1. Learn
    2. /
  2. Courses
    3. /
  3. Feature Engineering with PySpark

Exercise

Creating Time Splits

In the video, we learned why splitting data randomly can be dangerous for time series as data from the future can cause overfitting in our model. Often with time series, you acquire new data as it is made available and you will want to retrain your model using the newest data. In the video, we showed how to do a percentage split for test and training sets but suppose you wish to train on all available data except for the last 45days which you want to use for a test set.

In this exercise, we will create a function to find the split date for using the last 45 days of data for testing and the rest for training. Please note that timedelta() has already been imported for you from the standard python library datetime.

Instructions

100 XP
  • Create a function train_test_split_date() that takes in a dataframe, df, the date column to use for splitting split_col and the number of days to use for the test set, test_days and set it to have a default value of 45.
  • Find the min and max dates for split_col using ,().
  • Find the date to split the test and training sets using max_date and subtract test_days from it by using timedelta() which takes a days parameter, in this case, pass in `test_days,
  • Using OFFMKTDATE as the split_col find split_date and use it to filter the dataframe into two new ones, train_df and test_df, Where test_df is only the last 45 days of the data. Additionally, ensure that the test_df only contains homes listed as of the split date by filtering df['LISTDATE'] less than or equal to the split_date.