Model_training.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model building and Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from File_Operations import file_operations\n",
    "from Application_Logging import logger"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from xgboost import  XGBClassifier\n",
    "from sklearn.metrics import  classification_report,accuracy_score,roc_curve,roc_auc_score,confusion_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data preprocessing and splitting it into train and test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LIMIT_BAL</th>\n",
       "      <th>SEX</th>\n",
       "      <th>EDUCATION</th>\n",
       "      <th>MARRIAGE</th>\n",
       "      <th>AGE</th>\n",
       "      <th>PAY_0</th>\n",
       "      <th>PAY_2</th>\n",
       "      <th>PAY_3</th>\n",
       "      <th>PAY_4</th>\n",
       "      <th>PAY_5</th>\n",
       "      <th>...</th>\n",
       "      <th>BILL_AMT4</th>\n",
       "      <th>BILL_AMT5</th>\n",
       "      <th>BILL_AMT6</th>\n",
       "      <th>PAY_AMT1</th>\n",
       "      <th>PAY_AMT2</th>\n",
       "      <th>PAY_AMT3</th>\n",
       "      <th>PAY_AMT4</th>\n",
       "      <th>PAY_AMT5</th>\n",
       "      <th>PAY_AMT6</th>\n",
       "      <th>IsDefaulter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20000.0</td>\n",
       "      <td>2</td>\n",
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       "      <td>-2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>689.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>120000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3272.0</td>\n",
       "      <td>3455.0</td>\n",
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       "      <td>1000.0</td>\n",
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       "      <td>1000.0</td>\n",
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       "      <td>2000.0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>90000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>14331.0</td>\n",
       "      <td>14948.0</td>\n",
       "      <td>15549.0</td>\n",
       "      <td>1518.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>28314.0</td>\n",
       "      <td>28959.0</td>\n",
       "      <td>29547.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>1069.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50000.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>57</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>20940.0</td>\n",
       "      <td>19146.0</td>\n",
       "      <td>19131.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>36681.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9000.0</td>\n",
       "      <td>689.0</td>\n",
       "      <td>679.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   LIMIT_BAL  SEX  EDUCATION  MARRIAGE  AGE  PAY_0  PAY_2  PAY_3  PAY_4  \\\n",
       "0    20000.0    2          2         1   24      2      2     -1     -1   \n",
       "1   120000.0    2          2         2   26     -1      2      0      0   \n",
       "2    90000.0    2          2         2   34      0      0      0      0   \n",
       "3    50000.0    2          2         1   37      0      0      0      0   \n",
       "4    50000.0    1          2         1   57     -1      0     -1      0   \n",
       "\n",
       "   PAY_5  ...  BILL_AMT4  BILL_AMT5  BILL_AMT6  PAY_AMT1  PAY_AMT2  PAY_AMT3  \\\n",
       "0     -2  ...        0.0        0.0        0.0       0.0     689.0       0.0   \n",
       "1      0  ...     3272.0     3455.0     3261.0       0.0    1000.0    1000.0   \n",
       "2      0  ...    14331.0    14948.0    15549.0    1518.0    1500.0    1000.0   \n",
       "3      0  ...    28314.0    28959.0    29547.0    2000.0    2019.0    1200.0   \n",
       "4      0  ...    20940.0    19146.0    19131.0    2000.0   36681.0   10000.0   \n",
