银行风控案例-python学习笔记

前言：风险控制是挖掘中最为常见的应用，属于监督学习的“分类器”使用案例。我们通过以往历史数据判断用户违约的概率。本文使用了Logistic Regression 方法完成案例。注：根据CDA课程自己总结的学习笔记。使用的是ipython，数据及代码都已上传至个人网盘http://pan.baidu.com/s/1ntR2tmD。如果有任何问题或错误欢迎各位指正 liedward@qq.com谢谢。风控模型开发流程
·        数据抽取
·        数据探索
·        建模数据准备
·        变量选择
·        模型开发与验证
·        模型部署
·        模型监督加载包：importos
importsys
importstring
importpymysql
importnumpyasnp
importpandasaspd
importstatsmodels.apiassm
importmatplotlib.pyplotasplt
%matplotlibinline
importseabornassns
importsklearn.cross_validationascross_validation
importsklearn.treeastree
importsklearn.ensembleasensemble
importsklearn.linear_modelaslinear_model
importsklearn.svmassvm
importsklearn.feature_selectionasfeature_selection
importsklearn.metricsasmetrics
 数据抽取

model_data = pd.read_csv("credit_develop.csv")

model_data.head()#查看数据格式

读取数据后查看大致数据的情况，主要看每个字段的格式属性。比如Branch_of_Bank等字段为分类变量，后续需要进行虚拟化变量处理。数据探索数据探索是建模人员了解特征时使用的方法，可以通过数据表或是图形的方式了解整体数据。

model_data.describe().T

查看数据的分布。

data = model_data["Credit_Score"].dropna() # 去除缺失值

sns.distplot(data)查看信用分数的分布情况plt.boxplot(data)利用箱线图查看数据离散情况

model_data=model_data.drop_duplicates()#去除重复项

填充缺失值

model_data = model_data.fillna(model_data.mean()) #用均值来填充

变量相似度分析，变量聚类simpler = np.random.randint(0,len(model_data),size=50)
sns.clustermap(model_data.iloc[simpler,3:].T,col_cluster=False,row_cluster=True)
生成模型训练/测试数据集将分类变量变为虚拟变量

Area_Classification_dummy = pd.get_dummies(model_data["Area_Classification"],prefix="Area_Class")

model_data.join(Area_Classification_dummy)

model_data.join(model_data[="Branch"))

·        分成目标变量和应变量

target = model_data["target"]

pd.crosstab(target,"target")

data = model_data.ix[ :,'Age':]

·        分成训练集和测试集，比例为6:4

train_data, test_data, train_target, test_target = cross_validation.train_test_split(data, target, test_size=0.4, random_state=0)

筛选变量
因为使用的是普通的罗吉斯回归，所以变量筛选变得尤为重要。如果筛选不当会产生过拟合或是欠拟合现象。（当然可以使用一些更高级的算法完成筛选功能）首先使用最原始的方法线性相关系数。

corr_matrix = model_data.corr(method='pearson')

corr_matrix = corr_matrix.abs()

sns.set(rc={"figure.figsize": (10, 10)})

sns.heatmap(corr_matrix,square=True,cmap="Blues")

corr = model_data.corr(method='pearson').ix["target"].abs()

corr.sort(ascending=False)

corr.plot(kind="bar",title="corr",figsize=[12,6])·        使用随机森林方法来选择模型模型变量

rfc = ensemble.RandomForestClassifier(criterion='entropy', n_estimators=3, max_features=0.5, min_samples_split=5)

rfc_model = rfc.fit(train_data, train_target)

rfc_model.feature_importances_

rfc_fi = pd.DataFrame()

rfc_fi["features"] = list(data.columns)

rfc_fi["importance"] = list(rfc_model.feature_importances_)

rfc_fi=rfc_fi.set_index("features",drop=True)

rfc_fi.sort_index(by="importance",ascending=False).plot(kind="bar",title="corr",figsize=[12,6])

5.模型训练使用原始变量进行logistic回归In [39]: 

logistic_model = linear_model.LogisticRegression()

logistic_model.fit(train_data, train_target)

Out[39]:

LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)

In [40]: 

test_est = logistic_model.predict(test_data)

train_est = logistic_model.predict(train_data)

In [41]: 

test_est_p = logistic_model.predict_proba(test_data)[:,1]

train_est_p = logistic_model.predict_proba(train_data)[:,1]

In [42]: 

print metrics.classification_report(test_target, test_est)

precision    recall  f1-score   support

0       0.68      0.61      0.64      2825
1       0.64      0.71      0.67      2775

avg / total       0.66      0.66      0.66      5600

In [43]: 

print metrics.classification_report(train_target, train_est)

precision    recall  f1-score   support

0       0.67      0.63      0.65      4175
1       0.66      0.70      0.68      4225

avg / total       0.67      0.67      0.66      8400

In [44]: 

metrics.zero_one_loss(test_target, test_est)

Out[44]:

0.34053571428571427

In [45]: 

metrics.zero_one_loss(train_target, train_est)

Out[45]:

0.33476190476190482

目标样本和非目标样本的分数分布

In [46]:

x

red, blue = sns.color_palette("Set1",2)

 

sns.kdeplot(test_est_p[test_target==1], shade=True, color=red)

sns.kdeplot(test_est_p[test_target==0], shade=True, color=blue)

Out[47]:

<matplotlib.axes._subplots.AxesSubplot at 0x2183aa20>

ROC曲线

fpr_test, tpr_test, th_test = metrics.roc_curve(test_target, test_est_p)
fpr_train, tpr_train, th_train = metrics.roc_curve(train_target, train_est_p)
plt.figure(figsize=[6,6])
plt.plot(fpr_test, tpr_test, color=blue)
plt.plot(fpr_train, tpr_train, color=red)
plt.title('ROC curve')

其他机器学习算法

lr = linear_model.LogisticRegression()
lr_scores = cross_validation.cross_val_score(lr, train_data, train_target, cv=5)
print("logistic regression accuracy:")
print(lr_scores)
​
clf = tree.DecisionTreeClassifier(criterion='entropy', max_depth=8, min_samples_split=5)
clf_scores = cross_validation.cross_val_score(clf, train_data, train_target, cv=5)
print("decision tree accuracy:")
print(clf_scores)
​
rfc = ensemble.RandomForestClassifier(criterion='entropy', n_estimators=3, max_features=0.5, min_samples_split=5)
rfc_scores = cross_validation.cross_val_score(rfc, train_data, train_target, cv=5)
print("random forest accuracy:")
print(rfc_scores)
​
etc = ensemble.ExtraTreesClassifier(criterion='entropy', n_estimators=3, max_features=0.6, min_samples_split=5)
etc_scores = cross_validation.cross_val_score(etc, train_data, train_target, cv=5)
print("extra trees accuracy:")
print(etc_scores)
​
gbc = ensemble.GradientBoostingClassifier()
gbc_scores = cross_validation.cross_val_score(gbc, train_data, train_target, cv=5)
print("gradient boosting accuracy:")
print(gbc_scores)
​
svc = svm.SVC()
svc_scores = cross_validation.cross_val_score(svc, train_data, train_target, cv=5)
print("svm classifier accuracy:")
print(svc_scores)
​
abc = ensemble. AdaBoostClassifier(n_estimators=100)
abc_scores = cross_validation.cross_val_score(abc, train_data, train_target, cv=5)
print("abc classifier accuracy:")
print(abc_scores)

logistic regression accuracy:
[ 0.66785714  0.65416667  0.65357143  0.65535714  0.65833333]
decision tree accuracy:
[ 0.74107143  0.75654762  0.73988095  0.73035714  0.73690476]
random forest accuracy:
[ 0.7125      0.72142857  0.71190476  0.72083333  0.68809524]
extra trees accuracy:
[ 0.66964286  0.70714286  0.6827381   0.67738095  0.67797619]
gradient boosting accuracy:
[ 0.78214286  0.78035714  0.77202381  0.76071429  0.75119048]
svm classifier accuracy:
[ 0.50297619  0.50595238  0.50654762  0.50297619  0.50297619]
abc classifier accuracy:
[ 0.76785714  0.76488095  0.76904762  0.74940476  0.75      ]