Problem $4$b: Plotting the Prediction line

Now, we$$ll use the predict$\_$need function to plot the best$-$fit line for US consumption and a prediction out to $50$ years from now.

For this problem $($Problem $4$b$),$ add a line of code to call plot$\_$linear$\_$prediction$($data$,$ "USA"$)$ in your main code near the bottom of fishing.py $($it must come after the call to parse$\_$data$).$ Running your program should then result in no errors and a new file named USA$\_$need$\_$prediction.pngbeing created in the same folder as your program.

The plot$\_$linear$\_$prediction$($data$,$ country$\_$code$)$ function is given to you in the utils.py starter file. This function takes the return value from parse$\_$data as its first parameter and a country code $($e$.$g$.,$USA$)$ as its second parameter. It then calls the predict$\_$need function you wrote in Problem $4$a with the production need data for the given country code, and then plots the best$-$fit line and prediction. You do not need to write this function; it$$s already been written for you. But it does rely on correct implementations for previous problems.

This function has no return value, but calling plot$\_$linear$\_$prediction$($data$,$ "USA"$)$ should produce a plot that resembles the following:

Problem $5$: Total World$-$wide Production Need

Problem $5$a:

So$,$ now that we$$ve done all that work, how much seafood will the entire world need to produce $50$ years from now?

For this problem, write a function called total$\_$production$\_$need$($data$,$ years$\_$to$\_$predict$)$ that returns a single number: how many metric tonnes will the world need to produce years$\_$to$\_$predict years from now?

This function should do the following:

Take as input the data returned from Problem $1$s parse$\_$data and a number of years in the future to predict.

For each country code, predict the production need for years$\_$to$\_$predictyears from now using Problem $4$a$$s predict$\_$need function.

Get the last value in the predicted values.

For example, if you assign the return value from predict$\_$need to prediction, you should be able to get the last value with prediction$["$values$"][-1].$

Sum up all of the predicted values.

Return the total.

Running your program with small.csv as the input file to parse$\_$data, total$\_$production$\_$need should return a total production need of $13243690\mathrm{.}762868665.($Your degree of precision may vary.$)$

Problem $5$b: Running with the large file:

At this point, it$$s time to run with the larger data file. Change the call to parse$\_$data to use "large.csv$"$ as its input. Then, at the very end of your program, add a print statement to print out the total you return from total$\_$production$\_$need. Your program should print the following:

Metric tonnes of seafood needed to be produced in $50$ years: $245,637,243\mathrm{.}224$

Info

You can format very large numbers using Python$$s f$-$string syntax. For example, if total $=245637243\mathrm{.}223947,$ doing print$($f$\text{'}\{$total$\_$need:,$\mathrm{.}3$f$\})$ would print $245,637,243\mathrm{.}224.$