Regression¶

This tutorial uses Safe-DS on house sales data to predict house prices.

File and Imports¶

Start by creating a Python-File with the suffix .py.

Reading Data¶

Download the house sales data from here and load it into a Table:

In [1]:

from safeds.data.tabular.containers import Table

pricing = Table.from_csv_file("data/house_sales.csv")
# For visualisation purposes, we only print out the first 15 rows.
pricing.slice_rows(length=15)

from safeds.data.tabular.containers import Table pricing = Table.from_csv_file("data/house_sales.csv") # For visualisation purposes, we only print out the first 15 rows. pricing.slice_rows(length=15)

Out[1]:

shape: (15, 23)

id	year	month	day	zipcode	latitude	longitude	sqft_lot	sqft_living	sqft_above	sqft_basement	floors	bedrooms	bathrooms	waterfront	view	condition	grade	year_built	year_renovated	sqft_lot_15nn	sqft_living_15nn	price
i64	i64	i64	i64	i64	f64	f64	i64	i64	i64	i64	f64	i64	f64	i64	i64	i64	i64	i64	i64	i64	i64	i64
0	2014	5	2	98001	47.3406	-122.269	9397	2200	2200	0	2.0	4	2.5	0	1	3	8	1987	0	9176	2310	285000
1	2014	5	2	98003	47.3537	-122.303	10834	2090	1360	730	1.0	3	2.5	0	1	4	8	1987	0	8595	1750	285000
2	2014	5	2	98006	47.5443	-122.177	8119	2160	1080	1080	1.0	4	2.25	0	1	3	8	1966	0	9000	1850	440000
3	2014	5	2	98006	47.5746	-122.135	8800	1450	1450	0	1.0	4	1.0	0	1	4	7	1954	0	8942	1260	435000
4	2014	5	2	98006	47.5725	-122.133	10000	1920	1070	850	1.0	4	1.5	0	1	4	7	1954	0	10836	1450	430000
…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…
10	2014	5	2	98023	47.3256	-122.378	33151	3240	3240	0	2.0	3	2.5	0	3	3	10	1995	0	24967	4050	604000
11	2014	5	2	98024	47.5643	-121.897	16215	1580	1580	0	1.0	3	2.25	0	1	4	7	1978	0	16215	1450	335000
12	2014	5	2	98027	47.4635	-121.991	35100	1970	1970	0	2.0	3	2.25	0	1	4	9	1977	0	35100	2340	437500
13	2014	5	2	98027	47.4634	-121.987	37277	2710	2710	0	2.0	4	2.75	0	1	3	9	2000	0	39299	2390	630000
14	2014	5	2	98029	47.5794	-122.025	67518	2820	2820	0	2.0	5	2.5	0	1	3	8	1979	0	48351	2820	675000

Cleaning your Data¶

At this point it is usual to clean the data. Here's an example how to do so:

In [2]:

pricing_columns = (
    # Removes columns "latitude" and "longitude" from table
    pricing.remove_columns(["latitude", "longitude"])
    # Removes rows which contain missing values
    .remove_rows_with_missing_values()
    # Removes rows which contain outliers
    .remove_rows_with_outliers()
)
# For visualisation purposes, we only print out the first 5 rows.
pricing_columns.slice_rows(length=5)

pricing_columns = ( # Removes columns "latitude" and "longitude" from table pricing.remove_columns(["latitude", "longitude"]) # Removes rows which contain missing values .remove_rows_with_missing_values() # Removes rows which contain outliers .remove_rows_with_outliers() ) # For visualisation purposes, we only print out the first 5 rows. pricing_columns.slice_rows(length=5)

Out[2]:

shape: (5, 21)

id	year	month	day	zipcode	sqft_lot	sqft_living	sqft_above	sqft_basement	floors	bedrooms	bathrooms	waterfront	view	condition	grade	year_built	year_renovated	sqft_lot_15nn	sqft_living_15nn	price
i64	i64	i64	i64	i64	i64	i64	i64	i64	f64	i64	f64	i64	i64	i64	i64	i64	i64	i64	i64	i64
0	2014	5	2	98001	9397	2200	2200	0	2.0	4	2.5	0	1	3	8	1987	0	9176	2310	285000
1	2014	5	2	98003	10834	2090	1360	730	1.0	3	2.5	0	1	4	8	1987	0	8595	1750	285000
2	2014	5	2	98006	8119	2160	1080	1080	1.0	4	2.25	0	1	3	8	1966	0	9000	1850	440000
3	2014	5	2	98006	8800	1450	1450	0	1.0	4	1.0	0	1	4	7	1954	0	8942	1260	435000
4	2014	5	2	98006	10000	1920	1070	850	1.0	4	1.5	0	1	4	7	1954	0	10836	1450	430000

See how to perform further data cleaning in the dedicated Data Processing Tutorial.

Create Training and Testing Set¶

Split the house sales dataset into two tables. A training set, that will be used later to implement a training model to predict the house prices. It contains 60% of the data. The testing set contains the rest of the data.

