Please answer in python using template provided and so the output matches exactly

# import the necessary modules

fires $=$ # read in the csv file

# response variable

Y $=$ # set area as the response variable

# first forward selection step

model$1\_1=$ # generate the linear regression model for FFMC

# prints the p$-$value

print$("$model$1\_1$ p$-$values are $",$ model$1\_1.$pvalues$)$

model$1\_2=$ # generate the linear regression model for wind

print$("$model$1\_2$ p$-$values are $",$ model$1\_2.$pvalues$)$

model$1\_3=$ # generate the linear regression model for DC

print$("$model$1\_3$ p$-$values are $",$ model$1\_3.$pvalues$)$

model$1\_4=$ # generate the linear regression model for temp

print$("$model$1\_4$ p$-$values are $",$ model$1\_4.$pvalues$)$

# Second forward selection step using order FFMC$,$ wind, DC$,$ temp. Ex: If the best SLR model uses temp, the first MLR model should be Y ~ FFMC $+$ temp.

model$2\_1=$ # generate multiple regression model

print$("$model$2\_1$ p$-$values are $",$ model$2\_1.$pvalues$)$

model$2\_2=$ # generate multiple regression model

print$("$model$2\_2$ p$-$values are $",$ model$2\_2.$pvalues$)$

model$2\_3=$ # generate multiple regression model

print$("$model$2\_3$ p$-$values are $",$ model$2\_3.$pvalues$)$

# Third forward regression step using order FFMC$,$ wind, DC$,$ temp

model$3\_1=$ # generate multiple regression model

print$("$model$3\_1$ p$-$values are $",$ model$3\_1.$pvalues$)$

model$3\_2=$ # generate multiple regression model

print$("$model$3\_2$ p$-$values are $",$ model$3\_2.$pvalues$)$

# Continue until no additional predictor variables have p$-$values less than $0\mathrm{.}15.$The forestfires.csv data base contains meteorological information and the area burned for $517$ forest fires that occurred in Montesinho

Natural Park in Portugal. The columns of interest are FFMC$,$ DMC$,$ DC$,$ and ISI, as well as the response variable, area.

Read in the file forestfires.csv$.$

Use the ols$()$ function to perform forward selection stepwise regression on the variables FFMC$,$ DMC$,$ DC$,$ and ISI, in that order.

Continue the stepwise regression until no new variables have a p$-$value less than $0\mathrm{.}15.$

Ex: If the variable $RH$ is used instead of FFMC$,$ the output is: