我有一个数据帧，我在其上构建了一个预测模型。数据分为训练和测试，我使用了随机森林分类器。
现在，用户传递一个新数据，该数据需要通过此模型并给出结果。
它是一个文本数据，下面是数据帧：
Description Category
Rejoin this domain Network
Laptop crashed Hardware
Installation Error Software
法典：
############### Feature extraction ##############
countvec = CountVectorizer()
counts = countvec.fit_transform(read_data['Description'])
df = pd.DataFrame(counts.toarray())
df.columns = countvec.get_feature_names()
print(df)
########## Join with original data ##############
df = read_data.join(df)
a = list(df.columns.values)
########## Creating the dependent variable class for "Category" variable ###########
factor = pd.factorize(df['Category'])
df.Category = factor[0]
definitions = factor[1]
print(df.Category.head())
print(definitions)
########## Creating the dependent variable class for "Description" variable ###########
factor = pd.factorize(df['Description'])
df.Description = factor[0]
definitions_1 = factor[1]
print(df.Description.head())
print(definitions_1)
######### Split into Train and Test data #######################
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.80, random_state = 21)
############# Random forest classification model #########################
classifier = RandomForestClassifier(n_estimators = 10, criterion = 'entropy', random_state = 42)
classifier.fit(X_train, y_train)
######### Predicting the Test set results ##############
y_pred = classifier.predict(X_test)
#####Reverse factorize (converting y_pred from 0s,1s and 2s to original class for "Category" ###############
reversefactor = dict(zip(range(3),definitions))
y_test = np.vectorize(reversefactor.get)(y_test)
y_pred = np.vectorize(reversefactor.get)(y_pred)
#####Reverse factorize (converting y_pred from 0s,1s and 2s to original class for "Description" ###############
reversefactor = dict(zip(range(53),definitions_1))
X_test = np.vectorize(reversefactor.get)(X_test)
如果您只想对用户的数据进行预测，那么我只需加载包含用户数据的新 csv(或其他格式((确保列与原始训练数据集中的列相同，明显减去因变量(，您可以为您的任务提取预测：
user_df = pd.read_csv("user_data.csv")
#insert a preprocessing step if needed to make sure user_df is identical to the original dataset
new_predictions = classifier.predict(user_df)