In this tutorial, you use a
[linear regression model](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-glm)
in BigQuery ML to predict the weight of a penguin based on the
penguin's demographic information. A linear regression is a type of regression
model that generates a continuous value from a linear combination of input
features.
This tutorial uses the
[`bigquery-public-data.ml_datasets.penguins`](https://console.cloud.google.com/bigquery?p=bigquery-public-data&d=ml_datasets&t=penguins&page=table)
dataset.
## Objectives
In this tutorial, you will perform the following tasks:
- Create a linear regression model.
- Evaluate the model.
- Make predictions by using the model.
## Costs
This tutorial uses billable components of Google Cloud,
including the following:
- BigQuery
- BigQuery ML
For more information on BigQuery costs, see the
[BigQuery pricing](https://cloud.google.com/bigquery/pricing) page.
For more information on BigQuery ML costs, see
[BigQuery ML pricing](https://cloud.google.com/bigquery/pricing#bqml).
## Before you begin
1. In the Google Cloud console, on the project selector page,
select or create a Google Cloud project.
**Roles required to select or create a project**
- **Select a project**: Selecting a project doesn't require a specific IAM role---you can select any project that you've been granted a role on.
- **Create a project** : To create a project, you need the Project Creator role (`roles/resourcemanager.projectCreator`), which contains the `resourcemanager.projects.create` permission. [Learn how to grant
roles](https://docs.cloud.google.com/iam/docs/granting-changing-revoking-access).
> [!NOTE]
> **Note**: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.
[Go to project selector](https://console.cloud.google.com/projectselector2/home/dashboard)
2.
[Verify that billing is enabled for your Google Cloud project](https://docs.cloud.google.com/billing/docs/how-to/verify-billing-enabled#confirm_billing_is_enabled_on_a_project).
3.
Enable the BigQuery API, if it is not already enabled.
**Roles required to enable APIs**
To enable APIs, you need the `serviceusage.services.enable` permission. If you
created the project, then you likely already have this permission through the
Owner role (`roles/owner`). Otherwise, you can get this permission through the
Service Usage Admin role (`roles/serviceusage.serviceUsageAdmin`).
[Learn how to grant roles](https://docs.cloud.google.com/iam/docs/granting-changing-revoking-access).
[Enable the API](https://console.cloud.google.com/apis/enableflow?apiid=bigquery.googleapis.com)
## Required permissions
To create the model using BigQuery ML, you need the following
IAM permissions:
- `bigquery.jobs.create`
- `bigquery.models.create`
- `bigquery.models.getData`
- `bigquery.models.updateData`
- `bigquery.models.updateMetadata`
To run inference, you need the following permissions:
- `bigquery.models.getData` on the model
- `bigquery.jobs.create`
## Create a dataset
To create a BigQuery dataset, select one of the following options:
### Console
1. In the Google Cloud console, go to the **BigQuery** page.
[Go to BigQuery](https://console.cloud.google.com/bigquery)
2. In the left pane, click
**Explorer**:
If you don't see the left pane, click
**Expand left pane** to open the pane.
3. In **Explorer** , expand your project, and then click **Datasets**.
4. On the **Datasets** page, click
**Create dataset**.
5. In the **Create dataset** pane, do the following:
- For **Dataset ID** , enter `bqml_tutorial`.
- For **Data location** , select **US**.
Leave the remaining default settings as they are.
6. Click **Create dataset**.
### bq
To create a new dataset, use the
[`bq mk --dataset` command](https://docs.cloud.google.com/bigquery/docs/reference/bq-cli-reference#mk-dataset).
1. Create a dataset named `bqml_tutorial` with the data location set to `US`:
```
bq mk --dataset \
--location=US \
--description "BigQuery ML tutorial dataset." \
bqml_tutorial
```
2. Confirm that the dataset was created:
```bash
bq ls
```
### API
Call the [`datasets.insert`](https://docs.cloud.google.com/bigquery/docs/reference/rest/v2/datasets/insert)
method with a defined [dataset resource](https://docs.cloud.google.com/bigquery/docs/reference/rest/v2/datasets):
```json
{
"datasetReference": {
"datasetId": "bqml_tutorial"
}
}
```
### BigQuery DataFrames
Before trying this sample, follow the BigQuery DataFrames
setup instructions in the [BigQuery quickstart
using BigQuery DataFrames](https://docs.cloud.google.com/bigquery/docs/dataframes-quickstart).
