CreditScoring.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
    "from sklearn.compose import make_column_transformer\n",
    "from sklearn.compose import ColumnTransformer\n",
    "\n",
    "from sklearn.metrics import confusion_matrix as cm\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.pipeline import Pipeline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "production = pd.read_sas('C:/Users/kucza/OneDrive/Desktop/DataScience/CreditScoringPython/dane/production.sas7bdat',\n",
    "                        format = 'sas7bdat',\n",
    "                         encoding=\"ISO-8859-1\" )\n",
    "transactions = pd.read_sas('C:/Users/kucza/OneDrive/Desktop/DataScience/CreditScoringPython/dane/transactions.sas7bdat',\n",
    "                        format = 'sas7bdat',\n",
    "                         encoding=\"ISO-8859-1\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cid</th>\n",
       "      <th>aid</th>\n",
       "      <th>product</th>\n",
       "      <th>period</th>\n",
       "      <th>act_age</th>\n",
       "      <th>act_cc</th>\n",
       "      <th>act_loaninc</th>\n",
       "      <th>app_income</th>\n",
       "      <th>app_loan_amount</th>\n",
       "      <th>app_n_installments</th>\n",
       "      <th>...</th>\n",
       "      <th>act_CIns_All5y</th>\n",
       "      <th>act_CAll_Acp5y</th>\n",
       "      <th>act_CAll_All5y</th>\n",
       "      <th>APP_CHAR_GENDER</th>\n",
       "      <th>APP_CHAR_CITY</th>\n",
       "      <th>APP_CHAR_BRANCH</th>\n",
       "      <th>APP_CHAR_JOB_CODE</th>\n",
       "      <th>APP_CHAR_CARS</th>\n",
       "      <th>APP_CHAR_HOME_STATUS</th>\n",
       "      <th>APP_CHAR_MARITAL_STATUS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0000001237</td>\n",
       "      <td>css2006010100123</td>\n",
       "      <td>css</td>\n",
       "      <td>200601</td>\n",
       "      <td>84.0</td>\n",
       "      <td>0.577685</td>\n",
       "      <td>2.606882</td>\n",
       "      <td>1918.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0000003380</td>\n",
       "      <td>css2006010100350</td>\n",
       "      <td>css</td>\n",
       "      <td>200601</td>\n",
       "      <td>87.0</td>\n",
       "      <td>0.499747</td>\n",
       "      <td>2.529084</td>\n",
       "      <td>1977.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
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       "      <td>0000003467</td>\n",
       "      <td>css2006010100357</td>\n",
       "      <td>css</td>\n",
       "      <td>200601</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0.584158</td>\n",
       "      <td>4.125413</td>\n",
       "      <td>1212.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0000003620</td>\n",
       "      <td>css2006010100367</td>\n",
       "      <td>css</td>\n",
       "      <td>200601</td>\n",
       "      <td>94.0</td>\n",
       "      <td>0.602687</td>\n",
       "      <td>4.798464</td>\n",
       "      <td>1042.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0000003621</td>\n",
       "      <td>css2006010100368</td>\n",
       "      <td>css</td>\n",
       "      <td>200601</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0.604592</td>\n",
       "      <td>3.188776</td>\n",
       "      <td>1568.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 223 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          cid               aid product  period  act_age    act_cc  \\\n",
       "0  0000001237  css2006010100123     css  200601     84.0  0.577685   \n",
       "1  0000003380  css2006010100350     css  200601     87.0  0.499747   \n",
       "2  0000003467  css2006010100357     css  200601     75.0  0.584158   \n",
       "3  0000003620  css2006010100367     css  200601     94.0  0.602687   \n",
       "4  0000003621  css2006010100368     css  200601     70.0  0.604592   \n",
       "\n",
       "   act_loaninc  app_income  app_loan_amount  app_n_installments  ...  \\\n",
       "0     2.606882      1918.0           5000.0                24.0  ...   \n",
       "1     2.529084      1977.0           5000.0                24.0  ...   \n",
       "2     4.125413      1212.0           5000.0                24.0  ...   \n",
       "3     4.798464      1042.0           5000.0                24.0  ...   \n",
       "4     3.188776      1568.0           5000.0                24.0  ...   \n",
       "\n",
       "   act_CIns_All5y  act_CAll_Acp5y  act_CAll_All5y  APP_CHAR_GENDER  \\\n",
       "0             1.0             7.0            15.0              1.0   \n",
       "1             NaN            14.0            15.0              1.0   \n",
       "2             1.0            16.0            16.0              1.0   \n",
       "3             NaN            14.0            15.0              1.0   \n",
       "4             NaN            15.0            15.0              1.0   \n",
       "\n",
       "   APP_CHAR_CITY  APP_CHAR_BRANCH  APP_CHAR_JOB_CODE  APP_CHAR_CARS  \\\n",
       "0            0.0              0.0                4.0            1.0   \n",
       "1            3.0              0.0                4.0            0.0   \n",
       "2            1.0              0.0                4.0            1.0   \n",
       "3            0.0              0.0                4.0            1.0   \n",
       "4            0.0              0.0                4.0            1.0   \n",
       "\n",
       "   APP_CHAR_HOME_STATUS  APP_CHAR_MARITAL_STATUS  \n",
       "0                   0.0                      2.0  \n",
       "1                   0.0                      1.0  \n",
       "2                   0.0                      1.0  \n",
       "3                   0.0                      0.0  \n",
       "4                   0.0                      1.0  \n",
       "\n",
