Linear Regression

Linear regression is one of the most fundamental techniques in machine learning, forming the basis for more complex models. In this lesson, we'll build a linear regression model from scratch, implement R-squared to evaluate model fit, and apply feature engineering to extend into multiple linear regression. Let’s get started.

Linear regression from scratch

In the manufacturing industry, predictive maintenance is crucial for minimizing downtime and optimizing equipment performance. Sensors continuously monitor equipment conditions such as temperature, vibration, and pressure. By analyzing this data, we can predict when maintenance is needed before a failure occurs.

Create a class that fits the linear equation y = mx + b using gradient descent. This implementation will help predict future sensor values based on historical data, enabling timely maintenance and reducing operational costs.

Sample answer

To solve this problem, we can first start by thinking about what problem our simple LR model needs to solve. Given that a dataset X and corresponding target values y are defined, we need to create an instance of SimpleLinearRegression and fit it to the data. We can ...