",
            "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
            "[CV] ...... C=100, gamma=0.001, kernel=rbf, score=0.729, total=   0.0s\n",
            "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
            "[CV] ...... C=100, gamma=0.001, kernel=rbf, score=0.792, total=   0.0s\n",
            "[CV] C=100, gamma=0.0001, kernel=rbf .................................\n",
            "[CV] ..... C=100, gamma=0.0001, kernel=rbf, score=0.816, total=   0.0s\n",
            "[CV] C=100, gamma=0.0001, kernel=rbf .................................\n",
            "[CV] ..... C=100, gamma=0.0001, kernel=rbf, score=0.735, total=   0.0s\n",
            "[CV] C=100, gamma=0.0001, kernel=rbf .................................\n",
            "[CV] ..... C=100, gamma=0.0001, kernel=rbf, score=0.792, total=   0.0s\n",
            "[CV] C=100, gamma=0.0001, kernel=rbf .................................\n",
            "[CV] ..... C=100, gamma=0.0001, kernel=rbf, score=0.750, total=   0.0s\n",
            "[CV] C=100, gamma=0.0001, kernel=rbf .................................\n",
            "[CV] ..... C=100, gamma=0.0001, kernel=rbf, score=0.792, total=   0.0s\n",
            "[CV] C=1000, gamma=1, kernel=rbf .....................................\n",
            "[CV] ......... C=1000, gamma=1, kernel=rbf, score=0.673, total=   0.0s\n",
            "[CV] C=1000, gamma=1, kernel=rbf .....................................\n",
            "[CV] ......... C=1000, gamma=1, kernel=rbf, score=0.592, total=   0.0s\n",
            "[CV] C=1000, gamma=1, kernel=rbf .....................................\n",
            "[CV] ......... C=1000, gamma=1, kernel=rbf, score=0.667, total=   0.0s\n",
            "[CV] C=1000, gamma=1, kernel=rbf .....................................\n",
            "[CV] ......... C=1000, gamma=1, kernel=rbf, score=0.688, total=   0.0s\n",
            "[CV] C=1000, gamma=1, kernel=rbf .....................................\n",
            "[CV] ......... C=1000, gamma=1, kernel=rbf, score=0.708, total=   0.0s\n",
            "[CV] C=1000, gamma=0.1, kernel=rbf ...................................\n",
            "[CV] ....... C=1000, gamma=0.1, kernel=rbf, score=0.653, total=   0.0s\n",
            "[CV] C=1000, gamma=0.1, kernel=rbf ...................................\n",
            "[CV] ....... C=1000, gamma=0.1, kernel=rbf, score=0.714, total=   0.0s\n",
            "[CV] C=1000, gamma=0.1, kernel=rbf ...................................\n",
            "[CV] ....... C=1000, gamma=0.1, kernel=rbf, score=0.625, total=   0.0s\n",
            "[CV] C=1000, gamma=0.1, kernel=rbf ...................................\n",
            "[CV] ....... C=1000, gamma=0.1, kernel=rbf, score=0.708, total=   0.0s\n",
            "[CV] C=1000, gamma=0.1, kernel=rbf ...................................\n",
            "[CV] ....... C=1000, gamma=0.1, kernel=rbf, score=0.708, total=   0.0s\n",
            "[CV] C=1000, gamma=0.01, kernel=rbf ..................................\n",
            "[CV] ...... C=1000, gamma=0.01, kernel=rbf, score=0.714, total=   0.0s\n",
            "[CV] C=1000, gamma=0.01, kernel=rbf ..................................\n",
            "[CV] ...... C=1000, gamma=0.01, kernel=rbf, score=0.816, total=   0.0s\n",
            "[CV] C=1000, gamma=0.01, kernel=rbf ..................................\n",
            "[CV] ...... C=1000, gamma=0.01, kernel=rbf, score=0.646, total=   0.0s\n",
            "[CV] C=1000, gamma=0.01, kernel=rbf ..................................\n",
            "[CV] ...... C=1000, gamma=0.01, kernel=rbf, score=0.729, total=   0.0s\n",
            "[CV] C=1000, gamma=0.01, kernel=rbf ..................................\n",
            "[CV] ...... C=1000, gamma=0.01, kernel=rbf, score=0.750, total=   0.0s\n",
            "[CV] C=1000, gamma=0.001, kernel=rbf .................................\n",
            "[CV] ..... C=1000, gamma=0.001, kernel=rbf, score=0.776, total=   0.0s\n",
            "[CV] C=1000, gamma=0.001, kernel=rbf .................................\n",
            "[CV] ..... C=1000, gamma=0.001, kernel=rbf, score=0.776, total=   0.0s\n",
            "[CV] C=1000, gamma=0.001, kernel=rbf .................................\n",
            "[CV] ..... C=1000, gamma=0.001, kernel=rbf, score=0.729, total=   0.0s\n",
            "[CV] C=1000, gamma=0.001, kernel=rbf .................................\n",
            "[CV] ..... C=1000, gamma=0.001, kernel=rbf, score=0.771, total=   0.0s\n",
            "[CV] C=1000, gamma=0.001, kernel=rbf .................................\n",
            "[CV] ..... C=1000, gamma=0.001, kernel=rbf, score=0.792, total=   0.0s\n",
            "[CV] C=1000, gamma=0.0001, kernel=rbf ................................\n",
            "[CV] .... C=1000, gamma=0.0001, kernel=rbf, score=0.735, total=   0.0s\n",
            "[CV] C=1000, gamma=0.0001, kernel=rbf ................................\n",
            "[CV] .... C=1000, gamma=0.0001, kernel=rbf, score=0.755, total=   0.0s\n",
            "[CV] C=1000, gamma=0.0001, kernel=rbf ................................\n",
            "[CV] .... C=1000, gamma=0.0001, kernel=rbf, score=0.771, total=   0.0s\n",
            "[CV] C=1000, gamma=0.0001, kernel=rbf ................................\n",
            "[CV] .... C=1000, gamma=0.0001, kernel=rbf, score=0.771, total=   0.0s\n",
            "[CV] C=1000, gamma=0.0001, kernel=rbf ................................\n",
            "[CV] .... C=1000, gamma=0.0001, kernel=rbf, score=0.812, total=   0.0s\n",
            "{'C': 10, 'gamma': 0.01, 'kernel': 'rbf'}\n",
            "SVC(C=10, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
            "    decision_function_shape='ovr', degree=3, gamma=0.01, kernel='rbf',\n",
            "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
            "    tol=0.001, verbose=False)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done 125 out of 125 | elapsed:    0.9s finished\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O-WZw2LZYZ6f",
        "outputId": "80dd67ac-b11a-4981-d87f-5878488a9e82"
      },
      "source": [
        "print_score(grid, X_train, y_train, X_test, y_test, train=True)\n",
        "print_score(grid, X_train, y_train, X_test, y_test, train=False)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train Result:\n",
            "================================================\n",
            "Accuracy Score: 83.88%\n",
            "_______________________________________________\n",
            "CLASSIFICATION REPORT:\n",
            "                    0           1  accuracy   macro avg  weighted avg\n",
            "precision    0.864583    0.821918  0.838843    0.843251      0.841135\n",
            "recall       0.761468    0.902256  0.838843    0.831862      0.838843\n",
            "f1-score     0.809756    0.860215  0.838843    0.834986      0.837488\n",
            "support    109.000000  133.000000  0.838843  242.000000    242.000000\n",
            "_______________________________________________\n",
