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BabaCafe/Customer Approval 1

Created September 5, 2018 07:47
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Credit Card Approval part 1
Raw
Customer Approval 1
{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/babacafe/anaconda3/lib/python3.6/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
" \"This module will be removed in 0.20.\", DeprecationWarning)\n"
]
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import random as rnd\n",
"import math\n",
"from sklearn import preprocessing\n",
"from sklearn.cross_validation import train_test_split\n",
"from sklearn import metrics \n",
"from sklearn import feature_selection\n",
"label=preprocessing.LabelEncoder()\n",
"import random as rnd"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import seaborn as sns\n",
"import matplotlib as mpl\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.pylab as pylab\n",
"\n",
"mpl.style.use('ggplot')\n",
"sns.set_style('white')\n",
"pylab.rcParams['figure.figsize']=12,8\n",
"\n",
"def plot_correlation_map( df ):\n",
" corr = df.corr()\n",
" ht , ax = plt.subplots( figsize =( 16 , 12 ) )\n",
" cmap = sns.diverging_palette( 220 , 10 , as_cmap = True )\n",
" ht = sns.heatmap(\n",
" corr, \n",
" cmap = cmap,\n",
" square=True, \n",
" cbar_kws={ 'shrink' : .9 }, \n",
" ax=ax, \n",
" annot = True, \n",
" annot_kws = { 'fontsize' : 12 })\n",
"def describe_more( df ):\n",
" var = [] ; l = [] ; t = []\n",
" for x in df:\n",
" var.append( x )\n",
" l.append( len( pd.value_counts( df[ x ] ) ) )\n",
" t.append( df[ x ].dtypes )\n",
" #print(var,l,t)\n",
" levels = pd.DataFrame( { 'Variable' : var , 'Levels' : l , 'Datatype' : t } )\n",
" levels.sort_values( by = 'Levels' , inplace = True )\n",
" return levels\n",
"\n",
"def intervals(df):\n",
"# for i in ['year_tax_1','year_ops_1','year_ops_2', 'year_tax_2','year_combined_1','year_combined_2']:\n",
" for i in df.columns:\n",
"# df[i]=df[i].astype('float64')\n",
" if df[i].dtypes=='float64' or df[i].dtypes=='int64' :\n",
" print(i)\n",
" print(i,df[i].max(),df[i].min())\n",
" j=6\n",
" while j<7 and j>1:\n",
" try:\n",
" df[i+'_interval']=pd.qcut(df[i],j)\n",
" break\n",
" except:\n",
" j=j-1\n",
" if j==1:\n",
" j=3\n",
" df[i+'_interval']=pd.cut(df[i],j)\n",
" print('bins=',j)\n",
" df[i+'count']=df[i+'_interval']\n",
" print(df.groupby([i+'_interval'],as_index=False).agg({i+'count':'count'}))\n",
" return df"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"train=pd.read_csv('puzzle_train_dataset.csv')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
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" <th>ok_since</th>\n",
" <th>n_bankruptcies</th>\n",
" <th>n_defaulted_loans</th>\n",
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" <th>n_issues</th>\n",
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"text/plain": [
" ids default score_1 \\\n",
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"4 e643bf65-9288-92f2-df13-eed631fe237c False 4DLlLW62jReXaqbPaHp1vQ== \n",
"\n",
" score_2 score_3 score_4 score_5 score_6 \\\n",
"0 tHpS8e9F8d9zg3iOQM9tsA== 710.0 104.174961 0.661509 123.015325 \n",
"1 RO7MTL+j4PH2gNzbhNTq/A== 330.0 97.880798 0.531115 110.913484 \n",
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"3 tQUTfUyeuGkhRotd+6WjVg== 120.0 100.434557 0.139784 120.134718 \n",
"4 7h8PTkrlTWUPP3yuyP4rUg== 330.0 103.774638 0.002856 104.320462 \n",
"\n",
" risk_rate amount_borrowed ... state \\\n",
"0 0.43 20024.31 ... xsd3ZdsI3356I3xMxZeiqQ== \n",
"1 0.23 10046.51 ... xsd3ZdsI3356I3xMxZeiqQ== \n",
"2 0.30 21228.25 ... /L8vvVesB5WyAv190Hw/rQ== \n",
"3 0.15 23032.33 ... GW2VZ3dN3OGHSjQ6JkfqQw== \n",
"4 0.08 24026.29 ... sjJbkqJS7cXalHLBFA+EOQ== \n",
"\n",
" zip channel \\\n",
"0 i036nmJ7rfxo+3EvCD7Jnw== NCqL3QBx0pscDnx3ixKwXg== \n",
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"4 xTrDMEf/Cnewxc1LO+pfbg== NCqL3QBx0pscDnx3ixKwXg== \n",
"\n",
" job_name \\\n",
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"4 mLVIVxoGY7TUDJ1FyFoSIZi1SFcaBmO01AydRchaEiGYtU... \n",
