本數(shù)據(jù)為滴滴司機(jī)使用油品貸的數(shù)據(jù)。
油品貸的壞賬率高達(dá)5%荡含，非常高咒唆，一定是會(huì)賠錢的。并且能夠通過欺詐檢測释液。
來申請油品貸的司機(jī)本身是已經(jīng)有A卡了钧排，A卡評級為A-F。本來是只有F不放款均澳，但是在油品貸上只有評級為A放款才能不虧錢恨溜。
滴滴是和很多加油站有合作的，加油站會(huì)給滴滴提供司機(jī)數(shù)據(jù)找前。
導(dǎo)入包
import pandas as pd
import numpy as np
#顯示全部特征
pd.set_option('display.max_columns', None)
導(dǎo)入數(shù)據(jù)
data = pd.read_excel('oil_data_for_tree.xlsx')
data.head()
org_lst 不需要做特殊變換糟袁，直接去重
agg_lst 數(shù)值型變量做聚合
dstc_lst 文本型變量做cnt
org_lst = ['uid','create_dt','oil_actv_dt','class_new','bad_ind']
agg_lst = ['oil_amount','discount_amount','sale_amount','amount','pay_amount','coupon_amount','payment_coupon_amount']
dstc_lst = ['channel_code','oil_code','scene','source_app','call_source']
數(shù)據(jù)重組
df = data[org_lst].copy()
df[agg_lst] = data[agg_lst].copy()
df[dstc_lst] = data[dstc_lst].copy()
看一下缺失情況
df.isna().mean()
看一下基礎(chǔ)變量的describe
df.describe()
對creat_dt做補(bǔ)全，用oil_actv_dt來填補(bǔ)躺盛，并且截取6個(gè)月的數(shù)據(jù)项戴。
構(gòu)造變量的時(shí)候不能直接對歷史所有數(shù)據(jù)做累加。
否則隨著時(shí)間推移槽惫，變量分布會(huì)有很大的變化周叮。
def time_isna(x,y):
if str(x) == 'NaT':
x = y
else:
x = x
return x
df2 = df.sort_values(['uid','create_dt'],ascending = False)
df2['create_dt'] = df2.apply(lambda x: time_isna(x.create_dt,x.oil_actv_dt),axis = 1)
df2['dtn'] = (df2.oil_actv_dt - df2.create_dt).apply(lambda x :x.days)
df = df2[df2['dtn']<180]
df.head()
對org_list變量求歷史貸款天數(shù)的最大間隔，并且去重
base = df[org_lst]
base['dtn'] = df['dtn']
base = base.sort_values(['uid','create_dt'],ascending = False)
base = base.drop_duplicates(['uid'],keep = 'first')
base.shape
維度(11099, 6)
做變量衍生
gn = pd.DataFrame()
for i in agg_lst:
tp = pd.DataFrame(df.groupby('uid').apply(lambda df:len(df[i])).reset_index())
tp.columns = ['uid',i + '_cnt']
if gn.empty == True:
gn = tp
else:
gn = pd.merge(gn,tp,on = 'uid',how = 'left')
tp = pd.DataFrame(df.groupby('uid').apply(lambda df:np.where(df[i]>0,1,0).sum()).reset_index())
tp.columns = ['uid',i + '_num']
if gn.empty == True:
gn = tp
else:
gn = pd.merge(gn,tp,on = 'uid',how = 'left')
tp = pd.DataFrame(df.groupby('uid').apply(lambda df:np.nansum(df[i])).reset_index())
tp.columns = ['uid',i + '_tot']
if gn.empty == True:
gn = tp
else:
gn = pd.merge(gn,tp,on = 'uid',how = 'left')
tp = pd.DataFrame(df.groupby('uid').apply(lambda df:np.nanmean(df[i])).reset_index())
tp.columns = ['uid',i + '_avg']
if gn.empty == True:
gn = tp
else:
gn = pd.merge(gn,tp,on = 'uid',how = 'left')
tp = pd.DataFrame(df.groupby('uid').apply(lambda df:np.nanmax(df[i])).reset_index())
tp.columns = ['uid',i + '_max']
if gn.empty == True:
gn = tp
else:
gn = pd.merge(gn,tp,on = 'uid',how = 'left')
tp = pd.DataFrame(df.groupby('uid').apply(lambda df:np.nanmin(df[i])).reset_index())
tp.columns = ['uid',i + '_min']
if gn.empty == True:
gn = tp
else:
gn = pd.merge(gn,tp,on = 'uid',how = 'left')
tp = pd.DataFrame(df.groupby('uid').apply(lambda df:np.nanvar(df[i])).reset_index())
tp.columns = ['uid',i + '_var']
if gn.empty == True:
gn = tp
else:
gn = pd.merge(gn,tp,on = 'uid',how = 'left')
tp = pd.DataFrame(df.groupby('uid').apply(lambda df:np.nanmax(df[i]) -np.nanmin(df[i]) ).reset_index())
tp.columns = ['uid',i + '_var']
if gn.empty == True:
gn = tp
else:
gn = pd.merge(gn,tp,on = 'uid',how = 'left')
tp = pd.DataFrame(df.groupby('uid').apply(lambda df:np.nanmean(df[i])/max(np.nanvar(df[i]),1)).reset_index())
tp.columns = ['uid',i + '_var']
if gn.empty == True:
gn = tp
else:
gn = pd.merge(gn,tp,on = 'uid',how = 'left')
對dstc_lst變量求distinct個(gè)數(shù)
gc = pd.DataFrame()
for i in dstc_lst:
tp = pd.DataFrame(df.groupby('uid').apply(lambda df: len(set(df[i]))).reset_index())
tp.columns = ['uid',i + '_dstc']
if gc.empty == True:
gc = tp
else:
gc = pd.merge(gc,tp,on = 'uid',how = 'left')
將變量組合在一起
fn = pd.merge(base,gn,on= 'uid')
fn = pd.merge(fn,gc,on= 'uid')
fn.shape
維度(11099, 74)
fn = fn.fillna(0)
fn.head(100)
訓(xùn)練決策樹模型
x = fn.drop(['uid','oil_actv_dt','create_dt','bad_ind','class_new'],axis = 1)
y = fn.bad_ind.copy()
from sklearn import tree
dtree = tree.DecisionTreeRegressor(max_depth = 2,min_samples_leaf = 500,min_samples_split = 5000)
dtree = dtree.fit(x,y)
輸出決策樹圖像界斜，并作出決策
import pydotplus
from IPython.display import Image
from sklearn.externals.six import StringIO
import os
os.environ["PATH"] += os.pathsep + 'C:/Program Files (x86)/Graphviz2.38/bin/'
with open(path + "dt.dot", "w") as f:
tree.export_graphviz(dtree, out_file=f)
dot_data = StringIO()
tree.export_graphviz(dtree, out_file=dot_data,
feature_names=x.columns,
class_names=['bad_ind'],
filled=True, rounded=True,
special_characters=True)
graph = pydotplus.graph_from_dot_data(dot_data.getvalue())
Image(graph.create_png())
通過加入兩條規(guī)則仿耽，我們最終可以將壞客戶率控制在1.2%。
