Please explain what is each part of this matlab code:

$\%\%$part $2\%$ Step $1$: Import the two datasets and combine them into one global datasetdata$1=$ readtable$(\text{'}2021\_$Cars$\_$Aggregated.csv$\text{'})$; $\%$ Import the first datasetdata$2=$ readtable$(\text{'}2021.$Vans$\_$Aggregated.csv$\text{'})$; $\%$ Import the second dataset$\%$Set a fixed seed for reproducibilityseed $=50$; $\%$ Choose any integer value for the seedrng$($seed$)$; $\%$ Set the random number generator seed$\%$ Sample data for Diesel and Petrol carscarPosition $=$ linspace$(1,60,50)$;$\%$ Assumed CO$2$ emissions for Diesel and PetrolCO$2$Diesel $=20+2*$ exp$(-$carPosition $/60*2*$ pi$)-$ randn$(1,50)*2$;CO$2$Petrol $=18+2*($carPosition $/60*2*$ pi$).^0\mathrm{.}3+$ randn$(1,50)*2$;$\%$ Fit polynomial curves with a reduced degree of $2$pDiesel $=$ polyfit$($carPosition$,$ CO$2$Diesel, $2)$;pPetrol $=$ polyfit$($carPosition$,$ CO$2$Petrol, $2)$;$\%$ Generate points for best fit linesfitDiesel $=$ polyval$($pDiesel$,$ carPosition$($carPosition$<50))$;fitPetrol $=$ polyval$($pPetrol$,$ carPosition$)$;$\%$ Plotting the datafigure;hold on;$\%$ Plot Diesel best fit lineplot$($carPosition$($carPosition$<50),$ fitDiesel, 'Color', $[1,0\mathrm{.}5,0],$ 'LineWidth', $2)$;$\%$ Plot Petrol best fit lineplot$($carPosition$,$ fitPetrol, 'Color', $[0\mathrm{.}5,0,0\mathrm{.}5],$ 'LineWidth', $1)$;$\%$ Petrol datascatter$($carPosition$,$ CO$2$Petrol, $\text{'}$o$\text{'},$ 'MarkerEdgeColor', $[0,0\mathrm{.}5,1])$; $\%$ Blue for Petrol$\%$ Diesel datascatter$($carPosition$,$ CO$2$Diesel, $\text{'}$o$\text{'},$ 'MarkerEdgeColor', $[1,0\mathrm{.}5,0])$; $\%$ Orange for Diesel$\%$ Customize the plotxlabel$(\text{'}$Car Position'$)$;ylabel$(\text{'}$CO$2$ Weighted Percentage'$)$;title$(\text{'}$Weighted CO$2$ Emissions'$)$;xlim$([160])$;ylim$([1535])$;$\%$ Add a legend with custom nameslegend$(\text{'}$Diesel Best Fit', 'Petrol Best Fit', 'Petrol', 'Diesel'$)$;$\%$ Add grid linesgrid on;hold off;$\%$ Combine the datasetscombined$\_$data $=[$data$1$; data$2]$;$\%$ Step $2$a: Create a bar chart showing how many cars use distinct fuel types$\%$ Preprocess data: remove missing or empty values from 'FuelType' columnvalid$\_$indices $=$ ~ismissing$($combined$\_$data.FuelType$)$ & ~strcmp$($combined$\_$data.FuelType, $\text{'}\text{'})$;$\%$ Filter the combined data based on valid indicesvalid$\_$fuel$\_$data $=$ combined$\_$data$($valid$\_$indices, :$)$;$\%$ Get unique fuel types and count occurrences$[$fuel$\_$types, ~$,$ fuel$\_$type$\_$indices$]=$ unique$($valid$\_$fuel$\_$data.FuelType$)$;fuel$\_$counts $=$ histcounts$($fuel$\_$type$\_$indices, $1$:numel$($fuel$\_$types$)+1)$;$\%$ Plot the bar chartfigure;bar$(1$:numel$($fuel$\_$types$),$ fuel$\_$counts$)$;title$(\text{'}$Number of Cars Using Distinct Fuel Types'$)$;xlabel$(\text{'}$Fuel Type'$)$;ylabel$(\text{'}$Number of Cars'$)$;xticks$(1$:numel$($fuel$\_$types$))$;xticklabels$($fuel$\_$types$)$;$\%$ Step $2$b: Create a scatter plot showing the weighted CO$2$ Percentage for each car$[$sorted$\_$manufacturers, manufacturer$\_$indices$]=$ sort$($combined$\_$data.Manufacturer$)$; $\%$ Sort manufacturers alphabeticallysorted$\_$CO$2\_$percentage $=$ combined$\_$data.CO$2\_$Percentage$($manufacturer$\_$indices$)$; $\%$ Sort CO$2$ Percentage accordinglySet a fixed seed for reproducibilityseed $=50$; $\%$ Choose any integer value for the seedrng$($seed$)$; $\%$ Set the random number generator seed$\%$ Sample data for Diesel and Petrol carscarPosition $=$ linspace$(1,60,50)$;$\%$ Assumed CO$2$ emissions for Diesel and PetrolCO$2$Diesel $=20+2*$ exp$(-$carPosition $/60*2*$ pi$)-$ randn$(1,50)*2$;CO$2$Petrol $=18+2*($carPosition $/60*2*$ pi$).^0\mathrm{.}3+$ randn$(1,50)*2$;$\%$ Fit polynomial curves with a reduced degree of $2$pDiesel $=$ polyfit$($carPosition$,$ CO$2$Diesel, $2)$;pPetrol $=$ polyfit$($carPosition$,$ CO$2$Petrol, $2)$;$\%$ Generate points for best fit linesfitDiesel $=$ polyval$($pDiesel$,$ carPosition$($carPosition$<50))$;fitPetrol $=$ polyval$($pPetrol$,$ carPosition$)$;$\%$ Plotting the datafigure;hold on;