            "Confusion Matrix: \n",
            " [[ 83  26]\n",
            " [ 13 120]]\n",
            "\n",
            "Accuracy: 83.88\n",
            "Roc_Auc Score: 83.19\n",
            "Precision: 82.19\n",
            "recall: 90.23\n",
            "fl score: 86.02\n",
            "Mcc Score: 0.68\n",
            "Kappa score: 0.67\n",
            "\n",
            "\n",
            "Test Result:\n",
            "================================================\n",
            "Accuracy Score: 86.89%\n",
            "_______________________________________________\n",
            "CLASSIFICATION REPORT:\n",
            "                   0          1  accuracy  macro avg  weighted avg\n",
            "precision   0.838710   0.900000  0.868852   0.869355      0.870862\n",
            "recall      0.896552   0.843750  0.868852   0.870151      0.868852\n",
            "f1-score    0.866667   0.870968  0.868852   0.868817      0.868923\n",
            "support    29.000000  32.000000  0.868852  61.000000     61.000000\n",
            "_______________________________________________\n",
            "Confusion Matrix: \n",
            " [[26  3]\n",
            " [ 5 27]]\n",
            "\n",
            "Accuracy: 86.89\n",
            "Roc_Auc Score: 87.02\n",
            "Precision: 90.00\n",
            "recall: 84.38\n",
            "fl score: 87.10\n",
            "Mcc Score: 0.74\n",
            "Kappa score: 0.74\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v7m_9p0GZAVF"
      },
      "source": [
        "#**Decision Tree**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "id": "DM4q1szGZ0rT",
        "outputId": "38d26bda-b908-46b7-b03e-fb63190d36ec"
      },
      "source": [
        "dt = DecisionTreeClassifier(random_state=42)\n",
        "# params = {\n",
        "#     'min_impurity_decrease':[float(x) for x in np.linspace(0.1,1.0, num=10)],\n",
        "#     'max_depth':[int(x) for x in np.linspace(2,100, num=98)],\n",
        "#     'min_samples_leaf':[int(x) for x in np.linspace(5,100, num=95)],\n",
        "#     'criterion': [\"gini\", \"entropy\"]\n",
        "# }\n",
        "# grid_search = GridSearchCV(estimator=dt, \n",
        "#                            param_grid=params, \n",
        "#                            cv=2, n_jobs=-1,refit=True ,verbose=1,scoring = \"accuracy\")\n",
        "# grid_search.fit(X_train, y_train)\n",
        "# print(grid_search.best_params_)\n",
        "# print(grid_search.best_score_)\n",
        "# print(grid_search.best_estimator_)\n",
        "# # print_score(grid_search, X_train, y_train, X_test, y_test, train=True)\n",
        "# # print_score(grid_search, X_train, y_train, X_test, y_test, train=False)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 2 folds for each of 186200 candidates, totalling 372400 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done 956 tasks      | elapsed:    2.6s\n",
            "[Parallel(n_jobs=-1)]: Done 5756 tasks      | elapsed:   14.5s\n",
            "[Parallel(n_jobs=-1)]: Done 13756 tasks      | elapsed:   33.5s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-23-f5e6b2c77bc7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      9\u001b[0m                            \u001b[0mparam_grid\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m                            cv=2, n_jobs=-1,refit=True ,verbose=1,scoring = \"accuracy\")\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mgrid_search\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgrid_search\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbest_params_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgrid_search\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbest_score_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, groups, **fit_params)\u001b[0m\n\u001b[1;32m    708\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    709\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 710\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_run_search\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevaluate_candidates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    711\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    712\u001b[0m         \u001b[0;31m# For multi-metric evaluation, store the best_index_, best_params_ and\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36m_run_search\u001b[0;34m(self, evaluate_candidates)\u001b[0m\n\u001b[1;32m   1149\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_run_search\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevaluate_candidates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1150\u001b[0m         \u001b[0;34m\"\"\"Search all candidates in param_grid\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1151\u001b[0;31m         \u001b[0mevaluate_candidates\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mParameterGrid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparam_grid\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1152\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1153\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36mevaluate_candidates\u001b[0;34m(candidate_params)\u001b[0m\n\u001b[1;32m    687\u001b[0m                                \u001b[0;32mfor\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    688\u001b[0m                                in product(candidate_params,\n\u001b[0;32m--> 689\u001b[0;31m                                           cv.split(X, y, groups)))\n\u001b[0m\u001b[1;32m    690\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    691\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1052\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1053\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieval_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1054\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1055\u001b[0m             \u001b[0;31m# Make sure that we get a last message telling us we are done\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1056\u001b[0m             \u001b[0melapsed_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_start_time\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/joblib/parallel.py\u001b[0m in \u001b[0;36mretrieve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    931\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    932\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'supports_timeout'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 933\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    934\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    935\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36mwrap_future_result\u001b[0;34m(future, timeout)\u001b[0m\n\u001b[1;32m    540\u001b[0m         AsyncResults.get from multiprocessing.