       "\n",
       "   PAY_AMT4  PAY_AMT5  PAY_AMT6  IsDefaulter  \n",
       "0       0.0       0.0       0.0            1  \n",
       "1    1000.0       0.0    2000.0            1  \n",
       "2    1000.0    1000.0    5000.0            0  \n",
       "3    1100.0    1069.0    1000.0            0  \n",
       "4    9000.0     689.0     679.0            0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"EDA/Credit_Card_Trainingset.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_data = data.drop(\"IsDefaulter\",axis=1).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2.0000e+04,  2.0000e+00,  2.0000e+00,  1.0000e+00,  2.4000e+01,\n",
       "         2.0000e+00,  2.0000e+00, -1.0000e+00, -1.0000e+00, -2.0000e+00,\n",
       "        -2.0000e+00,  3.9130e+03,  3.1020e+03,  6.8900e+02,  0.0000e+00,\n",
       "         0.0000e+00,  0.0000e+00,  0.0000e+00,  6.8900e+02,  0.0000e+00,\n",
       "         0.0000e+00,  0.0000e+00,  0.0000e+00],\n",
       "       [ 1.2000e+05,  2.0000e+00,  2.0000e+00,  2.0000e+00,  2.6000e+01,\n",
       "        -1.0000e+00,  2.0000e+00,  0.0000e+00,  0.0000e+00,  0.0000e+00,\n",
       "         2.0000e+00,  2.6820e+03,  1.7250e+03,  2.6820e+03,  3.2720e+03,\n",
       "         3.4550e+03,  3.2610e+03,  0.0000e+00,  1.0000e+03,  1.0000e+03,\n",
       "         1.0000e+03,  0.0000e+00,  2.0000e+03],\n",
       "       [ 9.0000e+04,  2.0000e+00,  2.0000e+00,  2.0000e+00,  3.4000e+01,\n",
       "         0.0000e+00,  0.0000e+00,  0.0000e+00,  0.0000e+00,  0.0000e+00,\n",
       "         0.0000e+00,  2.9239e+04,  1.4027e+04,  1.3559e+04,  1.4331e+04,\n",
       "         1.4948e+04,  1.5549e+04,  1.5180e+03,  1.5000e+03,  1.0000e+03,\n",
       "         1.0000e+03,  1.0000e+03,  5.0000e+03],\n",
       "       [ 5.0000e+04,  2.0000e+00,  2.0000e+00,  1.0000e+00,  3.7000e+01,\n",
       "         0.0000e+00,  0.0000e+00,  0.0000e+00,  0.0000e+00,  0.0000e+00,\n",
       "         0.0000e+00,  4.6990e+04,  4.8233e+04,  4.9291e+04,  2.8314e+04,\n",
       "         2.8959e+04,  2.9547e+04,  2.0000e+03,  2.0190e+03,  1.2000e+03,\n",
       "         1.1000e+03,  1.0690e+03,  1.0000e+03],\n",
       "       [ 5.0000e+04,  1.0000e+00,  2.0000e+00,  1.0000e+00,  5.7000e+01,\n",
       "        -1.0000e+00,  0.0000e+00, -1.0000e+00,  0.0000e+00,  0.0000e+00,\n",
       "         0.0000e+00,  8.6170e+03,  5.6700e+03,  3.5835e+04,  2.0940e+04,\n",
       "         1.9146e+04,  1.9131e+04,  2.0000e+03,  3.6681e+04,  1.0000e+04,\n",
       "         9.0000e+03,  6.8900e+02,  6.7900e+02]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_data[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_data = data[\"IsDefaulter\"].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_data[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Standardization of input data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "m = logger.App_logger()\n",
    "file_inst = file_operations.File_Operations(\"training\",m,\"Logs/Training.log\")\n",
    "train_scaler = StandardScaler()\n",
    "scaler = train_scaler.fit(x_data)\n",
    "file_inst.save_model(scaler,\"scaler\")\n",
    "x_stand = scaler.transform(x_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test = train_test_split(x_stand,y_data,test_size=0.20,random_state=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(37382, 23)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9346, 23)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(37382,)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9346,)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 7 candidates, totalling 21 fits\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "             param_grid={&#x27;C&#x27;: [0.001, 0.01, 0.1, 1, 10, 100, 1000],\n",
       "                         &#x27;penalty&#x27;: [&#x27;l2&#x27;]},\n",
       "             scoring=&#x27;accuracy&#x27;, verbose=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=3, estimator=LogisticRegression(), n_jobs=-1,\n",
       "             param_grid={&#x27;C&#x27;: [0.001, 0.01, 0.1, 1, 10, 100, 1000],\n",
       "                         &#x27;penalty&#x27;: [&#x27;l2&#x27;]},\n",
       "             scoring=&#x27;accuracy&#x27;, verbose=3)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=3, estimator=LogisticRegression(), n_jobs=-1,\n",
       "             param_grid={'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000],\n",
       "                         'penalty': ['l2']},\n",
       "             scoring='accuracy', verbose=3)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l_r = LogisticRegression()\n",
    "param_grid = {'penalty':['l2'], 'C' : [0.001, 0.01, 0.1, 1, 10, 100, 1000] }\n",
    "grid_lr_clf = GridSearchCV(l_r, param_grid, scoring = 'accuracy', n_jobs = -1, verbose = 3, cv = 3)\n",
    "grid_lr_clf.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 0.01, 'penalty': 'l2'}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_lr_clf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_opt = LogisticRegression(penalty=\"l2\",C = 0.01)\n",
    "lr_opt.fit(x_train,y_train)\n",
    "y_pred = lr_opt.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Logistic Regression model: 0.7269420072758399\n",
      "Confusion matrix of Logistic Regression model:\n",
      " [[3438 1257]\n",
      " [1295 3356]]\n",
      "AUC score of Logistic Regression model: 0.7269168126954731\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy of Logistic Regression model:\",accuracy_score(y_test,y_pred))\n",
    "print(\"Confusion matrix of Logistic Regression model:\\n\",confusion_matrix(y_test,y_pred))\n",
    "lr_fpr, lr_tpr, _ = roc_curve(y_test, y_pred)\n",
    "lr_auc = roc_auc_score(y_test, y_pred)\n",
    "print(\"AUC score of Logistic Regression model:\",lr_auc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ns_probs = [0 for _ in range(len(y_test))]\n",
    "ns_fpr, ns_tpr, _ = roc_curve(y_test, ns_probs)\n",
    "ns_auc = roc_auc_score(y_test, ns_probs)\n",
    "plt.figure(figsize=(7,5))\n",
    "plt.plot(ns_fpr, ns_tpr,\"--\")\n",
    "plt.plot(lr_fpr,lr_tpr,marker =\".\")\n",
    "plt.title(\"ROC curve for Logistic Regression\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 4 candidates, totalling 12 fits\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=3, estimator=SVC(), n_jobs=-1,\n",
       "             param_grid={&#x27;C&#x27;: [0.1, 1, 10, 100], &#x27;kernel&#x27;: [&#x27;rbf&#x27;]},\n",
       "             scoring=&#x27;accuracy&#x27;, verbose=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=3, estimator=SVC(), n_jobs=-1,\n",
       "             param_grid={&#x27;C&#x27;: [0.1, 1, 10, 100], &#x27;kernel&#x27;: [&#x27;rbf&#x27;]},\n",
       "             scoring=&#x27;accuracy&#x27;, verbose=3)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=3, estimator=SVC(), n_jobs=-1,\n",
       "             param_grid={'C': [0.1, 1, 10, 100], 'kernel': ['rbf']},\n",
       "             scoring='accuracy', verbose=3)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc = SVC()\n",
    "param_grid = {'C': [0.1, 1, 10, 100],   \n",
    "              'kernel': ['rbf']} \n",
    "grid_svc_clf = GridSearchCV(svc, param_grid, scoring = 'accuracy', n_jobs = -1, verbose = 3, cv = 3)\n",
    "grid_svc_clf.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 100, 'kernel': 'rbf'}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_svc_clf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "svc_opt = SVC(C = 100,kernel=\"rbf\")\n",
    "svc_opt.fit(x_train,y_train)\n",
    "y_pred = svc_opt.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of SVC model: 0.785148726728012\n",
      "Confusion matrix of SVC model:\n",
      " [[3751  944]\n",
      " [1064 3587]]\n",
