• Python数据分析与机器学习48-案例之贷款利润最大化

    文章目录

    一. 数据源介绍

    下载地址:
    https://www.lendingclub.com/info/prospectus.action

    LoanStats3a.csv
    这是一个贷款的数据
    image.png

    image.png

    二. 数据预处理

    我们从上一步的数据中可以看到数据存在的一些问题:

    1. 首行是URL，第二行才开始是表格数据
    2. 存在很多的空列
    3. 有些id、member_id 这类流水id，有url、desc这些描述性字段 等一些对于分析意义不大的数据
    4. 有部分列只有唯一值，对于分析意义不大

    2.1 删除空列

    代码:

    import pandas as pd

# 跳过首行读取数据
loans_2007 = pd.read_csv('E:/file/LoanStats3a.csv', skiprows=1)

# 删除一些空列及不需要的列
half_count = len(loans_2007) / 2
loans_2007 = loans_2007.dropna(thresh=half_count, axis=1)
loans_2007 = loans_2007.drop(['desc', 'url'],axis=1)

# 将处理完的数据写入到文件
loans_2007.to_csv('E:/file/loans_2007.csv', index=False)

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    2.2 删除不必要的列并对字符数据进行编码

    代码:

    import pandas as pd

loans_2007 = pd.read_csv("E:/file/loans_2007.csv")

loans_2007 = loans_2007.drop(["id", "member_id", "funded_amnt", "funded_amnt_inv", "grade", "sub_grade", "emp_title", "issue_d"], axis=1)
loans_2007 = loans_2007.drop(["zip_code", "out_prncp", "out_prncp_inv", "total_pymnt", "total_pymnt_inv", "total_rec_prncp"], axis=1)
loans_2007 = loans_2007.drop(["total_rec_int", "total_rec_late_fee", "recoveries", "collection_recovery_fee", "last_pymnt_d", "last_pymnt_amnt"], axis=1)

print(loans_2007['loan_status'].value_counts())

# 将字符类型转换为数值
loans_2007 = loans_2007[(loans_2007['loan_status'] == "Fully Paid") | (loans_2007['loan_status'] == "Charged Off")]

status_replace = {
    "loan_status" : {
        "Fully Paid": 1,
        "Charged Off": 0,
    }
}

loans_2007 = loans_2007.replace(status_replace)

#将只包含唯一值的列进行删除
orig_columns = loans_2007.columns
drop_columns = []
for col in orig_columns:
    col_series = loans_2007[col].dropna().unique()
    if len(col_series) == 1:
        drop_columns.append(col)
loans_2007 = loans_2007.drop(drop_columns, axis=1)
print(drop_columns)
print(loans_2007.shape)

# 将处理完的数据写入到一个新文件
loans_2007.to_csv('E:/file/filtered_loans_2007.csv', index=False)

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    2.3 删除空值较多的列并对字符列进行编码

    代码:

    import pandas as pd

# 查看没一列 空值的个数,并将空值较多的列进行删除
loans = pd.read_csv('E:/file/filtered_loans_2007.csv')
null_counts = loans.isnull().sum()
print(null_counts)

loans = loans.drop("pub_rec_bankruptcies", axis=1)
loans = loans.dropna(axis=0)

# 查看列的数据类型,并将字符转为数值类型
object_columns_df = loans.select_dtypes(include=["object"])
print(object_columns_df.iloc[0])

cols = ['home_ownership', 'verification_status', 'emp_length', 'term', 'addr_state']
#for c in cols:
#    print(loans[c].value_counts())

# 这两列意义类似，删除其中之一
print(loans["purpose"].value_counts())
print(loans["title"].value_counts())

mapping_dict = {
    "emp_length": {
        "10+ years": 10,
        "9 years": 9,
        "8 years": 8,
        "7 years": 7,
        "6 years": 6,
        "5 years": 5,
        "4 years": 4,
        "3 years": 3,
        "2 years": 2,
        "1 year": 1,
        "< 1 year": 0,
        "n/a": 0
    }
}
loans = loans.drop(["last_credit_pull_d", "earliest_cr_line", "addr_state", "title"], axis=1)
loans["int_rate"] = loans["int_rate"].str.rstrip("%").astype("float")
loans["revol_util"] = loans["revol_util"].str.rstrip("%").astype("float")
loans = loans.replace(mapping_dict)