"\n",
" real_state ok_since n_bankruptcies n_defaulted_loans \\\n",
"0 n+xK9CfX0bCn77lClTWviw== 14.0 1.0 0.0 \n",
"1 n+xK9CfX0bCn77lClTWviw== 75.0 0.0 0.0 \n",
"2 N5/CE7lSkAfB04hVFFwllw== NaN 0.0 0.0 \n",
"3 N5/CE7lSkAfB04hVFFwllw== NaN 0.0 0.0 \n",
"4 N5/CE7lSkAfB04hVFFwllw== 15.0 0.0 0.0 \n",
"\n",
" n_accounts n_issues \n",
"0 9.0 9.0 \n",
"1 3.0 NaN \n",
"2 5.0 NaN \n",
"3 5.0 NaN \n",
"4 10.0 10.0 \n",
"\n",
"[5 rows x 29 columns]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train.head()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
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" <tbody>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>channel</td>\n",
" <td>1</td>\n",
" <td>object</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>default</td>\n",
" <td>2</td>\n",
" <td>object</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>facebook_profile</td>\n",
" <td>2</td>\n",
" <td>object</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>gender</td>\n",
" <td>2</td>\n",
" <td>object</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>borrowed_in_months</td>\n",
" <td>2</td>\n",
" <td>float64</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>n_defaulted_loans</td>\n",
" <td>5</td>\n",
" <td>float64</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>real_state</td>\n",
" <td>5</td>\n",
" <td>object</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>score_1</td>\n",
" <td>7</td>\n",
" <td>object</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>n_bankruptcies</td>\n",
" <td>7</td>\n",
" <td>float64</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>sign</td>\n",
" <td>12</td>\n",
" <td>object</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>score_2</td>\n",
" <td>35</td>\n",
" <td>object</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>n_accounts</td>\n",
" <td>45</td>\n",
" <td>float64</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>n_issues</td>\n",
" <td>45</td>\n",
" <td>float64</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>state</td>\n",
" <td>50</td>\n",
" <td>object</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>risk_rate</td>\n",
" <td>82</td>\n",
" <td>float64</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>score_3</td>\n",
" <td>88</td>\n",
" <td>float64</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>ok_since</td>\n",
" <td>104</td>\n",
" <td>float64</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>last_payment</td>\n",
" <td>108</td>\n",
" <td>object</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>end_last_loan</td>\n",
" <td>110</td>\n",
" <td>object</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>zip</td>\n",
" <td>830</td>\n",
" <td>object</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>reason</td>\n",
" <td>20345</td>\n",
" <td>object</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>credit_limit</td>\n",
" <td>28259</td>\n",
" <td>float64</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>job_name</td>\n",
" <td>44604</td>\n",
" <td>object</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>amount_borrowed</td>\n",
" <td>53348</td>\n",
" <td>float64</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>income</td>\n",
" <td>57430</td>\n",
" <td>float64</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>score_6</td>\n",
" <td>64592</td>\n",
" <td>float64</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>score_5</td>\n",
" <td>64592</td>\n",
" <td>float64</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>score_4</td>\n",
" <td>64592</td>\n",
" <td>float64</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>ids</td>\n",
" <td>64592</td>\n",
" <td>object</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Variable Levels Datatype\n",
"21 channel 1 object\n",
"1 default 2 object\n",
"16 facebook_profile 2 object\n",
"15 gender 2 object\n",
"10 borrowed_in_months 2 float64\n",
"26 n_defaulted_loans 5 float64\n",
"23 real_state 5 object\n",
