03 Santander_Customer_Satisfaction.ipynb

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     "text": [
      "/kaggle/input/santander-customer-satisfaction/sample_submission.csv\n",
      "/kaggle/input/santander-customer-satisfaction/train.csv\n",
      "/kaggle/input/santander-customer-satisfaction/test.csv\n"
     ]
    }
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   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
   ]
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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import missingno as msno\n",
    "import plotly.express as px\n",
    "\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV, cross_val_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, roc_auc_score\n",
    "from sklearn.metrics import f1_score, confusion_matrix, precision_recall_curve, roc_curve\n",
    "import lightgbm as lgb\n",
    "from lightgbm import plot_importance\n",
    "import xgboost as xgb\n",
    "\n",
    "\n",
    "from imblearn.under_sampling import TomekLinks\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "from imblearn.over_sampling import SMOTE\n",
    "\n",
    "\n",
    "plt.style.use('seaborn')\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "%matplotlib inline"
   ]
  },
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       "\n",
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       "   saldo_medio_var44_ult1  saldo_medio_var44_ult3          var38  TARGET  \n",
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       "[5 rows x 371 columns]"
      ]
     },
     "execution_count": 3,
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   ],
   "source": [
    "train_df = pd.read_csv('/kaggle/input/santander-customer-satisfaction/train.csv')\n",
    "test_df = pd.read_csv('/kaggle/input/santander-customer-satisfaction/test.csv')\n",
    "train_df.head()"
   ]
  },
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   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "Train Data Shape :  (76020, 371)\n",
      "Test Data Shape :  (75818, 370)\n"
     ]
    }
   ],
   "source": [
    "print(\"Train Data Shape : \",train_df.shape)\n",
    "print(\"Test Data Shape : \",test_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "organized-mistake",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 76020 entries, 0 to 76019\n",
      "Columns: 371 entries, ID to TARGET\n",
      "dtypes: float64(111), int64(260)\n",
      "memory usage: 215.2 MB\n"
     ]
    }
   ],
   "source": [
    "train_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "micro-samuel",
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       "      <td>76020.000000</td>\n",
       "      <td>76020.000000</td>\n",
       "      <td>76020.000000</td>\n",
       "      <td>76020.000000</td>\n",
       "      <td>76020.000000</td>\n",
       "      <td>76020.000000</td>\n",
       "      <td>76020.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>76020.000000</td>\n",
       "      <td>76020.000000</td>\n",
       "      <td>76020.000000</td>\n",
       "      <td>76020.000000</td>\n",
       "      <td>76020.000000</td>\n",
       "      <td>76020.000000</td>\n",
       "      <td>76020.000000</td>\n",
       "      <td>76020.000000</td>\n",
       "      <td>7.602000e+04</td>\n",
       "      <td>76020.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>75964.050723</td>\n",
       "      <td>-1523.199277</td>\n",
       "      <td>33.212865</td>\n",
       "      <td>86.208265</td>\n",
       "      <td>72.363067</td>\n",
       "      <td>119.529632</td>\n",
       "      <td>3.559130</td>\n",
       "      <td>6.472698</td>\n",
       "      <td>0.412946</td>\n",
       "      <td>0.567352</td>\n",
       "      <td>...</td>\n",
       "      <td>7.935824</td>\n",
       "      <td>1.365146</td>\n",
       "      <td>12.215580</td>\n",
       "      <td>8.784074</td>\n",
       "      <td>31.505324</td>\n",
       "      <td>1.858575</td>\n",
       "      <td>76.026165</td>\n",
       "      <td>56.614351</td>\n",
       "      <td>1.172358e+05</td>\n",
       "      <td>0.039569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>43781.947379</td>\n",
       "      <td>39033.462364</td>\n",
       "      <td>12.956486</td>\n",
       "      <td>1614.757313</td>\n",
       "      <td>339.315831</td>\n",
       "      <td>546.266294</td>\n",
       "      <td>93.155749</td>\n",
       "      <td>153.737066</td>\n",
       "      <td>30.604864</td>\n",
       "      <td>36.513513</td>\n",
       "      <td>...</td>\n",
       "      <td>455.887218</td>\n",
       "      <td>113.959637</td>\n",
       "      <td>783.207399</td>\n",
       "      <td>538.439211</td>\n",
       "      <td>2013.125393</td>\n",
       "      <td>147.786584</td>\n",
       "      <td>4040.337842</td>\n",
       "      <td>2852.579397</td>\n",
       "      <td>1.826646e+05</td>\n",
       "      <td>0.194945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-999999.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.163750e+03</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>38104.750000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.787061e+04</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>76043.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.064092e+05</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>113748.750000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.187563e+05</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>151838.000000</td>\n",
       "      <td>238.000000</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>210000.000000</td>\n",
       "      <td>12888.030000</td>\n",
       "      <td>21024.810000</td>\n",
       "      <td>8237.820000</td>\n",
       "      <td>11073.570000</td>\n",
       "      <td>6600.000000</td>\n",
       "      <td>6600.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>50003.880000</td>\n",
       "      <td>20385.720000</td>\n",
       "      <td>138831.630000</td>\n",
       "      <td>91778.730000</td>\n",
       "      <td>438329.220000</td>\n",
       "      <td>24650.010000</td>\n",
       "      <td>681462.900000</td>\n",
       "      <td>397884.300000</td>\n",
       "      <td>2.203474e+07</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 371 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  ID           var3         var15  imp_ent_var16_ult1  \\\n",
       "count   76020.000000   76020.000000  76020.000000        76020.000000   \n",
       "mean    75964.050723   -1523.199277     33.212865           86.208265   \n",
       "std     43781.947379   39033.462364     12.956486         1614.757313   \n",
       "min         1.000000 -999999.000000      5.000000            0.000000   \n",
       "25%     38104.750000       2.000000     23.000000            0.000000   \n",
       "50%     76043.000000       2.000000     28.000000            0.000000   \n",
       "75%    113748.750000       2.000000     40.000000            0.000000   \n",
       "max    151838.000000     238.000000    105.000000       210000.000000   \n",
       "\n",
       "       imp_op_var39_comer_ult1  imp_op_var39_comer_ult3  \\\n",
       "count             76020.000000             76020.000000   \n",
       "mean                 72.363067               119.529632   \n",
       "std                 339.315831               546.266294   \n",
       "min                   0.000000                 0.000000   \n",
       "25%                   0.000000                 0.000000   \n",
       "50%                   0.000000                 0.000000   \n",
       "75%                   0.000000                 0.000000   \n",
       "max               12888.030000             21024.810000   \n",
       "\n",
       "       imp_op_var40_comer_ult1  imp_op_var40_comer_ult3  \\\n",
       "count             76020.000000             76020.000000   \n",
       "mean                  3.559130                 6.472698   \n",
       "std                  93.155749               153.737066   \n",
       "min                   0.000000                 0.000000   \n",
       "25%                   0.000000                 0.000000   \n",
       "50%                   0.000000                 0.000000   \n",
       "75%                   0.000000                 0.000000   \n",
       "max                8237.820000             11073.570000   \n",
       "\n",
       "       imp_op_var40_efect_ult1  imp_op_var40_efect_ult3  ...  \\\n",
       "count             76020.000000             76020.000000  ...   \n",
       "mean                  0.412946                 0.567352  ...   \n",
       "std                  30.604864                36.513513  ...   \n",
       "min                   0.000000                 0.000000  ...   \n",
       "25%                   0.000000                 0.000000  ...   \n",
       "50%                   0.000000                 0.000000  ...   \n",
       "75%                   0.000000                 0.000000  ...   \n",
       "max                6600.000000              6600.000000  ...   \n",
       "\n",
       "       saldo_medio_var33_hace2  saldo_medio_var33_hace3  \\\n",
       "count             76020.000000             76020.000000   \n",
       "mean                  7.935824                 1.365146   \n",
       "std                 455.887218               113.959637   \n",
       "min                   0.000000                 0.000000   \n",
       "25%                   0.000000                 0.000000   \n",
       "50%                   0.000000                 0.000000   \n",
       "75%                   0.000000                 0.000000   \n",
       "max               50003.880000             20385.720000   \n",
       "\n",
       "       saldo_medio_var33_ult1  saldo_medio_var33_ult3  \\\n",
       "count            76020.000000            76020.000000   \n",
       "mean                12.215580                8.784074   \n",
       "std                783.207399              538.439211   \n",
       "min                  0.000000                0.000000   \n",
       "25%                  0.000000                0.000000   \n",
       "50%                  0.000000                0.000000   \n",
       "75%                  0.000000                0.000000   \n",
       "max             138831.630000            91778.730000   \n",
       "\n",
       "       saldo_medio_var44_hace2  saldo_medio_var44_hace3  \\\n",
       "count             76020.000000             76020.000000   \n",
       "mean                 31.505324                 1.858575   \n",
       "std                2013.125393               147.786584   \n",
       "min                   0.000000                 0.000000   \n",
       "25%                   0.000000                 0.000000   \n",
       "50%                   0.000000                 0.000000   \n",
       "75%                   0.000000                 0.000000   \n",
       "max              438329.220000             24650.010000   \n",
       "\n",
       "       saldo_medio_var44_ult1  saldo_medio_var44_ult3         var38  \\\n",
       "count            76020.000000            76020.000000  7.602000e+04   \n",
       "mean                76.026165               56.614351  1.172358e+05   \n",
       "std               4040.337842             2852.579397  1.826646e+05   \n",
       "min                  0.000000                0.000000  5.163750e+03   \n",
       "25%                  0.000000                0.000000  6.787061e+04   \n",
       "50%                  0.000000                0.000000  1.064092e+05   \n",
       "75%                  0.000000                0.000000  1.187563e+05   \n",
       "max             681462.900000           397884.300000  2.203474e+07   \n",
