使用python进行线性回归的简单预测 [英] Simple prediction using linear regression with python

问题描述

data2 = pd.DataFrame(data1['kwh'])
data2
                          kwh
date    
2012-04-12 14:56:50     1.256400
2012-04-12 15:11:55     1.430750
2012-04-12 15:27:01     1.369910
2012-04-12 15:42:06     1.359350
2012-04-12 15:57:10     1.305680
2012-04-12 16:12:10     1.287750
2012-04-12 16:27:14     1.245970
2012-04-12 16:42:19     1.282280
2012-04-12 16:57:24     1.365710
2012-04-12 17:12:28     1.320130
2012-04-12 17:27:33     1.354890
2012-04-12 17:42:37     1.343680
2012-04-12 17:57:41     1.314220
2012-04-12 18:12:44     1.311970
2012-04-12 18:27:46     1.338980
2012-04-12 18:42:51     1.357370
2012-04-12 18:57:54     1.328700
2012-04-12 19:12:58     1.308200
2012-04-12 19:28:01     1.341770
2012-04-12 19:43:04     1.278350
2012-04-12 19:58:07     1.253170
2012-04-12 20:13:10     1.420670
2012-04-12 20:28:15     1.292740
2012-04-12 20:43:15     1.322840
2012-04-12 20:58:18     1.247410
2012-04-12 21:13:20     0.568352
2012-04-12 21:28:22     0.317865
2012-04-12 21:43:24     0.233603
2012-04-12 21:58:27     0.229524
2012-04-12 22:13:29     0.236929
2012-04-12 22:28:34     0.233806
2012-04-12 22:43:38     0.235618
2012-04-12 22:58:43     0.229858
2012-04-12 23:13:43     0.235132
2012-04-12 23:28:46     0.231863
2012-04-12 23:43:55     0.237794
2012-04-12 23:59:00     0.229634
2012-04-13 00:14:02     0.234484
2012-04-13 00:29:05     0.234189
2012-04-13 00:44:09     0.237213
2012-04-13 00:59:09     0.230483
2012-04-13 01:14:10     0.234982
2012-04-13 01:29:11     0.237121
2012-04-13 01:44:16     0.230910
2012-04-13 01:59:22     0.238406
2012-04-13 02:14:21     0.250530
2012-04-13 02:29:24     0.283575
2012-04-13 02:44:24     0.302299
2012-04-13 02:59:25     0.322093
2012-04-13 03:14:30     0.327600
2012-04-13 03:29:31     0.324368
2012-04-13 03:44:31     0.301869
2012-04-13 03:59:42     0.322019
2012-04-13 04:14:43     0.325328
2012-04-13 04:29:43     0.306727
2012-04-13 04:44:46     0.299012
2012-04-13 04:59:47     0.303288
2012-04-13 05:14:48     0.326205
2012-04-13 05:29:49     0.344230
2012-04-13 05:44:50     0.353484
...

65701 rows × 1 columns

我有这个带有这个索引和1列的数据框.我想使用sklearn进行线性回归来做简单的预测.我非常困惑，我也不知道如何设置X和y(我希望将x值设置为是时间和 y 值 kwh...).我是 Python 新手，所以每一个帮助都很有价值.谢谢.

I have this dataframe with this index and 1 column.I want to do simple prediction using linear regression with sklearn.I'm very confused and I don't know how to set X and y(I want the x values to be the time and y values kwh...).I'm new to Python so every help is valuable.Thank you.

推荐答案

要做的第一件事是将数据分成两个数组，即X和y.X的每个元素将是一个日期，而y的相应元素将是关联的kwh.

The first thing you have to do is split your data into two arrays, X and y. Each element of X will be a date, and the corresponding element of y will be the associated kwh.

一旦有了，您将要使用sklearn.linear_model.LinearRegression进行回归.该文档位于此处.

Once you have that, you will want to use sklearn.linear_model.LinearRegression to do the regression. The documentation is here.

对于每个sklearn模型，有两个步骤.首先，您必须适合您的数据.然后，将要预测其千瓦时的日期放在另一个数组X_predict中，并使用predict方法预测该千瓦时.

As for every sklearn model, there is two step. First you must fit your data. Then, put the dates of which you want to predict the kwh in another array, X_predict, and predict the kwh using the predict method.

from sklearn.linear_model import LinearRegression

X = []  # put your dates in here
y = []  # put your kwh in here

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

X_predict = []  # put the dates of which you want to predict kwh here
y_predict = model.predict(X_predict)

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