"2 score_1 7 object\n",
"25 n_bankruptcies 7 float64\n",
"14 sign 12 object\n",
"3 score_2 35 object\n",
"27 n_accounts 45 float64\n",
"28 n_issues 45 float64\n",
"19 state 50 object\n",
"8 risk_rate 82 float64\n",
"4 score_3 88 float64\n",
"24 ok_since 104 float64\n",
"17 last_payment 108 object\n",
"18 end_last_loan 110 object\n",
"20 zip 830 object\n",
"12 reason 20345 object\n",
"11 credit_limit 28259 float64\n",
"22 job_name 44604 object\n",
"9 amount_borrowed 53348 float64\n",
"13 income 57430 float64\n",
"7 score_6 64592 float64\n",
"6 score_5 64592 float64\n",
"5 score_4 64592 float64\n",
"0 ids 64592 object"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"describe_more(train)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"test=pd.read_csv('puzzle_test_dataset.csv')"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
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" ids score_1 \\\n",
"0 e4366223-7aa2-0904-7a47-66479ae46b2a DGCQep2AE5QRkNCshIAlFQ== \n",
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"\n",
" score_2 score_3 score_4 score_5 score_6 \\\n",
"0 osCzpM4hJrxugqWWuZmMWw== 230.0 101.906686 0.677760 77.245320 \n",
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"4 d/7Hedyz7ovK9Pn1CYN4+A== 320.0 101.303601 0.234202 107.162760 \n",
"\n",
" risk_rate amount_borrowed borrowed_in_months ... \\\n",
"0 0.22 21016.38 36.0 ... \n",
"1 0.13 1211.74 36.0 ... \n",
"2 0.25 6990.63 36.0 ... \n",
"3 0.46 15025.67 60.0 ... \n",
"4 0.29 7027.81 36.0 ... \n",
"\n",
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"\n",
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"3 NCqL3QBx0pscDnx3ixKwXg== \n",
"4 NCqL3QBx0pscDnx3ixKwXg== \n",
"\n",
" job_name \\\n",
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"\n",
" real_state ok_since n_bankruptcies n_defaulted_loans \\\n",
"0 N5/CE7lSkAfB04hVFFwllw== 73.0 0.0 0.0 \n",
"1 N5/CE7lSkAfB04hVFFwllw== 39.0 0.0 0.0 \n",
"2 N5/CE7lSkAfB04hVFFwllw== 33.0 0.0 0.0 \n",
"3 n+xK9CfX0bCn77lClTWviw== 12.0 0.0 0.0 \n",
"4 n+xK9CfX0bCn77lClTWviw== 31.0 0.0 0.0 \n",
"\n",
" n_accounts n_issues \n",
"0 15.0 15.0 \n",
"1 11.0 NaN \n",
"2 21.0 NaN \n",
"3 10.0 10.0 \n",
"4 6.0 NaN \n",
"\n",
"[5 rows x 28 columns]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test.head()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(35000, 28)"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test.shape"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
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" <tr>\n",
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],
"text/plain": [
" Variable Levels Datatype\n",
"20 channel 1 object\n",
"15 facebook_profile 2 object\n",
"14 gender 2 object\n",
"9 borrowed_in_months 2 float64\n",
"25 n_defaulted_loans 5 float64\n",
"22 real_state 5 object\n",
"24 n_bankruptcies 6 float64\n",
"1 score_1 7 object\n",
"13 sign 12 object\n",
"2 score_2 35 object\n",
"27 n_issues 45 float64\n",
"26 n_accounts 46 float64\n",
"18 state 47 object\n",
"7 risk_rate 80 float64\n",
"3 score_3 83 float64\n",
"23 ok_since 100 float64\n",
"17 end_last_loan 105 object\n",
"16 last_payment 106 object\n",
"19 zip 821 object\n",
"11 reason 12013 object\n",
"10 credit_limit 16810 float64\n",
"21 job_name 25703 object\n",
"8 amount_borrowed 31059 float64\n",
"12 income 32616 float64\n",
"6 score_6 35000 float64\n",
"5 score_5 35000 float64\n",
"4 score_4 35000 float64\n",
"0 ids 35000 object"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"describe_more(test)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"big_train=pd.read_csv('big_case_train.csv')"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
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" <td>0</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>810e3277-619e-3154-7ba0-ebddfc5f7ea9</td>\n",
" <td>1285</td>\n",
" <td>1</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>810e3277-619e-3154-7ba0-ebddfc5f7ea9</td>\n",
" <td>1285</td>\n",
" <td>2</td>\n",
" <td>196.69</td>\n",
" <td>13.33</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>810e3277-619e-3154-7ba0-ebddfc5f7ea9</td>\n",
" <td>1285</td>\n",
" <td>3</td>\n",
" <td>151.73</td>\n",
" <td>106.26</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>810e3277-619e-3154-7ba0-ebddfc5f7ea9</td>\n",
" <td>1285</td>\n",
" <td>4</td>\n",
" <td>982.02</td>\n",
" <td>0.00</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" ids credit_line month spends \\\n",
"0 810e3277-619e-3154-7ba0-ebddfc5f7ea9 1285 0 0.00 \n",
"1 810e3277-619e-3154-7ba0-ebddfc5f7ea9 1285 1 0.00 \n",
"2 810e3277-619e-3154-7ba0-ebddfc5f7ea9 1285 2 196.69 \n",
"3 810e3277-619e-3154-7ba0-ebddfc5f7ea9 1285 3 151.73 \n",
"4 810e3277-619e-3154-7ba0-ebddfc5f7ea9 1285 4 982.02 \n",
"\n",
" revolving_balance card_request minutes_cs \n",
"0 0.00 1 0.0 \n",
"1 0.00 0 0.0 \n",
"2 13.33 0 0.0 \n",
"3 106.26 0 0.0 \n",
"4 0.00 0 0.0 "
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"big_train.head()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Variable</th>\n",
" <th>Levels</th>\n",
" <th>Datatype</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>card_request</td>\n",
" <td>2</td>\n",
" <td>int64</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>month</td>\n",
" <td>36</td>\n",
" <td>int64</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>minutes_cs</td>\n",
" <td>2151</td>\n",
" <td>float64</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>credit_line</td>\n",
" <td>15490</td>\n",
" <td>int64</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>ids</td>\n",
" <td>64592</td>\n",
" <td>object</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>revolving_balance</td>\n",
" <td>77617</td>\n",
" <td>float64</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>spends</td>\n",
" <td>311129</td>\n",
" <td>float64</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Variable Levels Datatype\n",
"5 card_request 2 int64\n",
"2 month 36 int64\n",
"6 minutes_cs 2151 float64\n",
"1 credit_line 15490 int64\n",
"0 ids 64592 object\n",
"4 revolving_balance 77617 float64\n",
"3 spends 311129 float64"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"describe_more(big_train)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"big_train.groupby('ids',as_index=False).agg({'credit_line':'nunique'}).credit_line.max()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(1406306, 7)"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"big_train.shape"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" 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>credit_line</th>\n",
" <th>month</th>\n",
" <th>spends</th>\n",
" <th>revolving_balance</th>\n",
" <th>card_request</th>\n",
" <th>minutes_cs</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>1.406306e+06</td>\n",
" <td>1.406306e+06</td>\n",
" <td>1.406306e+06</td>\n",
" <td>1.406306e+06</td>\n",
" <td>1.406306e+06</td>\n",
" <td>1.406306e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>6.439334e+03</td>\n",
" <td>1.209055e+01</td>\n",
" <td>2.875423e+03</td>\n",
" <td>4.809796e+02</td>\n",
" <td>9.294990e-02</td>\n",
" <td>2.788997e+00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>4.561354e+03</td>\n",
" <td>8.509357e+00</td>\n",
" <td>3.527589e+03</td>\n",
" <td>1.432618e+03</td>\n",
" <td>2.903623e-01</td>\n",
" <td>6.637394e+00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>5.040000e+02</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>2.946000e+03</td>\n",
" <td>5.000000e+00</td>\n",
" <td>3.350000e+02</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>5.295000e+03</td>\n",
" <td>1.100000e+01</td>\n",
" <td>1.565430e+03</td>\n",
" <td>1.530000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>8.722000e+03</td>\n",
" <td>1.800000e+01</td>\n",
" <td>4.122030e+03</td>\n",
" <td>1.429375e+02</td>\n",
" <td>0.000000e+00</td>\n",
" <td>1.720000e+00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>2.049000e+04</td>\n",
" <td>3.500000e+01</td>\n",
" <td>2.047574e+04</td>\n",
" <td>2.036944e+04</td>\n",
" <td>1.000000e+00</td>\n",