      "AUC score of SVC model: 0.7850835151967273\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy of SVC model:\",accuracy_score(y_test,y_pred))\n",
    "print(\"Confusion matrix of SVC model:\\n\",confusion_matrix(y_test,y_pred))\n",
    "svc_fpr, svc_tpr, _ = roc_curve(y_test, y_pred)\n",
    "svc_auc = roc_auc_score(y_test, y_pred)\n",
    "print(\"AUC score of SVC model:\",svc_auc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ns_probs = [0 for _ in range(len(y_test))]\n",
    "ns_fpr, ns_tpr, _ = roc_curve(y_test, ns_probs)\n",
    "ns_auc = roc_auc_score(y_test, ns_probs)\n",
    "plt.figure(figsize=(7,5))\n",
    "plt.plot(ns_fpr, ns_tpr,\"--\")\n",
    "plt.plot(svc_fpr,svc_tpr,marker =\".\")\n",
    "plt.title(\"ROC curve for SVC\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### GuassianNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 11 candidates, totalling 55 fits\n",
      "[CV 1/5] END ...............var_smoothing=1e-09;, score=0.599 total time=   0.0s\n",
      "[CV 2/5] END ...............var_smoothing=1e-09;, score=0.584 total time=   0.0s\n",
      "[CV 3/5] END ...............var_smoothing=1e-09;, score=0.584 total time=   0.0s\n",
      "[CV 4/5] END ...............var_smoothing=1e-09;, score=0.591 total time=   0.0s\n",
      "[CV 5/5] END ...............var_smoothing=1e-09;, score=0.584 total time=   0.0s\n",
      "[CV 1/5] END .................var_smoothing=0.1;, score=0.602 total time=   0.0s\n",
      "[CV 2/5] END .................var_smoothing=0.1;, score=0.588 total time=   0.0s\n",
      "[CV 3/5] END .................var_smoothing=0.1;, score=0.586 total time=   0.0s\n",
      "[CV 4/5] END .................var_smoothing=0.1;, score=0.595 total time=   0.0s\n",
      "[CV 5/5] END .................var_smoothing=0.1;, score=0.589 total time=   0.0s\n",
      "[CV 1/5] END ...............var_smoothing=0.001;, score=0.599 total time=   0.0s\n",
      "[CV 2/5] END ...............var_smoothing=0.001;, score=0.584 total time=   0.0s\n",
      "[CV 3/5] END ...............var_smoothing=0.001;, score=0.584 total time=   0.0s\n",
      "[CV 4/5] END ...............var_smoothing=0.001;, score=0.591 total time=   0.0s\n",
      "[CV 5/5] END ...............var_smoothing=0.001;, score=0.584 total time=   0.0s\n",
      "[CV 1/5] END .................var_smoothing=0.5;, score=0.612 total time=   0.0s\n",
      "[CV 2/5] END .................var_smoothing=0.5;, score=0.600 total time=   0.0s\n",
      "[CV 3/5] END .................var_smoothing=0.5;, score=0.596 total time=   0.0s\n",
      "[CV 4/5] END .................var_smoothing=0.5;, score=0.605 total time=   0.0s\n",
      "[CV 5/5] END .................var_smoothing=0.5;, score=0.596 total time=   0.0s\n",
      "[CV 1/5] END ................var_smoothing=0.05;, score=0.601 total time=   0.0s\n",
      "[CV 2/5] END ................var_smoothing=0.05;, score=0.586 total time=   0.0s\n",
      "[CV 3/5] END ................var_smoothing=0.05;, score=0.585 total time=   0.0s\n",
      "[CV 4/5] END ................var_smoothing=0.05;, score=0.593 total time=   0.0s\n",
      "[CV 5/5] END ................var_smoothing=0.05;, score=0.586 total time=   0.0s\n",
      "[CV 1/5] END ................var_smoothing=0.01;, score=0.599 total time=   0.0s\n",
      "[CV 2/5] END ................var_smoothing=0.01;, score=0.585 total time=   0.0s\n",
      "[CV 3/5] END ................var_smoothing=0.01;, score=0.585 total time=   0.0s\n",
      "[CV 4/5] END ................var_smoothing=0.01;, score=0.591 total time=   0.0s\n",
      "[CV 5/5] END ................var_smoothing=0.01;, score=0.585 total time=   0.0s\n",
      "[CV 1/5] END ...............var_smoothing=1e-08;, score=0.599 total time=   0.0s\n",
      "[CV 2/5] END ...............var_smoothing=1e-08;, score=0.584 total time=   0.0s\n",
      "[CV 3/5] END ...............var_smoothing=1e-08;, score=0.584 total time=   0.0s\n",
