Question

我正在处理这种类型的数据：

  Date Of Stop count
0 2012-01-01   180
1 2013-01-01   348
2 2014-02-01   537
3 2015-02-01   498
4 2016-03-01   719
5 2017-03-01   406

并尝试对我没有数据（日期）的日期进行预测

这是我的代码，其中我将日期分为前11个月和第12个月然后我尝试根据前11个月定义在12个月后可以得到什么

 dfhalf = groupbyClass[(groupbyClass['Date Of Stop'] > '01/01/2012') & 
         (groupbyClass['Date Of Stop'] < '12/01/2012')]
 dfpred = groupbyClass[(groupbyClass['Date Of Stop'] >= '12/01/2012') & 
         (groupbyClass['Date Of Stop'] < '01/01/2013')]

 from sklearn.linear_model import LinearRegression

 X = dfhalf['Date Of Stop']   # put dates in here
 y = dfhalf['count']          # put knowh in here

 model = LinearRegression()
 model.fit(X, y)

 X_predict = dfpred['Date Of Stop']  # dates for prediction
 y_predict = model.predict(X_predict)

不幸的是，这会向我抛出这样的东西：

 ValueError: Expected 2D array, got 1D array instead:
 array=['2012-01-02T00:00:00.000000000' '2012-01-03T00:00:00.000000000'
 '2012-01-04T00:00:00.000000000' '2012-01-05T00:00:00.000000000'
 '2012-01-06T00:00:00.000000000' '2012-01-07T00:00:00.000000000'
 '2012-01-08T00:00:00.000000000' '2012-01-09T00:00:00.000000000'
 '2012-01-10T00:00:00.000000000' '2012-01-11T00:00:00.000000000'
 ....
 Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.

在定义X或y时，我尝试使用.reshape（-1、1和（1，-1）的不同形式。但是没有运气。我不知道我需要做什么以及为什么。 / p>

使用Python和线性回归进行预测

0 个答案: