diabetics-pred-xgb-1.ipynb

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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>33.6</td>\n",
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       "      <td>23.3</td>\n",
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       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
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      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
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   ],
   "source": [
    "#importing dataset\n",
    "df = pd.read_csv('../input/pima-indians-diabetes-database/diabetes.csv')\n",
    "df.head()"
   ]
  },
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    {
     "data": {
      "text/plain": [
       "(268, 9)"
      ]
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     "execution_count": 3,
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   "source": [
    "df[df.Outcome==1].shape"
   ]
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       "(500, 9)"
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    "df[df.Outcome!=1].shape"
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   "source": [
    "df_nomiss = df[(df['Glucose']!=0) & (df['BloodPressure']!=0) & (df['SkinThickness']!=0)  & (df['Insulin']!=0) & (df['BMI']!=0)]\n",
    "df_miss  = df[(df['Glucose']==0) | (df['BloodPressure']==0) | (df['SkinThickness']==0)  | (df['Insulin']==0) | (df['BMI']==0)]"
   ]
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   "source": [
    "X_nomiss=df_nomiss.drop('Outcome',axis=1)\n",
    "y_nomiss=df_nomiss['Outcome']"
   ]
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   "source": [
    "X_miss=df_miss.drop('Outcome',axis=1)\n",
    "y_miss=df_miss['Outcome']"
   ]
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     "data": {
      "image/png": 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+emybT1dVJbkWuKOqrgVIcj2whtGDCY8EvjQ6w8UBjB73IA3KUNBSdwNwynghyUHAEcA9PPho+sC9+HN/1N5/PDaeW14GPABcVlWv2Ys/U3pMnj7SUrcNeGqS06B/Pel7GX0N5M3AUUn2S7Ka0bfUzfm/JE9q48uBU5Mc1vZxaKv/O6OnzgK8FvjXPejrSuClSZ7T9vm0JM/d019O2lOGgpa0Gj0R8ncY/ad+E/CfwH2M7i76EvBfjI4mzgG+NrbpZuCaJBe3p8q+E/hCkm8A72tz3gScnuQa4PX85FrDJH3tBt4AfKhtfwXwvJ/295Qm5VNSJUmdRwqSpM5QkCR1hoIkqTMUJEmdoSBJ6gwFSVJnKEiSuv8HHGGod05G2B8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=df[\"Outcome\"])\n",
    "plt.show()\n",
    "plt.savefig('/kaggle/working/output.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "48a4658d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-02T16:10:19.480173Z",
     "iopub.status.busy": "2023-02-02T16:10:19.479136Z",
     "iopub.status.idle": "2023-02-02T16:10:19.667980Z",
     "shell.execute_reply": "2023-02-02T16:10:19.668974Z",
     "shell.execute_reply.started": "2023-01-29T09:49:01.403526Z"
    },
    "papermill": {
     "duration": 0.269018,
     "end_time": "2023-02-02T16:10:19.669188",
     "exception": false,
     "start_time": "2023-02-02T16:10:19.400170",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train_nomiss,X_test_nomiss,y_train_nomiss,y_test_nomiss =  train_test_split(X_nomiss,y_nomiss,test_size=0.20, random_state=7,stratify=y_nomiss)\n",
    "X_train_miss,X_test_miss,y_train_miss,y_test_miss =  train_test_split(X_miss,y_miss,test_size=0.20, random_state=7,stratify=y_miss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "aeed1113",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-02T16:10:19.761886Z",
     "iopub.status.busy": "2023-02-02T16:10:19.761065Z",
     "iopub.status.idle": "2023-02-02T16:10:19.764990Z",
     "shell.execute_reply": "2023-02-02T16:10:19.765518Z",
     "shell.execute_reply.started": "2023-01-29T09:49:01.641638Z"
    },
    "papermill": {
     "duration": 0.054815,
     "end_time": "2023-02-02T16:10:19.765718",
     "exception": false,
     "start_time": "2023-02-02T16:10:19.710903",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_train_nomiss = pd.concat([X_train_nomiss,y_train_nomiss], axis=1)\n",
    "df_train_miss = pd.concat([X_train_miss,y_train_miss], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "daaee132",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-02T16:10:19.857774Z",
     "iopub.status.busy": "2023-02-02T16:10:19.856894Z",
     "iopub.status.idle": "2023-02-02T16:10:19.859111Z",
     "shell.execute_reply": "2023-02-02T16:10:19.858463Z",
     "shell.execute_reply.started": "2023-01-29T09:49:02.135022Z"
    },
    "papermill": {
     "duration": 0.051972,
     "end_time": "2023-02-02T16:10:19.859271",
     "exception": false,
     "start_time": "2023-02-02T16:10:19.807299",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_train = pd.concat([df_train_nomiss,df_train_miss], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "aced0236",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-02T16:10:19.955809Z",
     "iopub.status.busy": "2023-02-02T16:10:19.954915Z",
     "iopub.status.idle": "2023-02-02T16:10:19.957185Z",
     "shell.execute_reply": "2023-02-02T16:10:19.957698Z",
     "shell.execute_reply.started": "2023-01-29T09:49:02.476584Z"
    },
    "papermill": {
     "duration": 0.048927,
     "end_time": "2023-02-02T16:10:19.957854",
     "exception": false,
     "start_time": "2023-02-02T16:10:19.908927",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_test_nomiss = pd.concat([X_test_nomiss,y_test_nomiss], axis=1)\n",
    "df_test_miss = pd.concat([X_test_miss,y_test_miss], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f29f361d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-02T16:10:20.019673Z",
     "iopub.status.busy": "2023-02-02T16:10:20.018445Z",
     "iopub.status.idle": "2023-02-02T16:10:20.021210Z",
     "shell.execute_reply": "2023-02-02T16:10:20.020775Z",
     "shell.execute_reply.started": "2023-01-29T09:49:03.155109Z"
    },
    "papermill": {
     "duration": 0.035092,
     "end_time": "2023-02-02T16:10:20.021318",
     "exception": false,
     "start_time": "2023-02-02T16:10:19.986226",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_test = pd.concat([df_test_miss,df_test_miss], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "52924009",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-02T16:10:20.082749Z",
     "iopub.status.busy": "2023-02-02T16:10:20.081870Z",
     "iopub.status.idle": "2023-02-02T16:10:20.096316Z",
     "shell.execute_reply": "2023-02-02T16:10:20.095895Z",
     "shell.execute_reply.started": "2023-01-29T09:49:03.705690Z"
    },
    "papermill": {
     "duration": 0.04744,
     "end_time": "2023-02-02T16:10:20.096427",
     "exception": false,
     "start_time": "2023-02-02T16:10:20.048987",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "117.0\n",
      "72.0\n",
      "29.0\n",
      "125.0\n",
      "32.0\n"
     ]
    }
   ],
   "source": [
    "ms_col = ['Glucose','BloodPressure','SkinThickness','Insulin','BMI']\n",
    "for i in ms_col:\n",
    "    x= df_train[(df_train[i]!=0)][i].median()\n",
    "    print(x)\n",
    "    df_train.loc[((df_train[i] == 0)), i] =  df_train[(df_train[i]!=0)][i].median()\n",
    "    #df_train[ms_col].fillna(df_train[i].median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "223a3a71",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-02T16:10:20.160860Z",
     "iopub.status.busy": "2023-02-02T16:10:20.160148Z",
     "iopub.status.idle": "2023-02-02T16:10:20.162343Z",
     "shell.execute_reply": "2023-02-02T16:10:20.162758Z",
     "shell.execute_reply.started": "2023-01-29T09:49:04.964476Z"
    },
    "papermill": {
     "duration": 0.038226,
     "end_time": "2023-02-02T16:10:20.162895",
     "exception": false,
     "start_time": "2023-02-02T16:10:20.124669",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_test['Glucose'] = df_test['Glucose'].replace(0,117.0)\n",
    "df_test['BloodPressure'] = df_test['BloodPressure'].replace(0,72.0)\n",
    "df_test['SkinThickness'] = df_test['SkinThickness'].replace(0,29.0)\n",
    "df_test['Insulin'] = df_test['Insulin'].replace(0,125.0)\n",
    "df_test['BMI'] = df_test['BMI'].replace(0,32.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "934ca064",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-02T16:10:20.224324Z",
     "iopub.status.busy": "2023-02-02T16:10:20.223546Z",
     "iopub.status.idle": "2023-02-02T16:10:20.227772Z",
     "shell.execute_reply": "2023-02-02T16:10:20.228160Z",
     "shell.execute_reply.started": "2023-01-29T09:49:05.260439Z"
    },
    "papermill": {
     "duration": 0.036991,
     "end_time": "2023-02-02T16:10:20.228290",
     "exception": false,
     "start_time": "2023-02-02T16:10:20.191299",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X_train=df_train.drop('Outcome',axis=1)\n",
    "y_train=df_train['Outcome']\n",
    "X_test=df_test.drop('Outcome',axis=1)\n",
    "y_test=df_test['Outcome']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f0818ed7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-02T16:10:20.290282Z",
     "iopub.status.busy": "2023-02-02T16:10:20.289303Z",
     "iopub.status.idle": "2023-02-02T16:10:20.296937Z",
     "shell.execute_reply": "2023-02-02T16:10:20.297339Z",
     "shell.execute_reply.started": "2023-01-29T09:49:05.715221Z"
    },
    "papermill": {
     "duration": 0.040387,
     "end_time": "2023-02-02T16:10:20.297458",
     "exception": false,
     "start_time": "2023-02-02T16:10:20.257071",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaling_x=StandardScaler()\n",
    "X_train=scaling_x.fit_transform(X_train)\n",
    "X_test=scaling_x.fit_transform(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e79d2200",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-02T16:10:20.358141Z",
     "iopub.status.busy": "2023-02-02T16:10:20.357477Z",
     "iopub.status.idle": "2023-02-02T16:10:20.444771Z",
     "shell.execute_reply": "2023-02-02T16:10:20.444202Z",
     "shell.execute_reply.started": "2023-01-29T09:49:06.504627Z"
    },
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     "start_time": "2023-02-02T16:10:20.325870",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import xgboost as xgb\n",
    "from xgboost.sklearn import XGBClassifier\n",
    "from sklearn import metrics  \n",
    "from sklearn.model_selection import GridSearchCV, RandomizedSearchCV\n",
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "fd2e7347",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-02T16:10:20.508888Z",
     "iopub.status.busy": "2023-02-02T16:10:20.508288Z",
     "iopub.status.idle": "2023-02-02T16:10:23.436717Z",
     "shell.execute_reply": "2023-02-02T16:10:23.437106Z",
     "shell.execute_reply.started": "2023-01-29T09:49:07.735113Z"
    },
    "papermill": {
     "duration": 2.963247,
     "end_time": "2023-02-02T16:10:23.437259",
     "exception": false,
     "start_time": "2023-02-02T16:10:20.474012",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=0.8,\n",
       "              enable_categorical=False, eval_metric='auc', gamma=0, gpu_id=-1,\n",
       "              importance_type=None, interaction_constraints='',\n",
       "              learning_rate=0.1, max_delta_step=0, max_depth=5,\n",
       "              min_child_weight=1, missing=nan, monotone_constraints='()',\n",
       "              n_estimators=100, n_jobs=4, nthread=4, num_parallel_tree=1,\n",
       "              predictor='auto', random_state=27, reg_alpha=0.1, reg_lambda=1,\n",
       "              scale_pos_weight=1, seed=27, subsample=0.8, tree_method='exact',\n",
       "              use_label_encoder=False, ...)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb1 = XGBClassifier(\n",
    " learning_rate =0.1,\n",
    " n_estimators=100,\n",
    " max_depth=5,\n",
    " min_child_weight=1,\n",
    " gamma=0,\n",
    " subsample=0.8,\n",
    " colsample_bytree=0.8,\n",
    " objective= 'binary:logistic',\n",
    " nthread=4,\n",
    " reg_alpha=0.1,   \n",
    " scale_pos_weight=1,\n",
    " seed=27,\n",
    " use_label_encoder=False,  \n",
    " eval_metric='auc')\n",
    "\n",
    "xgb1.fit(X_train, \n",
    "          y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5af7f1f3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-02T16:10:23.503622Z",
     "iopub.status.busy": "2023-02-02T16:10:23.502721Z",
     "iopub.status.idle": "2023-02-02T16:10:23.519623Z",
     "shell.execute_reply": "2023-02-02T16:10:23.520150Z",
     "shell.execute_reply.started": "2023-01-29T09:49:08.981130Z"
    },
    "papermill": {
     "duration": 0.05387,
     "end_time": "2023-02-02T16:10:23.520326",
     "exception": false,
     "start_time": "2023-02-02T16:10:23.466456",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model Report\n",
      "Accuracy (Train): 98.21 %\n",
      "Accuracy (Test): 73.68 %\n",
      "AUC Score (Train): 99.84 %\n",
      "AUC Score (Test): 81.47 %\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nModel Report\")\n",
    "y_preds = xgb1.predict(X_test)\n",
    "y_pred_proba = xgb1.predict_proba(X_test)[:,1]\n",
    "\n",
    "\n",
    "y_preds1 = xgb1.predict(X_train)\n",
    "y_pred_proba1 = xgb1.predict_proba(X_train)[:,1]\n",
    "\n",
    "print('Accuracy (Train):', round(metrics.accuracy_score(y_train, y_preds1) * 100,2),'%')\n",
    "print('Accuracy (Test):', round(metrics.accuracy_score(y_test, y_preds) * 100,2),'%')\n",
    "print('AUC Score (Train):', round(metrics.roc_auc_score(y_train, y_pred_proba1) * 100,2),'%')\n",
    "print('AUC Score (Test):', round(metrics.roc_auc_score(y_test, y_pred_proba) * 100,2),'%')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "983fb30c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-02T16:10:23.594343Z",
     "iopub.status.busy": "2023-02-02T16:10:23.582844Z",
     "iopub.status.idle": "2023-02-02T16:10:25.292566Z",
     "shell.execute_reply": "2023-02-02T16:10:25.292086Z",
     "shell.execute_reply.started": "2023-01-29T05:38:53.137096Z"
    },
    "papermill": {
     "duration": 1.742691,
     "end_time": "2023-02-02T16:10:25.292704",
     "exception": false,
     "start_time": "2023-02-02T16:10:23.550013",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from xgboost import plot_tree\n",
    "plot_tree(xgb1)\n",
    "plt.show()\n",
    "plt.savefig('/kaggle/working/xgb1.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a7417f45",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-02T16:10:25.363829Z",
     "iopub.status.busy": "2023-02-02T16:10:25.363141Z",
     "iopub.status.idle": "2023-02-02T16:10:37.988336Z",
     "shell.execute_reply": "2023-02-02T16:10:37.988793Z",
     "shell.execute_reply.started": "2023-01-29T05:19:44.397908Z"
    },
    "papermill": {
     "duration": 12.666074,
     "end_time": "2023-02-02T16:10:37.988954",
     "exception": false,
     "start_time": "2023-02-02T16:10:25.322880",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\tvalidation_0-auc:0.69234\n",
      "[1]\tvalidation_0-auc:0.78051\n",
      "[2]\tvalidation_0-auc:0.80804\n",
      "[3]\tvalidation_0-auc:0.82664\n",
      "[4]\tvalidation_0-auc:0.82850\n",
      "[5]\tvalidation_0-auc:0.84561\n",
      "[6]\tvalidation_0-auc:0.84747\n",
      "[7]\tvalidation_0-auc:0.83854\n",
      "[8]\tvalidation_0-auc:0.83817\n",
      "[9]\tvalidation_0-auc:0.82998\n",
      "[10]\tvalidation_0-auc:0.83668\n",
      "[11]\tvalidation_0-auc:0.84189\n",
      "[0]\tvalidation_0-auc:0.74405\n",
      "[0]\tvalidation_0-auc:0.74144\n",
      "[1]\tvalidation_0-auc:0.81510\n",
      "[1]\tvalidation_0-auc:0.74554\n",
      "[2]\tvalidation_0-auc:0.81771\n",
      "[2]\tvalidation_0-auc:0.77158\n",
      "[3]\tvalidation_0-auc:0.83147\n",
      "[3]\tvalidation_0-auc:0.78609\n",
      "[4]\tvalidation_0-auc:0.82217\n",
      "[4]\tvalidation_0-auc:0.83259\n",
      "[5]\tvalidation_0-auc:0.81473\n",
      "[5]\tvalidation_0-auc:0.83222\n",
      "[6]\tvalidation_0-auc:0.83743\n",
      "[6]\tvalidation_0-auc:0.82887\n",
      "[7]\tvalidation_0-auc:0.82812\n",
      "[7]\tvalidation_0-auc:0.83259\n",
      "[8]\tvalidation_0-auc:0.81957\n",
      "[8]\tvalidation_0-auc:0.84077\n",
      "[9]\tvalidation_0-auc:0.82961\n",
      "[10]\tvalidation_0-auc:0.82775\n",
      "[11]\tvalidation_0-auc:0.82998\n",
      "[12]\tvalidation_0-auc:0.83296\n",
      "[13]\tvalidation_0-auc:0.83333\n",
      "[0]\tvalidation_0-auc:0.77083\n",
      "[1]\tvalidation_0-auc:0.76525\n",
      "[2]\tvalidation_0-auc:0.77232\n",
      "[3]\tvalidation_0-auc:0.79725\n",
      "[4]\tvalidation_0-auc:0.80283\n",
      "[5]\tvalidation_0-auc:0.81510\n",
      "[6]\tvalidation_0-auc:0.82664\n",
