How to create and optimise a baseline Linear Regression Model in Python
Linear Regression is a popular method of statistical analysis that can be used to predict a continuous variable based on a set of input variables. In this article, we will go over the basics of how to create and optimize a linear regression model in Python.
First, we need to import the necessary libraries such as Numpy and Pandas, which will help us handle our data. Next, we will import the LinearRegression class from the scikit-learn library, which will be used to create our model.
Once we have our libraries and classes imported, we can start creating our model. To do this, we will first need to load our data into a Pandas dataframe. We can do this by using the read_csv function, which will allow us to read in data from a CSV file.
Once our data is loaded, we will need to split it into training and testing sets. This is important because it allows us to test the accuracy of our model on unseen data. We can do this using the train_test_split function, which will randomly split our data into training and testing sets.
Now that our data is ready, we can create our model. We do this by instantiating the LinearRegression class and then fitting it to our training data using the fit method. Once the model is trained, we can use it to make predictions on our testing data using the predict method.
To check the accuracy of our model, we can use the mean squared error (MSE) metric. The lower the MSE, the better our model is at predicting the target variable.
Lastly, we need to optimise our model. One way to do this is by tuning the model’s parameters. For example, we can change the value of the learning rate, the number of iterations, or the regularization parameter. We can use a grid search to find the best combination of parameters for our data.
In conclusion, creating and optimising a baseline linear regression model in Python is a straightforward process. By using the scikit-learn library, we can easily load, split, and train our data, and make predictions on unseen data. With the right parameters, we can optimise the accuracy of our model and make more accurate predictions.
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