# 进行独热编码
cat_columns = ["home_ownership", "verification_status", "emp_length", "purpose", "term"]
dummy_df = pd.get_dummies(loans[cat_columns])
loans = pd.concat([loans, dummy_df], axis=1)
loans = loans.drop(cat_columns, axis=1)
loans = loans.drop("pymnt_plan", axis=1)

# 将处理完成的数据写入到文件
loans.to_csv('E:/file/cleaned_loans2007.csv', index=False)

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    2.4 预处理完成的数据

    代码:

    import pandas as pd

# 读取数据源
loans = pd.read_csv("E:/file/cleaned_loans2007.csv")
print(loans.info())

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    测试记录:
    如下可知，我们总共选择了37个特征，每个特征都是数值类型，且没有空值。

    
RangeIndex: 38428 entries, 0 to 38427
Data columns (total 37 columns):
 #   Column                               Non-Null Count  Dtype  
---  ------                               --------------  -----  
 0   loan_amnt                            38428 non-null  float64
 1   int_rate                             38428 non-null  float64
 2   installment                          38428 non-null  float64
 3   annual_inc                           38428 non-null  float64
 4   loan_status                          38428 non-null  int64  
 5   dti                                  38428 non-null  float64
 6   delinq_2yrs                          38428 non-null  float64
 7   inq_last_6mths                       38428 non-null  float64
 8   open_acc                             38428 non-null  float64
 9   pub_rec                              38428 non-null  float64
 10  revol_bal                            38428 non-null  float64
 11  revol_util                           38428 non-null  float64
 12  total_acc                            38428 non-null  float64
 13  home_ownership_MORTGAGE              38428 non-null  int64  
 14  home_ownership_NONE                  38428 non-null  int64  
 15  home_ownership_OTHER                 38428 non-null  int64  
 16  home_ownership_OWN                   38428 non-null  int64  
 17  home_ownership_RENT                  38428 non-null  int64  
 18  verification_status_Not Verified     38428 non-null  int64  
 19  verification_status_Source Verified  38428 non-null  int64  
 20  verification_status_Verified         38428 non-null  int64  
 21  purpose_car                          38428 non-null  int64  
 22  purpose_credit_card                  38428 non-null  int64  
 23  purpose_debt_consolidation           38428 non-null  int64  
 24  purpose_educational                  38428 non-null  int64  
 25  purpose_home_improvement             38428 non-null  int64  
 26  purpose_house                        38428 non-null  int64  
 27  purpose_major_purchase               38428 non-null  int64  
 28  purpose_medical                      38428 non-null  int64  
 29  purpose_moving                       38428 non-null  int64  
 30  purpose_other                        38428 non-null  int64  
 31  purpose_renewable_energy             38428 non-null  int64  
 32  purpose_small_business               38428 non-null  int64  
 33  purpose_vacation                     38428 non-null  int64  
 34  purpose_wedding                      38428 non-null  int64  
 35  term_ 36 months                      38428 non-null  int64  
 36  term_ 60 months                      38428 non-null  int64  
dtypes: float64(12), int64(25)
memory usage: 10.8 MB
None

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    三. 使用逻辑回归模型进行分析

    3.1 简单的逻辑回归模型

    import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_predict, KFold

# 读取数据源
loans = pd.read_csv("E:/file/cleaned_loans2007.csv")
#print(loans.info())

# 使用逻辑回归建模分析
lr = LogisticRegression()
cols = loans.columns
train_cols = cols.drop("loan_status")
features = loans[train_cols]
target = loans["loan_status"]

# 使用交叉验证
kf = KFold(n_splits=3, random_state=None)
predictions = cross_val_predict(lr, features, target, cv=kf)
predictions = pd.Series(predictions)

