credit_risk_model_dev.ipynb

{
 "cells": [
  {
   "cell_type": "code",
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   "metadata": {},
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   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PROBABILITY OF DEFAULT CREDIT RISK DEVELOPMENT using Kaggle Competition Data\n",
    "data set - https://www.kaggle.com/c/GiveMeSomeCredit\n",
    "Credit scoring algorithms, which make a guess at the probability of default, are the method banks use to determine whether or not a loan should be granted. Here we have started such development of PD model, absolutely from a beginner level and have explored the data widely to come up with a conclusive data which fits best for the model and have developed the model using XGBoost. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv(r'C:\\Users\\SHREYA\\OneDrive\\Desktop\\cs-training.csv').drop(['Unnamed: 0'],axis=1)\n",
    "\n",
    "test = pd.read_csv(r'C:\\Users\\SHREYA\\OneDrive\\Desktop\\cs-test.csv').drop(['Unnamed: 0'],axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150000, 11)"
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(101503, 11)"
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_redup = train.drop_duplicates() #storing duplicates in a df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def findMiss(df):\n",
    "    return round(df.isnull().sum()/df.shape[0]*100,2)#checking % missing values using a function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SeriousDlqin2yrs                         0.00\n",
       "RevolvingUtilizationOfUnsecuredLines     0.00\n",
       "age                                      0.00\n",
       "NumberOfTime30-59DaysPastDueNotWorse     0.00\n",
       "DebtRatio                                0.00\n",
       "MonthlyIncome                           19.56\n",
       "NumberOfOpenCreditLinesAndLoans          0.00\n",
       "NumberOfTimes90DaysLate                  0.00\n",
       "NumberRealEstateLoansOrLines             0.00\n",
       "NumberOfTime60-89DaysPastDueNotWorse     0.00\n",
       "NumberOfDependents                       2.56\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "findMiss(train_redup) #returns the % missing values in train_redup df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "   mode\n",
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     "execution_count": 9,
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   "source": [
    "train_redup['NumberOfDependents'].agg(['mode'])"
   ]
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  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NumberOfDependents\n",
       "0.0     86392\n",
       "1.0     26314\n",
       "2.0     19521\n",
       "3.0      9483\n",
       "4.0      2862\n",
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     "execution_count": 10,
     "metadata": {},
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   "source": [
    "train_redup.groupby(['NumberOfDependents']).size() #looking for the size of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating subset of dataframe and filling missing values of no of depend and monthly income\n",
    "fam_miss = train_redup[train_redup.NumberOfDependents.isnull()]\n",
    "fam_nmiss = train_redup[train_redup.NumberOfDependents.notnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-12-6a713ee960e3>:2: 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",
      "  fam_miss['NumberOfDependents'] = fam_miss['NumberOfDependents'].fillna(0)\n",
      "<ipython-input-12-6a713ee960e3>:3: 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",
      "  fam_miss['MonthlyIncome'] = fam_miss['MonthlyIncome'].fillna(0)\n"
     ]
    }
   ],
   "source": [
    "#filling missing values with zero\n",
    "fam_miss['NumberOfDependents'] = fam_miss['NumberOfDependents'].fillna(0)\n",
    "fam_miss['MonthlyIncome'] = fam_miss['MonthlyIncome'].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SeriousDlqin2yrs                        0.0\n",
       "RevolvingUtilizationOfUnsecuredLines    0.0\n",
       "age                                     0.0\n",
       "NumberOfTime30-59DaysPastDueNotWorse    0.0\n",
       "DebtRatio                               0.0\n",
       "MonthlyIncome                           0.0\n",
       "NumberOfOpenCreditLinesAndLoans         0.0\n",
       "NumberOfTimes90DaysLate                 0.0\n",
       "NumberRealEstateLoansOrLines            0.0\n",
       "NumberOfTime60-89DaysPastDueNotWorse    0.0\n",
       "NumberOfDependents                      0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "findMiss(fam_miss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SeriousDlqin2yrs                         0.00\n",
       "RevolvingUtilizationOfUnsecuredLines     0.00\n",
       "age                                      0.00\n",
       "NumberOfTime30-59DaysPastDueNotWorse     0.00\n",
       "DebtRatio                                0.00\n",
       "MonthlyIncome                           17.44\n",
       "NumberOfOpenCreditLinesAndLoans          0.00\n",
       "NumberOfTimes90DaysLate                  0.00\n",