      "[CV 4/5] END ...............var_smoothing=1e-08;, score=0.591 total time=   0.0s\n",
      "[CV 5/5] END ...............var_smoothing=1e-08;, score=0.584 total time=   0.0s\n",
      "[CV 1/5] END ...............var_smoothing=1e-07;, score=0.599 total time=   0.0s\n",
      "[CV 2/5] END ...............var_smoothing=1e-07;, score=0.584 total time=   0.0s\n",
      "[CV 3/5] END ...............var_smoothing=1e-07;, score=0.584 total time=   0.0s\n",
      "[CV 4/5] END ...............var_smoothing=1e-07;, score=0.591 total time=   0.0s\n",
      "[CV 5/5] END ...............var_smoothing=1e-07;, score=0.584 total time=   0.0s\n",
      "[CV 1/5] END ...............var_smoothing=1e-06;, score=0.599 total time=   0.0s\n",
      "[CV 2/5] END ...............var_smoothing=1e-06;, score=0.584 total time=   0.0s\n",
      "[CV 3/5] END ...............var_smoothing=1e-06;, score=0.584 total time=   0.0s\n",
      "[CV 4/5] END ...............var_smoothing=1e-06;, score=0.591 total time=   0.0s\n",
      "[CV 5/5] END ...............var_smoothing=1e-06;, score=0.584 total time=   0.0s\n",
      "[CV 1/5] END ...............var_smoothing=1e-10;, score=0.599 total time=   0.0s\n",
      "[CV 2/5] END ...............var_smoothing=1e-10;, score=0.584 total time=   0.0s\n",
      "[CV 3/5] END ...............var_smoothing=1e-10;, score=0.584 total time=   0.0s\n",
      "[CV 4/5] END ...............var_smoothing=1e-10;, score=0.591 total time=   0.0s\n",
      "[CV 5/5] END ...............var_smoothing=1e-10;, score=0.584 total time=   0.0s\n",
      "[CV 1/5] END ...............var_smoothing=1e-11;, score=0.599 total time=   0.0s\n",
      "[CV 2/5] END ...............var_smoothing=1e-11;, score=0.584 total time=   0.0s\n",
      "[CV 3/5] END ...............var_smoothing=1e-11;, score=0.584 total time=   0.0s\n",
      "[CV 4/5] END ...............var_smoothing=1e-11;, score=0.591 total time=   0.0s\n",
      "[CV 5/5] END ...............var_smoothing=1e-11;, score=0.584 total time=   0.0s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=GaussianNB(),\n",
       "             param_grid={&#x27;var_smoothing&#x27;: [1e-09, 0.1, 0.001, 0.5, 0.05, 0.01,\n",
       "                                           1e-08, 1e-07, 1e-06, 1e-10, 1e-11]},\n",
       "             verbose=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=GaussianNB(),\n",
       "             param_grid={&#x27;var_smoothing&#x27;: [1e-09, 0.1, 0.001, 0.5, 0.05, 0.01,\n",
       "                                           1e-08, 1e-07, 1e-06, 1e-10, 1e-11]},\n",
       "             verbose=3)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=GaussianNB(),\n",
       "             param_grid={'var_smoothing': [1e-09, 0.1, 0.001, 0.5, 0.05, 0.01,\n",
       "                                           1e-08, 1e-07, 1e-06, 1e-10, 1e-11]},\n",
       "             verbose=3)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gnb = GaussianNB()\n",
    "param_grid = {\"var_smoothing\": [1e-9,0.1, 0.001, 0.5,0.05,0.01,1e-8,1e-7,1e-6,1e-10,1e-11]}\n",
    "grid_gnb_clf = GridSearchCV(gnb,param_grid, cv=5,  verbose=3)\n",
    "grid_gnb_clf.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'var_smoothing': 0.5}"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_gnb_clf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "gnb_opt = GaussianNB(var_smoothing=0.5)\n",
    "gnb_opt.fit(x_train,y_train)\n",
    "y_pred = gnb_opt.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of GaussianNB model: 0.6061416648833725\n",
      "Confusion matrix of GaussianNB model:\n",
      " [[1445 3250]\n",
      " [ 431 4220]]\n",
      "AUC score of GaussianNB model: 0.6075529922567524\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy of GaussianNB model:\",accuracy_score(y_test,y_pred))\n",
    "print(\"Confusion matrix of GaussianNB model:\\n\",confusion_matrix(y_test,y_pred))\n",
