Python tutorial: Train a linear regression model with SQL machine learning
Applies to: 
SQL Server 2017 (14.x) and later
Azure SQL Managed Instance
In part three of this four-part tutorial series, you'll train a linear regression model in Python. In the next part of this series, you'll deploy this model in a SQL Server database with Machine Learning Services or on SQL Server 2019 Big Data Clusters.
In part three of this four-part tutorial series, you'll train a linear regression model in Python. In the next part of this series, you'll deploy this model in a SQL Server database with Machine Learning Services.
In part three of this four-part tutorial series, you'll train a linear regression model in Python. In the next part of this series, you'll deploy this model in an Azure SQL Managed Instance database with Machine Learning Services.
In this article, you'll learn how to:
- Train a linear regression model
- Make predictions using the linear regression model
In part one, you learned how to restore the sample database.
In part two, you learned how to load the data from a database into a Python data frame, and prepare the data in Python.
In part four, you'll learn how to store the model in a database, and then create stored procedures from the Python scripts you developed in parts two and three. The stored procedures will run in on the server to make predictions based on new data.
Prerequisites
- Part three of this tutorial assumes you have completed part one and its prerequisites, including installing the necessary Python packages.
Train the model
In order to predict, you have to find a function (model) that best describes the dependency between the variables in our dataset. This called training the model. The training dataset will be a subset of the entire dataset from the pandas data frame df that you created in part two of this series.
You will train model lin_model using a linear regression algorithm.
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
# Store the variable we'll be predicting on.
target = "Rentalcount"
# Generate the training set. Set random_state to be able to replicate results.
train = df.sample(frac=0.8, random_state=1)
# Select anything not in the training set and put it in the testing set.
test = df.loc[~df.index.isin(train.index)]
# Print the shapes of both sets.
print("Training set shape:", train.shape)
print("Testing set shape:", test.shape)
# Initialize the model class.
lin_model = LinearRegression()
# Fit the model to the training data.
lin_model.fit(train[columns], train[target])
You should see results similar to the following.
Training set shape: (362, 7)
Testing set shape: (91, 7)
Make predictions
Use a predict function to predict the rental counts using the model lin_model.
# Generate our predictions for the test set.
lin_predictions = lin_model.predict(test[columns])
print("Predictions:", lin_predictions)
# Compute error between our test predictions and the actual values.
lin_mse = mean_squared_error(lin_predictions, test[target])
print("Computed error:", lin_mse)
You should see results similar to the following.
Predictions: [124.41293228 123.8095075 117.67253182 209.39332151 135.46159387
199.50603805 472.14918499 90.15781602 216.61319499 120.30710327
89.47591091 127.71290441 207.44065517 125.68466139 201.38119194
204.29377218 127.4494643 113.42721447 127.37388762 94.66754136
90.21979191 173.86647615 130.34747586 111.81550069 118.88131715
124.74028405 211.95038051 202.06309706 123.53053083 167.06313191
206.24643852 122.64812937 179.98791527 125.1558454 168.00847713
120.2305587 196.60802649 117.00616326 173.20010759 89.9563518
92.11048236 120.91052805 175.47818992 129.65196995 120.97443971
175.95863082 127.24800008 135.05866542 206.49627783 91.63004147
115.78280925 208.92841718 213.5137192 212.83278197 96.74415948
95.1324457 199.9089665 206.10791806 126.16510228 120.0281266
209.08150631 132.88996619 178.84110582 128.85971386 124.67637239
115.58134503 96.82167192 514.61789505 125.48319717 207.50359894
121.64080826 201.9381774 113.22575025 202.46505762 90.7002328
92.31194658 201.25627228 516.97252195 91.36660136 599.27093251
199.6445585 123.66905128 117.4710676 173.12259514 129.60359486
209.59478573 206.29481361 210.69322009 205.50255751 210.88011563
207.65572019]
Computed error: 35003.54030828391
Next steps
In part three of this tutorial series, you completed these steps:
- Train a linear regression model
- Make predictions using the linear regression model
To deploy the machine learning model you've created, follow part four of this tutorial series: