贷款逾期--逻辑回归(1)

主要根据数据集来建立一个逻辑回归模型

数据集来源https://pan.baidu.com/s/1izJZerx0lfvQX6YRKYWv-g

主要问题是根据数据建立一个逻辑回归模型来预测贷款是否逾期。

下面是对其进行编写的代码:

主要分为五步:

1.数据分析

  • 对缺失值进行处理
  • 对包含中文数据进行映射
  • 对两个时间的不确定进行删除

2.划分训练集和验证集,验证集比例为test_size

3. 分标签和 训练数据

4.模型训练与模型预测

5.模型评分

代码是黑桃即胡联粤所写

import pickle
import pandas as pd #数据分析
from pandas import Series,DataFrame
from sklearn.model_selection import train_test_split
import time
from sklearn.linear_model import LogisticRegression
from datetime import datetime
from sklearn.metrics import f1_score
print("开始......")
t_start = time.time()
path = "E:/moxingxuexi/Task1/data.csv"
"""=====================================================================================================================
1 读取数据
"""
print("数据预处理")
data = pd.read_csv(path ,encoding='gb18030',engine='python')

"""
1.1 缺失值用100填充
"""
data=DataFrame(data.fillna(100))


"""
1.2 对reg_preference_for_trad 的处理  【映射】
    nan=0 境外=1 一线=5 二线=2 三线 =3 其他=4
"""
n=set(data['reg_preference_for_trad'])
dic={}
for i,j in enumerate(n):
    dic[j]=i
data['reg_preference_for_trad'] = data['reg_preference_for_trad'].map(dic)


"""
1.2 对source 的处理  【映射】
"""
n=set(data['source'])
dic={}
for i,j in enumerate(n):
    dic[j]=i
data['source'] = data['source'].map(dic)


"""
1.3 对bank_card_no 的处理  【映射】
"""
n=set(data['bank_card_no'])
dic={}
for i,j in enumerate(n):
    dic[j]=i
data['bank_card_no'] = data['bank_card_no'].map(dic)

"""
1.2 对 id_name的处理  【映射】
"""
n=set(data['id_name'])
dic={}
for i,j in enumerate(n):
    dic[j]=i
data['id_name'] = data['id_name'].map(dic)

"""
1.2 对 time 的处理  【删除】
"""
data.drop(["latest_query_time"],axis=1,inplace=True)
data.drop(["loans_latest_time"],axis=1,inplace=True)

status = data.status
# """=====================================================================================================================
# 4 time时间归一化 小时
# """
# data['time'] = pd.to_datetime(data['time'])
# time_now = data['time'].apply(lambda x:int((x-datetime(2018,11,14,0,0,0)).seconds/3600))
# data['time']= time_now

"""=====================================================================================================================
2 划分训练集和验证集,验证集比例为test_size
"""
print("划分训练集和验证集,验证集比例为test_size")
train, test = train_test_split(data, test_size=0.2, random_state=666)


"""=====================================================================================================================
3 分标签和 训练数据
"""
y_train= train.status
train.drop(["status"],axis=1,inplace=True)

y_test= test.status
test.drop(["status"],axis=1,inplace=True)
"""=====================================================================================================================
4 模型训练
"""
print("模型训练")
lr = LogisticRegression(C=120,dual=True)
lr.fit(train,y_train)

"""=====================================================================================================================
4 模型预测
"""
y_test_pre = lr.predict(test)

"""=====================================================================================================================
5 模型评分
"""
score_vali = f1_score(y_test, y_test_pre, average='macro')
print("验证集分数:{}".format(score_vali))

遇到的问题

1.encoding='gb18030',为什么改为utf-8不可以?

2.对"latest_query_time"与"loans_latest_time"这两列怎么处理?

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转载自blog.csdn.net/lgy54321/article/details/84101357
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