pls give python code

A travel services firm has a paid search campaign. Among the many keywords in its

campaign, we have data on four keywords, denoted by kw$8322228,$ kw$8322392,$ kw$8322393$

and kw$8322445.$ These are generic, non$-$branded keywords, where the prospect's query does

not indicate that he$/$she is leaning toward a specific brand. For each keyword, the firm tried

several bid values and recorded the corresponding number of clicks that it received.

The files are named clicksdata.kw$8322228.$csv$,$ clicksdata.kw$8322392.$csv$,$

clicksdata.kw$8322393.$csv$,$ clicksdata.kw$8322445.$csv respectively.

Part A: Estimate the alpha and beta parameters for each of these four keywords for this firm.

Hand$-$in: The eight numbers. No additional writeup required. Hint on checking your answers:

For kw$8322228,$ alpha should be between $70$ and $76,$ beta should between $0\mathrm{.}03$ and $0\mathrm{.}06,$ with

a residual$-$sum$-$of$-$squares of about $230.$

To estimate the alpha and beta for a keyword you need to run nonlinear regression n$.$clicks

as a function of bid.value and using the appropriate function form. In R$,$ nonlinear

regression using nls$()$ and the basic call is something like what is given below.

nls$\_$output $<-$ nls$($n$.$clicks ~ alpha$*(1-$exp$(-$beta$*$bid$.$value$)),$

start $=$ list$($alpha$=\{$starting value of alpha$\},$ beta$=\{$starting value of beta$\}),$

data$=\{$name of data frame$\})$

In Python you can use the scipy.optimize.curve$\_$fit function