       "\n",
       "             TARGET  \n",
       "count  76020.000000  \n",
       "mean       0.039569  \n",
       "std        0.194945  \n",
       "min        0.000000  \n",
       "25%        0.000000  \n",
       "50%        0.000000  \n",
       "75%        0.000000  \n",
       "max        1.000000  \n",
       "\n",
       "[8 rows x 371 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "differential-employer",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-10T09:30:10.424731Z",
     "iopub.status.busy": "2021-04-10T09:30:10.422820Z",
     "iopub.status.idle": "2021-04-10T09:30:10.425415Z",
     "shell.execute_reply": "2021-04-10T09:30:10.425819Z"
    },
    "papermill": {
     "duration": 0.113231,
     "end_time": "2021-04-10T09:30:10.426014",
     "exception": false,
     "start_time": "2021-04-10T09:30:10.312783",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "features = train_df.drop(['ID','TARGET'],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "studied-denial",
   "metadata": {
    "papermill": {
     "duration": 0.033393,
     "end_time": "2021-04-10T09:30:10.493222",
     "exception": false,
     "start_time": "2021-04-10T09:30:10.459829",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# EDA\n",
    "* Target Percent\n",
    "* Check Multicollinearity\n",
    "* Check Outlier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "gross-elder",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-10T09:30:10.564726Z",
     "iopub.status.busy": "2021-04-10T09:30:10.563609Z",
     "iopub.status.idle": "2021-04-10T09:30:10.574704Z",
     "shell.execute_reply": "2021-04-10T09:30:10.574163Z"
    },
    "papermill": {
     "duration": 0.048701,
     "end_time": "2021-04-10T09:30:10.574875",
     "exception": false,
     "start_time": "2021-04-10T09:30:10.526174",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TARGET</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>73012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3008</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   TARGET\n",
       "0   73012\n",
       "1    3008"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(train_df['TARGET'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "refined-germany",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-10T09:30:10.661950Z",
     "iopub.status.busy": "2021-04-10T09:30:10.650205Z",
     "iopub.status.idle": "2021-04-10T09:30:10.960179Z",
     "shell.execute_reply": "2021-04-10T09:30:10.959645Z"
    },
    "papermill": {
     "duration": 0.351011,
     "end_time": "2021-04-10T09:30:10.960347",
     "exception": false,
     "start_time": "2021-04-10T09:30:10.609336",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(1,2,figsize=(10,4))\n",
    "train_df['TARGET'].value_counts().plot.pie(\n",
    "    explode=[0,0.1],autopct='%1.1f%%',ax=ax[0],shadow=True\n",
    ")\n",
    "sns.countplot('TARGET', data=train_df, ax=ax[1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "wicked-marketing",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-10T09:30:11.038954Z",
     "iopub.status.busy": "2021-04-10T09:30:11.037745Z",
     "iopub.status.idle": "2021-04-10T09:30:11.169433Z",
     "shell.execute_reply": "2021-04-10T09:30:11.169937Z"
    },
    "papermill": {
     "duration": 0.17417,
     "end_time": "2021-04-10T09:30:11.170170",
     "exception": false,
     "start_time": "2021-04-10T09:30:10.996000",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Null values</th>\n",
       "      <th>Percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TARGET</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_op_var41_ult1</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_var26</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_var25_0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_var25</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>imp_trasp_var33_out_hace3</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>imp_trasp_var33_out_ult1</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>imp_venta_var44_hace3</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>imp_venta_var44_ult1</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>371 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Null values  Percent\n",
       "TARGET                               0      0.0\n",
       "num_op_var41_ult1                    0      0.0\n",
       "num_var26                            0      0.0\n",
       "num_var25_0                          0      0.0\n",
       "num_var25                            0      0.0\n",
       "...                                ...      ...\n",
       "imp_trasp_var33_out_hace3            0      0.0\n",
       "imp_trasp_var33_out_ult1             0      0.0\n",
       "imp_venta_var44_hace3                0      0.0\n",
       "imp_venta_var44_ult1                 0      0.0\n",
       "ID                                   0      0.0\n",
       "\n",
       "[371 rows x 2 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "null_value = train_df.isnull().sum().sort_values(ascending=False)\n",
    "null_percent = round(train_df.isnull().sum().sort_values(ascending=False)/len(train_df)*100,2)\n",
    "pd.concat([null_value, null_percent], axis=1, keys=['Null values', 'Percent'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "passing-marriage",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-10T09:30:11.257537Z",
     "iopub.status.busy": "2021-04-10T09:30:11.256841Z",
     "iopub.status.idle": "2021-04-10T09:30:11.287243Z",
     "shell.execute_reply": "2021-04-10T09:30:11.287732Z"
    },
    "papermill": {
     "duration": 0.07645,
     "end_time": "2021-04-10T09:30:11.287892",
     "exception": false,
     "start_time": "2021-04-10T09:30:11.211442",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>var3</th>\n",
       "      <th>var15</th>\n",
       "      <th>imp_ent_var16_ult1</th>\n",
       "      <th>imp_op_var39_comer_ult1</th>\n",
       "      <th>imp_op_var39_comer_ult3</th>\n",
       "      <th>imp_op_var40_comer_ult1</th>\n",
       "      <th>imp_op_var40_comer_ult3</th>\n",
       "      <th>imp_op_var40_efect_ult1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>var3</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.004452</td>\n",
       "      <td>0.001868</td>\n",
       "      <td>0.005989</td>\n",
       "      <td>0.006817</td>\n",
       "      <td>0.001518</td>\n",
       "      <td>0.001690</td>\n",
       "      <td>0.000530</td>\n",
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       "    <tr>\n",
       "      <th>var15</th>\n",
       "      <td>-0.004452</td>\n",
       "      <td>1.000000</td>\n",
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       "      <td>0.094762</td>\n",
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       "      <td>0.042754</td>\n",
       "      <td>0.048512</td>\n",
       "      <td>0.008805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>imp_ent_var16_ult1</th>\n",
       "      <td>0.001868</td>\n",
       "      <td>0.043722</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.041221</td>\n",
       "      <td>0.034879</td>\n",
       "      <td>0.009896</td>\n",
       "      <td>0.009377</td>\n",
       "      <td>0.000592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>imp_op_var39_comer_ult1</th>\n",
       "      <td>0.005989</td>\n",
       "      <td>0.094762</td>\n",
       "      <td>0.041221</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.886476</td>\n",
       "      <td>0.342709</td>\n",
       "      <td>0.295295</td>\n",
       "      <td>0.032280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>imp_op_var39_comer_ult3</th>\n",
       "      <td>0.006817</td>\n",
       "      <td>0.101177</td>\n",
       "      <td>0.034879</td>\n",
       "      <td>0.886476</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.316671</td>\n",
       "      <td>0.355670</td>\n",
       "      <td>0.029094</td>\n",
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       "    <tr>\n",
       "      <th>imp_op_var40_comer_ult1</th>\n",
       "      <td>0.001518</td>\n",
       "      <td>0.042754</td>\n",
       "      <td>0.009896</td>\n",
       "      <td>0.342709</td>\n",
       "      <td>0.316671</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.852206</td>\n",
       "      <td>0.061187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>imp_op_var40_comer_ult3</th>\n",
       "      <td>0.001690</td>\n",
       "      <td>0.048512</td>\n",
       "      <td>0.009377</td>\n",
       "      <td>0.295295</td>\n",
       "      <td>0.355670</td>\n",
       "      <td>0.852206</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.053579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>imp_op_var40_efect_ult1</th>\n",
       "      <td>0.000530</td>\n",
       "      <td>0.008805</td>\n",
       "      <td>0.000592</td>\n",
       "      <td>0.032280</td>\n",
       "      <td>0.029094</td>\n",
       "      <td>0.061187</td>\n",
       "      <td>0.053579</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             var3     var15  imp_ent_var16_ult1  \\\n",
       "var3                     1.000000 -0.004452            0.001868   \n",
       "var15                   -0.004452  1.000000            0.043722   \n",
       "imp_ent_var16_ult1       0.001868  0.043722            1.000000   \n",
       "imp_op_var39_comer_ult1  0.005989  0.094762            0.041221   \n",
       "imp_op_var39_comer_ult3  0.006817  0.101177            0.034879   \n",
       "imp_op_var40_comer_ult1  0.001518  0.042754            0.009896   \n",
       "imp_op_var40_comer_ult3  0.001690  0.048512            0.009377   \n",
       "imp_op_var40_efect_ult1  0.000530  0.008805            0.000592   \n",
       "\n",
       "                         imp_op_var39_comer_ult1  imp_op_var39_comer_ult3  \\\n",
       "var3                                    0.005989                 0.006817   \n",
       "var15                                   0.094762                 0.101177   \n",
       "imp_ent_var16_ult1                      0.041221                 0.034879   \n",
       "imp_op_var39_comer_ult1                 1.000000                 0.886476   \n",
       "imp_op_var39_comer_ult3                 0.886476                 1.000000   \n",
       "imp_op_var40_comer_ult1                 0.342709                 0.316671   \n",
       "imp_op_var40_comer_ult3                 0.295295                 0.355670   \n",
       "imp_op_var40_efect_ult1                 0.032280                 0.029094   \n",
       "\n",
       "                         imp_op_var40_comer_ult1  imp_op_var40_comer_ult3  \\\n",
       "var3                                    0.001518                 0.001690   \n",
       "var15                                   0.042754                 0.048512   \n",
       "imp_ent_var16_ult1                      0.009896                 0.009377   \n",
       "imp_op_var39_comer_ult1                 0.342709                 0.295295   \n",
       "imp_op_var39_comer_ult3                 0.316671                 0.355670   \n",
       "imp_op_var40_comer_ult1                 1.000000                 0.852206   \n",
       "imp_op_var40_comer_ult3                 0.852206                 1.000000   \n",
       "imp_op_var40_efect_ult1                 0.061187                 0.053579   \n",
       "\n",
       "                         imp_op_var40_efect_ult1  \n",
       "var3                                    0.000530  \n",
       "var15                                   0.008805  \n",
       "imp_ent_var16_ult1                      0.000592  \n",
       "imp_op_var39_comer_ult1                 0.032280  \n",
