使用多层感知器算法实现通信运营商 客户流失预测

# 代码 8-14

#导入库

import pandas as pd

from sklearn.model_selection import train_test_split

from sklearn.preprocessing import StandardScaler

from sklearn.neural_network import MLPClassifier

from sklearn.metrics import classification_report

# 导入数据

data_drop = pd.read_csv('../tmp/data_drop.csv', encoding='utf-8', index_col=0)

data_preprocessed = pd.read_csv('../tmp/data_preprocessed.csv', 

                                encoding='utf-8', index_col=0)

# 取data_preprocessed作为输入，取data_drop中三月份的数据的目标变量作为输出

data_target = data_drop.loc[:, ['USER_ID', 'IS_LOST']]

data_target = data_target.loc[data_target["USER_ID"].isin(data_preprocessed.index)]

data_target = data_target.loc[201603].drop_duplicates()

print('目标变量数据集的形状为:', data_target.shape)

# 划分数据集

x = data_preprocessed

y = data_target

x_train, x_test, y_train, y_test = train_test_split(x, y['IS_LOST'], test_size=0.2,

                                                    random_state=42)

print('训练集数据的形状为：', x_train.shape)

print('训练集标签的形状为：', y_train.shape)

print('测试集数据的形状为：', x_test.shape)

print('测试集标签的形状为：', y_test.shape)

# 数据标准化

stdScaler = StandardScaler().fit(x_train)

x_stdtrain = stdScaler.transform(x_train)

x_stdtest = stdScaler.transform(x_test)

print('标准化后的x_stdtrain:\n', x_stdtrain)

print('标准化后的x_stdtest:\n', x_stdtest)

# 代码 8-15

# 建立模型

bpnn = MLPClassifier(hidden_layer_sizes=(17, 10),\

                     max_iter=200, solver='lbfgs', random_state=50)

bpnn.fit(x_stdtrain, y_train)

print('构建的模型为:\n', bpnn)

# 代码 8-16

# 模型预测

y_pre = bpnn.predict(x_stdtest)

print('多层感知器预测结果评价报告：\n', classification_report(y_test, y_pre))

# %matplotlib inline

from sklearn.metrics import roc_curve

import matplotlib.pyplot as plt

# 绘制ROC曲线图

plt.rcParams['font.sans-serif'] = 'SimHei'  # 显示中文

plt.rcParams['axes.unicode_minus'] = False  # 显示负号

fpr, tpr, thresholds = roc_curve(y_pre, y_test)  # 求出TPR和FPR

plt.figure(figsize=(6, 4))  # 创建画布

plt.plot(fpr, tpr)  # 绘制曲线

plt.title('用户流失模型的ROC曲线')  # 标题

plt.xlabel('FPR')  # x轴标签

plt.ylabel('TPR')  # y轴标签

plt.show()  # 显示图形

plt.close