XGBoost_Trial_Only_Dummying.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ran\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import io\n",
    "import numpy as np\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pickle\n",
    "print ('Ran')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of training data: (307511, 122)\n",
      "Shape of test data: (48744, 121)\n"
     ]
    }
   ],
   "source": [
    "app_train = pd.read_csv('D:/Project_Data/application_train.csv')\n",
    "app_test = pd.read_csv('D:/Project_Data/application_test.csv')\n",
    "\n",
    "print(\"Shape of training data: {}\".format(app_train.shape))\n",
    "print(\"Shape of test data: {}\".format(app_test.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "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>SK_ID_CURR</th>\n",
       "      <th>TARGET</th>\n",
       "      <th>NAME_CONTRACT_TYPE</th>\n",
       "      <th>CODE_GENDER</th>\n",
       "      <th>FLAG_OWN_CAR</th>\n",
       "      <th>FLAG_OWN_REALTY</th>\n",
       "      <th>CNT_CHILDREN</th>\n",
       "      <th>AMT_INCOME_TOTAL</th>\n",
       "      <th>AMT_CREDIT</th>\n",
       "      <th>AMT_ANNUITY</th>\n",
       "      <th>...</th>\n",
       "      <th>FLAG_DOCUMENT_18</th>\n",
       "      <th>FLAG_DOCUMENT_19</th>\n",
       "      <th>FLAG_DOCUMENT_20</th>\n",
       "      <th>FLAG_DOCUMENT_21</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_HOUR</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_DAY</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_WEEK</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_MON</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_QRT</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_YEAR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100002</td>\n",
       "      <td>1</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>M</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>202500.0</td>\n",
       "      <td>406597.5</td>\n",
       "      <td>24700.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100003</td>\n",
       "      <td>0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>270000.0</td>\n",
       "      <td>1293502.5</td>\n",
       "      <td>35698.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100004</td>\n",
       "      <td>0</td>\n",
       "      <td>Revolving loans</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>67500.0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>6750.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100006</td>\n",
       "      <td>0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>312682.5</td>\n",
       "      <td>29686.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100007</td>\n",
       "      <td>0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>M</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>121500.0</td>\n",
       "      <td>513000.0</td>\n",
       "      <td>21865.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 122 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_CURR  TARGET NAME_CONTRACT_TYPE CODE_GENDER FLAG_OWN_CAR  \\\n",
       "0      100002       1         Cash loans           M            N   \n",
       "1      100003       0         Cash loans           F            N   \n",
       "2      100004       0    Revolving loans           M            Y   \n",
       "3      100006       0         Cash loans           F            N   \n",
       "4      100007       0         Cash loans           M            N   \n",
       "\n",
       "  FLAG_OWN_REALTY  CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  AMT_ANNUITY  \\\n",
       "0               Y             0          202500.0    406597.5      24700.5   \n",
       "1               N             0          270000.0   1293502.5      35698.5   \n",
       "2               Y             0           67500.0    135000.0       6750.0   \n",
       "3               Y             0          135000.0    312682.5      29686.5   \n",
       "4               Y             0          121500.0    513000.0      21865.5   \n",
       "\n",
       "   ...  FLAG_DOCUMENT_18 FLAG_DOCUMENT_19 FLAG_DOCUMENT_20 FLAG_DOCUMENT_21  \\\n",
       "0  ...                 0                0                0                0   \n",
       "1  ...                 0                0                0                0   \n",
