Home credit risk.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1- Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://www.kaggle.com/c/home-credit-default-risk\n",
    "\n",
    "Many people struggle to get loans due to insufficient or non-existent credit histories. And, unfortunately, this population is often taken advantage of by untrustworthy lenders.\n",
    "\n",
    "Home Credit strives to broaden financial inclusion for the unbanked population by providing a positive and safe borrowing experience. In order to make sure this underserved population has a positive loan experience, Home Credit makes use of a variety of alternative data--including telco and transactional information--to predict their clients' repayment abilities.\n",
    "\n",
    "While Home Credit is currently using various statistical and machine learning methods to make these predictions, they're challenging Kagglers to help them unlock the full potential of their data. Doing so will ensure that clients capable of repayment are not rejected and that loans are given with a principal, maturity, and repayment calendar that will empower their clients to be successful."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inspired by some great kernels:\n",
    "- sban: https://www.kaggle.com/shivamb/homecreditrisk-extensive-eda-baseline-0-772\n",
    "- Will Koehrsen: \n",
    "    - https://www.kaggle.com/willkoehrsen/introduction-to-manual-feature-engineering \n",
    "    - https://www.kaggle.com/willkoehrsen/start-here-a-gentle-introduction\n",
    "- [Aguiar[Updated 0.792 LB] LightGBM with Simple Features](https://www.kaggle.com/jsaguiar/updated-0-792-lb-lightgbm-with-simple-features/code)\n",
    "- [Bojan TunguzXGB Simple Features](https://www.kaggle.com/tunguz/xgb-simple-features/code)\n",
    "- [Ivan TimoshilovFork_LightGBM_with_Simple_Features](https://www.kaggle.com/znyksh/fork-lightgbm-with-simple-features)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2- About the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data frames we have are : \n",
    "\n",
    "#### application_{train|test}.csv \n",
    "\n",
    "* This is the main table, broken into two files for Train (with TARGET) and Test (without TARGET).\n",
    "Static data for all applications. One row represents one loan in our data sample.\n",
    "\n",
    "#### bureau.csv \n",
    "\n",
    "* All client's previous credits provided by other financial institutions that were reported to Credit Bureau (for clients who have a loan in our sample).\n",
    "For every loan in our sample, there are as many rows as number of credits the client had in Credit Bureau before the application date.\n",
    "\n",
    "#### bureau_balance.csv \n",
    "\n",
    "* Monthly balances of previous credits in Credit Bureau.\n",
    "This table has one row for each month of history of every previous credit reported to Credit Bureau – i.e the table has (#loans in sample * # of relative previous credits * # of months where we have some history observable for the previous credits) rows.\n",
    "\n",
    "#### POS_CASH_balance.csv \n",
    "\n",
    "* Monthly balance snapshots of previous POS (point of sales) and cash loans that the applicant had with Home Credit.\n",
    "This table has one row for each month of history of every previous credit in Home Credit (consumer credit and cash loans) related to loans in our sample – i.e. the table has (#loans in sample * # of relative previous credits * # of months in which we have some history observable for the previous credits) rows.\n",
    "\n",
    "#### credit_card_balance.csv \n",
    "\n",
    "* Monthly balance snapshots of previous credit cards that the applicant has with Home Credit.\n",
    "This table has one row for each month of history of every previous credit in Home Credit (consumer credit and cash loans) related to loans in our sample – i.e. the table has (#loans in sample * # of relative previous credit cards * # of months where we have some \n",
    "history observable for the previous credit card) rows.\n",
    "\n",
    "#### previous_application.csv \n",
    "\n",
    "* All previous applications for Home Credit loans of clients who have loans in our sample.\n",
    "There is one row for each previous application related to loans in our data sample.\n",
    "\n",
    "#### installments_payments.csv \n",
    "\n",
    "* Repayment history for the previously disbursed credits in Home Credit related to the loans in our sample.\n",
    "There is a) one row for every payment that was made plus b) one row each for missed payment.\n",
