python信用评分卡建模（附代码，博主录制）
机器学习合作项目可联系
QQ：231469242
数据源
https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.names
fnlwgt (final weight)
Description of fnlwgt (final weight) | | The weights on the CPS files are controlled to independent estimates of the | civilian noninstitutional population of the US. These are prepared monthly | for us by Population Division here at the Census Bureau. We use 3 sets of | controls. | These are: | 1. A single cell estimate of the population 16+ for each state. | 2. Controls for Hispanic Origin by age and sex. | 3. Controls by Race, age and sex. | | We use all three sets of controls in our weighting program and "rake" through | them 6 times so that by the end we come back to all the controls we used. | | The term estimate refers to population totals derived from CPS by creating | "weighted tallies" of any specified socio-economic characteristics of the | population. | | People with similar demographic characteristics should have | similar weights. There is one important caveat to remember | about this statement. That is that since the CPS sample is | actually a collection of 51 state samples, each with its own | probability of selection, the statement only applies within | state.
fnlwgt的描述（最终重量）
当前人口调查（CPS）档案中的权重受到对美国民间非机构人口的独立估计的控制。这些是由人口司每月为我们在人口普查局这里准备的。我们使用3套控件。这些是：
-
单个细胞估计每个州16岁以上的人口。
-
按年龄和性别控制西班牙裔。
-
按种族，年龄和性别控制。
我们在加权程序中使用所有三组控件，并通过它们“耙”6次，最终我们回到所有我们使用的控件。术语“估计”指的是通过创建人口任何特定社会经济特征的“加权统计”来源于CPS的人口总数。具有相似人口特征的人应具有相似的权重。要记住这个声明有一个重要的警告。这就是说，由于CPS样本实际上是51个状态样本的集合，每个样本都有自己的选择概率，所以该语句仅适用于状态。
对比原始数据和imputer处理后数据
数据集里删除了几个值，作为缺失数据
最后逻辑回归准确率8%左右
# -*- coding: utf-8 -*-
"""
Created on Tue Aug 14 10:34:11 2018
@author: zhi.li04
哑变量可以解决分类变量得缺失数据
连续变量缺失数据必须用Imputer 函数处理
"""
import pandas as pd
from sklearn import metrics
import numpy as np
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import Imputer
#读取文件
readFileName="income.xlsx"
#读取excel
data=pd.read_excel(readFileName)
#data=data[['age','workclass','education','sex','hours-per-week','occupation','income']]
data_dummies=pd.get_dummies(data)
#存入Excel
#data_dummies.to_excel("data_dummies.xlsx")
print('features after one-hot encoding:
',list(data_dummies.columns))
#features_test=data_dummies.ix[:,"age":'occupation_Transport-moving']
features=data_dummies.ix[:,"age":'native-country_ Yugoslavia']
x=features.values
#缺失数据处理
imp = Imputer(missing_values='NaN', strategy='most_frequent', axis=0)
imp.fit(x)
#x1是处理缺失数据后的值
x1=imp.transform(x)
y=data_dummies['income_ >50K'].values
x_train,x_test,y_train,y_test=train_test_split(x1,y,random_state=0)
logreg=LogisticRegression()
logreg.fit(x_train,y_train)
print("logistic regression:")
print("accuracy on the training subset:{:.3f}".format(logreg.score(x_train,y_train)))
print("accuracy on the test subset:{:.3f}".format(logreg.score(x_test,y_test)))
catboost.py
准确率达到0.88左右
# -*- coding: utf-8 -*-
"""
Created on Tue Aug 14 15:02:43 2018
@author: zhi.li04
"""
import pandas as pd
from sklearn import metrics
import numpy as np
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
import catboost as cb
from sklearn.preprocessing import Imputer
#读取文件
readFileName="income.xlsx"
#读取excel
data=pd.read_excel(readFileName)
#data=data[['age','workclass','education','sex','hours-per-week','occupation','income']]
data_dummies=pd.get_dummies(data)
#存入Excel
#data_dummies.to_excel("data_dummies.xlsx")
print('features after one-hot encoding:
',list(data_dummies.columns))
#features_test=data_dummies.ix[:,"age":'occupation_Transport-moving']
