线性回归预测d

Question

我有列车数据帧。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2016-12测试数据帧

^{pr2}$

我尝试使用linear regression

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)

但我有一个[ 2.17593916]。所有的数据都是不同的，但是我得到的所有值都在1.5 and 2.7之间。它是正确的吗？我怎样才能计算出与上一个数据相同的值呢？在