       "[5 rows x 223 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "production.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cid</th>\n",
       "      <th>aid</th>\n",
       "      <th>product</th>\n",
       "      <th>period</th>\n",
       "      <th>fin_period</th>\n",
       "      <th>status</th>\n",
       "      <th>due_installments</th>\n",
       "      <th>paid_installments</th>\n",
       "      <th>leftn_installments</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0000000002</td>\n",
       "      <td>css2006010500001</td>\n",
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       "      <td>200601</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0000000002</td>\n",
       "      <td>css2006010500001</td>\n",
       "      <td>css</td>\n",
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       "      <td>200601</td>\n",
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       "    <tr>\n",
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       "      <td>0000000002</td>\n",
       "      <td>css2006010500001</td>\n",
       "      <td>css</td>\n",
       "      <td>200604</td>\n",
       "      <td>200601</td>\n",
       "      <td>A</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0000000002</td>\n",
       "      <td>css2006010500001</td>\n",
       "      <td>css</td>\n",
       "      <td>200605</td>\n",
       "      <td>200601</td>\n",
       "      <td>A</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>20.0</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "          cid               aid product  period fin_period status  \\\n",
       "0  0000000002  css2006010500001     css  200601     200601      A   \n",
       "1  0000000002  css2006010500001     css  200602     200601      A   \n",
       "2  0000000002  css2006010500001     css  200603     200601      A   \n",
       "3  0000000002  css2006010500001     css  200604     200601      A   \n",
       "4  0000000002  css2006010500001     css  200605     200601      A   \n",
       "\n",
       "   due_installments  paid_installments  leftn_installments  \n",
       "0               0.0                0.0                24.0  \n",
       "1               0.0                1.0                23.0  \n",
       "2               0.0                2.0                22.0  \n",
       "3               0.0                3.0                21.0  \n",
       "4               0.0                4.0                20.0  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Default definition: if a product ever has n_installments_due or more it is considered as default, no matter if the client pays the loan back at a later time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "############## PROBLEM SPECIFIC FUNCTIONS ###########################\n",
    "def MaxDueInstallmentsMinPeriod(data_transactions):\n",
    "    #Creating table with product id, first installment period adn max due installements.\n",
    "    max_due_install_min_period = pd.pivot_table(data = data_transactions, index = 'aid',\n",
    "                                            values = ['due_installments', 'period'], aggfunc = ['max', 'min'])\n",
    "    max_due_install_min_period.columns = ['max_due_installments', 'max_period', 'min_due_installments', 'min_period']\n",
    "    max_due_install_min_period.drop(['max_period', 'min_due_installments'], axis = 1, inplace = True)\n",
    "    max_due_install_min_period['min_period'] = pd.to_numeric(max_due_install_min_period['min_period'])\n",
    "    return max_due_install_min_period\n",
    "\n",
    "def DefaultPerProduct(data_product, n_installments_due = 3, max_period = 200712):\n",
    "    #Creating dataframe with 0-1 variable if the product defaulted\n",
    "    col_name = 'default'+str(n_installments_due)\n",
    "    data_product.insert(len(data_product.columns),col_name, 0)\n",
    "    for index, row in data_product.iterrows():        \n",
    "        if row['max_due_installments'] >= n_installments_due and row['min_period'] <= max_period:\n",
    "            data_product.at[index, col_name] = 1\n",
    "        else:\n",
    "            data_product.at[index, col_name] = 0\n",
    "    data_product.drop(['min_period', 'max_due_installments'], axis = 1, inplace = True)\n",
    "    return data_product\n",
    "\n",
    "def ProductionDefault(data_product_default, data_production):\n",
    "    #Adding col1umn with 0-1 default to the main dataframe\n",
    "    data_production['period'] = pd.to_numeric(data_production['period'])\n",
    "    data_production = data_production[data_production['period'] <= 200712]\n",
    "    data_merged = data_production.merge(right = data_product_default, on = 'aid')\n",
    "    return data_merged\n",
    "\n",
    "############## PREPORCESSING FUNCTIONS ###########################\n",
    "\n",
    "def unique_count(dataframe):\n",
    "    uniques = dataframe.from_records([(col, dataframe[col].nunique()) for col in dataframe.columns], \n",
    "                                        columns=['Column_Name', 'Num_Unique']).sort_values(by=['Num_Unique'])\n",
    "    return uniques\n",
    "\n",
    "def DeleteMissing(dataframe, perc_missing_delete = 0.6):\n",
    "    return_df = dataframe[dataframe.columns[dataframe.isnull().mean() <= perc_missing_delete]]\n",
    "    return return_df\n",
    "\n",
    "def get_column_names_from_ColumnTransformer(column_transformer):    \n",
    "    col_name = []\n",
    "    for transformer_in_columns in column_transformer.transformers_[:-1]:#the last transformer is ColumnTransformer's 'remainder'\n",
    "        raw_col_name = transformer_in_columns[1]\n",
    "        if isinstance(transformer_in_columns[1],Pipeline): \n",
    "            transformer = transformer_in_columns[1].steps[-1][1]\n",
    "        else:\n",
    "            transformer = transformer_in_columns[1]\n",
    "        try:\n",
    "            names = transformer.get_feature_names()\n",
