BEST XGBOOST on combined 7 table csv file.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import confusion_matrix,classification_report\n",
    "# np.set_printoptions(threshold=np.nan)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Below XGBoosts models are having best of all AUC Metric and also detecting best amount of defaulters as compared to other Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = pd.read_csv(\"FINALHOMECREDIT.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>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>AMT_GOODS_PRICE</th>\n",
       "      <th>...</th>\n",
       "      <th>CC_NAME_CONTRACT_STATUS_Signed_MAX</th>\n",
       "      <th>CC_NAME_CONTRACT_STATUS_Signed_MEAN</th>\n",
       "      <th>CC_NAME_CONTRACT_STATUS_Signed_SUM</th>\n",
       "      <th>CC_NAME_CONTRACT_STATUS_Signed_VAR</th>\n",
       "      <th>CC_NAME_CONTRACT_STATUS_nan_MIN</th>\n",
       "      <th>CC_NAME_CONTRACT_STATUS_nan_MAX</th>\n",
       "      <th>CC_NAME_CONTRACT_STATUS_nan_MEAN</th>\n",
       "      <th>CC_NAME_CONTRACT_STATUS_nan_SUM</th>\n",
       "      <th>CC_NAME_CONTRACT_STATUS_nan_VAR</th>\n",
       "      <th>CC_COUNT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100002</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>202500.0</td>\n",
       "      <td>406597.5</td>\n",
       "      <td>24700.5</td>\n",
       "      <td>351000.0</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100003</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>270000.0</td>\n",
       "      <td>1293502.5</td>\n",
       "      <td>35698.5</td>\n",
       "      <td>1129500.0</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100004</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>67500.0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>6750.0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100006</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>312682.5</td>\n",
       "      <td>29686.5</td>\n",
       "      <td>297000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>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>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100007</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>121500.0</td>\n",
       "      <td>513000.0</td>\n",
       "      <td>21865.5</td>\n",
       "      <td>513000.0</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 798 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_CURR  TARGET  CODE_GENDER  FLAG_OWN_CAR  FLAG_OWN_REALTY  \\\n",
       "0      100002       1            0             0                0   \n",
       "1      100003       0            1             0                1   \n",
       "2      100004       0            0             1                0   \n",
       "3      100006       0            1             0                0   \n",
       "4      100007       0            0             0                0   \n",
       "\n",
       "   CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  AMT_ANNUITY  AMT_GOODS_PRICE  \\\n",
       "0             0          202500.0    406597.5      24700.5         351000.0   \n",
       "1             0          270000.0   1293502.5      35698.5        1129500.0   \n",
       "2             0           67500.0    135000.0       6750.0         135000.0   \n",
       "3             0          135000.0    312682.5      29686.5         297000.0   \n",
       "4             0          121500.0    513000.0      21865.5         513000.0   \n",
       "\n",
       "     ...     CC_NAME_CONTRACT_STATUS_Signed_MAX  \\\n",
       "0    ...                                    NaN   \n",
       "1    ...                                    NaN   \n",
       "2    ...                                    NaN   \n",
       "3    ...                                    0.0   \n",
       "4    ...                                    NaN   \n",
       "\n",
       "   CC_NAME_CONTRACT_STATUS_Signed_MEAN  CC_NAME_CONTRACT_STATUS_Signed_SUM  \\\n",
       "0                                  NaN                                 NaN   \n",
       "1                                  NaN                                 NaN   \n",
       "2                                  NaN                                 NaN   \n",
       "3                                  0.0                                 0.0   \n",
       "4                                  NaN                                 NaN   \n",
       "\n",
       "   CC_NAME_CONTRACT_STATUS_Signed_VAR  CC_NAME_CONTRACT_STATUS_nan_MIN  \\\n",
       "0                                 NaN                              NaN   \n",
       "1                                 NaN                              NaN   \n",
