Timestamps in Ridge Regression Scikit Learn

Question

I am trying to transform data for use in regression, most likely the Ridge or Lasso technique implemented in sklearn.linear_model.

My training data contains time stamps , which I believe may have predictive power. The time stamps reflect the time that a user placed an order for pizza. Here is an example:

Edit: Including labels in field elapsed_time, which is in seconds.

import pandas as pd
import sklearn.linear_model as linear_model

delivery_data = {
    'order_time' : ['2018-09-12 21:43:08', '2018-09-13 06:33:04', '2018-09-13 09:12:18'],
    'price' : [34.54, 8.63, 21.24],
    'miles' : [6, 3, 7],
    'home_type' : ['apartment', 'house', 'apartment'],
    'elapsed_time' : [2023, 1610, 1918]
}

df = pd.DataFrame(delivery_data)
df['order_time'] = pd.to_datetime(df['order_time'])

The resulting DataFrame looks like this:

           order_time  price  miles  home_type  elapsed_time
0 2018-09-12 21:43:08  34.54      6  apartment          2023
1 2018-09-13 06:33:04   8.63      3      house          1610
2 2018-09-13 09:12:18  21.24      7  apartment          1918

I am trying to predict the time to deliver pizza (elapsed_time) given timestamp, quantitative, and categorical data.

I suspect that time of day is predictive but that date is less predictive.

So far, I am considering extracting only the hour from the time stamp. In this example, order_time would become [21, 6, 9]. My first concern is that 23:59 has an hour of 23 and 00:01 has an hour of 0. The two values are far apart, even though the order times are two minutes apart.

Is there a better way to transform this datetime data?

Does it make a difference that the dataset contains other quantitative data (price, miles_from_store) and categorical data (home_type)?

Answer 1

The datetime type has arithmetic operations available. If you have two datetime types, you can find the delta between them - the result will be a datetime.timedelta class. The other quantitative data is easily incorporated into a linear regression model, either lasso or ridge. Pretty much all those scikit-learn models can use a vector of features.