Regression in machine learning is a type of supervised learning where the goal is to predict a continuous value or quantity based on input data. Unlike classification, which predicts discrete labels, regression deals with predicting numerical values, such as house prices, temperatures, or stock prices. In a regression task, the model is trained on data where the input features are associated with continuous target values. After training, the model can make predictions for new, unseen data based on the patterns it learned. ## Types of Regression ### 1. Linear Regression - Models the relationship between dependent and independent variables using a straight line. - Example: Predicting house prices based on square footage. Check further: [[Linear Regression]] ![[11111111.png]] ### 2. Polynomial Regression - Models non-linear relationships by fitting a polynomial curve to the data. - Example: Predicting the trajectory of a projectile. Check further: [[Polynomial Regression]] ### 3. Ridge Regression - Regularised linear regression to prevent overfitting. - Example: Predicting customer lifetime value with a large number of features. Check further: [[Ridge Regression]] ### 4. Lasso Regression - Similar to Ridge but uses L1 regularisation, which can shrink coefficients to zero, aiding feature selection. - Example: Determining key factors influencing sales. Check further:[[Lasso Regression]] ### 5. Decision Tree Regression - Splits data into branches to make predictions. - Example: Estimating car prices based on features. Check further: [[Decision Tree Regression]] ### 6. Random Forest Regression - Ensemble of decision trees for better accuracy and generalization. - Example: Predicting electricity consumption. Check further: [[Random Forest Regression]] ### 7. Gradient Boosting (e.g., XGBoost, LightGBM) - Advanced ensemble methods for improved accuracy. - Example: Predicting loan default risk. Check further: [[Gradient Boosting Regression]] ## Model Selection Criteria 1. Dataset size and complexity. 2. Relationship type (linear/non-linear). 3. Training time constraints. 4. Feature selection needs. 5. Computational resource availability. ## Performance Metrics - Mean Absolute Error (MAE) - Mean Squared Error (MSE) - Root Mean Squared Error (RMSE) - R-squared (R²) ## Best Practices 1. Perform exploratory data analysis (EDA). 2. Normalize/scale features when necessary. 3. Handle multicollinearity. 4. Apply cross-validation for robust evaluation. 5. Regularize models to avoid overfitting. ## Common Applications 1. House price prediction. 2. Sales forecasting. 3. Stock market prediction. 4. Energy consumption estimation. 5. Medical cost prediction. --- ## Implementation Example I am considering the Linear Regression ```python # Basic regression workflow from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.metrics import mean_squared_error, r2_score from sklearn.linear_model import LinearRegression # Example dataset (replace X and y with your data) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # Feature scaling scaler = StandardScaler() X_train_scaled = scaler.fit_transform(X_train) X_test_scaled = scaler.transform(X_test) # Model training model = LinearRegression() model.fit(X_train_scaled, y_train) # Evaluation predictions = model.predict(X_test_scaled) mse = mean_squared_error(y_test, predictions) r2 = r2_score(y_test, predictions) print(f"Mean Squared Error: {mse}") print(f"R² Score: {r2}") ``` ## Regression Resources ### Video Tutorials 1. **StatQuest with Josh Starmer** - [Linear Regression](https://www.youtube.com/watch?v=PaFPbb66DxQ): Clear explanation of linear regression fundamentals with visual examples - [Gradient Descent](https://www.youtube.com/watch?v=sDv4f4s2SB8): Step-by-step walkthrough of the optimization algorithm behind many regression models 2. **Sentdex's Python Programming for Finance** - [Linear Regression with Python](https://www.youtube.com/watch?v=JcI5Vnw0b2c): Practical implementation of linear regression for stock price prediction 3. **Krish Naik's Regression Series** - [Complete Regression Analysis](https://www.youtube.com/watch?v=0B5eIE_1vpU): Comprehensive overview of various regression techniques with real-world examples ### Books 1. **Introduction to Statistical Learning** - Authors: Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani - [Free PDF Download](https://www.statlearning.com/) - Excellent coverage of regression methods with practical examples and mathematical foundations 2. **Hands-On Machine Learning with Scikit-Learn** - Author: Aurélien Géron - [O'Reilly Link](https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/) - Practical guide to implementing regression models with scikit-learn ### Online Courses 1. **Coursera: Regression Models** - [Course Link](https://www.coursera.org/learn/regression-models) - Comprehensive course covering regression analysis fundamentals and advanced techniques 2. **DataCamp: Linear Regression in Python** - [Course Link](https://www.datacamp.com/courses/linear-regression-in-python) - Interactive course with hands-on exercises implementing regression models ### Datasets for Practice 1. **Boston Housing Dataset** - Classic dataset for predicting house prices based on various features 2. **Ames Housing Dataset** - More complex housing dataset with many features for advanced regression practice 3. **California Housing Dataset** - Large dataset for predicting median house values in California districts 4. **Bike Sharing Dataset** - Time series regression problem to predict bicycle rental demand These resources provide a solid foundation for understanding and implementing regression models, from basic linear regression to more advanced techniques.