    "gnb_fpr, gnb_tpr, _ = roc_curve(y_test, y_pred)\n",
    "gnb_auc = roc_auc_score(y_test, y_pred)\n",
    "print(\"AUC score of GaussianNB model:\",gnb_auc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ns_probs = [0 for _ in range(len(y_test))]\n",
    "ns_fpr, ns_tpr, _ = roc_curve(y_test, ns_probs)\n",
    "ns_auc = roc_auc_score(y_test, ns_probs)\n",
    "plt.figure(figsize=(7,5))\n",
    "plt.plot(ns_fpr, ns_tpr,\"--\")\n",
    "plt.plot(gnb_fpr,gnb_tpr,marker =\".\")\n",
    "plt.title(\"ROC curve for GuassianNavieBayes\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### RandomForsetClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 9 candidates, totalling 27 fits\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=3, estimator=RandomForestClassifier(), n_jobs=-1,\n",
       "             param_grid={&#x27;max_depth&#x27;: [10, 20, 30],\n",
       "                         &#x27;n_estimators&#x27;: [100, 150, 200]},\n",
       "             scoring=&#x27;accuracy&#x27;, verbose=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=3, estimator=RandomForestClassifier(), n_jobs=-1,\n",
       "             param_grid={&#x27;max_depth&#x27;: [10, 20, 30],\n",
       "                         &#x27;n_estimators&#x27;: [100, 150, 200]},\n",
       "             scoring=&#x27;accuracy&#x27;, verbose=3)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=3, estimator=RandomForestClassifier(), n_jobs=-1,\n",
       "             param_grid={'max_depth': [10, 20, 30],\n",
       "                         'n_estimators': [100, 150, 200]},\n",
       "             scoring='accuracy', verbose=3)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc = RandomForestClassifier()\n",
    "param_grid = {'n_estimators': [100,150,200], 'max_depth': [10,20,30]}\n",
    "grid_rfc_clf = GridSearchCV(RandomForestClassifier(), param_grid, scoring = 'accuracy', n_jobs = -1, verbose = 3, cv = 3)\n",
    "grid_rfc_clf.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 30, 'n_estimators': 150}"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_rfc_clf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc_opt = RandomForestClassifier(max_depth=30,n_estimators=150)\n",
    "rfc_opt.fit(x_train,y_train)\n",
    "y_pred = rfc_opt.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of RandomForestClassifier model: 0.8495613096511877\n",
      "Confusion matrix of RandomForestClassifier model:\n",
      " [[4044  651]\n",
      " [ 755 3896]]\n",
      "AUC score of RandomForestClassifier model: 0.8495055857306444\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy of RandomForestClassifier model:\",accuracy_score(y_test,y_pred))\n",
    "print(\"Confusion matrix of RandomForestClassifier model:\\n\",confusion_matrix(y_test,y_pred))\n",
    "rfc_fpr, rfc_tpr, _ = roc_curve(y_test, y_pred)\n",
    "rfc_auc = roc_auc_score(y_test, y_pred)\n",
    "print(\"AUC score of RandomForestClassifier model:\",rfc_auc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ns_probs = [0 for _ in range(len(y_test))]\n",
    "ns_fpr, ns_tpr, _ = roc_curve(y_test, ns_probs)\n",
    "ns_auc = roc_auc_score(y_test, ns_probs)\n",
    "plt.figure(figsize=(7,5))\n",
    "plt.plot(ns_fpr, ns_tpr,\"--\")\n",
    "plt.plot(rfc_fpr,rfc_tpr,marker =\".\")\n",
    "plt.title(\"ROC curve for RandomForestClassifier\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 12 candidates, totalling 36 fits\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=3,\n",
       "             estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                     callbacks=None, colsample_bylevel=None,\n",
       "                                     colsample_bynode=None,\n",
       "                                     colsample_bytree=0.8,\n",
       "                                     early_stopping_rounds=None,\n",
       "                                     enable_categorical=False, eval_metric=None,\n",