    "One row is equivalent to one payment of one installment OR one installment corresponding to one payment of one previous Home Credit credit related to loans in our sample.\n",
    "\n",
    "#### HomeCredit_columns_description.csv \n",
    "\n",
    "* This file contains descriptions for the columns in the various data files."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3- Import the Tools we need"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "#reading data , EDA , preprocessing tool , visualizations\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.impute import SimpleImputer\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "#impalanced category , train test split , model optimization\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn.model_selection import train_test_split\n",
    "import lightgbm as lgb\n",
    "from sklearn.metrics import roc_auc_score, accuracy_score\n",
    "\n",
    "#saving the model\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Annotation 2021-06-27 024230](https://user-images.githubusercontent.com/59618586/123529529-6121ce80-d6f1-11eb-93e3-ae8588aca8f9.png)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4- Read the Data + EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('application_train.csv')\n",
    "df1 = pd.read_csv('application_test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EXT_SOURCE_3                 -0.178919\n",
      "EXT_SOURCE_2                 -0.160472\n",
      "EXT_SOURCE_1                 -0.155317\n",
      "DAYS_EMPLOYED                -0.044932\n",
      "FLOORSMAX_AVG                -0.044003\n",
      "FLOORSMAX_MEDI               -0.043768\n",
      "FLOORSMAX_MODE               -0.043226\n",
      "AMT_GOODS_PRICE              -0.039645\n",
      "REGION_POPULATION_RELATIVE   -0.037227\n",
      "ELEVATORS_AVG                -0.034199\n",
      "Name: TARGET, dtype: float64\n",
      "\n",
      "FLAG_DOCUMENT_3                0.044346\n",
      "REG_CITY_NOT_LIVE_CITY         0.044395\n",
      "FLAG_EMP_PHONE                 0.045982\n",
      "REG_CITY_NOT_WORK_CITY         0.050994\n",
      "DAYS_ID_PUBLISH                0.051457\n",
      "DAYS_LAST_PHONE_CHANGE         0.055218\n",
      "REGION_RATING_CLIENT           0.058899\n",
      "REGION_RATING_CLIENT_W_CITY    0.060893\n",
      "DAYS_BIRTH                     0.078239\n",
      "TARGET                         1.000000\n",
      "Name: TARGET, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#correlation\n",
    "corr_Y = df.corr(method='pearson')[\"TARGET\"].sort_values()\n",
    "print(corr_Y.head(10),corr_Y.tail(10),sep='\\n\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#client age range\n",
    "plt.figure(figsize = (8,6))\n",
    "sns.kdeplot(df[df['TARGET']==0]['DAYS_BIRTH'], label = 'Target = 0')\n",
    "sns.kdeplot(df[df['TARGET']==1]['DAYS_BIRTH'], label = 'Target = 1')\n",
    "plt.title('Histogram of age of clients')\n",
    "plt.xlabel('Age [days]')\n",
    "plt.ylabel('Density')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zd\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#days birth\n",
    "plt.figure(figsize=(8,6))\n",
    "plt.title('Histogram of age of clients')\n",
    "plt.xlabel('Age [days]')\n",
    "plt.ylabel('Density')\n",
    "sns.distplot(df[\"DAYS_BIRTH\"])\n",
    "\n",
    "sns.kdeplot(df['DAYS_BIRTH'], label = 'Target = 0 & 1',color=\"black\")\n",
    "sns.kdeplot(df[df['TARGET']==0]['DAYS_BIRTH'], label = 'Target = 0',color=\"green\")\n",
    "sns.kdeplot(df[df['TARGET']==1]['DAYS_BIRTH'], label = 'Target = 1',color=\"b\")\n",
    "\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "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": [
    "# select only attributes with |corr| > 0.05\n",
    "indices = corr_Y[abs(corr_Y) > 0.05].index\n",
    "fea_corr = df[indices].corr()\n",
    "\n",
    "plt.figure(figsize=(10,8))\n",
    "sns.heatmap(fea_corr, cmap=\"RdYlBu\", annot=True, vmin=-0.2, vmax=0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make a label as to whether the instance belongs to train to test dataset\n",
    "df['is_train'] = 1\n",
    "df1['is_train'] = 0\n",
    "\n",
    "# target for train data\n",
    "trainY = df['TARGET']\n",
    "df.drop('TARGET',axis=1,inplace=True)\n",
    "\n",
    "\n",
    "# test id \n",
    "testID = df1['SK_ID_CURR']\n",
    "\n",
    "data = pd.concat([df,df1],axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SK_ID_CURR                      int64\n",