    "        except AttributeError: # if no 'get_feature_names' function, use raw column name\n",
    "            names = raw_col_name\n",
    "        if isinstance(names,np.ndarray): # eg.\n",
    "            col_name += names.tolist()\n",
    "        elif isinstance(names,list):\n",
    "            col_name += names    \n",
    "        elif isinstance(names,str):\n",
    "            col_name.append(names)\n",
    "    return col_name\n",
    "\n",
    "def Imputer_df(dataframe, num_cols_names, cat_cols_names, strategy_num_cols = 'mean',\n",
    "               strategy_cat_cols = 'most_frequent', remainder = 'passthrough'):\n",
    "    names = num_cols+cat_cols+[name for name in dataframe.columns if name not in num_cols+cat_cols]\n",
    "    ct1 = ColumnTransformer([\n",
    "        ('imput_mean', SimpleImputer(strategy = strategy_num_cols), num_cols),\n",
    "        ('imput_most_freq', SimpleImputer(strategy = strategy_cat_cols), cat_cols)\n",
    "        ], remainder = 'passthrough')\n",
    "    return_df = pd.DataFrame(data = ct1.fit_transform(dataframe), columns = names)\n",
    "    return return_df\n",
    "\n",
    "\n",
    "def ColumnValuesCheck(df1, df2):\n",
    "    # Columns in both dataframes must have the same names\n",
    "    #checks if columns with the same names have the same values. Columns do not have to be in the same order\n",
    "    #DO NOT USE AFTER IMPUTING MISSING VALUES\n",
    "    list = []\n",
    "    for column in df1:\n",
    "        column_values1 = df1[column]\n",
    "        column_values2 = df2[column]\n",
    "        if column_values1.equals(column_values2):\n",
    "            list = list + [1]\n",
    "        else:\n",
    "            list = list + [0]\n",
    "    return list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "#transformer_in_columns = transformer.transformers_[:-1]\n",
    "#raw_col_name = transformer_in_columns[1]\n",
    "#raw_col_name[1].get_feature_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = MaxDueInstallmentsMinPeriod(transactions)\n",
    "def_product = DefaultPerProduct(data, 3, 200712)\n",
    "data_merged = ProductionDefault(def_product, production)\n",
    "data_merged_nomisscol = DeleteMissing(data_merged, 0.6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_css = data_merged_nomisscol[data_merged_nomisscol['product'] == 'css']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_css_select = data_css.iloc[:,5:29].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_css_default3 = data_css.loc[:,'default3'].values\n",
    "y = data_css_default3.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
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       "<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>act_cc</th>\n",
       "      <th>act_loaninc</th>\n",
       "      <th>app_income</th>\n",
       "      <th>app_loan_amount</th>\n",
       "      <th>app_n_installments</th>\n",
       "      <th>app_number_of_children</th>\n",
       "      <th>app_spendings</th>\n",
       "      <th>app_installment</th>\n",
       "      <th>act_call_cc</th>\n",
       "      <th>act_cins_n_loan</th>\n",
       "      <th>...</th>\n",
       "      <th>act_cins_n_loans_hist</th>\n",
       "      <th>act_cins_n_statC</th>\n",
       "      <th>act_cins_n_statB</th>\n",
       "      <th>act_ccss_seniority</th>\n",
       "      <th>act_ccss_min_seniority</th>\n",
       "      <th>act_ccss_n_loans_hist</th>\n",
       "      <th>act_ccss_n_statC</th>\n",
       "      <th>act_ccss_n_statB</th>\n",
       "      <th>act_cus_active</th>\n",
       "      <th>act_CMaxA_Days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.577685</td>\n",
       "      <td>2.606882</td>\n",
       "      <td>1918.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>1.119917</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>307.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.499747</td>\n",
       "      <td>2.529084</td>\n",
       "      <td>1977.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>780.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>1.025797</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>252.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.584158</td>\n",
       "      <td>4.125413</td>\n",
       "      <td>1212.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>1.442244</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>309.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.602687</td>\n",
       "      <td>4.798464</td>\n",
       "      <td>1042.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>420.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>1.401152</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>299.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.604592</td>\n",
       "      <td>3.188776</td>\n",
       "      <td>1568.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>740.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>1.267857</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>307.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     act_cc  act_loaninc  app_income  app_loan_amount  app_n_installments  \\\n",
       "0  0.577685     2.606882      1918.0           5000.0                24.0   \n",
       "1  0.499747     2.529084      1977.0           5000.0                24.0   \n",
       "2  0.584158     4.125413      1212.0           5000.0                24.0   \n",
       "3  0.602687     4.798464      1042.0           5000.0                24.0   \n",
       "4  0.604592     3.188776      1568.0           5000.0                24.0   \n",
       "\n",
       "   app_number_of_children  app_spendings  app_installment  act_call_cc  \\\n",
       "0                     0.0          900.0            208.0     1.119917   \n",
       "1                     0.0          780.0            208.0     1.025797   \n",