       "NumberRealEstateLoansOrLines             0.00\n",
       "NumberOfTime60-89DaysPastDueNotWorse     0.00\n",
       "NumberOfDependents                       0.00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "findMiss(fam_nmiss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mean      6675.098269\n",
       "median    5400.000000\n",
       "min          0.000000\n",
       "Name: MonthlyIncome, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fam_nmiss['MonthlyIncome'].agg(['mean','median','min']) #hence not ideal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-16-d4f94639ff2e>:2: 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",
      "  fam_nmiss['MonthlyIncome'] = fam_nmiss['MonthlyIncome'].fillna(fam_nmiss['MonthlyIncome'].median()) #filling missing values\n"
     ]
    }
   ],
   "source": [
    "fam_nmiss['MonthlyIncome'].agg(['max']) #outliers are present\n",
    "fam_nmiss['MonthlyIncome'] = fam_nmiss['MonthlyIncome'].fillna(fam_nmiss['MonthlyIncome'].median()) #filling missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SeriousDlqin2yrs                        0.0\n",
       "RevolvingUtilizationOfUnsecuredLines    0.0\n",
       "age                                     0.0\n",
       "NumberOfTime30-59DaysPastDueNotWorse    0.0\n",
       "DebtRatio                               0.0\n",
       "MonthlyIncome                           0.0\n",
       "NumberOfOpenCreditLinesAndLoans         0.0\n",
       "NumberOfTimes90DaysLate                 0.0\n",
       "NumberRealEstateLoansOrLines            0.0\n",
       "NumberOfTime60-89DaysPastDueNotWorse    0.0\n",
       "NumberOfDependents                      0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "findMiss(fam_nmiss) #now there are no missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SeriousDlqin2yrs                        0.0\n",
       "RevolvingUtilizationOfUnsecuredLines    0.0\n",
       "age                                     0.0\n",
       "NumberOfTime30-59DaysPastDueNotWorse    0.0\n",
       "DebtRatio                               0.0\n",
       "MonthlyIncome                           0.0\n",
       "NumberOfOpenCreditLinesAndLoans         0.0\n",
       "NumberOfTimes90DaysLate                 0.0\n",
       "NumberRealEstateLoansOrLines            0.0\n",
       "NumberOfTime60-89DaysPastDueNotWorse    0.0\n",
       "NumberOfDependents                      0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#combining both together\n",
    "filled_train = fam_nmiss.append(fam_miss)\n",
    "filled_train.shape #total no.of records after filling missing data\n",
    "findMiss(filled_train) #shows no missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
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       "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
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      "text/plain": [
       "   SeriousDlqin2yrs  RevolvingUtilizationOfUnsecuredLines  age  \\\n",
       "0                 1                              0.766127   45   \n",
       "1                 0                              0.957151   40   \n",
       "2                 0                              0.658180   38   \n",
       "3                 0                              0.233810   30   \n",
       "4                 0                              0.907239   49   \n",
       "\n",
       "   NumberOfTime30-59DaysPastDueNotWorse  DebtRatio  MonthlyIncome  \\\n",
       "0                                     2   0.802982         9120.0   \n",
       "1                                     0   0.121876         2600.0   \n",
       "2                                     1   0.085113         3042.0   \n",
       "3                                     0   0.036050         3300.0   \n",
       "4                                     1   0.024926        63588.0   \n",
       "\n",
       "   NumberOfOpenCreditLinesAndLoans  NumberOfTimes90DaysLate  \\\n",
       "0                               13                        0   \n",
       "1                                4                        0   \n",
       "2                                2                        1   \n",
       "3                                5                        0   \n",
       "4                                7                        0   \n",
       "\n",
       "   NumberRealEstateLoansOrLines  NumberOfTime60-89DaysPastDueNotWorse  \\\n",
       "0                             6                                     0   \n",
       "1                             0                                     0   \n",
       "2                             0                                     0   \n",
       "3                             0                                     0   \n",
       "4                             1                                     0   \n",
       "\n",
       "   NumberOfDependents  \n",
       "0                 2.0  \n",
       "1                 1.0  \n",
       "2                 0.0  \n",
       "3                 0.0  \n",
       "4                 0.0  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filled_train.head() #analysing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SeriousDlqin2yrs\n",