       "NAME_CONTRACT_TYPE             object\n",
       "CODE_GENDER                    object\n",
       "FLAG_OWN_CAR                   object\n",
       "FLAG_OWN_REALTY                object\n",
       "                               ...   \n",
       "AMT_REQ_CREDIT_BUREAU_WEEK    float64\n",
       "AMT_REQ_CREDIT_BUREAU_MON     float64\n",
       "AMT_REQ_CREDIT_BUREAU_QRT     float64\n",
       "AMT_REQ_CREDIT_BUREAU_YEAR    float64\n",
       "is_train                        int64\n",
       "Length: 122, dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#discovering data types\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "COMMONAREA_MEDI             69.872297\n",
       "COMMONAREA_AVG              69.872297\n",
       "COMMONAREA_MODE             69.872297\n",
       "NONLIVINGAPARTMENTS_AVG     69.432963\n",
       "NONLIVINGAPARTMENTS_MODE    69.432963\n",
       "                              ...    \n",
       "EXT_SOURCE_2                 0.214626\n",
       "AMT_GOODS_PRICE              0.090403\n",
       "AMT_ANNUITY                  0.003902\n",
       "CNT_FAM_MEMBERS              0.000650\n",
       "DAYS_LAST_PHONE_CHANGE       0.000325\n",
       "Length: 67, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Nan values in each feature percentage\n",
    "total = df[df.columns[df.isnull().any()==True]]\n",
    "total.isnull().mean().sort_values(ascending=False)*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5- preprocessing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#filling by mode\n",
    "df['DAYS_EMPLOYED'].replace(df['DAYS_EMPLOYED'].mode()[0], np.nan, inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#feature generation also filling Nan values in common df's features\n",
    "def prepare_df(data):\n",
    "    data['NEW_CREDIT_TO_ANNUITY_RATIO'] = data['AMT_CREDIT'] / data['AMT_ANNUITY']\n",
    "    data['NEW_CREDIT_TO_GOODS_RATIO'] = data['AMT_CREDIT'] / data['AMT_GOODS_PRICE']\n",
    "    data['NEW_ANNUITY_TO_INCOME_RATIO'] = data['AMT_ANNUITY'] / data['AMT_INCOME_TOTAL']\n",
    "    data['NEW_CREDIT_TO_INCOME_RATIO'] = data['AMT_CREDIT'] / data['AMT_INCOME_TOTAL']\n",
    "\n",
    "    data['CNT_FAM_MEMBERS'].fillna(data['CNT_FAM_MEMBERS'].median(),inplace=True)\n",
    "    data['NEW_INC_PER_MEMB'] = data['AMT_INCOME_TOTAL'] / data['CNT_FAM_MEMBERS']\n",
    "    data['NEW_INC_PER_CHLD'] = data['AMT_INCOME_TOTAL'] / (1 + data['CNT_CHILDREN'])\n",
    "\n",
    "    data['NEW_EMPLOY_TO_BIRTH_RATIO'] = data['DAYS_EMPLOYED'] / data['DAYS_BIRTH']\n",
    "    data['NEW_CAR_TO_BIRTH_RATIO'] = data['OWN_CAR_AGE'] / data['DAYS_BIRTH']\n",
    "    data['NEW_CAR_TO_EMPLOY_RATIO'] = data['OWN_CAR_AGE'] / data['DAYS_EMPLOYED']\n",
    "    data['NEW_PHONE_TO_BIRTH_RATIO'] = data['DAYS_LAST_PHONE_CHANGE'] / data['DAYS_BIRTH']\n",
    "\n",
    "    data['NEW_SOURCES_PROD'] = data['EXT_SOURCE_1'] * data['EXT_SOURCE_2'] * data['EXT_SOURCE_3']\n",
    "    data['NEW_EXT_SOURCES_MEAN'] = data[['EXT_SOURCE_1', 'EXT_SOURCE_2', 'EXT_SOURCE_3']].mean(axis=1)\n",
    "    data['NEW_SCORES_STD'] = data[['EXT_SOURCE_1', 'EXT_SOURCE_2', 'EXT_SOURCE_3']].std(axis=1)\n",
    "    data['NEW_SCORES_STD'].fillna(data['NEW_SCORES_STD'].mean(),inplace=True)\n",
    "    \n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#removing ouliers using IQR(inter quartile range)\n",
    "def remove_outliers(df):\n",
    "#     d1 = df.select_dtypes(include='O')\n",
    "#     df = df.select_dtypes(exclude='O')\n",
    "    Q1 = df.quantile(0.25)\n",
    "    Q3 = df.quantile(0.75)\n",
    "    IQR = Q3 - Q1\n",
    "    df = df[~(df < (Q1 - 1.5 * IQR)) | (df > (Q3 + 1.5 * IQR))]\n",
    "#     df = pd.concat([df, d1], axis=1)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#removing features wo has more than 50% Nan values and filling other features\n",
    "def process_df(df, raw=False):\n",
    "    df_nan = df[df.columns[df.isnull().any()]]\n",
    "    df_na = df_nan[df_nan.columns[df_nan.isnull().mean()*100 < 50]]\n",
    "    df_not_nan = df[df.columns[~df.isnull().any()]]\n",
    "    df_na_cat = df_na.select_dtypes(include='O')\n",
    "    for col in df_na_cat:\n",
    "        df_na_cat[col].fillna(df_na_cat[col].mode()[0], inplace=True)\n",
    "    df_na_num = df_na.select_dtypes(exclude='O')\n",
    "    df_na_num = remove_outliers(df_na_num)\n",
    "    df_na_num = df_na_num.fillna(df_na_num.mean())\n",
    "    if raw:\n",
    "        df = pd.concat([df_not_nan.drop('SK_ID_CURR', axis=1), df_na_cat, df_na_num], axis=1)\n",
