Predicting a continuous value by fitting a line to the data.
Linear Regression is one of the most fundamental algorithms in machine learning and statistics. It's a supervised learning algorithm used for regression tasks, which means its goal is to predict a continuous, numerical output. The core idea is to find the best-fitting linear relationship between a set of input features (independent variables) and the output variable (dependent variable). In its simplest form, with one input feature, this relationship is represented by the equation of a straight line: y = mx + b, where 'y' is the predicted output, 'x' is the input feature, 'm' is the slope (weight), and 'b' is the y-intercept (bias). The goal of the learning process is to find the optimal values for 'm' and 'b' that minimize the overall error between the predicted values and the actual values in the training data. This error is typically measured using a cost function, most commonly the Mean Squared Error (MSE), which calculates the average of the squared differences between predicted and actual values. The model then uses an optimization algorithm, like Gradient Descent, to iteratively adjust the weights and bias to reduce this error. Despite its simplicity, Linear Regression is a powerful and interpretable model. It's easy to understand the impact of each feature on the prediction, making it a great starting point for many regression problems and a crucial baseline for evaluating more complex models.