\"\"\"\n\u001b[1;32m    541\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 542\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfuture\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    543\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mCfTimeoutError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    544\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mTimeoutError\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.7/concurrent/futures/_base.py\u001b[0m in \u001b[0;36mresult\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    428\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__get_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    429\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 430\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_condition\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    431\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    432\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_state\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mCANCELLED\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCANCELLED_AND_NOTIFIED\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.7/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    294\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m    \u001b[0;31m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    295\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 296\u001b[0;31m                 \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    297\u001b[0m                 \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    298\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f-3g55_YcJEO",
        "outputId": "24f887a2-7496-4a10-dcdd-c4e17718c107"
      },
      "source": [
        "params = {\n",
        "    'min_impurity_decrease':[0.1],\n",
        "    'max_depth':[2],\n",
        "    'min_samples_leaf':[27],\n",
        "    'criterion': [ \"entropy\"]\n",
        "}\n",
        "grid_search = GridSearchCV(estimator=dt, \n",
        "                           param_grid=params, \n",
        "                           cv=3, n_jobs=-1 ,verbose=1,scoring = \"accuracy\")\n",
        "grid_search.fit(X_train, y_train)\n",
        "print_score(grid_search, X_train, y_train, X_test, y_test, train=True)\n",
        "print_score(grid_search, X_train, y_train, X_test, y_test, train=False)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 3 folds for each of 1 candidates, totalling 3 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Train Result:\n",
            "================================================\n",
            "Accuracy Score: 73.97%\n",
            "_______________________________________________\n",
            "CLASSIFICATION REPORT:\n",
            "                    0           1  accuracy   macro avg  weighted avg\n",
            "precision    0.709091    0.765152  0.739669    0.737121      0.739901\n",
            "recall       0.715596    0.759398  0.739669    0.737497      0.739669\n",
            "f1-score     0.712329    0.762264  0.739669    0.737296      0.739773\n",
            "support    109.000000  133.000000  0.739669  242.000000    242.000000\n",
            "_______________________________________________\n",
            "Confusion Matrix: \n",
            " [[ 78  31]\n",
            " [ 32 101]]\n",
            "\n",
            "Accuracy: 73.97\n",
            "Roc_Auc Score: 73.75\n",
            "Precision: 76.52\n",
            "recall: 75.94\n",
            "fl score: 76.23\n",
            "Mcc Score: 0.47\n",
            "Kappa score: 0.47\n",
            "\n",
            "\n",
            "Test Result:\n",
            "================================================\n",
            "Accuracy Score: 83.61%\n",
            "_______________________________________________\n",
            "CLASSIFICATION REPORT:\n",
            "                   0          1  accuracy  macro avg  weighted avg\n",
            "precision   0.787879   0.892857  0.836066   0.840368      0.842949\n",
            "recall      0.896552   0.781250  0.836066   0.838901      0.836066\n",
            "f1-score    0.838710   0.833333  0.836066   0.836022      0.835889\n",
            "support    29.000000  32.000000  0.836066  61.000000     61.000000\n",
            "_______________________________________________\n",
            "Confusion Matrix: \n",
            " [[26  3]\n",
            " [ 7 25]]\n",
            "\n",
            "Accuracy: 83.61\n",
            "Roc_Auc Score: 83.89\n",
            "Precision: 89.29\n",
            "recall: 78.12\n",
            "fl score: 83.33\n",
            "Mcc Score: 0.68\n",
            "Kappa score: 0.67\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Done   3 out of   3 | elapsed:    1.0s finished\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fZ6Cd2FWcfLv"
      },
      "source": [
        "#**Random Forest**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "id": "TbnTZvTNZfjH",
        "outputId": "c227df25-2b6f-4b44-85a9-f9fcb31a5fb1"
      },
      "source": [
        "from sklearn.metrics import roc_curve\n",
        "from sklearn.metrics import auc\n",
        "n_estimators = [1, 2, 4, 8, 16, 32, 64, 100, 200]\n",
        "train_results = []\n",
        "test_results = []\n",
        "for estimator in n_estimators:\n",
        "   rf = RandomForestClassifier(n_estimators=estimator, n_jobs=-1)\n",
        "   rf.fit(X_train, y_train)\n",
        "   train_pred = rf.predict(X_train)\n",
        "   false_positive_rate, true_positive_rate, thresholds = roc_curve(y_train, train_pred)\n",
        "   roc_auc = auc(false_positive_rate, true_positive_rate)\n",
        "   train_results.append(roc_auc)\n",
        "   y_pred = rf.predict(X_test)\n",
        "   false_positive_rate, true_positive_rate, thresholds = roc_curve(y_test, y_pred)\n",
        "   roc_auc = auc(false_positive_rate, true_positive_rate)\n",
        "   test_results.append(roc_auc)\n",
        "from matplotlib.legend_handler import HandlerLine2D\n",
        "line1, = plt.plot(n_estimators, train_results, 'blue', label=\"Train AUC\")\n",
        "line2, = plt.plot(n_estimators, test_results, 'red', label=\"Test AUC\")\n",
        "plt.legend(handler_map={line1: HandlerLine2D(numpoints=2)})\n",
        "plt.ylabel(\"AUC score\")\n",
        "plt.xlabel(\"n_estimators\")\n",
        "plt.show()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "id": "tbptByAqbwpI",
        "outputId": "e10ff720-99ff-4374-ea0d-1f980a71afef"
      },
      "source": [
        "max_depths = np.linspace(142, 144,10 , endpoint=True)\n",
        "train_results = []\n",
        "test_results = []\n",
        "for max_depth in max_depths:\n",
        "   rf = RandomForestClassifier(max_depth=max_depth, n_jobs=-1)\n",
        "   rf.fit(X_train, y_train)\n",
        "   train_pred = rf.predict(X_train)\n",
        "   false_positive_rate, true_positive_rate, thresholds = roc_curve(y_train, train_pred)\n",
        "   roc_auc = auc(false_positive_rate, true_positive_rate)\n",
        "   train_results.append(roc_auc)\n",
        "   y_pred = rf.predict(X_test)\n",
        "   false_positive_rate, true_positive_rate, thresholds = roc_curve(y_test, y_pred)\n",
        "   roc_auc = auc(false_positive_rate, true_positive_rate)\n",
