然后对志愿者的数据进行详细的建模
时间: 2024-02-22 20:01:05 浏览: 17
好的,以下是一个对志愿者数据进行建模的示例:
```python
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
# 读取CSV文件
data = pd.read_csv('volunteer.csv')
# 将吸烟史列转换为数值
smoking_dict = {'never smoked': 0, 'formerly smoked': 1, 'smokes': 2}
data['smoking_status'] = data['smoking_status'].replace(smoking_dict)
# 将性别列转换为数值
gender_dict = {'Male': 0, 'Female': 1, 'Other': 2}
data['gender'] = data['gender'].replace(gender_dict)
# 将婚姻状况列转换为数值
marriage_dict = {'Never married': 0, 'Married': 1, 'Divorced': 2, 'Widowed': 3}
data['ever_married'] = data['ever_married'].replace(marriage_dict)
# 将居住地列转换为数值
residence_dict = {'Urban': 0, 'Rural': 1}
data['Residence_type'] = data['Residence_type'].replace(residence_dict)
# 将BMI列中的空值替换为平均值
mean_bmi = data['bmi'].mean()
data['bmi'] = data['bmi'].fillna(mean_bmi)
# 将年龄列按照十年为一组进行分组,并转换为数值
age_bins = [0, 10, 20, 30, 40, 50, 60, 70, 80, 90]
age_labels = [0, 1, 2, 3, 4, 5, 6, 7, 8]
data['age'] = pd.cut(data['age'], bins=age_bins, labels=age_labels)
data['age'] = data['age'].astype(int)
# 将训练集和测试集按照7:3的比例进行划分
train_data, test_data, train_label, test_label = train_test_split(data.drop(['stroke'], axis=1), data['stroke'], test_size=0.3)
# 对训练集和测试集进行标准化处理
scaler = StandardScaler()
train_data = scaler.fit_transform(train_data)
test_data = scaler.transform(test_data)
# 使用逻辑回归模型进行训练
model = LogisticRegression()
model.fit(train_data, train_label)
# 在测试集上进行预测并计算准确率
predict_label = model.predict(test_data)
accuracy = accuracy_score(test_label, predict_label)
print('模型准确率:', accuracy)
```
以上代码中,我们对吸烟史、性别、婚姻状况、居住地、BMI和年龄等列进行了转换和处理,然后将数据集划分为训练集和测试集,并对数据进行标准化处理。接着,我们使用逻辑回归模型进行了训练,并在测试集上进行了预测并计算了准确率。
请注意,这只是一个示例,您需要根据您的具体需求进行相应的更改。例如,如果您想使用其他模型进行训练,您需要修改代码以使用您需要的模型。