       "2  ...                 0                0                0                0   \n",
       "3  ...                 0                0                0                0   \n",
       "4  ...                 0                0                0                0   \n",
       "\n",
       "  AMT_REQ_CREDIT_BUREAU_HOUR AMT_REQ_CREDIT_BUREAU_DAY  \\\n",
       "0                        0.0                       0.0   \n",
       "1                        0.0                       0.0   \n",
       "2                        0.0                       0.0   \n",
       "3                        NaN                       NaN   \n",
       "4                        0.0                       0.0   \n",
       "\n",
       "   AMT_REQ_CREDIT_BUREAU_WEEK  AMT_REQ_CREDIT_BUREAU_MON  \\\n",
       "0                         0.0                        0.0   \n",
       "1                         0.0                        0.0   \n",
       "2                         0.0                        0.0   \n",
       "3                         NaN                        NaN   \n",
       "4                         0.0                        0.0   \n",
       "\n",
       "   AMT_REQ_CREDIT_BUREAU_QRT  AMT_REQ_CREDIT_BUREAU_YEAR  \n",
       "0                        0.0                         1.0  \n",
       "1                        0.0                         0.0  \n",
       "2                        0.0                         0.0  \n",
       "3                        NaN                         NaN  \n",
       "4                        0.0                         0.0  \n",
       "\n",
       "[5 rows x 122 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "app_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "app_train = pd.get_dummies(app_train,drop_first=True)\n",
    "app_test = pd.get_dummies(app_test,drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Features shape:  (307511, 230)\n",
      "Testing Features shape:  (48744, 226)\n"
     ]
    }
   ],
   "source": [
    "print('Training Features shape: ', app_train.shape)\n",
    "print('Testing Features shape: ', app_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Features shape:  (307511, 225)\n",
      "Testing Features shape:  (48744, 225)\n"
     ]
    }
   ],
   "source": [
    "train_labels = app_train['TARGET']\n",
    "train_sk_id_curr = app_train['SK_ID_CURR']\n",
    "test_sk_id_curr = app_test['SK_ID_CURR']\n",
    "\n",
    "app_train.drop('SK_ID_CURR', inplace=True, axis=1)\n",
    "app_test.drop('SK_ID_CURR', inplace=True, axis=1)\n",
    "\n",
    "app_train, app_test = app_train.align(app_test, join = 'inner', axis = 1)\n",
    "print('Training Features shape: ', app_train.shape)\n",
    "print('Testing Features shape: ', app_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer\n",
    "imputer = SimpleImputer(strategy = 'median')\n",
    "imputer.fit(app_train)\n",
    "app_train = imputer.transform(app_train)\n",
    "app_test = imputer.transform(app_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11.387150050352467"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratio = (train_labels == 0).sum()/ (train_labels == 1).sum()\n",
    "ratio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "0 - Non-defaulter and 1- Defaulter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost import XGBClassifier\n",
    "from sklearn.model_selection import cross_val_score, StratifiedKFold\n",
    "from sklearn.metrics import roc_auc_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Postive examples in train set: 226148\n",
      "Negative examples in train set: 19860\n",
      "Postive examples in test set: 56538\n",
      "Negative examples in test set: 4965\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(app_train, train_labels, test_size=0.2, stratify=train_labels, random_state=1)\n",
    "print(\"Postive examples in train set: {}\".format(np.sum(y_train==0)))\n",
    "print(\"Negative examples in train set: {}\".format(np.sum(y_train==1)))\n",
    "\n",
    "print(\"Postive examples in test set: {}\".format(np.sum(y_test==0)))\n",
    "print(\"Negative examples in test set: {}\".format(np.sum(y_test==1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\tvalidation_0-auc:0.684694\n",
      "Will train until validation_0-auc hasn't improved in 10 rounds.\n",
      "[1]\tvalidation_0-auc:0.69767\n",