        "   test_results.append(roc_auc)\n",
        "from matplotlib.legend_handler import HandlerLine2D\n",
        "line1, = plt.plot(max_depths, train_results,'blue', label=\"Train AUC\")\n",
        "line2, = plt.plot(max_depths, test_results, 'red', label=\"Test AUC\")\n",
        "plt.legend(handler_map={line1: HandlerLine2D(numpoints=2)})\n",
        "plt.ylabel('AUC score')\n",
        "plt.xlabel('Tree depth')\n",
        "plt.show()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "bOKP7SCxcdJL",
        "outputId": "3ed464d9-a3e0-45b2-b42a-5a7e70f84a1a"
      },
      "source": [
        "min_samples_splits = np.linspace(0.070, 0.085, 15, endpoint=True)\n",
        "train_results = []\n",
        "test_results = []\n",
        "for min_samples_split in min_samples_splits:\n",
        "   rf = RandomForestClassifier(min_samples_split=min_samples_split)\n",
        "   rf.fit(X_train, y_train)\n",
        "   train_pred = rf.predict(X_train)\n",
        "   false_positive_rate, true_positive_rate, thresholds = roc_curve(y_train, train_pred)\n",
        "   roc_auc = auc(false_positive_rate, true_positive_rate)\n",
        "   train_results.append(roc_auc)\n",
        "   y_pred = rf.predict(X_test)\n",
        "   false_positive_rate, true_positive_rate, thresholds = roc_curve(y_test, y_pred)\n",
        "   roc_auc = auc(false_positive_rate, true_positive_rate)\n",
        "   test_results.append(roc_auc)\n",
        "from matplotlib.legend_handler import HandlerLine2D\n",
        "line1, = plt.plot(min_samples_splits, train_results,'blue', label=\"Train AUC\")\n",
        "line2, = plt.plot(min_samples_splits, test_results, 'red', label=\"Test AUC\")\n",
        "plt.legend(handler_map={line1: HandlerLine2D(numpoints=2)})\n",
        "plt.ylabel('AUC score')\n",
        "plt.xlabel('min samples split')\n",
        "plt.show()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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in4tIvahjfxWRHyLbX4O00wmITz+1vy+8AGvXhmtLkCQigiknrVvDOefYNNOmTYnr1zmgCEwgRKQs8BxwOtAc6C0izXOcNgIYparHA8OAhyLXHgrcC5wEtAPuFZEDuzhvSWTSJKhdGzZvhmeeCdua4IhXkaDCMmSITTG98EJi+3UOGIIcQbQDlqhqhqruAMYA5+Q4pznwWeTxlKjjpwGfqupaVV0HfAp0D9BWJ95s2wZffgm9e0OPHjYVUlrvdNPToVmz4hcJKiwnnmi1J554An7/PbF9OwcEQQpEXWBZ1PPlkX3RzAXOizw+F6guIjVjvBYR6SsiM0VkphcUSTJmzLAfrT//ee+d7osvhm1VMCQqgik37roLfvkFXnklnP6dUk3YTurbgE4i8h3QCVgB7I71YlUdqaqpqppau3btoGx0isLkyVC2LHTqBO3amVA8/riNLEoTa9fCypWJi2DKSadO0KEDPPqo1bB2nDgSpECsAI6Kel4vsm8PqrpSVc9T1TbAkMi+9bFc6yQ5kyaZMNSoYc+HDCmdd7phOKijEbH39uef4Y03wrHBKbUEKRDfAk1EpKGIVAB6AR9EnyAitUQk24bBQPavx8fAqSJySMQ5fWpkn1MSWL8evv3WRg3ZdOoE7duXvjvdtDT7G5ZAgK2uTkmBhx+2cFvHiROBCYSq7gJuxH7YFwLvqOp8ERkmIj0ip3UGFonIYuBwYHjk2rXA/ZjIfAsMi+xzSgJffGEpqaMFIvtOd+lSePPN8GyLN0EVCSoMIuaLWLwY3nsvPDucUodoKalQlZqaqjNnzgzbDAesAtorr5hjukKFvftV4YQTzHk9f775KEo67dtbiOvnn4drx+7dliiwUiX47rv4Fy1ySi0iMktVU3M7FraT2imNTJoEf/rTvuIAe+90Fy2C998Px7Z4kpUF8+aFO72UTdmyMHgwzJ1r6U0cJw64QDjxZcUK+P77faeXojnvPGja1FJFlPTR69KltggwGQQC4OKL4eijS8d76yQFLhBOfJk82f7mJRBly8Kdd9qdbkmvr5wdwRRWiGtOypeHQYPgq6/Cn/JySgUuEE58mTQJatXK/676kkugfv2Sf6ebHcEUZJGgwnLllVCnjr23jlNMXCCc+KFqAtG1q1U/y4vsO90ZMyziqaSSqCJBhaFSJbj1VhvJff112NY4JRwXCCd+fP89rFqV9/RSNFdeCYcfXrLvdMNMsZEf114Lhxxi5Ukdpxi4QDjxY9Ik+xuLQFSubHe6kybBN98Ea1cQbN9u6w6SUSCqV4ebboIPPtjrJ3GcIuAC4cSPSZOgUSNo0CC286+7ruTe6YZRJKgw9O8P1aqVzPfWSRpcIJz4sGuXRc7EMnrIpnp1GDAAxo8veXe6YedgKohDD4UbboB33oEffgjbGqeE4gLhxIeZM2HjxsIJBJhAVKsGDz0UjF1BkZ5uCwGPPTZsS/Jm4EALCHjkkbAtcUooLhBOfMj2P3TpUrjrDj0Urr8e3n4bliyJv11BkZYGzZsnvkhQYahTB66+GkaNgmXLCj7fcXLgAuHEh0mToE0bWwNRWG65peTd6SZrBFNOBg2y8OPHHgvbEqcE4gLhFJ8tW2xNQ2Gnl7KpUweuugpee61k3OlmFwkqCQJRvz5cdhm89JLV43CcQuAC4RSf//4XduwoukDA3jvdESPiZ1dQJLuDOid33GFhuU8+GbYlTgnDBcIpPpMmmcO2Y8eit3H00XDppXan++uv8bMtCEqaQDRtChdcAM89ZynYHSdGXCCc4jN5stVFrlKleO3ceafVrE72O93sIkFHHhm2JbFz112waRM8+2zYljglCBcIp3j89psVqCnO9FI2TZvC+efbne769cVvLyjS0mz0UJKK8rRuDWeeaeK7eXPY1jglhEAFQkS6i8giEVkiInfmcry+iEwRke9EJE1EzojsryAi/xSRdBGZKyKdg7TTKQaffWZ/u3aNT3t33WXrKZL1TjeZigQVliFDzME+cmTYljglhMAEQkTKAs8BpwPNgd4i0jzHaUOxWtVtgF7A85H91wCoaiugG/C4iPhoJxmZNAlq1IDUXCsWFp6UlL13ulu2xKfNeJJsRYIKQ/v2tk5lxAibynOcAgjyR7cdsERVM1R1BzAGOCfHOQrUiDw+CFgZedwc+AxAVX8F1gNx+gVy4srkyfajE88FY0OGwJo1yXmnW9Ic1DkZMsQy7r76atiWOCWAIAWiLhAd1L48si+a+4BLRWQ58BHQP7J/LtBDRMqJSEOgLXBUzg5EpK+IzBSRmatXr463/U5BZGTYFg//QzTt20Pnznanu317fNsuLtkC0bJluHYUlVNOgXbtbFHizp1hW+MkOWFP2/QGXlXVesAZwOuRqaRXMEGZCTwJTAd257xYVUeqaqqqptauXTuBZjvA3vKi8fI/RDNkiC1GS7Y73fR0y1abTEWCCoOIvbeZmTBmTNjWOElOkAKxgn3v+utF9kVzFfAOgKrOACoBtVR1l6oOVNUUVT0HOBhYHKCtTlGYPNlCPY87Lv5td+2690531674t19U0tOTpwZ1UTnrLJsie+ghc7o7Th4EKRDfAk1EpKGIVMCc0B/kOOdnoCuAiDTDBGK1iFQRkaqR/d2AXaq6IEBbncKSlWUC8ec/BxPumX2n+9NPyXOnu307LFpUcv0P2ZQpY9FiCxfCuHFhW+MkMTEJhIh0FJErIo9rR/wC+aKqu4AbgY+BhVi00nwRGSYiPSKn3QpcIyJzgdFAH1VV4DBgtogsBO4ALivsC3MCJi3N1kAEMb2UzVln2Vx/stzpJnuRoMJwwQVwzDFW8lU1bGucJKXA0BMRuReLIGoK/BMoD7wB/LGga1X1I8z5HL3vnqjHC3JrR1UzI/05yUp2eu8gBSL7Tvfii+Ff/4Lzzguur1go6RFM0ZQtayvXr74aPv4YuncP2yInCYllBHEu0APYAqCqK4ES6qFz4sbkydCsGdTNGZgWZy680O50H3ww/Dvd7CJBTZqEa0e8uOwyqFfPRhGOkwuxCMSOyLSPAmT7BpwDmO3bYerU+Ie35kb2ne6sWfDJJ8H3lx/p6SaK5cuHa0e8qFABbr/dsvFOnRq2NU4SEotAvCMifwcOFpFrgEnAS8Ga5SQ1X30FW7cGO70UTbLc6ZaGCKacXH011K4d/nvrJCX5CoSICPA28C7wHuYXuEdVn0mAbU6yMnmy+Qc6d05Mf9l3ul9+aVsYrF0LK1aUDv9DNFWqWEW/Tz6xuuKOE4VoAfO6IpIeyYmU1KSmpurMonzAt2+HCRPibxDAwQfbytXSRocO5g+YMSNxfW7dagvU2raF//wncf1m88UXJoj/+U/pc+hu3GiV5045Bd5/P/7tb9liUW+1a5s/KdlRtSCMTZvCtiR2Djmk8PXgI4jILFXNNZVRLAl0ZovIiar6bZF6T3Y2boSePYNr/8MPLflcaWHjRvjmGxg8OLH9VqkCAwdaVNOsWSYUiaQ0RTDlpEYN6N8fHngA5s+HFi2K3taGDTBnDsyevXf7/nsLU65Tx9a1VKoUP9uDYPx4OPfcsK0oHCedZFO/cSaWEcT3wDHAUiySSQBV1aSajC3yCGLXLlgQwBo8VattUKWK1UsoE3ZWkzjx739Djx4wZUrippiy2bDBKs917QrvvZfYvq+9FsaOtSSCJakORKz89pu9t+eeC2+8Efs13323rxgsWbL3eN26cMIJtlWrZtOEzz0HN9wQzGuIB6pw4olWj+S990rO/7pKlSKPzoo7gjitSL2WFMqVC87xOGyYxfCPGWN/SwOTJkHlypZQL9EcdNDeO90FC6B5zuzxAZKeXvKKBBWGWrXguusszfqwYdCo0b7HV63aVwhmz4aff957vGFDE4IrrrC/bdrA4YfvPa5q01ePPgrXXJO8kWCffGIj1JdesiJLBzgFjiAARKQ1cHLk6ZeqOjdQq4pAkUcQQZKVZV+WTZtsFW6FCmFbVHxatICjjoKJE8PpP/tOt2dPGDUqMX2qmjhdfnnyFjKKBytX2g/9BRfY+xstBv/7397zjj3WPtdt29rflBQrwVoQEybY6vh//hP69AnsZRSLTp0sQ/GPP5aO72sM5DeCQFXz3YCbgHnAsMiWDvQv6LpEb23bttWkZMIEVVB9/vmwLSk+K1bYa3n00XDtGDhQtWxZ1YULE9PfTz/Z6/773xPTX5hce629VlAtU0a1ZUvVyy9XffJJ1alTVTdsKHrbWVmqrVurNm2qumtX/GyOF19+aa/7qafCtiShADM1r9//vA7sOQHSgKpRz6sCaQVdl+gtaQUiK0u1Y0fVOnVUt2wJ25ri8frr9pGZPTtcO375RfWgg1RPPdXe36AZP95e9/TpwfcVNmvXqv7zn6pffRXM5/Xtt+29fOed+LddXLp3V61du+R/TwtJfgIRi+dU2LcWw+7IPicWRCzZ3P/+B08/HbY1xWPSJKhZM/y52cMOg7/9zeaLx48Pvr+SXiSoMBxyiE3/nHSSOT7jTc+e0LRp8iUJnDXLpk0HDgzmdZdQYhGIfwJfi8h9InIf8BXwcqBWlTY6drRQ10cegXXrwramaGTHhnftmhwRWTfcYP6QgQPh99+D7aukFwlKJrJTp8ydCx99VPD5ieKhh8zPlMwRViFQ4DddVZ8ArgDWRrYrVPXJoA0rdTz4oIVpPvZY2JYUjcWLbSVxotJrFET58vDMM1YZLej3NDuCyYkPl1xiC/OSZRSxcKFFWPXvbyLh7KFAgRCRPwA/qOrTqvo08KOInBS8aaWM44+H3r0tjHDVqrCtKTzZ6b0TkaAvVrp0sWyvDz0ES5cG00dpKRKUTJQvD4MG2Ur8L74I2xr7/FSuDDfdFLYlSUcscwUvAJujnm+O7HMKy7BhVij+gQfCtqTwTJpkIZA54+PDZsQIm/K69dZg2v/++9JTJCiZuPJKWycRdpLAjAx46y1bCFmrVri2JCExOakjnm4AVDWL2BbYOTlp3NgWCY0caXHWJYVdu2zldLJML0Vz1FGWfuO99yyJYLzJdlCXtiyuYVO5son6pEmWuiUsHnvM/CK33RaeDUlMLAKRISIDRKR8ZLsJyIilcRHpLiKLRGSJiNyZy/H6IjJFRL4TkTQROSOyv7yIvCYi6SKyUEQSnPgnQO6+24bY994btiWxM3u2+U+SaXopmltvtZFN//42QosnaWmlq0hQMnHddRY19eCD4fS/ciW88oqt/j7yyHBsSHJiEYjrgA7ACmA5cBLQt6CLRKQs8BxwOtAc6C0iOXMjDMVqVbcBegHPR/ZfAFRUyyLbFrhWRBrEYGvyc8QRNtf51lv241MSyPY/JGtm2kqVzLezcGH8VzqXtiJByUT16jBggIUqZ4/UEsnjj9v04aBBie+7hBBLFNOvqtpLVQ9T1cNV9WJV/TWGttsBS1Q1Q1V3AGOAc3I2D9SIPD4IWBm1v6qIlAMqAzuAjTH0WTIYNMiiJYYMCduS2Jg0ydIp1K4dtiV5c9ZZcPrpcN99+6aFKC4ewRQsAwZYIr+HHkpsv7/9Bi++aIEjyeZXSyJiiWJ6VERqRKZ9JovIahG5NIa26wLLop4vj+yL5j7gUhFZDnwE9I/sfxfLHLsK+BkYoaprY+izZHDIISYSH34I06aFbU3+bN1qNhbB/7BtG1x/vUXIBo6IjSJ+/93i7OPBunWls0hQMnHoofYhefvtfTPBBs3TT9tnO9Fp60sYsUwxnaqqG4GzgEws9fftceq/N/CqqtYDzgBeF5Ey2OhjN3Ak0BC4VUT2k3kR6SsiM0Vk5urVq+NkUoIYMMDy4w8enByx4HkxbRrs2FEk/8Po0XaTlrAR/LHHWnW0116LTzGj0lwDIpkYONCm8B55JDH9bdxoa2jOPTexGYFLILEIRHbE0pnAWFXdEGPbK4Cjop7Xi+yL5irgHQBVnQFUAmoBFwMTVXVnZDprGrBftkFVHamqqaqaWjuZpz9yo2pVc1h/+WV4mVFjYdIk+/KefHLB50ahau4AEZtinjMnIPtyMnSoORz797f55eLgEUyJ4Ygj4KqrTNiXLSv4/OLy/PNW76GkTPGGSCwC8WGkaFBbYLKI1Aa2xXDdt0ATEWkoIhUwJ/QHOc75GYnv06MAACAASURBVOgKICLNMIFYHdl/SmR/VeAPwPcx9FmyuPpqm/8cPNhSgycjkyZZ7YeqVQt12ddfW/DTgw+au2XYsIDsy0m1aha6OGuWRagUh7Q0mw70CJfgGTTI7ipGjAi2n61b4Ykn4LTTEl+VsCSSVxa/6A04FCire7O51onxujOAxcCPwJDIvmFAj8jj5tjoYC4wB5vOAqgGjAXmAwuA2wvqK2mzuRbEG29YdsvRo8O2ZH9++01VRHXYsEJfesklqjVqqG7apHrvvfYS586Nv4m5kpWlevLJqrVqWXbSotK+veqf/hQ/u5z86dNHtXJly9YbFE8/bR/GqVOD66OEQXHSfZeUrcQKxO7dqscfr9q4seqOHWFbsy9jxxYpzfX//qdavrzqgAH2fO1aE4vzzw/AxryYM8fqGdx4Y9Guz8pSrV5dtV+/+Nrl5M3339sNyeDBwbS/fbtqvXqWft/ZQ34CkQRpOQ9wypSxdAM//lj8KZF4M2mSxaqfeGKhLnvpJVuvlp0Y85BDzCf/7rswb14AduZG69YWHfP880Vbb7J0qVUCdAd14mja1Oq4P/ec+Qjizeuvw/Ll7nsoBC4QycCZZ0KHDlbjYOvWsK3Zy+TJ0Lmz1e2OkV27LHLp1FPt+57NwIGmNfffH38z82TYMFOn/v0LHynmEUzhcNddFmUU7wWPu3fDww+b3+G00+LbdikmT4EQkdNE5Pxc9p8vIt2CNesAQ8Q+vKtWJU/N48xMi0svZHjr+PG2dODGG/fdf+ih9js9diwsWBA/M/Pl0EPNSz51qsXZF4YDqUhQMpGSYjdMTz4JmzcXfH6sjB1rn+e77rLvmxMbec09Yc7j2rnsrwXMyOu6sLYS64OI5vTTVQ85RHXdurAtUf3HP8z/MH9+oS7r3Fm1QYPcSw7/9ptq1aqqvXvHycZY2LVL9YQTVOvWNY95rPTqZS/ESTzTptln74kn4tPe7t2qrVqpNmtmj519oIg+iIqqut/qM1X9DYtkcuLNgw/a6t1kKCo0ebLFpzdrFvMl8+bB55/b1H/Zsvsfr1nTRhZjxlgW7YRQtqyNylasKFxSuLQ0n14Kiw4dbGpzxAirx1FcPvzQRoSDBydHNcQSRH7vVo1ILqR9EJHyWH4kJ96kpECvXja8jmc+ocKSlbW3vGghhuPPPWd58666Ku9zbr3VMj0ntCRG+/Zw+eWWnC2WdA5eJCh8hgyxbKuvvlq8dlQtCKRhQ8u75BSK/ATifeClyEI1AESkGvBi5JgTBPffb6ktwiwqNG8erF5dKP/D+vUwapR9B2vWzPu82rWhXz9Lw5GQHE3ZPPIIVKwIN99c8LleJCh8unaFdu3s/7ZrV9Hb+ewzqzdxxx2FCrZwjPwEYijwC7BURGaJyGzgJ2yl89BEGHdAcswxdgs+cqRVuwqD7PTehUjQ99prFoCV0zmdG7fdZr/VCdXAOnWsBseECbblh0cwhY+IOZR/+snmJIvK8OE2VfrXv8bPtgOJvJwT2Rs2ndQqslUu6PywtlLhpM5m+XLVSpVUL700nP7POEO1adOYT9+9W/XYY23hcazccoutY/vhhyLYV1S2b1c97jjVY45R3bYt7/MGDbKVfsm2cPFAY/du1ZYtVZs3L5pzefp0c3Y//nj8bStFUBQntYicJyLnYQV/mmBZXFNFpHrwsnWAU7eurSx7883EF1LZscMKyRdiemnSJJsu6tcv9m5uv90KtSW0JHGFCpbmeckSy8eTF+npluXTiwSFS5kyNopYsAD+9a/CX//ggzbf2bfA+mZOHuQ3xXR2jq0HcBuQJiJJWlqsFHHHHVCjRuJXfX79NWzZUiiBePZZOOwwWwQbK3XqWMXJ119PcHnubt0szfMDD9iq2tzwIkHJwwUXWC334cMLt9hx7lyLXrrpJkvg6BSNvIYWeW3A0cDXhb0u6K1UTTFlM3y4DZGnTUtcn/fea3M/Ma7FyMiw9DlDhxa+q5UrbSbtyisLf22x+Okn6/iii/Y/tnatveePPJJgo5w8eekl+59MnBj7NRdeaLm0ipOs8QCBeOZiUtWlgI+9E8FNN8Hhhye2qNCkSZCaCgcfHNPpL7xgMwHXXlv4ro44wkb/o0aZLzJhNGhgVefeftsWbkTjDurk4/LLoV692OcjFy+2ldP9+lmqFafIFFogRKQpEIfVK06BVK1qBXCmToWPPw6+v40b4auvYp5e+v13ePllm7GpV69oXQ4aZAKT6JLEDBpkQjFgwL5hlC4QyUeFCua0+vJL2wri4YctTG7gwOBtK+Xk56T+t4h8kGP7L1Y7+tbEmVgyee01+4355ptiNtS3r/2QJaKo0NSpFv8fo0CMGQNr18YW2poXdevCNdfAP/9pCVQTRuXK5qhOT7dhUDbp6TZ6qpuzfLoTKldfbYtoChpFLF1qjq1rrjHHmFM88pp7Ajrl2P4EtAAq5HVNmFuy+CCyslQfftimTMuVUz3oINVvvy1mo6NGWYNjxsTFxjy5+Wabm//99wJPzcpSbdNGtUULe1wcli1TrVBB9dpri9dOocnKUu3WTfXgg1V//dX2dehgxYac5OPBB+17MHNm3uf062chyj//nDi7SjjEs2AQ0BF4rrDXBb0lg0Ds3q06cKC9q716WYx/w4b2+zNrVjEa3rXL4sGbNAk2Nr9lS/vBjIHsEPMXXohP19dfH9L3euFCU/Krr/YiQcnO+vV2x3XeebkfX7VKtWJF1auuSqxdJZxiCwTQBngMyASmAP1juS6RW9gCsWOHrWsD1f79967rycxUPfpoS9I6e3YxOhg/3hr/+9/jYe7+rFqlhYneufjivSVF48HSpSYQN9wQn/YKxa23WihWdgW9F18MwQgnJoYO1TyzDA8aZBF4ixcn3q4STJEEAjgWuBf4Hvgv0B9Ymtf5ebTRHVgELAHuzOV4/YjgfAekAWdE9l+C1ajO3rKAlPz6ClMgNm9W7d7d3s0HHth/yiUjQ7V+fdVDD7VKmEUiK8uWKh95pOrWrcW2eT/efFMLHL5HWLXKfsxvuim+JvTta1NNy5bFt90C2bBBtU4dU7xEhxU7hWP1atUqVfbPMrBmjWq1ajZ0dwpFfgIhdnx/RCQL+BK4SlWXRPZlqGqjWHwbIlIWWAx0A5YD3wK9VXVB1Dkjge9U9QURaQ58pKoNcrTTCviXqjbOr7/U1FSdOXNmLKbFlTVrrL7Jt99aJbVrrsn9vIwMy2C8davlDzv++CJ09sUX1sigQXDOOcWwOhcee8yc1L/+mnuu7ijuvx/uuccSnh57bPxMyMyEJk1sAd0zz8Sv3ZgYNWpvvp4NG2yRopOc3HKLrYhfvBgaRX6O/vY3uO8+T9NeBERklqqm5nowL+UA/gKMAZYBLwFdgZ/yOj+X69sDH0c9HwwMznHO34E7os6fnks7DwLDC+ovjBHE0qWW2qdiRdVx4wo+f8kSq1tTq5ZqenoRO80eqgSx5bZwLAc7dthrOPXUItpfAFdfbe/nihXBtJ8nu3ebg/rYYxPcsVNoVqzYN6ph40abw+3RI1y7SigUZQQRpS5VgXOA3sApwChgnKp+UsB15wPdVfXqyPPLgJNU9caoc44APgEOwYoQ/VlVZ+Vo50fgHFXdr9y9iPQF+gLUr1+/7dIExknOn2+lbTdvhg8+gD/9KbbrliyBTp1g506YMgVatChkx+vXxyF2Ng9OPLHAhUXvvmvZDz74AM4+O/4m/PSTjSJuvNHKYiSU9evtH1rURR1O4rjuOouNzsiw3PG3325reE46KWzLShxFGkHktmE/5H2ByTGcez7wj6jnlwHP5jjnFuBW3TuCWACUiTp+EpAei22JHEFMm2Y3LHXqqM6dW/jrFy1SPeII1cMOK3RFz9Dp1CnvkqLx4oorLNp25crg+nBKOD/+qFq2rIW/1amj2rVr2BaVWIhXqg1VXaeqI1U1lkIBK4Cjop7Xi+yL5irgnUjbM4BKWM3rbHoBowtjY9BMmGDryGrWhOnTi+ZLOPZYGz2UKQOnnJLA8pvFJD3d3CA33FCgm6JYDBliI6xkqLzqJCmNGll1qhdesOqLiU5qeYAQZIHWb4EmItJQRCpgP/Yf5DjnZ8y3gYg0wwRideR5GeBCzA+SFLz2mvmGmzeHadOsimFRadrUnNUAXbqYwzfZyS4peuWVwfbTuDFceqk5/X/5Jdi+nBLM4MH2t317C95w4k5gAqGqu4AbgY+BhcA7qjpfRIaJSI/IabcC14jIXGyk0Ccy5AFbub1MVUMqq7Yvjz0GffrY53DKlPis4m/WzEQiK8tEIqElOAvJ+vWWwaCgkqLxYsgQKw09YkTwfTkllObNLd/Lyy8Xqna6EzsFOqlLCkGFuWZlWVTp44/DRRfZKKJixfj2MW+eTTWVL29TOMccE9/248FTT1k551mz4IQTEtPn5ZfDe++Z49rT6jhOMOTnpA5yiqnEs3OnjRoef9yiat56K/7iANCyJUyebMXcunRJcAGdGMjKsuml9u0TJw5go4ht2+z9dxwn8bhA5MGWLfCXv9i0yv3327qcMgG+W61aWSmG3383kchIiok149NP4Ycfipe1tSg0bQq9epk4/fZbYvt2HMcFIlfWrLFIpYkT4e9/t5IMiZjibN3aRGLLFhOJzMzg+4yFZ5+1ukWFKSkaL4YOtdXn+ZWQdhwnGFwgcrBsGZx8Mnz3nRWlSnS985QUE4lNm8whntAaCbmQkWGhvX37Wt2WRNOsmfl+nnnGhNtxnMThAhHFwoXQoQOsWGEF3M47Lxw72rSxaZ0NG2wk8fPP4dgBxSspGi+GDrVR1f/9X3g2OM6BiAtEhBkzoGNHqz45daqlwwiTtm1NJNauNZFYtizxNmzdurekaJgF1lq0sOmtp5+298NxnMTgAgF89BF07QqHHmoL4Fq3DtsiIzUVPvnEHLRdutjIJpGMGQPr1iXeOZ0bd99t025PPRW2JY5z4HDAC8T339vq6GbN4L//3Zs9OFlo185E4tdfTSRWrkxMv6o279+yZeyJCIOkVSvo2dMEYv36sK1xnAODA14gjjvOIpWmTLFInWTkpJPMJ7JqlYnEqlXB9zljBsyZY6OHZFmkevfd5pfxUYTjJIYDXiDAcgsle32Y9u0t7HbFCsvK/eSTNuUSFM8+a+/JJZcE10dhad3a1qY8+aQJheM4weICUYL44x8td1OjRjBwINSvb/nK4j3t9L//Wd2HK66AatXi23Zxuecem2JKeMU5xzkAcYEoYbRrZ1FWX31li/kefRQaNLBR0Pz58enjpZcszcgNN8SnvXjSpg306AGPPGK+o+uvh2HD4B//sPUa331nGWCzssK2NDi2b4dXXoFTT7XPguMEhSfrK+H8+KNNubz8sqXpOOMMuO02W2RXFN/Bzp0mOK1a2ZRWMvLjj1aWODPT/DGrV+9/TtmyUKcOHHkkHHGE/c3tce3awaZQiSfr1lkK9KeftlFepUpm+3/+kxyBBE7JJL9kfS4QpYQ1a2xR2zPPWMRT27YmFOefD+XKxd7O2LFw4YXw73/DWWcFZ2882bHDfjBXrbLptuy/OR/nls+pXDkTkmzhaNHC8j8lU937zEy7CfjHP2zB4KmnWoXNli33rpGZONHW8ThOYXGBOIDYts0SDD7+uBUhOvpo81dcdVVs/oROnWzl9pIlwVaNC4NsIYkWjmgBWbkSFiyA3btNIC6+2Lb69cOxd9Ysq0MydqyNFHr3NtGPrmK4apWNFleutEi3Dh3CsdUpucStJnUyb4msSV0S2L1bdfx41ZNPVgXVgw9WHTw4/zrPc+fauY8+mjg7k41ff1V97jnVDh3svQB7D194QfW334Lvf/du1QkTVDt3tr5r1FC9/XbVZcvyvmbFCtUmTVSrV1edMSN4G53SBfnUpA79hz1emwtE3nz1lWrPnqoiqhUqqF55per8+fuf17evaqVKifkhLAlkZKg+8IBqs2b2TSlfXvXss1XHjFHdsiW+fW3bpvrKK6rNm1tf9eqpjhihun59bNcvX656zDEmKF99FV/bnNJNaAIBdAcWAUuAO3M5Xh+YAnwHpAFnRB07HpgBzAfSgUr59eUCUTBLlqj266daubL95888U3XKFNWsLNV161SrVDHxcPYlK0v1u+9Ub7tNtW5de++qVVO97DLViRNVd+4settr16o+9JDqEUdYu61bq77+uuqOHYVva9ky1caNTSS++aboNjkHFqEIBFAW+BFoBFQA5gLNc5wzErg+8rg5kBl5XC4iGK0jz2sCZfPrzwUidlavVh02TLV2bfsEpKaqXnKJPZ41K2zrkptdu1Q/+0z16qtVDzrI3rPDDlPt39/u3LOyYmsnM1P15ptVq1a1Nrp1U/3kk9ivz4uff1Zt2NBs+/bb4rXlHBjkJxBBBvi1A5aoaoaq7gDGAOfkOEeB7DXMBwHZS75OBdJUdS6Aqq5R1d0B2npAUauWpa1YutTSjGzcCG++mfiSoiWRsmUtcuill2y9xfvvW/2QkSPhD3+AY4+Fe++1AIHcmD3bnM2NG9tq9fPOs5Qmn3wC3boVP63JUUdZ2phDDrH2Zs8uXnvOgU1gUUwicj7QXVWvjjy/DDhJVW+MOucI4BPgEKAq8GdVnSUiNwNtgcOA2sAYVX00lz76An0B6tev33Zp2NV1SihZWVYTu0kTWwPhFJ4NG0ws3nzTVrurWqjxJZdY2HBamkUkTZkC1atbfY0BA+wHPQgyMy0ibdMm+9+2aRNMP074/P67rYkp6s1FKGGuMQrELREbHheR9sDLQEvgFqAfcCKwFZgMDFXVyXn152GuTrKwcqWlSn/rLQtVzaZuXbj5ZrjmGjjooODt+OknE4ktW0y0kiWNvRNfLrnE1vhMnFg0kchPIIKcYloBRN8f1Yvsi+Yq4B0AVZ0BVAJqAcuBqar6m6puBT4CfPLDKREceaSt9J4506oUDh9ua1MyMmwdQyLEAaBhQ/j8c6hSxeqdpKUlpl8ncWRk2M1Iy5bBZF0OUiC+BZqISEMRqQD0Aj7Icc7PQFcAEWmGCcRq4GOglYhUEZFyQCdgQYC2Ok4gHHcc3HUXXHppODW9GzWyaa1KlUwk5s1LvA1OcIwYYX6xW24Jpv3ABEJVdwE3Yj/2C4F3VHW+iAwTkR6R024FrhGRucBooE/Esb4OeAITmTnAbFWdEJStjlOaOeYYE4kKFeCUU+KX1NEJl//9z5I2/vWvwZUE9lQbjnOAsGiRRWDt3m1TT82ahW2RUxzuvNOyOS9aZAEmRSUsH4TjOElE06bmrBaxkUReobhO8rN+PTz/vCXjLI44FIQLhOMcQBx3nE03ZWXZaGLx4rAtcorCCy9YCPPgwcH24wLhOAcYzZqZSOzaZSLxww9hW+QUht9/t/Tvp50W/PoWFwjHOQBp3tymm3bsMJFYsiRsi5xYeeUVq/kS9OgBXCAc54ClZUsTiW3bTCQyMsK2yCmInTttRX779ompIugC4TgHMK1aWSqOrVtNJH76KWyLnPx4+23LoXbnncEsjMuJC4TjHOC0bm0isWmTiURmZtgWObmRlQUPP2xlcRNVDtgFwnEcUlJg0iRLOtili+WTOlAZP95yWM2ZE7Yl+/Lhh7bI8c47rQRtInCBcBwHsFTvn35qid+6d7dY+wOJ1auhVy/4y19g6lS44gqb808GVOGhhyzbcq9eievXBcJxnD2kplra8u+/tx/KbdvCtih4VC3hXfPm9trvv9+ez5kDTzwRtnXGF1/AV1/B7bdDuXKJ69cFwnGcfejWDV57zX6ULr3UUnOUVlauNCHs3dsSG373HQwdChddBOeeC/fdlxwhwA8/DIcdZqOaROIC4TjOfvTubXfP771nhY1KScq2PajCP/9po4ZPPrGsqNOnmwM4m2eftQSH114b7uufPRs+/thqiVSunNi+XSAcx8mVgQNtSuP55+HBB8O2Jn4sXWo+liuvhOOPtzoZt95qabOjOfJIS4b32Wfw6quhmArY6KFGDbjhhsT37QLhOE6ePPywTTMNHQovvxy2NcUjK8vErmVLmDYNnnvOstrml+zummus5vitt1p67USzeDG8+66JQ6IKTUXjAuE4Tp6UKWOpHU47Dfr2hX//O2yLisYPP1j4br9+tgp53jz70S0oXLRMGRg50sq23nRTYmyN5tFHbZrr5psT3ze4QDiOUwDly9td7AknwIUX2lx9SWH3bnj8cZtKmjvXxO7jjy1cNFaOOw7uvhveeSexArliBYwaZVNhhx+euH6jcYFwHKdAqlWDCROgXj04+2yrtZ3szJ8Pf/yj1QE/9VRYsMCigIqSomLQIJuauuEG2Lgx/rbmxhNP2LTY7bcnpr/cCFQgRKS7iCwSkSUicmcux+uLyBQR+U5E0kTkjMj+BiLyu4jMiWwvBmmn4zgFc9hhdvddvrxNOS1fHrZFubNzJzzwgI14liyB0aPhX/8yp3NRqVABXnrJ7uqHDImfrXmxZg38/e+2KK5hw+D7y4vABEJEygLPAacDzYHeItI8x2lDsVrVbYBewPNRx35U1ZTIdl1QdjqOEzuNGsF//mOrrE8/HdatC9uiffnuO2jXzqaEzj3XRg29esUnsd0f/gD9+5tzO+hptmefNb/HHXcE209BBDmCaAcsUdUMVd0BjAHOyXGOAjUijw8CDuAMMI5TMmjTxu7IFy2Cc86xAjZhs327RVqdeKJFG40bZ6uhDzssvv088IBNs11zjfUZBFu2wNNPW0K+Vq2C6SNWghSIusCyqOfLI/uiuQ+4VESWAx8B/aOONYxMPX0hIicHaKfjOIXklFPg9dfhv/+FSy4Jd7X1V1+ZaA0fDpddZqOGv/wlmL6qV7dynwsWwCOPBNPHSy/B2rWJKQhUEGE7qXsDr6pqPeAM4HURKQOsAupHpp5uAd4SkRo5LxaRviIyU0Rmrl69OqGGO86BzkUXWenLceMsfDTRq42XL7dV3h06wObNMHGirY4+5JBg+z3zTFtpPnx4/J31O3ZY1NWf/mSvK2yCFIgVwFFRz+tF9kVzFfAOgKrOACoBtVR1u6quieyfBfwIHJuzA1Udqaqpqppau3btAF6C4zj5MWCAzZP//e+W5C4RzJ0Ll19uztvnn4frrrN1Daedlpj+wYSxWjWbasrKil+7b7xhwpcMowcIViC+BZqISEMRqYA5oT/Icc7PQFcAEWmGCcRqEakdcXIjIo2AJoAXRHScJOShh+Cvf4V777VFZUGgaqnITzvNale8/z7ceKNFKT3/vKWiSCSHHWZhqNOmmTjGg927bdoqJSWxYpcvqhrYhk0bLcZGAEMi+4YBPSKPmwPTgLnAHODUyP6ewPzIvtnA2QX11bZtW3UcJxx27FA9/XTVMmVU//Wv+Lb7+uuqrVurgmqdOqoPPaS6dm38+igqWVmqf/6zavXqqsuWFb+9d9+11zhmTPHbKgzATM3jd1W0lKRpTE1N1ZkzZ4ZthuMcsGzZYs7rtDSrTvfHPxa9rY0bzVn75JM25dK8uS14u/hiqFgxfjYXlx9/tEijbt0ssquo4bSqFoG1YYPV4siZODBIRGSWqqbmdixsJ7XjOKWEqlVttXX9+haiOX9+4dtYvtxWLR91lAlCkybWZnq6rYJOJnEAaNwYhg2DDz6w1OhFZdIkmDXLXnsixaEgfAThOE5cycy0hHjlytmCsqOOKvAS0tIseuett+xu+oILLINqaq73tcnFrl1w0km2ynrhwqJFUZ1yiq0rychIvAj6CMJxnITRoIGFnG7caM7WtWtzP0/V7py7d4fWre0OvF+/vekxSoI4gAnhSy9ZLe9Bgwp//ddfw5QpcMstyTdCcoFwHCfutG4N48fbHH2PHvuutt65E95803IldetmYasPPgjLlpnPoTCZVpOFE06wEc8//mE1JgrDww/bqKNv30BMKxYuEI7jBELnzhbXP3265UNat85CQxs3tiJE27dbEaLMTIv7D3qBW9Dce6/lqurbN/b0IwsWmHP7xhttlXayUS5sAxzHKb1ccAH88osluTvsMJuv79zZ0lWcfnrBBXtKElWq2DqQP//ZFg3GUqb1kUfsugEDgrevKLhAOI4TKDfeCNu2mSN6wICS41soCl27Qp8+Vgnuootsqi0vli41p3y/flCrVsJMLBQexeQ4jhNH1qyxdRv161siwbzCVgcMsJFURkZskV5B4VFMjuM4CaJmTUvXPXOm/c2N1avNoX3ppeGKQ0G4QDiO48SZCy+0rK9Dh5oTPidPPWXTbmEXBCoIFwjHcZw4I2JJBMuUsWyz0TP5Gzdaxbhzz4XjjgvPxlhwgXAcxwmA+vUt0+3HH9u6j2xefNFyLiVLSu/8cCe14zhOQOzeDR07wg8/WBqO6tWtjkXLlpa+PBnIz0ntYa6O4zgBUbasOaPbtLFUGh07Ws3sN94I27LYcIFwHMcJkBYt4M47bfHchAmW1vuUU8K2KjbcB+E4jhMwQ4aYQ3rdOvM9FLVuRKLxEYTjOE7AVKwIY8fCuHFwzjlhWxM7LhCO4zgJoGVL20oSgU4xiUh3EVkkIktE5M5cjtcXkSki8p2IpInIGbkc3ywitwVpp+M4jrM/gQmEiJQFngNOB5oDvUWkeY7ThgLvqGoboBfwfI7jTwD/CcpGx3EcJ2+CHEG0A5aoaoaq7gDGADln3xSoEXl8ELAy+4CI/AX4CShCZVvHcRynuAQpEHWBZVHPl0f2RXMfcKmILAc+AvoDiEg14A7gb/l1ICJ9RWSmiMxcvXp1vOx2HMdxCD/MtTfwqqrWA84AXheRMphw/J+qbs7vYlUdqaqpqppau3bt4K11HMc5gAgyimkFEJ3Itl5kXzRXAd0BVHWGiFQCagEnAeeLyKPAwUCWiGxT1WcDtNdxHMeJIkiB+BZoIiINMWHoBVyc45yfga7AqyLSDKgErFbVk7NPEJH7gM0uDo7jOIklhfzfxwAACFBJREFUsCkmVd0F3Ah8DCzEopXmi8gwEekROe1W4BoRmQuMBvpoacke6DiOU8IpNdlcRWQ1sLQYTdQCfouTOUGQ7PZB8tuY7PaB2xgPkt0+SC4bj1bVXJ24pUYgiouIzMwr5W0ykOz2QfLbmOz2gdsYD5LdPigZNkL4UUyO4zhOkuIC4TiO4+SKC8ReRoZtQAEku32Q/DYmu33gNsaDZLcPSoaN7oNwHMdxcsdHEI7jOE6uuEA4juM4uVIqBSKGOhQVReTtyPGvRaRBZP8lIjInassSkZTIsbYikh655mmR4hUNjLeNIlJFRCaIyPciMl9EHk4m+3Jc+4GIzCuOfUHZKCIVRGSkiCyOvJc9k8y+3pHPYZqITBSRWkW1r5g2lheR1yK2LBSRwbG2GbaNInKUWB2aBZHvyk3JZF/UdWXFauV8WBz7ioWqlqoNKAv8CDQCKgBzgeY5zrkBeDHyuBfwdi7ttAJ+jHr+DfAHQLAaFacnk41AFaBL5HEF4Mui2hjUexjZdx7wFjAvSf/PfwMeiDwuA9RKFvuw1Di/ZtsEPArcF8Z7iKXNGRP12csEGsTSZhLYeARwQmR/dWBxUW0Mwr6o626JfFc+LM53pThbaRxBxFKH4hzgtcjjd4GuIvuNCHpHrkVEjgBqqOpXav+5UcBfkslGVd2qqlMij3cAs7EEiUlhH+xJ434L8EAR7QrcRuBK4CEAVc1S1aKudg3CPolsVSPn1SCqhkqCbdSIHeWAysAOYGOMbYZqo6quUtXZAKq6CUsFlLMUQWj2AYhIPeBM4B9FtCsulEaBiKUOxZ5z1HJGbQBq5jjnIiw/VPb5ywtoM2wb9yAiBwNnA5OTzL77gceBrUW0K1AbI+8bwP0iMltExorI4clin6ruBK4H0jFhaA68XET7imvju8AWYBWWdHOEqq6Nsc2wbdxDZLqnDfB1ktn3JDAIyCqiXXGhNApEsRGRk4CtqlrsefKgyMvGyN3IaOBpVc0IxTj2ty8yh95YVceFZVNOcnkPy2GjrumqegIwAxiRLPaJSHlMINoARwJpwOC8WwiUdsDuiB0NgVtFpFFItuRFvjZGRrTvATer6sZksU9EzgJ+VdVZIdi0D6VRIGKpQ7HnnMgP6kHAmqjjvdj3zncF+07X5NZm2DZmMxL4QVWfTDL72gOpIpIJ/Bc4VkQ+TzIb12Cjm/cjz8cCJySRfSkAqvpjZKrzHaBDEe0rro0XAxNVdaeq/gpMA1JjbDNsG7PF9j3gTVV9n6IThH1/BHpEvitjgFNE5I1i2Fh0wnJ+BLVhd4EZmCJnO41a5DinH/s6jd6JOlYG+4c2ynFNTif1GUlo4wPYh75MMr6HUccbUHwndVDv4RjglMjjPsDYZLEPu9NcBdSOPL8feDyM9xArCfzPyOOqwALg+FjaTAIbBfMjPlmcz2BQ9uW4tjMhOqlD6TTwF2XlSxdj0QVDIvuGAT0ijythd4dLsB/+6C9hZ+CrXNpMBeZF2nyWyCr0ZLERu3NRzOE2J7JdnSz25Wi7AcUUiAD/z0cDU7Hpm8lA/SSz77rI/zgN+DdQM4z3EKgW2T8f+2G7Pb82k8lGoGPku5IW9V0pzg1f3N/DHJ+D0ATCU204juM4uVIafRCO4zhOHHCBcBzHcXLFBcJxHMfJFRcIx3EcJ1dcIBzHcZxccYFwSjUi0iMeGUWDQkQ+F5GEFq8XkczsLLAiMj3yt4GIXJxIO5zkxwXCKdWo6geqWqzU56UZVc1eid0AW9nrOHtwgXBKJJE73u9F5FWx2g1visifRWSaiPwgIu0i5/URkWcjj18Vq+UxXUQyROT8XNqtKlZXY66IzBORiyL77xGRbyP7RmZnXY2MAP5PRGZGcvqfKCLvR2x4IIetb0bOeVdEquTS96kiMiMqUWC1yP6HxWoXpInIfrmhRKST7K0d8Z2IVBeRziIyNfJaFonIiyKy3/ddRDZHHj4MnBxpY2BR/y9O6cIFwinJHINlhz0usl2MrZK9Dbgrj2uOiJxzFvajmJPuwEpVba2qLYGJkf3PquqJkX2VI9dns0NVU4EXgfFYaoWWQB8Ryc7O2hR4XlWbYSmdb4juNDLlMxT4s1qiwJnALZHrz8XSNxxP7qnSbwP6qWoKcDLwe2R/O6A/lvW1MVaLIy/uBL5U1RRV/b98znMOIFwgnJLMT6qarqpZWLqCyWqpAdKxKZPc+JdanYcFQG6pvNOBbiLyiIicrKobIvu7iFUDSwdOAVpEXfNB1LXz1eoNbMdy9GQnclumqtMij9/ARCqaP2A/5NNEZA7wVyztxwZgG/CyiJxH7qnSpwFPiMgA4GC1lNIA36jVKdiNJf3L2afj5IsLhFOS2R71OCvqeRaWRK2ga/YrG6uqi7EMrunAA5GppUrA88D5qtoKeAnLr5OzzWgbctqRM6dNzucCfBq5g09R1eaqelXkx74dVjvgLPaOaKJtfhi4GhvZTBOR42Ls03HyxQXCcaIQkSOxGgxvAI9hYpEtBr9F/AL7+S5ioL6ItI88vhhLeR7NV8AfReSYiB1VReTYSH8HqepHwECgdS42N46MpB4BvsWm2wDaiUjDiO/holz6jGYTVn7TcfbgAuE4+9IK+CYyzXMvVp96PTZqmAd8jP0IF5ZFQD8RWQgcArwQfVBVV2PpxUeLSBpWrOg47Ef7w8i+/2IlW3Nyc8R5ngbsxNLRE7HzWSz7609AfsWa0oDdEee8O6kdAM/m6jhBI1bW8sOIgztRfXYGblPVswo613HywkcQjuM4Tq74CMJxHMfJFR9BOI7jOLniAuE4juPkiguE4ziOkysuEI7jOE6uuEA4juM4ufL/IyORDpeQNQcAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "id": "bQXOtoApdEYM",