" <td>7.558000e+01</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" credit_line month spends revolving_balance \\\n",
"count 1.406306e+06 1.406306e+06 1.406306e+06 1.406306e+06 \n",
"mean 6.439334e+03 1.209055e+01 2.875423e+03 4.809796e+02 \n",
"std 4.561354e+03 8.509357e+00 3.527589e+03 1.432618e+03 \n",
"min 5.040000e+02 0.000000e+00 0.000000e+00 0.000000e+00 \n",
"25% 2.946000e+03 5.000000e+00 3.350000e+02 0.000000e+00 \n",
"50% 5.295000e+03 1.100000e+01 1.565430e+03 1.530000e+00 \n",
"75% 8.722000e+03 1.800000e+01 4.122030e+03 1.429375e+02 \n",
"max 2.049000e+04 3.500000e+01 2.047574e+04 2.036944e+04 \n",
"\n",
" card_request minutes_cs \n",
"count 1.406306e+06 1.406306e+06 \n",
"mean 9.294990e-02 2.788997e+00 \n",
"std 2.903623e-01 6.637394e+00 \n",
"min 0.000000e+00 0.000000e+00 \n",
"25% 0.000000e+00 0.000000e+00 \n",
"50% 0.000000e+00 0.000000e+00 \n",
"75% 0.000000e+00 1.720000e+00 \n",
"max 1.000000e+00 7.558000e+01 "
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"big_train.describe()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(64592, 29)"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train[train.ids.isin(big_train.ids.unique())].shape"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(64592, 29)"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train.shape"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(0, 28)"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test[test.ids.isin(big_train.ids.unique())].shape"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(35000, 28)"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test.shape"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(0, 7)"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"big_train[big_train.ids.isin(test.ids.unique())].shape"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [],
"source": [
"# train['end_last_loan_date']=pd.to_datetime(train['end_last_loan'], errors='coerce')\n",
"# train['last_payment_date']=pd.to_datetime(train['last_payment'], errors='coerce')\n",
"train['months']=train.end_last_loan_date - train.last_payment_date"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(0, 32)"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train[train.months.astype('int64').isnull()].shape"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"def_id=train[train.default==True].ids.unique()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"nondef_id=train[train.default==False].ids.unique()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"50456"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(nondef_id)"
]
},
{
"cell_type": "code",
"execution_count": 163,
"metadata": {},
"outputs": [
{
"data": {
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>ids</th>\n",
" <th>credit_line</th>\n",
" <th>month</th>\n",
" <th>spends</th>\n",
" <th>revolving_balance</th>\n",
" <th>card_request</th>\n",
" <th>minutes_cs</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1320109</th>\n",
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" <tr>\n",
" <th>1320145</th>\n",
" <td>97fb3d11-5949-2697-d125-9596f723001f</td>\n",
" <td>3992</td>\n",
" <td>3</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <tr>\n",
" <th>1320156</th>\n",
" <td>3ec4c7ed-3826-b1d7-f83a-9876a8a05cff</td>\n",
" <td>907</td>\n",
" <td>3</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1320175</th>\n",
" <td>dbcae5fa-6a5d-32d4-356a-1e74a74f1227</td>\n",
" <td>4379</td>\n",
" <td>4</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" <td>7.92</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1320181</th>\n",
" <td>1ee49ae0-fdce-50e6-a74c-4323c84ac1fb</td>\n",
" <td>6667</td>\n",
" <td>5</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" ids credit_line month spends \\\n",
"1320109 7f93590f-4a91-aec4-5062-cb1ba2c16a84 4937 0 0.0 \n",
"1320145 97fb3d11-5949-2697-d125-9596f723001f 3992 3 0.0 \n",
"1320156 3ec4c7ed-3826-b1d7-f83a-9876a8a05cff 907 3 0.0 \n",
"1320175 dbcae5fa-6a5d-32d4-356a-1e74a74f1227 4379 4 0.0 \n",