For more information, see the
[BigQuery DataFrames reference documentation](https://docs.cloud.google.com/python/docs/reference/bigframes/latest).
To authenticate to BigQuery, set up Application Default Credentials.
For more information, see [Set
up ADC for a local development environment](https://docs.cloud.google.com/docs/authentication/set-up-adc-local-dev-environment).
import google.cloud.bigquery
bqclient = google.cloud.https://docs.cloud.google.com/python/docs/reference/bigquery/latest.https://docs.cloud.google.com/python/docs/reference/bigquery/latest/google.cloud.bigquery.client.Client.html()
bqclient.https://docs.cloud.google.com/python/docs/reference/bigquery/latest/google.cloud.bigquery.client.Client.html#google_cloud_bigquery_client_Client_create_dataset("bqml_tutorial", exists_ok=True)
## Create the model
Create a linear regression model using the Analytics sample
dataset for BigQuery.
### SQL
You can create a linear regression model by using the
[`CREATE MODEL` statement](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-glm)
and specifying `LINEAR_REG` for the model type. Creating the model includes
training the model.
The following are useful things to know about the `CREATE MODEL` statement:
- The [`input_label_cols`](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-glm#input_label_cols) option specifies which column in the `SELECT` statement to use as the label column. Here, the label column is `body_mass_g`. For linear regression models, the label column must be real-valued, that is, the column values must be real numbers.
- This query's `SELECT` statement uses the following columns in the
`bigquery-public-data.ml_datasets.penguins` table to predict a penguin's
weight:
- `species`: the species of penguin.
- `island`: the island that the penguin resides on.
- `culmen_length_mm`: the length of the penguin's culmen in millimeters.
- `culmen_depth_mm`: the depth of the penguin's culmen in millimeters.
- `flipper_length_mm`: the length of the penguin's flippers in millimeters.
- `sex`: the sex of the penguin.
- The `WHERE` clause in this query's `SELECT` statement, `WHERE body_mass_g IS
NOT NULL`, excludes rows where the `body_mass_g` column is `NULL`.
Run the query that creates your linear regression model:
1. In the Google Cloud console, go to the **BigQuery** page.
[Go to BigQuery](https://console.cloud.google.com/bigquery)
2. In the query editor, run the following query:
```googlesql
CREATE OR REPLACE MODEL `bqml_tutorial.penguins_model`
OPTIONS
(model_type='linear_reg',
input_label_cols=['body_mass_g']) AS
SELECT
*
FROM
`bigquery-public-data.ml_datasets.penguins`
WHERE
body_mass_g IS NOT NULL;
```
3. It takes about 30 seconds to create the `penguins_model` model.
To see the model, follow these steps:
1. In the left pane, click **Explorer**:
If you don't see the left pane, click **Expand left pane** to open the pane.
2. In the **Explorer** pane, expand your project and click **Datasets**.
3. Click the `bqml_tutorial` dataset.
4. Click the **Models** tab.
### BigQuery DataFrames
Before trying this sample, follow the BigQuery DataFrames
setup instructions in the [BigQuery quickstart
using BigQuery DataFrames](https://docs.cloud.google.com/bigquery/docs/dataframes-quickstart).
For more information, see the
[BigQuery DataFrames reference documentation](https://docs.cloud.google.com/python/docs/reference/bigframes/latest).
To authenticate to BigQuery, set up Application Default Credentials.
For more information, see [Set
up ADC for a local development environment](https://docs.cloud.google.com/docs/authentication/set-up-adc-local-dev-environment).
import bigframes.pandas as bpd
from bigframes.bigquery import ml
# Set partial ordering mode for BigQuery DataFrames.
# For more information, see the BigQuery DataFrames performance documentation:
# https://cloud.google.com/bigquery/docs/dataframes-performance#partial-ordering-mode
bpd.options.bigquery.ordering_mode = "partial"
# Load data from BigQuery
bq_df = bpd.read_gbq("bigquery-public-data.ml_datasets.penguins")
# Drop rows with nulls to get training data
training_data = bq_df.dropna(subset=["body_mass_g"])