    "    else:\n",
    "        df = pd.concat([df_not_nan, df_na_cat, df_na_num], axis=1)\n",
    "    df = pd.get_dummies(df, drop_first=True)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#lets use our function\n",
    "data = prepare_df(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#read other datasets\n",
    "bureau = pd.read_csv('bureau.csv')\n",
    "bureau_balance = pd.read_csv('bureau_balance.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#labeling STATUS \n",
    "bureau_balance['STATUS'] = bureau_balance['STATUS'].replace(['C','X'],'0').apply(lambda x:int(x))\n",
    "bb = bureau_balance['STATUS'].groupby(bureau_balance['SK_ID_BUREAU']).sum().to_frame()\n",
    "\n",
    "# merge the sum of DPD\n",
    "bureau = pd.merge(bureau,bb,left_on='SK_ID_BUREAU',right_index=True,how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#lets process our data\n",
    "bureau = process_df(bureau)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# examine fraction of missing value in each attribute\n",
    "def missing_df(df):\n",
    "        \"\"\"\n",
    "        return a data frame containing the statistics of missing value\n",
    "        \"\"\"\n",
    "        \n",
    "        total = df.isnull().sum().sort_values(ascending=False)\n",
    "        fraction = 100 * total / df.shape[0]\n",
    "        \n",
    "        # keep to two decimal places\n",
    "        fraction = fraction.apply(lambda x: round(x,2))\n",
    "        \n",
    "        df_missing = pd.concat([total,fraction], axis=1, keys=['Total','Fraction'])\n",
    "        df_missing.index.name = 'Attributes'\n",
    "        \n",
    "        return df_missing\n",
    "    \n",
    "    \n",
    "def cut_missing_fea(df, mis_threshold=50):\n",
    "    \"\"\"\n",
    "    cut off features with ratio of missing values bigger than 60%\n",
    "    \"\"\"\n",
    "    missing = missing_df(df)\n",
    "    keep_fea = missing[missing['Fraction']<mis_threshold].index\n",
    "\n",
    "    return df[keep_fea]\n",
    "\n",
    "\n",
    "    \n",
    "def prepare_data(df, group_var, prefix):\n",
    "    \n",
    "    # cut off featues with too much missing values\n",
    "    df = cut_missing_fea(df)\n",
    "    \n",
    "    # if only one or two categories, factorize()\n",
    "    for col in df:\n",
    "        if df[col].dtype == 'object' and len(list(df[col].unique())) <= 2:\n",
    "            df.loc[:,col], _ = pd.factorize(df[col])\n",
    "    \n",
    "    # one hot encoding if more than two categories, including Null\n",
    "    df = pd.get_dummies(df,dummy_na=True)\n",
    "    \n",
    "    # compute sum, mean, std for each column\n",
    "    d1 = df.drop([group_var],axis=1).groupby('SK_ID_CURR').agg(['sum','mean','std'])\n",
    "    d1.columns = ['_'.join(col).strip('_ ') for col in d1.columns.values]\n",
    "    d1.add_prefix(prefix+'_')\n",
    "    \n",
    "    # count group_var for each SK_ID_CURR\n",
    "    d2 = df[['SK_ID_CURR',group_var]].groupby('SK_ID_CURR').count()\n",
    "    \n",
    "    # merge \n",
    "    df = pd.merge(d1,d2,left_index=True,right_index=True,how='left')\n",
    "    \n",
    "    # add prefix\n",
    "    df = df.add_prefix(prefix+'_')\n",
    "    \n",
    "    return df.reset_index()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bureau.shape =  (305811, 92)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(356255, 226)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#prepare the bureau df with the same prepare function\n",
    "bureau = prepare_data(bureau,group_var='SK_ID_BUREAU',prefix='bureau')\n",
    "print(\"bureau.shape = \",bureau.shape)\n",
    "\n",
    "\n",
    "# merge with  data\n",
    "data = pd.merge(data,bureau,how='left')\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#delete old data from the memory to allocate faster\n",
    "del bureau, bureau_balance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#read the previous applications data\n",
    "prev_app = pd.read_csv('previous_application.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#replacing the Nan values with the mode of the days 365243\n",
    "col_DAYS = []\n",
    "for col in prev_app:\n",
    "    if 'DAYS' in col:\n",
    "        col_DAYS.append(col)\n",
    "\n",
    "for col in col_DAYS:\n",
    "    prev_app[col].replace(365243,np.nan,inplace=True)\n",
    "del col_DAYS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zd\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1743: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  isetter(ilocs[0], value)\n"