       "2                                 NaN                              NaN   \n",
       "3                                 0.0                              0.0   \n",
       "4                                 NaN                              NaN   \n",
       "\n",
       "   CC_NAME_CONTRACT_STATUS_nan_MAX  CC_NAME_CONTRACT_STATUS_nan_MEAN  \\\n",
       "0                              NaN                               NaN   \n",
       "1                              NaN                               NaN   \n",
       "2                              NaN                               NaN   \n",
       "3                              0.0                               0.0   \n",
       "4                              NaN                               NaN   \n",
       "\n",
       "   CC_NAME_CONTRACT_STATUS_nan_SUM  CC_NAME_CONTRACT_STATUS_nan_VAR  CC_COUNT  \n",
       "0                              NaN                              NaN       NaN  \n",
       "1                              NaN                              NaN       NaN  \n",
       "2                              NaN                              NaN       NaN  \n",
       "3                              0.0                              0.0       6.0  \n",
       "4                              NaN                              NaN       NaN  \n",
       "\n",
       "[5 rows x 798 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(307507, 798)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train[\"TARGET\"].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    282682\n",
       "1     24825\n",
       "Name: TARGET, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train[\"TARGET\"].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "       colsample_bytree=1, eval_metric='auc', gamma=16, learning_rate=0.1,\n",
       "       max_delta_step=0, max_depth=6, min_child_weight=1, missing=None,\n",
       "       n_estimators=100, n_jobs=16, nthread=None,\n",
       "       objective='binary:logistic', random_state=0, reg_alpha=40,\n",
       "       reg_lambda=40, scale_pos_weight=4, seed=None, silent=True,\n",
       "       subsample=1)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split( df_train.drop('TARGET',axis=1) , df_train['TARGET'] , test_size=0.2, random_state=42)\n",
    "model = XGBClassifier(max_depth=6, learning_rate=0.1,n_estimators=100,n_jobs=16,scale_pos_weight=4,missing=np.nan,gamma=16, eval_metric=\"auc\",reg_lambda=40,reg_alpha=40)\n",
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train AUC 0.8229\n",
      "Test AUC 0.7818\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "y_train_predicted = model.predict_proba(X_train)[:,1]\n",
    "y_test_predicted = model.predict_proba(X_test)[:,1]\n",
    "print(\"Train AUC %.4f\"%roc_auc_score(y_train,y_train_predicted))\n",
    "print(\"Test AUC %.4f\"%roc_auc_score(y_test,y_test_predicted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy 0.8980\n",
      "Test Accuracy 0.8917\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    }
   ],
   "source": [
    "y_train_pred = model.predict(X_train)\n",
    "y_test_pred = model.predict(X_test)\n",
    "print(\"Train Accuracy %.4f\" % accuracy_score(y_train,y_train_pred))\n",
    "print(\"Test Accuracy %.4f\" % accuracy_score(y_test,y_test_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[53235  3246]\n",
      " [ 3417  1604]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "confmat=confusion_matrix(y_test,y_test_pred)\n",
    "print(confmat)\n",
    "# plt.imshow(confmat, cmap='binary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Function to calculate missing values by column# Funct \n",
    "def missing_values_table(df):\n",
    "        # Total missing values\n",
    "        mis_val = df.isnull().sum()\n",
    "        \n",
    "        # Percentage of missing values\n",
    "        mis_val_percent = 100 * df.isnull().sum() / len(df)\n",
    "        \n",
    "        # Make a table with the results\n",
    "        mis_val_table = pd.concat([mis_val, mis_val_percent], axis=1)\n",
    "        # Rename the columns\n",
    "        mis_val_table_ren_columns = mis_val_table.rename(\n",
    "        columns = {0 : 'Missing Values', 1 : '% of Total Values'})\n",
    "        \n",
    "        # Sort the table by percentage of missing descending\n",
    "        mis_val_table_ren_columns = mis_val_table_ren_columns[\n",
    "            mis_val_table_ren_columns.iloc[:,1] != 0].sort_values(\n",
    "        '% of Total Values', ascending=False).round(1)\n",
    "        \n",
    "        # Print some summary information\n",
    "        print (\"Your selected dataframe has \" + str(df.shape[1]) + \" columns.\\n\"      \n",
