kaggle中的单车数据集怎么进行knn模型的构建,写出详细的代码
时间: 2024-03-03 10:51:50 浏览: 19
Kaggle中的单车租赁数据集被广泛用于机器学习的练习和竞赛。下面是使用KNN模型进行预测的代码示例:
```
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsRegressor
from sklearn.metrics import mean_squared_error
# 加载数据集
data = pd.read_csv('bike_rental.csv')
data = data[['atemp', 'humidity', 'windspeed', 'season','count','season','holiday','workingday','weather']]
data = pd.get_dummies(data, columns=['season','holiday','workingday','weather'])
# 划分训练集和测试集
train, test = train_test_split(data, test_size=0.3, random_state=42)
# 准备特征和标签
X_train, y_train = train.drop('count', axis=1), train['count']
X_test, y_test = test.drop('count', axis=1), test['count']
# 训练KNN模型
knn = KNeighborsRegressor(n_neighbors=5)
knn.fit(X_train, y_train)
# 在测试集上进行预测
y_pred = knn.predict(X_test)
# 计算均方误差
mse = mean_squared_error(y_test, y_pred)
print('MSE: ', mse)
```
上述代码首先加载数据集,然后进行数据清洗和特征工程。接着,它将数据集划分为训练集和测试集,并准备好特征和标签。然后,它使用`KNeighborsRegressor`类来训练一个KNN回归器,并在测试集上进行预测。最后,它计算均方误差来评估模型的预测性能。