Python Sklearn：预测数据的线性回归

我有火车数据帧。这是他们的一部分

date    city    brand   model   price   count
2016-03 moscow  bmw 5-series    1 млн - 2 млн   5
2016-05 moscow  bmw 5-series    500 тыс - 1 млн 3
2016-06 moscow  bmw 5-series    1 млн - 2 млн   4
2016-09 moscow  bmw 5-series    до 200 тыс  4

我需要预测它将在2016-12测试数据帧

date    city    brand   model
2016-12 moscow  bmw 5-series

我尝试使用

线性回归

X = pd.read_excel('result_drom2.xlsx')
X_predict = pd.read_excel('test.xlsx')
y = pd.DataFrame()

y['count'] = X['count']
del X['count']
label = LabelEncoder()
def cat_to_num(df, column):
    dicts = {}
    label.fit(df[column].drop_duplicates())
    dicts[column] = list(label.classes_)
    df[column] = label.transform(df[column])

cat_to_num(X, 'date')    
cat_to_num(X, 'city')
cat_to_num(X, 'brand')
cat_to_num(X, 'model')
cat_to_num(X, 'price')

cat_to_num(X_predict, 'date') 
cat_to_num(X_predict, 'city')
cat_to_num(X_predict, 'brand')
cat_to_num(X_predict, 'model')
cat_to_num(X_predict, 'price')

model = LinearRegression()
model.fit(X, y)
y_predict = model.predict(X_predict)

但是我有一个

[2.17593916]

。所有数据都不同，但我得到的所有值都在

1.5和2.7之间。它正确吗？我怎样才能计算出与上次数据相同的值