python linear regression predict by date

Question

I want to predict a value at a date in the future with simple linear regression, but I can't due to the date format.

This is the dataframe I have:

data_df =  date          value 2016-01-15    1555 2016-01-16    1678 2016-01-17    1789 ...    y = np.asarray(data_df['value']) X = data_df[['date']] X_train, X_test, y_train, y_test = train_test_split              (X,y,train_size=.7,random_state=42)  model = LinearRegression() #create linear regression object model.fit(X_train, y_train) #train model on train data model.score(X_train, y_train) #check score  print (‘Coefficient: \n’, model.coef_) print (‘Intercept: \n’, model.intercept_)  coefs = zip(model.coef_, X.columns) model.__dict__ print "sl = %.1f + " % model.intercept_ + \      " + ".join("%.1f %s" % coef for coef in coefs) #linear model 

I tried to convert the date unsuccessfully

data_df['conv_date'] = data_df.date.apply(lambda x: x.toordinal())  data_df['conv_date'] = pd.to_datetime(data_df.date, format="%Y-%M-%D") 

Answer 1

Linear regression doesn't work on date data. Therefore we need to convert it into numerical value.The following code will convert the date into numerical value:

import datetime as dt data_df['Date'] = pd.to_datetime(data_df['Date']) data_df['Date']=data_df['Date'].map(dt.datetime.toordinal)