       "                                     feature_types=None, gamma=0, gpu_id=None,\n",
       "                                     grow_policy=None, importance_type=None,\n",
       "                                     interaction_constraints=None,\n",
       "                                     learning_rate=0.1, max_bin=None,\n",
       "                                     max_cat_threshold=None,\n",
       "                                     max_cat_to_onehot=None,\n",
       "                                     max_delta_step=None, max_depth=5,\n",
       "                                     max_leaves=None, min_child_weight=1,\n",
       "                                     missing=nan, monotone_constraints=None,\n",
       "                                     n_estimators=140, n_jobs=None, nthread=4,\n",
       "                                     num_parallel_tree=None, predictor=None, ...),\n",
       "             n_jobs=-1,\n",
       "             param_grid={&#x27;max_depth&#x27;: range(3, 10, 2),\n",
       "                         &#x27;min_child_weight&#x27;: range(1, 6, 2)},\n",
       "             scoring=&#x27;accuracy&#x27;, verbose=2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=3,\n",
       "             estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                     callbacks=None, colsample_bylevel=None,\n",
       "                                     colsample_bynode=None,\n",
       "                                     colsample_bytree=0.8,\n",
       "                                     early_stopping_rounds=None,\n",
       "                                     enable_categorical=False, eval_metric=None,\n",
       "                                     feature_types=None, gamma=0, gpu_id=None,\n",
       "                                     grow_policy=None, importance_type=None,\n",
       "                                     interaction_constraints=None,\n",
       "                                     learning_rate=0.1, max_bin=None,\n",
       "                                     max_cat_threshold=None,\n",
       "                                     max_cat_to_onehot=None,\n",
       "                                     max_delta_step=None, max_depth=5,\n",
       "                                     max_leaves=None, min_child_weight=1,\n",
       "                                     missing=nan, monotone_constraints=None,\n",
       "                                     n_estimators=140, n_jobs=None, nthread=4,\n",
       "                                     num_parallel_tree=None, predictor=None, ...),\n",
       "             n_jobs=-1,\n",
       "             param_grid={&#x27;max_depth&#x27;: range(3, 10, 2),\n",
       "                         &#x27;min_child_weight&#x27;: range(1, 6, 2)},\n",
       "             scoring=&#x27;accuracy&#x27;, verbose=2)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=0.8, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=0, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=5, max_leaves=None,\n",
       "              min_child_weight=1, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=140, n_jobs=None, nthread=4, num_parallel_tree=None,\n",
       "              predictor=None, ...)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=0.8, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=0, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=5, max_leaves=None,\n",
       "              min_child_weight=1, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=140, n_jobs=None, nthread=4, num_parallel_tree=None,\n",
       "              predictor=None, ...)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=3,\n",
       "             estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                     callbacks=None, colsample_bylevel=None,\n",
