Linear, Random Forest and Neural Network Regression

When analysing data with outliers, it is sometimes harder to develop model. This folder contains three supervised learners.

%% Acknowledgements

% Data Source:

% The climate data used in this analysis was obtained from:

% NOAA Global Summary of the Day dataset

% Link: https://datahub.io/core/global-temp/r/annual.csv

(if for some reason, you can not download the data from url, you can find the excel file in the folder, simply edit first few command by load....csv)

% This dataset contains annual global mean temperature data.

% Function Sources:

% - detectImportOptions:

% This function automatically detects the import options for reading a table from a file or URL.

% Documentation: https://www.mathworks.com/help/matlab/ref/detectimportoptions.html

% - readtable:

% This function reads tabular data from a file or URL and creates a table.

% Documentation: https://www.mathworks.com/help/matlab/ref/readtable.html

% - TreeBagger:

% This function creates a Random Forest model using bagging for regression.

% Documentation: https://www.mathworks.com/help/stats/treebagger.html

% - plot:

% This function creates a line plot of data.

% Documentation: https://www.mathworks.com/help/matlab/ref/plot.html

% - saveas:

% This function saves the current figure to a specified file format.

% Documentation: https://www.mathworks.com/help/matlab/ref/saveas.html