This Jupyter Notebook provides a step-by-step guide on how to perform linear regression using the scikit-learn library and the California Housing dataset. The goal is to predict the median house value (MedHouseVal) based on the given features.
Linear regression is a popular machine learning algorithm used for predicting continuous outcomes. In this example, we will use the scikit-learn library to build a linear regression model using the California Housing dataset. We will explore the relationship between the features and the target variable, split the dataset into training and testing sets, train the model, make predictions, and evaluate the model’s performance.
The following libraries are required for this analysis:
You can install these libraries using pip:
pip install numpy pandas matplotlib seaborn scikit-learn
The California Housing dataset is used for this analysis. It is a classic dataset for regression problems and is available in scikit-learn. The dataset contains various features related to houses in California, such as median income, average occupancy, and median house value.
The code for this analysis can be found in the following GitHub repository: Link to GitHub Repository
In this example, we demonstrated how to perform linear regression using the scikit-learn library and the California Housing dataset. We imported the necessary libraries, loaded the dataset, explored the relationship between the features and the target variable, split the dataset into training and testing sets, trained the linear regression model, made predictions, and evaluated the model’s performance using the Root Mean Squared Error (RMSE).
To further enhance this analysis, you can explore using more features from the dataset, try different regression models, and tune their parameters. This will allow you to improve the predictive accuracy of the model and gain more insights from the data.
By following this example, you can apply linear regression to other regression problems and datasets, providing a valuable tool for predicting continuous outcomes based on given features.