       "0    93.3\n",
       "1     6.7\n",
       "dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#finding proportion of defaulter and non defaulter & converting it into percentage\n",
    "round(filled_train.groupby(['SeriousDlqin2yrs']).size()/filled_train.shape[0]*100,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    149391.000000\n",
       "mean          6.071087\n",
       "std         250.263672\n",
       "min           0.000000\n",
       "25%           0.030132\n",
       "50%           0.154235\n",
       "75%           0.556494\n",
       "max       50708.000000\n",
       "Name: RevolvingUtilizationOfUnsecuredLines, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#since the data is highly imbalanced the model will be biased so let's evaluate the variables\n",
    "filled_train.RevolvingUtilizationOfUnsecuredLines.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(149150, 11)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dropping values>10 from col RevolvingUtilizationOfUnsecuredLines as ideal range is 0-1.\n",
    "(filled_train[filled_train['RevolvingUtilizationOfUnsecuredLines']>10]).describe()\n",
    "util_dropped=filled_train.drop(filled_train[filled_train['RevolvingUtilizationOfUnsecuredLines']>10].index)\n",
    "util_dropped.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\SHREYA\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: 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",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='age'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#checking for outliers in col age\n",
    "sns.boxplot(util_dropped['age']) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NumberOfTime30-59DaysPastDueNotWorse\n",
       "0     125239\n",
       "1      16020\n",
       "2       4588\n",
       "3       1750\n",
       "4        747\n",
       "5        342\n",
       "6        140\n",
       "7         54\n",
       "8         25\n",
       "9         12\n",
       "10         4\n",
       "11         1\n",
       "12         2\n",
       "13         1\n",
       "96         5\n",
       "98       220\n",
       "dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "util_dropped.groupby(['NumberOfTime30-59DaysPastDueNotWorse']).size() \n",
    "#shows that there are some misoccurrences or error since no of times has jumped directly to 96 after 13"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NumberOfTime60-89DaysPastDueNotWorse\n",
       "0     141601\n",
       "1       5723\n",
       "2       1117\n",
       "3        318\n",
       "4        104\n",
       "5         34\n",
       "6         16\n",
       "7          8\n",
       "8          2\n",
       "9          1\n",
       "11         1\n",
       "96         5\n",
       "98       220\n",
       "dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "util_dropped.groupby(['NumberOfTime60-89DaysPastDueNotWorse']).size() \n",
    "#same error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NumberOfTimes90DaysLate\n",
       "0     140875\n",
       "1       5227\n",
       "2       1554\n",
       "3        666\n",
       "4        291\n",
       "5        131\n",
       "6         80\n",
       "7         38\n",
       "8         20\n",
       "9         19\n",
       "10         8\n",
       "11         5\n",
       "12         2\n",
       "13         4\n",
       "14         2\n",
       "15         2\n",
       "17         1\n",
       "96         5\n",
       "98       220\n",
       "dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "util_dropped.groupby(['NumberOfTimes90DaysLate']).size() \n",
    "#same error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SeriousDlqin2yrs\n",
       "0     89\n",
       "1    136\n",
       "dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#finding out the no of defaulters who are 96>=  days late\n",
    "util_dropped[util_dropped['NumberOfTimes90DaysLate']>=96].groupby(['SeriousDlqin2yrs']).size()\n",
    "#there are significant amt of defaulters henc the data can't be simply dropped\n",
    "#can treat the outlier value by bringing them close to the last nearby value thro winsorizing\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    149150.000000\n",
       "mean        354.085328\n",
       "std        2042.235713\n",
       "min           0.000000\n",
       "25%           0.177334\n",
       "50%           0.368200\n",
       "75%           0.874241\n",
       "max      329664.000000\n",
       "Name: DebtRatio, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#evaluating next variable DebtRatio\n",
    "util_dropped['DebtRatio'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='DebtRatio', ylabel='Density'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualizing col DebtRatio\n",
    "sns.kdeplot(util_dropped['DebtRatio'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SeriousDlqin2yrs\n",
       "0    1366\n",
       "1     111\n",
       "dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#finding the proportions how DebtRatio is distributed\n",