       "imp_op_var39_comer_ult3                 0.029094  \n",
       "imp_op_var40_comer_ult1                 0.061187  \n",
       "imp_op_var40_comer_ult3                 0.053579  \n",
       "imp_op_var40_efect_ult1                 1.000000  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features[features.columns[:8]].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "selected-pixel",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-10T09:30:11.374340Z",
     "iopub.status.busy": "2021-04-10T09:30:11.373115Z",
     "iopub.status.idle": "2021-04-10T09:30:12.063179Z",
     "shell.execute_reply": "2021-04-10T09:30:12.063689Z"
    },
    "papermill": {
     "duration": 0.736357,
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     "exception": false,
     "start_time": "2021-04-10T09:30:11.327515",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(features[features.columns[:8]].corr(),annot=True,cmap='YlGnBu')\n",
    "fig=plt.gcf()\n",
    "fig.set_size_inches(10,8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dying-microphone",
   "metadata": {
    "papermill": {
     "duration": 0.036253,
     "end_time": "2021-04-10T09:30:12.138938",
     "exception": false,
     "start_time": "2021-04-10T09:30:12.102685",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "=> We Can Check Multicollinearity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "surprising-choir",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-10T09:30:12.234856Z",
     "iopub.status.busy": "2021-04-10T09:30:12.228916Z",
     "iopub.status.idle": "2021-04-10T09:30:14.659466Z",
     "shell.execute_reply": "2021-04-10T09:30:14.658480Z"
    },
    "papermill": {
     "duration": 2.483,
     "end_time": "2021-04-10T09:30:14.659617",
     "exception": false,
     "start_time": "2021-04-10T09:30:12.176617",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SJGlNGUwlSZIkSWvKYCpJkiRJWlMGU0mSJEnSmjKYSpIkSdIQ2LNnD5df/pGOjzv77OezZ8+ePrTogFJfzy5JkiRJWtYHPvC+np7v6U9/Zsvte/fu4YorPsLjH/+ERc9XKhVKpebR8E1vektP2teKwVSSJEmShsA73/lWbrjhBk4//cmUSiVGR0eZnJzkuuuu49JLL+ecc17MjTfeyPz8PE94wmk89rGPB+DUU/+U97zng8zMTHP22c/nXvc6iR/+8AdMTU3x+tf/I2Nj4ytum0N5JUmSJGkIPOc5Z3HMMcdw0UUX87znPZ+f/eynvOAFZ3PppZcDcM45r+TCCz/Ee9/7AT760UvZvXvXQefYtu16Hv/4J/ChD13Gpk2TfOUrX+pJ26yYSpIkSdIQuvvd78HRRx+z//FHPnIpV175FQBuuulGrr/+ejZv3rLomKOOOpq73CUAEMLd+PWvf9WTthhMJUmSJGkIbdiwYf/X3/3ut/n2t7/JBRe8j/Hxcc4889nMz88ddMzIyMj+rwuFItXqwft0w2DaQ1c+9anMzlWW3Xbv89+9yq2RJEmSpAMmJiaYnp5edtu+fXuZnDyM8fFxrrvuWn7ykx+tatsMppIkSZI0BDZv3sI973kiT3vaExkbG+fwww/fv+3kkx/Exz52OU95yqnc4Q7HcvzxJ6xq25J6vb6qF2xl+/Y966cxnapWueHpT2T75BbqhYPXlLJiemiZmppk+/b+3stJa89+Hg7283Cwn4eHfT0c7OfBNDU1mTTb5qq8PTL65S9y0he+wG23Xb/WTZEkSZKkgWIw7ZHkllsAGFlmgrAkSZIkqTmDaY8klXTRo6Q2uKORJUmSJGktGEx7pVwGoFCvrXFDJEmSJGmwGEx7pZIGUyumkiRJktQZg2mPJOV0KG+hZsVUkiRJkjphMO2VbChvso5uvyNJkiRJDXv27OHyyz/S1bGXXXYxs7OzPW7RAaW+nXnIzO9N76OUWDGVJEmSlMP4B97X0/PNPv2ZLbfv3buHK674CI9//BM6Pvdll13CIx/5x4yPj3fbvJb6FkxDCAH48IKn7gS8Msb45n5dcy19+2v/xSOB6TlvFyNJkiRp/XnnO9/KDTfcwOmnP5n73e9ktm7dype+9EXK5Xke8pCHccYZf8nMzAyvfOVLuemmm6jVqpx++rPYsWMHN9+8nec//y/ZvHkLb33rBT1vW9+CaYwxAicBhBCKwA3AFf263lorT+8DoFItr3FLJEmSJOlgz3nOWVxzzc+56KKL+eY3v86Xv/zvvPvd76der/PSl76I733vu+zatZMjj5zijW88D4C9e/eyadMmPvzhf+Etb7mALVu29KVtqzWU9+HAz2OM163S9Vbd/vuYVp1jKkmSJGl9++Y3v863vvV1nvnMpwAwMzPNtm2/5F73ujdve9ubOf/8t/DgB/8eJ55471Vpz2oF09OAS9rttHXrBKVScRWa03sl0kBaSOqMjx38sk5NTa52k9Rn9ulwsJ+Hg/08HOzn4WFfD4dDop8neztfc7LNazI3t5FSqcjU1CQbNozw3Oc+h9NOO+2g/T7+8Y/x1a9+lYsuehcPeMADOPPMMykWCxxxxEYOP7w/r3vfg2kIYRR4DHBOu3137pzud3P6pjY7D0C9UmV2rnLQ9u3b96x2k9RHU1OT9ukQsJ+Hg/08HOzn4WFfD4dDpZ/H9/R2ldvZNq/J7GydW2/dw/bte7jnPe/Du9/9Dh74wIcxMTHB9u03USqVqFarTE4exoMe9AfU6yN86lMfY/v2PYyNjXP99TdRrY503b5Wv0xYjYrpo4HvxhhvXIVrrZlCNRvK66q8kiRJktahzZu3cM97nsjTnvZEHvCAB/OIRzyK5zwnXcl3w4YJXvnKv2Xbtus5//zzSJICpVKJs89+KQCPecz/x4tffBZHHjk1WIsfLfAkcgzjHXRJpZr+bTCVJEmSlEO727v0w7nnvnbR4yc+8UmLHh9zzO04+eQHHnTcqaeexqmnHjzst1cKfTszEELYCDwCuLyf11kPio2Kad3FjyRJkiSpE32tmMYY9wFH9PMa60WhasVUkiRJkrrR14rpMClkgbRQN5hKkiRJUicMpj1S3F8xdSivJEmSJHXCYNojxVoaTAvOMZUkSZKkjhhMe8ShvJIkSZLUHYNpjxT3B1MrppIkSZLUCYNpj5QawdQ5ppIkSZLUEYNpjxSzSmnRiqkkSZIkdcRg2iMlh/JKkiRJUlcMpj1SsmIqSZIkSV0xmPZII5haMZUkSZKkzhhMe6QRTEsYTCVJkiSpEwbTHrFiKkmSJEndMZj2yEj2t3NMJUmSJKkzBtMeqNfr+4NpaU1bIkmSJEmDx2DaA+Vy2YqpJEmSJHXJYNoDC4NpCcBwKkmSJEm5GUx7oDI/t2gIb2IwlSRJkqTcDKY9UJ6eXvQ4qdXWqCWSJEmSNHgMpj1QnZ1Z9LhQN5hKkiRJUl4G0x6ozCwOpknNobySJEmSlJfBtAeqs7OLHidWTCVJkiQpN4NpDxwUTK2YSpIkSVJuBtMeWBpMCy5+JEmSJEm5GUx7oDbnUF5JkiRJ6pbBtAeqSxY/smIqSZIkSfkZTHugNje36HFSd46pJEmSJOVlMO2Bg4KpFVNJkiRJys1g2gPVOe9jKkmSJEndMpj2QG1uftHjgosfSZIkSVJuBtMeqGdDeRvx1IqpJEmSJOVnMO2B+nwaTKezx0mtunaNkSRJkqQBYzDtgcZQ3kYwrbv4kSRJkiTlZjDthfKSYFq1YipJkiRJeZX6efIQwhbgPcAJQB348xjj1/p5zbXQmGPaCKY1g6kkSZIk5dbviul5wL/FGO8GnAhc1efrrY2lFVPnmEqSJElSbn2rmIYQNgMPAU4HiDHOc2Dh2kNKfb4MwGzjcdU5ppIkSZKUVz+H8h4HbAfeF0I4EfgO8IIY475mB2zdOkGpVOxjk/pjrJDeHmauUIBajVJSZ3xs8Us7NTW5Fk1TH9mnw8F+Hg7283Cwn4eHfT0c7OdDSz+DaQn4HeCsGOM3QgjnAS8F/qbZATt3TjfbtK7N7knbPZ8kAFTmK8zOVRbts337nlVvl/pnamrSPh0C9vNwsJ+Hg/08POzr4WA/D6ZWv0zo5xzTbcC2GOM3sscfJQ2qh55sjul8IX05a84xlSRJkqTc+hZMY4y/Aa4PIYTsqYcDP+nX9dZSUkmro41gmngfU0mSJEnKra+3iwHOAv4lhDAKXAM8s8/XWxvldPGjchZMcfEjSZIkScqtr8E0xvg94L79vMZ6kFTSYFopZgs3OZRXkiRJknLr931Mh0JSTofyHgim9TVsjSRJkiQNFoNpDyTVNJhWG8G0bsVUkiRJkvIymPZAY/GjaikbGe0cU0mSJEnKzWDaA41gWs8qpgVX5ZUkSZKk3AymPVCopkN3642Kad05ppIkSZKUl8G0BxoVU0ZG0sdWTCVJkiQpN4NpDxSziqnBVJIkSZI6ZzDtgcZQXrKhvEndYCpJkiRJeRlMe6ARTAujo+nf3sdUkiRJknIzmPZAsbZkKK8VU0mSJEnKzWDaA8XGfUsbQ3mtmEqSJElSbgbTHihmix3VsmBasGIqSZIkSbkZTHugUKtRBZIsmBa9j6kkSZIk5WYw7YFSrUYZKBaLgIsfSZIkSVInDKY9UKzXqCQJxWKRKlCwYipJkiRJuRlMe6BUq1FOEkpJQhnnmEqSJElSJwymPVCs16kkCSPFYjqk14qpJEmSJOVmMO2BkSyYlpKECg7llSRJkqROGEx7oJQF02KhkFVM17pFkiRJkjQ4DKY9MFKvUy0UGGkEU0ymkiRJkpSXwbQHSkA1KVDaXzE1mEqSJElSXgbTFarX64wA1UJhwVBeg6kkSZIk5WUwXaFKpbI/mI4UClSAkrlUkiRJknIzmK5QuVxOg2lxwVBe55hKkiRJUm4G0xWqlOfTOaaFIsUkoUy6Sq8kSZIkKR+D6QrN75sGoFoskmT3MS2tbZMkSZIkaaAYTFeoNpsG01oxfSmrGEwlSZIkqRMG0xWqzMwAUCumcbSSJOmL6nBeSZIkScrFYLpClZlZAOrFYvo4ez6p1daoRZIkSZI0WAymK1SbS4Npo2JaTRIACnWDqSRJkiTlYTBdoepsFkxLWcU0C6ZJzaG8kiRJkpSHwXSFGsG0Xkorpo06acGhvJIkSZKUi8F0hRpDeRvBdH/F1MWPJEmSJCmXvt7ZJIRwLbCH9C4qlRjjfft5vbVQm5sDoF4aSR/vH8prxVSSJEmS8liNW24+LMZ48ypcZ00sHMqbcGDxo8TFjyRJkiQpF4fyrlBtNr2PKSOLK6YFFz+SJEmSpFz6HUzrwOdDCN8JITy7z9daE7W5eeDgobzUqmvVJEmSJEkaKP0eyvu7McYbQgi3Ab4QQvhpjPHKZjtv3TpBKbvtyqDYUEoro6ObNlABaoU0648UYHzswMs7NTW5Fs1TH9mnw8F+Hg7283Cwn4eHfT0c7OdDS1+DaYzxhuzvm0IIVwD3B5oG0507p/vZnL7Yu3MPAPO1hAIH5pjOzpaZnavs32/79j1r0Tz1ydTUpH06BOzn4WA/Dwf7eXjY18PBfh5MrX6Z0LehvCGEjSGEycbXwCOBH/XremulPp+uyrt0jmm9Wml2iCRJkiRpgX5WTG8LXBFCaFzn4hjjv/XxemuiPl8GIBkdBaCWpFm/VnVVXkmSJEnKo2/BNMZ4DXBiv86/XtTn08WPGhXTeqNi6uJHkiRJkpSLt4tZoUYwTcbGgAOLH9WrBlNJkiRJysNgulLlLJiOpsG0XmhUTB3KK0mSJEl5GExXqlExzeaY1hMrppIkSZLUCYPpSpUXB9NaVjHFOaaSJEmSlIvBdKXK6aq8hWyOKYUiAHVX5ZUkSZKkXAymK7UkmNatmEqSJElSRwymK1WpAAsWP8rmmGLFVJIkSZJyMZiuUJJVTIvjjaG82eJHrsorSZIkSbkYTFco2V8xHU+fyOaYUjeYSpIkSVIeBtMVagTT4oY0mNaL6UuaeLsYSZIkScrFYLpChUpjKG+jYpq9pA7llSRJkqRcDKYrtL9iOr4heyKrmDqUV5IkSZJyMZiuUCEbstuomCbFRsW0vlZNkiRJkqSBYjBdoYMqptniR4lDeSVJkiQpF4PpCi2tmFI0mEqSJElSJwymK3QgmKYV08ZQXueYSpIkSVI+BtMVKtTSYFrakAXTbFXexDmmkiRJkpSLwXSFitW0MlrK7mOaZHNMCw7llSRJkqRcDKYr1KiYMjqWPs7mmBbqVkwlSZIkKQ+D6QqVsoopIyPp367KK0mSJEkdMZiuULFWowqQzS0tZIsfWTGVJEmSpHwMpitUrNcoL3ic7B/Ka8VUkiRJkvIwmK5QsVajkiT7HxdKpfRvK6aSJEmSlIvBdIVKtToVFgTTgosfSZIkSVInDKYrVKzXqBzIpftX5S0aTCVJkiQpF4PpCpXqdSqFAy9j0dvFSJIkSVJHcgXTEMI7Qwgn9Lsxg6hUry+aY1osFihjxVSSJEmS8irl3O9nwOUhhN8Abwf+NcZY6V+zBsdIvc5sUmCs8biQBlMrppIkSZKUT66KaYzxn2KMdwVeBzwFuDaE8OoQwlF9bd0AGAGqhQUV0yQNpiWDqSRJkiTl0ukc068DXwFqwAOBb4UQXtjjNg2Mer2eBdPi/udKhQIVHMorSZIkSXnlnWN6nxDChcCPgN8CHhJjfCRwPPCiPrZvXatWq1kwXbD4UZKkc0zXrFWSJEmSNFjyzjG9CHgrcGaMcbrxZIzx1hDCa/vRsEFQLpcPCqZJI5haMZUkSZKkXPIO5X1hjPFdC0NpCOEPAGKMF/SlZQOgUp6nBFSLi1/GCs4xlSRJkqS88gbTNy7z3Jt62ZBBNL8vzem1wuKBu1Xyl6IlSZIkadi1zE8hhN8G7gocFkL44wWbNgMTeS4QQigC3wZuiDGe0m1D16Pq7AwAteLiYFpOEueYSpIkSVJO7Qp7DwZOB24L/N8Fz98KvDjnNV4AXAUc1mnj1rtmwdShvJIkSZKUX8tgGmN8P/D+EMLpMcaLOj15COF2wJ8Ar+UQXL23Mp0G02pp8ctYTRKDqSRJkiTl1G4o73Exxl8A3wwhHL90e4zxJ23O/2bgr4HJPI3ZunWCUmlwBsHu3pBO0U1GR5iamuQqYHysRAUYyb5umJrK9RJogNinw8F+Hg7283Cwn4eHfT0c7OdDS7uhvG8FTgE+vcy2OnCnZgeGEE4BbooxfieE8NA8jdm5c7r9TuvIzb/ewW8DlaTA9u17AJidqzCWJIwAs7NlSBKA/dt1aJiamrRPh4D9PBzs5+FgPw8P+3o42M+DqdUvE9oN5T0l+/u4Lq77YOAx2aJJ46QLKH0oxvjULs61LtXm0qG89WWG8gIk9Tr17GtJkiRJ0vJy3dUkhHBX4JcxxtkQwh8B9wYuiDHubHZMjPEc4Jzs+IcCZx9KoRSgOjsLQL24JJjSCKY16rnvyCNJkiRJwylvaroMqIYQjgMuIB3C+/6+tWpA1ObmAKiPLK2Ypn8nNRdAkiRJkqR28gbTWoyxTLrC7vkxxmcDd8h7kRjjVw61e5jCgmC6pGJaawzlrVZXvU2SJEmSNGjyBtPxEMJtgT8FvpQ9N/STJ2tz6VBeRkYWPd+YY1qtGUwlSZIkqZ28wfTNQAT2xhi/HUK4E7C7b60aEI1gWi8tH0zrVkwlSZIkqa1cix/FGN8FvGvBU9cCf9iPBg2S2ux8+sXo4mBaS9K8X61UGZy7skqSJEnS2sgVTAFCCA8H7rzkmPN73qIBUp9P55geNJS3YMVUkiRJkvLKe7uY9wP3Ab4LNNLW0C85eyCYji5+Ppt+W3OOqSRJkiS1lbdi+kDgHtnKvMrU59OhvMno4mBas2IqSZIkSbnlXfzo+r62YkA1gunSobyNOaY1g6kkSZIktZW3Yvoz4N9DCB8DZhtPxhiHfI6pFVNJkiRJWqm8wXQc+DlwzwXPDf0cU8rLB9N6VjE1mEqSJElSe3lvF/PMfjdkENXn0ym3yejY4ucLWTCt1Va9TZIkSZI0aPKuyjsBnAPcKcb4lBDC3YC7xRg/1s/GrXvlLJiOLRnKmyTZF1ZMJUmSJKmdvIsfvQMYAU7KHm8DXtWPBg2U/XNMF1dMKTiUV5IkSZLyyhtM7xVjfCkwDxBj3NvBsYespJIG08LY4mBay4IpVafhSpIkSVI7ecPl3MIHIYTxDo49dJUrwMHBlMbiR3UrppIkSZLUTt5weWUI4WXAWAjhocBlwMf71qpBkc0xXRpM68VGxdTFjyRJkiSpnbzB9OVAAuwB3gB8Ezi3T20aGEm1UTHdsHhDwcWPJEmSJCmvtqvyhhDuB5wNnJA99UPgczHGSj8bNgiSbChvcXx80fP1pJhu93YxkiRJktRWy4ppCOGBwOeBa0irpq/Ivv5cCOHk/jdvfTtQMV08lDfxPqaSJEmSlFu7iulfA38eY7xiwXNXhBC+QXpf08f1q2GDoFBpUjHN5phaMZUkSZKk9trNMb3HklAKQIzx48Dx/WnS4GhUTJcGUwoO5ZUkSZKkvNoF0+kutw2FYiVd3Ki4YWLR80ljVV6DqSRJkiS11W4o72gI4e6kK/IetK0P7RkozSum2VDeusFUkiRJktppF0wngM802VbvcVsGTqGaVUzHF98uprH4UVIb+pdIkiRJktpqGUxjjHdcpXYMpEI1rYiWNiwJpkXnmEqSJElSXu3mmKqFYlYxHZlYHExd/EiSJEmS8jOYrkChlgZTRhZPty1kFdNC3aG8kiRJktSOwXQFio2K6MjI4g3ZHNOCix9JkiRJUlsG0xUo1mrUALIKaUNh/xxTK6aSJEmS1I7BdAVKtRrlZZ5PHMorSZIkSbkZTFegWKtRTg6+xeuBOaYO5ZUkSZKkdgymK1Cq16ks83yhmN6Fx4qpJEmSJLVnMF2BUruKqXNMJUmSJKktg+kKFOt1qsnBL2Gh2FiV12AqSZIkSe2U+nXiEMI4cCUwll3nozHGV/XremuhVK9TKSYUlzyfZEN5ixhMJUmSJKmdflZM54A/iDGeCJwEPCqE8IA+Xm/VjdRZtmJayobyFh3KK0mSJElt9a1iGmOsA3uzhyPZn0MqqZWos69w8BxTsqG8VkwlSZIkqb2+BVOAEEIR+A7w28DbY4zf6Of1VtsIUC0cXDFNCkVqpHNQJUmSJEmt9TWYxhirwEkhhC3AFSGEE2KMP2q2/9atE5RKS2dsrk/VapV5oFYsMjU1CcBVwPhY+pKWSYNp43FjHx067NPhYD8PB/t5ONjPw8O+Hg7286Glr8G0Ica4K4TwZeBRQNNgunPn9Go0pydmZ2c5CqgkBbZv33Pg+bn0zqaNYNp4vHAfDb6pqUn7dAjYz8PBfh4O9vPwsK+Hg/08mFr9MqFvix+FEKaySikhhA3AI4Cf9ut6q61SnqcEVIvLv4QVVin1S5IkSdKA62d2Ogp4fzbPtABcFmP8VB+vt6rK02l1t1ZYfuhxBeeYSpIkSVIe/VyV9wfAvft1/rVWnp4BoFpsEkyTxIqpJEmSJOXQz/uYHtJqc2kwrTULpkDJiqkkSZIktWUw7VJlJg2m9VbBdBXbI0mSJEmDymDapersHAC10vLxs+pQXkmSJEnKxWDapepsu4qpwVSSJEmS8jCYdqmWBdPmFVMYWc0GSZIkSdKAMph2qTo7C0C9tHz8rCYJI0DdBZAkSZIkqSWDaZeqc+kc03qLOaYjQKVeW8VWSZIkSdLgMZh2qd4umJJQBCrV6iq2SpIkSZIGj8G0S9W5bCjvyPJDeWtJku5XtWIqSZIkSa0YTLtUy+aY0qxiWkiDab1SWa0mSZIkSdJAMph2qTY/n37RpGJa3V8xdSivJEmSJLViMO1SfT6dY8ro6LLbG0N56zWDqSRJkiS1YjDtUmPxI5rcLqYRTGtWTCVJkiSpJYNplxrBNGlaMU1f2ppzTCVJkiSpJYNpl+rlcvp3u6G8rsorSZIkSS0ZTLtUzxY/alYxrRecYypJkiRJeRhMu9UIpiNthvI6x1SSJEmSWjKYdisbymvFVJIkSZJWxmDarXJaMS2MNQum6UtbrxhMJUmSJKkVg2mXCrOzANQ3bFx2e1IoAlCpuiqvJEmSJLViMO1SsXG7mI0Ty25Pilkw9XYxkiRJktSSwbRLpSyYsnHTstsbwbTqUF5JkiRJaslg2qVSY1Xejc2G8jZW5bViKkmSJEmtGEy7VJrPhvJumlx2e6FYArxdjCRJkiS1YzDt0sh8eruYwqblh/IWit7HVJIkSZLyMJh2aaRxu5gmwbSYVUyrBlNJkiRJaslg2qXRcqNi2mQobyld/KhuMJUkSZKklgymXRqplJkFxiaWv11Mo2JqMJUkSZKk1gymXRqdL7MPmGgWTEtZMK0ZTCVJkiSpFYNpl0YrFaaBiYnlbxdTKjVW5a2tYqskSZIkafAYTLs0Vq0wnSQUi8VltyeN562YSpIkSVJLBtMujVerzBWav3z1JEm/qFkxlSRJkqRWDKbdqNfZUK8zmy1wtJxaFloTg6kkSZIktWQw7cbcHAVgvtQqmKZDeYuuyitJkiRJLTVPVisUQrg98AHgtkAdeFeM8bx+XW81JTPTAMyPjDTdp5qF1vF6fVXaJEmSJEmDqp8V0wrw4hjj8cADgL8KIRzfx+utmuqttwJQGR1tuk9lJAumDuWVJEmSpJb6FkxjjL+OMX43+3oPcBVwTL+ut5rmdu4AoDI21nSfRsV0I1AxnEqSJElSU30byrtQCOGOwL2Bb7Tab+vWCUql5W+/sp5sr82mX0xsYGpqcv/zVwHjY+lLWkzGAZgE6kUW7adDg306HOzn4WA/Dwf7eXjY18PBfj609D2YhhA2Af8KvDDGeGurfXfunO53