     ]
    }
   ],
   "source": [
    "#prepare data with exceptions\n",
    "prev_app = prepare_data(prev_app,group_var='SK_ID_PREV',prefix='prev_app')\n",
    "\n",
    "#merging the important data with the \"data1\"\n",
    "data1 = pd.merge(data,prev_app,how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#read the cash balance data\n",
    "pos_bal = pd.read_csv('POS_CASH_balance.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#prepare the cash balance data\n",
    "pos_bal = prepare_data(pos_bal,group_var='SK_ID_PREV',prefix='pos')\n",
    "\n",
    "# merge with main work data\n",
    "data = pd.merge(data,pos_bal,how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#delete the un-needed data to ease the allocation for the memory\n",
    "del pos_bal, prev_app"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#read the install payments dataset\n",
    "install_pay = pd.read_csv('installments_payments.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#prepare the payments dataset\n",
    "install_pay = prepare_data(install_pay,group_var='SK_ID_PREV',prefix='install')\n",
    "\n",
    "# merge with data\n",
    "data = pd.merge(data,install_pay,how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#read the credit balance dataset\n",
    "credit_bal = pd.read_csv('credit_card_balance.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#prepare the credit balance dataset\n",
    "credit_bal = prepare_data(credit_bal,group_var='SK_ID_PREV',prefix='credit')\n",
    "\n",
    "# merge with data\n",
    "data = pd.merge(data,credit_bal,how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#delete the un-needed data to ease the allocation for the memory\n",
    "del credit_bal, install_pay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data.to_csv(\"data.csv\") #save the prepared work data as .csv file\n",
    "#data = pd.read_csv('data.csv') #red the work data if it is already saved"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zd\\Anaconda3\\lib\\site-packages\\pandas\\core\\series.py:4536: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  downcast=downcast,\n"
     ]
    }
   ],
   "source": [
    "# process the wholde data\n",
    "data = process_df(data, raw=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6- Scaling Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(data)\n",
    "scaled_data = scaler.transform(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>CNT_CHILDREN</th>\n",
       "      <th>AMT_INCOME_TOTAL</th>\n",
       "      <th>AMT_CREDIT</th>\n",
       "      <th>REGION_POPULATION_RELATIVE</th>\n",
       "      <th>DAYS_BIRTH</th>\n",
       "      <th>DAYS_EMPLOYED</th>\n",
       "      <th>DAYS_REGISTRATION</th>\n",
       "      <th>DAYS_ID_PUBLISH</th>\n",
       "      <th>FLAG_MOBIL</th>\n",
       "      <th>...</th>\n",
       "      <th>OCCUPATION_TYPE_Medicine staff</th>\n",
       "      <th>OCCUPATION_TYPE_Private service staff</th>\n",
       "      <th>OCCUPATION_TYPE_Realty agents</th>\n",
       "      <th>OCCUPATION_TYPE_Sales staff</th>\n",
       "      <th>OCCUPATION_TYPE_Secretaries</th>\n",
       "      <th>OCCUPATION_TYPE_Security staff</th>\n",
       "      <th>OCCUPATION_TYPE_Waiters/barmen staff</th>\n",
       "      <th>HOUSETYPE_MODE_specific housing</th>\n",
       "      <th>HOUSETYPE_MODE_terraced house</th>\n",
       "      <th>EMERGENCYSTATE_MODE_Yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100002</td>\n",
       "      <td>0</td>\n",
       "      <td>202500.0</td>\n",
       "      <td>406597.5</td>\n",
       "      <td>0.018801</td>\n",
       "      <td>-9461</td>\n",
       "      <td>-637</td>\n",
       "      <td>-3648.0</td>\n",
       "      <td>-2120</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100003</td>\n",
       "      <td>0</td>\n",
       "      <td>270000.0</td>\n",
       "      <td>1293502.5</td>\n",
       "      <td>0.003541</td>\n",
       "      <td>-16765</td>\n",
       "      <td>-1188</td>\n",
       "      <td>-1186.0</td>\n",
       "      <td>-291</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100004</td>\n",
       "      <td>0</td>\n",
       "      <td>67500.0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>0.010032</td>\n",
       "      <td>-19046</td>\n",
       "      <td>-225</td>\n",
       "      <td>-4260.0</td>\n",
       "      <td>-2531</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100006</td>\n",
