基于python 信用卡评分系统 的数据分析

基于python 信用卡评分系统 的数据分析

import pandas as pd

import matplotlib.pyplot as plt #导入图像库

from sklearn.ensemble import RandomForestRegressor

# 用随机森林对缺失值预测填充函数

def set_missing(df):

    # 把已有的数值型特征取出来

    process_df = df.ix[:,[5,0,1,2,3,4,6,7,8,9]]

    # 分成已知该特征和未知该特征两部分

    known = process_df[process_df.MonthlyIncome.notnull()].as_matrix()

    unknown = process_df[process_df.MonthlyIncome.isnull()].as_matrix()

    # X为特征属性值

    X = known[:, 1:]

    # y为结果标签值

    y = known[:, 0]

    # fit到RandomForestRegressor之中

    rfr = RandomForestRegressor(random_state=0, n_estimators=200,max_depth=3,n_jobs=-1)

    rfr.fit(X,y)

    # 用得到的模型进行未知特征值预测

    predicted = rfr.predict(unknown[:, 1:]).round(0)

    print(predicted)

    # 用得到的预测结果填补原缺失数据

    df.loc[(df.MonthlyIncome.isnull()), 'MonthlyIncome'] = predicted

    return df

data = pd.read_csv(r'E:\Python\Source\CreditScore\cs-training.csv')

process_df = data.iloc[:,[5,0,1,2,3,4,6,7,8,9]]

known = process_df[process_df.MonthlyIncome.notnull()].as_matrix()

unknown = process_df[process_df.MonthlyIncome.isnull()].as_matrix()

X = known[:, 1:]

y = known[:, 0]

# fit到RandomForestRegressor之中

rfr = RandomForestRegressor(random_state=0, n_estimators=200,max_depth=3,n_jobs=-1)

rfr.fit(X,y)

# 用得到的模型进行未知特征值预测

predicted = rfr.predict(unknown[:, 1:]).round(0)

print(predicted)

data.loc[(data.MonthlyIncome.isnull()), 'MonthlyIncome'] = predicted

 

 

 

[8311. 1159. 8311. ... 1159. 2554. 2554.]

data=data.dropna()#删除比较少的缺失值

data = data.drop_duplicates()#删除重复项

#异常值处理

#x1 = data["age"]

x2 = data["RevolvingUtilizationOfUnsecuredLines"]

x3 = data["DebtRatio"]

fig = plt.figure(1)

ax = fig.add_subplot(111)

ax.boxplot([x2,x3])

ax.set_xticklabels(["RevolvingUtilizationOfUnsecuredLines","DebtRatio"])

 

Out[48]:

[Text(0,0,'RevolvingUtilizationOfUnsecuredLines'), Text(0,0,'DebtRatio')]

 

#异常值处理

data = data[data["age"] > 0]

data = data[data['NumberOfTime30-59DaysPastDueNotWorse'] < 90]#剔除异常值

# 好坏客户的整体分析

data['SeriousDlqin2yrs']=1-data['SeriousDlqin2yrs']

grouped = data["SeriousDlqin2yrs"].groupby(data["SeriousDlqin2yrs"]).count()

print("坏客户占比：{:.2%}".format(grouped[0]/grouped[1]))

print(grouped)

grouped.plot(kind="bar")

坏客户占比：7.16%
SeriousDlqin2yrs
0      9706
1    135648
Name: SeriousDlqin2yrs, dtype: int64

Out[54]:

 

 Y = data['SeriousDlqin2yrs']

 

本文通过对kaggle上的Give Me Some Credit数据的挖掘分析，结合信用评分卡的建立原理，从数据的预处理、变量选择、建模分析到创建信用评分，创建了一个简单的信用评分系统。本项目还有许多不足之处，比如分箱应当使用最优分箱或卡方分箱，减少人为分箱的随机性，此外模型采用的是逻辑回归算法，还可以多多尝试其他模型。

 

 

 

 

 

 

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转载于:https://www.cnblogs.com/gylhaut/p/9887471.html