mean_squared_error (y_test, predictions ) ) Final Thoughts mean_squared_error (y_test, predictions ) mean_absolute_error (y_test, predictions ) hist (y_test - predictions ) from sklearn import metrics predict (x_test ) # plt.scatter(y_test, predictions) X_train, x_test, y_train, y_test = train_test_split (x, y, test_size = 0.3 ) from sklearn. #How to import seaborn in python jupyter notebook codeHere is the code you'll need to generate predictions from our model using the predict method: Since the predict variable is designed to make predictions, it only accepts an x-array parameter. You simply need to call the predict method on the model variable that we created earlier. Scikit-learn makes it very easy to make predictions from a machine learning model. ![]() Now that we've generated our first machine learning linear regression model, it's time to use the model to make predictions from our test data set. Similarly, small values have small impact. Said differently, large coefficients on a specific variable mean that that variable has a large impact on the value of the variable you're trying to predict. What this means is that if you hold all other variables constant, then a one-unit increase in Area Population will result in a 15-unit increase in the predicted variable - in this case, Price. Let's look at the Area Population variable specifically, which has a coefficient of approximately 15. Let's take a moment to understand what these coefficients mean. The output in this case is much easier to interpret: You can import pandas with the following statement: It is convention to import pandas under the alias pd. The first library that we need to import is pandas, which is a portmanteau of "panel data" and is the most popular Python library for working with tabular data. The Libraries We Will Use in This Tutorial Before we build the model, we'll first need to import the required libraries. More specifically, we will be working with a data set of housing data and attempting to predict housing prices. This will allow you to focus on learning the machine learning concepts and avoid spending unnecessary time on cleaning or manipulating data. Since linear regression is the first machine learning model that we are learning in this course, we will work with artificially-created datasets in this tutorial. The Data Set We Will Use in This Tutorial
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