       "      <td>0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>312682.5</td>\n",
       "      <td>0.008019</td>\n",
       "      <td>-19005</td>\n",
       "      <td>-3039</td>\n",
       "      <td>-9833.0</td>\n",
       "      <td>-2437</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100007</td>\n",
       "      <td>0</td>\n",
       "      <td>121500.0</td>\n",
       "      <td>513000.0</td>\n",
       "      <td>0.028663</td>\n",
       "      <td>-19932</td>\n",
       "      <td>-3038</td>\n",
       "      <td>-4311.0</td>\n",
       "      <td>-3458</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 352 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_CURR  CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  \\\n",
       "0      100002             0          202500.0    406597.5   \n",
       "1      100003             0          270000.0   1293502.5   \n",
       "2      100004             0           67500.0    135000.0   \n",
       "3      100006             0          135000.0    312682.5   \n",
       "4      100007             0          121500.0    513000.0   \n",
       "\n",
       "   REGION_POPULATION_RELATIVE  DAYS_BIRTH  DAYS_EMPLOYED  DAYS_REGISTRATION  \\\n",
       "0                    0.018801       -9461           -637            -3648.0   \n",
       "1                    0.003541      -16765          -1188            -1186.0   \n",
       "2                    0.010032      -19046           -225            -4260.0   \n",
       "3                    0.008019      -19005          -3039            -9833.0   \n",
       "4                    0.028663      -19932          -3038            -4311.0   \n",
       "\n",
       "   DAYS_ID_PUBLISH  FLAG_MOBIL  ...  OCCUPATION_TYPE_Medicine staff  \\\n",
       "0            -2120           1  ...                               0   \n",
       "1             -291           1  ...                               0   \n",
       "2            -2531           1  ...                               0   \n",
       "3            -2437           1  ...                               0   \n",
       "4            -3458           1  ...                               0   \n",
       "\n",
       "   OCCUPATION_TYPE_Private service staff  OCCUPATION_TYPE_Realty agents  \\\n",
       "0                                      0                              0   \n",
       "1                                      0                              0   \n",
       "2                                      0                              0   \n",
       "3                                      0                              0   \n",
       "4                                      0                              0   \n",
       "\n",
       "   OCCUPATION_TYPE_Sales staff  OCCUPATION_TYPE_Secretaries  \\\n",
       "0                            0                            0   \n",
       "1                            0                            0   \n",
       "2                            0                            0   \n",
       "3                            0                            0   \n",
       "4                            0                            0   \n",
       "\n",
       "   OCCUPATION_TYPE_Security staff  OCCUPATION_TYPE_Waiters/barmen staff  \\\n",
       "0                               0                                     0   \n",
       "1                               0                                     0   \n",
       "2                               0                                     0   \n",
       "3                               0                                     0   \n",
       "4                               0                                     0   \n",
       "\n",
       "   HOUSETYPE_MODE_specific housing  HOUSETYPE_MODE_terraced house  \\\n",
       "0                                0                              0   \n",
       "1                                0                              0   \n",
       "2                                0                              0   \n",
       "3                                0                              0   \n",
       "4                                0                              0   \n",
       "\n",
       "   EMERGENCYSTATE_MODE_Yes  \n",
       "0                        0  \n",
       "1                        0  \n",
       "2                        0  \n",
       "3                        0  \n",
       "4                        0  \n",
       "\n",
