Comparing Linear Regression and Random Forest Algorithms for Big Data Analysis

This project compares the performance of two machine learning algorithms, linear regression and random forest, in Weka for big data analysis. It includes experimental setups, results, and discussions on dataset 1 and dataset 2. The analysis focuses on the classification problems and the use of feature selection algorithms. The results show the performance metrics such as correlation coefficient, mean absolute error, root absolute error, and root relative squared error for each setup. The significance test and AUC curve analysis are also discussed. The project concludes with a critical understanding of the challenges in big data analysis.