      "[7]\tvalidation_0-auc:0.83333\n",
      "[8]\tvalidation_0-auc:0.83259\n",
      "[9]\tvalidation_0-auc:0.82143\n",
      "[10]\tvalidation_0-auc:0.83333\n",
      "[11]\tvalidation_0-auc:0.83036\n",
      "[0]\tvalidation_0-auc:0.71875\n",
      "[1]\tvalidation_0-auc:0.76414\n",
      "[2]\tvalidation_0-auc:0.78646\n",
      "[3]\tvalidation_0-auc:0.79576\n",
      "[4]\tvalidation_0-auc:0.80246\n",
      "[5]\tvalidation_0-auc:0.79278\n",
      "[6]\tvalidation_0-auc:0.80729\n",
      "[7]\tvalidation_0-auc:0.80432\n",
      "[8]\tvalidation_0-auc:0.81176\n",
      "[9]\tvalidation_0-auc:0.81101\n",
      "[10]\tvalidation_0-auc:0.81473\n",
      "[11]\tvalidation_0-auc:0.81696\n",
      "[12]\tvalidation_0-auc:0.82441\n",
      "[13]\tvalidation_0-auc:0.82441\n",
      "[14]\tvalidation_0-auc:0.83147\n",
      "[15]\tvalidation_0-auc:0.83519\n",
      "[16]\tvalidation_0-auc:0.83147\n",
      "[17]\tvalidation_0-auc:0.84152\n",
      "[18]\tvalidation_0-auc:0.84301\n",
      "[19]\tvalidation_0-auc:0.84896\n",
      "[20]\tvalidation_0-auc:0.84970\n",
      "[21]\tvalidation_0-auc:0.85193\n",
      "[22]\tvalidation_0-auc:0.84821\n",
      "[23]\tvalidation_0-auc:0.84747\n",
      "[24]\tvalidation_0-auc:0.84524\n",
      "[25]\tvalidation_0-auc:0.84821\n",
      "[26]\tvalidation_0-auc:0.85193\n",
      "[0]\tvalidation_0-auc:0.72731\n",
      "[0]\tvalidation_0-auc:0.77083\n",
      "[1]\tvalidation_0-auc:0.79167\n",
      "[1]\tvalidation_0-auc:0.77902\n",
      "[2]\tvalidation_0-auc:0.81548\n",
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      "[10]\tvalidation_0-auc:0.84077\n",
      "[5]\tvalidation_0-auc:0.86309\n",
      "[9]\tvalidation_0-auc:0.82552\n",
      "[11]\tvalidation_0-auc:0.84077\n",
      "[5]\tvalidation_0-auc:0.80692\n",
      "[12]\tvalidation_0-auc:0.83929\n",
      "[10]\tvalidation_0-auc:0.82812\n",
      "[6]\tvalidation_0-auc:0.85938\n",
      "[11]\tvalidation_0-auc:0.83631\n",
      "[12]\tvalidation_0-auc:0.83482\n",
      "[6]\tvalidation_0-auc:0.79688\n",
      "[13]\tvalidation_0-auc:0.83482\n",
      "[14]\tvalidation_0-auc:0.82664\n",
      "[7]\tvalidation_0-auc:0.84933\n",
      "[7]\tvalidation_0-auc:0.79985\n",
      "[15]\tvalidation_0-auc:0.83036\n",
      "[8]\tvalidation_0-auc:0.84189\n",
      "[0]\tvalidation_0-auc:0.77455\n",
      "[8]\tvalidation_0-auc:0.80580\n",
      "[1]\tvalidation_0-auc:0.82775\n",
      "[2]\tvalidation_0-auc:0.80878\n",
      "[3]\tvalidation_0-auc:0.81399\n",
      "[4]\tvalidation_0-auc:0.81250\n",
      "[5]\tvalidation_0-auc:0.80580\n",
      "[0]\tvalidation_0-auc:0.72024\n",
      "[1]\tvalidation_0-auc:0.75484\n",
      "[2]\tvalidation_0-auc:0.78720\n",
      "[3]\tvalidation_0-auc:0.80097\n",
      "[4]\tvalidation_0-auc:0.80915\n",
      "[5]\tvalidation_0-auc:0.79948\n",
      "[6]\tvalidation_0-auc:0.79725\n",
      "[7]\tvalidation_0-auc:0.78609\n",
      "[8]\tvalidation_0-auc:0.78906\n",
      "[0]\tvalidation_0-auc:0.76116\n",
      "[1]\tvalidation_0-auc:0.82961\n",
      "[2]\tvalidation_0-auc:0.86570\n",
      "[3]\tvalidation_0-auc:0.85342\n",
      "[4]\tvalidation_0-auc:0.84710\n",
      "[5]\tvalidation_0-auc:0.84226\n",
      "[6]\tvalidation_0-auc:0.83966\n",
      "[7]\tvalidation_0-auc:0.83929\n",
      "Tuned Paramerers ==>  {'max_depth': 3, 'min_child_weight': 5}\n",
      "Best Score ==>  0.8291338646144357\n",
      "Accuracy on Train set ==>  0.8554095519171762\n",
      "Accuracy on Test1 set ==>  0.8656994047619048\n"
     ]
    }
   ],
   "source": [
    "param_test1 = {\n",
    " 'max_depth':range(3,10,2),\n",
    " 'min_child_weight':range(1,6,2)\n",
    "}\n",
    "\n",
    "gsearch1 = GridSearchCV(estimator=XGBClassifier(learning_rate =0.1,\n",
    "                                                n_estimators=100,\n",
    "                                                #max_depth=5,\n",
    "                                                #min_child_weight=1,\n",
    "                                                gamma=0,\n",
    "                                                subsample=0.8,\n",
    "                                                colsample_bytree=0.8,\n",
    "                                                objective= 'binary:logistic',\n",
    "                                                nthread=4,\n",
    "                                                scale_pos_weight=1,\n",
    "                                                seed=27,\n",
    "                                                use_label_encoder=False,  \n",
    "                                                eval_metric='auc'),\n",
    "                        param_grid = param_test1, \n",
    "                        scoring='roc_auc',\n",
    "                        n_jobs=4,\n",
    "                        cv=5)\n",
    "eval_set = [(X_test, y_test)]\n",
    "\n",
    "gsearch1.fit(X_train, y_train,early_stopping_rounds=5,eval_set=eval_set)\n",
    "print(\"Tuned Paramerers ==> \",gsearch1.best_params_)\n",
    "print(\"Best Score ==> \",gsearch1.best_score_)\n",
    "print(\"Accuracy on Train set ==> \", gsearch1.score(X_train,y_train))\n",
    "print(\"Accuracy on Test1 set ==> \", gsearch1.score(X_test,y_test))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2d0e9bb3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-02T16:10:38.073290Z",
     "iopub.status.busy": "2023-02-02T16:10:38.071864Z",
     "iopub.status.idle": "2023-02-02T16:10:50.676275Z",
     "shell.execute_reply": "2023-02-02T16:10:50.675648Z",
     "shell.execute_reply.started": "2023-01-29T05:21:42.830458Z"
    },
    "papermill": {
     "duration": 12.649862,
     "end_time": "2023-02-02T16:10:50.676444",
     "exception": false,
     "start_time": "2023-02-02T16:10:38.026582",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "[12]\tvalidation_0-auc:0.84338\n",
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      "[24]\tvalidation_0-auc:0.83854\n",
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      "[25]\tvalidation_0-auc:0.83929\n",
      "[12]\tvalidation_0-auc:0.84338\n",
      "[26]\tvalidation_0-auc:0.83929\n",
      "[27]\tvalidation_0-auc:0.84226\n",
      "[28]\tvalidation_0-auc:0.84375\n",
      "[29]\tvalidation_0-auc:0.84226\n",
      "[0]\tvalidation_0-auc:0.74851\n",
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      "[32]\tvalidation_0-auc:0.84226\n",
      "[33]\tvalidation_0-auc:0.83854\n",
      "[3]\tvalidation_0-auc:0.81622\n",
      "[34]\tvalidation_0-auc:0.83780\n",
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      "[0]\tvalidation_0-auc:0.74851\n",
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      "[1]\tvalidation_0-auc:0.79018\n",
      "[6]\tvalidation_0-auc:0.84375\n",
      "[2]\tvalidation_0-auc:0.80506\n",
      "[7]\tvalidation_0-auc:0.83929\n",
      "[3]\tvalidation_0-auc:0.81622\n",
      "[8]\tvalidation_0-auc:0.84077\n",
      "[4]\tvalidation_0-auc:0.81027\n",
      "[9]\tvalidation_0-auc:0.83482\n",
      "[10]\tvalidation_0-auc:0.83408\n",
      "[5]\tvalidation_0-auc:0.80580\n",
      "[11]\tvalidation_0-auc:0.83482\n",
      "[6]\tvalidation_0-auc:0.80915\n",
      "[7]\tvalidation_0-auc:0.80692\n",
      "[0]\tvalidation_0-auc:0.71949\n",
      "[0]\tvalidation_0-auc:0.73772\n",
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      "[2]\tvalidation_0-auc:0.77865\n",
      "[2]\tvalidation_0-auc:0.82961\n",
      "[3]\tvalidation_0-auc:0.84375\n",
      "[3]\tvalidation_0-auc:0.79316\n",
      "[4]\tvalidation_0-auc:0.84412\n",
      "[4]\tvalidation_0-auc:0.79241\n",
      "[5]\tvalidation_0-auc:0.85231\n",
      "[5]\tvalidation_0-auc:0.78981\n",
      "[6]\tvalidation_0-auc:0.80134\n",
      "[6]\tvalidation_0-auc:0.84821\n",
      "[7]\tvalidation_0-auc:0.80134\n",
      "[7]\tvalidation_0-auc:0.86161\n",
      "[8]\tvalidation_0-auc:0.81324\n",
      "[8]\tvalidation_0-auc:0.84821\n",
      "[9]\tvalidation_0-auc:0.84301\n",
      "[9]\tvalidation_0-auc:0.80729\n",
      "[10]\tvalidation_0-auc:0.81176\n",
      "[10]\tvalidation_0-auc:0.84449\n",
      "[11]\tvalidation_0-auc:0.81176\n",
      "[11]\tvalidation_0-auc:0.84449\n",
      "[12]\tvalidation_0-auc:0.82515\n",
      "[13]\tvalidation_0-auc:0.81845\n",