       "[5 rows x 352 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7- dealing with imbalanced target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zd\\Anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "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": [
    "#visualize the imbalaced target label\n",
    "sns.countplot(trainY);\n",
    "plt.title(\"impalanced target data\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ignoring features we wont need and prepare for modeling\n",
    "ignore_features = ['SK_ID_CURR', 'is_train']\n",
    "relevant_features = [col for col in data.columns if col not in ignore_features]\n",
    "trainX = data[data['is_train'] == 1][relevant_features]\n",
    "testX = data[data['is_train'] == 0][relevant_features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#another imputations by median\n",
    "imputer = SimpleImputer(strategy=\"median\")\n",
    "imputer.fit(trainX)\n",
    "trainX = imputer.transform(trainX)\n",
    "testX = imputer.transform(testX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dealing with imbalanced by SMOTE\n",
    "smt = SMOTE()\n",
    "X_smote_res, y_smote_res = smt.fit_resample(trainX, trainY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zd\\Anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "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": [
    "sns.countplot(y_smote_res);\n",
    "plt.title(\"impalanced target data after SMOTE\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8- splitting data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#train test splits\n",
    "x_train, x_val, y_train, y_val = train_test_split(X_smote_res, y_smote_res, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9- training the model and evaluating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "#lighGBM model data preparation\n",
    "lgb_train = lgb.Dataset(data=x_train, label=y_train)\n",
    "lgb_eval = lgb.Dataset(data=x_val, label=y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zd\\Anaconda3\\lib\\site-packages\\lightgbm\\engine.py:148: UserWarning: Found `num_iteration` in params. Will use it instead of argument\n",
      "  warnings.warn(\"Found `{}` in params. Will use it instead of argument\".format(alias))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 100 rounds\n",
      "[20]\tvalid_0's auc: 0.96257\n",
      "[40]\tvalid_0's auc: 0.965815\n",
      "[60]\tvalid_0's auc: 0.969024\n",
      "[80]\tvalid_0's auc: 0.970722\n",
      "[100]\tvalid_0's auc: 0.971997\n",
      "[120]\tvalid_0's auc: 0.972824\n",
      "[140]\tvalid_0's auc: 0.973472\n",
      "[160]\tvalid_0's auc: 0.97399\n",
      "[180]\tvalid_0's auc: 0.97447\n",
      "[200]\tvalid_0's auc: 0.974896\n",
      "[220]\tvalid_0's auc: 0.975262\n",
      "[240]\tvalid_0's auc: 0.975617\n",
      "[260]\tvalid_0's auc: 0.975929\n",
      "[280]\tvalid_0's auc: 0.976199\n",
      "[300]\tvalid_0's auc: 0.976434\n",
      "[320]\tvalid_0's auc: 0.976644\n",
      "[340]\tvalid_0's auc: 0.976846\n",
      "[360]\tvalid_0's auc: 0.977043\n",
      "[380]\tvalid_0's auc: 0.977225\n",
      "[400]\tvalid_0's auc: 0.977417\n",
      "[420]\tvalid_0's auc: 0.97759\n",
      "[440]\tvalid_0's auc: 0.977761\n",
      "[460]\tvalid_0's auc: 0.977911\n",
      "[480]\tvalid_0's auc: 0.978063\n",
      "[500]\tvalid_0's auc: 0.978191\n",
      "[520]\tvalid_0's auc: 0.978321\n",
      "[540]\tvalid_0's auc: 0.978443\n",
      "[560]\tvalid_0's auc: 0.978563\n",
      "[580]\tvalid_0's auc: 0.978679\n",
      "[600]\tvalid_0's auc: 0.978781\n",
      "[620]\tvalid_0's auc: 0.978882\n",
      "[640]\tvalid_0's auc: 0.978977\n",
      "[660]\tvalid_0's auc: 0.979059\n",
      "[680]\tvalid_0's auc: 0.97915\n",
      "[700]\tvalid_0's auc: 0.979226\n",
      "[720]\tvalid_0's auc: 0.979291\n",
      "[740]\tvalid_0's auc: 0.979366\n",
      "[760]\tvalid_0's auc: 0.979432\n",
      "[780]\tvalid_0's auc: 0.979495\n",
      "[800]\tvalid_0's auc: 0.97955\n",
      "[820]\tvalid_0's auc: 0.979604\n",
      "[840]\tvalid_0's auc: 0.979656\n",
      "[860]\tvalid_0's auc: 0.979705\n",
      "[880]\tvalid_0's auc: 0.979748\n",
      "[900]\tvalid_0's auc: 0.979793\n",
      "[920]\tvalid_0's auc: 0.979833\n",
      "[940]\tvalid_0's auc: 0.979874\n",
      "[960]\tvalid_0's auc: 0.979911\n",
      "[980]\tvalid_0's auc: 0.97995\n",
      "[1000]\tvalid_0's auc: 0.979983\n",
      "[1020]\tvalid_0's auc: 0.980026\n",
      "[1040]\tvalid_0's auc: 0.980058\n",
      "[1060]\tvalid_0's auc: 0.980088\n",
      "[1080]\tvalid_0's auc: 0.980118\n",
      "[1100]\tvalid_0's auc: 0.980147\n",
