现有数据集(Edu-Data),包含440个学生记录和对应的16个特征。这些特征分为三大类: (1)性别和国籍等人口统计特征; (2)学历背景特征,如教育阶段,年级和隶属教室; (3)行为特征,如上课举手,访问资源,家长回答问卷调查,学校满意度等。我需要python的具体代码
时间: 2024-02-25 21:55:29 浏览: 20
好的,以下是使用Python对Edu-Data数据集进行分析的代码示例:
首先,我们需要导入所需的库,包括Pandas,Numpy和Scikit-learn:
```python
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score
```
然后,我们需要读取数据集,并对数据进行预处理:
```python
df = pd.read_csv('Edu-Data.csv')
# 将分类变量转换为数值变量
df['gender'] = df['gender'].map({'M': 0, 'F': 1})
df['NationalITy'] = df['NationalITy'].astype('category').cat.codes
df['PlaceofBirth'] = df['PlaceofBirth'].astype('category').cat.codes
df['StageID'] = df['StageID'].astype('category').cat.codes
df['GradeID'] = df['GradeID'].astype('category').cat.codes
df['SectionID'] = df['SectionID'].astype('category').cat.codes
df['Topic'] = df['Topic'].astype('category').cat.codes
df['Semester'] = df['Semester'].astype('category').cat.codes
df['Relation'] = df['Relation'].astype('category').cat.codes
df['ParentAnsweringSurvey'] = df['ParentAnsweringSurvey'].map({'Yes': 1, 'No': 0})
df['ParentschoolSatisfaction'] = df['ParentschoolSatisfaction'].map({'Good': 1, 'Bad': 0})
df['StudentAbsenceDays'] = df['StudentAbsenceDays'].map({'Under-7': 0, 'Above-7': 1})
# 将数据集分为特征和标签
X = df.drop('Class', axis=1)
y = df['Class']
# 将数据集分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
```
接下来,我们需要训练一个决策树分类器,并使用测试集评估其准确性:
```python
# 训练决策树分类器
clf = DecisionTreeClassifier(random_state=42)
clf.fit(X_train, y_train)
# 使用测试集评估分类器的准确性
y_pred = clf.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print('Accuracy:', accuracy)
```
最后,我们可以使用这个分类器来预测新数据的类别:
```python
# 预测新数据的类别
new_data = np.array([[1, 4, 2, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0]])
y_pred = clf.predict(new_data)
print('Predicted class:', y_pred[0])
```
这个代码示例展示了如何使用Python对Edu-Data数据集进行分析,并训练一个决策树分类器来预测学生的成绩等级。