In this project, I performed CO2 emission regression analysis on the Canada dataset using various regression techniques. The goal was to predict CO2 emissions based on different features and compare the performance of several regression algorithms. The following regression techniques were implemented and evaluated:
Lasso regression is a linear regression technique that performs both variable selection and regularization. It helps in reducing the complexity of the model by shrinking the coefficients of less significant features to zero. By applying L1 regularization, the Lasso regression can effectively handle datasets with a large number of features.
Ridge regression is another type of linear regression that incorporates L2 regularization to prevent overfitting and improve the generalization of the model. It works by adding a penalty term to the loss function, which reduces the magnitude of the coefficients. Ridge regression is particularly useful when dealing with multicollinear features.
Polynomial regression is a non-linear regression technique that models the relationship between the independent variable(s) and the dependent variable as an nth-degree polynomial. It can capture more complex relationships by introducing polynomial terms. In this project, I employed polynomial regression to capture potential non-linear relationships between the CO2 emissions and the dataset's features.
Decision tree regression builds a model by recursively splitting the dataset based on feature values. Each internal node represents a test on a particular feature, while each leaf node holds a predicted response value. Decision tree regression is advantageous in capturing non-linear relationships and handling both categorical and numerical features.
Random forest regression is an ensemble learning method that combines multiple decision trees to make predictions. Each tree is trained on a random subset of the dataset, and the final prediction is the average or majority vote of the individual tree predictions. Random forest regression often achieves better performance by reducing overfitting and increasing model robustness.
By implementing these regression techniques, I aimed to identify the most effective approach for predicting CO2 emissions in the Canada dataset. The evaluation was based on various metrics such as mean squared error, R-squared value, and cross-validation scores. The comparison of these algorithms provides insights into their respective strengths and weaknesses in tackling CO2 emission regression problems.