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顾客购买服装的分析与预测
【实验内容】
采用决策树算法，对“双十一”期间顾客是否买服装的数据集进行分析与预测。
顾客购买服装数据集：包含review(商品评价变量)、discount(打折程度)、needed(是否必需)、shipping(是否包邮)、buy(是否购买)。
【实验要求】
1.读取顾客购买服装的数据集(数据集路径：data/data7[vite:vue] [plugin vite:vue] docs/Python/机器学习实验之肿瘤分类与预测( SVM).md (57:42): Attribute name cannot contain U+0022 ("), U+0027 ('), and U+003C (<). file: D:/Project/Power/blog/docs/Python/机器学习实验之肿瘤分类与预测(SVM).md:57:42 6088/3_buy.csv)，探索数据。
2.分别用ID3算法和CART算法进行决策树模型的配置、模型的训练、模型的预测、模型的评估。
3.扩展内容(选做)：对不同算法生成的决策树结构图进行可视化。
python
import pandas as pd
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import numpy as np
from sklearn import tree # 导入决策树包
from jupyterthemes import jtplot
jtplot.style(theme='monokai') # 选择一个绘图主题读取顾客购买服装的数据集
python
data = pd.read_csv("./datasets/3_buy.csv")
data| review | discount | needed | shipping | buy | |
|---|---|---|---|---|---|
| 0 | 3 | 3 | 0 | 1 | 1 |
| 1 | 3 | 3 | 0 | 0 | 1 |
| 2 | 2 | 3 | 0 | 1 | 0 |
| 3 | 1 | 2 | 0 | 1 | 0 |
| 4 | 1 | 1 | 1 | 1 | 0 |
| 5 | 1 | 1 | 1 | 0 | 1 |
| 6 | 2 | 1 | 1 | 0 | 0 |
| 7 | 3 | 2 | 0 | 1 | 1 |
| 8 | 3 | 1 | 1 | 1 | 0 |
| 9 | 1 | 2 | 1 | 1 | 0 |
| 10 | 3 | 2 | 1 | 0 | 0 |
| 11 | 2 | 2 | 0 | 0 | 1 |
| 12 | 2 | 3 | 1 | 1 | 0 |
| 13 | 1 | 2 | 0 | 0 | 1 |
分别用ID3算法和CART算法进行决策树模型的配置、模型的训练、模型的预测、模型的评估
数据集分割
python
x, y = np.split(data, indices_or_sections=(4,), axis=1)
# print(x)
# print(y)python
x_train, x_test, y_train, y_test = train_test_split(
x, y, test_size=0.30)
print("x_train.shape:", x_train.shape)
print("y_train.shape:", y_train.shape)
print("x_test.shape:", x_test.shape)
print("y_test.shape:", y_test.shape)x_train.shape: (9, 4) y_train.shape: (9, 1) x_test.shape: (5, 4) y_test.shape: (5, 1)
配置模型
python
clf_CART = tree.DecisionTreeClassifier(criterion='gini', max_depth=4) # CART基尼系数
clf_ID3 = tree.DecisionTreeClassifier(criterion='entropy', max_depth=4) # ID3信息熵训练模型
python
clf_CART.fit(x_train, y_train) # 模型训练
clf_ID3.fit(x_train, y_train) # 模型训练DecisionTreeClassifier(criterion='entropy', max_depth=4)
模型预测
python
predictions_CART = clf_CART.predict(x_test) # 模型测试
print("predictions_CART", predictions_CART)
predictions_ID3 = clf_ID3.predict(x_test) # 模型测试
print("predictions_ID3", predictions_ID3)predictions_CART [0 0 1 0 0] predictions_ID3 [0 0 1 0 0]
模型评估
python
from sklearn.metrics import accuracy_score # 导入准确率评价指标
print('Accuracy of CART: %s' % accuracy_score(y_test, predictions_CART))
from sklearn.metrics import accuracy_score # 导入准确率评价指标
print('Accuracy of ID3: %s' % accuracy_score(y_test, predictions_ID3))Accuracy of CART: 0.8 Accuracy of ID3: 0.8