    "#shows the people with very high liabilities than asset\n",
    "util_dropped[util_dropped['DebtRatio']>3500]['SeriousDlqin2yrs'].describe()\n",
    "#analyzing defaulters under differeent liabilities range\n",
    "util_dropped[util_dropped['DebtRatio']>3000].groupby(['SeriousDlqin2yrs']).size()\n",
    "util_dropped[util_dropped['DebtRatio']>5000].groupby(['SeriousDlqin2yrs']).size()\n",
    "#shows that less the debt ratio less are defaulters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "#considering 3492 as threshold & checking how many monthly income entries are zero in col SeriousDlqin2yrs\n",
    "temp = util_dropped[(util_dropped['DebtRatio']>3492)&(util_dropped['SeriousDlqin2yrs']==util_dropped['MonthlyIncome'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>SeriousDlqin2yrs</th>\n",
       "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
       "      <th>age</th>\n",
       "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
       "      <th>DebtRatio</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
       "      <th>NumberOfTimes90DaysLate</th>\n",
       "      <th>NumberRealEstateLoansOrLines</th>\n",
       "      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n",
       "      <th>NumberOfDependents</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>0</td>\n",
       "      <td>0.039388</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>15466.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>0</td>\n",
       "      <td>0.013115</td>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>4211.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>508</th>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>73</td>\n",
       "      <td>0</td>\n",
       "      <td>8500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>844</th>\n",
       "      <td>0</td>\n",
       "      <td>0.734284</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>11311.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3370</th>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>9260.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      SeriousDlqin2yrs  RevolvingUtilizationOfUnsecuredLines  age  \\\n",
       "90                   0                              0.039388   51   \n",
       "326                  0                              0.013115   43   \n",
       "508                  0                              1.000000   73   \n",
       "844                  0                              0.734284   45   \n",
       "3370                 0                              1.000000   45   \n",
       "\n",
       "      NumberOfTime30-59DaysPastDueNotWorse  DebtRatio  MonthlyIncome  \\\n",
       "90                                       0    15466.0            0.0   \n",
       "326                                      0     4211.0            0.0   \n",
       "508                                      0     8500.0            0.0   \n",
       "844                                      0    11311.0            0.0   \n",
       "3370                                     0     9260.0            0.0   \n",
       "\n",
       "      NumberOfOpenCreditLinesAndLoans  NumberOfTimes90DaysLate  \\\n",
       "90                                  7                        0   \n",
       "326                                21                        0   \n",
       "508                                 2                        0   \n",
       "844                                 6                        0   \n",
       "3370                                7                        0   \n",
       "\n",
       "      NumberRealEstateLoansOrLines  NumberOfTime60-89DaysPastDueNotWorse  \\\n",
       "90                               0                                     0   \n",
       "326                              2                                     0   \n",
       "508                              0                                     0   \n",
       "844                              2                                     1   \n",
       "3370                             4                                     0   \n",
       "\n",
       "      NumberOfDependents  \n",
       "90                   0.0  \n",
       "326                  2.0  \n",
       "508                  0.0  \n",
       "844                  0.0  \n",
       "3370                 4.0  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SeriousDlqin2yrs\n",
       "0    380\n",
       "1      2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#analyzing temp data further\n",
    "temp.groupby(['SeriousDlqin2yrs']).size() #hence mostly non-defaulters have monthly income as zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>SeriousDlqin2yrs</th>\n",
       "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
       "      <th>age</th>\n",
       "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
       "      <th>DebtRatio</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
       "      <th>NumberOfTimes90DaysLate</th>\n",
       "      <th>NumberRealEstateLoansOrLines</th>\n",
       "      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n",
