信用评分模型数据清洗和标注流程设计

import pandas as pd

# 加载数据
data = __________

# 显示前五行的数据
__________

import matplotlib.pyplot as plt
import seaborn as sns

# 设置图像尺寸
plt.figure(figsize=(12, 8))

# 识别数值列用于箱线图
numeric_cols = data.select_dtypes(include=['float64', 'int64']).columns

# 创建箱线图
for i, col in enumerate(numeric_cols, 1):
    plt.subplot(3, 4, i)
    sns.boxplot(x=data[col])
    plt.title(col)

plt.tight_layout()
plt.show()

# 使用IQR处理异常值
Q1 = __________(0.25)
Q3 = __________(0.75)
IQR = __________

# 移除异常值
data_cleaned = data[~((data[numeric_cols] < (Q1 - 1.5 * __________)) | (data[numeric_cols] > (Q3 + 1.5 * __________))).any(axis=1)]

# 检查处理重复值
duplicates = __________()
num_duplicates = duplicates.sum()
data_cleaned = data_cleaned[~duplicates]

print(f'删除的重复行数: {num_duplicates}')

#对数据进行归一化处理
from sklearn.preprocessing import MinMaxScaler

scaler = MinMaxScaler()
data_cleaned[numeric_cols] = __________

# 设定目标变量
target_variable = __________

from sklearn.model_selection import train_test_split

# 定义特征和目标
X = __________(columns=[__________])   #1分
y = __________                         #1分

# 划分数据（训练集占80%）
X_train, X_test, y_train, y_test = __________(__________, random_state=42)

# 显示划分后的数据形状
print(f'训练数据形状: {X_train.shape}')
print(f'测试数据形状: {X_test.shape}')

# 保存清洗后的数据到CSV
cleaned_file_path = '2.1.3_cleaned_data.csv'
__________(__________, index=False)