c3ripmu3MQWUR8fYvn3Pom2zcxUAkqxIugm4cXruoP002KamJu3TIWA/Dwf7eTjYz8PDvh4O9vNgavXLhL6uyhtCGCENpf8SY7y8n9daTeVduwCob9jQdJ96ocBckrAJmK1UVqdhkiRJkjSA+hZMQwgJ8F7gqhjjP/XrOmuhvHs3APUNG1vuN5sU0mBaNZhKkiRJUjP9HMr7YOBpwA9DCN/LnntZjPEzfbzmqqjemgbTZFPrYDpXLLCpVmXOe5lKkiRJUlN9C6Yxxv8Ekn6dfy1V96Tj2ZONm1ruN1coOJRXkiRJktro6xzTQ1UtC6aFycNa7lcuFtkEVkwlSZIkqQWDaRfq+/YBUJxsvUR1uVhkFCjPz69CqyRJkiRpMBlMu9AIpqXNm1vuVy5mI6XL5X43SZIkSZIGlsG0C8lMGkxHNm9puV+llAbTuhVTSZIkSWrKYNqFZGYGgNGtW1vuV82CaWLFVJIkSZKaMph2oTg7C8DoltbBtJYF00LZiqkkSZIkNWMw7UJxbg6AscMPb7lfbWQEgIK3i5EkSZKkpgymXRjJgumGI45ouV99ZBSAosFUkiRJkpoymHahVC5TBTZsbj2Ul9G0YlqsOMdUkiRJkpoxmHZhrFxmHzA6NtZ6x6xiOlKt9r9RkiRJkjSgDKZdGK1WmEkSkiRpuV9hLAumFYOpJEmSJDVjMO3CeLXKTKH9S5c0KqY1g6kkSZIkNWMw7cJ4rcZcsdh2v2q2Ku+YQ3klSZIkqSmDaRc21OvMFUtt96tm9zEdq9X63SRJkiRJGlgG0w7VymXGgfmR9sG0UkorpuMGU0mSJElqymDaodkdOwAoZ/NHW2lUTDfU6n1tkyRJkiQNMoNph2ZvuQWASrtbxQDVUjoPdaJuxVSSJEmSmjGYdmhuZ1oxzRNM64Ui88AEUHM4ryRJkiQty2DaofmdacW0Nj6ea/+ZJGETMDMz08dWSZIkSdLgMph2aH7XLgBqGzbk2n+mUDCYSpIkSVILBtMOlbNgyoaJXPvPZsF0enpf39okSZIkSYPMYNqhyp5b0y8mNubaf86KqSRJkiS1ZDDtUHX3bgCSTZty7T9fKDIOzNy6u4+tkiRJkqTBZTDtUG3vHgAKeYNpMb1lzPzOnX1rkyRJkiQNMoNph2p79wJQmDws1/7lLJiWs9vMSJIkSZIWM5h2KlvEqHTY5ly7l0slAKq7d/WrRZIkSZI00AymHUqmpwEobc4XTKultGJaaazmK0mSJElaxGDaoUYwHd2yJdf+1dIIAHUXP5IkSZKkZRlMO1SYTW/7MrJla679a9lQ3vqePX1rkyRJkiQNMoNph4qzswCMbT081/61kaxiajCVJEmSpGUZTDtUmp8HYPyII3PtX8+CabJvX9/aJEmSJEmDzGDaodL8HADjh+cbysvIKACF6b39apIkSZIkDTSDaYdGyxUAko2bcu2fjI4BUJie6VubJEmSJGmQlfp14hDChcApwE0xxhP6dZ3VNlopMwNQyJfpk9F0KG9x1mAqSZIkScvpZ8X0IuBRfTz/mhirVpnJGUoBCmNpxXQkWzRJkiRJkrRY34JpjPFKYEe/zr9Wxqs15grF3PuXsqG8I3Nz/WqSJEmSJA20vg3l7cbWrROUSvlD31qo1WvMlkY5amryoG1XAeNji1/SEhMAjFXKTC1zjAaX/Tkc7OfhYD8PB/t5eNjXw8F+PrSsq2C6c+f0Wjehpfn5eQ4DdpVKbN++/H1JZ+cqix4n1fTvkbm5psdo8ExNTdqfQ8B+Hg7283Cwn4eHfT0c7OfB1OqXCa7K24F9e/ewEShn9ybNo14oUAY2VCpt95UkSZKkYWQw7cDMrl0UgHI2bzSXJGEfsKFS7VezJEmSJGmg9S2YhhAuAb6Wfhm2hRDO6Ne1VsvsLTcDUB0d7ei4mSRhQ81gKkmSJEnL6dsc0xjjk/p17rUyt3MnANXxDR0dN10oMFmtUq/XSZKkH02TJEmSpIHlUN4OlHelwbQ2Pt7RcbNJwiagXC73oVWSJEmSNNgMph2Yz4JpfWKio+NmCwUmgOk9t/ahVZIkSZI02AymHajs3g10HkznCunLPLdjR8/bJEmSJEmDzmDagcqtacUz2bipo+Pmi8X07x239LxNkiRJkjToDKYdqGVDcQubOgum5SyYzt1iMJUkSZKkpQymHajt3QtAYXKyo+Pmi+nix5Vdu3rdJEmSJEkaeAbTDtT3pcG0dNjmjo6rlNKKaSVbPEmSJEmSdIDBtBP79gFQPOywjg6rZhXT2u5dvW6RJEmSJA08g2kHkpkZAEa3bOnouOpIFkxv3d3rJkmSJEnSwDOYdqAwMw3AyOatHR1XK40AUN+zt+dtkiRJkqRBZzDtQGF2FoDRrR0G05HR9Iu9t/a6SZIkSZI08AymHSjNzwEwtvXwjo6rZ0N5k2yOqiRJkiTpAINpB0pz8+nfmztblTcZGQOgMG0wlSRJkqSlDKYdGCmnwbS+YUNHxyWj6RzTwvRMz9skSZIkSYPOYNqB0XIFgPrExo6OS0bTimlx1mAqSZIkSUsZTDswVq1QARgZ6ei4wli6+NFItniSJEmSJOkAg2kHxqtVZgoFSJKOjiuOjQMwki2eJEmSJEk6wGCaU7VaZbxeZ65Y7PjYkdERqsDo/HzvGyZJkiRJA85gmtPMzDQbgblSqeNjR4sl9gLj5XLP2yVJkiRJg85gmtO+ffuYAModzi8FSJKEfUnCeKXS+4ZJkiRJ0oAzmOa0b98+NgKVkdGujp8uFNhQrfa2UZIkSZJ0CDCY5jR9627GgPJYd8F0plBkolbrbaMkSZIk6RBgMM1pbudOAKrZCrudmi2V2Fivg+FUkiRJkhYxmOY0t3MHALXxLoPpSLZo0vR0r5okSZIkSYcEg2lO5V27AKhv2NDV8fONual79/SoRZIkSZJ0aDCY5lTevRuA2sTGro6fHxsDYPbmm3vWJkmSJEk6FBhMc6rs3gVAMjHR1fHlLJjO79jRqyZJkiRJ0iHBYJpTLRuCm2ya7Or4SjYEuLzzlp61SZIkSZIOBQbTnKp70mBa2LSpu+PH02BayeaqSpIkSZJSBtOc6nv3AlA87LDujt+Yzk2t7NrZszZJkiRJ0qHAYJpTfd8+AIqTm7s6vrFoUi1bREmSJEmSlDKY5pTMpMF0ZMuW7k6QzU2t3WowlSRJkqSFDKY5FWZmABjZvKWr45PJNJju+OV1vWqSJEmSJB0S+hpMQwiPCiHEEMLVIYSX9vNa/VYulgAYPfbYro6/14MeDMAPv/Il3ve+9/SsXZIGRL1OsuMWqNfXuiWSJEnrTt+CaQihCLwdeDRwPPCkEMLx/bpev93/vR/gP950HoXfuW9Xx9/m5AdSGxnhxcDhL3kR7//HN/S2gZLWpZ1f+2+ufuoTmT3uaI6823HM3vl2XHPG09j7/f9Z66ZJkiStG6U+nvv+wNUxxmsAQgiXAo8FftLHa/bNMXe6M9zpzl0fXzvqaHZf8RlGX/g8nnX1/3LLG17LV37wPe7x0ldCsvwxSdJkQ47t7Y5tclDnx6xQfZ1Vjw5qT7P2bd/Ijh37Vrc96+Cl2t+eddNvdZI+vjC1rRvZuTN/P8/ecjO7vvF1Kj/6ASPX/Jyjfnkdd82mAUwD/5Uk3GfvHm7/yY/DJz/OD0slfrlxI9ObJrntM85gy/3uz+hB89gT6tkPiYWf66Sw+POaJMn+z/Ciz3+Hn+uOPpP1esef4ab7N3u6m/davdbR7tWtE837eZnrd/09d3Bc212X7FDv9HOw6GdL+2Nzf88L9uv8dVr8uN1nu9P36r4tLfp5+YOanqvFZTpSr1Yp791Lee8eKtP7qMzOUBwZpTi+geL4OKXxcUobJiht2EBpwwQjExMUisXOLjKECjs2smsV/o1W73XyT1Zx5yZ279jbv8YMorFxNt/u9mvdiq71M5geA1y/4PE24OQ+Xm/dq9z/ZCpXfoOb/+F1HHHeP/J/Pvtp+Oyn17pZ6tLUWjdAq+K2Kzh2HvjPrYez/eGP4A5/9QL2vu3NXHLLLVT/N3K3G2/kgZUy99y9G3bvhte9uldNVhd+a60boFVxx7VugFbNEWvdAK2Kw9e6AetMFXjfqU/ksecP5rTBpF8VqxDCqcCjYozPyh4/DTg5xnhmXy4oSZIkSRpI/Vz86AZgYS35dtlzkiRJkiTt18+hvN8C7hJCOI40kJ4GPLmP15MkSZIkDaC+VUxjjBXgTOBzwFXAZTHGH/frepIkSZKkwdS3OaaSJEmSJOXRzzmmkiRJkiS1ZTCVJEmSJK2pfi5+dEgJITwKOA8oAu+JMb5+yfYx4APAfYBbgD+LMV6bbTsHOIP09kLPjzF+bhWbrg7k6OcXAc8CKsB24M9jjNdl26rAD7NdfxljfMyqNVwdydHPpwNv5MBK4m+LMb4n2/YM4BXZ838XY3z/qjRaXcnR1/8MPCx7OAHcJsa4JdvmZ3oAhBAuBE4BbooxnrDM9oT0PfDHwDRweozxu9k2P88DJEdfPwV4CZAAe4Dnxhi/n227NnuuClRijPddrXarMzn6+aHAx4FfZE9dHmN8Tbat5c98rW8G0xxCCEXg7cAjgG3At0IIn4gx/mTBbmcAO2OMvx1COA14A/BnIYTjSVckvgdwNPDFEMJdY4zV1f0u1E7Ofv4f4L4xxukQwnOBfwD+LNs2E2M8aTXbrM7l7GeADy+973II4XDgVcB9gTrwnezYnavQdHUoT1/HGP//BfufBdx7wSn8TA+Gi4C3kf5yeDmPBu6S/TkZeAdwsp/ngXQRrfv6F8Dvxxh3hhAeDbyLtM8bHhZjvLm/TVQPXETrfgb4jxjjKQuf6ODfd61TDuXN5/7A1THGa2KM88ClwGOX7PNYoPGb1o8CD89+S/tY4NIY41yM8RfA1dn5tP607ecY45djjNPZw6+T3p9XgyXP57mZPwK+EGPckf3n9QvAo/rUTq1cp339JOCSVWmZeibGeCWwo8UujwU+EGOsxxi/DmwJIRyFn+eB066vY4z/veAXC/4bPaByfKabWcm/71oHDKb5HANcv+Dxtuy5ZffJbpWzGzgi57FaHzrtqzOAzy54PB5C+HYI4eshhMf1oX3qjbz9/H9CCD8IIXw0hHD7Do/V+pC7v0IIxwLHAV9a8LSf6UNDs/eBn+dD29J/o+vA50MI3wkhPHuN2qTeeWAI4fshhM+GEO6RPednesAZTKUuhBCeSjr8640Lnj42m7PyZODNIYQ7r0nj1AufBO4YY7wXaRXFeWeHvtOAjy6ZZuFnWhpAIYSHkQbTlyx4+ndjjL9DOrT7r0IID1mTxqkXvkv68/lE4K3Ax9a2OeoVg2k+NwC3X/D4dhxYFOWgfUIIJWAz6SJIeY7V+pCrr0IIfwi8HHhMjHGu8XyM8Ybs72uAr7B4rprWj7b9HGO8ZUHfvod0UbNcx2pd6aS/TmPJMF4/04eMZu8DP8+HoBDCvUh/bj82xnhL4/kFn+ebgCtwWtXAijHeGmPcm339GWAkhHAkfqYHnsE0n28BdwkhHBdCGCX9D8wnluzzCeAZ2denAl+KMdaz508LIYyFEI4jXXzhm6vUbnWmbT+HEO4NXEAaSm9a8PzWbGVmsh+ODwacbL8+5ennoxY8fAxwVfb154BHZv29FXhk9pzWpzw/uwkh3A3YCnxtwXN+pg8dnwCeHkJIQggPAHbHGH+Nn+dDTgjhDsDlwNNijD9b8PzGEMJk42vSvv7R2rRSKxVC+K1sHRdCCPcnzTO3kPNnvtYvV+XNIcZYCSGcSfoPVhG4MMb44xDCa4Bvxxg/AbwX+GAI4WrSCdunZcf+OIRwGel/aCrAX7ki7/qUs5/fCGwCPhJCgAO3kLg7cEEIoUb6A/L1rgK3PuXs5+eHEB5D+pndAZyeHbsjhPC3pP/4AbwmxtjNAg1aBTn7GtKf15dmv0xs8DM9IEIIlwAPBY4MIWwjXWl3BCDG+E7gM6S3irma9HYxz8y2+XkeMDn6+pWk63ucn/0b3bgtzG2BK7LnSsDFMcZ/W/VvQLnk6OdTgeeGECrADHBa9vN72Z/5a/AtqEtJvV5vv5ckSZIkSX3iUF5JkiRJ0poymEqSJEmS1pTBVJIkSZK0pgymkiRJkqQ15aq8kiRJkqSWQggXAqcAN8UYT2iz7x2A9wNbSFdJfml239mmrJhKkiRJktq5CHhUzn1fAVwWY7w36W3Zzm93gBVTSZIkSVJLMcYrQwh3XPhcCOHOwNuBKdJ7Rf9FjPGnQB04LNttM/Crdue3YipJkiRJ6sa7gLNijPcBzuZAZfRc4KkhhG3AZ4Cz2p3IYCpJkiRJ6kgIYRPwIOAjIYTvARcAR2WbnwRcFGO8HfDHwAdDCC2zp0N5JUmSJEmdKgC7YownLbPtDLL5qDHGr4UQxoEjgZtanUySJEmSpNxijLcCvwghPAEghJCEEE7MNv8SeHj2/N2BcWB7q/Ml9Xq9j82VJEmSJA26EMIlwENJK583Aq8CvgS8g3QI7whwaYzxNSGE44F3A5tIF0L66xjj51ud32AqSZIkSVpTDuWVJEmSJK0pg6kkSZIkaU0ZTCVJkiRJa8pgKkmSJElaUwZTSZIkSdKaMphKkiRJktaUwVSSJEmStKYMppIkSZKkNfX/AHtRdq3hFNlcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,6))\n",
    "plt.title(\"Distribution of mean values per row in the train and test set\")\n",
    "sns.distplot(train_df[features.columns].mean(axis=1),color=\"black\", kde=True,bins=120, label='train')\n",
    "sns.distplot(test_df[features.columns].mean(axis=1),color=\"red\", kde=True,bins=120, label='test')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "dated-morrison",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-10T09:30:14.762204Z",
     "iopub.status.busy": "2021-04-10T09:30:14.759793Z",
     "iopub.status.idle": "2021-04-10T09:30:17.006201Z",
     "shell.execute_reply": "2021-04-10T09:30:17.006691Z"
    },
    "papermill": {
     "duration": 2.308513,
     "end_time": "2021-04-10T09:30:17.006870",
     "exception": false,
     "start_time": "2021-04-10T09:30:14.698357",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,6))\n",
    "plt.title(\"Distribution of std values per rows in the train and test set\")\n",
    "sns.distplot(train_df[features.columns].std(axis=1),color=\"blue\",kde=True,bins=120, label='train')\n",
    "sns.distplot(test_df[features.columns].std(axis=1),color=\"green\", kde=True,bins=120, label='test')\n",
    "plt.legend(); plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "multiple-roots",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-10T09:30:17.098785Z",
     "iopub.status.busy": "2021-04-10T09:30:17.097753Z",
     "iopub.status.idle": "2021-04-10T09:30:19.059778Z",
     "shell.execute_reply": "2021-04-10T09:30:19.059210Z"
    },
    "papermill": {
     "duration": 2.010971,
     "end_time": "2021-04-10T09:30:19.059925",
     "exception": false,
     "start_time": "2021-04-10T09:30:17.048954",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "t0 = train_df[train_df['TARGET'] == 0]\n",
    "t1 = train_df[train_df['TARGET'] == 1]\n",
    "plt.figure(figsize=(16,6))\n",
    "plt.title(\"Distribution of skew values per row in the train set\")\n",
    "sns.distplot(t0[features.columns].skew(axis=1),color=\"red\", kde=True,bins=120, label='target = 0')\n",
    "sns.distplot(t1[features.columns].skew(axis=1),color=\"blue\", kde=True,bins=120, label='target = 1')\n",
    "plt.legend(); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "challenging-religious",
   "metadata": {
    "papermill": {
     "duration": 0.038339,
     "end_time": "2021-04-10T09:30:19.138352",
     "exception": false,
     "start_time": "2021-04-10T09:30:19.100013",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "=> We Can Check Outlier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "sexual-locator",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-10T09:30:19.292152Z",
     "iopub.status.busy": "2021-04-10T09:30:19.291019Z",
     "iopub.status.idle": "2021-04-10T09:30:20.736357Z",
     "shell.execute_reply": "2021-04-10T09:30:20.735793Z"
    },
    "papermill": {
     "duration": 1.558729,
     "end_time": "2021-04-10T09:30:20.736523",
     "exception": false,
     "start_time": "2021-04-10T09:30:19.177794",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>var3</th>\n",
       "      <th>var15</th>\n",
       "      <th>imp_ent_var16_ult1</th>\n",
       "      <th>imp_op_var39_comer_ult1</th>\n",
       "      <th>imp_op_var39_comer_ult3</th>\n",
       "      <th>imp_op_var40_comer_ult1</th>\n",
       "      <th>imp_op_var40_comer_ult3</th>\n",
       "      <th>imp_op_var40_efect_ult1</th>\n",
       "      <th>imp_op_var40_efect_ult3</th>\n",
       "      <th>...</th>\n",
       "      <th>saldo_medio_var33_hace2</th>\n",
       "      <th>saldo_medio_var33_hace3</th>\n",
       "      <th>saldo_medio_var33_ult1</th>\n",
       "      <th>saldo_medio_var33_ult3</th>\n",
       "      <th>saldo_medio_var44_hace2</th>\n",
       "      <th>saldo_medio_var44_hace3</th>\n",
       "      <th>saldo_medio_var44_ult1</th>\n",
       "      <th>saldo_medio_var44_ult3</th>\n",
       "      <th>var38</th>\n",
       "      <th>TARGET</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>76020.000000</td>\n",
       "      <td>76020.000000</td>\n",
       "      <td>76020.000000</td>\n",
       "      <td>76020.000000</td>\n",
       "      <td>76020.000000</td>\n",
       "      <td>76020.000000</td>\n",
       "      <td>76020.000000</td>\n",
       "      <td>76020.000000</td>\n",
       "      <td>76020.000000</td>\n",
       "      <td>76020.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>76020.000000</td>\n",
       "      <td>76020.000000</td>\n",
       "      <td>76020.000000</td>\n",
       "      <td>76020.000000</td>\n",
       "      <td>76020.000000</td>\n",
       "      <td>76020.000000</td>\n",
       "      <td>76020.000000</td>\n",
       "      <td>76020.000000</td>\n",
       "      <td>7.602000e+04</td>\n",
       "      <td>76020.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>75964.050723</td>\n",
       "      <td>-1523.199277</td>\n",
       "      <td>33.212865</td>\n",
       "      <td>86.208265</td>\n",
       "      <td>72.363067</td>\n",
       "      <td>119.529632</td>\n",
       "      <td>3.559130</td>\n",