features=data_dummies.ix[:,"age":'native-country_ Yugoslavia']
x=features.values
#缺失数据处理
imp = Imputer(missing_values='NaN', strategy='most_frequent', axis=0)
imp.fit(x)
#x1是处理缺失数据后的值
x1=imp.transform(x)
y=data_dummies['income_ >50K'].values
x_train,x_test,y_train,y_test=train_test_split(x1,y,random_state=0)
cb=cb.CatBoostClassifier()
cb.fit(x_train,y_train)
print("logistic regression:")
print("accuracy on the training subset:{:.3f}".format(cb.score(x_train,y_train)))
print("accuracy on the test subset:{:.3f}".format(cb.score(x_test,y_test)))
lightgbm1.py
准确性0.87左右
# -*- coding: utf-8 -*-
"""
Created on Tue Aug 14 15:24:14 2018
@author: zhi.li04
"""
import pandas as pd
from sklearn import metrics
import numpy as np
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
import catboost as cb
from xgboost import XGBClassifier
import lightgbm as lgb
from sklearn.preprocessing import Imputer
#读取文件
readFileName="income.xlsx"
#读取excel
data=pd.read_excel(readFileName)
#data=data[['age','workclass','education','sex','hours-per-week','occupation','income']]
data_dummies=pd.get_dummies(data)
#存入Excel
#data_dummies.to_excel("data_dummies.xlsx")
print('features after one-hot encoding:
',list(data_dummies.columns))
#features_test=data_dummies.ix[:,"age":'occupation_Transport-moving']
features=data_dummies.ix[:,"age":'native-country_ Yugoslavia']
x=features.values
#缺失数据处理
imp = Imputer(missing_values='NaN', strategy='most_frequent', axis=0)
imp.fit(x)
#x1是处理缺失数据后的值
x1=imp.transform(x)
y=data_dummies['income_ >50K'].values
x_train,x_test,y_train,y_test=train_test_split(x1,y,random_state=0)
clf=lgb.LGBMClassifier()
clf.fit(x_train,y_train)
print("logistic regression:")
print("accuracy on the training subset:{:.3f}".format(clf.score(x_train,y_train)))
print("accuracy on the test subset:{:.3f}".format(clf.score(x_test,y_test)))
xgboost模型
准确率0.87左右
# -*- coding: utf-8 -*-
"""
Created on Tue Aug 14 15:13:17 2018
@author: zhi.li04
"""
import pandas as pd
from sklearn import metrics
import numpy as np
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
import catboost as cb
from xgboost import XGBClassifier
from sklearn.preprocessing import Imputer
#读取文件
readFileName="income.xlsx"
#读取excel
data=pd.read_excel(readFileName)
#data=data[['age','workclass','education','sex','hours-per-week','occupation','income']]
data_dummies=pd.get_dummies(data)
#存入Excel
#data_dummies.to_excel("data_dummies.xlsx")
print('features after one-hot encoding:
',list(data_dummies.columns))
#features_test=data_dummies.ix[:,"age":'occupation_Transport-moving']
features=data_dummies.ix[:,"age":'native-country_ Yugoslavia']
x=features.values
#缺失数据处理
imp = Imputer(missing_values='NaN', strategy='most_frequent', axis=0)
imp.fit(x)
#x1是处理缺失数据后的值
x1=imp.transform(x)
y=data_dummies['income_ >50K'].values
x_train,x_test,y_train,y_test=train_test_split(x1,y,random_state=0)
clf=XGBClassifier(n_estimators=1000)
clf.fit(x_train,y_train)
print("logistic regression:")
print("accuracy on the training subset:{:.3f}".format(clf.score(x_train,y_train)))
print("accuracy on the test subset:{:.3f}".format(clf.score(x_test,y_test)))
AUC: 0.9107 ACC: 0.8547 Recall: 0.5439 F1-score: 0.6457 Precesion: 0.7944
# -*- coding: utf-8 -*-
"""
Created on Tue Apr 24 22:42:47 2018
@author: Administrator
出现module 'xgboost' has no attribute 'DMatrix'的临时解决方法
初学者或者说不太了解Python才会犯这种错误，其实只需要注意一点！不要使用任何模块名作为文件名，任何类型的文件都不可以！我的错误根源是在文件夹中使用xgboost.*的文件名，当import xgboost时会首先在当前文件中查找，才会出现这样的问题。
所以，再次强调：不要用任何的模块名作为文件名！
"""
import xgboost as xgb
from sklearn.cross_validation import train_test_split
