PLEASE HELP WITH PYTHON CODING

For this assignment, you will be using the data set car$\_$data.csv$.$ This data set is a modified version of the data

There are two columns in this data set.

One is the weight of the vehicle in pounds, and the other is the highway miles per gallon.

Make a scatterplot of the highway miles per gallon $(y-$axis$)$ versus the weight $(x-$axis$).$

Based on your plot, what is the general trend of how highway miles per gallon varies with the weight?

If you were to build a linear model using this data to predict highway miles per gallon from weight, would you

expect the slope to be positive or negative? Explain.

If the slope of a linear model predicting highway miles per gallon from the weight, interpret the meaning of the

slope being $-0\mathrm{.}05.$

Write code to add a line to the graph you made in problem $(1).$ Adjust the slope and $y-$intercept of this line

until you think you have found the line that best fits the data. Record the slope and $y-$intercept.

Use Python to find the best$-$fit line. The Scikit$-$learn package is a good choice to use for this.

Find the root mean squared error $($RMSE$)$ of the prediction line you found in problem $(4)$ and the actual best$-$fit

line found in problem $(5).$ How do these compare?

Use the best$-$fit line in problem $(5)$ to predict the highway mpg of a car that weighs $3200$ pounds.

Provided below is the data for the 'car$\_$data.csv$\text{'}$ and I need to know how to import it and read it as well with pd$.$read.csv$(\text{'}$car$\_$data.csv$\text{'})$

weight hwy$\_$mpg

$254827$

$254827$

$282326$

$233730$

$282425$

$284425$

$295425$

$308620$

$305322$

$239529$

$271028$

$276528$

$305525$

$323022$

$338022$

$350520$

$148853$

$187443$

$190943$

$187641$

$187638$

$212830$

$196738$

$198938$

$198938$

$219130$

$253530$

$281124$

$171354$

$181938$

$183742$

$194034$

$195634$

$201034$

$202434$

$223633$

$228933$

$230433$

$237233$

$246528$

$229331$

$233729$

$187443$

$190943$

$273429$

$406619$

$406619$

$395017$

$189031$

$190038$

$190538$

$194538$

$195038$

$238023$

$238023$

$238523$

$250023$

$238532$

$241032$

$238532$

$241032$

$244342$

$242532$

$267027$

$270039$

$351525$

$375025$

$349525$

$377025$

$374018$

$368518$

$390016$

$371516$

$291024$

$191841$

$194438$

$200438$

$214530$

$237030$

$232832$

$283324$

$292124$

$292624$

$236532$

$240532$

$240330$

$240330$

$188937$

$201750$

$191837$

$193837$

$202437$

$195137$

$202837$

$197137$

$203737$

$200837$

$232434$

$230234$

$309522$

$329622$

$306025$

$307125$

$313923$

$313925$

$302024$

$319733$

$323024$

$343025$

$307524$

$325233$

$328524$

$348525$

$307524$

$325233$

$313024$

$191841$

$212830$

$196738$

$198938$

$219138$

$253530$

$281824$

$277827$

$275625$

$275625$

$280025$

$336628$

$257931$

$246031$

$265828$

$269528$

$270728$

$275828$

$280826$

$284726$

$205036$

$212031$

$224031$

$214537$

$219033$

$234032$

$238525$

$251029$

$229032$

$245531$

$242029$

$265023$

$198539$

$204038$

$201538$

$228037$

$229032$

$311032$

$208137$

$210937$

$227536$

$227547$

$209447$

$212234$

$214034$

$216934$

$220434$

$226529$

$230029$