我已经应用了一个热编码，然后决策树，并得到了一个很好的分数为我的部门和数量列。
当我这样做：

prediction = dt_clf.predict([[0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0]])
print(prediction)

我得到以下输出：

ValueError: could not convert string to float: 'Beef'

但是我想把它转换成pickle并部署到pythonflask，这样用户就可以给予并检索预测。
我必须做一个热编码再次如果是这样请纠正这段代码为我：

from sklearn.tree import DecisionTreeRegressor
from sklearn.model_selection import train_test_split

# Split the data into training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=10)

# Train a decision tree regression model with the best hyperparameters found by grid search
dt_clf = DecisionTreeRegressor(criterion='friedman_mse', splitter='random')
dt_clf.fit(X_train, y_train)

# Evaluate the model on the test set
dt_score = dt_clf.score(X_test, y_test)
print("Decision Tree Regression Test Score:", dt_score)`

我希望用户通过网页给予他们的输入来预测。有什么反馈吗？