       "      <td>6.472698</td>\n",
       "      <td>0.412946</td>\n",
       "      <td>0.567352</td>\n",
       "      <td>...</td>\n",
       "      <td>7.935824</td>\n",
       "      <td>1.365146</td>\n",
       "      <td>12.215580</td>\n",
       "      <td>8.784074</td>\n",
       "      <td>31.505324</td>\n",
       "      <td>1.858575</td>\n",
       "      <td>76.026165</td>\n",
       "      <td>56.614351</td>\n",
       "      <td>1.172358e+05</td>\n",
       "      <td>0.039569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>43781.947379</td>\n",
       "      <td>39033.462364</td>\n",
       "      <td>12.956486</td>\n",
       "      <td>1614.757313</td>\n",
       "      <td>339.315831</td>\n",
       "      <td>546.266294</td>\n",
       "      <td>93.155749</td>\n",
       "      <td>153.737066</td>\n",
       "      <td>30.604864</td>\n",
       "      <td>36.513513</td>\n",
       "      <td>...</td>\n",
       "      <td>455.887218</td>\n",
       "      <td>113.959637</td>\n",
       "      <td>783.207399</td>\n",
       "      <td>538.439211</td>\n",
       "      <td>2013.125393</td>\n",
       "      <td>147.786584</td>\n",
       "      <td>4040.337842</td>\n",
       "      <td>2852.579397</td>\n",
       "      <td>1.826646e+05</td>\n",
       "      <td>0.194945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-999999.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.163750e+03</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>38104.750000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.787061e+04</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>76043.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.064092e+05</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>113748.750000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.187563e+05</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>151838.000000</td>\n",
       "      <td>238.000000</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>210000.000000</td>\n",
       "      <td>12888.030000</td>\n",
       "      <td>21024.810000</td>\n",
       "      <td>8237.820000</td>\n",
       "      <td>11073.570000</td>\n",
       "      <td>6600.000000</td>\n",
       "      <td>6600.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>50003.880000</td>\n",
       "      <td>20385.720000</td>\n",
       "      <td>138831.630000</td>\n",
       "      <td>91778.730000</td>\n",
       "      <td>438329.220000</td>\n",
       "      <td>24650.010000</td>\n",
       "      <td>681462.900000</td>\n",
       "      <td>397884.300000</td>\n",
       "      <td>2.203474e+07</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 371 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  ID           var3         var15  imp_ent_var16_ult1  \\\n",
       "count   76020.000000   76020.000000  76020.000000        76020.000000   \n",
       "mean    75964.050723   -1523.199277     33.212865           86.208265   \n",
       "std     43781.947379   39033.462364     12.956486         1614.757313   \n",
       "min         1.000000 -999999.000000      5.000000            0.000000   \n",
       "25%     38104.750000       2.000000     23.000000            0.000000   \n",
       "50%     76043.000000       2.000000     28.000000            0.000000   \n",
       "75%    113748.750000       2.000000     40.000000            0.000000   \n",
       "max    151838.000000     238.000000    105.000000       210000.000000   \n",
       "\n",
       "       imp_op_var39_comer_ult1  imp_op_var39_comer_ult3  \\\n",
       "count             76020.000000             76020.000000   \n",
       "mean                 72.363067               119.529632   \n",
       "std                 339.315831               546.266294   \n",
       "min                   0.000000                 0.000000   \n",
       "25%                   0.000000                 0.000000   \n",
       "50%                   0.000000                 0.000000   \n",
       "75%                   0.000000                 0.000000   \n",
       "max               12888.030000             21024.810000   \n",
       "\n",
       "       imp_op_var40_comer_ult1  imp_op_var40_comer_ult3  \\\n",
       "count             76020.000000             76020.000000   \n",
       "mean                  3.559130                 6.472698   \n",
       "std                  93.155749               153.737066   \n",
       "min                   0.000000                 0.000000   \n",
       "25%                   0.000000                 0.000000   \n",
       "50%                   0.000000                 0.000000   \n",
       "75%                   0.000000                 0.000000   \n",
       "max                8237.820000             11073.570000   \n",
       "\n",
       "       imp_op_var40_efect_ult1  imp_op_var40_efect_ult3  ...  \\\n",
       "count             76020.000000             76020.000000  ...   \n",
       "mean                  0.412946                 0.567352  ...   \n",
       "std                  30.604864                36.513513  ...   \n",
       "min                   0.000000                 0.000000  ...   \n",
       "25%                   0.000000                 0.000000  ...   \n",
       "50%                   0.000000                 0.000000  ...   \n",
       "75%                   0.000000                 0.000000  ...   \n",
       "max                6600.000000              6600.000000  ...   \n",
       "\n",
       "       saldo_medio_var33_hace2  saldo_medio_var33_hace3  \\\n",
       "count             76020.000000             76020.000000   \n",
       "mean                  7.935824                 1.365146   \n",
       "std                 455.887218               113.959637   \n",
       "min                   0.000000                 0.000000   \n",
       "25%                   0.000000                 0.000000   \n",
       "50%                   0.000000                 0.000000   \n",
       "75%                   0.000000                 0.000000   \n",
       "max               50003.880000             20385.720000   \n",
       "\n",
       "       saldo_medio_var33_ult1  saldo_medio_var33_ult3  \\\n",
       "count            76020.000000            76020.000000   \n",
       "mean                12.215580                8.784074   \n",
       "std                783.207399              538.439211   \n",
       "min                  0.000000                0.000000   \n",
       "25%                  0.000000                0.000000   \n",
       "50%                  0.000000                0.000000   \n",
       "75%                  0.000000                0.000000   \n",
       "max             138831.630000            91778.730000   \n",
       "\n",
       "       saldo_medio_var44_hace2  saldo_medio_var44_hace3  \\\n",
       "count             76020.000000             76020.000000   \n",
       "mean                 31.505324                 1.858575   \n",
       "std                2013.125393               147.786584   \n",
       "min                   0.000000                 0.000000   \n",
       "25%                   0.000000                 0.000000   \n",
       "50%                   0.000000                 0.000000   \n",
       "75%                   0.000000                 0.000000   \n",
       "max              438329.220000             24650.010000   \n",
       "\n",
       "       saldo_medio_var44_ult1  saldo_medio_var44_ult3         var38  \\\n",
       "count            76020.000000            76020.000000  7.602000e+04   \n",
       "mean                76.026165               56.614351  1.172358e+05   \n",
       "std               4040.337842             2852.579397  1.826646e+05   \n",
       "min                  0.000000                0.000000  5.163750e+03   \n",
       "25%                  0.000000                0.000000  6.787061e+04   \n",
       "50%                  0.000000                0.000000  1.064092e+05   \n",
       "75%                  0.000000                0.000000  1.187563e+05   \n",
       "max             681462.900000           397884.300000  2.203474e+07   \n",
       "\n",
       "             TARGET  \n",
       "count  76020.000000  \n",
       "mean       0.039569  \n",
       "std        0.194945  \n",
       "min        0.000000  \n",
       "25%        0.000000  \n",
       "50%        0.000000  \n",
       "75%        0.000000  \n",
       "max        1.000000  \n",
       "\n",
       "[8 rows x 371 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "alien-expression",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-10T09:30:20.846567Z",
     "iopub.status.busy": "2021-04-10T09:30:20.845574Z",
     "iopub.status.idle": "2021-04-10T09:30:20.947011Z",
     "shell.execute_reply": "2021-04-10T09:30:20.947475Z"
    },
    "papermill": {
     "duration": 0.166387,
     "end_time": "2021-04-10T09:30:20.947629",
     "exception": false,
     "start_time": "2021-04-10T09:30:20.781242",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x7fc714643d50>,\n",
       "  <matplotlib.lines.Line2D at 0x7fc714635450>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x7fc714635d50>,\n",
       "  <matplotlib.lines.Line2D at 0x7fc714635510>],\n",
       " 'boxes': [<matplotlib.lines.Line2D at 0x7fc7146430d0>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x7fc714628910>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x7fc714628850>],\n",
       " 'means': []}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(train_df['var3'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "subject-diversity",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-10T09:30:21.037116Z",
     "iopub.status.busy": "2021-04-10T09:30:21.036179Z",
     "iopub.status.idle": "2021-04-10T09:30:21.150551Z",
     "shell.execute_reply": "2021-04-10T09:30:21.150989Z"
    },
    "papermill": {
     "duration": 0.161176,
     "end_time": "2021-04-10T09:30:21.151170",
     "exception": false,
     "start_time": "2021-04-10T09:30:20.989994",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x7fc7145878d0>,\n",
       "  <matplotlib.lines.Line2D at 0x7fc714587e90>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x7fc7145105d0>,\n",
       "  <matplotlib.lines.Line2D at 0x7fc714510b90>],\n",
       " 'boxes': [<matplotlib.lines.Line2D at 0x7fc714587510>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x7fc714519290>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x7fc714519990>],\n",
       " 'means': []}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(train_df['var38'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "interpreted-spread",
   "metadata": {
    "papermill": {
     "duration": 0.043992,
     "end_time": "2021-04-10T09:30:21.238920",
     "exception": false,
     "start_time": "2021-04-10T09:30:21.194928",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Preprocessing\n",
    "* Processing Outlier Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "starting-afghanistan",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-10T09:30:21.329714Z",
     "iopub.status.busy": "2021-04-10T09:30:21.328663Z",
     "iopub.status.idle": "2021-04-10T09:30:22.622258Z",
     "shell.execute_reply": "2021-04-10T09:30:22.622990Z"
    },
    "papermill": {
     "duration": 1.341621,
     "end_time": "2021-04-10T09:30:22.623220",
     "exception": false,
     "start_time": "2021-04-10T09:30:21.281599",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>var3</th>\n",
       "      <th>var15</th>\n",
       "      <th>imp_ent_var16_ult1</th>\n",
       "      <th>imp_op_var39_comer_ult1</th>\n",
       "      <th>imp_op_var39_comer_ult3</th>\n",
       "      <th>imp_op_var40_comer_ult1</th>\n",
