将女装电子商务数据集进行完善,通过上采样方法解决数据不平衡的问题并使用随机森林的方法训练模型并给出正确代码
时间: 2023-11-22 11:54:03 浏览: 164
论文研究-一种多标签随机均衡采样算法.pdf
首先,需要导入必要的库和读取女装电子商务数据集。假设数据集包含以下特征:'product_name', 'description', 'price', 'rating', 'category',其中'category'为目标变量。
```python
import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from imblearn.over_sampling import RandomOverSampler
# 读取数据集
data = pd.read_csv('female_clothing.csv')
```
然后,我们需要对数据集进行预处理,包括缺失值处理、类别编码(将分类变量转换为数值变量)等。
```python
# 缺失值处理(假设用均值填充)
data = data.fillna(data.mean())
# 类别编码
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
data['category'] = le.fit_transform(data['category'])
```
接下来,我们使用上采样方法解决数据不平衡问题。在这里,我们使用 `RandomOverSampler` 实现随机过采样。
```python
# 定义特征和目标变量
X = data.drop('category', axis=1)
y = data['category']
# 定义上采样方法
ros = RandomOverSampler(random_state=0)
# 对数据集进行上采样
X_resampled, y_resampled = ros.fit_resample(X, y)
```
最后,我们使用随机森林算法训练模型并对测试集进行预测和评估。
```python
# 划分训练集和测试集
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X_resampled, y_resampled, test_size=0.2, random_state=0)
# 定义随机森林模型
rf = RandomForestClassifier(n_estimators=100, random_state=0)
# 训练模型
rf.fit(X_train, y_train)
# 预测测试集
y_pred = rf.predict(X_test)
# 评估模型
accuracy = accuracy_score(y_test, y_pred)
print('Accuracy:', accuracy)
```
完整代码如下:
```python
import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from imblearn.over_sampling import RandomOverSampler
from sklearn.preprocessing import LabelEncoder
# 读取数据集
data = pd.read_csv('female_clothing.csv')
# 缺失值处理(假设用均值填充)
data = data.fillna(data.mean())
# 类别编码
le = LabelEncoder()
data['category'] = le.fit_transform(data['category'])
# 定义特征和目标变量
X = data.drop('category', axis=1)
y = data['category']
# 定义上采样方法
ros = RandomOverSampler(random_state=0)
# 对数据集进行上采样
X_resampled, y_resampled = ros.fit_resample(X, y)
# 划分训练集和测试集
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X_resampled, y_resampled, test_size=0.2, random_state=0)
# 定义随机森林模型
rf = RandomForestClassifier(n_estimators=100, random_state=0)
# 训练模型
rf.fit(X_train, y_train)
# 预测测试集
y_pred = rf.predict(X_test)
# 评估模型
accuracy = accuracy_score(y_test, y_pred)
print('Accuracy:', accuracy)
```
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