    "            \"There are \" + str(mis_val_table_ren_columns.shape[0]) +\n",
    "              \" columns that have missing values.\")\n",
    "        \n",
    "        # Return the dataframe with missing information\n",
    "        return mis_val_table_ren_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your selected dataframe has 798 columns.\n",
      "There are 604 columns that have missing values.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Index(['REFUSED_RATE_DOWN_PAYMENT_MIN', 'REFUSED_RATE_DOWN_PAYMENT_MEAN',\n",
       "       'REFUSED_RATE_DOWN_PAYMENT_MAX', 'REFUSED_AMT_DOWN_PAYMENT_MEAN',\n",
       "       'REFUSED_AMT_DOWN_PAYMENT_MAX', 'REFUSED_AMT_DOWN_PAYMENT_MIN',\n",
       "       'REFUSED_APP_CREDIT_PERC_VAR', 'CC_AMT_PAYMENT_CURRENT_VAR',\n",
       "       'CC_CNT_DRAWINGS_OTHER_CURRENT_VAR', 'CC_AMT_DRAWINGS_ATM_CURRENT_VAR',\n",
       "       'CC_CNT_DRAWINGS_ATM_CURRENT_VAR', 'CC_CNT_DRAWINGS_POS_CURRENT_VAR',\n",
       "       'CC_AMT_DRAWINGS_OTHER_CURRENT_VAR', 'CC_AMT_DRAWINGS_POS_CURRENT_VAR',\n",
       "       'CC_AMT_PAYMENT_CURRENT_MEAN', 'CC_AMT_PAYMENT_CURRENT_MAX',\n",
       "       'CC_AMT_PAYMENT_CURRENT_MIN', 'CC_CNT_DRAWINGS_ATM_CURRENT_MEAN',\n",
       "       'CC_AMT_DRAWINGS_OTHER_CURRENT_MEAN',\n",
       "       'CC_AMT_DRAWINGS_OTHER_CURRENT_MAX', 'CC_CNT_DRAWINGS_ATM_CURRENT_MAX',\n",
       "       'CC_CNT_DRAWINGS_ATM_CURRENT_MIN', 'CC_AMT_DRAWINGS_POS_CURRENT_MIN',\n",
       "       'CC_AMT_DRAWINGS_POS_CURRENT_MAX', 'CC_AMT_DRAWINGS_OTHER_CURRENT_MIN',\n",
       "       'CC_CNT_DRAWINGS_OTHER_CURRENT_MIN',\n",
       "       'CC_CNT_DRAWINGS_OTHER_CURRENT_MAX',\n",
       "       'CC_CNT_DRAWINGS_OTHER_CURRENT_MEAN', 'CC_CNT_DRAWINGS_POS_CURRENT_MIN',\n",
       "       'CC_AMT_DRAWINGS_ATM_CURRENT_MEAN', 'CC_CNT_DRAWINGS_POS_CURRENT_MAX',\n",
       "       'CC_CNT_DRAWINGS_POS_CURRENT_MEAN', 'CC_AMT_DRAWINGS_ATM_CURRENT_MAX',\n",
       "       'CC_AMT_DRAWINGS_ATM_CURRENT_MIN', 'CC_AMT_DRAWINGS_POS_CURRENT_MEAN',\n",
       "       'ACTIVE_AMT_ANNUITY_MAX', 'ACTIVE_AMT_ANNUITY_MEAN',\n",
       "       'CLOSED_AMT_ANNUITY_MAX', 'CLOSED_AMT_ANNUITY_MEAN',\n",
       "       'BURO_AMT_ANNUITY_MAX', 'BURO_AMT_ANNUITY_MEAN',\n",
       "       'CC_AMT_DRAWINGS_CURRENT_VAR', 'CC_NAME_CONTRACT_STATUS_Completed_VAR',\n",
       "       'CC_AMT_INST_MIN_REGULARITY_VAR', 'CC_AMT_PAYMENT_TOTAL_CURRENT_VAR'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Missing values statistics\n",
    "missing_values = missing_values_table(df_train)\n",
    "missing_values.index[:45]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "       colsample_bytree=1, eval_metric='auc', gamma=16, learning_rate=0.1,\n",
       "       max_delta_step=0, max_depth=6, min_child_weight=1, missing=None,\n",
       "       n_estimators=100, n_jobs=16, nthread=None,\n",
       "       objective='binary:logistic', random_state=0, reg_alpha=40,\n",
       "       reg_lambda=40, scale_pos_weight=4, seed=None, silent=True,\n",
       "       subsample=1)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "newX=df_train.drop(missing_values.index[:15],axis=1)\n",
    "X_train, X_test, y_train, y_test = train_test_split( newX.drop('TARGET',axis=1) , df_train['TARGET'] , test_size=0.2, random_state=42)\n",
    "model = XGBClassifier(max_depth=6, learning_rate=0.1,n_estimators=100,n_jobs=16,scale_pos_weight=4,missing=np.nan,gamma=16, eval_metric=\"auc\",reg_lambda=40,reg_alpha=40)\n",
    "model.fit(X_train,y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_train.drop(missing_values.index[:30],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train AUC 0.8229\n",
      "Test AUC 0.7813\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "y_train_predicted = model.predict_proba(X_train)[:,1]\n",
    "y_test_predicted = model.predict_proba(X_test)[:,1]\n",
    "print(\"Train AUC %.4f\"%roc_auc_score(y_train,y_train_predicted))\n",
    "print(\"Test AUC %.4f\"%roc_auc_score(y_test,y_test_predicted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy 0.8984\n",
      "Test Accuracy 0.8927\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    }
   ],
   "source": [
    "y_train_pred = model.predict(X_train)\n",
    "y_test_pred = model.predict(X_test)\n",
    "print(\"Train Accuracy %.4f\" % accuracy_score(y_train,y_train_pred))\n",
    "print(\"Test Accuracy %.4f\" % accuracy_score(y_test,y_test_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[53303  3178]\n",
      " [ 3419  1602]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "confmat=confusion_matrix(y_test,y_test_pred)\n",
    "print(confmat)\n",
    "# plt.imshow(confmat, cmap='binary')"
   ]
  }
 ],
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