In [3]:

train_table, testing_table = pricing_columns.split_rows(0.60)

train_table, testing_table = pricing_columns.split_rows(0.60)

Mark the price Column as the target variable to be predicted. Include the id column only as an extra column, which is completely ignored by the model:

In [4]:

extra_names = ["id"]

train_tabular_dataset = train_table.to_tabular_dataset("price", extra_names=extra_names)

extra_names = ["id"] train_tabular_dataset = train_table.to_tabular_dataset("price", extra_names=extra_names)

Creating and Fitting a Regressor¶

Use Decision Tree regressor as a model for the regression. Pass the "train_tabular_dataset" table to the fit function of the model:

In [5]:

from safeds.ml.classical.regression import DecisionTreeRegressor

fitted_model = DecisionTreeRegressor().fit(train_tabular_dataset)

from safeds.ml.classical.regression import DecisionTreeRegressor fitted_model = DecisionTreeRegressor().fit(train_tabular_dataset)

Predicting with the Fitted Regressor¶

Use the fitted decision tree regression model, that we trained on the training dataset to predict the price of a house in the test dataset.

In [6]:

prediction = fitted_model.predict(testing_table)
# For visualisation purposes we only print out the first 15 rows.
prediction.to_table().slice_rows(length=15)

prediction = fitted_model.predict(testing_table) # For visualisation purposes we only print out the first 15 rows. prediction.to_table().slice_rows(length=15)

Out[6]:

shape: (15, 21)

id	year	month	day	zipcode	sqft_lot	sqft_living	sqft_above	sqft_basement	floors	bedrooms	bathrooms	waterfront	view	condition	grade	year_built	year_renovated	sqft_lot_15nn	sqft_living_15nn	price
i64	i64	i64	i64	i64	i64	i64	i64	i64	f64	i64	f64	i64	i64	i64	i64	i64	i64	i64	i64	f64
10549	2014	10	13	98103	5000	1240	1000	240	1.0	2	1.0	0	1	3	7	1920	0	3500	1480	550661.111111
17590	2015	3	14	98002	7312	2010	2010	0	1.0	4	2.0	0	1	4	7	1976	0	7650	2010	269944.444444
10889	2014	10	17	98103	3220	1120	1120	0	1.0	2	1.0	0	1	4	7	1923	0	3220	1440	550661.111111
12511	2014	11	14	98144	2457	1950	1950	0	3.0	2	2.5	0	1	3	8	2009	0	1639	1650	382300.0
20572	2015	4	27	98056	5038	1220	1220	0	1.0	3	1.0	0	1	5	6	1942	0	5038	1140	195130.555556
…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…
9097	2014	9	18	98003	7400	1130	1130	0	1.0	4	1.0	0	1	4	7	1969	0	7379	1540	223916.666667
8454	2014	9	8	98023	8470	840	840	0	1.0	3	1.0	0	1	4	6	1961	0	8450	840	109291.666667
7829	2014	8	26	98126	4025	820	820	0	1.0	2	1.0	0	3	5	6	1922	0	5750	1410	330190.0
19952	2015	4	17	98198	10187	1120	1120	0	1.0	3	1.75	0	1	3	7	1968	0	8736	1900	201880.0
18382	2015	3	26	98042	5929	2210	2210	0	2.0	4	2.5	0	1	3	8	2004	0	5901	2200	311491.666667

Evaluating the Fitted Regressor¶

You can test the mean absolute error of that model with the initial testing_table as follows:

In [7]:

fitted_model.mean_absolute_error(testing_table)

fitted_model.mean_absolute_error(testing_table)

Out[7]:

92480.98339349499

Full Code¶

In [8]:

from safeds.data.tabular.containers import Table
from safeds.ml.classical.regression import DecisionTreeRegressor

pricing = Table.from_csv_file("data/house_sales.csv")

pricing_columns = (
    pricing.remove_columns(["latitude", "longitude"])
    .remove_rows_with_missing_values()
    .remove_rows_with_outliers()
)

train_table, testing_table = pricing_columns.split_rows(0.60)

extra_names = ["id"]
train_tabular_dataset = train_table.to_tabular_dataset("price", extra_names=extra_names)

fitted_model = DecisionTreeRegressor().fit(train_tabular_dataset)
prediction = fitted_model.predict(testing_table)

fitted_model.mean_absolute_error(testing_table)

from safeds.data.tabular.containers import Table from safeds.ml.classical.regression import DecisionTreeRegressor pricing = Table.from_csv_file("data/house_sales.csv") pricing_columns = ( pricing.remove_columns(["latitude", "longitude"]) .remove_rows_with_missing_values() .remove_rows_with_outliers() ) train_table, testing_table = pricing_columns.split_rows(0.60) extra_names = ["id"] train_tabular_dataset = train_table.to_tabular_dataset("price", extra_names=extra_names) fitted_model = DecisionTreeRegressor().fit(train_tabular_dataset) prediction = fitted_model.predict(testing_table) fitted_model.mean_absolute_error(testing_table)

Out[8]:

92552.00101077871