# False positives.
fp_filter = (predictions == 1) & (loans["loan_status"] == 0)
fp = len(predictions[fp_filter])

# True positives.
tp_filter = (predictions == 1) & (loans["loan_status"] == 1)
tp = len(predictions[tp_filter])

# False negatives.
fn_filter = (predictions == 0) & (loans["loan_status"] == 1)
fn = len(predictions[fn_filter])

# True negatives
tn_filter = (predictions == 0) & (loans["loan_status"] == 0)
tn = len(predictions[tn_filter])

# Rates
tpr = tp / float((tp + fn))
fpr = fp / float((fp + tn))

print(tpr)
print(fpr)
print(predictions[:20])

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    测试记录:
    我们可以看到tpr和fpr都是很高的数值，但是我们想贷款利润最大化，就要求 tpr越大越好，fpr越小越好。
    这样的结果是达不到我们的要求的，原来是因为样本分布不均匀的原因，正样本数远远大于负样本数。

    0.9992428143077808
0.9983367214932545
0     1
1     1
2     1
3     1
4     1
5     1
6     1
7     1
8     1
9     1
10    1
11    1
12    1
13    1
14    1
15    1
16    1
17    1
18    1
19    1
dtype: int64

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    3.2 逻辑回归+改变样本权重

    对比上一章节，我这边只修改了一行代码，就是训练模型时候的样本权重值。

    代码:

    import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_predict, KFold

# 读取数据源
loans = pd.read_csv("E:/file/cleaned_loans2007.csv")
#print(loans.info())

# 使用逻辑回归建模分析,改变正负样本的权重项
lr = LogisticRegression(class_weight="balanced")
cols = loans.columns
train_cols = cols.drop("loan_status")
features = loans[train_cols]
target = loans["loan_status"]

# 使用交叉验证
kf = KFold(n_splits=3, random_state=None)
predictions = cross_val_predict(lr, features, target, cv=kf)
predictions = pd.Series(predictions)

# False positives.
fp_filter = (predictions == 1) & (loans["loan_status"] == 0)
fp = len(predictions[fp_filter])

# True positives.
tp_filter = (predictions == 1) & (loans["loan_status"] == 1)
tp = len(predictions[tp_filter])

# False negatives.
fn_filter = (predictions == 0) & (loans["loan_status"] == 1)
fn = len(predictions[fn_filter])

# True negatives
tn_filter = (predictions == 0) & (loans["loan_status"] == 0)
tn = len(predictions[tn_filter])

# Rates
tpr = tp / float((tp + fn))
fpr = fp / float((fp + tn))

print(tpr)
print(fpr)
print(predictions[:20])

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    测试记录:
    tpr 0.5559,这个比较低了
    fpr 0.37，1 - 0.37 = 0.63，模型的效果只能分辨出63%的负样本数据。

    0.5559257352273071
0.37737941230826094
0     0
1     0
2     0
3     1
4     1
5     0
6     0
7     0
8     0
9     1
10    1
11    0
12    0
13    1
14    0
15    0
16    1
17    1
18    1
19    0
dtype: int64

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    四. 随机森林

    4.1 初步的随机森林

    代码:

    import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_predict, KFold
from sklearn.ensemble import RandomForestClassifier

# 读取数据源
loans = pd.read_csv("E:/file/cleaned_loans2007.csv")
#print(loans.info())

cols = loans.columns
train_cols = cols.drop("loan_status")
features = loans[train_cols]
target = loans["loan_status"]

# 使用随机森林进行建模
rf = RandomForestClassifier(n_estimators=10,class_weight="balanced", random_state=1)
#print help(RandomForestClassifier)
kf = KFold(n_splits=3, random_state=None)
predictions = cross_val_predict(rf, features, target, cv=kf)
predictions = pd.Series(predictions)

# False positives.
fp_filter = (predictions == 1) & (loans["loan_status"] == 0)
fp = len(predictions[fp_filter])

# True positives.
tp_filter = (predictions == 1) & (loans["loan_status"] == 1)
tp = len(predictions[tp_filter])

# False negatives.
fn_filter = (predictions == 0) & (loans["loan_status"] == 1)
fn = len(predictions[fn_filter])

# True negatives
tn_filter = (predictions == 0) & (loans["loan_status"] == 0)
tn = len(predictions[tn_filter])

# Rates
tpr = tp / float((tp + fn))
fpr = fp / float((fp + tn))

print(tpr)
print(fpr)
print(predictions[:20])

