Regression algorithms are machine learning methods used to predict continuous values based on input features. Techniques like Linear Regression, Decision Trees, Random Forests, and Gradient Boosting analyze relationships between variables to deliver accurate numeric predictions. They help solve real‑world problems such as forecasting prices, estimating demand, and analyzing trends with reliable, data‑driven insights.
Learn the fundamentals of Linear Regression, including how it works, key assumptions, and step‑by‑step implementation. This lesson helps you understand relationships between variables and build accurate predictive models using real data.
Explore Multiple and Polynomial Regression techniques to capture complex patterns in data. This lesson teaches how to model multiple features and nonlinear relationships for more powerful and flexible predictions.
Master regularization techniques like Ridge, Lasso, and Elastic Net to reduce overfitting and improve model stability. This lesson explains how these methods handle multicollinearity and enhance regression model performance.
Apply regression techniques in a hands‑on House Price Prediction project. Learn to preprocess data, engineer features, select models, and evaluate performance to build a real‑world predictive analytics solution.