       "      <th>imp_op_var40_comer_ult3</th>\n",
       "      <th>imp_op_var40_efect_ult1</th>\n",
       "      <th>imp_op_var40_efect_ult3</th>\n",
       "      <th>...</th>\n",
       "      <th>saldo_medio_var33_hace2</th>\n",
       "      <th>saldo_medio_var33_hace3</th>\n",
       "      <th>saldo_medio_var33_ult1</th>\n",
       "      <th>saldo_medio_var33_ult3</th>\n",
       "      <th>saldo_medio_var44_hace2</th>\n",
       "      <th>saldo_medio_var44_hace3</th>\n",
       "      <th>saldo_medio_var44_ult1</th>\n",
       "      <th>saldo_medio_var44_ult3</th>\n",
       "      <th>var38</th>\n",
       "      <th>TARGET</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>76020.000000</td>\n",
       "      <td>76020.000000</td>\n",
       "      <td>76020.000000</td>\n",
       "      <td>76020.000000</td>\n",
       "      <td>76020.000000</td>\n",
       "      <td>76020.000000</td>\n",
       "      <td>76020.000000</td>\n",
       "      <td>76020.000000</td>\n",
       "      <td>76020.000000</td>\n",
       "      <td>76020.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>76020.000000</td>\n",
       "      <td>76020.000000</td>\n",
       "      <td>76020.000000</td>\n",
       "      <td>76020.000000</td>\n",
       "      <td>76020.000000</td>\n",
       "      <td>76020.000000</td>\n",
       "      <td>76020.000000</td>\n",
       "      <td>76020.000000</td>\n",
       "      <td>7.602000e+04</td>\n",
       "      <td>76020.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>75964.050723</td>\n",
       "      <td>2.716483</td>\n",
       "      <td>33.212865</td>\n",
       "      <td>86.208265</td>\n",
       "      <td>72.363067</td>\n",
       "      <td>119.529632</td>\n",
       "      <td>3.559130</td>\n",
       "      <td>6.472698</td>\n",
       "      <td>0.412946</td>\n",
       "      <td>0.567352</td>\n",
       "      <td>...</td>\n",
       "      <td>7.935824</td>\n",
       "      <td>1.365146</td>\n",
       "      <td>12.215580</td>\n",
       "      <td>8.784074</td>\n",
       "      <td>31.505324</td>\n",
       "      <td>1.858575</td>\n",
       "      <td>76.026165</td>\n",
       "      <td>56.614351</td>\n",
       "      <td>1.172358e+05</td>\n",
       "      <td>0.039569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>43781.947379</td>\n",
       "      <td>9.447971</td>\n",
       "      <td>12.956486</td>\n",
       "      <td>1614.757313</td>\n",
       "      <td>339.315831</td>\n",
       "      <td>546.266294</td>\n",
       "      <td>93.155749</td>\n",
       "      <td>153.737066</td>\n",
       "      <td>30.604864</td>\n",
       "      <td>36.513513</td>\n",
       "      <td>...</td>\n",
       "      <td>455.887218</td>\n",
       "      <td>113.959637</td>\n",
       "      <td>783.207399</td>\n",
       "      <td>538.439211</td>\n",
       "      <td>2013.125393</td>\n",
       "      <td>147.786584</td>\n",
       "      <td>4040.337842</td>\n",
       "      <td>2852.579397</td>\n",
       "      <td>1.826646e+05</td>\n",
       "      <td>0.194945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.163750e+03</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>38104.750000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.787061e+04</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>76043.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.064092e+05</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>113748.750000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.187563e+05</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>151838.000000</td>\n",
       "      <td>238.000000</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>210000.000000</td>\n",
       "      <td>12888.030000</td>\n",
       "      <td>21024.810000</td>\n",
       "      <td>8237.820000</td>\n",
       "      <td>11073.570000</td>\n",
       "      <td>6600.000000</td>\n",
       "      <td>6600.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>50003.880000</td>\n",
       "      <td>20385.720000</td>\n",
       "      <td>138831.630000</td>\n",
       "      <td>91778.730000</td>\n",
       "      <td>438329.220000</td>\n",
       "      <td>24650.010000</td>\n",
       "      <td>681462.900000</td>\n",
       "      <td>397884.300000</td>\n",
       "      <td>2.203474e+07</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 371 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  ID          var3         var15  imp_ent_var16_ult1  \\\n",
       "count   76020.000000  76020.000000  76020.000000        76020.000000   \n",
       "mean    75964.050723      2.716483     33.212865           86.208265   \n",
       "std     43781.947379      9.447971     12.956486         1614.757313   \n",
       "min         1.000000      0.000000      5.000000            0.000000   \n",
       "25%     38104.750000      2.000000     23.000000            0.000000   \n",
       "50%     76043.000000      2.000000     28.000000            0.000000   \n",
       "75%    113748.750000      2.000000     40.000000            0.000000   \n",
       "max    151838.000000    238.000000    105.000000       210000.000000   \n",
       "\n",
       "       imp_op_var39_comer_ult1  imp_op_var39_comer_ult3  \\\n",
       "count             76020.000000             76020.000000   \n",
       "mean                 72.363067               119.529632   \n",
       "std                 339.315831               546.266294   \n",
       "min                   0.000000                 0.000000   \n",
       "25%                   0.000000                 0.000000   \n",
       "50%                   0.000000                 0.000000   \n",
       "75%                   0.000000                 0.000000   \n",
       "max               12888.030000             21024.810000   \n",
       "\n",
       "       imp_op_var40_comer_ult1  imp_op_var40_comer_ult3  \\\n",
       "count             76020.000000             76020.000000   \n",
       "mean                  3.559130                 6.472698   \n",
       "std                  93.155749               153.737066   \n",
       "min                   0.000000                 0.000000   \n",
       "25%                   0.000000                 0.000000   \n",
       "50%                   0.000000                 0.000000   \n",
       "75%                   0.000000                 0.000000   \n",
       "max                8237.820000             11073.570000   \n",
       "\n",
       "       imp_op_var40_efect_ult1  imp_op_var40_efect_ult3  ...  \\\n",
       "count             76020.000000             76020.000000  ...   \n",
       "mean                  0.412946                 0.567352  ...   \n",
       "std                  30.604864                36.513513  ...   \n",
       "min                   0.000000                 0.000000  ...   \n",
       "25%                   0.000000                 0.000000  ...   \n",
       "50%                   0.000000                 0.000000  ...   \n",
       "75%                   0.000000                 0.000000  ...   \n",
       "max                6600.000000              6600.000000  ...   \n",
       "\n",
       "       saldo_medio_var33_hace2  saldo_medio_var33_hace3  \\\n",
       "count             76020.000000             76020.000000   \n",
       "mean                  7.935824                 1.365146   \n",
       "std                 455.887218               113.959637   \n",
       "min                   0.000000                 0.000000   \n",
       "25%                   0.000000                 0.000000   \n",
       "50%                   0.000000                 0.000000   \n",
       "75%                   0.000000                 0.000000   \n",
       "max               50003.880000             20385.720000   \n",
       "\n",
       "       saldo_medio_var33_ult1  saldo_medio_var33_ult3  \\\n",
       "count            76020.000000            76020.000000   \n",
       "mean                12.215580                8.784074   \n",
       "std                783.207399              538.439211   \n",
       "min                  0.000000                0.000000   \n",
       "25%                  0.000000                0.000000   \n",
       "50%                  0.000000                0.000000   \n",
       "75%                  0.000000                0.000000   \n",
       "max             138831.630000            91778.730000   \n",
       "\n",
       "       saldo_medio_var44_hace2  saldo_medio_var44_hace3  \\\n",
       "count             76020.000000             76020.000000   \n",
       "mean                 31.505324                 1.858575   \n",
       "std                2013.125393               147.786584   \n",
       "min                   0.000000                 0.000000   \n",
       "25%                   0.000000                 0.000000   \n",
       "50%                   0.000000                 0.000000   \n",
       "75%                   0.000000                 0.000000   \n",
       "max              438329.220000             24650.010000   \n",
       "\n",
       "       saldo_medio_var44_ult1  saldo_medio_var44_ult3         var38  \\\n",
       "count            76020.000000            76020.000000  7.602000e+04   \n",
       "mean                76.026165               56.614351  1.172358e+05   \n",
       "std               4040.337842             2852.579397  1.826646e+05   \n",
       "min                  0.000000                0.000000  5.163750e+03   \n",
       "25%                  0.000000                0.000000  6.787061e+04   \n",
       "50%                  0.000000                0.000000  1.064092e+05   \n",
       "75%                  0.000000                0.000000  1.187563e+05   \n",
       "max             681462.900000           397884.300000  2.203474e+07   \n",
       "\n",
       "             TARGET  \n",
       "count  76020.000000  \n",
       "mean       0.039569  \n",
       "std        0.194945  \n",
       "min        0.000000  \n",
       "25%        0.000000  \n",
       "50%        0.000000  \n",
       "75%        0.000000  \n",
       "max        1.000000  \n",
       "\n",
       "[8 rows x 371 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['var3'].replace(-999999,2,inplace=True)\n",
    "train_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "objective-estate",
   "metadata": {
    "papermill": {
     "duration": 0.043784,
     "end_time": "2021-04-10T09:30:22.714674",
     "exception": false,
     "start_time": "2021-04-10T09:30:22.670890",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Feature Engineering\n",
    "* Split Data to Train / Test \n",
    "* Train Data to Standard Scaler\n",
    "* Target Data to Oversampling by SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "amino-johns",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-10T09:30:22.922593Z",
     "iopub.status.busy": "2021-04-10T09:30:22.853057Z",
     "iopub.status.idle": "2021-04-10T09:30:22.947199Z",
     "shell.execute_reply": "2021-04-10T09:30:22.946689Z"
    },
    "papermill": {
     "duration": 0.190094,
     "end_time": "2021-04-10T09:30:22.947344",
     "exception": false,
     "start_time": "2021-04-10T09:30:22.757250",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_df.drop('ID',axis=1,inplace=True)\n",
    "test_df.drop('ID',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "honest-charleston",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-10T09:30:23.107800Z",
     "iopub.status.busy": "2021-04-10T09:30:23.086344Z",
     "iopub.status.idle": "2021-04-10T09:30:23.111031Z",
     "shell.execute_reply": "2021-04-10T09:30:23.110577Z"
    },
    "papermill": {
     "duration": 0.12009,
     "end_time": "2021-04-10T09:30:23.111160",