      "[1120]\tvalid_0's auc: 0.980171\n",
      "[1140]\tvalid_0's auc: 0.980202\n",
      "[1160]\tvalid_0's auc: 0.980232\n",
      "[1180]\tvalid_0's auc: 0.980262\n",
      "[1200]\tvalid_0's auc: 0.980288\n",
      "[1220]\tvalid_0's auc: 0.980319\n",
      "[1240]\tvalid_0's auc: 0.98034\n",
      "[1260]\tvalid_0's auc: 0.980367\n",
      "[1280]\tvalid_0's auc: 0.980394\n",
      "[1300]\tvalid_0's auc: 0.980415\n",
      "[1320]\tvalid_0's auc: 0.98044\n",
      "[1340]\tvalid_0's auc: 0.980461\n",
      "[1360]\tvalid_0's auc: 0.980482\n",
      "[1380]\tvalid_0's auc: 0.980498\n",
      "[1400]\tvalid_0's auc: 0.980514\n",
      "[1420]\tvalid_0's auc: 0.980529\n",
      "[1440]\tvalid_0's auc: 0.980553\n",
      "[1460]\tvalid_0's auc: 0.980569\n",
      "[1480]\tvalid_0's auc: 0.980586\n",
      "[1500]\tvalid_0's auc: 0.980603\n",
      "[1520]\tvalid_0's auc: 0.980622\n",
      "[1540]\tvalid_0's auc: 0.980641\n",
      "[1560]\tvalid_0's auc: 0.980658\n",
      "[1580]\tvalid_0's auc: 0.980677\n",
      "[1600]\tvalid_0's auc: 0.980699\n",
      "[1620]\tvalid_0's auc: 0.980715\n",
      "[1640]\tvalid_0's auc: 0.980734\n",
      "[1660]\tvalid_0's auc: 0.980757\n",
      "[1680]\tvalid_0's auc: 0.98077\n",
      "[1700]\tvalid_0's auc: 0.980782\n",
      "[1720]\tvalid_0's auc: 0.980794\n",
      "[1740]\tvalid_0's auc: 0.980809\n",
      "[1760]\tvalid_0's auc: 0.980822\n",
      "[1780]\tvalid_0's auc: 0.980836\n",
      "[1800]\tvalid_0's auc: 0.980847\n",
      "[1820]\tvalid_0's auc: 0.98086\n",
      "[1840]\tvalid_0's auc: 0.980873\n",
      "[1860]\tvalid_0's auc: 0.980884\n",
      "[1880]\tvalid_0's auc: 0.980894\n",
      "[1900]\tvalid_0's auc: 0.980905\n",
      "[1920]\tvalid_0's auc: 0.980912\n",
      "[1940]\tvalid_0's auc: 0.980922\n",
      "[1960]\tvalid_0's auc: 0.980934\n",
      "[1980]\tvalid_0's auc: 0.980942\n",
      "[2000]\tvalid_0's auc: 0.98095\n",
      "[2020]\tvalid_0's auc: 0.980957\n",
      "[2040]\tvalid_0's auc: 0.980963\n",
      "[2060]\tvalid_0's auc: 0.980973\n",
      "[2080]\tvalid_0's auc: 0.980977\n",
      "[2100]\tvalid_0's auc: 0.980987\n",
      "[2120]\tvalid_0's auc: 0.980994\n",
      "[2140]\tvalid_0's auc: 0.981001\n",
      "[2160]\tvalid_0's auc: 0.981006\n",
      "[2180]\tvalid_0's auc: 0.98101\n",
      "[2200]\tvalid_0's auc: 0.981016\n",
      "[2220]\tvalid_0's auc: 0.981022\n",
      "[2240]\tvalid_0's auc: 0.981026\n",
      "[2260]\tvalid_0's auc: 0.98103\n",
      "[2280]\tvalid_0's auc: 0.981037\n",
      "[2300]\tvalid_0's auc: 0.98104\n",
      "[2320]\tvalid_0's auc: 0.981047\n",
      "[2340]\tvalid_0's auc: 0.981054\n",
      "[2360]\tvalid_0's auc: 0.981055\n",
      "[2380]\tvalid_0's auc: 0.981058\n",
      "[2400]\tvalid_0's auc: 0.98106\n",
      "[2420]\tvalid_0's auc: 0.981064\n",
      "[2440]\tvalid_0's auc: 0.981066\n",
      "[2460]\tvalid_0's auc: 0.98107\n",
      "[2480]\tvalid_0's auc: 0.981071\n",
      "[2500]\tvalid_0's auc: 0.981069\n",
      "[2520]\tvalid_0's auc: 0.981075\n",
      "[2540]\tvalid_0's auc: 0.981075\n",
      "[2560]\tvalid_0's auc: 0.981078\n",
      "[2580]\tvalid_0's auc: 0.981082\n",
      "[2600]\tvalid_0's auc: 0.981083\n",
      "[2620]\tvalid_0's auc: 0.981085\n",
      "[2640]\tvalid_0's auc: 0.981086\n",
      "[2660]\tvalid_0's auc: 0.981089\n",
      "[2680]\tvalid_0's auc: 0.981087\n",
      "[2700]\tvalid_0's auc: 0.981086\n",
      "[2720]\tvalid_0's auc: 0.981087\n",
      "[2740]\tvalid_0's auc: 0.981087\n",
      "Early stopping, best iteration is:\n",
      "[2651]\tvalid_0's auc: 0.98109\n"
     ]
    }
   ],
   "source": [
    "#train the model with the hyper-parameters\n",
    "\n",
    "params = {'task': 'train', 'boosting_type': 'gbdt', 'objective': 'binary', 'metric': 'auc', \n",
    "          'learning_rate': 0.01, 'num_leaves': 48, 'num_iteration': 5000, 'verbose': 0 ,\n",
    "          'colsample_bytree':.8, 'subsample':.9, 'max_depth':7, 'reg_alpha':.1, 'reg_lambda':.1, \n",
    "          'min_split_gain':.01, 'min_child_weight':1}\n",
    "\n",
    "model = lgb.train(params, lgb_train, valid_sets=lgb_eval, early_stopping_rounds=100, verbose_eval=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "#saving the model\n",
    "filename = 'finalized_model.sav'\n",
    "pickle.dump(model, open(filename, 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load the model from disk\n",
    "#loaded_model = pickle.load(open(filename, 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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