       "2                     0.0          500.0            208.0     1.442244   \n",
       "3                     0.0          420.0            208.0     1.401152   \n",
       "4                     0.0          740.0            208.0     1.267857   \n",
       "\n",
       "   act_cins_n_loan  ...  act_cins_n_loans_hist  act_cins_n_statC  \\\n",
       "0              0.0  ...                   21.0              21.0   \n",
       "1              0.0  ...                    9.0               6.0   \n",
       "2              0.0  ...                    6.0               6.0   \n",
       "3              0.0  ...                   11.0              11.0   \n",
       "4              0.0  ...                   11.0              11.0   \n",
       "\n",
       "   act_cins_n_statB  act_ccss_seniority  act_ccss_min_seniority  \\\n",
       "0               0.0               307.0                     4.0   \n",
       "1               3.0               252.0                     4.0   \n",
       "2               0.0               309.0                     4.0   \n",
       "3               0.0               299.0                     4.0   \n",
       "4               0.0               307.0                     4.0   \n",
       "\n",
       "   act_ccss_n_loans_hist  act_ccss_n_statC  act_ccss_n_statB  act_cus_active  \\\n",
       "0                   51.0              43.0               2.0             1.0   \n",
       "1                   40.0              32.0               2.0             1.0   \n",
       "2                   76.0              70.0               0.0             1.0   \n",
       "3                   69.0              62.0               2.0             1.0   \n",
       "4                   69.0              62.0               1.0             1.0   \n",
       "\n",
       "   act_CMaxA_Days  \n",
       "0            15.0  \n",
       "1            14.0  \n",
       "2            14.0  \n",
       "3            15.0  \n",
       "4            14.0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_css_select.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "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>Column_Name</th>\n",
       "      <th>Num_Unique</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>act_cus_active</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>app_loan_amount</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>app_n_installments</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>app_installment</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>app_number_of_children</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>act_cins_n_loan</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>act_ccss_n_loan</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>act_call_n_loan</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>act_cins_n_statB</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>act_CMaxA_Days</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>act_cins_n_statC</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>act_cins_n_loans_hist</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>act_ccss_n_statB</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>act_ccss_n_statC</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>act_ccss_n_loans_hist</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>app_spendings</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>act_ccss_min_seniority</td>\n",
       "      <td>277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>act_cins_min_seniority</td>\n",
       "      <td>297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>act_ccss_seniority</td>\n",
       "      <td>327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>act_cins_seniority</td>\n",
       "      <td>335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>app_income</td>\n",
       "      <td>2933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>act_loaninc</td>\n",
       "      <td>2933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>act_cc</td>\n",
       "      <td>12068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>act_call_cc</td>\n",
       "      <td>19976</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Column_Name  Num_Unique\n",
       "22          act_cus_active           1\n",
       "3          app_loan_amount           1\n",
       "4       app_n_installments           1\n",
       "7          app_installment           1\n",
       "5   app_number_of_children           4\n",
       "9          act_cins_n_loan           4\n",
       "10         act_ccss_n_loan           7\n",
       "11         act_call_n_loan           8\n",
       "16        act_cins_n_statB          22\n",
       "23          act_CMaxA_Days          25\n",
       "15        act_cins_n_statC          29\n",
       "14   act_cins_n_loans_hist          32\n",
       "21        act_ccss_n_statB          33\n",
       "20        act_ccss_n_statC          76\n",
       "19   act_ccss_n_loans_hist          81\n",
       "6            app_spendings         170\n",
       "18  act_ccss_min_seniority         277\n",
       "13  act_cins_min_seniority         297\n",
       "17      act_ccss_seniority         327\n",
       "12      act_cins_seniority         335\n",
       "2               app_income        2933\n",
       "1              act_loaninc        2933\n",
       "0                   act_cc       12068\n",
       "8              act_call_cc       19976"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_count(data_css_select)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda\\lib\\site-packages\\numpy\\lib\\histograms.py:824: RuntimeWarning: invalid value encountered in greater_equal\n",
      "  keep = (tmp_a >= first_edge)\n",
      "C:\\Anaconda\\lib\\site-packages\\numpy\\lib\\histograms.py:825: RuntimeWarning: invalid value encountered in less_equal\n",
