1、导入数据 略
2、查看数据
train_data.columns
test_data.columns
# isDefault是Y变量，test_data多了n2.2和n2.3字段
train_data.info()
train_data.describe()
2.1查看缺失值的两种方法
missing = train_data.isnull().sum()/len(train_data)
missing = missing[missing > 0]
missing.sort_values(inplace = True)
missing.plot.bar()
缺失值可视化
def missing_data(data):
total = data.isnull().sum().sort_values(ascending=False)
percent = (data.isnull().sum()/data.isnull().count()*100).sort_values(ascending=False)
return pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])
missing_data(train_data).head(20)
缺失值表格
2.2查看只有一个值的特征
①nunique() 返回唯一值个数
②unique() 返回唯一值array
one_value_fea = [col for col in train_data.columns if train_data[col].nunique()<=1]
one_value_fea_test = [col for col in test_data.columns if test_data[col].nunique()<=1]
print(one_value_fea)
print(one_value_fea_test)
#['policyCode']
print(f'There are {len(one_value_fea)} columns in train dataset with one unique value.')
print(f'There are {len(one_value_fea_test)} columns in test dataset with one unique value.')
#There are 1 columns in test dataset with one unique value.
2.3根据数据类型进行特征分类
分成category，numerical两类，其中numerical再细分成连续和离散两类
numerical_fea = list(train_data.select_dtypes(exclude=['object']).columns)
catagory_fea = list(filter(lambda x:x not in numeric_fea, list(train_data.columns)))
将数值型变量分类为连续性变量和离散型变量
#过滤数值型类别特征
def get_numerical_serial_fea(data,feas):
numerical_serial_fea = []
numerical_noserial_fea = []
for fea in feas:
temp = data[fea].nunique()
if temp <= 20:
numerical_noserial_fea.append(fea)
continue
numerical_serial_fea.append(fea)
return numerical_serial_fea,numerical_noserial_fea
numerical_serial_fea,numerical_noserial_fea = get_numerical_serial_fea(train_data,numerical_fea)
#可以和后面的单因素分析结合
离散型数值数据的阈值可以取10,20,30，根据需要来
numerical_noserial_fea
#['term', 'homeOwnership', 'verificationStatus', 'isDefault', 'purpose', 'pubRecBankruptcies', 'initialListStatus', 'applicationType', 'policyCode', 'n11', 'n12']
2.4连续型变量分布
原代码运行时间比较长，且没有去掉id编码，所以也尝试了另一种代码。比较粗糙，但是也符合查看各变量分布的需求。
f = pd.melt(train_data, value_vars=numerical_serial_fea)
g = sns.FacetGrid(f, col="variable", col_wrap=2, sharex=False, sharey=False)
g = g.map(sns.distplot, "value")
train_data[numerical_serial_fea].drop('id',axis=1).hist(bins=30, figsize=(15,20))
plt.show()
各变量分布
2.5样本均衡性
#1表示违约，0表示正常还款
temp = train_data['isDefault'].value_counts()
df = pd.DataFrame({'labels':temp.index,
'values':temp.values})
plt.figure(figsize = (6,6))
plt.title('违约情况分布')
sns.set_color_codes(palette='bright')
sns.barplot(x = 'labels', y = 'values', data=df)
plt.show()
#总体违约率
train_data['isDefault'].mean()
#0.1995125
总体违约率19.95%，属于不均衡样本。
2.6查看单因素违约率
def plot_stats(feature,label_rotation=False,horizontal_layout=True):
temp = train_data[feature].value_counts()
df1 = pd.DataFrame({feature: temp.index,'Number of contracts': temp.values})
# 违约率[0,1]，可以用平均值计算比例
cat_perc = train_data[[feature, 'isDefault']].groupby([feature],as_index=False).mean()
cat_perc.sort_values(by='isDefault', ascending=False, inplace=True)
#ncols，2列1行，nrows，1列2行
if(horizontal_layout):
fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(12,6))
