Machine Learning Project -- 2
Budget: $30 – $250 USD
I need a project, preferably in R language, that does three steps:
1) take a data set, composed of 1 explained variable (Y) and 80 explanatory variables (X1, X2, ..., X80) and perform an importance analysis of them (by two different algorithms) and automatically select 10 most important to move on to the next step;
2) Having selected the 10 most important variables, use them to predict the value of Y using the 5 main machine learning algorithms (decision tree, random forest, gradient boosting, XGboosting, geographic random forest);
03) Finally, calculate the main performance parameters of the modeling (RMSE, RME, MAE, MAPE, R2, CODE, PRD) and present the results in a table.
1) take a data set, composed of 1 explained variable (Y) and 80 explanatory variables (X1, X2, ..., X80) and perform an importance analysis of them (by two different algorithms) and automatically select 10 most important to move on to the next step;
2) Having selected the 10 most important variables, use them to predict the value of Y using the 5 main machine learning algorithms (decision tree, random forest, gradient boosting, XGboosting, geographic random forest);
03) Finally, calculate the main performance parameters of the modeling (RMSE, RME, MAE, MAPE, R2, CODE, PRD) and present the results in a table.