Linear Regression using Turicreate

Any data scientist or analyst must grasp linear regression because it is the foundation of predictive modelling. There are several libraries that can be used to implement this technique in Python, each with their own merits. TuriCreate, a machine learning toolkit offered by Apple, is one such library. It is simple to use, incredibly scalable, and effective. This article takes you on a thorough tour of TuriCreate's implementation of linear regression while utilising real-world examples to aid comprehension.

Introduction to Linear Regression

An approach to predictive modelling is linear regression. Based on one or more independent factors, it is used to forecast the value of a dependent variable. The dependent variable, often known as the response variable, is the variable we want to forecast. The independent variables (also known as predictors) are the factors we utilise to forecast the value of the dependent variable.

Unveiling TuriCreate

Apple's TuriCreate makes it easier to create unique machine learning models. The fundamental methods don't require in-depth understanding on your part. TuriCreate is a very adaptable and effective toolbox that makes machine learning jobs easy to experiment with.

Getting Started with Linear Regression in TuriCreate

Make sure TuriCreate is installed in your Python environment before diving into the examples:

pip install turicreate

Example 1: Simple Linear Regression

Let's start out with a straightforward linear regression in which there is just one independent variable. We'll make use of TuriCreate's house_data dataset, which is pre-built.

Let's load the data first:

import turicreate as tc

# Load house_data
house_data = tc.SFrame('house_data.sframe')

We'll divide the data into training and test sets after that:

train_data, test_data = house_data.random_split(.8,seed=0)

We can now train a straightforward linear regression model in which we try to forecast pricing using sqft_living (the size of the home):

# Create a linear regression model
model = tc.linear_regression.create(train_data, target='price', features=['sqft_living'])

Example 2: Making Predictions

We may forecast using our test data after training our model:

# Make predictions
predictions = model.predict(test_data)

# Print the predictions
print(predictions)

Example 3: Evaluating the Model

Our model's performance may be easily assessed using TuriCreate −

# Evaluate the model and save the results into a dictionary
results = model.evaluate(test_data)

# Print the results
print(results)

The root mean squared error (RMSE), a widely used statistic for regression models, is calculated by the evaluate function. The RMSE tells us how concentrated the data is around the line of best fit because it indicates the sample standard deviation of the discrepancies between predicted and observed values.

Interpreting the Results

If all other qualities remain the same, the coefficient for sqft_living shows us how much the price will increase each additional square foot of living area. The RMSE indicates the average mistake our model makes in its predictions, expressed in price units.

Conclusion

TuriCreate may be used efficiently to execute the statistical and predictive method known as linear regression. This advanced, user-friendly tool makes it simple to create machine learning models quickly, which aids in streamlining the predictive analytics procedure.

This article provided concrete examples to help you better grasp linear regression and TuriCreate's use of it. You may learn more about linear regression and how to use TuriCreate in Python by paying attention to these examples.

Keep in mind that real-world data frequently contains several variables and may necessitate more complicated models. Consider this your starting point from which to construct more complex prediction models. You have access to a wide range of TuriCreate tools and features that make this process simple and natural.

You'll discover a variety of more methods and algorithms as you continue to delve into this robust library that you can utilise to tackle challenging data science issues. These include, among others, neural networks, clustering algorithms, and decision tree algorithms.