一数据
csv数据
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class0表示可以贷款1表示不可贷款
time列有0 0两个编号就是表示有两个相同办理的位置
其中V1—V28都是经过脱敏处理的，为的就是保护用户数据安全，数据是存放数据库里的，数据库在服务器里
import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
import matplotlib.pyplot as plt
from pylab import mpl
# 数据读取
data = pd.read_csv(r"./creditcard.csv")
# 数据预处理：Amount标准化、删除Time列
scaler = StandardScaler()
data['Amount'] = scaler.fit_transform(data[['Amount']])
data = data.drop(['Time'], axis=1)
# 数据集切分（特征与标签分离、训练集测试集划分）
X_whole = data.drop('Class', axis=1)
y_whole = data['Class']
x_train_w, x_test_w, y_train_w, y_test_w = train_test_split(
X_whole, y_whole, test_size=0.3, random_state=1000
)
# 逻辑回归建模：训练、预测、评估
lr = LogisticRegression(C=0.01)
lr.fit(x_train_w, y_train_w)
test_predicted = lr.predict(x_test_w)
result = lr.score(x_test_w, y_test_w)
# 分类报告（如需可启用）
from sklearn import metrics
print(metrics.classification_report(y_test_w, test_predicted))
# 正负样本数量可视化（解决中文显示问题）
mpl.rcParams['font.sans-serif'] = ['Microsoft YaHei']
mpl.rcParams['axes.unicode_minus'] = False
labels_count = pd.value_counts(data['Class'])
labels_count.plot(kind='bar')
plt.title("正负例样本数")
plt.xlabel("类别")
plt.ylabel("频数")
plt.show()读取
data = pd.read_csv(r"./creditcard.csv")对数据处理
scaler = StandardScaler()
data['Amount'] = scaler.fit_transform(data[['Amount']])
data = data.drop(['Time'], axis=1)如何处理？
1.Z标准化，2.归一化0~1
1·Z标准化
数据标准化
0~1 标准化：
也叫离差标准化，是对原始数据的线性变换，使结果映射到([0,1])区间。
2.归一化0~1
数据标准化
Z 标准化：
这种方法基于原始数据的均值（mean）和标准差（standard deviation）进行数据的标准化。将 A 的原始值 x 使用 z - score 标准化到 x′ 。
（标准差（standard deviation ）是统计学里用于衡量数据离散程度的指标，反映数据相对于均值的波动情况，公式及解读如下：）
切分
X_whole = data.drop('Class', axis=1)
y_whole = data['Class']
x_train_w, x_test_w, y_train_w, y_test_w = train_test_split(
X_whole, y_whole, test_size=0.3, random_state=1000
)
对比
0与1可以看出相差许多需要处理
银行只会看1的那行recall的那一列注意是0.54不到99%所以银行是不会用这个模型的，如果你能做到就可以去银行应聘了