      "[12]\tvalidation_0-auc:0.84673\n",
      "[14]\tvalidation_0-auc:0.82292\n",
      "[15]\tvalidation_0-auc:0.82441\n",
      "[16]\tvalidation_0-auc:0.82217\n",
      "[17]\tvalidation_0-auc:0.82812\n",
      "[18]\tvalidation_0-auc:0.82664\n",
      "[19]\tvalidation_0-auc:0.83259\n",
      "[20]\tvalidation_0-auc:0.83333\n",
      "[21]\tvalidation_0-auc:0.83259\n",
      "[22]\tvalidation_0-auc:0.83408\n",
      "[23]\tvalidation_0-auc:0.83631\n",
      "[24]\tvalidation_0-auc:0.83854\n",
      "[25]\tvalidation_0-auc:0.83929\n",
      "[26]\tvalidation_0-auc:0.83929\n",
      "[27]\tvalidation_0-auc:0.84226\n",
      "[28]\tvalidation_0-auc:0.84375\n",
      "[29]\tvalidation_0-auc:0.84226\n",
      "[30]\tvalidation_0-auc:0.84449\n",
      "[31]\tvalidation_0-auc:0.84449\n",
      "[32]\tvalidation_0-auc:0.84226\n",
      "[33]\tvalidation_0-auc:0.83854\n",
      "[34]\tvalidation_0-auc:0.83780\n",
      "[35]\tvalidation_0-auc:0.84003\n",
      "[0]\tvalidation_0-auc:0.76116\n",
      "[1]\tvalidation_0-auc:0.82961\n",
      "[2]\tvalidation_0-auc:0.86570\n",
      "[3]\tvalidation_0-auc:0.85342\n",
      "[4]\tvalidation_0-auc:0.84710\n",
      "[5]\tvalidation_0-auc:0.84226\n",
      "[6]\tvalidation_0-auc:0.83966\n",
      "[7]\tvalidation_0-auc:0.83929\n",
      "Tuned Paramerers ==>  {'n_estimators': 100}\n",
      "Best Score ==>  0.8291338646144357\n",
      "Accuracy on Train set ==>  0.8554095519171762\n",
      "Accuracy on Test1 set ==>  0.8656994047619048\n"
     ]
    }
   ],
   "source": [
    "param_test2 = {\n",
    " 'n_estimators':range(100,2000,25)\n",
    "}\n",
    "\n",
    "gsearch2 = GridSearchCV(estimator=XGBClassifier(learning_rate =0.1,\n",
    "                                                #n_estimators=100,\n",
    "                                                max_depth=3,\n",
    "                                                min_child_weight=5,\n",
    "                                                gamma=0,\n",
    "                                                subsample=0.8,\n",
    "                                                colsample_bytree=0.8,\n",
    "                                                objective= 'binary:logistic',\n",
    "                                                nthread=4,\n",
    "                                                scale_pos_weight=1,\n",
    "                                                seed=27,\n",
    "                                                use_label_encoder=False,  \n",
    "                                                eval_metric='auc'),\n",
    "                        param_grid = param_test2, \n",
    "                        scoring='roc_auc',\n",
    "                        n_jobs=4,\n",
    "                        cv=5)\n",
    "eval_set = [(X_test, y_test)]\n",
    "\n",
    "gsearch2.fit(X_train, y_train,early_stopping_rounds=5,eval_set=eval_set)\n",
    "print(\"Tuned Paramerers ==> \",gsearch2.best_params_)\n",
    "print(\"Best Score ==> \",gsearch2.best_score_)\n",
    "print(\"Accuracy on Train set ==> \", gsearch2.score(X_train,y_train))\n",
    "print(\"Accuracy on Test1 set ==> \", gsearch2.score(X_test,y_test))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "46335fe5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-02T16:10:50.864843Z",
     "iopub.status.busy": "2023-02-02T16:10:50.864088Z",
     "iopub.status.idle": "2023-02-02T16:11:03.478527Z",
     "shell.execute_reply": "2023-02-02T16:11:03.479145Z",
     "shell.execute_reply.started": "2023-01-29T05:22:45.523011Z"
    },
    "papermill": {
     "duration": 12.712642,
     "end_time": "2023-02-02T16:11:03.479530",
     "exception": false,
     "start_time": "2023-02-02T16:10:50.766888",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "[6]\tvalidation_0-auc:0.82180\n",
      "[11]\tvalidation_0-auc:0.83817\n",
      "[3]\tvalidation_0-auc:0.81176\n",
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      "[7]\tvalidation_0-auc:0.81957\n",
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      "[4]\tvalidation_0-auc:0.81287\n",
      "[8]\tvalidation_0-auc:0.82031\n",
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      "[18]\tvalidation_0-auc:0.84673\n",
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      "[19]\tvalidation_0-auc:0.85045\n",
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      "[15]\tvalidation_0-auc:0.84933\n",
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      "[10]\tvalidation_0-auc:0.81399\n",
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      "[21]\tvalidation_0-auc:0.84747\n",
      "[11]\tvalidation_0-auc:0.81473\n",
      "[22]\tvalidation_0-auc:0.84747\n",
      "[17]\tvalidation_0-auc:0.84933\n",
      "[18]\tvalidation_0-auc:0.84263\n",
      "[23]\tvalidation_0-auc:0.84673\n",
      "[19]\tvalidation_0-auc:0.84338\n",
      "[0]\tvalidation_0-auc:0.76339\n",
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      "[2]\tvalidation_0-auc:0.79204\n",
      "[3]\tvalidation_0-auc:0.80618\n",
      "[4]\tvalidation_0-auc:0.82254\n",
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      "[6]\tvalidation_0-auc:0.80580\n",
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      "[2]\tvalidation_0-auc:0.76860\n",
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      "[3]\tvalidation_0-auc:0.79502\n",
      "[5]\tvalidation_0-auc:0.79725\n",
      "[4]\tvalidation_0-auc:0.80208\n",
      "[6]\tvalidation_0-auc:0.82366\n",
      "[5]\tvalidation_0-auc:0.80059\n",
      "[7]\tvalidation_0-auc:0.81734\n",
      "[6]\tvalidation_0-auc:0.81250\n",
      "[7]\tvalidation_0-auc:0.80580\n",
      "[8]\tvalidation_0-auc:0.82850\n",
      "[9]\tvalidation_0-auc:0.83184\n",
      "[8]\tvalidation_0-auc:0.81622\n",
      "[10]\tvalidation_0-auc:0.83519\n",
      "[11]\tvalidation_0-auc:0.83891\n",
      "[12]\tvalidation_0-auc:0.83966\n",
      "[9]\tvalidation_0-auc:0.80766\n",
      "[13]\tvalidation_0-auc:0.84189\n",
      "[14]\tvalidation_0-auc:0.84189\n",
      "[10]\tvalidation_0-auc:0.81138\n",
      "[15]\tvalidation_0-auc:0.84338\n",
      "[16]\tvalidation_0-auc:0.84338\n",
      "[17]\tvalidation_0-auc:0.84040\n",
      "[18]\tvalidation_0-auc:0.84933\n",
      "[11]\tvalidation_0-auc:0.82106\n",
      "[19]\tvalidation_0-auc:0.84487\n",
      "[20]\tvalidation_0-auc:0.84561\n",
      "[21]\tvalidation_0-auc:0.84747\n",
      "[12]\tvalidation_0-auc:0.82106\n",
      "[13]\tvalidation_0-auc:0.82329\n",
      "[14]\tvalidation_0-auc:0.82812\n",
      "[22]\tvalidation_0-auc:0.84747\n",
      "[15]\tvalidation_0-auc:0.83110\n",
      "[23]\tvalidation_0-auc:0.84263\n",
      "[16]\tvalidation_0-auc:0.83110\n",
      "[17]\tvalidation_0-auc:0.83557\n",
      "[18]\tvalidation_0-auc:0.83631\n",
      "[19]\tvalidation_0-auc:0.84152\n",
      "[20]\tvalidation_0-auc:0.84598\n",
      "[21]\tvalidation_0-auc:0.84301\n",
      "[22]\tvalidation_0-auc:0.84747\n",
      "[23]\tvalidation_0-auc:0.84821\n",
      "[24]\tvalidation_0-auc:0.84673\n",
      "[25]\tvalidation_0-auc:0.84821\n",
      "[26]\tvalidation_0-auc:0.84747\n",
      "[27]\tvalidation_0-auc:0.85268\n",
      "[28]\tvalidation_0-auc:0.85491\n",
      "[29]\tvalidation_0-auc:0.84821\n",
      "[30]\tvalidation_0-auc:0.84970\n",
      "[31]\tvalidation_0-auc:0.85342\n",
      "[32]\tvalidation_0-auc:0.85342\n",
      "[33]\tvalidation_0-auc:0.85640\n",
      "[34]\tvalidation_0-auc:0.85566\n",
      "[35]\tvalidation_0-auc:0.85938\n",
      "[36]\tvalidation_0-auc:0.85640\n",
      "[37]\tvalidation_0-auc:0.85714\n",
      "[38]\tvalidation_0-auc:0.85417\n",
      "[39]\tvalidation_0-auc:0.85491\n",
      "[40]\tvalidation_0-auc:0.85640\n",
      "[0]\tvalidation_0-auc:0.59524\n",
      "[1]\tvalidation_0-auc:0.66109\n",
      "[2]\tvalidation_0-auc:0.79129\n",
      "[3]\tvalidation_0-auc:0.81734\n",
      "[4]\tvalidation_0-auc:0.83110\n",
      "[5]\tvalidation_0-auc:0.84747\n",
      "[6]\tvalidation_0-auc:0.84747\n",
      "[7]\tvalidation_0-auc:0.84933\n",
      "[8]\tvalidation_0-auc:0.84263\n",
      "[9]\tvalidation_0-auc:0.84710\n",
      "[10]\tvalidation_0-auc:0.83854\n",
      "[11]\tvalidation_0-auc:0.84449\n",
      "Tuned Paramerers ==>  {'colsample_bytree': 0.6, 'gamma': 0.0, 'subsample': 0.9}\n",
