Problem $4$a: Calculating best$-$fit prediction for US Consumption

Now, we$$ll use the same code we used in Problem $2$b $($the get$\_$production$\_$need function$)$ to plot the best$-$fit line for US consumption and a prediction out to $50$ years from now.

Write a function called predict$\_$need$($data$,$ country$\_$code, predict$\_$years$)$ that returns a best$-$fit prediction line of production necessity data for the given country. It should return it as a dictionary in the form:

$\{$ "years": $[...],$ "values": $[...]\}$

You should use the approach described in the Calculating Best$-$fit Lines section above. Specifically, your code should:

Similar to Problem $3$a$,$ use Problem $2$b$$s get$\_$production$\_$need function to get the observed data for each year:

From data$["$min$\_$year"$]$ to data$["$max$\_$year"$]($inclusive$),$ append the year to a $$years$$ list and the value from get$\_$production$\_$need to a $$values$$ list.Important: polyfit does not accept None values, and so if there are any None values in the data, you need to omit both the value and the year that it$$s for. $($The example return data below shows that even though Ankh$-$Morpork has production data for $1960,$ that year is not included in the returned data because we do not have consumption data for that country in $1960.)$

Use poly.polyfit to calculate the best$-$fit line for the data. Remember:

Your file should have import numpy.polynomial as poly at the top. $($It should already be there for you.$)$The polyfit function takes three parameters. The first two are the x $($years$)$ and y $($production need for each year$)$ values, respectively. The third parameter should be $1.$polyfit returns two values, in order: b and then m$.$

Generate the values for the best$-$fit line and prediction.

Hint: You can do this in a single loop by creating a new list for both the x and y values, looping from the min year $($data$["$min$\_$year"$])$ to the max year $($data$["$max$\_$year"$])+51.$ The body of the loop would append the year to the x list, and mx $+$ b to the y list.

Returns a dictionary: $\{"$years$"$: $[...],$ "values": $[...]\}$