# A Linear Regression model predicts a continuous numerical value.
#
# Use ml.create_model to create and train the model in BigQuery.
# The options parameter specifies the model type and the label column.
# For more information, see the BigQuery DataFrames API reference documentation:
# https://dataframes.bigquery.dev/reference/api/bigframes.bigquery.ml.create_model.html#bigframes.bigquery.ml.create_model
ml.https://docs.cloud.google.com/python/docs/reference/bigframes/latest/bigframes.bigquery.ml.html(
your_model_id, # For example: "bqml_tutorial.penguins_model",
options={
"model_type": "LINEAR_REG",
"input_label_cols": ["body_mass_g"],
},
training_data=training_data,
replace=True,
)
It takes about 30 seconds to create the model. To see the model, follow these
steps:
1. In the left pane, click **Explorer**:
2. In the **Explorer** pane, expand your project and click **Datasets**.
3. Click the `bqml_tutorial` dataset.
4. Click the **Models** tab.
## Get training statistics
To see the results of the model training, you can use the
[`ML.TRAINING_INFO` function](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-train),
or you can view the statistics in the Google Cloud console. In this
tutorial, you use the Google Cloud console.
A machine learning algorithm builds a model by examining many examples and
attempting to find a model that minimizes loss. This process is called empirical
risk minimization.
Loss is the penalty for a bad prediction. It is a number indicating
how bad the model's prediction was on a single example. If the model's
prediction is perfect, the loss is zero; otherwise, the loss is greater. The
goal of training a model is to find a set of weights and biases that have low
loss, on average, across all examples.
See the model training statistics that were generated when you ran the
`CREATE MODEL` query:
1. In the left pane, click **Explorer**:
2. In the **Explorer** pane, expand your project and click **Datasets**.
3. Click the `bqml_tutorial` dataset.
4. Click the **Models** tab.
5. To open the model information pane, click **penguins_model**.
6. Click the **Training** tab, and then click **Table**. The results should look
similar to the following:
The **Training Data Loss** column represents the loss metric calculated
after the model is trained on the training dataset. Since you performed a
linear regression, this column shows the
[mean squared error](https://developers.google.com/machine-learning/glossary/#MSE)
value. A
[normal_equation](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-glm#optimize_strategy)
optimization strategy is automatically used for this training, so only one
iteration is required to converge to the final model. For more information
on setting the model optimization strategy, see
[`optimize_strategy`](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-glm#optimize_strategy).
## Evaluate the model
After creating the model, evaluate the model's performance by using the
[`ML.EVALUATE` function](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-evaluate)
or the
[`bigframes.bigquery.ml.evaluate` BigQuery DataFrames function](https://dataframes.bigquery.dev/reference/api/bigframes.bigquery.ml.evaluate.html#bigframes.bigquery.ml.evaluate)
to evaluate the predicted values generated by the model against the actual data.
### SQL
For input, the `ML.EVALUATE` function takes the trained model and a dataset
that matches the schema of the data that you used to train the model. In
a production environment, you should
evaluate the model on different data than the data you used to train the model.
If you run `ML.EVALUATE` without providing input data, the function retrieves
the evaluation metrics calculated during training. These metrics are calculated
by using the automatically reserved evaluation dataset:
SELECT
*
FROM
ML.EVALUATE(MODEL bqml_tutorial.penguins_model);
Run the `ML.EVALUATE` query:
1. In the Google Cloud console, go to the **BigQuery** page.
[Go to BigQuery](https://console.cloud.google.com/bigquery)
2. In the query editor, run the following query:
```googlesql
SELECT
*
FROM
ML.EVALUATE(MODEL `bqml_tutorial.penguins_model`,
(
SELECT
*
FROM
`bigquery-public-data.ml_datasets.penguins`
WHERE
body_mass_g IS NOT NULL));
```
### BigQuery DataFrames
Before trying this sample, follow the BigQuery DataFrames
setup instructions in the [BigQuery quickstart
using BigQuery DataFrames](https://docs.cloud.google.com/bigquery/docs/dataframes-quickstart).
For more information, see the
[BigQuery DataFrames reference documentation](https://docs.cloud.google.com/python/docs/reference/bigframes/latest).
To authenticate to BigQuery, set up Application Default Credentials.
For more information, see [Set
up ADC for a local development environment](https://docs.cloud.google.com/docs/authentication/set-up-adc-local-dev-environment).