       "      <th>NumberOfDependents</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.766127</td>\n",
       "      <td>45</td>\n",
       "      <td>2</td>\n",
       "      <td>0.802982</td>\n",
       "      <td>9120.0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0.957151</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0.121876</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0.658180</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>0.085113</td>\n",
       "      <td>3042.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0.233810</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0.036050</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0.907239</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>0.024926</td>\n",
       "      <td>63588.0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SeriousDlqin2yrs  RevolvingUtilizationOfUnsecuredLines  age  \\\n",
       "0                 1                              0.766127   45   \n",
       "1                 0                              0.957151   40   \n",
       "2                 0                              0.658180   38   \n",
       "3                 0                              0.233810   30   \n",
       "4                 0                              0.907239   49   \n",
       "\n",
       "   NumberOfTime30-59DaysPastDueNotWorse  DebtRatio  MonthlyIncome  \\\n",
       "0                                     2   0.802982         9120.0   \n",
       "1                                     0   0.121876         2600.0   \n",
       "2                                     1   0.085113         3042.0   \n",
       "3                                     0   0.036050         3300.0   \n",
       "4                                     1   0.024926        63588.0   \n",
       "\n",
       "   NumberOfOpenCreditLinesAndLoans  NumberOfTimes90DaysLate  \\\n",
       "0                               13                        0   \n",
       "1                                4                        0   \n",
       "2                                2                        1   \n",
       "3                                5                        0   \n",
       "4                                7                        0   \n",
       "\n",
       "   NumberRealEstateLoansOrLines  NumberOfTime60-89DaysPastDueNotWorse  \\\n",
       "0                             6                                     0   \n",
       "1                             0                                     0   \n",
       "2                             0                                     0   \n",
       "3                             0                                     0   \n",
       "4                             1                                     0   \n",
       "\n",
       "   NumberOfDependents  \n",
       "0                 2.0  \n",
       "1                 1.0  \n",
       "2                 0.0  \n",
       "3                 0.0  \n",
       "4                 0.0  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#removing those 382 entries\n",
    "dRatio = util_dropped.drop(util_dropped[(util_dropped['DebtRatio']>3492) & (util_dropped['SeriousDlqin2yrs']==util_dropped['MonthlyIncome'])].index)\n",
    "dRatio.shape\n",
    "dRatio.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating model for the dataframe using xgboost\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import confusion_matrix , classification_report\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = XGBClassifier(tree_method = 'exact')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating to dataframes to train the model\n",
    "x = dRatio.drop(['SeriousDlqin2yrs'],axis=1)\n",
    "y = dRatio['SeriousDlqin2yrs']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "#predicting the model\n",
    "model.fit(x,y.values.ravel())\n",
    "y_pred = model.predict(x)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "94.71"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calculating accuracy\n",
    "round(accuracy_score(y,y_pred)*100,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "#confusion_matrix\n",
    "cm = confusion_matrix(y,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(33.0, 0.5, 'actuals')"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#heatmap\n",
    "sns.heatmap(cm,annot=True,fmt='d',cmap='Oranges',linewidths=0.5,linecolor='Black')\n",
    "plt.xticks(np.arange(2)+.5,['No def','def'])\n",
    "plt.yticks(np.arange(2)+.5,['No def','def'])\n",
    "plt.xlabel(\"predicted\")\n",
    "plt.ylabel(\"actuals\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.99      0.97    138778\n",
      "           1       0.79      0.29      0.42      9990\n",
      "\n",
      "    accuracy                           0.95    148768\n",
      "   macro avg       0.87      0.64      0.70    148768\n",
      "weighted avg       0.94      0.95      0.94    148768\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#classification report of model\n",
    "print(classification_report(y,y_pred))"
   ]
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}