     "exception": false,
     "start_time": "2021-04-10T09:30:22.991070",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "x = train_df.drop('TARGET',axis=1)\n",
    "y = train_df['TARGET']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "possible-single",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-10T09:30:23.209151Z",
     "iopub.status.busy": "2021-04-10T09:30:23.207792Z",
     "iopub.status.idle": "2021-04-10T09:30:24.155479Z",
     "shell.execute_reply": "2021-04-10T09:30:24.154951Z"
    },
    "papermill": {
     "duration": 1.001235,
     "end_time": "2021-04-10T09:30:24.155631",
     "exception": false,
     "start_time": "2021-04-10T09:30:23.154396",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "scaler = StandardScaler().fit(x)\n",
    "x_scaler = scaler.transform(x)\n",
    "test_df_scler = scaler.transform(test_df)\n",
    "x_scaler_df = pd.DataFrame(x_scaler, columns=x.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "subjective-walter",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-10T09:30:24.246867Z",
     "iopub.status.busy": "2021-04-10T09:30:24.245829Z",
     "iopub.status.idle": "2021-04-10T09:30:28.726482Z",
     "shell.execute_reply": "2021-04-10T09:30:28.725973Z"
    },
    "papermill": {
     "duration": 4.528332,
     "end_time": "2021-04-10T09:30:28.726624",
     "exception": false,
     "start_time": "2021-04-10T09:30:24.198292",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "pca = PCA(n_components=0.95)\n",
    "x_scaler_pca = pca.fit_transform(x_scaler)\n",
    "x_scaler_pca_df = pd.DataFrame(x_scaler_pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "academic-notebook",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-10T09:30:28.837090Z",
     "iopub.status.busy": "2021-04-10T09:30:28.836300Z",
     "iopub.status.idle": "2021-04-10T09:30:28.840095Z",
     "shell.execute_reply": "2021-04-10T09:30:28.840581Z"
    },
    "papermill": {
     "duration": 0.070204,
     "end_time": "2021-04-10T09:30:28.840741",
     "exception": false,
     "start_time": "2021-04-10T09:30:28.770537",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
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       "      <th>99</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>-1.554275</td>\n",
       "      <td>-0.208119</td>\n",
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       "      <td>0.164041</td>\n",
       "      <td>1.350834</td>\n",
       "      <td>0.160862</td>\n",
       "      <td>-0.115949</td>\n",
       "      <td>-0.029225</td>\n",
       "      <td>-0.196244</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.064338</td>\n",
       "      <td>-0.171155</td>\n",
       "      <td>-0.143007</td>\n",
       "      <td>0.012559</td>\n",
       "      <td>-0.011739</td>\n",
       "      <td>0.094623</td>\n",
       "      <td>-0.019372</td>\n",
       "      <td>-0.026646</td>\n",
       "      <td>-0.035459</td>\n",
       "      <td>0.020311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.960521</td>\n",
       "      <td>7.752362</td>\n",
       "      <td>-4.106652</td>\n",
       "      <td>5.334768</td>\n",
       "      <td>2.385043</td>\n",
       "      <td>0.218341</td>\n",
       "      <td>0.133414</td>\n",
       "      <td>0.049123</td>\n",
       "      <td>-0.556414</td>\n",
       "      <td>-0.166837</td>\n",
       "      <td>...</td>\n",
       "      <td>0.766588</td>\n",
       "      <td>-0.308978</td>\n",
       "      <td>0.503549</td>\n",
       "      <td>0.719637</td>\n",
       "      <td>-0.275620</td>\n",
       "      <td>0.127514</td>\n",
       "      <td>0.409062</td>\n",
       "      <td>0.184099</td>\n",
       "      <td>-0.448965</td>\n",
       "      <td>0.320545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.986447</td>\n",
       "      <td>-0.934208</td>\n",
       "      <td>0.009955</td>\n",
       "      <td>-0.080542</td>\n",
       "      <td>0.109740</td>\n",
       "      <td>-0.162644</td>\n",
       "      <td>0.014606</td>\n",
       "      <td>-0.116430</td>\n",
       "      <td>0.041589</td>\n",
       "      <td>0.070014</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.139155</td>\n",
       "      <td>0.271461</td>\n",
       "      <td>0.155290</td>\n",
       "      <td>0.115685</td>\n",
       "      <td>0.208813</td>\n",
       "      <td>0.074755</td>\n",
       "      <td>-0.045338</td>\n",
       "      <td>0.123473</td>\n",
       "      <td>0.893015</td>\n",
       "      <td>-0.667017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.439496</td>\n",
       "      <td>-1.457601</td>\n",
       "      <td>0.036698</td>\n",
       "      <td>-0.104464</td>\n",
       "      <td>-0.232233</td>\n",
       "      <td>-2.798434</td>\n",
       "      <td>-0.389224</td>\n",
       "      <td>0.137367</td>\n",
       "      <td>0.160411</td>\n",
       "      <td>0.296752</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.480409</td>\n",
       "      <td>0.099165</td>\n",
       "      <td>0.499151</td>\n",
       "      <td>0.148508</td>\n",
       "      <td>0.060842</td>\n",
       "      <td>-0.096960</td>\n",
       "      <td>0.117620</td>\n",
       "      <td>0.259210</td>\n",
       "      <td>-0.120035</td>\n",
       "      <td>0.193365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.078684</td>\n",
       "      <td>8.541310</td>\n",
       "      <td>3.888339</td>\n",
       "      <td>-10.484813</td>\n",
       "      <td>-5.649927</td>\n",
       "      <td>1.674919</td>\n",
       "      <td>-0.269189</td>\n",
       "      <td>0.813126</td>\n",
       "      <td>-0.521450</td>\n",
       "      <td>0.028506</td>\n",
       "      <td>...</td>\n",
       "      <td>0.697077</td>\n",
       "      <td>-2.086193</td>\n",
       "      <td>-1.250446</td>\n",
       "      <td>-0.445839</td>\n",
       "      <td>-0.150258</td>\n",
       "      <td>0.738608</td>\n",
       "      <td>-0.187255</td>\n",
       "      <td>0.019829</td>\n",
       "      <td>-0.319196</td>\n",
       "      <td>0.006682</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 100 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         0         1         2          3         4         5         6   \\\n",
       "0 -2.674145 -1.554275 -0.208119   0.281410  0.164041  1.350834  0.160862   \n",
       "1 -0.960521  7.752362 -4.106652   5.334768  2.385043  0.218341  0.133414   \n",
       "2 -1.986447 -0.934208  0.009955  -0.080542  0.109740 -0.162644  0.014606   \n",
       "3  4.439496 -1.457601  0.036698  -0.104464 -0.232233 -2.798434 -0.389224   \n",
       "4  2.078684  8.541310  3.888339 -10.484813 -5.649927  1.674919 -0.269189   \n",
       "\n",
       "         7         8         9   ...        90        91        92        93  \\\n",
       "0 -0.115949 -0.029225 -0.196244  ... -0.064338 -0.171155 -0.143007  0.012559   \n",
       "1  0.049123 -0.556414 -0.166837  ...  0.766588 -0.308978  0.503549  0.719637   \n",
       "2 -0.116430  0.041589  0.070014  ... -0.139155  0.271461  0.155290  0.115685   \n",
       "3  0.137367  0.160411  0.296752  ... -0.480409  0.099165  0.499151  0.148508   \n",
       "4  0.813126 -0.521450  0.028506  ...  0.697077 -2.086193 -1.250446 -0.445839   \n",
       "\n",
       "         94        95        96        97        98        99  \n",
       "0 -0.011739  0.094623 -0.019372 -0.026646 -0.035459  0.020311  \n",
       "1 -0.275620  0.127514  0.409062  0.184099 -0.448965  0.320545  \n",
       "2  0.208813  0.074755 -0.045338  0.123473  0.893015 -0.667017  \n",
       "3  0.060842 -0.096960  0.117620  0.259210 -0.120035  0.193365  \n",
       "4 -0.150258  0.738608 -0.187255  0.019829 -0.319196  0.006682  \n",
       "\n",
       "[5 rows x 100 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_scaler_pca_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "committed-berry",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-10T09:30:28.937349Z",
     "iopub.status.busy": "2021-04-10T09:30:28.936342Z",
     "iopub.status.idle": "2021-04-10T09:30:28.940300Z",
     "shell.execute_reply": "2021-04-10T09:30:28.940805Z"
    },
    "papermill": {
     "duration": 0.053612,
     "end_time": "2021-04-10T09:30:28.940973",
     "exception": false,
     "start_time": "2021-04-10T09:30:28.887361",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.08084426, 0.05228955, 0.03647597, 0.03479578, 0.03418844,\n",
       "       0.0312059 , 0.02941499, 0.02827235, 0.02609924, 0.02561362,\n",
       "       0.02258139, 0.0213216 , 0.02111292, 0.02044311, 0.01988079,\n",
       "       0.01751307, 0.01632014, 0.01558715, 0.01391018, 0.01323561,\n",
       "       0.01292253, 0.01234885, 0.01223925, 0.01180389, 0.01076486,\n",
       "       0.01060342, 0.01031062, 0.01010831, 0.00997942, 0.00964471,\n",
       "       0.00953121, 0.00935437, 0.00915166, 0.00874128, 0.00847179,\n",
       "       0.00803298, 0.00737756, 0.00715547, 0.00676623, 0.00670803,\n",
       "       0.00648847, 0.00625966, 0.0062385 , 0.00607502, 0.00601847,\n",
       "       0.00599934, 0.00581773, 0.00578601, 0.00569563, 0.00552583,\n",
       "       0.00521093, 0.00509666, 0.00453945, 0.00435819, 0.00429771,\n",
       "       0.00412857, 0.00411041, 0.00395858, 0.00389107, 0.00382694,\n",
       "       0.00369233, 0.00364883, 0.00355661, 0.00341691, 0.00330774,\n",
       "       0.00310196, 0.0030283 , 0.00297678, 0.00294249, 0.00283271,\n",
       "       0.00279356, 0.00277341, 0.00267043, 0.00255626, 0.00252827,\n",
       "       0.00247618, 0.00245714, 0.00237941, 0.00231859, 0.00228646,\n",
       "       0.00221366, 0.00218226, 0.00216175, 0.00212089, 0.00208838,\n",
       "       0.00204427, 0.00202612, 0.00198258, 0.00196149, 0.00194829,\n",
       "       0.00190237, 0.00183131, 0.00180495, 0.0016681 , 0.00163391,\n",
       "       0.00158843, 0.0015384 , 0.00145791, 0.00142274, 0.00137803])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "apparent-respondent",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-10T09:30:29.052183Z",
     "iopub.status.busy": "2021-04-10T09:30:29.051277Z",
     "iopub.status.idle": "2021-04-10T09:30:30.526530Z",
     "shell.execute_reply": "2021-04-10T09:30:30.526986Z"
    },
    "papermill": {
     "duration": 1.540039,
     "end_time": "2021-04-10T09:30:30.527130",
     "exception": false,
     "start_time": "2021-04-10T09:30:28.987091",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x_scaler_pca_df.loc[:, 0], x_scaler_pca_df.loc[:, 1], c=y,  cmap=\"copper_r\")\n",
    "plt.axis('off')\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "industrial-future",
   "metadata": {
    "papermill": {
     "duration": 0.045353,
     "end_time": "2021-04-10T09:30:30.619214",
     "exception": false,
     "start_time": "2021-04-10T09:30:30.573861",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "=> We cant use PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "collect-trading",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-10T09:30:30.717088Z",
     "iopub.status.busy": "2021-04-10T09:30:30.716226Z",
     "iopub.status.idle": "2021-04-10T09:30:30.940549Z",
     "shell.execute_reply": "2021-04-10T09:30:30.939950Z"
    },
    "papermill": {
     "duration": 0.275173,
     "end_time": "2021-04-10T09:30:30.940693",
     "exception": false,
     "start_time": "2021-04-10T09:30:30.665520",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "placed-holiday",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-10T09:30:31.112902Z",
     "iopub.status.busy": "2021-04-10T09:30:31.111913Z",
     "iopub.status.idle": "2021-04-10T09:30:31.490231Z",
     "shell.execute_reply": "2021-04-10T09:30:31.491474Z"
    },
    "papermill": {