      "Best Score ==>  0.8295960634032831\n",
      "Accuracy on Train set ==>  0.8838275595530884\n",
      "Accuracy on Test set ==>  0.8493303571428571\n"
     ]
    }
   ],
   "source": [
    "param_test4 = {\n",
    " 'gamma':[i/10.0 for i in range(0,5)],\n",
    " 'subsample':[i/10.0 for i in range(6,10)],\n",
    " 'colsample_bytree':[i/10.0 for i in range(6,10)]\n",
    "}\n",
    "gsearch4 = GridSearchCV(estimator=XGBClassifier(learning_rate =0.1,\n",
    "                                                n_estimators=100,\n",
    "                                                max_depth=3,\n",
    "                                                min_child_weight=5,\n",
    "                                                #gamma=0,\n",
    "                                                #subsample=0.8,\n",
    "                                                #colsample_bytree=0.8,\n",
    "                                                objective= 'binary:logistic',\n",
    "                                                nthread=4,\n",
    "                                                scale_pos_weight=1,\n",
    "                                                seed=27,\n",
    "                                                use_label_encoder=False,  \n",
    "                                                eval_metric='auc'),\n",
    "                        param_grid = param_test4, \n",
    "                        scoring='roc_auc',\n",
    "                        n_jobs=4,\n",
    "                        cv=5)\n",
    "eval_set = [(X_test, y_test)]\n",
    "gsearch4.fit(X_train, y_train,early_stopping_rounds=5,eval_set=eval_set)\n",
    "print(\"Tuned Paramerers ==> \",gsearch4.best_params_)\n",
    "print(\"Best Score ==> \",gsearch4.best_score_)\n",
    "print(\"Accuracy on Train set ==> \", gsearch4.score(X_train,y_train))\n",
    "print(\"Accuracy on Test set ==> \", gsearch4.score(X_test,y_test))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "90699c6c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-02T16:11:04.164297Z",
     "iopub.status.busy": "2023-02-02T16:11:04.163477Z",
     "iopub.status.idle": "2023-02-02T16:11:06.923543Z",
     "shell.execute_reply": "2023-02-02T16:11:06.924081Z",
     "shell.execute_reply.started": "2023-01-29T05:23:43.969042Z"
    },
    "papermill": {
     "duration": 3.123741,
     "end_time": "2023-02-02T16:11:06.924250",
     "exception": false,
     "start_time": "2023-02-02T16:11:03.800509",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "[8]\tvalidation_0-auc:0.80469\n",
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      "[10]\tvalidation_0-auc:0.81324\n",
      "[11]\tvalidation_0-auc:0.82292\n",
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      "[12]\tvalidation_0-auc:0.82180\n",
      "[5]\tvalidation_0-auc:0.78981\n",
      "[13]\tvalidation_0-auc:0.80990\n",
      "[0]\tvalidation_0-auc:0.60677\n",
      "[6]\tvalidation_0-auc:0.80022\n",
      "[14]\tvalidation_0-auc:0.81287\n",
      "[1]\tvalidation_0-auc:0.75260\n",
      "[15]\tvalidation_0-auc:0.81882\n",
      "[2]\tvalidation_0-auc:0.75856\n",
      "[7]\tvalidation_0-auc:0.79836\n",
      "[3]\tvalidation_0-auc:0.81138\n",
      "[4]\tvalidation_0-auc:0.80878\n",
      "[8]\tvalidation_0-auc:0.80506\n",
      "[5]\tvalidation_0-auc:0.81250\n",
      "[9]\tvalidation_0-auc:0.80134\n",
      "[6]\tvalidation_0-auc:0.81250\n",
      "[7]\tvalidation_0-auc:0.80655\n",
      "[10]\tvalidation_0-auc:0.81213\n",
      "[0]\tvalidation_0-auc:0.60417\n",
      "[8]\tvalidation_0-auc:0.80878\n",
      "[1]\tvalidation_0-auc:0.77976\n",
      "[2]\tvalidation_0-auc:0.79836\n",
      "[3]\tvalidation_0-auc:0.83594\n",
      "[11]\tvalidation_0-auc:0.82180\n",
      "[4]\tvalidation_0-auc:0.81659\n",
      "[9]\tvalidation_0-auc:0.80655\n",
      "[5]\tvalidation_0-auc:0.82180\n",
      "[6]\tvalidation_0-auc:0.81064\n",
      "[10]\tvalidation_0-auc:0.80506\n",
      "[7]\tvalidation_0-auc:0.81994\n",
      "[12]\tvalidation_0-auc:0.82180\n",
      "[13]\tvalidation_0-auc:0.80990\n",
      "[14]\tvalidation_0-auc:0.81287\n",
      "[15]\tvalidation_0-auc:0.81882\n",
      "[16]\tvalidation_0-auc:0.81585\n",
      "[0]\tvalidation_0-auc:0.59524\n",
      "[1]\tvalidation_0-auc:0.66109\n",
      "[2]\tvalidation_0-auc:0.79129\n",
      "[3]\tvalidation_0-auc:0.81734\n",
      "[4]\tvalidation_0-auc:0.83110\n",
      "[5]\tvalidation_0-auc:0.84747\n",
      "[6]\tvalidation_0-auc:0.84747\n",
      "[7]\tvalidation_0-auc:0.84933\n",
      "[8]\tvalidation_0-auc:0.84263\n",
      "[9]\tvalidation_0-auc:0.84710\n",
      "[10]\tvalidation_0-auc:0.83854\n",
      "[11]\tvalidation_0-auc:0.84449\n",
      "[12]\tvalidation_0-auc:0.84301\n",
      "Tuned Paramerers ==>  {'nthread': 1}\n",
      "Best Score ==>  0.8295960634032831\n",
      "Accuracy on Train set ==>  0.8838275595530884\n",
      "Accuracy on Test set ==>  0.8493303571428571\n"
     ]
    }
   ],
   "source": [
    "param_test6 = {\n",
    " 'nthread':range(1,10,1),\n",
    "}\n",
    "gsearch6 = GridSearchCV(estimator=XGBClassifier(learning_rate =0.1,\n",
    "                                                n_estimators=100,\n",
    "                                                max_depth=3,\n",
    "                                                min_child_weight=5,\n",
    "                                                gamma=0.0,\n",
    "                                                subsample=0.9,\n",
    "                                                colsample_bytree=0.6,\n",
    "                                                objective= 'binary:logistic',\n",
    "                                                #nthread=4,\n",
    "                                                scale_pos_weight=1,\n",
    "                                                seed=27,\n",
    "                                                use_label_encoder=False,  \n",
    "                                                eval_metric='auc'),\n",
    "                        param_grid = param_test6, \n",
    "                        scoring='roc_auc',\n",
    "                        n_jobs=4,\n",
    "                        cv=5)\n",
    "eval_set = [(X_test, y_test)]\n",
    "gsearch6.fit(X_train, y_train,early_stopping_rounds=5,eval_set=eval_set)\n",
    "print(\"Tuned Paramerers ==> \",gsearch6.best_params_)\n",
    "print(\"Best Score ==> \",gsearch6.best_score_)\n",
    "print(\"Accuracy on Train set ==> \", gsearch6.score(X_train,y_train))\n",
    "print(\"Accuracy on Test set ==> \", gsearch6.score(X_test,y_test))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6dbd14bc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-02T16:11:07.169206Z",
     "iopub.status.busy": "2023-02-02T16:11:07.163386Z",
     "iopub.status.idle": "2023-02-02T16:11:07.669067Z",
     "shell.execute_reply": "2023-02-02T16:11:07.668429Z",
     "shell.execute_reply.started": "2023-01-29T05:25:06.383966Z"
    },
    "papermill": {
     "duration": 0.626382,
     "end_time": "2023-02-02T16:11:07.669242",
     "exception": false,
     "start_time": "2023-02-02T16:11:07.042860",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "[23]\tvalidation_0-auc:0.83557\n",
      "[24]\tvalidation_0-auc:0.83631\n",
      "[25]\tvalidation_0-auc:0.82961\n",
      "[0]\tvalidation_0-auc:0.50000\n",
      "[26]\tvalidation_0-auc:0.82961\n",
      "[0]\tvalidation_0-auc:0.56920\n",
      "[1]\tvalidation_0-auc:0.50000\n",
      "[2]\tvalidation_0-auc:0.50000\n",
      "[3]\tvalidation_0-auc:0.50000\n",
      "[1]\tvalidation_0-auc:0.74814\n",
      "[2]\tvalidation_0-auc:0.76786\n",
      "[3]\tvalidation_0-auc:0.81250\n",
      "[4]\tvalidation_0-auc:0.80990\n",
      "[5]\tvalidation_0-auc:0.80543\n",
      "[6]\tvalidation_0-auc:0.80357\n",
      "[4]\tvalidation_0-auc:0.50000\n",
      "[7]\tvalidation_0-auc:0.80655\n",
      "[8]\tvalidation_0-auc:0.80878\n",
      "[0]\tvalidation_0-auc:0.50000\n",
      "[1]\tvalidation_0-auc:0.50000\n",
      "[2]\tvalidation_0-auc:0.50000\n",
      "[3]\tvalidation_0-auc:0.50000\n",
      "[4]\tvalidation_0-auc:0.50000\n",
      "[0]\tvalidation_0-auc:0.59524\n",
      "[1]\tvalidation_0-auc:0.66109\n",