# Use the ml.evaluate method to evaluate the model with test data.
# For more information, see the BigQuery DataFrames API reference documentation:
# https://dataframes.bigquery.dev/reference/api/bigframes.bigquery.ml.evaluate.html#bigframes.bigquery.ml.evaluate
ml.evaluate(
your_model_id, # For example: "bqml_tutorial.penguins_model",
input_=training_data,
)
# Expected output results:
# index mean_absolute_error mean_squared_error mean_squared_log_error median_absolute_error r2_score explained_variance
# 0 227.012237 81838.159892 0.00507 173.080816 0.872377 0.872377
# 1 rows x 6 columns
The results should look similar to the following:
Because you performed a linear regression, the results include the following
columns:
- `mean_absolute_error`
- `mean_squared_error`
- `mean_squared_log_error`
- `median_absolute_error`
- `r2_score`
- `explained_variance`
An important metric in the evaluation results is the
[R^2^ score](https://en.wikipedia.org/wiki/Coefficient_of_determination).
The R^2^ score is a statistical measure that determines if the linear
regression predictions approximate the actual data. A value of `0` indicates
that the model explains none of the variability of the response data around the
mean. A value of `1` indicates that the model explains all the variability of
the response data around the mean.
You can also look at the model's information pane in the Google Cloud console
to view the evaluation metrics:
## Use the model to predict outcomes
Now that you have evaluated your model, the next step is to use it to predict
an outcome. You can run the
[`ML.PREDICT` function](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-predict)
or the
[`bigframes.bigquery.ml.predict` BigQuery DataFrames function](https://dataframes.bigquery.dev/reference/api/bigframes.bigquery.ml.predict.html#bigframes.bigquery.ml.predict)
on the model to predict the body mass in grams of all penguins that reside on
the Biscoe Islands.
### SQL
For input, the `ML.PREDICT` function takes the trained model and a dataset that
matches the schema of the data that you used to train the model, excluding the
label column.
Run the `ML.PREDICT` query:
1. In the Google Cloud console, go to the **BigQuery** page.
[Go to BigQuery](https://console.cloud.google.com/bigquery)
2. In the query editor, run the following query:
```googlesql
SELECT
*
FROM
ML.PREDICT(MODEL `bqml_tutorial.penguins_model`,
(
SELECT
*
FROM
`bigquery-public-data.ml_datasets.penguins`
WHERE island = 'Biscoe'));
```
### BigQuery DataFrames
Before trying this sample, follow the BigQuery DataFrames
setup instructions in the [BigQuery quickstart
using BigQuery DataFrames](https://docs.cloud.google.com/bigquery/docs/dataframes-quickstart).
For more information, see the
[BigQuery DataFrames reference documentation](https://docs.cloud.google.com/python/docs/reference/bigframes/latest).
To authenticate to BigQuery, set up Application Default Credentials.
For more information, see [Set
up ADC for a local development environment](https://docs.cloud.google.com/docs/authentication/set-up-adc-local-dev-environment).
# Load data from BigQuery
bq_df = bpd.read_gbq("bigquery-public-data.ml_datasets.penguins")
# Use 'contains' function to filter by island containing the string
# "Biscoe".
biscoe_data = bq_df[bq_df["island"].str.contains("Biscoe")]
# Use the ml.predict method to predict results using your model.
# For more information, see the BigQuery DataFrames API reference documentation:
# https://dataframes.bigquery.dev/reference/api/bigframes.bigquery.ml.predict.html#bigframes.bigquery.ml.predict
ml.predict(
your_model_id, # For example: "bqml_tutorial.penguins_model",
input_=biscoe_data,
)
# Expected output results:
# predicted_body_mass_g species island culmen_length_mm culmen_depth_mm body_mass_g flipper_length_mm sex
# 23 4681.782896 Gentoo penguin (Pygoscelis papua) Biscoe
# 332 4740.7907 Gentoo penguin (Pygoscelis papua) Biscoe 46.2 14.4 214.0 4650.0
# 160 4731.310452 Gentoo penguin (Pygoscelis papua) Biscoe 44.5 14.3 216.0 4100.0
The results should look similar to the following:
## Explain the prediction results
### SQL
To understand why the model is generating these prediction results, you can use
the
[`ML.EXPLAIN_PREDICT` function](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-explain-predict).
`ML.EXPLAIN_PREDICT` is an extended version of the `ML.PREDICT` function.
`ML.EXPLAIN_PREDICT` not only outputs prediction results, but also outputs
additional columns to explain the prediction results. In practice, you can run
`ML.EXPLAIN_PREDICT` instead of `ML.PREDICT`. For more information, see
[BigQuery ML explainable AI overview](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-xai-overview#explainable_ai_offerings_in_bigquery_ml).