      "  keep &= (tmp_a <= last_edge)\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": [
    "plt.hist(data_css_select['act_ccss_n_loans_hist'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_cols = unique_count(data_css_select)['Column_Name'][0:4].values.tolist()\n",
    "cat_cols = unique_count(data_css_select)['Column_Name'][4:8].values.tolist()\n",
    "num_cols = unique_count(data_css_select)['Column_Name'][8:23].values.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "ct1 = ColumnTransformer([\n",
    "    ('imput_mean', SimpleImputer(strategy = 'mean'), num_cols),\n",
    "    ('imput_most_freq', SimpleImputer(strategy = 'most_frequent'), cat_cols)\n",
    "], remainder = 'drop')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "ct2 = ColumnTransformer([\n",
    "    ('stand_scale', StandardScaler(), num_cols),\n",
    "    ('ohe', OneHotEncoder(categories = 'auto'), cat_cols)\n",
    "], remainder = 'drop')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline = Pipeline([\n",
    "    ('column transformer', transformer),\n",
    "    ('ling_reg', LogisticRegression())    \n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pipeline.fit(data_css_select, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ColumnValuesCheck(data_X, data_css_select)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_X = Imputer_df(dataframe = data_css_select, num_cols_names = num_cols, cat_cols_names = cat_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "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>act_cc</th>\n",
       "      <th>act_loaninc</th>\n",
       "      <th>app_income</th>\n",
       "      <th>app_loan_amount</th>\n",
       "      <th>app_n_installments</th>\n",
       "      <th>app_number_of_children</th>\n",
       "      <th>app_spendings</th>\n",
       "      <th>app_installment</th>\n",
       "      <th>act_call_cc</th>\n",
       "      <th>act_cins_n_loan</th>\n",
       "      <th>...</th>\n",
       "      <th>act_cins_n_loans_hist</th>\n",
       "      <th>act_cins_n_statC</th>\n",
       "      <th>act_cins_n_statB</th>\n",
       "      <th>act_ccss_seniority</th>\n",
       "      <th>act_ccss_min_seniority</th>\n",
       "      <th>act_ccss_n_loans_hist</th>\n",
       "      <th>act_ccss_n_statC</th>\n",
       "      <th>act_ccss_n_statB</th>\n",
       "      <th>act_cus_active</th>\n",
       "      <th>act_CMaxA_Days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.577685</td>\n",
       "      <td>2.606882</td>\n",
       "      <td>1918.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>1.119917</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>307.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.499747</td>\n",
       "      <td>2.529084</td>\n",
       "      <td>1977.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>780.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>1.025797</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>252.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.584158</td>\n",
       "      <td>4.125413</td>\n",
       "      <td>1212.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>1.442244</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>309.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.602687</td>\n",
       "      <td>4.798464</td>\n",
       "      <td>1042.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>420.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>1.401152</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>299.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.604592</td>\n",
       "      <td>3.188776</td>\n",
       "      <td>1568.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>740.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>1.267857</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>307.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     act_cc  act_loaninc  app_income  app_loan_amount  app_n_installments  \\\n",
       "0  0.577685     2.606882      1918.0           5000.0                24.0   \n",
       "1  0.499747     2.529084      1977.0           5000.0                24.0   \n",
       "2  0.584158     4.125413      1212.0           5000.0                24.0   \n",
       "3  0.602687     4.798464      1042.0           5000.0                24.0   \n",
       "4  0.604592     3.188776      1568.0           5000.0                24.0   \n",
       "\n",
       "   app_number_of_children  app_spendings  app_installment  act_call_cc  \\\n",
       "0                     0.0          900.0            208.0     1.119917   \n",
       "1                     0.0          780.0            208.0     1.025797   \n",
       "2                     0.0          500.0            208.0     1.442244   \n",
       "3                     0.0          420.0            208.0     1.401152   \n",
       "4                     0.0          740.0            208.0     1.267857   \n",
       "\n",
       "   act_cins_n_loan  ...  act_cins_n_loans_hist  act_cins_n_statC  \\\n",
       "0              0.0  ...                   21.0              21.0   \n",
       "1              0.0  ...                    9.0               6.0   \n",
       "2              0.0  ...                    6.0               6.0   \n",
       "3              0.0  ...                   11.0              11.0   \n",
       "4              0.0  ...                   11.0              11.0   \n",
       "\n",
       "   act_cins_n_statB  act_ccss_seniority  act_ccss_min_seniority  \\\n",
       "0               0.0               307.0                     4.0   \n",
       "1               3.0               252.0                     4.0   \n",
       "2               0.0               309.0                     4.0   \n",
       "3               0.0               299.0                     4.0   \n",
       "4               0.0               307.0                     4.0   \n",
       "\n",
       "   act_ccss_n_loans_hist  act_ccss_n_statC  act_ccss_n_statB  act_cus_active  \\\n",
       "0                   51.0              43.0               2.0             1.0   \n",
       "1                   40.0              32.0               2.0             1.0   \n",
       "2                   76.0              70.0               0.0             1.0   \n",
       "3                   69.0              62.0               2.0             1.0   \n",
       "4                   69.0              62.0               1.0             1.0   \n",
       "\n",
       "   act_CMaxA_Days  \n",
       "0            15.0  \n",
       "1            14.0  \n",
       "2            14.0  \n",
       "3            15.0  \n",
       "4            14.0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_css_select.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>act_cins_n_statB</th>\n",
       "      <th>act_CMaxA_Days</th>\n",
       "      <th>act_cins_n_statC</th>\n",
       "      <th>act_cins_n_loans_hist</th>\n",
       "      <th>act_ccss_n_statB</th>\n",
       "      <th>act_ccss_n_statC</th>\n",
       "      <th>act_ccss_n_loans_hist</th>\n",
       "      <th>app_spendings</th>\n",
       "      <th>act_ccss_min_seniority</th>\n",
       "      <th>act_cins_min_seniority</th>\n",
       "      <th>...</th>\n",
       "      <th>act_cc</th>\n",
       "      <th>app_number_of_children</th>\n",
       "      <th>act_cins_n_loan</th>\n",
       "      <th>act_ccss_n_loan</th>\n",
       "      <th>act_call_n_loan</th>\n",
       "      <th>app_loan_amount</th>\n",
       "      <th>app_n_installments</th>\n",
       "      <th>app_installment</th>\n",
       "      <th>act_call_cc</th>\n",
       "      <th>act_cus_active</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.577685</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>1.119917</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>780.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.499747</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>1.025797</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.584158</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>1.442244</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>420.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.602687</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>1.401152</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>740.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.604592</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>1.267857</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   act_cins_n_statB  act_CMaxA_Days  act_cins_n_statC  act_cins_n_loans_hist  \\\n",
       "0               0.0            15.0              21.0                   21.0   \n",
       "1               3.0            14.0               6.0                    9.0   \n",
       "2               0.0            14.0               6.0                    6.0   \n",
       "3               0.0            15.0              11.0                   11.0   \n",
       "4               0.0            14.0              11.0                   11.0   \n",
       "\n",
       "   act_ccss_n_statB  act_ccss_n_statC  act_ccss_n_loans_hist  app_spendings  \\\n",
       "0               2.0              43.0                   51.0          900.0   \n",
       "1               2.0              32.0                   40.0          780.0   \n",
       "2               0.0              70.0                   76.0          500.0   \n",
       "3               2.0              62.0                   69.0          420.0   \n",
       "4               1.0              62.0                   69.0          740.0   \n",
       "\n",
       "   act_ccss_min_seniority  act_cins_min_seniority  ...    act_cc  \\\n",
       "0                     4.0                    28.0  ...  0.577685   \n",
       "1                     4.0                   104.0  ...  0.499747   \n",
       "2                     4.0                    28.0  ...  0.584158   \n",
       "3                     4.0                   100.0  ...  0.602687   \n",
       "4                     4.0                    76.0  ...  0.604592   \n",
       "\n",
       "   app_number_of_children  act_cins_n_loan  act_ccss_n_loan  act_call_n_loan  \\\n",
       "0                     0.0              0.0              6.0              6.0   \n",
       "1                     0.0              0.0              6.0              6.0   \n",
       "2                     0.0              0.0              6.0              6.0   \n",
       "3                     0.0              0.0              5.0              5.0   \n",
       "4                     0.0              0.0              6.0              6.0   \n",
       "\n",
       "   app_loan_amount  app_n_installments  app_installment  act_call_cc  \\\n",
       "0           5000.0                24.0            208.0     1.119917   \n",
       "1           5000.0                24.0            208.0     1.025797   \n",
       "2           5000.0                24.0            208.0     1.442244   \n",
       "3           5000.0                24.0            208.0     1.401152   \n",
       "4           5000.0                24.0            208.0     1.267857   \n",
       "\n",
       "   act_cus_active  \n",
       "0             1.0  \n",
       "1             1.0  \n",
       "2             1.0  \n",
       "3             1.0  \n",
       "4             1.0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_X = transformer.fit_transform(data_css_select)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_X = ct1.fit_transform(data_css_select)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data_css_select)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(data_css_select, y, test_size = 0.2, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Input contains NaN, infinity or a value too large for dtype('float64').",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-87-b87598fac5f6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mlin_reg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mLogisticRegression\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mlin_reg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mC:\\Anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m   1530\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1531\u001b[0m         X, y = check_X_y(X, y, accept_sparse='csr', dtype=_dtype, order=\"C\",\n\u001b[1;32m-> 1532\u001b[1;33m                          accept_large_sparse=solver != 'liblinear')\n\u001b[0m\u001b[0;32m   1533\u001b[0m         \u001b[0mcheck_classification_targets\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1534\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclasses_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munique\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_X_y\u001b[1;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, warn_on_dtype, estimator)\u001b[0m\n\u001b[0;32m    717\u001b[0m                     \u001b[0mensure_min_features\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mensure_min_features\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    718\u001b[0m                     \u001b[0mwarn_on_dtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mwarn_on_dtype\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 719\u001b[1;33m                     estimator=estimator)\n\u001b[0m\u001b[0;32m    720\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mmulti_output\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    721\u001b[0m         y = check_array(y, 'csr', force_all_finite=True, ensure_2d=False,\n",
      "\u001b[1;32mC:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[0;32m    540\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    541\u001b[0m             _assert_all_finite(array,\n\u001b[1;32m--> 542\u001b[1;33m                                allow_nan=force_all_finite == 'allow-nan')\n\u001b[0m\u001b[0;32m    543\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    544\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mensure_min_samples\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[1;34m(X, allow_nan)\u001b[0m\n\u001b[0;32m     54\u001b[0m                 not allow_nan and not np.isfinite(X).all()):\n\u001b[0;32m     55\u001b[0m             \u001b[0mtype_err\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'infinity'\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mallow_nan\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;34m'NaN, infinity'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 56\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmsg_err\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtype_err\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     57\u001b[0m     \u001b[1;31m# for object dtype data, we only check for NaNs (GH-13254)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     58\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'object'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mallow_nan\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float64')."
     ]
    }
   ],
   "source": [
    "lin_reg = LogisticRegression()\n",
    "lin_reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_prob = lin_reg.predict_proba(X_test)\n",
    "y_pred = lin_reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qmNsSxV8xCvZ5X4OFVcZcVSerantVLVTVAqPzDG+sqvX9IYDNY8jv9z8wOoFOku2MlmkemmqV4zVkzN8ALgVI8jJG4X5iqlVO3xHgre2qmUuAk1X12Lq/26zPIA88y3w5oz8A8nXgfa3tjxj954bRD/5vgWPAF4AXz7rmKYz5n4FvAve2jyOzrnnSYz6t7x10cLXMwJ91gPcz+psIXwaunnXNUxjzRcDnGV1Jcy/wi7OueQxj/gTwGPADRrP0/cDbgbcv+Tl/sP2bfHmjv9/efkCSOjQPyzKSpDUy3CWpQ4a7JHXIcJekDhnuktQhw12SOmS4S1KH/h+th4W4LEV47QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred_prob_plot = [y_pred_prob[i][1] for i in range(len(y_pred_prob))]\n",
    "plt.hist(y_pred_prob_plot, bins=100)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "cv = cross_val_score(lin_reg, data_X, y, cv = 5, scoring = 'f1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "avg. accuracy = 0.3798 std. of accuracy = 0.0063 std. coef = 0.0166\n"
     ]
    }
   ],
   "source": [
    "print(\"avg. accuracy = \"+str(round(np.mean(cv),4))+\n",
    "      \" std. of accuracy = \"+str(round(np.std(cv),4))+\n",
    "      \" std. coef = \"+str(round(np.std(cv)/np.mean(cv),4)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[4345  237]\n",
      " [1164  428]]\n"
     ]
    }
   ],
   "source": [
    "print(cm(y_true = y_test, y_pred = y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['x0_0.0',\n",
       " 'x0_1.0',\n",
       " 'x0_2.0',\n",
       " 'x0_3.0',\n",
       " 'x1_0.0',\n",
       " 'x1_1.0',\n",
       " 'x1_2.0',\n",
       " 'x1_3.0',\n",
       " 'x2_1.0',\n",
       " 'x2_2.0',\n",
       " 'x2_3.0',\n",
       " 'x2_4.0',\n",
       " 'x2_5.0',\n",
       " 'x2_6.0',\n",
       " 'x2_7.0',\n",
       " 'drop']"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_column_names_from_ColumnTransformer(transformer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>cid</th>\n",
       "      <th>aid</th>\n",
       "      <th>product</th>\n",
       "      <th>period</th>\n",
       "      <th>act_age</th>\n",
       "      <th>act_cc</th>\n",
       "      <th>act_loaninc</th>\n",
       "      <th>app_income</th>\n",
       "      <th>app_loan_amount</th>\n",
       "      <th>app_n_installments</th>\n",
       "      <th>...</th>\n",
       "      <th>act_CAll_Acp5y</th>\n",
       "      <th>act_CAll_All5y</th>\n",
       "      <th>APP_CHAR_GENDER</th>\n",
       "      <th>APP_CHAR_CITY</th>\n",
       "      <th>APP_CHAR_BRANCH</th>\n",
       "      <th>APP_CHAR_JOB_CODE</th>\n",
       "      <th>APP_CHAR_CARS</th>\n",
       "      <th>APP_CHAR_HOME_STATUS</th>\n",
       "      <th>APP_CHAR_MARITAL_STATUS</th>\n",
       "      <th>default3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0000001237</td>\n",
       "      <td>css2006010100123</td>\n",
       "      <td>css</td>\n",
       "      <td>200601</td>\n",
       "      <td>84.0</td>\n",
       "      <td>0.577685</td>\n",
       "      <td>2.606882</td>\n",
       "      <td>1918.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0000003380</td>\n",
       "      <td>css2006010100350</td>\n",
       "      <td>css</td>\n",
       "      <td>200601</td>\n",
       "      <td>87.0</td>\n",
       "      <td>0.499747</td>\n",
       "      <td>2.529084</td>\n",
       "      <td>1977.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0000003467</td>\n",
       "      <td>css2006010100357</td>\n",
       "      <td>css</td>\n",
       "      <td>200601</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0.584158</td>\n",
       "      <td>4.125413</td>\n",
       "      <td>1212.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>...</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0000003620</td>\n",
       "      <td>css2006010100367</td>\n",
       "      <td>css</td>\n",
       "      <td>200601</td>\n",
       "      <td>94.0</td>\n",
       "      <td>0.602687</td>\n",
       "      <td>4.798464</td>\n",
       "      <td>1042.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0000003621</td>\n",
       "      <td>css2006010100368</td>\n",
       "      <td>css</td>\n",
       "      <td>200601</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0.604592</td>\n",
       "      <td>3.188776</td>\n",
       "      <td>1568.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>...</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 85 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          cid               aid product  period  act_age    act_cc  \\\n",
       "0  0000001237  css2006010100123     css  200601     84.0  0.577685   \n",
       "1  0000003380  css2006010100350     css  200601     87.0  0.499747   \n",
       "2  0000003467  css2006010100357     css  200601     75.0  0.584158   \n",
       "3  0000003620  css2006010100367     css  200601     94.0  0.602687   \n",
       "4  0000003621  css2006010100368     css  200601     70.0  0.604592   \n",
       "\n",
       "   act_loaninc  app_income  app_loan_amount  app_n_installments  ...  \\\n",
       "0     2.606882      1918.0           5000.0                24.0  ...   \n",
       "1     2.529084      1977.0           5000.0                24.0  ...   \n",
       "2     4.125413      1212.0           5000.0                24.0  ...   \n",
       "3     4.798464      1042.0           5000.0                24.0  ...   \n",
       "4     3.188776      1568.0           5000.0                24.0  ...   \n",
       "\n",
       "   act_CAll_Acp5y  act_CAll_All5y  APP_CHAR_GENDER  APP_CHAR_CITY  \\\n",
       "0             7.0            15.0              1.0            0.0   \n",
       "1            14.0            15.0              1.0            3.0   \n",
       "2            16.0            16.0              1.0            1.0   \n",
       "3            14.0            15.0              1.0            0.0   \n",
       "4            15.0            15.0              1.0            0.0   \n",
       "\n",
       "   APP_CHAR_BRANCH  APP_CHAR_JOB_CODE  APP_CHAR_CARS  APP_CHAR_HOME_STATUS  \\\n",
       "0              0.0                4.0            1.0                   0.0   \n",
       "1              0.0                4.0            0.0                   0.0   \n",
       "2              0.0                4.0            1.0                   0.0   \n",
       "3              0.0                4.0            1.0                   0.0   \n",
       "4              0.0                4.0            1.0                   0.0   \n",
       "\n",
       "   APP_CHAR_MARITAL_STATUS  default3  \n",
       "0                      2.0         0  \n",
       "1                      1.0         0  \n",
       "2                      1.0         0  \n",
       "3                      0.0         0  \n",
       "4                      1.0         0  \n",
       "\n",
       "[5 rows x 85 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_merged_nomisscol.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "agr3_Mean_CMaxA_Due     0.588670\n",
       "agr3_Max_CMaxA_Due      0.588670\n",
       "agr3_Min_CMaxA_Due      0.588670\n",
       "act_CMaxA_Days          0.587548\n",
       "act_cus_active          0.582405\n",
       "                          ...   \n",
       "APP_CHAR_BRANCH         0.000000\n",
       "APP_CHAR_JOB_CODE       0.000000\n",
       "APP_CHAR_CARS           0.000000\n",
       "APP_CHAR_HOME_STATUS    0.000000\n",
       "cid                     0.000000\n",
       "Length: 85, dtype: float64"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_merged_nomiss.isna().mean().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'data_merged_nomiss' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-28-495cf0da7cd0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata_merged_nomiss\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mascending\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbins\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'data_merged_nomiss' is not defined"
     ]
    }
   ],
   "source": [
    "plt.hist(data_merged_nomiss.isna().mean().sort_values(ascending=False), bins=100)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(x = unique_count(data_merged_nomiss)['Num_Unique'], bins = 10000)\n",
    "plt.xlim(0, 100)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "79328"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data_merged)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}