"1320181 1ee49ae0-fdce-50e6-a74c-4323c84ac1fb 6667 5 0.0 \n",
"\n",
" revolving_balance card_request minutes_cs \n",
"1320109 0.0 1 0.00 \n",
"1320145 0.0 0 0.00 \n",
"1320156 0.0 0 0.00 \n",
"1320175 0.0 1 7.92 \n",
"1320181 0.0 0 0.00 "
]
},
"execution_count": 163,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"big_train.loc[(big_train.ids.isin(def_id))&(big_train.revolving_balance==0)&(big_train.spends==0),:].head()"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>ids</th>\n",
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" <th>month</th>\n",
" <th>spends</th>\n",
" <th>revolving_balance</th>\n",
" <th>card_request</th>\n",
" <th>minutes_cs</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1320176</th>\n",
" <td>1ee49ae0-fdce-50e6-a74c-4323c84ac1fb</td>\n",
" <td>6667</td>\n",
" <td>0</td>\n",
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" <td>7.82</td>\n",
" <td>1</td>\n",
" <td>2.71</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1320177</th>\n",
" <td>1ee49ae0-fdce-50e6-a74c-4323c84ac1fb</td>\n",
" <td>6667</td>\n",
" <td>1</td>\n",
" <td>3257.30</td>\n",
" <td>3099.08</td>\n",
" <td>0</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>1320178</th>\n",
" <td>1ee49ae0-fdce-50e6-a74c-4323c84ac1fb</td>\n",
" <td>6667</td>\n",
" <td>2</td>\n",
" <td>73.16</td>\n",
" <td>2.91</td>\n",
" <td>0</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1320179</th>\n",
" <td>1ee49ae0-fdce-50e6-a74c-4323c84ac1fb</td>\n",
" <td>6667</td>\n",
" <td>3</td>\n",
" <td>493.12</td>\n",
" <td>55.48</td>\n",
" <td>0</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1320180</th>\n",
" <td>1ee49ae0-fdce-50e6-a74c-4323c84ac1fb</td>\n",
" <td>6667</td>\n",
" <td>4</td>\n",
" <td>53.17</td>\n",
" <td>19.96</td>\n",
" <td>0</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1320181</th>\n",
" <td>1ee49ae0-fdce-50e6-a74c-4323c84ac1fb</td>\n",
" <td>6667</td>\n",
" <td>5</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1320182</th>\n",
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"\n",
" revolving_balance card_request minutes_cs \n",
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"1320188 0.02 0 10.28 \n",
"1320189 0.00 0 2.71 \n",
"1320190 8.42 0 0.00 "
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"big_train[big_train.ids=='1ee49ae0-fdce-50e6-a74c-4323c84ac1fb']"
]
},
{
"cell_type": "code",
"execution_count": 141,
"metadata": {},
"outputs": [
{
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"text/plain": [
" ids credit_line month spends \\\n",
"1320072 acbb594c-fdfa-feee-5a57-a21f077bff9b 1235 0 219.04 \n",
"1320073 acbb594c-fdfa-feee-5a57-a21f077bff9b 1235 1 154.29 \n",
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"1320077 acbb594c-fdfa-feee-5a57-a21f077bff9b 1235 5 1197.47 \n",
"1320078 acbb594c-fdfa-feee-5a57-a21f077bff9b 1235 6 83.87 \n",
"1320079 acbb594c-fdfa-feee-5a57-a21f077bff9b 1235 7 612.96 \n",
"1320080 acbb594c-fdfa-feee-5a57-a21f077bff9b 1235 8 1086.06 \n",
"1320081 acbb594c-fdfa-feee-5a57-a21f077bff9b 1235 9 1120.70 \n",
"1320082 acbb594c-fdfa-feee-5a57-a21f077bff9b 1235 10 96.68 \n",
"\n",
" revolving_balance card_request minutes_cs \n",
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
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},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
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"text/plain": [
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},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
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"source": [
"big_train[big_train.revolving_balance > big_train.spends]"
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{
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"execution_count": 24,
"metadata": {},
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{
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},
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],
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},
{
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{