else:
fig, (ax1, ax2) = plt.subplots(nrows=2, figsize=(12,14))
sns.set_color_codes("pastel")
s = sns.barplot(ax=ax1, x = feature, y="Number of contracts",data=df1)
if(label_rotation):
s.set_xticklabels(s.get_xticklabels(),rotation=90)
s = sns.barplot(ax=ax2, x = feature, y='isDefault', order=cat_perc[feature], data=cat_perc)
if(label_rotation):
s.set_xticklabels(s.get_xticklabels(),rotation=90)
#增加平均逾期率参考线
plt.axhline(0.1995125, color = 'red', linewidth = 2, linestyle='--')
plt.ylabel('Percent of target with value 1 [%]', fontsize=10)
plt.tick_params(axis='both', which='major', labelsize=10)
plt.show()
plot_stats('grade')
plot_stats('term')
plot_stats('homeOwnership')
plot_stats('employmentLength',1,0)
'grade'
'term'
'homeOwnership'
'employmentLength'
此外，通过2.3发现的离散型数据特征n11和n12具有良好的区分性。
2.7数值型变量不同y值分布差异
def plot_distribution_comp(var,nrow=2):
i = 0
t1 = train_data.loc[train_data['isDefault'] != 0]
t0 = train_data.loc[train_data['isDefault'] == 0]
sns.set_style('whitegrid')
plt.figure()
fig, ax = plt.subplots(nrow,2,figsize=(12,6*nrow))
for feature in var:
i += 1
plt.subplot(nrow,2,i)
sns.kdeplot(t1[feature].dropna(), bw=0.1, label="isDefault = 1")
sns.kdeplot(t0[feature].dropna(), bw=0.1, label="isDefault = 0")
plt.ylabel('Density plot', fontsize=12)
plt.xlabel(feature, fontsize=12)
locs, labels = plt.xticks()
plt.tick_params(axis='both', which='major', labelsize=12)
plt.show()
var = ['purpose', 'homeOwnership', 'dti', 'revolUtil', 'delinquency_2years', 'regionCode']
plot_distribution_comp(var, nrow=3)
9.png
选取的几个特征违约与否的分布无明显差异，需要后续特征处理再进行区分。
2.8类别型变量不同y值分布差异
train_data1 = train_data.loc[train_data['isDefault'] != 0]
train_data0 = train_data.loc[train_data['isDefault'] == 0]
fig,((ax1,ax2),(ax3,ax4)) = plt.subplots(2,2,figsize=(12,8))
train_data1.groupby('grade')['grade'].count().plot(kind='barh', ax=ax1,
title='Fraud-grade distribution')
train_data0.groupby('grade')['grade'].count().plot(kind='barh', ax=ax2,
title='non_Fraud-grade distribution')
train_data1.groupby('employmentLength')['employmentLength'].count().plot(kind='barh', ax=ax3,
title='Fraud-employmentLength distribution')
train_data0.groupby('employmentLength')['employmentLength'].count().plot(kind='barh', ax=ax4,
title='non_Fraud-employmentLength distribution')
10.png
grade有比较明显的差异，和单因素违约率的结果也相符
2.9比较train_data和test_data时间段是否一致
#转化成时间格式 issueDateDT特征表示数据日期离数据集中日期最早的日期（2007-06-01）的天数
train_data['issueDate'] = pd.to_datetime(train_data['issueDate'],format='%Y-%m-%d')
startdate = datetime.datetime.strptime('2007-06-01', '%Y-%m-%d')
train_data['issueDateDT'] = train_data['issueDate'].apply(lambda x: x-startdate).dt.days
#转化成时间格式
test_data['issueDate'] = pd.to_datetime(test_data['issueDate'],format='%Y-%m-%d')
startdate = datetime.datetime.strptime('2007-06-01', '%Y-%m-%d')
test_data['issueDateDT'] = test_data['issueDate'].apply(lambda x: x-startdate).dt.days
plt.hist(train_data['issueDateDT'], label='train')
plt.hist(test_data['issueDateDT'], label='test')
plt.legend()
plt.title('Distribution of issueDateDT dates')
11.png
时间分布一致，后面可以直接建模拟合。
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