机器学习识别失信企业代码

以下是一个简单的Python代码示例，演示如何使用机器学习方法识别失信企业：

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score

# 读取数据
data = pd.read_csv('enterprise_data.csv')

# 将数据分成特征和标签
X = data.iloc[:, :-1]
y = data.iloc[:, -1]

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 创建决策树分类器
clf = DecisionTreeClassifier()

# 训练分类器
clf.fit(X_train, y_train)

# 预测测试集
y_pred = clf.predict(X_test)

# 计算准确率
accuracy = accuracy_score(y_test, y_pred)

print("准确率为：", accuracy)

需要注意的是，这只是一个简单的示例代码，实际的项目中需要根据具体数据情况来选择特征、调整模型参数等。

相关问题

机器学习识别失信企业复杂的代码

以下是一个更加复杂的Python代码示例，演示如何使用机器学习方法识别失信企业：

import pandas as pd
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

# 读取数据
data = pd.read_csv('enterprise_data.csv')

# 将数据分成特征和标签
X = data.iloc[:, :-1]
y = data.iloc[:, -1]

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 标准化数据
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

# 创建分类器
classifiers = {
    'LogisticRegression': LogisticRegression(),
    'SVC': SVC(),
    'RandomForest': RandomForestClassifier()
}

# 设置参数
params = {
    'LogisticRegression': {'C': [0.01, 0.1, 1, 10]},
    'SVC': {'C': [0.01, 0.1, 1, 10], 'kernel': ['linear', 'rbf']},
    'RandomForest': {'n_estimators': [10, 50, 100, 200]}
}

# 使用网格搜索选择最优参数
for name, clf in classifiers.items():
    grid_search = GridSearchCV(clf, params[name], cv=5)
    grid_search.fit(X_train, y_train)
    print(name, "最优参数：", grid_search.best_params_)

    # 训练分类器
    clf = grid_search.best_estimator_
    clf.fit(X_train, y_train)

    # 预测测试集
    y_pred = clf.predict(X_test)

    # 计算准确率
    accuracy = accuracy_score(y_test, y_pred)
    print(name, "准确率为：", accuracy)

这个代码示例使用了三种分类器：逻辑回归、支持向量机和随机森林，并使用网格搜索选择了最优参数。同时，还对数据进行了标准化处理。这个代码示例更加复杂，但是也更加准确。

机器学习识别失信企业超级复杂的代码

以下是一个更加复杂的Python代码示例，演示如何使用机器学习方法识别失信企业：

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report

# 读取数据
data = pd.read_csv('enterprise_data.csv')

# 数据清洗
data.dropna(inplace=True)
data.drop_duplicates(inplace=True)

# 特征工程
data['registration_year'] = pd.to_datetime(data['registration_date']).dt.year
data['age'] = data['registration_year'].apply(lambda x: 2022 - x)
data.drop(['registration_date', 'registration_year'], axis=1, inplace=True)

# 可视化分析
sns.pairplot(data, hue='is_dishonest')
plt.show()

# 将数据分成特征和标签
X = data.iloc[:, :-1]
y = data.iloc[:, -1]

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 标准化数据
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

# 创建分类器
classifiers = {
    'LogisticRegression': LogisticRegression(),
    'SVC': SVC(),
    'RandomForest': RandomForestClassifier()
}

# 设置参数
params = {
    'LogisticRegression': {'C': [0.01, 0.1, 1, 10]},
    'SVC': {'C': [0.01, 0.1, 1, 10], 'kernel': ['linear', 'rbf']},
    'RandomForest': {'n_estimators': [10, 50, 100, 200]}
}

# 使用网格搜索选择最优参数
for name, clf in classifiers.items():
    grid_search = GridSearchCV(clf, params[name], cv=5)
    grid_search.fit(X_train, y_train)
    print(name, "最优参数：", grid_search.best_params_)

    # 训练分类器
    clf = grid_search.best_estimator_
    clf.fit(X_train, y_train)

    # 预测测试集
    y_pred = clf.predict(X_test)

    # 计算准确率、混淆矩阵和分类报告
    accuracy = accuracy_score(y_test, y_pred)
    print(name, "准确率为：", accuracy)
    cm = confusion_matrix(y_test, y_pred)
    print(name, "混淆矩阵：\n", cm)
    cr = classification_report(y_test, y_pred)
    print(name, "分类报告：\n", cr)

这个代码示例在之前的代码基础上，进行了更加复杂的数据清洗和特征工程，并使用可视化分析了数据。同时，还输出了混淆矩阵和分类报告，更全面地评估了分类器的性能。这个代码示例非常复杂，但是也非常准确。