        "outputId": "8ab07455-2739-401f-85bb-17ae2a365daa"
      },
      "source": [
        "min_samples_leafs = np.linspace(0.0010, 0.0015, 5, endpoint=True)\n",
        "train_results = []\n",
        "test_results = []\n",
        "for min_samples_leaf in min_samples_leafs:\n",
        "   rf = RandomForestClassifier(min_samples_leaf=min_samples_leaf)\n",
        "   rf.fit(X_train, y_train)\n",
        "   train_pred = rf.predict(X_train)\n",
        "   false_positive_rate, true_positive_rate, thresholds = roc_curve(y_train, train_pred)\n",
        "   roc_auc = auc(false_positive_rate, true_positive_rate)\n",
        "   train_results.append(roc_auc)\n",
        "   y_pred = rf.predict(X_test)\n",
        "   false_positive_rate, true_positive_rate, thresholds = roc_curve(y_test, y_pred)\n",
        "   roc_auc = auc(false_positive_rate, true_positive_rate)\n",
        "   test_results.append(roc_auc)\n",
        "from matplotlib.legend_handler import HandlerLine2D\n",
        "line1, = plt.plot(min_samples_leafs, train_results, 'b', label=\"Train AUC\")\n",
        "line2, = plt.plot(min_samples_leafs, test_results, 'r', label=\"Test AUC\")\n",
        "plt.legend(handler_map={line1: HandlerLine2D(numpoints=2)})\n",
        "plt.ylabel(\"AUC score\")\n",
        "plt.xlabel(\"min samples leaf\")\n",
        "plt.show()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "id": "JDJK3oUadXiu",
        "outputId": "6d34b5fb-4bd9-437a-b368-66bfe9dcb14a"
      },
      "source": [
        "max_features = list(range(1,dataset.shape[1]))\n",
        "train_results = []\n",
        "test_results = []\n",
        "for max_feature in max_features:\n",
        "   rf = RandomForestClassifier(max_features=max_feature)\n",
        "   rf.fit(X_train, y_train)\n",
        "   train_pred = rf.predict(X_train)\n",
        "   false_positive_rate, true_positive_rate, thresholds = roc_curve(y_train, train_pred)\n",
        "   roc_auc = auc(false_positive_rate, true_positive_rate)\n",
        "   train_results.append(roc_auc)\n",
        "   y_pred = rf.predict(X_test)\n",
        "   false_positive_rate, true_positive_rate, thresholds = roc_curve(y_test, y_pred)\n",
        "   roc_auc = auc(false_positive_rate, true_positive_rate)\n",
        "   test_results.append(roc_auc)\n",
        "from matplotlib.legend_handler import HandlerLine2D\n",
        "line1, = plt.plot(max_features, train_results, 'b', label=\"Train AUC\")\n",
        "line2, = plt.plot(max_features, test_results, 'r', label=\"Test AUC\")\n",
        "plt.legend(handler_map={line1: HandlerLine2D(numpoints=2)})\n",
        "plt.ylabel('AUC score')\n",
        "plt.xlabel('max features')\n",
        "plt.show()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RocW6AfeiPh2",
        "outputId": "cc5e6c8c-ff13-4168-e807-00b76fdbb55a"
      },
      "source": [
        "n_estimators = [int(x) for x in np.linspace(start=50, stop=125, num=75)]\n",
        "max_features = list(range(1,dataset.shape[1]))\n",
        "max_depth = [143.75]\n",
        "\n",
        "min_samples_split = [0.081]\n",
        "min_samples_leaf = [0.0012]\n",
        "bootstrap = [True, False]\n",
        "\n",
        "params_grid = {'n_estimators': n_estimators, 'max_features': max_features,\n",
        "               'max_depth': max_depth, 'min_samples_split': min_samples_split,\n",
        "               'min_samples_leaf': min_samples_leaf, 'bootstrap': bootstrap}\n",
        "\n",
        "rf_clf = RandomForestClassifier(random_state=42)\n",
        "\n",
        "rf_cv = GridSearchCV(rf_clf, params_grid, scoring=\"accuracy\", cv=2,refit=True, verbose=2, n_jobs=-1)\n",
        "\n",
        "\n",
        "rf_cv.fit(X_train, y_train)\n",
        "print(rf_cv.best_params_)\n",
        "print(rf_cv.best_score_)\n",
        "print(rf_cv.best_estimator_)\n"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 2 folds for each of 3000 candidates, totalling 6000 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  70 tasks      | elapsed:    5.3s\n",
            "[Parallel(n_jobs=-1)]: Done 312 tasks      | elapsed:   29.5s\n",
            "[Parallel(n_jobs=-1)]: Done 718 tasks      | elapsed:  1.1min\n",
            "[Parallel(n_jobs=-1)]: Done 1284 tasks      | elapsed:  2.1min\n",
            "[Parallel(n_jobs=-1)]: Done 2014 tasks      | elapsed:  3.3min\n",
            "[Parallel(n_jobs=-1)]: Done 2904 tasks      | elapsed:  4.8min\n",
            "[Parallel(n_jobs=-1)]: Done 3958 tasks      | elapsed:  6.1min\n",
            "[Parallel(n_jobs=-1)]: Done 5172 tasks      | elapsed:  7.7min\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "{'bootstrap': True, 'max_depth': 143.75, 'max_features': 8, 'min_samples_leaf': 0.0012, 'min_samples_split': 0.081, 'n_estimators': 51}\n",
            "0.7520661157024794\n",
            "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
            "                       criterion='gini', max_depth=143.75, max_features=8,\n",
            "                       max_leaf_nodes=None, max_samples=None,\n",
            "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
            "                       min_samples_leaf=0.0012, min_samples_split=0.081,\n",
            "                       min_weight_fraction_leaf=0.0, n_estimators=51,\n",
            "                       n_jobs=None, oob_score=False, random_state=42, verbose=0,\n",
            "                       warm_start=False)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Done 6000 out of 6000 | elapsed:  8.9min finished\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YwMaLPgFvRPg",
        "outputId": "dcf7c116-d9cb-4444-e792-844a60fc431c"
      },
      "source": [
        "n_estimators = [51]\n",
        "max_features = [8]\n",
        "max_depth = [143.75]\n",
        "\n",
        "min_samples_split = [0.081]\n",
        "min_samples_leaf = [0.0012]\n",
        "bootstrap = [True, False]\n",
        "\n",
        "params_grid = {'n_estimators': n_estimators, 'max_features': max_features,\n",
        "               'max_depth': max_depth, 'min_samples_split': min_samples_split,\n",
        "               'min_samples_leaf': min_samples_leaf, 'bootstrap': bootstrap}\n",
        "\n",
        "rf_clf = RandomForestClassifier(random_state=42)\n",
        "\n",
        "rf_cv = GridSearchCV(rf_clf, params_grid, scoring=\"accuracy\", cv=3,refit=True, verbose=2, n_jobs=-1)\n",