      "[2]\tvalidation_0-auc:0.79129\n",
      "[3]\tvalidation_0-auc:0.82403\n",
      "[4]\tvalidation_0-auc:0.83259\n",
      "[5]\tvalidation_0-auc:0.84821\n",
      "[6]\tvalidation_0-auc:0.84747\n",
      "[7]\tvalidation_0-auc:0.84784\n",
      "[8]\tvalidation_0-auc:0.83966\n",
      "[9]\tvalidation_0-auc:0.84784\n",
      "Tuned Paramerers ==>  {'reg_alpha': 0.1}\n",
      "Best Score ==>  0.8302640197232852\n",
      "Accuracy on Train set ==>  0.8797051038811983\n",
      "Accuracy on Test set ==>  0.8482142857142858\n"
     ]
    }
   ],
   "source": [
    "param_test7 = {\n",
    " 'reg_alpha':[1e-5, 1e-2, 0.1, 1, 100]\n",
    "}\n",
    "gsearch7 = GridSearchCV(estimator=XGBClassifier(learning_rate =0.1,\n",
    "                                                n_estimators=100,\n",
    "                                                max_depth=3,\n",
    "                                                min_child_weight=5,\n",
    "                                                gamma=0.0,\n",
    "                                                subsample=0.9,\n",
    "                                                colsample_bytree=0.6,\n",
    "                                                #reg_alpha=,\n",
    "                                                objective= 'binary:logistic',\n",
    "                                                nthread=1,\n",
    "                                                scale_pos_weight=1,\n",
    "                                                seed=27,\n",
    "                                                use_label_encoder=False,  \n",
    "                                                eval_metric='auc'),\n",
    "                        param_grid = param_test7, \n",
    "                        scoring='roc_auc',\n",
    "                        n_jobs=4,\n",
    "                        cv=5)\n",
    "eval_set = [(X_test, y_test)]\n",
    "gsearch7.fit(X_train, y_train,early_stopping_rounds=5,eval_set=eval_set)\n",
    "print(\"Tuned Paramerers ==> \",gsearch7.best_params_)\n",
    "print(\"Best Score ==> \",gsearch7.best_score_)\n",
    "print(\"Accuracy on Train set ==> \", gsearch7.score(X_train,y_train))\n",
    "print(\"Accuracy on Test set ==> \", gsearch7.score(X_test,y_test))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "75984a36",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-02T16:11:07.922334Z",
     "iopub.status.busy": "2023-02-02T16:11:07.921480Z",
     "iopub.status.idle": "2023-02-02T16:11:09.271042Z",
     "shell.execute_reply": "2023-02-02T16:11:09.271614Z",
     "shell.execute_reply.started": "2023-01-29T05:25:44.411981Z"
    },
    "papermill": {
     "duration": 1.477466,
     "end_time": "2023-02-02T16:11:09.271774",
     "exception": false,
     "start_time": "2023-02-02T16:11:07.794308",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "[6]\tvalidation_0-auc:0.79799\n",
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      "[7]\tvalidation_0-auc:0.80208\n",
      "[1]\tvalidation_0-auc:0.78125\n",
      "[2]\tvalidation_0-auc:0.79911\n",
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      "[4]\tvalidation_0-auc:0.80990\n",
      "[5]\tvalidation_0-auc:0.81808\n",
      "[9]\tvalidation_0-auc:0.80729\n",
      "[6]\tvalidation_0-auc:0.80915\n",
      "[7]\tvalidation_0-auc:0.81845\n",
      "[10]\tvalidation_0-auc:0.81510\n",
      "[8]\tvalidation_0-auc:0.81287\n",
      "[11]\tvalidation_0-auc:0.82106\n",
      "[12]\tvalidation_0-auc:0.82403\n",
      "[13]\tvalidation_0-auc:0.82329\n",
      "[14]\tvalidation_0-auc:0.82478\n",
      "[15]\tvalidation_0-auc:0.82924\n",
      "[16]\tvalidation_0-auc:0.82924\n",
      "[17]\tvalidation_0-auc:0.83073\n",
      "[18]\tvalidation_0-auc:0.83371\n",
      "[19]\tvalidation_0-auc:0.82850\n",
      "[20]\tvalidation_0-auc:0.83519\n",
      "[21]\tvalidation_0-auc:0.83594\n",
      "[22]\tvalidation_0-auc:0.83296\n",
      "[23]\tvalidation_0-auc:0.83371\n",
      "[24]\tvalidation_0-auc:0.82924\n",
      "[25]\tvalidation_0-auc:0.82627\n",
      "[26]\tvalidation_0-auc:0.82998\n",
      "[0]\tvalidation_0-auc:0.59524\n",
      "[1]\tvalidation_0-auc:0.66109\n",
      "[2]\tvalidation_0-auc:0.79278\n",
      "[3]\tvalidation_0-auc:0.81882\n",
      "[4]\tvalidation_0-auc:0.83110\n",
      "[5]\tvalidation_0-auc:0.84449\n",
      "[6]\tvalidation_0-auc:0.84896\n",
      "[7]\tvalidation_0-auc:0.84933\n",
      "[8]\tvalidation_0-auc:0.84189\n",
      "[9]\tvalidation_0-auc:0.84784\n",
      "[10]\tvalidation_0-auc:0.83929\n",
      "[11]\tvalidation_0-auc:0.84524\n",
      "[12]\tvalidation_0-auc:0.84226\n",
      "Tuned Paramerers ==>  {'learning_rate': 0.06}\n",
      "Best Score ==>  0.8275959880566888\n",
      "Accuracy on Train set ==>  0.8826622631344716\n",
      "Accuracy on Test1 set ==>  0.8493303571428572\n"
     ]
    }
   ],
   "source": [
    "param_test8 = {\n",
    " 'learning_rate':[i/100.0 for i in range(1,10)]\n",
    "}\n",
    "gsearch8 = GridSearchCV(estimator=XGBClassifier(#learning_rate =0.1,\n",
    "                                                n_estimators=100,\n",
    "                                                max_depth=3,\n",
    "                                                min_child_weight=5,\n",
    "                                                gamma=0.0,\n",
    "                                                subsample=0.9,\n",
    "                                                colsample_bytree=0.6,\n",
    "                                                reg_alpha= 0.1,\n",
    "                                                objective= 'binary:logistic',\n",
    "                                                nthread=4,\n",
    "                                                scale_pos_weight=1,\n",
    "                                                seed=27,\n",
    "                                                use_label_encoder=False,  \n",
    "                                                eval_metric='auc'),\n",
    "                        param_grid = param_test8, \n",
    "                        scoring='roc_auc',\n",
    "                        n_jobs=4,\n",
    "                        cv=5)\n",
    "eval_set = [(X_test, y_test)]\n",
    "gsearch8.fit(X_train, y_train,early_stopping_rounds=5,eval_set=eval_set)\n",
    "print(\"Tuned Paramerers ==> \",gsearch8.best_params_)\n",
    "print(\"Best Score ==> \",gsearch8.best_score_)\n",
    "print(\"Accuracy on Train set ==> \", gsearch8.score(X_train,y_train))\n",
    "print(\"Accuracy on Test1 set ==> \", gsearch8.score(X_test,y_test))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "8dbcb1b0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-02T16:11:09.539069Z",
     "iopub.status.busy": "2023-02-02T16:11:09.537741Z",
     "iopub.status.idle": "2023-02-02T16:11:15.425485Z",
     "shell.execute_reply": "2023-02-02T16:11:15.426665Z",
     "shell.execute_reply.started": "2023-01-29T05:26:23.637566Z"
    },
    "papermill": {
     "duration": 6.025882,
     "end_time": "2023-02-02T16:11:15.426869",
     "exception": false,
     "start_time": "2023-02-02T16:11:09.400987",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
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     "output_type": "stream",
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      "[11]\tvalidation_0-auc:0.81808\n",
      "[7]\tvalidation_0-auc:0.78497\n",
      "[8]\tvalidation_0-auc:0.77865\n",
      "[9]\tvalidation_0-auc:0.77790\n",
      "[10]\tvalidation_0-auc:0.77976\n",
      "[11]\tvalidation_0-auc:0.78683\n",
      "[0]\tvalidation_0-auc:0.60417\n",
      "[1]\tvalidation_0-auc:0.78571\n",
      "[2]\tvalidation_0-auc:0.80841\n",
      "[3]\tvalidation_0-auc:0.82292\n",
      "[4]\tvalidation_0-auc:0.83184\n",
      "[5]\tvalidation_0-auc:0.81734\n",
      "[6]\tvalidation_0-auc:0.81548\n",
      "[7]\tvalidation_0-auc:0.81101\n",
      "[8]\tvalidation_0-auc:0.81771\n",
      "[0]\tvalidation_0-auc:0.59524\n",
      "[1]\tvalidation_0-auc:0.66109\n",
      "[2]\tvalidation_0-auc:0.77232\n",
      "[3]\tvalidation_0-auc:0.80432\n",
      "[4]\tvalidation_0-auc:0.81548\n",
      "[5]\tvalidation_0-auc:0.83594\n",
      "[6]\tvalidation_0-auc:0.84115\n",