Run the `ML.EXPLAIN_PREDICT` query:
1. In the Google Cloud console, go to the **BigQuery** page.
[Go to BigQuery](https://console.cloud.google.com/bigquery)
1. In the query editor, run the following query:
```googlesql
SELECT
*
FROM
ML.EXPLAIN_PREDICT(MODEL `bqml_tutorial.penguins_model`,
(
SELECT
*
FROM
`bigquery-public-data.ml_datasets.penguins`
WHERE island = 'Biscoe'),
STRUCT(3 as top_k_features));
```
1. The results should look similar to the following:
> [!NOTE]
> **Note:** The `ML.EXPLAIN_PREDICT` query outputs all the input feature columns, similar to what `ML.PREDICT` does. For readability purposes, only one feature column, `species`, is shown in the preceding figure.
### BigQuery DataFrames
Before trying this sample, follow the BigQuery DataFrames
setup instructions in the [BigQuery quickstart
using BigQuery DataFrames](https://docs.cloud.google.com/bigquery/docs/dataframes-quickstart).
For more information, see the
[BigQuery DataFrames reference documentation](https://docs.cloud.google.com/python/docs/reference/bigframes/latest).
To authenticate to BigQuery, set up Application Default Credentials.
For more information, see [Set
up ADC for a local development environment](https://docs.cloud.google.com/docs/authentication/set-up-adc-local-dev-environment).
# Use the ml.explain_predict method to understand why the model is
# generating these prediction results.
# For more information, see the BigQuery DataFrames API reference documentation:
# https://dataframes.bigquery.dev/reference/api/bigframes.bigquery.ml.explain_predict.html#bigframes.bigquery.ml.explain_predict
#
# Using the trained model and utilizing data specific to Biscoe Island,
# explain the predictions of the top 3 features.
ml.explain_predict(
your_model_id, # For example: "bqml_tutorial.penguins_model",
input_=biscoe_data,
top_k_features=3,
)
# Expected results:
# predicted_body_mass_g top_feature_attributions baseline_prediction_value prediction_value approximation_error species island culmen_length_mm culmen_depth_mm flipper_length_mm body_mass_g sex
# 0 5413.510134 [{'feature': 'island', 'attribution': 7348.877... -5320.222128 5413.510134 0.0 Gentoo penguin (Pygoscelis papua) Biscoe 45.2 16.4 223.0 5950.0 MALE
# 1 4768.351092 [{'feature': 'island', 'attribution': 7348.877... -5320.222128 4768.351092 0.0 Gentoo penguin (Pygoscelis papua) Biscoe 46.5 14.5 213.0 4400.0 FEMALE
# 2 3235.896372 [{'feature': 'island', 'attribution': 7348.877... -5320.222128 3235.896372 0.0 Adelie Penguin (Pygoscelis adeliae) Biscoe 37.7 16.0 183.0 3075.0 FEMALE
# 3 5349.603734 [{'feature': 'island', 'attribution': 7348.877... -5320.222128 5349.603734 0.0 Gentoo penguin (Pygoscelis papua) Biscoe 46.4 15.6 221.0 5000.0 MALE
# 4 4637.165037 [{'feature': 'island', 'attribution': 7348.877... -5320.222128 4637.165037 0.0 Gentoo penguin (Pygoscelis papua) Biscoe 46.1 13.2 211.0 4500.0 FEMALE
For linear regression models, Shapley values are used to generate feature
attribution values for each feature in the model. The output includes
the top three feature attributions per row of the `penguins` table because
`top_k_features` was set to `3`. These attributions are sorted by
the absolute value of the attribution in descending order. In all examples, the
feature `sex` contributed the most to the overall prediction.
## Globally explain the model
### SQL
To know which features are generally the most important to determine penguin
weight, you can use the
[`ML.GLOBAL_EXPLAIN` function](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-global-explain).
In order to use `ML.GLOBAL_EXPLAIN`, you must retrain the model with the
`ENABLE_GLOBAL_EXPLAIN` option set to `TRUE`.