      "[2]\tvalidation_0-auc:0.701373\n",
      "[3]\tvalidation_0-auc:0.705643\n",
      "[4]\tvalidation_0-auc:0.70868\n",
      "[5]\tvalidation_0-auc:0.710959\n",
      "[6]\tvalidation_0-auc:0.712839\n",
      "[7]\tvalidation_0-auc:0.714636\n",
      "[8]\tvalidation_0-auc:0.715176\n",
      "[9]\tvalidation_0-auc:0.717006\n",
      "[10]\tvalidation_0-auc:0.718343\n",
      "[11]\tvalidation_0-auc:0.720083\n",
      "[12]\tvalidation_0-auc:0.722222\n",
      "[13]\tvalidation_0-auc:0.723856\n",
      "[14]\tvalidation_0-auc:0.724811\n",
      "[15]\tvalidation_0-auc:0.725467\n",
      "[16]\tvalidation_0-auc:0.72659\n",
      "[17]\tvalidation_0-auc:0.727612\n",
      "[18]\tvalidation_0-auc:0.729942\n",
      "[19]\tvalidation_0-auc:0.73148\n",
      "[20]\tvalidation_0-auc:0.732952\n",
      "[21]\tvalidation_0-auc:0.73428\n",
      "[22]\tvalidation_0-auc:0.734449\n",
      "[23]\tvalidation_0-auc:0.734913\n",
      "[24]\tvalidation_0-auc:0.735365\n",
      "[25]\tvalidation_0-auc:0.735641\n",
      "[26]\tvalidation_0-auc:0.736876\n",
      "[27]\tvalidation_0-auc:0.737942\n",
      "[28]\tvalidation_0-auc:0.738443\n",
      "[29]\tvalidation_0-auc:0.738795\n",
      "[30]\tvalidation_0-auc:0.73983\n",
      "[31]\tvalidation_0-auc:0.740936\n",
      "[32]\tvalidation_0-auc:0.741948\n",
      "[33]\tvalidation_0-auc:0.74225\n",
      "[34]\tvalidation_0-auc:0.742864\n",
      "[35]\tvalidation_0-auc:0.743098\n",
      "[36]\tvalidation_0-auc:0.743801\n",
      "[37]\tvalidation_0-auc:0.744229\n",
      "[38]\tvalidation_0-auc:0.744429\n",
      "[39]\tvalidation_0-auc:0.745656\n",
      "[40]\tvalidation_0-auc:0.746302\n",
      "[41]\tvalidation_0-auc:0.746411\n",
      "[42]\tvalidation_0-auc:0.746934\n",
      "[43]\tvalidation_0-auc:0.747013\n",
      "[44]\tvalidation_0-auc:0.747365\n",
      "[45]\tvalidation_0-auc:0.747728\n",
      "[46]\tvalidation_0-auc:0.74809\n",
      "[47]\tvalidation_0-auc:0.748676\n",
      "[48]\tvalidation_0-auc:0.74901\n",
      "[49]\tvalidation_0-auc:0.74949\n",
      "[50]\tvalidation_0-auc:0.749671\n",
      "[51]\tvalidation_0-auc:0.749806\n",
      "[52]\tvalidation_0-auc:0.749903\n",
      "[53]\tvalidation_0-auc:0.74988\n",
      "[54]\tvalidation_0-auc:0.749877\n",
      "[55]\tvalidation_0-auc:0.750274\n",
      "[56]\tvalidation_0-auc:0.750551\n",
      "[57]\tvalidation_0-auc:0.750909\n",
      "[58]\tvalidation_0-auc:0.751525\n",
      "[59]\tvalidation_0-auc:0.751726\n",
      "[60]\tvalidation_0-auc:0.751947\n",
      "[61]\tvalidation_0-auc:0.751998\n",
      "[62]\tvalidation_0-auc:0.752063\n",
      "[63]\tvalidation_0-auc:0.752302\n",
      "[64]\tvalidation_0-auc:0.75278\n",
      "[65]\tvalidation_0-auc:0.752751\n",
      "[66]\tvalidation_0-auc:0.75278\n",
      "[67]\tvalidation_0-auc:0.75309\n",
      "[68]\tvalidation_0-auc:0.753255\n",
      "[69]\tvalidation_0-auc:0.75338\n",
      "[70]\tvalidation_0-auc:0.753559\n",
      "[71]\tvalidation_0-auc:0.753967\n",
      "[72]\tvalidation_0-auc:0.754123\n",
      "[73]\tvalidation_0-auc:0.754059\n",
      "[74]\tvalidation_0-auc:0.754118\n",
      "[75]\tvalidation_0-auc:0.754243\n",
      "[76]\tvalidation_0-auc:0.754321\n",
      "[77]\tvalidation_0-auc:0.754556\n",
      "[78]\tvalidation_0-auc:0.754566\n",
      "[79]\tvalidation_0-auc:0.754963\n",
      "[80]\tvalidation_0-auc:0.755173\n",
      "[81]\tvalidation_0-auc:0.75523\n",
      "[82]\tvalidation_0-auc:0.755415\n",
      "[83]\tvalidation_0-auc:0.755533\n",
      "[84]\tvalidation_0-auc:0.755632\n",
      "[85]\tvalidation_0-auc:0.755705\n",
      "[86]\tvalidation_0-auc:0.755763\n",
      "[87]\tvalidation_0-auc:0.755875\n",
      "[88]\tvalidation_0-auc:0.75589\n",
      "[89]\tvalidation_0-auc:0.75595\n",
      "[90]\tvalidation_0-auc:0.755959\n",
      "[91]\tvalidation_0-auc:0.755919\n",
      "[92]\tvalidation_0-auc:0.755897\n",
      "[93]\tvalidation_0-auc:0.755881\n",
      "[94]\tvalidation_0-auc:0.755995\n",
      "[95]\tvalidation_0-auc:0.756052\n",
      "[96]\tvalidation_0-auc:0.756244\n",
      "[97]\tvalidation_0-auc:0.75632\n",
      "[98]\tvalidation_0-auc:0.756447\n",
      "[99]\tvalidation_0-auc:0.756571\n",
      "[100]\tvalidation_0-auc:0.756657\n",
      "[101]\tvalidation_0-auc:0.756742\n",
      "[102]\tvalidation_0-auc:0.75669\n",
      "[103]\tvalidation_0-auc:0.756922\n",
      "[104]\tvalidation_0-auc:0.756945\n",