       "                                     colsample_bynode=None,\n",
       "                                     colsample_bytree=0.8,\n",
       "                                     early_stopping_rounds=None,\n",
       "                                     enable_categorical=False, eval_metric=None,\n",
       "                                     feature_types=None, gamma=0, gpu_id=None,\n",
       "                                     grow_policy=None, importance_type=None,\n",
       "                                     interaction_constraints=None,\n",
       "                                     learning_rate=0.1, max_bin=None,\n",
       "                                     max_cat_threshold=None,\n",
       "                                     max_cat_to_onehot=None,\n",
       "                                     max_delta_step=None, max_depth=5,\n",
       "                                     max_leaves=None, min_child_weight=1,\n",
       "                                     missing=nan, monotone_constraints=None,\n",
       "                                     n_estimators=140, n_jobs=None, nthread=4,\n",
       "                                     num_parallel_tree=None, predictor=None, ...),\n",
       "             n_jobs=-1,\n",
       "             param_grid={'max_depth': range(3, 10, 2),\n",
       "                         'min_child_weight': range(1, 6, 2)},\n",
       "             scoring='accuracy', verbose=2)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid = {\n",
    " 'max_depth':range(3,10,2),\n",
    " 'min_child_weight':range(1,6,2)\n",
    "}\n",
    "grid_xgb_clf = GridSearchCV(estimator = XGBClassifier( learning_rate =0.1, n_estimators=140, max_depth=5,\n",
    " min_child_weight=1, gamma=0, subsample=0.8, colsample_bytree=0.8,\n",
    " objective= 'binary:logistic', nthread=4, scale_pos_weight=1, seed=27), \n",
    " param_grid = param_grid, scoring='accuracy',n_jobs=-1, cv=3, verbose = 2)\n",
    "grid_xgb_clf.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 9, 'min_child_weight': 1}"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_xgb_clf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_opt = XGBClassifier(max_depth=9,min_child_weight = 1)\n",
    "xgb_opt.fit(x_train,y_train)\n",
    "y_pred = xgb_opt.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of XGBClassifier model: 0.8423924673657179\n",
      "Confusion matrix of XGBClassifier model:\n",
      " [[4031  664]\n",
      " [ 809 3842]]\n",
      "AUC score of XGBClassifier model: 0.8423159310043369\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy of XGBClassifier model:\",accuracy_score(y_test,y_pred))\n",
    "print(\"Confusion matrix of XGBClassifier model:\\n\",confusion_matrix(y_test,y_pred))\n",
    "xgb_fpr, xgb_tpr, _ = roc_curve(y_test, y_pred)\n",
    "xgb_auc = roc_auc_score(y_test, y_pred)\n",
    "print(\"AUC score of XGBClassifier model:\",xgb_auc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ns_probs = [0 for _ in range(len(y_test))]\n",
    "ns_fpr, ns_tpr, _ = roc_curve(y_test, ns_probs)\n",
    "ns_auc = roc_auc_score(y_test, ns_probs)\n",
    "plt.figure(figsize=(7,5))\n",
    "plt.plot(ns_fpr, ns_tpr,\"--\")\n",
    "plt.plot(xgb_fpr,xgb_tpr,marker =\".\")\n",
    "plt.title(\"ROC curve for XGBClassifier\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above model evaluation process it is observed that the models RandomForestClassifier and XGBClassifier have high accuracy and ROC_AUC score compared to other models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Both RandomForestClassifier and XGBClassifier is choosen for model serialization and prediction"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.13 ('CCDP')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "3845146c9f6afb2daa69b7ea31270afdb0d9c9fe7162f9e50427b85edf32c60d"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}