Retrain and get global explanations for the model:
1. In the Google Cloud console, go to the **BigQuery** page.
[Go to BigQuery](https://console.cloud.google.com/bigquery)
1. In the query editor, run the following query to retrain the model:
```googlesql
#standardSQL
CREATE OR REPLACE MODEL `bqml_tutorial.penguins_model`
OPTIONS (
model_type = 'linear_reg',
input_label_cols = ['body_mass_g'],
enable_global_explain = TRUE)
AS
SELECT
*
FROM
`bigquery-public-data.ml_datasets.penguins`
WHERE
body_mass_g IS NOT NULL;
```
2. In the query editor, run the following query to get global explanations:
```googlesql
SELECT
*
FROM
ML.GLOBAL_EXPLAIN(MODEL `bqml_tutorial.penguins_model`)
```
3. The results should look similar to the following:
### BigQuery DataFrames
Before trying this sample, follow the BigQuery DataFrames
setup instructions in the [BigQuery quickstart
using BigQuery DataFrames](https://docs.cloud.google.com/bigquery/docs/dataframes-quickstart).
For more information, see the
[BigQuery DataFrames reference documentation](https://docs.cloud.google.com/python/docs/reference/bigframes/latest).
To authenticate to BigQuery, set up Application Default Credentials.
For more information, see [Set
up ADC for a local development environment](https://docs.cloud.google.com/docs/authentication/set-up-adc-local-dev-environment).
# To use the ml.global_explain method, the model must be created with
# enable_global_explain set to True.
#
# Use ml.create_model to create and train the model in BigQuery.
# The options parameter specifies the model type and the label column.
# For more information, see the BigQuery DataFrames API reference documentation:
# https://dataframes.bigquery.dev/reference/api/bigframes.bigquery.ml.create_model.html#bigframes.bigquery.ml.create_model
training_data = bq_df.dropna(subset=["body_mass_g"])
ml.create_model(
your_model_id, # For example: "bqml_tutorial.penguins_model",
options={
"model_type": "LINEAR_REG",
"input_label_cols": ["body_mass_g"],
"enable_global_explain": True,
},
training_data=training_data,
replace=True,
)
# Use the ml.global_explain method to explain the model.
# For more information, see the BigQuery DataFrames API reference documentation:
# https://dataframes.bigquery.dev/reference/api/bigframes.bigquery.ml.global_explain.html#bigframes.bigquery.ml.global_explain
ml.global_explain(
your_model_id, # For example: "bqml_tutorial.penguins_model",
)
# Expected results:
# attribution
# feature
# island 5737.315921
# species 4073.280549
# sex 622.070896
# flipper_length_mm 193.612051
# culmen_depth_mm 117.084944
# culmen_length_mm 94.366793
## Clean up
To avoid incurring charges to your Google Cloud account for the resources used in this
tutorial, either delete the project that contains the resources, or keep the project and
delete the individual resources.
- You can delete the project you created.
- Or you can keep the project and delete the dataset.
### Delete your dataset
Deleting your project removes all datasets and all tables in the project. If you
prefer to reuse the project, you can delete the dataset you created in this
tutorial:
1. If necessary, open the BigQuery page in the
Google Cloud console.
[Go to the BigQuery page](https://console.cloud.google.com/bigquery)
2. In the navigation, click the **bqml_tutorial** dataset you created.
3. Click **Delete dataset** on the right side of the window.
This action deletes the dataset, the table, and all the data.
4. In the **Delete dataset** dialog box, confirm the delete command by typing
the name of your dataset (`bqml_tutorial`) and then click **Delete**.
### Delete your project
To delete the project:
> [!CAUTION]
> **Caution** : Deleting a project has the following effects:
>
> - **Everything in the project is deleted.** If you used an existing project for the tasks in this document, when you delete it, you also delete any other work you've done in the project.
> - **Custom project IDs are lost.** When you created this project, you might have created a custom project ID that you want to use in the future. To preserve the URLs that use the project ID, such as an `appspot.com` URL, delete selected resources inside the project instead of deleting the whole project.
>
>
> If you plan to explore multiple architectures, tutorials, or quickstarts, reusing projects
> can help you avoid exceeding project quota limits.
1. In the Google Cloud console, go to the **Manage resources** page.
[Go to Manage resources](https://console.cloud.google.com/iam-admin/projects)
2. In the project list, select the project that you want to delete, and then click **Delete**.
3. In the dialog, type the project ID, and then click **Shut down** to delete the project.
## What's next
- For an overview of BigQuery ML, see [Introduction to BigQuery ML](https://docs.cloud.google.com/bigquery/docs/bqml-introduction).
- For information on creating models, see the [`CREATE MODEL`](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create) syntax page.