As with most machine learning algorithms, we have to split our dataset into:
- Training set: the data used to fit the model
- Test set: the data partitioned away at the very start of the experiment (to provide an unbiased evaluation of the model)
In general, putting 80% of your data in the training set and 20% of your data in the test set is a good place to start.
Suppose you have some values in x and some values in y:
Here are the parameters:
train_size: the proportion of the dataset to include in the train split (between 0.0 and 1.0)test_size: the proportion of the dataset to include in the test split (between 0.0 and 1.0)random_state: the seed used by the random number generator [optional]
To learn more, here is a Training Set vs Validation Set vs Test Set article.
Instructions
Import train_test_split from sklearn.model_selection.
Create a DataFrame x that selects the following columns from the main df DataFrame:
'bedrooms''bathrooms''size_sqft''min_to_subway''floor''building_age_yrs''no_fee''has_roofdeck''has_washer_dryer''has_doorman''has_elevator''has_dishwasher''has_patio''has_gym'
Create a DataFrame y that selects the rent column from the main df DataFrame.
These are the columns we want to use for our regression model.
Use scikit-learn’s train_test_split() method to split x into 80% training set and 20% testing set and generate:
x_trainx_testy_trainy_test
Set the random_state to 6.
Let’s take a look at the shapes of x_train, x_test, y_train, and y_test to see we got the proportion we wanted.
We have 14 features that we’re looking for for each apartment, and 1 label we’re looking for for each apartment.