      "[7]\tvalidation_0-auc:0.84077\n",
      "[8]\tvalidation_0-auc:0.83929\n",
      "[9]\tvalidation_0-auc:0.83557\n",
      "[10]\tvalidation_0-auc:0.82961\n",
      "Tuned Paramerers ==>  {'learning_rate': 0.12}\n",
      "Best Score ==>  0.8319186659797861\n",
      "Accuracy on Train set ==>  0.8818307450870166\n",
      "Accuracy on Test1 set ==>  0.8411458333333334\n"
     ]
    }
   ],
   "source": [
    "param_test9 = {\n",
    " 'learning_rate':[i/100.0 for i in range(7,50)]\n",
    "}\n",
    "gsearch9 = GridSearchCV(estimator=XGBClassifier(#learning_rate =0.1,\n",
    "                                                n_estimators=100,\n",
    "                                                max_depth=3,\n",
    "                                                min_child_weight=5,\n",
    "                                                gamma=0.0,\n",
    "                                                subsample=0.9,\n",
    "                                                colsample_bytree=0.6,\n",
    "                                                reg_alpha= 0.1,\n",
    "                                                objective= 'binary:logistic',\n",
    "                                                nthread=4,\n",
    "                                                scale_pos_weight=1,\n",
    "                                                seed=27,\n",
    "                                                use_label_encoder=False,  \n",
    "                                                eval_metric='auc'),\n",
    "                        param_grid = param_test9, \n",
    "                        scoring='roc_auc',\n",
    "                        n_jobs=4,\n",
    "                        cv=5)\n",
    "eval_set = [(X_test, y_test)]\n",
    "gsearch9.fit(X_train, y_train,early_stopping_rounds=5,eval_set=eval_set)\n",
    "print(\"Tuned Paramerers ==> \",gsearch9.best_params_)\n",
    "print(\"Best Score ==> \",gsearch9.best_score_)\n",
    "print(\"Accuracy on Train set ==> \", gsearch9.score(X_train,y_train))\n",
    "print(\"Accuracy on Test1 set ==> \", gsearch9.score(X_test,y_test))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "dc21ddf5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-02T16:11:15.796591Z",
     "iopub.status.busy": "2023-02-02T16:11:15.795592Z",
     "iopub.status.idle": "2023-02-02T16:11:16.786679Z",
     "shell.execute_reply": "2023-02-02T16:11:16.787078Z",
     "shell.execute_reply.started": "2023-01-29T05:27:39.186774Z"
    },
    "papermill": {
     "duration": 1.141335,
     "end_time": "2023-02-02T16:11:16.787234",
     "exception": false,
     "start_time": "2023-02-02T16:11:15.645899",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "[10]\tvalidation_0-auc:0.84673\n",
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      "[6]\tvalidation_0-auc:0.82478\n",
      "[12]\tvalidation_0-auc:0.85268\n",
      "[7]\tvalidation_0-auc:0.82478\n",
      "[6]\tvalidation_0-auc:0.81399\n",
      "[8]\tvalidation_0-auc:0.82924\n",
      "[7]\tvalidation_0-auc:0.81548\n",
      "[9]\tvalidation_0-auc:0.82812\n",
      "[8]\tvalidation_0-auc:0.82589\n",
      "[10]\tvalidation_0-auc:0.83184\n",
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      "[13]\tvalidation_0-auc:0.83780\n",
      "[14]\tvalidation_0-auc:0.82664\n",
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      "[16]\tvalidation_0-auc:0.82738\n",
      "[17]\tvalidation_0-auc:0.83036\n",
      "[0]\tvalidation_0-auc:0.62723\n",
      "[0]\tvalidation_0-auc:0.62054\n",
      "[1]\tvalidation_0-auc:0.78013\n",
      "[1]\tvalidation_0-auc:0.77418\n",
      "[0]\tvalidation_0-auc:0.62872\n",
      "[2]\tvalidation_0-auc:0.77902\n",
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      "[3]\tvalidation_0-auc:0.81771\n",
      "[3]\tvalidation_0-auc:0.79055\n",
      "[2]\tvalidation_0-auc:0.78348\n",
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      "[0]\tvalidation_0-auc:0.61533\n",
      "[4]\tvalidation_0-auc:0.82701\n",
      "[1]\tvalidation_0-auc:0.73400\n",
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      "[5]\tvalidation_0-auc:0.83929\n",
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      "[6]\tvalidation_0-auc:0.82627\n",
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      "[7]\tvalidation_0-auc:0.84487\n",
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      "[6]\tvalidation_0-auc:0.80990\n",
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      "[1]\tvalidation_0-auc:0.72582\n",
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      "[0]\tvalidation_0-auc:0.62872\n",
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      "[6]\tvalidation_0-auc:0.82329\n",
      "[8]\tvalidation_0-auc:0.82589\n",
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      "[11]\tvalidation_0-auc:0.83631\n",
      "[6]\tvalidation_0-auc:0.80432\n",
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      "[7]\tvalidation_0-auc:0.80208\n",
      "[13]\tvalidation_0-auc:0.83966\n",
      "[14]\tvalidation_0-auc:0.83371\n",
      "[8]\tvalidation_0-auc:0.80804\n",
      "[15]\tvalidation_0-auc:0.83631\n",
      "[9]\tvalidation_0-auc:0.82887\n",
      "[16]\tvalidation_0-auc:0.83705\n",
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      "[10]\tvalidation_0-auc:0.81399\n",
      "[1]\tvalidation_0-auc:0.75893\n",
      "[11]\tvalidation_0-auc:0.83259\n",
      "[2]\tvalidation_0-auc:0.80990\n",
      "[12]\tvalidation_0-auc:0.82143\n",
      "[13]\tvalidation_0-auc:0.82366\n",
      "[3]\tvalidation_0-auc:0.80618\n",
      "[14]\tvalidation_0-auc:0.81548\n",
      "[15]\tvalidation_0-auc:0.81436\n",
      "[4]\tvalidation_0-auc:0.83110\n",
      "[16]\tvalidation_0-auc:0.81436\n",
      "[5]\tvalidation_0-auc:0.83184\n",
      "[6]\tvalidation_0-auc:0.82738\n",
      "[7]\tvalidation_0-auc:0.83333\n",
      "[8]\tvalidation_0-auc:0.83296\n",
      "[9]\tvalidation_0-auc:0.83073\n",
      "[10]\tvalidation_0-auc:0.83296\n",
      "[11]\tvalidation_0-auc:0.84040\n",
      "[12]\tvalidation_0-auc:0.83743\n",
      "[13]\tvalidation_0-auc:0.83445\n",
      "[14]\tvalidation_0-auc:0.82850\n",
      "[15]\tvalidation_0-auc:0.82627\n",
      "[16]\tvalidation_0-auc:0.82478\n",
      "[0]\tvalidation_0-auc:0.59524\n",
      "[1]\tvalidation_0-auc:0.66109\n",
      "[2]\tvalidation_0-auc:0.77232\n",
      "[3]\tvalidation_0-auc:0.80432\n",
      "[4]\tvalidation_0-auc:0.81548\n",
      "[5]\tvalidation_0-auc:0.83594\n",
      "[6]\tvalidation_0-auc:0.84115\n",
      "[7]\tvalidation_0-auc:0.84077\n",
      "[8]\tvalidation_0-auc:0.83929\n",
      "[9]\tvalidation_0-auc:0.83557\n",
      "[10]\tvalidation_0-auc:0.82961\n",
      "[11]\tvalidation_0-auc:0.83705\n",
      "Tuned Paramerers ==>  {'scale_pos_weight': 1}\n",
      "Best Score ==>  0.8319186659797861\n",
      "Accuracy on Train set ==>  0.8818307450870166\n",
      "Accuracy on Test set ==>  0.8411458333333334\n"
     ]
    }
   ],
   "source": [
    "param_test10 = {\n",
    " 'scale_pos_weight':range(1,8,1)\n",
    "}\n",
    "gsearch10 = GridSearchCV(estimator=XGBClassifier(learning_rate =0.12,\n",
    "                                                n_estimators=100,\n",
    "                                                max_depth=3,\n",
    "                                                min_child_weight=5,\n",
    "                                                gamma=0.0,\n",
    "                                                subsample=0.9,\n",
    "                                                colsample_bytree=0.6,\n",
    "                                                reg_alpha= 0.1,\n",
    "                                                objective= 'binary:logistic',\n",
    "                                                nthread=4,\n",
    "                                                #scale_pos_weight=1,\n",
    "                                                seed=27,\n",
    "                                                use_label_encoder=False,  \n",
    "                                                eval_metric='auc'),\n",
    "                        param_grid = param_test10, \n",
    "                        scoring='roc_auc',\n",
    "                        n_jobs=4,\n",
    "                        cv=5)\n",
    "eval_set = [(X_test, y_test)]\n",
    "gsearch10.fit(X_train, y_train,early_stopping_rounds=5,eval_set=eval_set)\n",
    "print(\"Tuned Paramerers ==> \",gsearch10.best_params_)\n",
    "print(\"Best Score ==> \",gsearch10.best_score_)\n",
    "print(\"Accuracy on Train set ==> \", gsearch10.score(X_train,y_train))\n",
    "print(\"Accuracy on Test set ==> \", gsearch10.score(X_test,y_test))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9701da9c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-02T16:11:17.109514Z",
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     "shell.execute_reply": "2023-02-02T16:11:17.177546Z",
     "shell.execute_reply.started": "2023-01-29T10:03:42.678901Z"
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    "papermill": {
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     "exception": false,
     "start_time": "2023-02-02T16:11:16.954553",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\tvalidation_0-auc:0.59524\n",
      "[1]\tvalidation_0-auc:0.66109\n",
      "[2]\tvalidation_0-auc:0.78460\n",
      "[3]\tvalidation_0-auc:0.82403\n",
      "[4]\tvalidation_0-auc:0.82664\n",
      "[5]\tvalidation_0-auc:0.84115\n",
      "[6]\tvalidation_0-auc:0.84115\n",
      "[7]\tvalidation_0-auc:0.84115\n",
      "[8]\tvalidation_0-auc:0.83519\n",
      "[9]\tvalidation_0-auc:0.84263\n",
      "[10]\tvalidation_0-auc:0.83929\n",
      "[11]\tvalidation_0-auc:0.84152\n",
      "[12]\tvalidation_0-auc:0.84598\n",
      "[13]\tvalidation_0-auc:0.84003\n",
      "[14]\tvalidation_0-auc:0.83408\n",
      "[15]\tvalidation_0-auc:0.83780\n",
      "[16]\tvalidation_0-auc:0.83929\n",
      "[17]\tvalidation_0-auc:0.84301\n",
      "\n",
      "Model Report\n",
      "Accuracy (Train): 80.75 %\n",
      "Accuracy (Test): 81.58 %\n",
      "AUC Score (Train): 89.01 %\n",
      "AUC Score (Test): 84.6 %\n"
     ]
    }
   ],
   "source": [
    "xgb =XGBClassifier(learning_rate =0.12,\n",
    "                   n_estimators=100,\n",
    "                   max_depth=3,\n",
    "                   min_child_weight=5,\n",
    "                   gamma=0.0,\n",
    "                   subsample=0.9,\n",
    "                   colsample_bytree=0.6,\n",
    "                   objective= 'binary:logistic',\n",
    "                   nthread=4,\n",
    "                   scale_pos_weight=1,\n",
    "                   seed=27,\n",
    "                   use_label_encoder=False,  \n",
    "                   eval_metric='auc')\n",
    "eval_set = [(X_test, y_test)]\n",
    "\n",
    "xgb.fit(X_train, y_train,early_stopping_rounds=5,eval_set=eval_set)\n",
    "\n",
    "print(\"\\nModel Report\")\n",
    "y_predsx = xgb.predict(X_test)\n",
    "y_predx_proba = xgb.predict_proba(X_test)[:,1]\n",
    "\n",
    "\n",
    "y_preds1x = xgb.predict(X_train)\n",
    "y_predx_proba1 = xgb.predict_proba(X_train)[:,1]\n",
    "\n",
    "print('Accuracy (Train):', round(metrics.accuracy_score(y_train, y_preds1x) * 100,2),'%')\n",
    "print('Accuracy (Test):', round(metrics.accuracy_score(y_test, y_predsx) * 100,2),'%')\n",
    "print('AUC Score (Train):', round(metrics.roc_auc_score(y_train, y_predx_proba1) * 100,2),'%')\n",
    "print('AUC Score (Test):', round(metrics.roc_auc_score(y_test, y_predx_proba) * 100,2),'%')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8f7f3619",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-02T16:11:17.555532Z",
     "iopub.status.busy": "2023-02-02T16:11:17.554378Z",
     "iopub.status.idle": "2023-02-02T16:11:17.772811Z",
     "shell.execute_reply": "2023-02-02T16:11:17.772310Z",
     "shell.execute_reply.started": "2023-01-29T06:18:26.560820Z"
    },
    "papermill": {
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     "end_time": "2023-02-02T16:11:17.772948",
     "exception": false,
     "start_time": "2023-02-02T16:11:17.329689",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, roc_auc_score\n",
    "y_score1 = xgb1.predict_proba(X_test)[:,1]\n",
    "fpr1, tpr1, th1 = roc_curve(y_test, y_score1)\n",
    "y_score2 = xgb.predict_proba(X_test)[:,1]\n",
    "fpr2, tpr2, th2 = roc_curve(y_test, y_score2)\n",
    "#print('roc_auc_score before tuning: ', roc_auc_score(y_test, y_score2))  \n",
    "#print('roc_auc_score after tuning: ', roc_auc_score(y_test, y_score1))  \n",
    "roc_auc1 = roc_auc_score(y_test, y_score1)\n",
    "roc_auc2 = roc_auc_score(y_test, y_score2)\n",
    "plt.subplots(1, figsize=(6,5))\n",
    "plt.plot(fpr1, tpr1, 'c', label = 'AUC (w/o Tuning) = %0.2f' % roc_auc1)\n",
    "plt.plot(fpr2, tpr2, 'b', label = 'AUC (with Tuning) = %0.2f' % roc_auc2)\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.plot([0, 1], ls=\"--\")\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c5c7c4fb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-02T16:11:18.088046Z",
     "iopub.status.busy": "2023-02-02T16:11:18.086151Z",
     "iopub.status.idle": "2023-02-02T16:11:18.461632Z",
     "shell.execute_reply": "2023-02-02T16:11:18.462054Z",
     "shell.execute_reply.started": "2023-01-29T10:03:21.164335Z"
    },
    "papermill": {
     "duration": 0.535705,
     "end_time": "2023-02-02T16:11:18.462233",
     "exception": false,
     "start_time": "2023-02-02T16:11:17.926528",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, roc_auc_score\n",
    "y_score1 = xgb1.predict_proba(X_train)[:,1]\n",
    "fpr1, tpr1, th1 = roc_curve(y_train, y_score1)\n",
    "y_score2 = xgb1.predict_proba(X_test)[:,1]\n",
    "fpr2, tpr2, th2 = roc_curve(y_test, y_score2)\n",
    "#print('roc_auc_score before tuning: ', roc_auc_score(y_test, y_score2))  \n",
    "#print('roc_auc_score after tuning: ', roc_auc_score(y_test, y_score1))  \n",
    "roc_auc1 = roc_auc_score(y_train, y_score1)\n",
    "roc_auc2 = roc_auc_score(y_test, y_score2)\n",
    "plt.subplots(1, figsize=(6,5))\n",
    "plt.plot(fpr1, tpr1, 'c', label = 'AUC (Training) = %0.2f' % roc_auc1)\n",
    "plt.plot(fpr2, tpr2, 'b', label = 'AUC (Testing) = %0.2f' % roc_auc2)\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.plot([0, 1], ls=\"--\")\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.title(\"Before Tuning\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "0f856dfd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-02T16:11:18.770461Z",
     "iopub.status.busy": "2023-02-02T16:11:18.769670Z",
     "iopub.status.idle": "2023-02-02T16:11:18.979303Z",
     "shell.execute_reply": "2023-02-02T16:11:18.978301Z",
     "shell.execute_reply.started": "2023-01-29T10:03:47.254081Z"
    },
    "papermill": {
     "duration": 0.364485,
     "end_time": "2023-02-02T16:11:18.979435",
     "exception": false,
     "start_time": "2023-02-02T16:11:18.614950",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, roc_auc_score\n",
    "y_score1 = xgb.predict_proba(X_train)[:,1]\n",
    "fpr1, tpr1, th1 = roc_curve(y_train, y_score1)\n",
    "y_score2 = xgb.predict_proba(X_test)[:,1]\n",
    "fpr2, tpr2, th2 = roc_curve(y_test, y_score2)\n",
    "#print('roc_auc_score before tuning: ', roc_auc_score(y_test, y_score2))  \n",
    "#print('roc_auc_score after tuning: ', roc_auc_score(y_test, y_score1))  \n",
    "roc_auc1 = roc_auc_score(y_train, y_score1)\n",
    "roc_auc2 = roc_auc_score(y_test, y_score2)\n",
    "plt.subplots(1, figsize=(6,5))\n",
    "plt.plot(fpr1, tpr1, 'c', label = 'AUC (Training) = %0.2f' % roc_auc1)\n",
    "plt.plot(fpr2, tpr2, 'b', label = 'AUC (Testing) = %0.2f' % roc_auc2)\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.plot([0, 1], ls=\"--\")\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.title(\"After Tuning\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7fa60373",
   "metadata": {
    "papermill": {
     "duration": 0.152773,
     "end_time": "2023-02-02T16:11:19.284977",
     "exception": false,
     "start_time": "2023-02-02T16:11:19.132204",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.12"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 70.95525,
   "end_time": "2023-02-02T16:11:20.849659",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2023-02-02T16:10:09.894409",
   "version": "2.3.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}