import pandas as pd
import matplotlib.pylab as plt
#读取文件
readFileName="income.xlsx"
#读取excel
data=pd.read_excel(readFileName)
#data=data[['age','workclass','education','sex','hours-per-week','occupation','income']]
data_dummies=pd.get_dummies(data)
print('features after one-hot encoding:
',list(data_dummies.columns))
features=data_dummies.ix[:,"age":'native-country_Yugoslavia']
x=features.values
y=data_dummies['income_>50K'].values
x_train,x_test,y_train,y_test=train_test_split(x,y,random_state=0)
names=features.columns
dtrain=xgb.DMatrix(x_train,label=y_train)
dtest=xgb.DMatrix(x_test)
params={'booster':'gbtree',
#'objective': 'reg:linear',
'objective': 'binary:logistic',
'eval_metric': 'auc',
'max_depth':4,
'lambda':10,
'subsample':0.75,
'colsample_bytree':0.75,
'min_child_weight':2,
'eta': 0.025,
'seed':0,
'nthread':8,
'silent':1}
watchlist = [(dtrain,'train')]
bst=xgb.train(params,dtrain,num_boost_round=100,evals=watchlist)
ypred=bst.predict(dtest)
# 设置阈值, 输出一些评价指标
y_pred = (ypred >= 0.5)*1
#模型校验
from sklearn import metrics
print ('AUC: %.4f' % metrics.roc_auc_score(y_test,ypred))
print ('ACC: %.4f' % metrics.accuracy_score(y_test,y_pred))
print ('Recall: %.4f' % metrics.recall_score(y_test,y_pred))
print ('F1-score: %.4f' %metrics.f1_score(y_test,y_pred))
print ('Precesion: %.4f' %metrics.precision_score(y_test,y_pred))
metrics.confusion_matrix(y_test,y_pred)
'''
AUC: 0.9107
ACC: 0.8547
Recall: 0.5439
F1-score: 0.6457
Precesion: 0.7944
Out[28]:
array([[5880, 279],
[ 904, 1078]], dtype=int64)
'''
print("xgboost:")
print('Feature importances:{}'.format(bst.get_fscore()))
'''
Feature importances:{'f33': 76, 'f3': 273, 'f4': 157, 'f25': 11, 'f0': 167,
'f42': 34, 'f2': 193, 'f5': 132, 'f56': 1, 'f64': 14, 'f24': 11, 'f53': 15,
'f58': 24, 'f39': 2, 'f1': 20, 'f29': 3, 'f35': 9, 'f48': 20, 'f12': 11,
'f65': 3, 'f27': 3, 'f50': 3, 'f26': 7, 'f60': 2, 'f43': 8, 'f85': 1,
'f10': 1, 'f46': 5, 'f11': 1, 'f49': 1, 'f7': 1, 'f52': 3, 'f66': 1,
'f54': 1, 'f23': 1}
'''
随机森林randomForest.py
0.856左右准确性
# -*- coding: utf-8 -*-
"""
Created on Tue Aug 14 15:29:13 2018
@author: zhi.li04
"""
import pandas as pd
from sklearn import metrics
import numpy as np
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
import catboost as cb
from xgboost import XGBClassifier
import lightgbm as lgb
from sklearn.preprocessing import Imputer
#读取文件
readFileName="income.xlsx"
#读取excel
data=pd.read_excel(readFileName)
#data=data[['age','workclass','education','sex','hours-per-week','occupation','income']]
data_dummies=pd.get_dummies(data)
#存入Excel
#data_dummies.to_excel("data_dummies.xlsx")
print('features after one-hot encoding:
',list(data_dummies.columns))
#features_test=data_dummies.ix[:,"age":'occupation_Transport-moving']
features=data_dummies.ix[:,"age":'native-country_ Yugoslavia']
x=features.values
#缺失数据处理
imp = Imputer(missing_values='NaN', strategy='most_frequent', axis=0)
imp.fit(x)
#x1是处理缺失数据后的值
x1=imp.transform(x)
y=data_dummies['income_ >50K'].values
x_train,x_test,y_train,y_test=train_test_split(x1,y,random_state=0)
clf=RandomForestClassifier(n_estimators=1000,random_state=0)
clf.fit(x_train,y_train)
print("logistic regression:")
print("accuracy on the training subset:{:.3f}".format(clf.score(x_train,y_train)))
print("accuracy on the test subset:{:.3f}".format(clf.score(x_test,y_test)))
python风控建模实战lendingClub(博主录制，catboost，lightgbm建模，2K超清分辨率)
https://study.163.com/course/courseMain.htm?courseId=1005988013&share=2&shareId=400000000398149
微信扫二维码，免费学习更多python资源