      "[105]\tvalidation_0-auc:0.756972\n",
      "[106]\tvalidation_0-auc:0.757067\n",
      "[107]\tvalidation_0-auc:0.757223\n",
      "[108]\tvalidation_0-auc:0.757276\n",
      "[109]\tvalidation_0-auc:0.757335\n",
      "[110]\tvalidation_0-auc:0.757315\n",
      "[111]\tvalidation_0-auc:0.75747\n",
      "[112]\tvalidation_0-auc:0.757477\n",
      "[113]\tvalidation_0-auc:0.757466\n",
      "[114]\tvalidation_0-auc:0.757707\n",
      "[115]\tvalidation_0-auc:0.757772\n",
      "[116]\tvalidation_0-auc:0.757794\n",
      "[117]\tvalidation_0-auc:0.757764\n",
      "[118]\tvalidation_0-auc:0.757865\n",
      "[119]\tvalidation_0-auc:0.757923\n",
      "[120]\tvalidation_0-auc:0.758132\n",
      "[121]\tvalidation_0-auc:0.758125\n",
      "[122]\tvalidation_0-auc:0.75827\n",
      "[123]\tvalidation_0-auc:0.758317\n",
      "[124]\tvalidation_0-auc:0.758385\n",
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      "[250]\tvalidation_0-auc:0.76131\n",
      "[251]\tvalidation_0-auc:0.761297\n",
      "Stopping. Best iteration:\n",
      "[241]\tvalidation_0-auc:0.76136\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "       colsample_bynode=1, colsample_bytree=1, gamma=0.1,\n",
       "       learning_rate=0.1, max_delta_step=0, max_depth=3,\n",
       "       min_child_weight=1, missing=None, n_estimators=1000, n_jobs=1,\n",
       "       nthread=None, objective='binary:logistic', random_state=0,\n",
       "       reg_alpha=0, reg_lambda=1, scale_pos_weight=11.387150050352467,\n",
       "       seed=None, silent=None, subsample=0.5, verbosity=1)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = XGBClassifier(n_estimators=1000, objective='binary:logistic', gamma=0.1, subsample=0.5, scale_pos_weight=ratio )\n",
    "clf.fit(X_train, y_train, eval_set=[(X_test, y_test)], eval_metric='auc', early_stopping_rounds=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score  0.7008926393834447\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "y_pred = clf.predict(X_test)\n",
    "print('Accuracy score ',accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\tvalidation_0-auc:0.686937\n",
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      "[235]\tvalidation_0-auc:0.771369\n",
      "[236]\tvalidation_0-auc:0.771426\n",
      "[237]\tvalidation_0-auc:0.77148\n",
      "[238]\tvalidation_0-auc:0.771529\n",
      "[239]\tvalidation_0-auc:0.771576\n",
      "[240]\tvalidation_0-auc:0.771667\n",
      "[241]\tvalidation_0-auc:0.771717\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "       colsample_bynode=1, colsample_bytree=1, gamma=0.1,\n",
       "       learning_rate=0.1, max_delta_step=0, max_depth=3,\n",
       "       min_child_weight=1, missing=None, n_estimators=242, n_jobs=1,\n",
       "       nthread=None, objective='binary:logistic', random_state=0,\n",
       "       reg_alpha=0, reg_lambda=1, scale_pos_weight=11.387150050352467,\n",
       "       seed=None, silent=None, subsample=0.5, verbosity=1)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_estimators = clf.best_ntree_limit\n",
    "clf = XGBClassifier(n_estimators=n_estimators, objective='binary:logistic', gamma=0.1, subsample=0.5, scale_pos_weight=ratio )\n",
    "clf.fit(app_train, train_labels, eval_set=[(app_train, train_labels)], eval_metric='auc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = clf.predict(app_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission = pd.DataFrame({'SK_ID_CURR': test_sk_id_curr, 'TARGET': predictions})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\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>SK_ID_CURR</th>\n",
       "      <th>TARGET</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100001</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100005</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100013</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100028</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100038</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_CURR  TARGET\n",
       "0      100001       0\n",
       "1      100005       1\n",
       "2      100013       0\n",
       "3      100028       0\n",
       "4      100038       1"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission.to_csv('baseline_xgboost_trial_only_dummying.csv', index = False)"
   ]
  }
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