     "duration": 0.484688,
     "end_time": "2021-04-10T09:30:31.491727",
     "exception": false,
     "start_time": "2021-04-10T09:30:31.007039",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "scaler_x_train, scaler_x_test, scaler_y_train, scaler_y_test = train_test_split(x_scaler, y, test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "indie-respect",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-10T09:30:31.665777Z",
     "iopub.status.busy": "2021-04-10T09:30:31.656367Z",
     "iopub.status.idle": "2021-04-10T09:30:32.449209Z",
     "shell.execute_reply": "2021-04-10T09:30:32.448657Z"
    },
    "papermill": {
     "duration": 0.879811,
     "end_time": "2021-04-10T09:30:32.449360",
     "exception": false,
     "start_time": "2021-04-10T09:30:31.569549",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Count_target'}>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "smote = SMOTE()\n",
    "x_over, y_over = smote.fit_resample(scaler_x_train,scaler_y_train)\n",
    "y_over.value_counts().plot(kind='bar',title='Count_target',color=['blue','orange'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "linear-fabric",
   "metadata": {
    "papermill": {
     "duration": 0.048004,
     "end_time": "2021-04-10T09:30:32.545240",
     "exception": false,
     "start_time": "2021-04-10T09:30:32.497236",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Modeling\n",
    "* LogisticRegression\n",
    "* LightGBM Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "norwegian-surgery",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-10T09:30:32.647997Z",
     "iopub.status.busy": "2021-04-10T09:30:32.647351Z",
     "iopub.status.idle": "2021-04-10T09:30:32.652002Z",
     "shell.execute_reply": "2021-04-10T09:30:32.651414Z"
    },
    "papermill": {
     "duration": 0.05953,
     "end_time": "2021-04-10T09:30:32.652140",
     "exception": false,
     "start_time": "2021-04-10T09:30:32.592610",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_clf_eval(y_test, pred = None, pred_proba = None):\n",
    "    confusion = confusion_matrix(y_test, pred)\n",
    "    accuacy = accuracy_score(y_test, pred)\n",
    "    precision = precision_score(y_test, pred)\n",
    "    recall = recall_score(y_test, pred)\n",
    "    f1 = f1_score(y_test, pred)\n",
    "    roc_auc = roc_auc_score(y_test, pred_proba)\n",
    "    \n",
    "    print('confusion')\n",
    "    print(confusion)\n",
    "    print('Accuacy : {}'.format(np.around(accuacy,4)))\n",
    "    print('Precision: {}'.format(np.around(precision,4)))\n",
    "    print('Recall : {}'.format(np.around(recall,4)))\n",
    "    print('F1 : {}'.format(np.around(f1,4)))  \n",
    "    print('ROC_AUC : {}'.format(np.around(roc_auc,4)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "working-mathematics",
   "metadata": {
    "papermill": {
     "duration": 0.047644,
     "end_time": "2021-04-10T09:30:32.748450",
     "exception": false,
     "start_time": "2021-04-10T09:30:32.700806",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "* LogisticRegression "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "friendly-sword",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-10T09:30:32.848989Z",
     "iopub.status.busy": "2021-04-10T09:30:32.847935Z",
     "iopub.status.idle": "2021-04-10T09:30:40.043602Z",
     "shell.execute_reply": "2021-04-10T09:30:40.043118Z"
    },
    "papermill": {
     "duration": 7.248831,
     "end_time": "2021-04-10T09:30:40.043733",
     "exception": false,
     "start_time": "2021-04-10T09:30:32.794902",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "confusion\n",
      "[[15330  6559]\n",
      " [  242   675]]\n",
      "Accuacy : 0.7018\n",
      "Precision: 0.0933\n",
      "Recall : 0.7361\n",
      "F1 : 0.1656\n",
      "ROC_AUC : 0.7897\n"
     ]
    }
   ],
   "source": [
    "lg_reg = LogisticRegression()\n",
    "\n",
    "lg_reg.fit(x_over, y_over)\n",
    "pred = lg_reg.predict(scaler_x_test)\n",
    "pred_proba = lg_reg.predict_proba(scaler_x_test)[:,1]\n",
    "get_clf_eval(scaler_y_test, pred, pred_proba)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fresh-crystal",
   "metadata": {
    "papermill": {
     "duration": 0.048212,
     "end_time": "2021-04-10T09:30:40.141181",
     "exception": false,
     "start_time": "2021-04-10T09:30:40.092969",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "* LightGBM Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "hollow-trade",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-10T09:30:40.247991Z",
     "iopub.status.busy": "2021-04-10T09:30:40.246574Z",
     "iopub.status.idle": "2021-04-10T09:30:40.269645Z",
     "shell.execute_reply": "2021-04-10T09:30:40.270257Z"
    },
    "papermill": {
     "duration": 0.079436,
     "end_time": "2021-04-10T09:30:40.270446",
     "exception": false,
     "start_time": "2021-04-10T09:30:40.191010",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "scaler_x_test, scaler_x_val, scaler_y_test, scaler_y_val = train_test_split(scaler_x_test, scaler_y_test, test_size=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "northern-entity",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-10T09:30:40.373748Z",
     "iopub.status.busy": "2021-04-10T09:30:40.372987Z",
     "iopub.status.idle": "2021-04-10T09:30:40.377244Z",
     "shell.execute_reply": "2021-04-10T09:30:40.376801Z"
    },
    "papermill": {
     "duration": 0.058534,
     "end_time": "2021-04-10T09:30:40.377387",
     "exception": false,
     "start_time": "2021-04-10T09:30:40.318853",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_data = lgb.Dataset(x_over, label=y_over)\n",
    "val_data = lgb.Dataset(scaler_x_val, label=scaler_y_val)\n",
    "params = {\n",
    "    'n_estimators': 5000,\n",
    "    'num_leaves': 20,\n",
    "    'max_depth': -1,\n",
    "    'min_data_in_leaf': 80,\n",
    "    'learning_rate': 0.001,\n",
    "    'boosting': 'gbdt',\n",
    "    'objective': 'binary',\n",
    "    'metric': 'auc',\n",
    "    'n_jobs': -1\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "unable-russian",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-10T09:30:40.478598Z",
     "iopub.status.busy": "2021-04-10T09:30:40.477944Z",
     "iopub.status.idle": "2021-04-10T09:32:16.603450Z",
     "shell.execute_reply": "2021-04-10T09:32:16.604319Z"
    },
    "papermill": {
     "duration": 96.179053,
     "end_time": "2021-04-10T09:32:16.604505",
     "exception": false,
     "start_time": "2021-04-10T09:30:40.425452",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Info] Number of positive: 51123, number of negative: 51123\n",
      "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.143504 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 36158\n",
      "[LightGBM] [Info] Number of data points in the train set: 102246, number of used features: 225\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n",
      "[1]\ttrain's auc: 0.788042\n",
      "Training until validation scores don't improve for 300 rounds\n",
      "[2]\ttrain's auc: 0.788042\n",
      "[3]\ttrain's auc: 0.788042\n",
      "[4]\ttrain's auc: 0.788042\n",
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      "[1997]\ttrain's auc: 0.813591\n",
      "[1998]\ttrain's auc: 0.813606\n",
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      "[2003]\ttrain's auc: 0.813654\n",
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      "[2005]\ttrain's auc: 0.813673\n",
      "[2006]\ttrain's auc: 0.813675\n",
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      "[2009]\ttrain's auc: 0.813699\n",
      "[2010]\ttrain's auc: 0.813702\n",
      "[2011]\ttrain's auc: 0.81371\n",
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      "[2013]\ttrain's auc: 0.813731\n",
      "[2014]\ttrain's auc: 0.813746\n",
      "[2015]\ttrain's auc: 0.813745\n",
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      "[2794]\ttrain's auc: 0.814064\n",
      "[2795]\ttrain's auc: 0.814059\n",
      "[2796]\ttrain's auc: 0.814039\n",
      "[2797]\ttrain's auc: 0.814048\n",
      "[2798]\ttrain's auc: 0.814052\n",
      "[2799]\ttrain's auc: 0.814044\n",
      "[2800]\ttrain's auc: 0.814042\n",
      "[2801]\ttrain's auc: 0.814058\n",
      "[2802]\ttrain's auc: 0.814055\n",
      "[2803]\ttrain's auc: 0.814042\n",
      "[2804]\ttrain's auc: 0.814044\n",
      "Early stopping, best iteration is:\n",
      "[2504]\ttrain's auc: 0.814662\n"
     ]
    }
   ],
   "source": [
    "model = lgb.train(params,\n",
    "                  train_data,\n",
    "                  valid_sets=val_data, \n",
    "                  valid_names=['train','valid'],\n",
    "                  early_stopping_rounds=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "alone-vegetable",
   "metadata": {
    "papermill": {
     "duration": 0.167524,
     "end_time": "2021-04-10T09:32:16.942817",
     "exception": false,
     "start_time": "2021-04-10T09:32:16.775293",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "dietary-bumper",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-10T09:32:17.277325Z",
     "iopub.status.busy": "2021-04-10T09:32:17.276766Z",
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    },
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     "duration": 0.205856,
     "end_time": "2021-04-10T09:32:17.309949",
     "exception": false,
     "start_time": "2021-04-10T09:32:17.104093",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>TARGET</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
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      "text/plain": [
       "   ID  TARGET\n",
       "0   2       0\n",
       "1   5       0\n",
       "2   6       0\n",
       "3   7       0\n",
       "4   9       0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission = pd.read_csv('/kaggle/input/santander-customer-satisfaction/sample_submission.csv')\n",
    "submission.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "distinct-privacy",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-10T09:32:17.646602Z",
     "iopub.status.busy": "2021-04-10T09:32:17.645487Z",
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    },
    "papermill": {
     "duration": 9.744359,
     "end_time": "2021-04-10T09:32:27.220460",
     "exception": false,
     "start_time": "2021-04-10T09:32:17.476101",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "target = model.predict(test_df_scler)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "multiple-concrete",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-10T09:32:27.565129Z",
     "iopub.status.busy": "2021-04-10T09:32:27.564248Z",
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
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   "outputs": [],
   "source": [
    "submission['TARGET'] = target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "practical-physiology",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-10T09:32:27.907101Z",
     "iopub.status.busy": "2021-04-10T09:32:27.906420Z",
     "iopub.status.idle": "2021-04-10T09:32:28.347360Z",
     "shell.execute_reply": "2021-04-10T09:32:28.346825Z"
    },
    "papermill": {
     "duration": 0.611126,
     "end_time": "2021-04-10T09:32:28.347493",
     "exception": false,
     "start_time": "2021-04-10T09:32:27.736367",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "submission.to_csv('submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "abstract-lightweight",
   "metadata": {
    "papermill": {
     "duration": 0.166113,
     "end_time": "2021-04-10T09:32:28.681741",
     "exception": false,
     "start_time": "2021-04-10T09:32:28.515628",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  }
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