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"execution_count": 109,
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},
{
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{
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{
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},
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]
}
],
"source": [
"defaulted['profits']=1\n",
"for j,i in enumerate(def_id):\n",
"# print(i)\n",
"# defaulted.loc[defaulted.ids==i,'profits'].apply(lambda z: 2\\\n",
"# if big_train[big_train.ids==i][-1:].revolving_balance.values[0]==0\\\n",
"# else profits(big_train,i,j=2))\n",
"# break\n",
" print(j,i)\n",
" if big_train[big_train.ids==i][-1:].revolving_balance.values[0]==0:\n",
" defaulted.loc[defaulted.ids==i,'profit']=(big_train[big_train.ids==i][:-1].spends.sum())*0.05+\\\n",
" (big_train[big_train.ids==i][:-1].revolving_balance.sum())*0.17\\\n",
" -big_train[big_train.ids==i][-1:].spends.values[0]\n",
" else:# big_train[big_train.ids==i][-1:].revolving_balance.values[0]>0:\n",
" defaulted.loc[defaulted.ids==i,'profit']=(big_train[big_train.ids==i].spends.sum())*0.05\\\n",
" +(big_train[big_train.ids==i][:-1].revolving_balance.sum())*0.17\\\n",
" -big_train[big_train.ids==i][-1:].revolving_balance.values[0]*1.17"
]
},
{
"cell_type": "code",
"execution_count": 162,
"metadata": {},
"outputs": [],
"source": [
"defaulted.to_csv('Defaulted.csv',index=False)"
]
},
{
"cell_type": "code",
"execution_count": 160,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"ids 9510\n",
"credit_line 9510\n",
"card_request 9510\n",
"minutes_cs 9510\n",
"revolving_balance 9510\n",
"spends 9510\n",
"month 9510\n",
"profit 9510\n",
"profits 9510\n",
"dtype: int64"
]
},
"execution_count": 160,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"defaulted.notnull().sum()"
]
},
{
"cell_type": "code",
"execution_count": 134,
"metadata": {},
"outputs": [],
"source": [
"# def profits(big_train,i,j):\n",
"# if j==1:\n",
"# p=(big_train[big_train.ids==i][:-1].spends.sum())*0.05+\\\n",
"# (big_train[big_train.ids==i][:-1].revolving_balance.sum())*0.17\\\n",
"# -big_train[big_train.ids==i][-1:].spends.values[0]\n",
"# elif j==2:\n",
"# p=(big_train[big_train.ids==i].spends.sum())*0.05\\\n",
"# +(big_train[big_train.ids==i][:-1].revolving_balance.sum())*0.17\\\n",
"# -big_train[big_train.ids==i][-1:].revolving_balance.values[0]*1.17\n",
"# return p"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {},
"outputs": [],
"source": [
"# defaulted=big_train[big_train.ids.isin(def_id)].groupby(['ids','credit_line'],as_index=False).agg({'card_request':'sum','minutes_cs':'sum','revolving_balance':'sum','spends':'sum','month':'nunique'})"
]
},
{
"cell_type": "code",
"execution_count": 130,
"metadata": {},
"outputs": [],
"source": [
"non_defaulted=big_train[big_train.ids.isin(nondef_id)].groupby(['ids','credit_line'],as_index=False).agg({'card_request':'sum','minutes_cs':'sum','revolving_balance':'sum','spends':'sum','month':'nunique'})"
]
},
{
"cell_type": "code",
"execution_count": 132,
"metadata": {},
"outputs": [],
"source": [
"non_defaulted['profit']=non_defaulted['revolving_balance']*0.17+ non_defaulted['spends']*0.05"
]
},
{
"cell_type": "code",
"execution_count": 164,
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
" ids credit_line card_request \\\n",
"0 0005967a-2048-6197-a054-30954efe93b1 11728 1 \n",
"1 000779f6-0dd7-ae04-80c6-fcc030b909bc 4994 3 \n",
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"4 002b83b9-302c-1b64-f441-cfa545803746 1366 1 \n",
"\n",
" minutes_cs revolving_balance spends month profit profits \n",
"0 54.37 3856.89 46426.93 9 2962.4118 1 \n",
"1 25.84 1000.69 15947.04 8 965.5531 1 \n",
"2 43.53 25658.14 132438.37 14 7426.6383 1 \n",
"3 10.85 3877.92 16060.00 6 1295.8319 1 \n",
"4 87.56 1727.31 9077.43 14 351.2360 1 "
]
},
"execution_count": 164,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"defaulted.head()"
]
}
],
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