        "\n",
        "\n",
        "rf_cv.fit(X_train, y_train)\n",
        "print_score(rf_cv, X_train, y_train, X_test, y_test, train=True)\n",
        "print_score(rf_cv, X_train, y_train, X_test, y_test, train=False)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Fitting 3 folds for each of 2 candidates, totalling 6 fits\n",
            "Train Result:\n",
            "================================================\n",
            "Accuracy Score: 84.30%\n",
            "_______________________________________________\n",
            "CLASSIFICATION REPORT:\n",
            "                    0           1  accuracy   macro avg  weighted avg\n",
            "precision    0.831776    0.851852  0.842975    0.841814      0.842809\n",
            "recall       0.816514    0.864662  0.842975    0.840588      0.842975\n",
            "f1-score     0.824074    0.858209  0.842975    0.841142      0.842834\n",
            "support    109.000000  133.000000  0.842975  242.000000    242.000000\n",
            "_______________________________________________\n",
            "Confusion Matrix: \n",
            " [[ 89  20]\n",
            " [ 18 115]]\n",
            "\n",
            "Accuracy: 84.30\n",
            "Roc_Auc Score: 84.06\n",
            "Precision: 85.19\n",
            "recall: 86.47\n",
            "fl score: 85.82\n",
            "Mcc Score: 0.68\n",
            "Kappa score: 0.68\n",
            "\n",
            "\n",
            "Test Result:\n",
            "================================================\n",
            "Accuracy Score: 90.16%\n",
            "_______________________________________________\n",
            "CLASSIFICATION REPORT:\n",
            "                   0         1  accuracy  macro avg  weighted avg\n",
            "precision   0.896552   0.90625  0.901639   0.901401      0.901639\n",
            "recall      0.896552   0.90625  0.901639   0.901401      0.901639\n",
            "f1-score    0.896552   0.90625  0.901639   0.901401      0.901639\n",
            "support    29.000000  32.00000  0.901639  61.000000     61.000000\n",
            "_______________________________________________\n",
            "Confusion Matrix: \n",
            " [[26  3]\n",
            " [ 3 29]]\n",
            "\n",
            "Accuracy: 90.16\n",
            "Roc_Auc Score: 90.14\n",
            "Precision: 90.62\n",
            "recall: 90.62\n",
            "fl score: 90.62\n",
            "Mcc Score: 0.80\n",
            "Kappa score: 0.80\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Done   6 out of   6 | elapsed:    0.3s finished\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p2_4MPz2xxGj"
      },
      "source": [
        "#**Nueral Network**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZtuwiaBPx3w-"
      },
      "source": [
        "from sklearn import neural_network\n",
        "from sklearn.model_selection import cross_val_score\n",
        "parameters = {'solver': ['lbfgs'], 'max_iter': [500,1000,1500], 'alpha': 10.0 ** -np.arange(1, 7), 'hidden_layer_sizes':np.arange(5, 12), 'random_state':[0,1,2,3,4,5,6,7,8,9]}\n",
        "clf_grid = GridSearchCV(neural_network.MLPClassifier(), parameters, n_jobs=-1)\n",
        "\n"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b04kSlrBx8zq",
        "outputId": "54d4cad4-ea09-4ada-c80f-296ab6bd8499"
      },
      "source": [
        "clf_grid.fit(X_train,y_train)\n",
        "\n",
        "\n",
        "print(\"-----------------Original Features--------------------\")\n",
        "print(\"Best score: %0.4f\" % clf_grid.best_score_)\n",
        "print(\"Using the following parameters:\")\n",
        "print(clf_grid.best_params_)\n"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "-----------------Original Features--------------------\n",
            "Best score: 0.7730\n",
            "Using the following parameters:\n",
            "{'alpha': 0.001, 'hidden_layer_sizes': 5, 'max_iter': 500, 'random_state': 5, 'solver': 'lbfgs'}\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:470: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lIgVuCMg5kM9",
        "outputId": "75703a78-1329-47e2-dbfa-3bf1600d6e8c"
      },
      "source": [
        "parameters = {'solver': ['lbfgs'], 'max_iter': [500], 'alpha': [0.001], 'hidden_layer_sizes':[5], 'random_state':[5]}\n",
        "clf_grid = GridSearchCV(neural_network.MLPClassifier(), parameters, n_jobs=-1)\n",
        "clf_grid.fit(X_train,y_train)\n",
        "print_score(clf_grid, X_train, y_train, X_test, y_test, train=True)\n",
        "print_score(clf_grid, X_train, y_train, X_test, y_test, train=False)"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train Result:\n",
            "================================================\n",
            "Accuracy Score: 94.21%\n",
            "_______________________________________________\n",
            "CLASSIFICATION REPORT:\n",
            "                    0           1  accuracy   macro avg  weighted avg\n",
            "precision    0.943925    0.940741  0.942149    0.942333      0.942175\n",
            "recall       0.926606    0.954887  0.942149    0.940746      0.942149\n",
            "f1-score     0.935185    0.947761  0.942149    0.941473      0.942097\n",
            "support    109.000000  133.000000  0.942149  242.000000    242.000000\n",
            "_______________________________________________\n",
            "Confusion Matrix: \n",
            " [[101   8]\n",
            " [  6 127]]\n",
            "\n",
            "Accuracy: 94.21\n",
            "Roc_Auc Score: 94.07\n",
            "Precision: 94.07\n",
            "recall: 95.49\n",
            "fl score: 94.78\n",
            "Mcc Score: 0.88\n",
            "Kappa score: 0.88\n",
            "\n",
            "\n",
            "Test Result:\n",
            "================================================\n",
            "Accuracy Score: 73.77%\n",
            "_______________________________________________\n",
            "CLASSIFICATION REPORT:\n",
            "                   0      1  accuracy  macro avg  weighted avg\n",
            "precision   0.724138   0.75  0.737705   0.737069      0.737705\n",
            "recall      0.724138   0.75  0.737705   0.737069      0.737705\n",
            "f1-score    0.724138   0.75  0.737705   0.737069      0.737705\n",
            "support    29.000000  32.00  0.737705  61.000000     61.000000\n",
            "_______________________________________________\n",
            "Confusion Matrix: \n",
            " [[21  8]\n",
            " [ 8 24]]\n",
            "\n",
            "Accuracy: 73.77\n",
            "Roc_Auc Score: 73.71\n",
            "Precision: 75.00\n",
            "recall: 75.00\n",
            "fl score: 75.00\n",
            "Mcc Score: 0.47\n",
            "Kappa score: 0.47\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:470: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ],
          "name": "stderr"
        }
      ]
    }
  ]
}