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    测试记录:
    tpr 高，fpr也高，所以这个模型效果也不理想

    0.9763152315473846
0.9406763999260765
0     1
1     0
2     1
3     1
4     1
5     1
6     1
7     1
8     1
9     1
10    1
11    1
12    1
13    1
14    1
15    1
16    1
17    1
18    1
19    1
dtype: int64

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    4.2 调整参数

    我们使用for循环去遍历参数，查看最优的参数

    代码:

    import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_predict, KFold
from sklearn.ensemble import RandomForestClassifier

# 读取数据源
loans = pd.read_csv("E:/file/cleaned_loans2007.csv")
#print(loans.info())

cols = loans.columns
train_cols = cols.drop("loan_status")
features = loans[train_cols]
target = loans["loan_status"]

rf_trees = [2,5,8,10,12,15]
rf_depthe = [3,4,5,6,7,8,9,10,11,12,13,14,15]

result = {}
results = []
results2 = []


for t in rf_trees:
    for d in rf_depthe:
        # 使用随机森林进行建模
        rf = RandomForestClassifier(n_estimators=t, max_depth=d, class_weight="balanced", random_state=1)
        #print help(RandomForestClassifier)
        kf = KFold(n_splits=3, random_state=None)
        predictions = cross_val_predict(rf, features, target, cv=kf)
        predictions = pd.Series(predictions)

        # False positives.
        fp_filter = (predictions == 1) & (loans["loan_status"] == 0)
        fp = len(predictions[fp_filter])

        # True positives.
        tp_filter = (predictions == 1) & (loans["loan_status"] == 1)
        tp = len(predictions[tp_filter])

        # False negatives.
        fn_filter = (predictions == 0) & (loans["loan_status"] == 1)
        fn = len(predictions[fn_filter])

        # True negatives
        tn_filter = (predictions == 0) & (loans["loan_status"] == 0)
        tn = len(predictions[tn_filter])

        # Rates
        tpr = tp / float((tp + fn))
        fpr = fp / float((fp + tn))

        #print("trees:" + str(t) + "   " + "depths:" + str(d))

        #print('trp:'+ str(tpr))
        #print('fpr:'+ str(fpr))
        #print("##########################")


        result['trees'] = t;
        result['depths'] = d;
        result['trp'] = tpr;
        result['fpr'] = fpr;
        # json字典在循环外 顾需要加上copy
        results.append(result.copy())


for r in results:
    print(r)

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    测试记录:

    {'trees': 2, 'depths': 3, 'trp': 0.6363691431686707, 'fpr': 0.40713361670670856}
{'trees': 2, 'depths': 4, 'trp': 0.6348244843565436, 'fpr': 0.40177416374052854}
{'trees': 2, 'depths': 5, 'trp': 0.6153193809249781, 'fpr': 0.3962299020513768}
{'trees': 2, 'depths': 6, 'trp': 0.6316443044492231, 'fpr': 0.40620957309184996}
{'trees': 2, 'depths': 7, 'trp': 0.6363994305963595, 'fpr': 0.42247274071336166}
{'trees': 2, 'depths': 8, 'trp': 0.6567525820032105, 'fpr': 0.4514877102199224}
{'trees': 2, 'depths': 9, 'trp': 0.6668382954235696, 'fpr': 0.46331546849011274}
{'trees': 2, 'depths': 10, 'trp': 0.6982463579368204, 'fpr': 0.5139530585843651}
{'trees': 2, 'depths': 11, 'trp': 0.691552836417603, 'fpr': 0.5058214747736093}
{'trees': 2, 'depths': 12, 'trp': 0.742375140079353, 'fpr': 0.5989650711513583}
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    参考：

    1. https://study.163.com/course/introduction.htm?courseId=1003590004#/courseDetail?tab=1
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  • 原文地址：https://blog.csdn.net/u010520724/article/details/126222883